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Wang YH, Li W, McDermott M, Son GY, Maiti G, Zhou F, Tao AY, Raphael D, Moreira AL, Shen B, Vaeth M, Nadorp B, Chakravarti S, Lacruz RS, Feske S. IFN-γ-producing T H1 cells and dysfunctional regulatory T cells contribute to the pathogenesis of Sjögren's disease. Sci Transl Med 2024; 16:eado4856. [PMID: 39693412 DOI: 10.1126/scitranslmed.ado4856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 08/06/2024] [Accepted: 10/02/2024] [Indexed: 12/20/2024]
Abstract
Sjögren's disease (SjD) is an autoimmune disorder characterized by progressive salivary and lacrimal gland dysfunction, inflammation, and destruction, as well as extraglandular manifestations. SjD is associated with autoreactive B and T cells, but its pathophysiology remains incompletely understood. Abnormalities in regulatory T (Treg) cells occur in several autoimmune diseases, but their role in SjD is ambiguous. We had previously shown that the function and development of Treg cells depend on store-operated Ca2+ entry (SOCE), which is mediated by ORAI1 Ca2+ channels and stromal interaction protein 1 (STIM1) and STIM2. Here, we show that mice with a Foxp3+ Treg cell-specific deletion of Stim1 and Stim2 develop a phenotype that fulfills all classification criteria of human SjD. Mutant mice have salivary and lacrimal gland inflammation characterized by strong lymphocyte infiltration and transcriptional signatures dominated by T helper 1 (TH1) and interferon (IFN) signaling. CD4+ T cells from mutant mice are sufficient to induce SjD-like disease in an IFN-γ-dependent manner. Inhibition of IFN signaling with the JAK1/2 inhibitor baricitinib alleviated CD4+ T cell-induced SjD in mice. These findings are consistent with the transcriptional profiles of CD4+ T cells from patients with SjD, which indicate enhanced TH1 but reduced memory Treg cell function. Together, our study provides evidence for a critical role of dysfunctional Treg cells and IFN-γ-producing TH1 cells in the pathogenesis of SjD.
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Affiliation(s)
- Yin-Hu Wang
- Department of Pathology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Wenyi Li
- Department of Pathology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Maxwell McDermott
- Department of Pathology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Ga-Yeon Son
- Department of Molecular Pathobiology, New York University College of Dentistry, New York, NY 10010, USA
| | - George Maiti
- Department of Ophthalmology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Fang Zhou
- Department of Pathology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Anthony Y Tao
- Department of Pathology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Dimitrius Raphael
- Department of Pathology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Andre L Moreira
- Department of Pathology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Boheng Shen
- Department of Pathology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Martin Vaeth
- Department of Pathology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Bettina Nadorp
- Department of Pathology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Division of Precision Medicine, Department of Medicine, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Shukti Chakravarti
- Department of Pathology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Ophthalmology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Rodrigo S Lacruz
- Department of Molecular Pathobiology, New York University College of Dentistry, New York, NY 10010, USA
| | - Stefan Feske
- Department of Pathology, New York University Grossman School of Medicine, New York, NY 10016, USA
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2
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Pouyabahar D, Andrews T, Bader G. Interpretable single-cell factor decomposition using sciRED. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.01.605536. [PMID: 39149356 PMCID: PMC11326131 DOI: 10.1101/2024.08.01.605536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) maps gene expression heterogeneity within a tissue. However, identifying biological signals in this data is challenging due to confounding technical factors, sparsity, and high dimensionality. Data factorization methods address this by separating and identifying signals in the data, such as gene expression programs, but the resulting factors must be manually interpreted. We developed Single-Cell Interpretable Residual Decomposition (sciRED) to improve the interpretation of scRNA-seq factor analysis. sciRED removes known confounding effects, uses rotations to improve factor interpretability, maps factors to known covariates, identifies unexplained factors that may capture hidden biological phenomena and determines the genes and biological processes represented by the resulting factors. We apply sciRED to multiple scRNA-seq datasets and identify sex-specific variation in a kidney map, discern strong and weak immune stimulation signals in a PBMC dataset, reduce ambient RNA contamination in a rat liver atlas to help identify strain variation, and reveal rare cell type signatures and anatomical zonation gene programs in a healthy human liver map. These demonstrate that sciRED is useful in characterizing diverse biological signals within scRNA-seq data.
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3
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Thompson JR, Nelson ED, Tippani M, Ramnauth AD, Divecha HR, Miller RA, Eagles NJ, Pattie EA, Kwon SH, Bach SV, Kaipa UM, Yao J, Hou C, Kleinman JE, Collado-Torres L, Han S, Maynard KR, Hyde TM, Martinowich K, Page SC, Hicks SC. An integrated single-nucleus and spatial transcriptomics atlas reveals the molecular landscape of the human hippocampus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.590643. [PMID: 38712198 PMCID: PMC11071618 DOI: 10.1101/2024.04.26.590643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
The hippocampus contains many unique cell types, which serve the structure's specialized functions, including learning, memory and cognition. These cells have distinct spatial organization, morphology, physiology, and connectivity, highlighting the importance of transcriptome-wide profiling strategies that retain cytoarchitectural organization. Here, we generated spatially-resolved transcriptomics (SRT) and single-nucleus RNA-sequencing (snRNA-seq) data from adjacent tissue sections of the anterior human hippocampus in ten adult neurotypical donors to define molecular profiles for hippocampal cell types and spatial domains. Using non-negative matrix factorization (NMF) and label transfer, we integrated these data by defining gene expression patterns within the snRNA-seq data and inferring their expression in the SRT data. We identified NMF patterns that captured transcriptional variation across neuronal cell types and indicated that the response of excitatory and inhibitory postsynaptic specializations were prioritized in different SRT spatial domains. We used the NMF and label transfer approach to leverage existing rodent datasets, identifying patterns of activity-dependent transcription and subpopulations of dentate gyrus granule cells in our SRT dataset that may be predisposed to participate in learning and memory ensembles. Finally, we characterized the spatial organization of NMF patterns corresponding to non- cornu ammonis pyramidal neurons and identified snRNA-seq clusters mapping to distinct regions of the retrohippocampus, to three subiculum layers, and to a population of presubiculum neurons. To make this comprehensive molecular atlas accessible to the scientific community, both raw and processed data are freely available, including through interactive web applications.
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4
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Jin X, Tian Y, Zhu H, Sun Y, Zhang Z. Computer-aided analysis reveals metallothionein-positive cancer-associated fibroblasts promote angiogenesis in gastric adenocarcinoma. Discov Oncol 2024; 15:751. [PMID: 39636347 PMCID: PMC11621267 DOI: 10.1007/s12672-024-01614-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Accepted: 11/21/2024] [Indexed: 12/07/2024] Open
Abstract
Gastric adenocarcinoma (GC), along with its tumor microenvironment (TME), poses great challenges for clinical treatment strategies. Single-cell sequencing has become an important tool for analyzing TME heterogeneity, cell subpopulation, and gene expression patterns. 56 GC single-cell sequencing samples were analyzed, focusing on TME by delineating cancer cells, cancer-associated fibroblasts (CAFs), and macrophages. The spatial transcriptome was used to clarify the distribution characteristics of each cellular component in the tissue slice. Despite the widespread genetic mutations observed in cancer cells, certain recurrent alterations were identified in specific chromosomal regions. The heterogeneity among GC cells is profound, four cancer cell subpopulations were identified through drug sensitivity profiling. Subtype 4, although only present in some samples, demonstrates the strongest stemness and metabolic activity, possibly indicative of an early-stage cancer subpopulation. Their drug sensitivity profiles may hold promise for guiding clinical intervention. In addition, robust spatial co-localization patterns were observed between CAFs, M2 macrophages, and endothelial cells. CAFs were further categorized into six subgroups, among which a novel subgroup termed metallothionein(mt)-positive CAF (mtCAF), characterized by elevated expression of metallothionein 1X (MT1X) and subsequent vascular endothelial growth factor A (VEGFA) secretion, was identified. Immunohistochemistry preliminary confirmed the presence of this unique CAF subgroup. Additionally, M2d macrophages, besides exhibiting high VEGFA expression, also demonstrated various growth factors such as Aamphiregulin (AREG). The M2d-mtCAF axis may play an important role in GC angiogenesis. This study not only enhances our understanding of the TME heterogeneity in GC but also sheds light on the interaction between CAFs and tumor-associated macrophages (TAMs) in tumor angiogenesis.
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Affiliation(s)
- Xiaolong Jin
- Department of Gastroenterology, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, China
| | - Yu Tian
- Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Haoran Zhu
- Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yuewen Sun
- Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Zhenxing Zhang
- Department of Stomatology, Taizhou Central Hospital (Taizhou University Hospital), No. 999, Donghai Avenue, Taizhou, 318000, Zhejiang, People's Republic of China.
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5
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Agrawal A, Thomann S, Basu S, Grün D. NiCo identifies extrinsic drivers of cell state modulation by niche covariation analysis. Nat Commun 2024; 15:10628. [PMID: 39639035 PMCID: PMC11621405 DOI: 10.1038/s41467-024-54973-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2024] [Accepted: 11/22/2024] [Indexed: 12/07/2024] Open
Abstract
Cell states are modulated by intrinsic driving forces such as gene expression noise and extrinsic signals from the tissue microenvironment. The distinction between intrinsic and extrinsic cell state determinants is essential for understanding the regulation of cell fate in tissues during development, homeostasis and disease. The rapidly growing availability of single-cell resolution spatial transcriptomics makes it possible to meet this challenge. However, available computational methods to infer topological tissue domains, spatially variable genes, or ligand-receptor interactions are limited in their capacity to capture cell state changes driven by crosstalk between individual cell types within the same niche. We present NiCo, a computational framework for integrating single-cell resolution spatial transcriptomics with matched single-cell RNA-sequencing reference data to infer the influence of the spatial niche on the cell state. By applying NiCo to mouse embryogenesis, adult small intestine and liver data, we demonstrate the ability to predict novel niche interactions that govern cell state variation underlying tissue development and homeostasis. In particular, NiCo predicts a feedback mechanism between Kupffer cells and neighboring stellate cells dampening stellate cell activation in the normal liver. NiCo provides a powerful tool to elucidate tissue architecture and to identify drivers of cellular states in local niches.
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Affiliation(s)
- Ankit Agrawal
- Würzburg Institute of Systems Immunology, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Stefan Thomann
- Würzburg Institute of Systems Immunology, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Sukanya Basu
- Würzburg Institute of Systems Immunology, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Dominic Grün
- Würzburg Institute of Systems Immunology, Julius-Maximilians-Universität Würzburg, Würzburg, Germany.
- CAIDAS - Center for Artificial Intelligence and Data Science, Würzburg, Germany.
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6
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Li H, Côté P, Kuoch M, Ezike J, Frenis K, Afanassiev A, Greenstreet L, Tanaka-Yano M, Tarantino G, Zhang S, Whangbo J, Butty VL, Moiso E, Falchetti M, Lu K, Connelly GG, Morris V, Wang D, Chen AF, Bianchi G, Daley GQ, Garg S, Liu D, Chou ST, Regev A, Lummertz da Rocha E, Schiebinger G, Rowe RG. The dynamics of hematopoiesis over the human lifespan. Nat Methods 2024:10.1038/s41592-024-02495-0. [PMID: 39639169 DOI: 10.1038/s41592-024-02495-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 09/19/2024] [Indexed: 12/07/2024]
Abstract
Over a lifetime, hematopoietic stem cells (HSCs) adjust their lineage output to support age-aligned physiology. In model organisms, stereotypic waves of hematopoiesis have been observed corresponding to defined age-biased HSC hallmarks. However, how the properties of hematopoietic stem and progenitor cells change over the human lifespan remains unclear. To address this gap, we profiled individual transcriptome states of human hematopoietic stem and progenitor cells spanning gestation, maturation and aging. Here we define the gene expression networks dictating age-specific differentiation of HSCs and the dynamics of fate decisions and lineage priming throughout life. We additionally identifiy and functionally validate a fetal-specific HSC state with robust engraftment and multilineage capacity. Furthermore, we observe that classification of acute myeloid leukemia against defined transcriptional age states demonstrates that utilization of early life transcriptional programs associates with poor prognosis. Overall, we provide a disease-relevant framework for heterochronic orientation of stem cell ontogeny along the real time axis of the human lifespan.
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Affiliation(s)
- Hojun Li
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Pediatrics, University of California, San Diego, CA, USA.
- Division of Hematology/Oncology, Rady Children's Hospital, San Diego, CA, USA.
| | - Parker Côté
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Pediatrics, University of California, San Diego, CA, USA
| | - Michael Kuoch
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jideofor Ezike
- Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Katie Frenis
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA
| | - Anton Afanassiev
- Department of Mathematics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Laura Greenstreet
- Department of Mathematics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Mayuri Tanaka-Yano
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Giuseppe Tarantino
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Stephen Zhang
- Department of Mathematics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Jennifer Whangbo
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Vor Biopharma, Cambridge, MA, USA
| | - Vincent L Butty
- Barbara K. Ostrom Bioinformatics Facility, Integrated Genomics and Bioinformatics Core of the Koch Institute, Cambridge, MA, USA
| | - Enrico Moiso
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Marcelo Falchetti
- Departments of Microbiology, Immunology and Parasitology, Federal University of Santa Catarina, Florianopolis, Brazil
| | - Kate Lu
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Guinevere G Connelly
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Vivian Morris
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA
| | - Dahai Wang
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA
| | - Antonia F Chen
- Harvard Medical School, Boston, MA, USA
- Department of Orthopedic Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Giada Bianchi
- Harvard Medical School, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - George Q Daley
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Salil Garg
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - David Liu
- Harvard Medical School, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Stella T Chou
- Division of Hematology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Aviv Regev
- Division of Hematology/Oncology, Rady Children's Hospital, San Diego, CA, USA
- Genentech, South San Francisco, CA, USA
| | - Edroaldo Lummertz da Rocha
- Departments of Microbiology, Immunology and Parasitology, Federal University of Santa Catarina, Florianopolis, Brazil
| | - Geoffrey Schiebinger
- Department of Mathematics, University of British Columbia, Vancouver, British Columbia, Canada
| | - R Grant Rowe
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
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7
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Guo F, Gan D, Li J. Cell-to-cell distance that combines gene expression and gene embeddings. Comput Struct Biotechnol J 2024; 23:3929-3937. [PMID: 39582889 PMCID: PMC11584677 DOI: 10.1016/j.csbj.2024.10.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2024] [Revised: 10/14/2024] [Accepted: 10/27/2024] [Indexed: 11/26/2024] Open
Abstract
The application of large-language models (LLMs) to single-cell gene-expression data has introduced a new type of data that includes a gene-embedding matrix, in addition to the experimentally obtained gene-expression matrix. This paper addresses a fundamental problem in analyzing such data: how to effectively combine the information from both matrices to better define cell-to-cell distance. We identify a computationally feasible solution that demonstrates superior ability to cluster cells of the same type across all six real datasets we tested, underscoring its advantage as a measure of cell-to-cell distance.
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Affiliation(s)
- Fangfang Guo
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Dailin Gan
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Jun Li
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN 46556, USA
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8
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Chakraborty S, Guan Z, Kostrzewa CE, Shen R, Begg CB. Identifying somatic fingerprints of cancers defined by germline and environmental risk factors. Genet Epidemiol 2024; 48:455-467. [PMID: 38686586 PMCID: PMC11522022 DOI: 10.1002/gepi.22565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 01/18/2024] [Accepted: 04/16/2024] [Indexed: 05/02/2024]
Abstract
Numerous studies over the past generation have identified germline variants that increase specific cancer risks. Simultaneously, a revolution in sequencing technology has permitted high-throughput annotations of somatic genomes characterizing individual tumors. However, examining the relationship between germline variants and somatic alteration patterns is hugely challenged by the large numbers of variants in a typical tumor, the rarity of most individual variants, and the heterogeneity of tumor somatic fingerprints. In this article, we propose statistical methodology that frames the investigation of germline-somatic relationships in an interpretable manner. The method uses meta-features embodying biological contexts of individual somatic alterations to implicitly group rare mutations. Our team has used this technique previously through a multilevel regression model to diagnose with high accuracy tumor site of origin. Herein, we further leverage topic models from computational linguistics to achieve interpretable lower-dimensional embeddings of the meta-features. We demonstrate how the method can identify distinctive somatic profiles linked to specific germline variants or environmental risk factors. We illustrate the method using The Cancer Genome Atlas whole-exome sequencing data to characterize somatic tumor fingerprints in breast cancer patients with germline BRCA1/2 mutations and in head and neck cancer patients exposed to human papillomavirus.
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Affiliation(s)
| | - Zoe Guan
- Mass General Research Institute, Boston, Massachusetts, USA
| | | | - Ronglai Shen
- Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Colin B Begg
- Memorial Sloan Kettering Cancer Center, New York, New York, USA
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9
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Venkat A, Leone S, Youlten SE, Fagerberg E, Attanasio J, Joshi NS, Perlmutter M, Krishnaswamy S. Mapping the gene space at single-cell resolution with gene signal pattern analysis. NATURE COMPUTATIONAL SCIENCE 2024; 4:955-977. [PMID: 39706866 DOI: 10.1038/s43588-024-00734-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 10/30/2024] [Indexed: 12/23/2024]
Abstract
In single-cell sequencing analysis, several computational methods have been developed to map the cellular state space, but little has been done to map or create embeddings of the gene space. Here we formulate the gene embedding problem, design tasks with simulated single-cell data to evaluate representations, and establish ten relevant baselines. We then present a graph signal processing approach, called gene signal pattern analysis (GSPA), that learns rich gene representations from single-cell data using a dictionary of diffusion wavelets on the cell-cell graph. GSPA enables characterization of genes based on their patterning and localization on the cellular manifold. We motivate and demonstrate the efficacy of GSPA as a framework for diverse biological tasks, such as capturing gene co-expression modules, condition-specific enrichment and perturbation-specific gene-gene interactions. Then we showcase the broad utility of gene representations derived from GSPA, including for cell-cell communication (GSPA-LR), spatial transcriptomics (GSPA-multimodal) and patient response (GSPA-Pt) analysis.
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Affiliation(s)
- Aarthi Venkat
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA
| | - Sam Leone
- Applied Math Program, Yale University, New Haven, CT, USA
| | | | - Eric Fagerberg
- Department of Immunobiology, Yale University, New Haven, CT, USA
| | - John Attanasio
- Department of Immunobiology, Yale University, New Haven, CT, USA
| | - Nikhil S Joshi
- Department of Immunobiology, Yale University, New Haven, CT, USA
| | - Michael Perlmutter
- Department of Mathematics, Boise State University, Boise, ID, USA
- Program in Computing, Boise State University, Boise, ID, USA
| | - Smita Krishnaswamy
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA.
- Applied Math Program, Yale University, New Haven, CT, USA.
- Department of Genetics, Yale University, New Haven, CT, USA.
- Department of Computer Science, Yale University, New Haven, CT, USA.
- Wu Tsai Institute, Yale University, New Haven, CT, USA.
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10
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Bedia JS, Huang YW, Gonzalez AD, Gonzalez VD, Funingana IG, Rahil Z, Mike A, Lowber A, Vias M, Ashworth A, Brenton JD, Fantl WJ. Coordinated protein modules define DNA damage responses to carboplatin at single cell resolution in human ovarian carcinoma models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.11.21.624591. [PMID: 39605494 PMCID: PMC11601625 DOI: 10.1101/2024.11.21.624591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/29/2024]
Abstract
Tubo-ovarian high-grade serous carcinoma (HGSC) is the most lethal gynecological malignancy and frequently responds to platinum-based chemotherapy because of common genetic and somatic impairment of DNA damage repair (DDR) pathways. The mechanisms of clinical platinum resistance are diverse and poorly molecularly defined. Consequently, there are no biomarkers or medicines that improve patient outcomes. Herein we use single cell mass cytometry (CyTOF) to systematically evaluate the phosphorylation and abundance of proteins known to participate in the DNA damage response (DDR). Single cell analyses of highly characterized HGSC cell lines that phenocopy human patients show that cells with comparable levels of intranuclear platinum, a proxy for carboplatin uptake, undergo different cell fates. Unsupervised analyses revealed a continuum of DDR responses. Decompositional methods were used to identify eight distinct protein modules of carboplatin resistance and sensitivity at single cell resolution. CyTOF profiling of primary and secondary platinum-resistance patient models shows that a complex DDR sensitivity module is strongly associated with response, suggesting it as a potential tool to clinically characterize complex drug resistance phenotypes.
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Affiliation(s)
- Jacob S. Bedia
- Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ying-Wen Huang
- Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | | | - Veronica D. Gonzalez
- Baxter Laboratory for Stem Cell Biology, Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ionut-Gabriel Funingana
- Department of Oncology, University of Cambridge, Cambridgeshire, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, Cambridgeshire, CB2 0RE, UK
- Department of Oncology, Addenbrooke’s Hospital, Cambridge University Hospitals, NHS Foundation Trust, Cambridge, UK
| | - Zainab Rahil
- Baxter Laboratory for Stem Cell Biology, Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Alyssa Mike
- Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Alexis Lowber
- Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Maria Vias
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, Cambridgeshire, CB2 0RE, UK
| | - Alan Ashworth
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, 1450 Third Street, San Francisco, CA 94158, USA
| | - James D. Brenton
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, Cambridgeshire, CB2 0RE, UK
- Department of Oncology, Addenbrooke’s Hospital, Cambridge University Hospitals, NHS Foundation Trust, Cambridge, UK
| | - Wendy J. Fantl
- Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Stanford Comprehensive Cancer Institute
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA 94305, USA
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11
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Dong P, Song L, Bendl J, Misir R, Shao Z, Edelstien J, Davis DA, Haroutunian V, Scott WK, Acker S, Lawless N, Hoffman GE, Fullard JF, Roussos P. A multi-regional human brain atlas of chromatin accessibility and gene expression facilitates promoter-isoform resolution genetic fine-mapping. Nat Commun 2024; 15:10113. [PMID: 39578476 PMCID: PMC11584674 DOI: 10.1038/s41467-024-54448-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 11/08/2024] [Indexed: 11/24/2024] Open
Abstract
Brain region- and cell-specific transcriptomic and epigenomic features are associated with heritability for neuropsychiatric traits, but a systematic view, considering cortical and subcortical regions, is lacking. Here, we provide an atlas of chromatin accessibility and gene expression profiles in neuronal and non-neuronal nuclei across 25 distinct human cortical and subcortical brain regions from 6 neurotypical controls. We identified extensive gene expression and chromatin accessibility differences across brain regions, including variation in alternative promoter-isoform usage and enhancer-promoter interactions. Genes with distinct promoter-isoform usage across brain regions were strongly enriched for neuropsychiatric disease risk variants. Moreover, we built enhancer-promoter interactions at promoter-isoform resolution across different brain regions and highlighted the contribution of brain region-specific and promoter-isoform-specific regulation to neuropsychiatric disorders. Including promoter-isoform resolution uncovers additional distal elements implicated in the heritability of diseases, thereby increasing the power to fine-map risk genes. Our results provide a valuable resource for studying molecular regulation across multiple regions of the human brain and underscore the importance of considering isoform information in gene regulation.
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Affiliation(s)
- Pengfei Dong
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Liting Song
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ruth Misir
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zhiping Shao
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jonathan Edelstien
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David A Davis
- Brain Endowment Bank, Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Vahram Haroutunian
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA
| | - William K Scott
- Brain Endowment Bank, Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA
- John P. Hussman Institute for Human Genomics and Dr. John T. Macdonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Susanne Acker
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharma GmbH and Co. KG, Biberach, Germany
| | - Nathan Lawless
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharma GmbH and Co. KG, Biberach, Germany
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, USA
| | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA.
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, USA.
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12
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Ma XR, Conley SD, Kosicki M, Bredikhin D, Cui R, Tran S, Sheth MU, Qiu WL, Chen S, Kundu S, Kang HY, Amgalan D, Munger CJ, Duan L, Dang K, Rubio OM, Kany S, Zamirpour S, DePaolo J, Padmanabhan A, Olgin J, Damrauer S, Andersson R, Gu M, Priest JR, Quertermous T, Qiu X, Rabinovitch M, Visel A, Pennacchio L, Kundaje A, Glass IA, Gifford CA, Pirruccello JP, Goodyer WR, Engreitz JM. Molecular convergence of risk variants for congenital heart defects leveraging a regulatory map of the human fetal heart. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.11.20.24317557. [PMID: 39606363 PMCID: PMC11601760 DOI: 10.1101/2024.11.20.24317557] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/29/2024]
Abstract
Congenital heart defects (CHD) arise in part due to inherited genetic variants that alter genes and noncoding regulatory elements in the human genome. These variants are thought to act during fetal development to influence the formation of different heart structures. However, identifying the genes, pathways, and cell types that mediate these effects has been challenging due to the immense diversity of cell types involved in heart development as well as the superimposed complexities of interpreting noncoding sequences. As such, understanding the molecular functions of both noncoding and coding variants remains paramount to our fundamental understanding of cardiac development and CHD. Here, we created a gene regulation map of the healthy human fetal heart across developmental time, and applied it to interpret the functions of variants associated with CHD and quantitative cardiac traits. We collected single-cell multiomic data from 734,000 single cells sampled from 41 fetal hearts spanning post-conception weeks 6 to 22, enabling the construction of gene regulation maps in 90 cardiac cell types and states, including rare populations of cardiac conduction cells. Through an unbiased analysis of all 90 cell types, we find that both rare coding variants associated with CHD and common noncoding variants associated with valve traits converge to affect valvular interstitial cells (VICs). VICs are enriched for high expression of known CHD genes previously identified through mapping of rare coding variants. Eight CHD genes, as well as other genes in similar molecular pathways, are linked to common noncoding variants associated with other valve diseases or traits via enhancers in VICs. In addition, certain common noncoding variants impact enhancers with activities highly specific to particular subanatomic structures in the heart, illuminating how such variants can impact specific aspects of heart structure and function. Together, these results implicate new enhancers, genes, and cell types in the genetic etiology of CHD, identify molecular convergence of common noncoding and rare coding variants on VICs, and suggest a more expansive view of the cell types instrumental in genetic risk for CHD, beyond the working cardiomyocyte. This regulatory map of the human fetal heart will provide a foundational resource for understanding cardiac development, interpreting genetic variants associated with heart disease, and discovering targets for cell-type specific therapies.
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Affiliation(s)
- X Rosa Ma
- Basic Science and Engineering (BASE) Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Stephanie D Conley
- Basic Science and Engineering (BASE) Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Michael Kosicki
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Danila Bredikhin
- Basic Science and Engineering (BASE) Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Ran Cui
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Steven Tran
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Maya U Sheth
- Basic Science and Engineering (BASE) Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Wei-Lin Qiu
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Sijie Chen
- Basic Science and Engineering (BASE) Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Soumya Kundu
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Helen Y Kang
- Basic Science and Engineering (BASE) Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
- Current address: PhD Program in Computational and Systems Biology, MIT, Cambridge, MA, USA
| | - Dulguun Amgalan
- Basic Science and Engineering (BASE) Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
| | - Chad J Munger
- Basic Science and Engineering (BASE) Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Lauren Duan
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Katherine Dang
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Oriane Matthys Rubio
- Basic Science and Engineering (BASE) Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Shinwan Kany
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Cardiology, University Heart and Vascular Center Hamburg-Eppendorf, Hamburg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck, Hamburg, Germany
| | - Siavash Zamirpour
- School of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - John DePaolo
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arun Padmanabhan
- Gladstone Institutes, San Francisco, CA, USA
- Department of Medicine, University of California San Francisco School of Medicine, San Francisco, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Jeffrey Olgin
- Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California San Francisco, San Francisco, CA, USA
| | - Scott Damrauer
- Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Robin Andersson
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Mingxia Gu
- Center for Stem Cell and Organoid Medicine, Division of Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- University of Cincinnati School of Medicine, Cincinnati, OH, USA
| | - James R Priest
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Tenaya Therapeutics, South San Francisco, CA, USA
| | - Thomas Quertermous
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Medicine, Stanford University, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Xiaojie Qiu
- Basic Science and Engineering (BASE) Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Stanford Bio-X, Stanford University, Stanford, CA, USA
| | - Marlene Rabinovitch
- Basic Science and Engineering (BASE) Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Vera Moulton Wall Center for Pulmonary Vascular Diseases, Stanford University, Stanford, CA, USA
| | - Axel Visel
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- School of Natural Sciences, University of California, Merced, Merced, CA, USA
| | - Len Pennacchio
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Comparative Biochemistry Program, University of California, Berkeley, CA, 94720, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Ian A Glass
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
- Department of Pediatrics and Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - Casey A Gifford
- Basic Science and Engineering (BASE) Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
| | - James P Pirruccello
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, CA, USA
- Bakar Computation Health Sciences Institute, University of California, San Francisco, CA, USA
| | - William R Goodyer
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Jesse M Engreitz
- Basic Science and Engineering (BASE) Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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13
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Das S, Parigi SM, Luo X, Fransson J, Kern BC, Okhovat A, Diaz OE, Sorini C, Czarnewski P, Webb AT, Morales RA, Lebon S, Monasterio G, Castillo F, Tripathi KP, He N, Pelczar P, Schaltenberg N, De la Fuente M, López-Köstner F, Nylén S, Larsen HL, Kuiper R, Antonson P, Hermoso MA, Huber S, Biton M, Scharaw S, Gustafsson JÅ, Katajisto P, Villablanca EJ. Liver X receptor unlinks intestinal regeneration and tumorigenesis. Nature 2024:10.1038/s41586-024-08247-6. [PMID: 39567700 DOI: 10.1038/s41586-024-08247-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 10/17/2024] [Indexed: 11/22/2024]
Abstract
Uncontrolled regeneration leads to neoplastic transformation1-3. The intestinal epithelium requires precise regulation during continuous homeostatic and damage-induced tissue renewal to prevent neoplastic transformation, suggesting that pathways unlinking tumour growth from regenerative processes must exist. Here, by mining RNA-sequencing datasets from two intestinal damage models4,5 and using pharmacological, transcriptomics and genetic tools, we identified liver X receptor (LXR) pathway activation as a tissue adaptation to damage that reciprocally regulates intestinal regeneration and tumorigenesis. Using single-cell RNA sequencing, intestinal organoids, and gain- and loss-of-function experiments, we demonstrate that LXR activation in intestinal epithelial cells induces amphiregulin (Areg), enhancing regenerative responses. This response is coordinated by the LXR-ligand-producing enzyme CYP27A1, which was upregulated in damaged intestinal crypt niches. Deletion of Cyp27a1 impaired intestinal regeneration, which was rescued by exogenous LXR agonists. Notably, in tumour models, Cyp27a1 deficiency led to increased tumour growth, whereas LXR activation elicited anti-tumour responses dependent on adaptive immunity. Consistently, human colorectal cancer specimens exhibited reduced levels of CYP27A1, LXR target genes, and B and CD8 T cell gene signatures. We therefore identify an epithelial adaptation mechanism to damage, whereby LXR functions as a rheostat, promoting tissue repair while limiting tumorigenesis.
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Affiliation(s)
- Srustidhar Das
- Division of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and University Hospital, Stockholm, Sweden.
- Center of Molecular Medicine, Stockholm, Sweden.
| | - S Martina Parigi
- Division of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and University Hospital, Stockholm, Sweden
- Center of Molecular Medicine, Stockholm, Sweden
- Robin Chemers Neustein Laboratory of Mammalian Cell Biology and Development, The Rockefeller University, New York, NY, USA
| | - Xinxin Luo
- Division of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and University Hospital, Stockholm, Sweden
- Center of Molecular Medicine, Stockholm, Sweden
| | - Jennifer Fransson
- Division of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and University Hospital, Stockholm, Sweden
- Center of Molecular Medicine, Stockholm, Sweden
| | - Bianca C Kern
- Division of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and University Hospital, Stockholm, Sweden
- Center of Molecular Medicine, Stockholm, Sweden
| | - Ali Okhovat
- Division of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and University Hospital, Stockholm, Sweden
- Center of Molecular Medicine, Stockholm, Sweden
- Structural Genomics Consortium, Division of Rheumatology, Department of Medicine Solna, Karolinska Institute and University Hospital, Stockholm, Sweden
| | - Oscar E Diaz
- Division of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and University Hospital, Stockholm, Sweden
- Center of Molecular Medicine, Stockholm, Sweden
| | - Chiara Sorini
- Division of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and University Hospital, Stockholm, Sweden
- Center of Molecular Medicine, Stockholm, Sweden
| | - Paulo Czarnewski
- Division of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and University Hospital, Stockholm, Sweden
- Center of Molecular Medicine, Stockholm, Sweden
- Science for Life Laboratory, Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Stockholm University, Solna, Sweden
| | - Anna T Webb
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Rodrigo A Morales
- Division of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and University Hospital, Stockholm, Sweden
- Center of Molecular Medicine, Stockholm, Sweden
| | - Sacha Lebon
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Gustavo Monasterio
- Division of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and University Hospital, Stockholm, Sweden
- Center of Molecular Medicine, Stockholm, Sweden
| | - Francisca Castillo
- Division of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and University Hospital, Stockholm, Sweden
- Center of Molecular Medicine, Stockholm, Sweden
| | - Kumar P Tripathi
- Division of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and University Hospital, Stockholm, Sweden
- Center of Molecular Medicine, Stockholm, Sweden
| | - Ning He
- Division of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and University Hospital, Stockholm, Sweden
- Center of Molecular Medicine, Stockholm, Sweden
| | - Penelope Pelczar
- I. Medizinische Klinik, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Nicola Schaltenberg
- I. Medizinische Klinik, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Marjorie De la Fuente
- Center of Biomedical Research (CIBMED), School of Medicine, Faculty of Medicine-Clinica Las Condes, Universidad Finis Terrae, Santiago, Chile
- Laboratory of Innate Immunity, Program of Immunology, Institute of Biomedical Sciences, Faculty of Medicine, Universidad de Chile, Santiago, Chile
| | - Francisco López-Köstner
- Centro de Enfermedades Digestivas, Programa Enfermedad Inflamatoria Intestinal, Clínica Universidad de Los Andes, Universidad de Los Andes, Santiago, Chile
| | - Susanne Nylén
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - Hjalte List Larsen
- Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), University of Copenhagen, Copenhagen, Denmark
| | - Raoul Kuiper
- Section for Aquatic Biosecurity Research, Norwegian Veterinary Institute, Ås, Norway
- Department of Laboratory Medicine, Karolinska Institutet, Huddinge, Sweden
| | - Per Antonson
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
| | - Marcela A Hermoso
- Laboratory of Innate Immunity, Program of Immunology, Institute of Biomedical Sciences, Faculty of Medicine, Universidad de Chile, Santiago, Chile
| | - Samuel Huber
- I. Medizinische Klinik, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Moshe Biton
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Sandra Scharaw
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Jan-Åke Gustafsson
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
- Center for Nuclear Receptors and Cell Signaling, Department of Biology and Biochemistry, University of Houston, Houston, TX, USA
| | - Pekka Katajisto
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Eduardo J Villablanca
- Division of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and University Hospital, Stockholm, Sweden.
- Center of Molecular Medicine, Stockholm, Sweden.
- Clinical Immunology and Transfusion Medicine, Karolinska University Hospital, Stockholm, Sweden.
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14
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Kiviaho A, Eerola SK, Kallio HML, Andersen MK, Hoikka M, Tiihonen AM, Salonen I, Spotbeen X, Giesen A, Parker CTA, Taavitsainen S, Hantula O, Marttinen M, Hermelo I, Ismail M, Midtbust E, Wess M, Devlies W, Sharma A, Krossa S, Häkkinen T, Afyounian E, Vandereyken K, Kint S, Kesseli J, Tolonen T, Tammela TLJ, Viset T, Størkersen Ø, Giskeødegård GF, Rye MB, Murtola T, Erickson A, Latonen L, Bova GS, Mills IG, Joniau S, Swinnen JV, Voet T, Mirtti T, Attard G, Claessens F, Visakorpi T, Rautajoki KJ, Tessem MB, Urbanucci A, Nykter M. Single cell and spatial transcriptomics highlight the interaction of club-like cells with immunosuppressive myeloid cells in prostate cancer. Nat Commun 2024; 15:9949. [PMID: 39550375 PMCID: PMC11569175 DOI: 10.1038/s41467-024-54364-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Accepted: 11/08/2024] [Indexed: 11/18/2024] Open
Abstract
Prostate cancer treatment resistance is a significant challenge facing the field. Genomic and transcriptomic profiling have partially elucidated the mechanisms through which cancer cells escape treatment, but their relation toward the tumor microenvironment (TME) remains elusive. Here we present a comprehensive transcriptomic landscape of the prostate TME at multiple points in the standard treatment timeline employing single-cell RNA-sequencing and spatial transcriptomics data from 120 patients. We identify club-like cells as a key epithelial cell subtype that acts as an interface between the prostate and the immune system. Tissue areas enriched with club-like cells have depleted androgen signaling and upregulated expression of luminal progenitor cell markers. Club-like cells display a senescence-associated secretory phenotype and their presence is linked to increased polymorphonuclear myeloid-derived suppressor cell (PMN-MDSC) activity. Our results indicate that club-like cells are associated with myeloid inflammation previously linked to androgen deprivation therapy resistance, providing a rationale for their therapeutic targeting.
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Affiliation(s)
- Antti Kiviaho
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland
| | - Sini K Eerola
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland
| | - Heini M L Kallio
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland
| | - Maria K Andersen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Surgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Miina Hoikka
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland
| | - Aliisa M Tiihonen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland
| | - Iida Salonen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland
| | - Xander Spotbeen
- Laboratory of Lipid Metabolism and Cancer, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium
- KU Leuven Institute for Single Cell Omics (LISCO), KU Leuven, Leuven, Belgium
| | - Alexander Giesen
- Department of Urology, University Hospitals Leuven, Leuven, Belgium
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | | | - Sinja Taavitsainen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland
| | - Olli Hantula
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland
| | - Mikael Marttinen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland
- European Molecular Biology Laboratory, Structural and Computational Biology Unit, Heidelberg, Germany
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Ismaïl Hermelo
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland
| | | | - Elise Midtbust
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Surgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Maximilian Wess
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Surgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Wout Devlies
- Department of Urology, University Hospitals Leuven, Leuven, Belgium
- Molecular Endocrinology Laboratory, Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium
| | - Abhibhav Sharma
- Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Sebastian Krossa
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Central staff, St. Olavs Hospital HF, 7006, Trondheim, Norway
| | - Tomi Häkkinen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland
| | - Ebrahim Afyounian
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland
| | - Katy Vandereyken
- KU Leuven Institute for Single Cell Omics (LISCO), KU Leuven, Leuven, Belgium
- Laboratory of Reproductive Genomics, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Sam Kint
- KU Leuven Institute for Single Cell Omics (LISCO), KU Leuven, Leuven, Belgium
- Laboratory of Reproductive Genomics, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Juha Kesseli
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland
| | - Teemu Tolonen
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland
- Department of Pathology, Fimlab Laboratories, Ltd, Tampere University Hospital, Tampere, Finland
| | - Teuvo L J Tammela
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Urology, Tampere University Hospital, Tampere, Finland
| | - Trond Viset
- Department of Pathology, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Øystein Størkersen
- Department of Pathology, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Guro F Giskeødegård
- Clinic of Surgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Morten B Rye
- Clinic of Surgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Teemu Murtola
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland
| | - Andrew Erickson
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- ICAN-Digital Precision Cancer Medicine Flagship, Helsinki, Finland
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Leena Latonen
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - G Steven Bova
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland
| | - Ian G Mills
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Patrick G Johnston Centre for Cancer Research, Queen's University of Belfast, Belfast, UK
| | - Steven Joniau
- Department of Urology, University Hospitals Leuven, Leuven, Belgium
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Johannes V Swinnen
- Laboratory of Lipid Metabolism and Cancer, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium
- KU Leuven Institute for Single Cell Omics (LISCO), KU Leuven, Leuven, Belgium
| | - Thierry Voet
- KU Leuven Institute for Single Cell Omics (LISCO), KU Leuven, Leuven, Belgium
- Laboratory of Reproductive Genomics, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Tuomas Mirtti
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- ICAN-Digital Precision Cancer Medicine Flagship, Helsinki, Finland
- Department of Pathology, University of Helsinki & Helsinki University Hospital, Helsinki, Finland
| | - Gerhardt Attard
- University College London Cancer Institute, London, UK
- University College London Hospitals, London, UK
| | - Frank Claessens
- Molecular Endocrinology Laboratory, Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium
| | - Tapio Visakorpi
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland
- Fimlab Laboratories, Ltd, Tampere University Hospital, Tampere, Finland
| | - Kirsi J Rautajoki
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland
| | - May-Britt Tessem
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Surgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Alfonso Urbanucci
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland.
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.
| | - Matti Nykter
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
- Prostate Cancer Research Center, Tampere University and TAYS Cancer Center, Tampere, Finland.
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15
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McClune CJ, Liu JCT, Wick C, De La Peña R, Lange BM, Fordyce PM, Sattely ES. Multiplexed perturbation of yew reveals cryptic proteins that enable a total biosynthesis of baccatin III and Taxol precursors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.11.06.622305. [PMID: 39574719 PMCID: PMC11580873 DOI: 10.1101/2024.11.06.622305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/01/2024]
Abstract
Plants make complex and potent therapeutic molecules, but difficulties in sourcing from natural producers or chemical synthesis can challenge their use in the clinic. A prominent example is the anti-cancer therapeutic paclitaxel (Taxol ® ). Identification of the full paclitaxel biosynthetic pathway would enable heterologous drug production, but it has eluded discovery despite a half century of intensive research. Within the search space of Taxus' large, enzyme-rich genome, we suspected the complex paclitaxel pathway would be difficult to resolve using conventional gene co-expression analysis and small sample sets. To improve the resolution of gene set identification, we developed a multiplexed perturbation strategy to transcriptionally profile cell states spanning tissues, cell types, developmental stages, and elicitation conditions. This approach revealed a set of paclitaxel biosynthetic genes that segregate into expression modules that suggest consecutive biosynthetic sub-pathways. These modules resolved seven new genes that, when combined with previously known enzymes, are sufficient for the de novo biosynthesis and isolation of baccatin III, an industrial precursor for Taxol, in Nicotiana benthamiana leaves at levels comparable to the natural abundance in Taxus needles. Included are taxane 1β-hydroxylase (T1βH), taxane 9α-hydroxylase (T9αH), taxane 7β- O -acyltransferase (T7ΑΤ), taxane 7β- O -deacetylase (T7dA), taxane 9α- O -deacetylase (T9dA), and taxane 9-oxidase (T9ox). Importantly, the T9αH we discovered is distinct and independently evolved from those recently reported, which failed to yield baccatin III with downstream enzymes. Unexpectedly, we also found a nuclear transport factor 2 (NTF2)-like protein (FoTO1) crucial for high yields of taxanes; this gene promotes the formation of the desired product during the first taxane oxidation step, resolving a longstanding bottleneck in paclitaxel pathway reconstitution. Together with a new β-phenylalanine-CoA-ligase, the eight genes discovered in this study enables the complete reconstitution of 3'- N -debenzoyl-2'-deoxy-paclitaxel with a 20-enzyme pathway in Nicotiana plants. More broadly, we establish a generalizable approach for pathway discovery that scales the power of co-expression studies to match the complexity of specialized metabolism, enabling discovery of gene sets responsible for high-value biological functions.
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Affiliation(s)
- Conor James McClune
- Department of Chemical Engineering, Stanford University, California 94305
- Howard Hughes Medical Institute, Stanford University, Stanford, California 94305
| | | | - Chloe Wick
- Department of Chemical Engineering, Stanford University, California 94305
| | - Ricardo De La Peña
- Department of Chemical Engineering, Stanford University, California 94305
| | - Bernd Markus Lange
- Institute of Biological Chemistry, Washington State University, Pullman, Washington 99164
| | - Polly M. Fordyce
- Department of Bioengineering, Stanford University, California 94305
- Department of Genetics, Stanford University, California 94305
| | - Elizabeth S. Sattely
- Department of Chemical Engineering, Stanford University, California 94305
- Howard Hughes Medical Institute, Stanford University, Stanford, California 94305
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16
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Nolbrant S, Wallace JL, Ding J, Zhu T, Sevetson JL, Kajtez J, Baldacci IA, Corrigan EK, Hoglin K, McMullen R, Schmitz MT, Breevoort A, Swope D, Wu F, Pavlovic BJ, Salama SR, Kirkeby A, Huang H, Schaefer NK, Pollen AA. INTERSPECIES ORGANOIDS REVEAL HUMAN-SPECIFIC MOLECULAR FEATURES OF DOPAMINERGIC NEURON DEVELOPMENT AND VULNERABILITY. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.11.14.623592. [PMID: 39605599 PMCID: PMC11601475 DOI: 10.1101/2024.11.14.623592] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/29/2024]
Abstract
The disproportionate expansion of telencephalic structures during human evolution involved tradeoffs that imposed greater connectivity and metabolic demands on midbrain dopaminergic neurons. Despite the central role of dopaminergic neurons in human-enriched disorders, molecular specializations associated with human-specific features and vulnerabilities of the dopaminergic system remain unexplored. Here, we establish a phylogeny-in-a-dish approach to examine gene regulatory evolution by differentiating pools of human, chimpanzee, orangutan, and macaque pluripotent stem cells into ventral midbrain organoids capable of forming long-range projections, spontaneous activity, and dopamine release. We identify human-specific gene expression changes related to axonal transport of mitochondria and reactive oxygen species buffering and candidate cis- and trans-regulatory mechanisms underlying gene expression divergence. Our findings are consistent with a model of evolved neuroprotection in response to tradeoffs related to brain expansion and could contribute to the discovery of therapeutic targets and strategies for treating disorders involving the dopaminergic system.
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Affiliation(s)
- Sara Nolbrant
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
- These authors contributed equally
| | - Jenelle L. Wallace
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
- These authors contributed equally
| | - Jingwen Ding
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
- These authors contributed equally
| | - Tianjia Zhu
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Jess L. Sevetson
- Department of Molecular, Cellular, and Developmental Biology, University of California Santa Cruz, CA, United States of America
- Genomics Institute, University of California Santa Cruz, CA, United States of America
| | - Janko Kajtez
- Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW)), Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Isabella A. Baldacci
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Emily K. Corrigan
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Kaylynn Hoglin
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Reed McMullen
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Matthew T. Schmitz
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Arnar Breevoort
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Dani Swope
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Fengxia Wu
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Anatomy and Neurobiology, Shandong University, Jinan, Shandong Province, China
| | - Bryan J. Pavlovic
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Sofie R. Salama
- Department of Molecular, Cellular, and Developmental Biology, University of California Santa Cruz, CA, United States of America
- Genomics Institute, University of California Santa Cruz, CA, United States of America
| | - Agnete Kirkeby
- Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW)), Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Experimental Medical Sciences, Wallenberg Center for Molecular Medicine (WCMM) and Lund Stem Cell Center, Lund University, Lund, Sweden
| | - Hao Huang
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Nathan K. Schaefer
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Alex A. Pollen
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
- Lead contact
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17
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Basanta S, Stadtmauer DJ, Maziarz JD, McDonough-Goldstein CE, Cole AG, Dagdas G, Wagner GP, Pavličev M. Hallmarks of uterine receptivity predate placental mammals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.11.04.621939. [PMID: 39574771 PMCID: PMC11580939 DOI: 10.1101/2024.11.04.621939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2024]
Abstract
Embryo implantation requires tightly coordinated signaling between the blastocyst and the endometrium, and is crucial for the establishment of a uteroplacental unit that persists until term in eutherian mammals. In contrast, marsupials, with a unique life cycle and short gestation, make only brief fetal-maternal contact and lack implantation. To better understand the evolutionary link between eutherian implantation and its ancestral equivalent in marsupials, we compare single-cell transcriptomes from the receptive and non-receptive endometrium of the mouse and guinea pig with that of the opossum, a marsupial. We identify substantial differences between rodent peri-implantation endometrium and opossum placental attachment, including differences in the diversity and abundance of stromal and epithelial cells which parallel the difference between histotrophic and hemotrophic provisioning strategies. We also identify a window of conserved epithelial gene expression between the opossum shelled blastocyst stage and rodent peri-implantation, including IHH and LIF . We find strong conservation of blastocyst proteases, steroid synthetases, Wnt and BMP signals between eutherians and the opossum despite its lack of implantation. Finally, we show that the signaling repertoire of the maternal uterine epithelium during implantation displays substantial overlap with that of the post-implantation placental trophoblast, suggesting that the fetal trophoblast can compensate for the loss of endometrial epithelium in eutherian invasive placentation. Together, our results suggest that eutherian implantation primarily involved the re-wiring of maternal signaling networks, some of which were already present in the therian ancestor, and points towards an essential role of maternal innovations in the evolution of invasive placentation.
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Affiliation(s)
- Silvia Basanta
- Department of Evolutionary Biology, University of Vienna, Vienna, Austria
| | - Daniel J. Stadtmauer
- Department of Evolutionary Biology, University of Vienna, Vienna, Austria
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA
| | - Jamie D. Maziarz
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, USA
| | - Caitlin E. McDonough-Goldstein
- Department of Evolutionary Biology, University of Vienna, Vienna, Austria
- Department of Integrative Biology, University of Wisconsin-Madison, WI, USA
| | - Alison G. Cole
- Department of Neuroscience and Developmental Biology, University of Vienna, Vienna, Austria
| | - Gülay Dagdas
- Department of Evolutionary Biology, University of Vienna, Vienna, Austria
| | - Günter P. Wagner
- Department of Evolutionary Biology, University of Vienna, Vienna, Austria
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA
- Department of Animal Science, Texas A&M University, College Station, TX, USA
| | - Mihaela Pavličev
- Department of Evolutionary Biology, University of Vienna, Vienna, Austria
- Complexity Science Hub Vienna, Vienna, Austria
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18
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Goeva A, Dolan MJ, Luu J, Garcia E, Boiarsky R, Gupta RM, Macosko E. HiDDEN: a machine learning method for detection of disease-relevant populations in case-control single-cell transcriptomics data. Nat Commun 2024; 15:9468. [PMID: 39487129 PMCID: PMC11530671 DOI: 10.1038/s41467-024-53666-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 10/15/2024] [Indexed: 11/04/2024] Open
Abstract
In case-control single-cell RNA-seq studies, sample-level labels are transferred onto individual cells, labeling all case cells as affected, when in reality only a small fraction of them may actually be perturbed. Here, using simulations, we demonstrate that the standard approach to single cell analysis fails to isolate the subset of affected case cells and their markers when either the affected subset is small, or when the strength of the perturbation is mild. To address this fundamental limitation, we introduce HiDDEN, a computational method that refines the case-control labels to accurately reflect the perturbation status of each cell. We show HiDDEN's superior ability to recover biological signals missed by the standard analysis workflow in simulated ground truth datasets of cell type mixtures. When applied to a dataset of human multiple myeloma precursor conditions, HiDDEN recapitulates the expert manual annotation and discovers malignancy in early stage samples missed in the original analysis. When applied to a mouse model of demyelination, HiDDEN identifies an endothelial subpopulation playing a role in early stage blood-brain barrier dysfunction. We anticipate that HiDDEN should find wide usage in contexts that require the detection of subtle transcriptional changes in cell types across conditions.
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Affiliation(s)
- Aleksandrina Goeva
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.
| | - Michael-John Dolan
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Judy Luu
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Eric Garcia
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Rebecca Boiarsky
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
- CSAIL and IMES, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Rajat M Gupta
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
- Divisions of Cardiovascular Medicine and Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Evan Macosko
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.
- Massachusetts General Hospital, Department of Psychiatry, Boston, MA, USA.
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19
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Piran Z, Cohen N, Hoshen Y, Nitzan M. Disentanglement of single-cell data with biolord. Nat Biotechnol 2024; 42:1678-1683. [PMID: 38225466 PMCID: PMC11554562 DOI: 10.1038/s41587-023-02079-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 11/30/2023] [Indexed: 01/17/2024]
Abstract
Biolord is a deep generative method for disentangling single-cell multi-omic data to known and unknown attributes, including spatial, temporal and disease states, used to reveal the decoupled biological signatures over diverse single-cell modalities and biological systems. By virtually shifting cells across states, biolord generates experimentally inaccessible samples, outperforming state-of-the-art methods in predictions of cellular response to unseen drugs and genetic perturbations. Biolord is available at https://github.com/nitzanlab/biolord .
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Affiliation(s)
- Zoe Piran
- School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel
| | - Niv Cohen
- School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel
| | - Yedid Hoshen
- School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel
| | - Mor Nitzan
- School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel.
- Racah Institute of Physics, The Hebrew University, Jerusalem, Israel.
- Faculty of Medicine, The Hebrew University, Jerusalem, Israel.
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20
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Dong L, Hu S, Li X, Pei S, Jin L, Zhang L, Chen X, Min A, Yin M. SPP1 + TAM Regulates the Metastatic Colonization of CXCR4 + Metastasis-Associated Tumor Cells by Remodeling the Lymph Node Microenvironment. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400524. [PMID: 39236316 PMCID: PMC11600252 DOI: 10.1002/advs.202400524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 05/06/2024] [Indexed: 09/07/2024]
Abstract
Lymph node metastasis, the initial step in distant metastasis, represents a primary contributor to mortality in patients diagnosed with oral squamous cell carcinoma (OSCC). However, the underlying mechanisms of lymph node metastasis in OSCC remain incompletely understood. Here, the transcriptomes of 56 383 single cells derived from paired tissues of six OSCC patients are analyzed. This study founds that CXCR4+ epithelial cells, identified as highly malignant disseminated tumor cells (DTCs), exhibited a propensity for lymph node metastasis. Importantly, a distinct subset of tumor-associated macrophages (TAMs) characterized by exclusive expression of phosphoprotein 1 (SPP1) is discovered. These TAMs may remodel the metastatic lymph node microenvironment by potentially activating fibroblasts and promoting T cell exhaustion through SPP1-CD44 and CD155-CD226 ligand-receptor interactions, thereby facilitating colonization and proliferation of disseminated tumor cells. The research advanced the mechanistic understanding of metastatic tumor microenvironment (TME) and provided a foundation for the development of personalized treatments for OSCC patients with metastasis.
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Affiliation(s)
- Liang Dong
- Department of DermatologyHunan Engineering Research Center of Skin Health and DiseaseHunan Key Laboratory of Skin Cancer and PsoriasisXiangya HospitalCentral South UniversityChangshaHunan410008China
- Clinical Research Center (CRC)Medical Pathology Center (MPC)Cancer Early Detection and Treatment Center (CEDTC)Chongqing University Three Gorges HospitalChongqing UniversityChongqing404100China
- Translational Medicine Research Center (TMRC)School of Medicine Chongqing UniversityChongqing404100China
- National Clinical Research Center for Geriatric DisordersXiangya HospitalCentral South UniversityChangshaHunan410008China
| | - Shujun Hu
- National Clinical Research Center for Geriatric DisordersXiangya HospitalCentral South UniversityChangshaHunan410008China
- Department of Oral and Maxillofacial SurgeryCenter of StomatologyXiangya HospitalCentral South UniversityChangshaHunan410008China
- Research Center of Oral and Maxillofacail TumorXiangya HospitalCentral South UniversityChangshaHunan410008China
- Insititute of Oral Cancer and Precancerous LesionsCentral South UniversityChangshaHunan410008China
| | - Xin Li
- Clinical Research Center (CRC)Medical Pathology Center (MPC)Cancer Early Detection and Treatment Center (CEDTC)Chongqing University Three Gorges HospitalChongqing UniversityChongqing404100China
- Translational Medicine Research Center (TMRC)School of Medicine Chongqing UniversityChongqing404100China
| | - Shiyao Pei
- Department of DermatologyHunan Engineering Research Center of Skin Health and DiseaseHunan Key Laboratory of Skin Cancer and PsoriasisXiangya HospitalCentral South UniversityChangshaHunan410008China
- National Clinical Research Center for Geriatric DisordersXiangya HospitalCentral South UniversityChangshaHunan410008China
- Department of DermatologyThird Xiangya HospitalCentral South UniversityChangsha410008China
| | - Liping Jin
- Department of DermatologyHunan Engineering Research Center of Skin Health and DiseaseHunan Key Laboratory of Skin Cancer and PsoriasisXiangya HospitalCentral South UniversityChangshaHunan410008China
- National Clinical Research Center for Geriatric DisordersXiangya HospitalCentral South UniversityChangshaHunan410008China
| | - Lining Zhang
- Clinical Research Center (CRC)Medical Pathology Center (MPC)Cancer Early Detection and Treatment Center (CEDTC)Chongqing University Three Gorges HospitalChongqing UniversityChongqing404100China
- Translational Medicine Research Center (TMRC)School of Medicine Chongqing UniversityChongqing404100China
| | - Xiang Chen
- Department of DermatologyHunan Engineering Research Center of Skin Health and DiseaseHunan Key Laboratory of Skin Cancer and PsoriasisXiangya HospitalCentral South UniversityChangshaHunan410008China
- National Clinical Research Center for Geriatric DisordersXiangya HospitalCentral South UniversityChangshaHunan410008China
| | - Anjie Min
- National Clinical Research Center for Geriatric DisordersXiangya HospitalCentral South UniversityChangshaHunan410008China
- Department of Oral and Maxillofacial SurgeryCenter of StomatologyXiangya HospitalCentral South UniversityChangshaHunan410008China
- Research Center of Oral and Maxillofacail TumorXiangya HospitalCentral South UniversityChangshaHunan410008China
- Insititute of Oral Cancer and Precancerous LesionsCentral South UniversityChangshaHunan410008China
| | - Mingzhu Yin
- Department of DermatologyHunan Engineering Research Center of Skin Health and DiseaseHunan Key Laboratory of Skin Cancer and PsoriasisXiangya HospitalCentral South UniversityChangshaHunan410008China
- Clinical Research Center (CRC)Medical Pathology Center (MPC)Cancer Early Detection and Treatment Center (CEDTC)Chongqing University Three Gorges HospitalChongqing UniversityChongqing404100China
- Translational Medicine Research Center (TMRC)School of Medicine Chongqing UniversityChongqing404100China
- National Clinical Research Center for Geriatric DisordersXiangya HospitalCentral South UniversityChangshaHunan410008China
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21
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Oliver AJ, Huang N, Bartolome-Casado R, Li R, Koplev S, Nilsen HR, Moy M, Cakir B, Polanski K, Gudiño V, Melón-Ardanaz E, Sumanaweera D, Dimitrov D, Milchsack LM, FitzPatrick MEB, Provine NM, Boccacino JM, Dann E, Predeus AV, To K, Prete M, Chapman JA, Masi AC, Stephenson E, Engelbert J, Lobentanzer S, Perera S, Richardson L, Kapuge R, Wilbrey-Clark A, Semprich CI, Ellams S, Tudor C, Joseph P, Garrido-Trigo A, Corraliza AM, Oliver TRW, Hook CE, James KR, Mahbubani KT, Saeb-Parsy K, Zilbauer M, Saez-Rodriguez J, Høivik ML, Bækkevold ES, Stewart CJ, Berrington JE, Meyer KB, Klenerman P, Salas A, Haniffa M, Jahnsen FL, Elmentaite R, Teichmann SA. Single-cell integration reveals metaplasia in inflammatory gut diseases. Nature 2024; 635:699-707. [PMID: 39567783 PMCID: PMC11578898 DOI: 10.1038/s41586-024-07571-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 05/15/2024] [Indexed: 11/22/2024]
Abstract
The gastrointestinal tract is a multi-organ system crucial for efficient nutrient uptake and barrier immunity. Advances in genomics and a surge in gastrointestinal diseases1,2 has fuelled efforts to catalogue cells constituting gastrointestinal tissues in health and disease3. Here we present systematic integration of 25 single-cell RNA sequencing datasets spanning the entire healthy gastrointestinal tract in development and in adulthood. We uniformly processed 385 samples from 189 healthy controls using a newly developed automated quality control approach (scAutoQC), leading to a healthy reference atlas with approximately 1.1 million cells and 136 fine-grained cell states. We anchor 12 gastrointestinal disease datasets spanning gastrointestinal cancers, coeliac disease, ulcerative colitis and Crohn's disease to this reference. Utilizing this 1.6 million cell resource (gutcellatlas.org), we discover epithelial cell metaplasia originating from stem cells in intestinal inflammatory diseases with transcriptional similarity to cells found in pyloric and Brunner's glands. Although previously linked to mucosal healing4, we now implicate pyloric gland metaplastic cells in inflammation through recruitment of immune cells including T cells and neutrophils. Overall, we describe inflammation-induced changes in stem cells that alter mucosal tissue architecture and promote further inflammation, a concept applicable to other tissues and diseases.
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Affiliation(s)
- Amanda J Oliver
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Ni Huang
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Raquel Bartolome-Casado
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Department of Pathology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway
| | - Ruoyan Li
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, US
| | - Simon Koplev
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Hogne R Nilsen
- Department of Pathology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway
| | - Madelyn Moy
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Batuhan Cakir
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | | | - Victoria Gudiño
- Inflammatory Bowel Disease Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Hospital Clínic, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
| | - Elisa Melón-Ardanaz
- Inflammatory Bowel Disease Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Hospital Clínic, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
| | | | - Daniel Dimitrov
- Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Bioquant, Heidelberg, Germany
| | | | - Michael E B FitzPatrick
- Translational Gastroenterology and Liver Unit, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Nicholas M Provine
- Translational Gastroenterology and Liver Unit, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - Emma Dann
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | | | - Ken To
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Martin Prete
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Jonathan A Chapman
- Translational and Clinical Research Institute, Newcastle University, Newcastle, UK
| | - Andrea C Masi
- Translational and Clinical Research Institute, Newcastle University, Newcastle, UK
| | - Emily Stephenson
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Translational and Clinical Research Institute, Newcastle University, Newcastle, UK
| | - Justin Engelbert
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Translational and Clinical Research Institute, Newcastle University, Newcastle, UK
| | - Sebastian Lobentanzer
- Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Bioquant, Heidelberg, Germany
| | - Shani Perera
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Laura Richardson
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Rakeshlal Kapuge
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | | | | | - Sophie Ellams
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Catherine Tudor
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | | | - Alba Garrido-Trigo
- Inflammatory Bowel Disease Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Hospital Clínic, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
| | - Ana M Corraliza
- Inflammatory Bowel Disease Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Hospital Clínic, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
| | - Thomas R W Oliver
- Department of Histopathology and Cytology, Cambridge University Hospitals, Cambridge, UK
| | | | - Kylie R James
- Translational Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | - Krishnaa T Mahbubani
- Department of Surgery, University of Cambridge, Cambridge, UK
- Cambridge Biorepository for Translational Medicine, Cambridge NIHR Biomedical Research Centre, Cambridge, UK
- Department of Haematology, Cambridge Stem Cell Institute, Cambridge, UK
| | - Kourosh Saeb-Parsy
- Department of Surgery, University of Cambridge, Cambridge, UK
- Cambridge Biorepository for Translational Medicine, Cambridge NIHR Biomedical Research Centre, Cambridge, UK
| | - Matthias Zilbauer
- Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- University Department of Paediatrics, University of Cambridge, Cambridge, UK
- Department of Paediatric Gastroenterology, Hepatology and Nutrition, Cambridge University Hospitals, Cambridge, UK
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Bioquant, Heidelberg, Germany
| | - Marte Lie Høivik
- Department of Gastroenterology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Espen S Bækkevold
- Department of Pathology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway
| | | | - Janet E Berrington
- Translational and Clinical Research Institute, Newcastle University, Newcastle, UK
| | - Kerstin B Meyer
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Paul Klenerman
- Translational Gastroenterology and Liver Unit, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Azucena Salas
- Inflammatory Bowel Disease Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Hospital Clínic, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
| | - Muzlifah Haniffa
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
- Department of Dermatology and National Institute for Health Research (NIHR) Newcastle Biomedical Research Centre, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Frode L Jahnsen
- Department of Pathology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway
| | - Rasa Elmentaite
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Ensocell Therapeutics, BioData Innovation Centre, Wellcome Genome Campus, Cambridge, UK
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
- Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
- Ensocell Therapeutics, BioData Innovation Centre, Wellcome Genome Campus, Cambridge, UK.
- Theory of Condensed Matter, Cavendish Laboratory/Department of Physics, University of Cambridge, Cambridge, UK.
- Department of Medicine, University of Cambridge, Cambridge, UK.
- CIFAR Macmillan Multi-scale Human Program, CIFAR, Toronto, Ontario, Canada.
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22
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Zeng AG, Iacobucci I, Shah S, Mitchell A, Wong G, Bansal S, Chen D, Gao Q, Kim H, Kennedy JA, Arruda A, Minden MD, Haferlach T, Mullighan CG, Dick JE. Single-cell transcriptional mapping reveals genetic and non-genetic determinants of aberrant differentiation in AML. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.26.573390. [PMID: 38234771 PMCID: PMC10793439 DOI: 10.1101/2023.12.26.573390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
In acute myeloid leukemia (AML), genetic mutations distort hematopoietic differentiation, resulting in the accumulation of leukemic blasts. Yet, it remains unclear how these mutations intersect with cellular origins and whether they converge upon similar differentiation patterns. Single-cell RNA sequencing (scRNA-seq) has enabled high-resolution mapping of the relationship between leukemia and normal cell states, yet this application is hampered by imprecise reference maps of normal hematopoiesis and small sample sizes among patient cohorts. As a first step we constructed a reference atlas of human bone marrow hematopoiesis from 263,519 single-cell transcriptomes spanning 55 cellular states, that was benchmarked against independent datasets of immunophenotypically pure hematopoietic stem and progenitor cells. Using this reference atlas, we mapped over 1.2 million single-cell transcriptomes spanning 318 AML, mixed phenotype acute leukemia (MPAL), and acute erythroid leukemia (AEL) samples. This large-scale analysis, together with systematic mapping of genotype-to-phenotype associations between driver mutations and differentiation landscapes, revealed convergence of diverse genetic alterations on twelve recurrent patterns of aberrant differentiation in AML. This included unconventional lymphoid and erythroid priming linked to RUNX1 and TP53 mutations, respectively. We also identified non-genetic determinants of AML differentiation such as two subgroups of KMT2A-rearranged AML that differ in the identity of their leukemic stem cells (LSCs), likely reflecting distinct cellular origins. Furthermore, distinct LSC-driven hierarchies can co-exist within individual patients, providing insights into AML evolution. Together, precise mapping of normal and malignant cell states provides a framework for advancing the study and disease classification of hematologic malignancies thereby informing therapy development.
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Affiliation(s)
- Andy G.X. Zeng
- Princess Margaret Cancer Centre, University Health Network; Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto; Toronto, ON, Canada
| | - Ilaria Iacobucci
- Department of Pathology, St Jude Children’s Research Hospital, Memphis, TN, USA
| | - Sayyam Shah
- Princess Margaret Cancer Centre, University Health Network; Toronto, ON, Canada
| | - Amanda Mitchell
- Princess Margaret Cancer Centre, University Health Network; Toronto, ON, Canada
| | - Gordon Wong
- Princess Margaret Cancer Centre, University Health Network; Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto; Toronto, ON, Canada
| | - Suraj Bansal
- Princess Margaret Cancer Centre, University Health Network; Toronto, ON, Canada
| | - David Chen
- Princess Margaret Cancer Centre, University Health Network; Toronto, ON, Canada
| | - Qingsong Gao
- Department of Pathology, St Jude Children’s Research Hospital, Memphis, TN, USA
| | - Hyerin Kim
- Princess Margaret Cancer Centre, University Health Network; Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto; Toronto, ON, Canada
| | - James A. Kennedy
- Division of Medical Oncology and Hematology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Andrea Arruda
- Princess Margaret Cancer Centre, University Health Network; Toronto, ON, Canada
| | - Mark D. Minden
- Princess Margaret Cancer Centre, University Health Network; Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- Division of Medical Oncology and Hematology, University Health Network, Toronto, ON, Canada
| | | | - Charles G. Mullighan
- Department of Pathology, St Jude Children’s Research Hospital, Memphis, TN, USA
- Center of Excellence for Leukemia Studies, St. Jude Children’s Research Hospital, Memphis, TN
| | - John E. Dick
- Princess Margaret Cancer Centre, University Health Network; Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto; Toronto, ON, Canada
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23
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Thomas T, Friedrich M, Rich-Griffin C, Pohin M, Agarwal D, Pakpoor J, Lee C, Tandon R, Rendek A, Aschenbrenner D, Jainarayanan A, Voda A, Siu JHY, Sanches-Peres R, Nee E, Sathananthan D, Kotliar D, Todd P, Kiourlappou M, Gartner L, Ilott N, Issa F, Hester J, Turner J, Nayar S, Mackerodt J, Zhang F, Jonsson A, Brenner M, Raychaudhuri S, Kulicke R, Ramsdell D, Stransky N, Pagliarini R, Bielecki P, Spies N, Marsden B, Taylor S, Wagner A, Klenerman P, Walsh A, Coles M, Jostins-Dean L, Powrie FM, Filer A, Travis S, Uhlig HH, Dendrou CA, Buckley CD. A longitudinal single-cell atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease. Nat Immunol 2024; 25:2152-2165. [PMID: 39438660 PMCID: PMC11519010 DOI: 10.1038/s41590-024-01994-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/18/2024] [Indexed: 10/25/2024]
Abstract
Precision medicine in immune-mediated inflammatory diseases (IMIDs) requires a cellular understanding of treatment response. We describe a therapeutic atlas for Crohn's disease (CD) and ulcerative colitis (UC) following adalimumab, an anti-tumour necrosis factor (anti-TNF) treatment. We generated ~1 million single-cell transcriptomes, organised into 109 cell states, from 216 gut biopsies (41 subjects), revealing disease-specific differences. A systems biology-spatial analysis identified granuloma signatures in CD and interferon (IFN)-response signatures localising to T cell aggregates and epithelial damage in CD and UC. Pretreatment differences in epithelial and myeloid compartments were associated with remission outcomes in both diseases. Longitudinal comparisons demonstrated disease progression in nonremission: myeloid and T cell perturbations in CD and increased multi-cellular IFN signalling in UC. IFN signalling was also observed in rheumatoid arthritis (RA) synovium with a lymphoid pathotype. Our therapeutic atlas represents the largest cellular census of perturbation with the most common biologic treatment, anti-TNF, across multiple inflammatory diseases.
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Affiliation(s)
- Tom Thomas
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
- Centre for Human Genetics, University of Oxford, Oxford, UK
- Translational Gastroenterology & Liver Unit, John Radcliffe Hospital, Headington, Oxford, UK
| | - Matthias Friedrich
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
- Translational Gastroenterology & Liver Unit, John Radcliffe Hospital, Headington, Oxford, UK
| | | | - Mathilde Pohin
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Devika Agarwal
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Julia Pakpoor
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
- Centre for Human Genetics, University of Oxford, Oxford, UK
- Translational Gastroenterology & Liver Unit, John Radcliffe Hospital, Headington, Oxford, UK
| | - Carl Lee
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Ruchi Tandon
- University College London Hospitals NHS Foundation Trust, London, UK
| | - Aniko Rendek
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Dominik Aschenbrenner
- Translational Gastroenterology & Liver Unit, John Radcliffe Hospital, Headington, Oxford, UK
| | | | - Alexandru Voda
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | | | | | - Eloise Nee
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Dharshan Sathananthan
- University of Adelaide, Adelaide, Australia
- Lyell McEwin Hospital, Adelaide, Australia
| | - Dylan Kotliar
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Peter Todd
- Centre for Human Genetics, University of Oxford, Oxford, UK
| | | | - Lisa Gartner
- Translational Gastroenterology & Liver Unit, John Radcliffe Hospital, Headington, Oxford, UK
| | - Nicholas Ilott
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Fadi Issa
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Joanna Hester
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Jason Turner
- Rheumatology Research Group, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
| | - Saba Nayar
- Rheumatology Research Group, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- National Institute for Health Research (NIHR) Birmingham Biomedical Research Centre and NIHR Clinical Research Facility, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Birmingham Tissue Analytics, Institute of Translational Medicine, University of Birmingham, Birmingham, UK
| | - Jonas Mackerodt
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Fan Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Center for Health AI, University of Colorado Anschutz, Anschutz, CO, USA
| | - Anna Jonsson
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Michael Brenner
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Soumya Raychaudhuri
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | | | | | | | | | | | - Noah Spies
- Celsius Therapeutics, Cambridge, MA, USA
| | - Brian Marsden
- Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Stephen Taylor
- Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Allon Wagner
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
- The Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Paul Klenerman
- Translational Gastroenterology & Liver Unit, John Radcliffe Hospital, Headington, Oxford, UK
| | - Alissa Walsh
- Translational Gastroenterology & Liver Unit, John Radcliffe Hospital, Headington, Oxford, UK
| | - Mark Coles
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | | | - Fiona M Powrie
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Andrew Filer
- Rheumatology Research Group, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- National Institute for Health Research (NIHR) Birmingham Biomedical Research Centre and NIHR Clinical Research Facility, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Birmingham Tissue Analytics, Institute of Translational Medicine, University of Birmingham, Birmingham, UK
| | - Simon Travis
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.
- Translational Gastroenterology & Liver Unit, John Radcliffe Hospital, Headington, Oxford, UK.
- NIHR Oxford Biomedical Research Centre, Oxford, UK.
| | - Holm H Uhlig
- Translational Gastroenterology & Liver Unit, John Radcliffe Hospital, Headington, Oxford, UK.
- NIHR Oxford Biomedical Research Centre, Oxford, UK.
- Department of Paediatrics, University of Oxford, Oxford, UK.
| | - Calliope A Dendrou
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.
- Centre for Human Genetics, University of Oxford, Oxford, UK.
- NIHR Oxford Biomedical Research Centre, Oxford, UK.
| | - Christopher D Buckley
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.
- Translational Gastroenterology & Liver Unit, John Radcliffe Hospital, Headington, Oxford, UK.
- Rheumatology Research Group, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK.
- NIHR Oxford Biomedical Research Centre, Oxford, UK.
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24
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Kaur H, Heiser CN, McKinley ET, Ventura-Antunes L, Harris CR, Roland JT, Farrow MA, Selden HJ, Pingry EL, Moore JF, Ehrlich LIR, Shrubsole MJ, Spraggins JM, Coffey RJ, Lau KS, Vandekar SN. Consensus tissue domain detection in spatial omics data using multiplex image labeling with regional morphology (MILWRM). Commun Biol 2024; 7:1295. [PMID: 39478141 PMCID: PMC11525554 DOI: 10.1038/s42003-024-06281-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Accepted: 05/02/2024] [Indexed: 11/02/2024] Open
Abstract
Spatially resolved molecular assays provide high dimensional genetic, transcriptomic, proteomic, and epigenetic information in situ and at various resolutions. Pairing these data across modalities with histological features enables powerful studies of tissue pathology in the context of an intact microenvironment and tissue structure. Increasing dimensions across molecular analytes and samples require new data science approaches to functionally annotate spatially resolved molecular data. A specific challenge is data-driven cross-sample domain detection that allows for analysis within and between consensus tissue compartments across high volumes of multiplex datasets stemming from tissue atlasing efforts. Here, we present MILWRM (multiplex image labeling with regional morphology)-a Python package for rapid, multi-scale tissue domain detection and annotation at the image- or spot-level. We demonstrate MILWRM's utility in identifying histologically distinct compartments in human colonic polyps, lymph nodes, mouse kidney, and mouse brain slices through spatially-informed clustering in two different spatial data modalities from different platforms. We used tissue domains detected in human colonic polyps to elucidate the molecular distinction between polyp subtypes, and explored the ability of MILWRM to identify anatomical regions of the brain tissue and their respective distinct molecular profiles.
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Affiliation(s)
- Harsimran Kaur
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Cody N Heiser
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Eliot T McKinley
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | | | - Coleman R Harris
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Joseph T Roland
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Melissa A Farrow
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Hilary J Selden
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
| | - Ellie L Pingry
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - John F Moore
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
| | - Lauren I R Ehrlich
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
| | - Martha J Shrubsole
- Department of Medicine, Division of Epidemiology, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt-Ingram Cancer Center, Nashville, TN, USA
| | - Jeffrey M Spraggins
- Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt-Ingram Cancer Center, Nashville, TN, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Robert J Coffey
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt-Ingram Cancer Center, Nashville, TN, USA
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ken S Lau
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA.
- Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN, USA.
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA.
- Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN, USA.
- Department of Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.
- Vanderbilt-Ingram Cancer Center, Nashville, TN, USA.
| | - Simon N Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
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25
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Aguirre M, Spence JP, Sella G, Pritchard JK. Gene regulatory network structure informs the distribution of perturbation effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.04.602130. [PMID: 39005431 PMCID: PMC11245109 DOI: 10.1101/2024.07.04.602130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Gene regulatory networks (GRNs) govern many core developmental and biological processes underlying human complex traits. Even with broad-scale efforts to characterize the effects of molecular perturbations and interpret gene coexpression, it remains challenging to infer the architecture of gene regulation in a precise and efficient manner. Key properties of GRNs, like hierarchical structure, modular organization, and sparsity, provide both challenges and opportunities for this objective. Here, we seek to better understand properties of GRNs using a new approach to simulate their structure and model their function. We produce realistic network structures with a novel generating algorithm based on insights from small-world network theory, and we model gene expression regulation using stochastic differential equations formulated to accommodate modeling molecular perturbations. With these tools, we systematically describe the effects of gene knockouts within and across GRNs, finding a subset of networks that recapitulate features of a recent genome-scale perturbation study. With deeper analysis of these exemplar networks, we consider future avenues to map the architecture of gene expression regulation using data from cells in perturbed and unperturbed states, finding that while perturbation data are critical to discover specific regulatory interactions, data from unperturbed cells may be sufficient to reveal regulatory programs.
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Affiliation(s)
- Matthew Aguirre
- Department of Biomedical Data Science, Stanford University, Stanford CA
| | | | - Guy Sella
- Department of Biological Sciences, Columbia University, New York NY
- Program for Mathematical Genomics, Columbia University, New York NY
| | - Jonathan K Pritchard
- Department of Genetics, Stanford University, Stanford CA
- Department of Biology, Stanford University, Stanford CA
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26
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Gong D, Arbesfeld-Qiu JM, Perrault E, Bae JW, Hwang WL. Spatial oncology: Translating contextual biology to the clinic. Cancer Cell 2024; 42:1653-1675. [PMID: 39366372 DOI: 10.1016/j.ccell.2024.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 08/01/2024] [Accepted: 09/06/2024] [Indexed: 10/06/2024]
Abstract
Microscopic examination of cells in their tissue context has been the driving force behind diagnostic histopathology over the past two centuries. Recently, the rise of advanced molecular biomarkers identified through single cell profiling has increased our understanding of cellular heterogeneity in cancer but have yet to significantly impact clinical care. Spatial technologies integrating molecular profiling with microenvironmental features are poised to bridge this translational gap by providing critical in situ context for understanding cellular interactions and organization. Here, we review how spatial tools have been used to study tumor ecosystems and their clinical applications. We detail findings in cell-cell interactions, microenvironment composition, and tissue remodeling for immune evasion and therapeutic resistance. Additionally, we highlight the emerging role of multi-omic spatial profiling for characterizing clinically relevant features including perineural invasion, tertiary lymphoid structures, and the tumor-stroma interface. Finally, we explore strategies for clinical integration and their augmentation of therapeutic and diagnostic approaches.
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Affiliation(s)
- Dennis Gong
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jeanna M Arbesfeld-Qiu
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard University, Graduate School of Arts and Sciences, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Ella Perrault
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard University, Graduate School of Arts and Sciences, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Jung Woo Bae
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - William L Hwang
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard University, Graduate School of Arts and Sciences, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA.
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27
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Brattig-Correia R, Almeida JM, Wyrwoll MJ, Julca I, Sobral D, Misra CS, Di Persio S, Guilgur LG, Schuppe HC, Silva N, Prudêncio P, Nóvoa A, Leocádio AS, Bom J, Laurentino S, Mallo M, Kliesch S, Mutwil M, Rocha LM, Tüttelmann F, Becker JD, Navarro-Costa P. The conserved genetic program of male germ cells uncovers ancient regulators of human spermatogenesis. eLife 2024; 13:RP95774. [PMID: 39388236 PMCID: PMC11466473 DOI: 10.7554/elife.95774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2024] Open
Abstract
Male germ cells share a common origin across animal species, therefore they likely retain a conserved genetic program that defines their cellular identity. However, the unique evolutionary dynamics of male germ cells coupled with their widespread leaky transcription pose significant obstacles to the identification of the core spermatogenic program. Through network analysis of the spermatocyte transcriptome of vertebrate and invertebrate species, we describe the conserved evolutionary origin of metazoan male germ cells at the molecular level. We estimate the average functional requirement of a metazoan male germ cell to correspond to the expression of approximately 10,000 protein-coding genes, a third of which defines a genetic scaffold of deeply conserved genes that has been retained throughout evolution. Such scaffold contains a set of 79 functional associations between 104 gene expression regulators that represent a core component of the conserved genetic program of metazoan spermatogenesis. By genetically interfering with the acquisition and maintenance of male germ cell identity, we uncover 161 previously unknown spermatogenesis genes and three new potential genetic causes of human infertility. These findings emphasize the importance of evolutionary history on human reproductive disease and establish a cross-species analytical pipeline that can be repurposed to other cell types and pathologies.
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Affiliation(s)
- Rion Brattig-Correia
- Instituto Gulbenkian de CiênciaOeirasPortugal
- Department of Systems Science and Industrial Engineering, Binghamton UniversityNew YorkUnited States
| | - Joana M Almeida
- Instituto Gulbenkian de CiênciaOeirasPortugal
- EvoReproMed Lab, Environmental Health Institute (ISAMB), Associate Laboratory TERRA, Faculty of Medicine, University of LisbonLisbonPortugal
| | - Margot Julia Wyrwoll
- Centre of Medical Genetics, Institute of Reproductive Genetics, University and University Hospital of MünsterMünsterGermany
| | - Irene Julca
- School of Biological Sciences, Nanyang Technological UniversitySingaporeSingapore
| | - Daniel Sobral
- Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University LisbonLisbonPortugal
- UCIBIO - Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, NOVA University LisbonCaparicaPortugal
| | - Chandra Shekhar Misra
- Instituto Gulbenkian de CiênciaOeirasPortugal
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de LisboaOeirasPortugal
| | - Sara Di Persio
- Centre of Reproductive Medicine and Andrology, University Hospital MünsterMünsterGermany
| | | | - Hans-Christian Schuppe
- Clinic of Urology, Pediatric Urology and Andrology, Justus-Liebig-UniversityGiessenGermany
| | - Neide Silva
- Instituto Gulbenkian de CiênciaOeirasPortugal
| | - Pedro Prudêncio
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de LisboaLisboaPortugal
| | - Ana Nóvoa
- Instituto Gulbenkian de CiênciaOeirasPortugal
| | | | - Joana Bom
- Instituto Gulbenkian de CiênciaOeirasPortugal
| | - Sandra Laurentino
- Centre of Reproductive Medicine and Andrology, University Hospital MünsterMünsterGermany
| | | | - Sabine Kliesch
- Centre of Reproductive Medicine and Andrology, University Hospital MünsterMünsterGermany
| | - Marek Mutwil
- School of Biological Sciences, Nanyang Technological UniversitySingaporeSingapore
| | - Luis M Rocha
- Instituto Gulbenkian de CiênciaOeirasPortugal
- Department of Systems Science and Industrial Engineering, Binghamton UniversityNew YorkUnited States
| | - Frank Tüttelmann
- Centre of Medical Genetics, Institute of Reproductive Genetics, University and University Hospital of MünsterMünsterGermany
| | - Jörg D Becker
- Instituto Gulbenkian de CiênciaOeirasPortugal
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de LisboaOeirasPortugal
| | - Paulo Navarro-Costa
- Instituto Gulbenkian de CiênciaOeirasPortugal
- EvoReproMed Lab, Environmental Health Institute (ISAMB), Associate Laboratory TERRA, Faculty of Medicine, University of LisbonLisbonPortugal
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28
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Manoharan VT, Abdelkareem A, Gill G, Brown S, Gillmor A, Hall C, Seo H, Narta K, Grewal S, Dang NH, Ahn BY, Osz K, Lun X, Mah L, Zemp F, Mahoney D, Senger DL, Chan JA, Morrissy AS. Spatiotemporal modeling reveals high-resolution invasion states in glioblastoma. Genome Biol 2024; 25:264. [PMID: 39390467 PMCID: PMC11465563 DOI: 10.1186/s13059-024-03407-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 09/29/2024] [Indexed: 10/12/2024] Open
Abstract
BACKGROUND Diffuse invasion of glioblastoma cells through normal brain tissue is a key contributor to tumor aggressiveness, resistance to conventional therapies, and dismal prognosis in patients. A deeper understanding of how components of the tumor microenvironment (TME) contribute to overall tumor organization and to programs of invasion may reveal opportunities for improved therapeutic strategies. RESULTS Towards this goal, we apply a novel computational workflow to a spatiotemporally profiled GBM xenograft cohort, leveraging the ability to distinguish human tumor from mouse TME to overcome previous limitations in the analysis of diffuse invasion. Our analytic approach, based on unsupervised deconvolution, performs reference-free discovery of cell types and cell activities within the complete GBM ecosystem. We present a comprehensive catalogue of 15 tumor cell programs set within the spatiotemporal context of 90 mouse brain and TME cell types, cell activities, and anatomic structures. Distinct tumor programs related to invasion align with routes of perivascular, white matter, and parenchymal invasion. Furthermore, sub-modules of genes serving as program network hubs are highly prognostic in GBM patients. CONCLUSION The compendium of programs presented here provides a basis for rational targeting of tumor and/or TME components. We anticipate that our approach will facilitate an ecosystem-level understanding of the immediate and long-term consequences of such perturbations, including the identification of compensatory programs that will inform improved combinatorial therapies.
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Affiliation(s)
- Varsha Thoppey Manoharan
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada
- Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
| | - Aly Abdelkareem
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada
- Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
| | - Gurveer Gill
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada
- Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
| | - Samuel Brown
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada
- Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
| | - Aaron Gillmor
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada
- Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
| | - Courtney Hall
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada
- Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
| | - Heewon Seo
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada
- Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
| | - Kiran Narta
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada
- Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
| | - Sean Grewal
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada
- Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
| | - Ngoc Ha Dang
- Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
| | - Bo Young Ahn
- Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
| | - Kata Osz
- Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
| | - Xueqing Lun
- Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
| | - Laura Mah
- Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
- Department of Microbiology, Immunology and Infectious Diseases, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Franz Zemp
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada
- Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
| | - Douglas Mahoney
- Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
- Department of Microbiology, Immunology and Infectious Diseases, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Donna L Senger
- Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada.
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.
| | - Jennifer A Chan
- Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada.
- Department of Pathology and Laboratory Medicine, University of Calgary, Calgary, AB, Canada.
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada.
| | - A Sorana Morrissy
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada.
- Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada.
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada.
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29
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Dong M, Su D, Kluger H, Fan R, Kluger Y. SIMVI reveals intrinsic and spatial-induced states in spatial omics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.28.554970. [PMID: 37693629 PMCID: PMC10491129 DOI: 10.1101/2023.08.28.554970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Spatial omics technologies enable the analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular interactions, and may thus fail to capture spatial regulations for further biological discoveries. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free framework that disentangles cell intrinsic and spatial-induced latent variables for modeling gene expression in spatial omics data. We derive theoretical support for SIMVI in disentangling intrinsic and spatial-induced variations. By this disentanglement, SIMVI enables estimation of spatial effects (SE) at a single-cell resolution, and opens up various opportunities for novel downstream analyses. To demonstrate the potential of SIMVI, we applied SIMVI to spatial omics data from diverse platforms and tissues (MERFISH human cortex, Slide-seqv2 mouse hippocampus, Slide-tags human tonsil, spatial multiome human melanoma, cohort-level CosMx melanoma). In all tested datasets, SIMVI effectively disentangles variations and infers accurate spatial effects compared with alternative methods. Moreover, on these datasets, SIMVI uniquely uncovers complex spatial regulations and dynamics of biological significance. In the human tonsil data, SIMVI illuminates the cyclical spatial dynamics of germinal center B cells during maturation. Applying SIMVI to both RNA and ATAC modalities of the multiome melanoma data reveals potential tumor epigenetic reprogramming states. Application of SIMVI on our newly-collected cohort-level CosMx melanoma dataset uncovers space-and-outcome-dependent macrophage states and the underlying cellular communication machinery in the tumor microenvironments.
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Affiliation(s)
- Mingze Dong
- Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - David Su
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
- Yale Center for Immuno-Oncology, Yale School of Medicine, New Haven, CT, USA
| | - Harriet Kluger
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
- Yale Center for Immuno-Oncology, Yale School of Medicine, New Haven, CT, USA
| | - Rong Fan
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Yuval Kluger
- Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
- Applied Mathematics Program, Yale University, New Haven, CT, USA
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30
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Liu N, Kattan WE, Mead BE, Kummerlowe C, Cheng T, Ingabire S, Cheah JH, Soule CK, Vrcic A, McIninch JK, Triana S, Guzman M, Dao TT, Peters JM, Lowder KE, Crawford L, Amini AP, Blainey PC, Hahn WC, Cleary B, Bryson B, Winter PS, Raghavan S, Shalek AK. Scalable, compressed phenotypic screening using pooled perturbations. Nat Biotechnol 2024:10.1038/s41587-024-02403-z. [PMID: 39375446 DOI: 10.1038/s41587-024-02403-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 08/26/2024] [Indexed: 10/09/2024]
Abstract
High-throughput phenotypic screens using biochemical perturbations and high-content readouts are constrained by limitations of scale. To address this, we establish a method of pooling exogenous perturbations followed by computational deconvolution to reduce required sample size, labor and cost. We demonstrate the increased efficiency of compressed experimental designs compared to conventional approaches through benchmarking with a bioactive small-molecule library and a high-content imaging readout. We then apply compressed screening in two biological discovery campaigns. In the first, we use early-passage pancreatic cancer organoids to map transcriptional responses to a library of recombinant tumor microenvironment protein ligands, uncovering reproducible phenotypic shifts induced by specific ligands distinct from canonical reference signatures and correlated with clinical outcome. In the second, we identify the pleotropic modulatory effects of a chemical compound library with known mechanisms of action on primary human peripheral blood mononuclear cell immune responses. In sum, our approach empowers phenotypic screens with information-rich readouts to advance drug discovery efforts and basic biological inquiry.
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Affiliation(s)
- Nuo Liu
- Institute for Medical Engineering and Science (IMES) and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
- Program in Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Walaa E Kattan
- Institute for Medical Engineering and Science (IMES) and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Benjamin E Mead
- Institute for Medical Engineering and Science (IMES) and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Conner Kummerlowe
- Institute for Medical Engineering and Science (IMES) and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
- Program in Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Thomas Cheng
- Institute for Medical Engineering and Science (IMES) and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Sarah Ingabire
- Institute for Medical Engineering and Science (IMES) and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Jaime H Cheah
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christian K Soule
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anita Vrcic
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jane K McIninch
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sergio Triana
- Institute for Medical Engineering and Science (IMES) and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Manuel Guzman
- Institute for Medical Engineering and Science (IMES) and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Tyler T Dao
- Institute for Medical Engineering and Science (IMES) and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Joshua M Peters
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kristen E Lowder
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Dana Farber Cancer Institute, Boston, MA, USA
| | - Lorin Crawford
- Microsoft Research, Cambridge, MA, USA
- Center for Computational Biology, Brown University, Providence, RI, USA
- Department of Biostatistics, Brown University, Providence, RI, USA
| | | | - Paul C Blainey
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - William C Hahn
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Dana Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Brian Cleary
- Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Department of Biology, Boston University, Boston, MA, USA
| | - Bryan Bryson
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Srivatsan Raghavan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Dana Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Alex K Shalek
- Institute for Medical Engineering and Science (IMES) and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA.
- Program in Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Program in Immunology, Harvard Medical School, Boston, MA, USA.
- Harvard Stem Cell Institute, Cambridge, MA, USA.
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31
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Chafamo D, Shanmugam V, Tokcan N. C-ziptf: stable tensor factorization for zero-inflated multi-dimensional genomics data. BMC Bioinformatics 2024; 25:323. [PMID: 39369208 PMCID: PMC11456250 DOI: 10.1186/s12859-024-05886-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 07/30/2024] [Indexed: 10/07/2024] Open
Abstract
In the past two decades, genomics has advanced significantly, with single-cell RNA-sequencing (scRNA-seq) marking a pivotal milestone. ScRNA-seq provides unparalleled insights into cellular diversity and has spurred diverse studies across multiple conditions and samples, resulting in an influx of complex multidimensional genomics data. This highlights the need for robust methodologies capable of handling the complexity and multidimensionality of such genomics data. Furthermore, single-cell data grapples with sparsity due to issues like low capture efficiency and dropout effects. Tensor factorizations (TF) have emerged as powerful tools to unravel the complex patterns from multi-dimensional genomics data. Classic TF methods, based on maximum likelihood estimation, struggle with zero-inflated count data, while the inherent stochasticity in TFs further complicates result interpretation and reproducibility. Our paper introduces Zero Inflated Poisson Tensor Factorization (ZIPTF), a novel method for high-dimensional zero-inflated count data factorization. We also present Consensus-ZIPTF (C-ZIPTF), merging ZIPTF with a consensus-based approach to address stochasticity. We evaluate our proposed methods on synthetic zero-inflated count data, simulated scRNA-seq data, and real multi-sample multi-condition scRNA-seq datasets. ZIPTF consistently outperforms baseline matrix and tensor factorization methods, displaying enhanced reconstruction accuracy for zero-inflated data. When dealing with high probabilities of excess zeros, ZIPTF achieves up to 2.4 × better accuracy. Moreover, C-ZIPTF notably enhances the factorization's consistency. When tested on synthetic and real scRNA-seq data, ZIPTF and C-ZIPTF consistently uncover known and biologically meaningful gene expression programs. Access our data and code at: https://github.com/klarman-cell-observatory/scBTF and https://github.com/klarman-cell-observatory/scbtf_experiments .
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Affiliation(s)
- Daniel Chafamo
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Vignesh Shanmugam
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, 02215, USA
| | - Neriman Tokcan
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
- Department of Mathematics, University of Massachusetts Boston, Boston, MA, 02125, USA.
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32
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Mou L, Lu Y, Wu Z, Pu Z, Wang M. Integrating genomics and AI to uncover molecular targets for mRNA vaccine development in lupus nephritis. Front Immunol 2024; 15:1381445. [PMID: 39430760 PMCID: PMC11486652 DOI: 10.3389/fimmu.2024.1381445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Accepted: 09/02/2024] [Indexed: 10/22/2024] Open
Abstract
Lupus nephritis (LN), a complex complication of systemic lupus erythematosus, requires in-depth cellular and molecular analysis for advanced treatment strategies, including mRNA vaccine development. In this study, we analyzed single-cell RNA sequencing data from 24 LN patients and 10 healthy controls, supplemented by bulk RNA-seq data from additional LN patients and controls. By applying non-negative matrix factorization (NMF), we identified four distinct leukocyte meta-programs in LN, highlighting diverse immune functions and potential mRNA vaccine targets. Utilizing 12 machine learning algorithms, we developed 417 predictive models incorporating gene sets linked to key biological pathways, such as MTOR signaling, autophagy, Toll-like receptor, and adaptive immunity pathways. These models were instrumental in identifying potential targets for mRNA vaccine development. Our functional network analysis further revealed intricate gene interactions, providing novel insights into the molecular basis of LN. Additionally, we validated the mRNA expression levels of potential vaccine targets across multiple cohorts and correlated them with clinical parameters such as the glomerular filtration rate (GFR) and pathological stage. This study represents a significant advance in LN research by merging single-cell genomics with the precision of NMF and machine learning, broadening our understanding of LN at the cellular and molecular levels. More importantly, our findings shed light on the development of targeted mRNA vaccines, offering new possibilities for diagnostics and therapeutics for this complex autoimmune disease.
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Affiliation(s)
- Lisha Mou
- Department of Rheumatology and Immunology, Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, China
- MetaLife Center, Shenzhen Institute of Translational Medicine, Shenzhen, Guangdong, China
| | - Ying Lu
- MetaLife Center, Shenzhen Institute of Translational Medicine, Shenzhen, Guangdong, China
| | - Zijing Wu
- MetaLife Center, Shenzhen Institute of Translational Medicine, Shenzhen, Guangdong, China
| | - Zuhui Pu
- MetaLife Center, Shenzhen Institute of Translational Medicine, Shenzhen, Guangdong, China
- Imaging Department, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, China
| | - Meiying Wang
- Department of Rheumatology and Immunology, Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, China
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33
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Dong X, Zhang D, Zhang X, Liu Y, Liu Y. Network modeling links kidney developmental programs and the cancer type-specificity of VHL mutations. NPJ Syst Biol Appl 2024; 10:114. [PMID: 39362887 PMCID: PMC11449910 DOI: 10.1038/s41540-024-00445-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Accepted: 09/21/2024] [Indexed: 10/05/2024] Open
Abstract
Elucidating the molecular dependencies behind the cancer-type specificity of driver mutations may reveal new therapeutic opportunities. We hypothesized that developmental programs would impact the transduction of oncogenic signaling activated by a driver mutation and shape its cancer-type specificity. Therefore, we designed a computational analysis framework by combining single-cell gene expression profiles during fetal organ development, latent factor discovery, and information theory-based differential network analysis to systematically identify transcription factors that selectively respond to driver mutations under the influence of organ-specific developmental programs. After applying this approach to VHL mutations, which are highly specific to clear cell renal cell carcinoma (ccRCC), we revealed important regulators downstream of VHL mutations in ccRCC and used their activities to cluster patients with ccRCC into three subtypes. This classification revealed a more significant difference in prognosis than the previous mRNA profile-based method and was validated in an independent cohort. Moreover, we found that EP300, a key epigenetic factor maintaining the regulatory network of the subtype with the worst prognosis, can be targeted by a small inhibitor, suggesting a potential treatment option for a subset of patients with ccRCC. This work demonstrated an intimate relationship between organ development and oncogenesis from the perspective of systems biology, and the method can be generalized to study the influence of other biological processes on cancer driver mutations.
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Affiliation(s)
- Xiaobao Dong
- Department of Genetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
| | - Donglei Zhang
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Xian Zhang
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yun Liu
- Department of Pediatric Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Yuanyuan Liu
- Department of Genetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
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Gosai SJ, Castro RI, Fuentes N, Butts JC, Mouri K, Alasoadura M, Kales S, Nguyen TTL, Noche RR, Rao AS, Joy MT, Sabeti PC, Reilly SK, Tewhey R. Machine-guided design of cell-type-targeting cis-regulatory elements. Nature 2024; 634:1211-1220. [PMID: 39443793 PMCID: PMC11525185 DOI: 10.1038/s41586-024-08070-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 09/18/2024] [Indexed: 10/25/2024]
Abstract
Cis-regulatory elements (CREs) control gene expression, orchestrating tissue identity, developmental timing and stimulus responses, which collectively define the thousands of unique cell types in the body1-3. While there is great potential for strategically incorporating CREs in therapeutic or biotechnology applications that require tissue specificity, there is no guarantee that an optimal CRE for these intended purposes has arisen naturally. Here we present a platform to engineer and validate synthetic CREs capable of driving gene expression with programmed cell-type specificity. We take advantage of innovations in deep neural network modelling of CRE activity across three cell types, efficient in silico optimization and massively parallel reporter assays to design and empirically test thousands of CREs4-8. Through large-scale in vitro validation, we show that synthetic sequences are more effective at driving cell-type-specific expression in three cell lines compared with natural sequences from the human genome and achieve specificity in analogous tissues when tested in vivo. Synthetic sequences exhibit distinct motif vocabulary associated with activity in the on-target cell type and a simultaneous reduction in the activity of off-target cells. Together, we provide a generalizable framework to prospectively engineer CREs from massively parallel reporter assay models and demonstrate the required literacy to write fit-for-purpose regulatory code.
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Affiliation(s)
- Sager J Gosai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Graduate Program in Biological and Biomedical Science, Boston, MA, USA.
- Department Of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.
- Howard Hughes Medical Institute, Chevy Chase, MD, USA.
| | | | - Natalia Fuentes
- The Jackson Laboratory, Bar Harbor, ME, USA
- Harvard College, Harvard University, Cambridge, MA, USA
| | - John C Butts
- The Jackson Laboratory, Bar Harbor, ME, USA
- Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME, USA
| | | | | | | | | | - Ramil R Noche
- Department of Comparative Medicine, Yale School of Medicine, New Haven, CT, USA
- Yale Zebrafish Research Core, Yale School of Medicine, New Haven, CT, USA
| | - Arya S Rao
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Mary T Joy
- The Jackson Laboratory, Bar Harbor, ME, USA
| | - Pardis C Sabeti
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department Of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Department of Immunology and Infectious Diseases, Harvard T H Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Steven K Reilly
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA.
- Wu Tsai Institute, Yale University, New Haven, CT, USA.
| | - Ryan Tewhey
- The Jackson Laboratory, Bar Harbor, ME, USA.
- Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME, USA.
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA, USA.
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35
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Najibi AJ, Lane RS, Sobral MC, Bovone G, Kang S, Freedman BR, Gutierrez Estupinan J, Elosegui-Artola A, Tringides CM, Dellacherie MO, Williams K, Ijaz H, Müller S, Turley SJ, Mooney DJ. Durable lymph-node expansion is associated with the efficacy of therapeutic vaccination. Nat Biomed Eng 2024; 8:1226-1242. [PMID: 38710838 PMCID: PMC11485260 DOI: 10.1038/s41551-024-01209-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 03/30/2024] [Indexed: 05/08/2024]
Abstract
Following immunization, lymph nodes dynamically expand and contract. The mechanical and cellular changes enabling the early-stage expansion of lymph nodes have been characterized, yet the durability of such responses and their implications for adaptive immunity and vaccine efficacy are unknown. Here, by leveraging high-frequency ultrasound imaging of the lymph nodes of mice, we report more potent and persistent lymph-node expansion for animals immunized with a mesoporous silica vaccine incorporating a model antigen than for animals given bolus immunization or standard vaccine formulations such as alum, and that durable and robust lymph-node expansion was associated with vaccine efficacy and adaptive immunity for 100 days post-vaccination in a mouse model of melanoma. Immunization altered the mechanical and extracellular-matrix properties of the lymph nodes, drove antigen-dependent proliferation of immune and stromal cells, and altered the transcriptional features of dendritic cells and inflammatory monocytes. Strategies that robustly maintain lymph-node expansion may result in enhanced vaccination outcomes.
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Affiliation(s)
- Alexander J Najibi
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Ryan S Lane
- Department of Cancer Immunology, Genentech, Inc., South San Francisco, CA, USA
| | - Miguel C Sobral
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Giovanni Bovone
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Shawn Kang
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Benjamin R Freedman
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Joel Gutierrez Estupinan
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Alberto Elosegui-Artola
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
- Institute for Bioengineering of Catalonia, Barcelona, Spain
| | - Christina M Tringides
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
- Harvard Program in Biophysics, Harvard University, Cambridge, MA, USA
| | - Maxence O Dellacherie
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Katherine Williams
- Department of Cancer Immunology, Genentech, Inc., South San Francisco, CA, USA
| | - Hamza Ijaz
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Sören Müller
- Department of Cancer Immunology, Genentech, Inc., South San Francisco, CA, USA
| | - Shannon J Turley
- Department of Cancer Immunology, Genentech, Inc., South San Francisco, CA, USA
| | - David J Mooney
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA.
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36
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Parker J. Organ Evolution: Emergence of Multicellular Function. Annu Rev Cell Dev Biol 2024; 40:51-74. [PMID: 38960448 DOI: 10.1146/annurev-cellbio-111822-121620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2024]
Abstract
Instances of multicellularity across the tree of life have fostered the evolution of complex organs composed of distinct cell types that cooperate, producing emergent biological functions. How organs originate is a fundamental evolutionary problem that has eluded deep mechanistic and conceptual understanding. Here I propose a cell- to organ-level transitions framework, whereby cooperative division of labor originates and becomes entrenched between cell types through a process of functional niche creation, cell-type subfunctionalization, and irreversible ratcheting of cell interdependencies. Comprehending this transition hinges on explaining how these processes unfold molecularly in evolving populations. Recent single-cell transcriptomic studies and analyses of terminal fate specification indicate that cellular functions are conferred by modular gene expression programs. These discrete components of functional variation may be deployed or combined within cells to introduce new properties into multicellular niches, or partitioned across cells to establish division of labor. Tracing gene expression program evolution at the level of single cells in populations may reveal transitions toward organ complexity.
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Affiliation(s)
- Joseph Parker
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA;
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37
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Almet AA, Tsai YC, Watanabe M, Nie Q. Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics. Nat Methods 2024; 21:1806-1817. [PMID: 39187683 PMCID: PMC11466815 DOI: 10.1038/s41592-024-02380-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 07/23/2024] [Indexed: 08/28/2024]
Abstract
From single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), one can extract high-dimensional gene expression patterns that can be described by intercellular communication networks or decoupled gene modules. These two descriptions of information flow are often assumed to occur independently. However, intercellular communication drives directed flows of information that are mediated by intracellular gene modules, in turn triggering outflows of other signals. Methodologies to describe such intercellular flows are lacking. We present FlowSig, a method that infers communication-driven intercellular flows from scRNA-seq or ST data using graphical causal modeling and conditional independence. We benchmark FlowSig using newly generated experimental cortical organoid data and synthetic data generated from mathematical modeling. We demonstrate FlowSig's utility by applying it to various studies, showing that FlowSig can capture stimulation-induced changes to paracrine signaling in pancreatic islets, demonstrate shifts in intercellular flows due to increasing COVID-19 severity and reconstruct morphogen-driven activator-inhibitor patterns in mouse embryogenesis.
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Affiliation(s)
- Axel A Almet
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA
- NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA
| | - Yuan-Chen Tsai
- Department of Anatomy & Neurobiology, University of California, Irvine, Irvine, CA, USA
- Sue & Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, USA
- School of Medicine, University of California, Irvine, Irvine, CA, USA
| | - Momoko Watanabe
- Department of Anatomy & Neurobiology, University of California, Irvine, Irvine, CA, USA
- Sue & Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, USA
- School of Medicine, University of California, Irvine, Irvine, CA, USA
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA.
- NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA.
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA.
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38
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Zhao J, Zhang X, Wang G, Lin Y, Liu T, Chang RB, Zhao H. INSPIRE: interpretable, flexible and spatially-aware integration of multiple spatial transcriptomics datasets from diverse sources. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.23.614539. [PMID: 39386646 PMCID: PMC11463460 DOI: 10.1101/2024.09.23.614539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/12/2024]
Abstract
Recent advances in spatial transcriptomics technologies have led to a growing number of diverse datasets, offering unprecedented opportunities to explore tissue organizations and functions within spatial contexts. However, it remains a significant challenge to effectively integrate and interpret these data, often originating from different samples, technologies, and developmental stages. In this paper, we present INSPIRE, a deep learning method for integrative analyses of multiple spatial transcriptomics datasets to address this challenge. With designs of graph neural networks and an adversarial learning mechanism, INSPIRE enables spatially informed and adaptable integration of data from varying sources. By incorporating non-negative matrix factorization, INSPIRE uncovers interpretable spatial factors with corresponding gene programs, revealing tissue architectures, cell type distributions and biological processes. We demonstrate the capabilities of INSPIRE by applying it to human cortex slices from different samples, mouse brain slices with complementary views, mouse hippocampus and embryo slices generated through different technologies, and spatiotemporal organogenesis atlases containing half a million spatial spots. INSPIRE shows superior performance in identifying detailed biological signals, effectively borrowing information across distinct profiling technologies, and elucidating dynamical changes during embryonic development. Furthermore, we utilize INSPIRE to build 3D models of tissues and whole organisms from multiple slices, demonstrating its power and versatility.
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Affiliation(s)
- Jia Zhao
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, USA
| | - Xiangyu Zhang
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, USA
| | - Gefei Wang
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, USA
| | - Yingxin Lin
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, USA
| | - Tianyu Liu
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, USA
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Rui B. Chang
- Department of Neuroscience, School of Medicine, Yale University, New Haven, CT, USA
- Department of Cellular and Molecular Physiology, School of Medicine, Yale University, New Haven, CT, USA
| | - Hongyu Zhao
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, USA
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
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39
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Stöber MC, Chamorro González R, Brückner L, Conrad T, Wittstruck N, Szymansky A, Eggert A, Schulte JH, Koche RP, Henssen AG, Schwarz RF, Haase K. Intercellular extrachromosomal DNA copy-number heterogeneity drives neuroblastoma cell state diversity. Cell Rep 2024; 43:114711. [PMID: 39255063 DOI: 10.1016/j.celrep.2024.114711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 05/20/2024] [Accepted: 08/20/2024] [Indexed: 09/12/2024] Open
Abstract
Neuroblastoma exhibits significant inter- and intra-tumor genetic heterogeneity and varying clinical outcomes. Extrachromosomal DNAs (ecDNAs) may drive this heterogeneity by independently segregating during cell division, leading to rapid oncogene amplification. While ecDNA-mediated oncogene amplification is linked to poor prognosis in various cancers, the effects of ecDNA copy-number heterogeneity on intermediate phenotypes are poorly understood. Here, we leverage DNA and RNA sequencing from the same single cells in cell lines and neuroblastoma patients to investigate these effects. By analyzing ecDNA amplicon structures, we reveal extensive intercellular ecDNA copy-number heterogeneity. We also provide direct evidence of how this heterogeneity influences the expression of cargo genes, including MYCN and its downstream targets, and the overall transcriptional state of neuroblastoma cells. Our findings highlight the role of ecDNA copy number in promoting rapid adaptability of cellular states within tumors, underscoring the need for ecDNA-specific treatment strategies to address tumor formation and adaptation.
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Affiliation(s)
- Maja C Stöber
- Berlin Institute for Medical Systems Biology at the Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 10115 Berlin, Germany; Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany; Humboldt-Universität zu Berlin, Faculty of Life Science, 10099 Berlin, Germany
| | - Rocío Chamorro González
- Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin, 13353 Berlin, Germany
| | - Lotte Brückner
- Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin, 13353 Berlin, Germany
| | - Thomas Conrad
- Berlin Institute for Medical Systems Biology at the Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 10115 Berlin, Germany; Berlin Institute of Health, 10178 Berlin, Germany
| | - Nadine Wittstruck
- Berlin Institute for Medical Systems Biology at the Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 10115 Berlin, Germany; Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin, 13353 Berlin, Germany
| | - Annabell Szymansky
- Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin, 13353 Berlin, Germany
| | - Angelika Eggert
- Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin, 13353 Berlin, Germany; German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Johannes H Schulte
- Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin, 13353 Berlin, Germany; German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Richard P Koche
- Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Anton G Henssen
- Berlin Institute for Medical Systems Biology at the Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 10115 Berlin, Germany; Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin, 13353 Berlin, Germany; German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Berlin Institute of Health, 10178 Berlin, Germany; Experimental and Clinical Research Center (ECRC) of the MDC and Charité Berlin, 13125 Berlin, Germany.
| | - Roland F Schwarz
- Institute for Computational Cancer Biology (ICCB), Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany; BIFOLD - Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany; Berlin Institute for Medical Systems Biology at the Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 10115 Berlin, Germany.
| | - Kerstin Haase
- Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin, 13353 Berlin, Germany; German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
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40
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Loh L, Carcy S, Krovi HS, Domenico J, Spengler A, Lin Y, Torres J, Prabakar RK, Palmer W, Norman PJ, Stone M, Brunetti T, Meyer HV, Gapin L. Unraveling the phenotypic states of human innate-like T cells: Comparative insights with conventional T cells and mouse models. Cell Rep 2024; 43:114705. [PMID: 39264810 PMCID: PMC11552652 DOI: 10.1016/j.celrep.2024.114705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 07/23/2024] [Accepted: 08/16/2024] [Indexed: 09/14/2024] Open
Abstract
The "innate-like" T cell compartment, known as Tinn, represents a diverse group of T cells that straddle the boundary between innate and adaptive immunity. We explore the transcriptional landscape of Tinn compared to conventional T cells (Tconv) in the human thymus and blood using single-cell RNA sequencing (scRNA-seq) and flow cytometry. In human blood, the majority of Tinn cells share an effector program driven by specific transcription factors, distinct from those governing Tconv cells. Conversely, only a fraction of thymic Tinn cells displays an effector phenotype, while others share transcriptional features with developing Tconv cells, indicating potential divergent developmental pathways. Unlike the mouse, human Tinn cells do not differentiate into multiple effector subsets but develop a mixed type 1/type 17 effector potential. Cross-species analysis uncovers species-specific distinctions, including the absence of type 2 Tinn cells in humans, which implies distinct immune regulatory mechanisms across species.
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Affiliation(s)
- Liyen Loh
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Salomé Carcy
- School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Joanne Domenico
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Andrea Spengler
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Yong Lin
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Joshua Torres
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Rishvanth K Prabakar
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - William Palmer
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Paul J Norman
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Tonya Brunetti
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Hannah V Meyer
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Laurent Gapin
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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41
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Stadtmauer DJ, Basanta Martínez S, Maziarz JD, Cole AG, Dagdas G, Smith GR, van Breukelen F, Pavličev M, Wagner GP. Cell type and cell signaling innovations underlying mammalian pregnancy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.01.591945. [PMID: 38746137 PMCID: PMC11092578 DOI: 10.1101/2024.05.01.591945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
How fetal and maternal cell types have co-evolved to enable mammalian placentation poses a unique evolutionary puzzle. Here, we present a multi-species atlas integrating single-cell transcriptomes from six species bracketing therian mammal diversity. We find that invasive trophoblasts share a gene-expression signature across eutherians, and evidence that endocrine decidual cells evolved stepwise from an immunomodulatory cell type retained in Tenrec with affinity to human decidua of menstruation. We recover evolutionary patterns in ligand-receptor signaling: fetal and maternal cells show a pronounced tendency towards disambiguation, but a predicted arms race dynamic between them is limited. We reconstruct cell communication networks of extinct mammalian ancestors, finding strong integration of fetal trophoblast into maternal networks. Together, our results reveal a dynamic history of cell type and signaling evolution. Synopsis The fetal-maternal interface is one of the most intense loci of cell-cell signaling in the human body. Invasion of cells from the fetal placenta into the uterus, and the corresponding transformation of maternal tissues called decidualization, first evolved in the stem lineage of eutherian mammals( 1 , 2 ). Single-cell studies of the human fetal-maternal interface have provided new insight into the cell type diversity and cell-cell interactions governing this chimeric organ( 3-5 ). However, the fetal-maternal interface is also one of the most rapidly evolving, and hence most diverse, characters among mammals( 6 ), and an evolutionary analysis is missing. Here, we present and compare single-cell data from the fetal-maternal interface of species bracketing key events in mammal phylogeny: a marsupial (opossum, Monodelphis domestica ), the afrotherian Tenrec ecaudatus, and four Euarchontoglires - guinea pig and mouse (Rodentia) together with recent macaque and human data (primates) ( 4 , 5 , 7 ). We infer cell type homologies, identify a gene-expression signature of eutherian invasive trophoblast conserved over 99 million years, and discover a predecidual cell in the tenrec which suggests stepwise evolution of the decidual stromal cell. We reconstruct ancestral cell signaling networks, revealing the integration of fetal cell types into the interface. Finally, we test two long-standing theoretical predictions, the disambiguation hypothesis( 8 ) and escalation hypothesis( 9 ), at transcriptome-wide scale, finding divergence between fetal and maternal signaling repertoires but arms race dynamics restricted to a small subset of ligand-receptor pairs. In so doing, we trace the co-evolutionary history of cell types and their signaling across mammalian viviparity.
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42
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Cahill R, Wang Y, Xian RP, Lee AJ, Zeng H, Yu B, Tasic B, Abbasi-Asl R. Unsupervised pattern identification in spatial gene expression atlas reveals mouse brain regions beyond established ontology. Proc Natl Acad Sci U S A 2024; 121:e2319804121. [PMID: 39226356 PMCID: PMC11406299 DOI: 10.1073/pnas.2319804121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 07/24/2024] [Indexed: 09/05/2024] Open
Abstract
The rapid growth of large-scale spatial gene expression data demands efficient and reliable computational tools to extract major trends of gene expression in their native spatial context. Here, we used stability-driven unsupervised learning (i.e., staNMF) to identify principal patterns (PPs) of 3D gene expression profiles and understand spatial gene distribution and anatomical localization at the whole mouse brain level. Our subsequent spatial correlation analysis systematically compared the PPs to known anatomical regions and ontology from the Allen Mouse Brain Atlas using spatial neighborhoods. We demonstrate that our stable and spatially coherent PPs, whose linear combinations accurately approximate the spatial gene data, are highly correlated with combinations of expert-annotated brain regions. These PPs yield a brain ontology based purely on spatial gene expression. Our PP identification approach outperforms principal component analysis and typical clustering algorithms on the same task. Moreover, we show that the stable PPs reveal marked regional imbalance of brainwide genetic architecture, leading to region-specific marker genes and gene coexpression networks. Our findings highlight the advantages of stability-driven machine learning for plausible biological discovery from dense spatial gene expression data, streamlining tasks that are infeasible by conventional manual approaches.
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Affiliation(s)
- Robert Cahill
- Department of Neurology, University of California, San Francisco, CA 94143
- UCSF Weill Institute for Neurosciences, San Francisco, CA 94143
| | - Yu Wang
- Department of Statistics, University of California, Berkeley, CA 94720
| | - R Patrick Xian
- Department of Neurology, University of California, San Francisco, CA 94143
- UCSF Weill Institute for Neurosciences, San Francisco, CA 94143
| | - Alex J Lee
- Department of Neurology, University of California, San Francisco, CA 94143
- UCSF Weill Institute for Neurosciences, San Francisco, CA 94143
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109
| | - Bin Yu
- Department of Statistics, University of California, Berkeley, CA 94720
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720
| | | | - Reza Abbasi-Asl
- Department of Neurology, University of California, San Francisco, CA 94143
- UCSF Weill Institute for Neurosciences, San Francisco, CA 94143
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94143
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Luo Y, Xia Y, Liu D, Li X, Li H, Liu J, Zhou D, Dong Y, Li X, Qian Y, Xu C, Tao K, Li G, Pan W, Zhong Q, Liu X, Xu S, Wang Z, Liu R, Zhang W, Shan W, Fang T, Wang S, Peng Z, Jin P, Jin N, Shi S, Chen Y, Wang M, Jiao X, Luo M, Gong W, Wang Y, Yao Y, Zhao Y, Huang X, Ji X, He Z, Zhao G, Liu R, Wu M, Chen G, Hong L, Ma D, Fang Y, Liang H, Gao Q. Neoadjuvant PARPi or chemotherapy in ovarian cancer informs targeting effector Treg cells for homologous-recombination-deficient tumors. Cell 2024; 187:4905-4925.e24. [PMID: 38971151 DOI: 10.1016/j.cell.2024.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 02/12/2024] [Accepted: 06/10/2024] [Indexed: 07/08/2024]
Abstract
Homologous recombination deficiency (HRD) is prevalent in cancer, sensitizing tumor cells to poly (ADP-ribose) polymerase (PARP) inhibition. However, the impact of HRD and related therapies on the tumor microenvironment (TME) remains elusive. Our study generates single-cell gene expression and T cell receptor profiles, along with validatory multimodal datasets from >100 high-grade serous ovarian cancer (HGSOC) samples, primarily from a phase II clinical trial (NCT04507841). Neoadjuvant monotherapy with the PARP inhibitor (PARPi) niraparib achieves impressive 62.5% and 73.6% response rates per RECIST v.1.1 and GCIG CA125, respectively. We identify effector regulatory T cells (eTregs) as key responders to HRD and neoadjuvant therapies, co-occurring with other tumor-reactive T cells, particularly terminally exhausted CD8+ T cells (Tex). TME-wide interferon signaling correlates with cancer cells upregulating MHC class II and co-inhibitory ligands, potentially driving Treg and Tex fates. Depleting eTregs in HRD mouse models, with or without PARP inhibition, significantly suppresses tumor growth without observable toxicities, underscoring the potential of eTreg-focused therapeutics for HGSOC and other HRD-related tumors.
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Affiliation(s)
- Yikai Luo
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yu Xia
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Dan Liu
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xiong Li
- Department of Gynecology & Obstetrics, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, China
| | - Huayi Li
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jiahao Liu
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Dongchen Zhou
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yu Dong
- Precision Scientific (Beijing) Co., Ltd., Beijing 100085, China
| | - Xin Li
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yiyu Qian
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Cheng Xu
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Kangjia Tao
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Guannan Li
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Wen Pan
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Qing Zhong
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xingzhe Liu
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Sen Xu
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zhi Wang
- Department of Gynecology & Obstetrics, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, China
| | - Ronghua Liu
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Wei Zhang
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Wanying Shan
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Tian Fang
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Siyuan Wang
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zikun Peng
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Ping Jin
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Ning Jin
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Shennan Shi
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yuxin Chen
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Mengjie Wang
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xiaofei Jiao
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Mengshi Luo
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Wenjian Gong
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Ya Wang
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yue Yao
- Precision Scientific (Beijing) Co., Ltd., Beijing 100085, China
| | - Yi Zhao
- Precision Scientific (Beijing) Co., Ltd., Beijing 100085, China
| | - Xinlin Huang
- Precision Scientific (Beijing) Co., Ltd., Beijing 100085, China
| | - Xuwo Ji
- Precision Scientific (Beijing) Co., Ltd., Beijing 100085, China
| | - Zhaoren He
- BioMap (Beijing) Intelligence Technology Limited, Beijing 100089, China
| | - Guangnian Zhao
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Rong Liu
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Mingfu Wu
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Gang Chen
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Li Hong
- Department of Gynecology and Obstetrics, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Ding Ma
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
| | - Yong Fang
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
| | - Han Liang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Qinglei Gao
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
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Engreitz JM, Lawson HA, Singh H, Starita LM, Hon GC, Carter H, Sahni N, Reddy TE, Lin X, Li Y, Munshi NV, Chahrour MH, Boyle AP, Hitz BC, Mortazavi A, Craven M, Mohlke KL, Pinello L, Wang T, Kundaje A, Yue F, Cody S, Farrell NP, Love MI, Muffley LA, Pazin MJ, Reese F, Van Buren E, Dey KK, Kircher M, Ma J, Radivojac P, Balliu B, Williams BA, Huangfu D, Park CY, Quertermous T, Das J, Calderwood MA, Fowler DM, Vidal M, Ferreira L, Mooney SD, Pejaver V, Zhao J, Gazal S, Koch E, Reilly SK, Sunyaev S, Carpenter AE, Buenrostro JD, Leslie CS, Savage RE, Giric S, Luo C, Plath K, Barrera A, Schubach M, Gschwind AR, Moore JE, Ahituv N, Yi SS, Hallgrimsdottir I, Gaulton KJ, Sakaue S, Booeshaghi S, Mattei E, Nair S, Pachter L, Wang AT, Shendure J, Agarwal V, Blair A, Chalkiadakis T, Chardon FM, Dash PM, Deng C, Hamazaki N, Keukeleire P, Kubo C, Lalanne JB, Maass T, Martin B, McDiarmid TA, Nobuhara M, Page NF, Regalado S, Sims J, Ushiki A, Best SM, Boyle G, Camp N, Casadei S, Da EY, Dawood M, Dawson 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Sverchkov Y, Weng Z, Garber M, Fu Y, Haas N, Li X, Phalke N, Shan SC, Shedd N, Yu T, Zhang Y, Zhou H, Battle A, Jerby L, Kotler E, Kundu S, Marderstein AR, Montgomery SB, Nigam A, Padhi EM, Patel A, Pritchard J, Raine I, Ramalingam V, Rodrigues KB, Schreiber JM, Singhal A, Sinha R, Wang AT, Abundis M, Bisht D, Chakraborty T, Fan J, Hall DR, Rarani ZH, Jain AK, Kaundal B, Keshari S, McGrail D, Pease NA, Yi VF, Wu H, Kannan S, Song H, Cai J, Gao Z, Kurzion R, Leu JI, Li F, Liang D, Ming GL, Musunuru K, Qiu Q, Shi J, Su Y, Tishkoff S, Xie N, Yang Q, Yang W, Zhang H, Zhang Z, Beer MA, Hadjantonakis AK, Adeniyi S, Cho H, Cutler R, Glenn RA, Godovich D, Hu N, Jovanic S, Luo R, Oh JW, Razavi-Mohseni M, Shigaki D, Sidoli S, Vierbuchen T, Wang X, Williams B, Yan J, Yang D, Yang Y, Sander M, Gaulton KJ, Ren B, Bartosik W, Indralingam HS, Klie A, Mummey H, Okino ML, Wang G, Zemke NR, Zhang K, Zhu H, Zaitlen N, Ernst J, Langerman J, Li T, Sun Y, Rudensky AY, Periyakoil PK, Gao VR, Smith MH, Thomas NM, Donlin LT, Lakhanpal A, Southard KM, Ardy RC, Cherry JM, Gerstein MB, Andreeva K, Assis PR, Borsari B, Douglass E, Dong S, Gabdank I, Graham K, Jolanki O, Jou J, Kagda MS, Lee JW, Li M, Lin K, Miyasato SR, Rozowsky J, Small C, Spragins E, Tanaka FY, Whaling IM, Youngworth IA, Sloan CA, Belter E, Chen X, Chisholm RL, Dickson P, Fan C, Fulton L, Li D, Lindsay T, Luan Y, Luo Y, Lyu H, Ma X, Macias-Velasco J, Miga KH, Quaid K, Stitziel N, Stranger BE, Tomlinson C, Wang J, Zhang W, Zhang B, Zhao G, Zhuo X, Brennand K, Ciccia A, Hayward SB, Huang JW, Leuzzi G, Taglialatela A, Thakar T, Vaitsiankova A, Dey KK, Ali TA, Kim A, Grimes HL, Salomonis N, Gupta R, Fang S, Lee-Kim V, Heinig M, Losert C, Jones TR, Donnard E, Murphy M, Roberts E, Song S, Mostafavi S, Sasse A, Spiro A, Pennacchio LA, Kato M, Kosicki M, Mannion B, Slaven N, Visel A, Pollard KS, Drusinsky S, Whalen S, Ray J, Harten IA, Ho CH, Sanjana NE, Caragine C, Morris JA, Seruggia D, Kutschat AP, Wittibschlager S, Xu H, Fu R, He W, Zhang L, Osorio D, Bly Z, Calluori S, Gilchrist DA, Hutter CM, Morris SA, Samer EK. Deciphering the impact of genomic variation on function. Nature 2024; 633:47-57. [PMID: 39232149 DOI: 10.1038/s41586-024-07510-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 05/02/2024] [Indexed: 09/06/2024]
Abstract
Our genomes influence nearly every aspect of human biology-from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.
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Chen K, Yu Q, Sha Q, Wang J, Fang J, Li X, Shen X, Fu B, Guo C. Single-cell transcriptomic analysis of immune cell dynamics in the healthy human endometrium. Biochem Biophys Rep 2024; 39:101802. [PMID: 39161579 PMCID: PMC11332207 DOI: 10.1016/j.bbrep.2024.101802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 06/25/2024] [Accepted: 07/23/2024] [Indexed: 08/21/2024] Open
Abstract
The microenvironment of the endometrial immune system is crucial to the success of placental implantation and healthy pregnancy. However, the functionalities of immune cells across various stages of the reproductive cycle have yet to be fully comprehended. To address this, we conducted advanced bioinformatic analysis on 230,049 high-quality single-cell transcriptomes from healthy endometrial samples obtained during the proliferative, secretory, early pregnancy, and late pregnancy stages. Our investigation has unveiled that proliferative natural killer (NK) cells, a potential source of endometrial NK cells, exhibit the most robust proliferative and differentiation potential during non-pregnant stages. We have also identified similar differentiation trajectories of NK cells originating from proliferative NK cells across four stages. Notably, during early pregnancy, NK cells demonstrate the highest oxidative phosphorylation metabolism activity, and, in conjunction with macrophages and T cells, exhibit the strongest type II interferon response. With spatial transcriptome data, we have discerned that the most robust immune-non-immune interactions are associated with the promotion and inhibition of cell proliferation, differentiation and migration during four stages. Furthermore, we have compiled lists of stage-specific risk genes implicated in reproductive diseases, which hold promise as potential disease biomarkers. Our study provides insights into the dynamics of the endometrial immune microenvironment during different reproductive cycle stages, thus serving as a reference for detecting pathological changes during pregnancy.
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Affiliation(s)
- Kaixing Chen
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230021, China
- CAS Center for Excellence in Molecular Cell Sciences, The CAS Key Laboratory of Innate Immunity and Chronic Disease, University of Science and Technology of China, 230027, Hefei, Anhui, China
| | - Qiaoni Yu
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230021, China
| | - Qing Sha
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230021, China
| | - Junyu Wang
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230021, China
| | - Jingwen Fang
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230021, China
- HanGene Biotech, Xiaoshan Innovation Polis, Hangzhou, Zhejiang, 311200, China
| | - Xin Li
- Department of Rheumatology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, 121001, China
| | - Xiaokun Shen
- Department of Immunology, School of Basic Medical Science, Jinzhou Medical University, Jinzhou, 121001, China
| | - Binqing Fu
- CAS Center for Excellence in Molecular Cell Sciences, The CAS Key Laboratory of Innate Immunity and Chronic Disease, University of Science and Technology of China, 230027, Hefei, Anhui, China
| | - Chuang Guo
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230021, China
- CAS Center for Excellence in Molecular Cell Sciences, The CAS Key Laboratory of Innate Immunity and Chronic Disease, University of Science and Technology of China, 230027, Hefei, Anhui, China
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Liu J, Castillo-Hair SM, Du LY, Wang Y, Carte AN, Colomer-Rosell M, Yin C, Seelig G, Schier AF. Dissecting the regulatory logic of specification and differentiation during vertebrate embryogenesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.27.609971. [PMID: 39253514 PMCID: PMC11383055 DOI: 10.1101/2024.08.27.609971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
The interplay between transcription factors and chromatin accessibility regulates cell type diversification during vertebrate embryogenesis. To systematically decipher the gene regulatory logic guiding this process, we generated a single-cell multi-omics atlas of RNA expression and chromatin accessibility during early zebrafish embryogenesis. We developed a deep learning model to predict chromatin accessibility based on DNA sequence and found that a small number of transcription factors underlie cell-type-specific chromatin landscapes. While Nanog is well-established in promoting pluripotency, we discovered a new function in priming the enhancer accessibility of mesendodermal genes. In addition to the classical stepwise mode of differentiation, we describe instant differentiation, where pluripotent cells skip intermediate fate transitions and terminally differentiate. Reconstruction of gene regulatory interactions reveals that this process is driven by a shallow network in which maternally deposited regulators activate a small set of transcription factors that co-regulate hundreds of differentiation genes. Notably, misexpression of these transcription factors in pluripotent cells is sufficient to ectopically activate their targets. This study provides a rich resource for analyzing embryonic gene regulation and reveals the regulatory logic of instant differentiation.
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Affiliation(s)
- Jialin Liu
- Biozentrum, University of Basel, Basel, 4056, Switzerland
- Allen Discovery Center for Cell Lineage Tracing, University of Washington, Seattle, WA, 98195, USA
| | | | - Lucia Y. Du
- Biozentrum, University of Basel, Basel, 4056, Switzerland
- Allen Discovery Center for Cell Lineage Tracing, University of Washington, Seattle, WA, 98195, USA
| | - Yiqun Wang
- Biozentrum, University of Basel, Basel, 4056, Switzerland
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, UCSD, La Jolla, CA, 92037, USA
| | - Adam N. Carte
- Biozentrum, University of Basel, Basel, 4056, Switzerland
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, 02115, USA
| | - Mariona Colomer-Rosell
- Biozentrum, University of Basel, Basel, 4056, Switzerland
- Allen Discovery Center for Cell Lineage Tracing, University of Washington, Seattle, WA, 98195, USA
| | - Christopher Yin
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, 98195, USA
| | - Georg Seelig
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, 98195, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, 98195, USA
| | - Alexander F. Schier
- Biozentrum, University of Basel, Basel, 4056, Switzerland
- Allen Discovery Center for Cell Lineage Tracing, University of Washington, Seattle, WA, 98195, USA
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Yates J, Mathey-Andrews C, Park J, Garza A, Gagné A, Hoffman S, Bi K, Titchen B, Hennessey C, Remland J, Shannon E, Camp S, Balamurali S, Cavale SK, Li Z, Raghawan AK, Kraft A, Boland G, Aguirre AJ, Sethi NS, Boeva V, Van Allen E. Cell states and neighborhoods in distinct clinical stages of primary and metastatic esophageal adenocarcinoma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.17.608386. [PMID: 39229240 PMCID: PMC11370330 DOI: 10.1101/2024.08.17.608386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Esophageal adenocarcinoma (EAC) is a highly lethal cancer of the upper gastrointestinal tract with rising incidence in western populations. To decipher EAC disease progression and therapeutic response, we performed multiomic analyses of a cohort of primary and metastatic EAC tumors, incorporating single-nuclei transcriptomic and chromatin accessibility sequencing, along with spatial profiling. We identified tumor microenvironmental features previously described to associate with therapy response. We identified five malignant cell programs, including undifferentiated, intermediate, differentiated, epithelial-to-mesenchymal transition, and cycling programs, which were associated with differential epigenetic plasticity and clinical outcomes, and for which we inferred candidate transcription factor regulons. Furthermore, we revealed diverse spatial localizations of malignant cells expressing their associated transcriptional programs and predicted their significant interactions with microenvironmental cell types. We validated our findings in three external single-cell RNA-seq and three bulk RNA-seq studies. Altogether, our findings advance the understanding of EAC heterogeneity, disease progression, and therapeutic response.
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Affiliation(s)
- Josephine Yates
- Institute for Machine Learning, Department of Computer Science, ETH Zürich, Zurich, Switzerland
- ETH AI Center, ETH Zurich, Zurich, Switzerland
- Swiss Institute for Bioinformatics (SIB), Lausanne, Switzerland
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Camille Mathey-Andrews
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jihye Park
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Amanda Garza
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Andréanne Gagné
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Samantha Hoffman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Division of Medical Sciences, Harvard University, Boston, Massachusetts, USA
| | - Kevin Bi
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Breanna Titchen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Division of Medical Sciences, Harvard University, Boston, Massachusetts, USA
| | | | - Joshua Remland
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Erin Shannon
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Sabrina Camp
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Siddhi Balamurali
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Shweta Kiran Cavale
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Zhixin Li
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Akhouri Kishore Raghawan
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Agnieszka Kraft
- Institute for Machine Learning, Department of Computer Science, ETH Zürich, Zurich, Switzerland
- Swiss Institute for Bioinformatics (SIB), Lausanne, Switzerland
| | - Genevieve Boland
- Department of Surgery, Division of Gastrointestinal and Surgical Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Andrew J Aguirre
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Division of Medical Sciences, Harvard University, Boston, Massachusetts, USA
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Nilay S Sethi
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Valentina Boeva
- Institute for Machine Learning, Department of Computer Science, ETH Zürich, Zurich, Switzerland
- ETH AI Center, ETH Zurich, Zurich, Switzerland
- Swiss Institute for Bioinformatics (SIB), Lausanne, Switzerland
- Cochin Institute, Inserm U1016, CNRS UMR 8104, Paris Descartes University UMR-S1016, Paris 75014, France
| | - Eliezer Van Allen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Division of Medical Sciences, Harvard University, Boston, Massachusetts, USA
- Parker Institute for Cancer Immunotherapy, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
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Mou L, Lu Y, Wu Z, Pu Z, Huang X, Wang M. Applying 12 machine learning algorithms and Non-negative Matrix Factorization for robust prediction of lupus nephritis. Front Immunol 2024; 15:1391218. [PMID: 39224582 PMCID: PMC11366613 DOI: 10.3389/fimmu.2024.1391218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 07/23/2024] [Indexed: 09/04/2024] Open
Abstract
Lupus nephritis (LN) is a challenging condition with limited diagnostic and treatment options. In this study, we applied 12 distinct machine learning algorithms along with Non-negative Matrix Factorization (NMF) to analyze single-cell datasets from kidney biopsies, aiming to provide a comprehensive profile of LN. Through this analysis, we identified various immune cell populations and their roles in LN progression and constructed 102 machine learning-based immune-related gene (IRG) predictive models. The most effective models demonstrated high predictive accuracy, evidenced by Area Under the Curve (AUC) values, and were further validated in external cohorts. These models highlight six hub IRGs (CD14, CYBB, IFNGR1, IL1B, MSR1, and PLAUR) as key diagnostic markers for LN, showing remarkable diagnostic performance in both renal and peripheral blood cohorts, thus offering a novel approach for noninvasive LN diagnosis. Further clinical correlation analysis revealed that expressions of IFNGR1, PLAUR, and CYBB were negatively correlated with the glomerular filtration rate (GFR), while CYBB also positively correlated with proteinuria and serum creatinine levels, highlighting their roles in LN pathophysiology. Additionally, protein-protein interaction (PPI) analysis revealed significant networks involving hub IRGs, emphasizing the importance of the interleukin family and chemokines in LN pathogenesis. This study highlights the potential of integrating advanced genomic tools and machine learning algorithms to improve diagnosis and personalize management of complex autoimmune diseases like LN.
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Affiliation(s)
- Lisha Mou
- Department of Rheumatology and Immunology, Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
- MetaLife Lab, Shenzhen Institute of Translational Medicine, Shenzhen, Guangdong, China
| | - Ying Lu
- Department of Rheumatology and Immunology, Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
- MetaLife Lab, Shenzhen Institute of Translational Medicine, Shenzhen, Guangdong, China
| | - Zijing Wu
- Department of Rheumatology and Immunology, Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
- MetaLife Lab, Shenzhen Institute of Translational Medicine, Shenzhen, Guangdong, China
| | - Zuhui Pu
- Imaging Department, Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
| | - Xiaoyan Huang
- Department of Nephrology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Meiying Wang
- Department of Rheumatology and Immunology, Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
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Moriano J, Leonardi O, Vitriolo A, Testa G, Boeckx C. A multi-layered integrative analysis reveals a cholesterol metabolic program in outer radial glia with implications for human brain evolution. Development 2024; 151:dev202390. [PMID: 39114968 PMCID: PMC11385646 DOI: 10.1242/dev.202390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 07/18/2024] [Indexed: 08/28/2024]
Abstract
The definition of molecular and cellular mechanisms contributing to brain ontogenetic trajectories is essential to investigate the evolution of our species. Yet their functional dissection at an appropriate level of granularity remains challenging. Capitalizing on recent efforts that have extensively profiled neural stem cells from the developing human cortex, we develop an integrative computational framework to perform trajectory inference and gene regulatory network reconstruction, (pseudo)time-informed non-negative matrix factorization for learning the dynamics of gene expression programs, and paleogenomic analysis for a higher-resolution mapping of derived regulatory variants in our species in comparison with our closest relatives. We provide evidence for cell type-specific regulation of gene expression programs during indirect neurogenesis. In particular, our analysis uncovers a key role for a cholesterol program in outer radial glia, regulated by zinc-finger transcription factor KLF6. A cartography of the regulatory landscape impacted by Homo sapiens-derived variants reveals signals of selection clustering around regulatory regions associated with GLI3, a well-known regulator of radial glial cell cycle, and impacting KLF6 regulation. Our study contributes to the evidence of significant changes in metabolic pathways in recent human brain evolution.
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Affiliation(s)
- Juan Moriano
- Department of General Linguistics, University of Barcelona, 08007 Barcelona, Spain
- University of Barcelona Institute of Complex Systems, 08007 Barcelona, Spain
| | | | - Alessandro Vitriolo
- Human Technopole, Viale Rita Levi-Montalcini 1, 20157 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Via Santa Sofia 9, 20122 Milan, Italy
| | - Giuseppe Testa
- Human Technopole, Viale Rita Levi-Montalcini 1, 20157 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Via Santa Sofia 9, 20122 Milan, Italy
| | - Cedric Boeckx
- Department of General Linguistics, University of Barcelona, 08007 Barcelona, Spain
- University of Barcelona Institute of Complex Systems, 08007 Barcelona, Spain
- University of Barcelona Institute of Neurosciences, 08007 Barcelona, Spain
- Catalan Institute for Research and Advanced Studies (ICREA), 08007 Barcelona, Spain
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50
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He H, Chen S, Yu Y, Fan Z, Qian Y, Dong Y, Song Y, Zhong C, Sun X, Cao Q, Li S, Huang W, Li W, Zhuang M, Yang J, Wang X, Wang J, Wu D, Wang H, Wen W. Comprehensive single-cell analysis deciphered microenvironmental dynamics and immune regulator olfactomedin 4 in pathogenesis of gallbladder cancer. Gut 2024; 73:1529-1542. [PMID: 38719336 PMCID: PMC11347255 DOI: 10.1136/gutjnl-2023-331773] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 04/20/2024] [Indexed: 08/10/2024]
Abstract
OBJECTIVE Elucidating complex ecosystems and molecular features of gallbladder cancer (GBC) and benign gallbladder diseases is pivotal to proactive cancer prevention and optimal therapeutic intervention. DESIGN We performed single-cell transcriptome analysis on 230 737 cells from 15 GBCs, 4 cholecystitis samples, 3 gallbladder polyps, 5 gallbladder adenomas and 16 adjacent normal tissues. Findings were validated through large-scale histological assays, digital spatial profiler multiplexed immunofluorescence (GeoMx), etc. Further molecular mechanism was demonstrated with in vitro and in vivo studies. RESULTS The cell atlas unveiled an altered immune landscape across different pathological states of gallbladder diseases. GBC featured a more suppressive immune microenvironment with distinct T-cell proliferation patterns and macrophage attributions in different GBC subtypes. Notably, mutual exclusivity between stromal and immune cells was identified and remarkable stromal ecosystem (SC) heterogeneity during GBC progression was unveiled. Specifically, SC1 demonstrated active interaction between Fibro-iCAF and Endo-Tip cells, correlating with poor prognosis. Moreover, epithelium genetic variations within adenocarcinoma (AC) indicated an evolutionary similarity between adenoma and AC. Importantly, our study identified elevated olfactomedin 4 (OLFM4) in epithelial cells as a central player in GBC progression. OLFM4 was related to T-cell malfunction and tumour-associated macrophage infiltration, leading to a worse prognosis in GBC. Further investigations revealed that OLFM4 upregulated programmed death-ligand 1 (PD-L1) expression through the MAPK-AP1 axis, facilitating tumour cell immune evasion. CONCLUSION These findings offer a valuable resource for understanding the pathogenesis of gallbladder diseases and indicate OLFM4 as a potential biomarker and therapeutic target for GBC.
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Affiliation(s)
- Huisi He
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- International Cooperation Laboratory on Signal Transduction, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Shuzhen Chen
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- International Cooperation Laboratory on Signal Transduction, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Yong Yu
- Department I of Biliary Tract Surgery, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Zhecai Fan
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- International Cooperation Laboratory on Signal Transduction, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Youwen Qian
- Department of Pathology, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Yaping Dong
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yuting Song
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- International Cooperation Laboratory on Signal Transduction, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Caiming Zhong
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- Department of Laboratory Diagnosis, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Xiaojuan Sun
- Department of Laboratory Diagnosis, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Qiqi Cao
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- International Cooperation Laboratory on Signal Transduction, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Shiyao Li
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- International Cooperation Laboratory on Signal Transduction, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Weihan Huang
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- Department of Laboratory Diagnosis, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Wenxin Li
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- Department of Laboratory Diagnosis, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Mingzhu Zhuang
- Department of Laboratory Diagnosis, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Jinxian Yang
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- International Cooperation Laboratory on Signal Transduction, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Xianming Wang
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jiaqian Wang
- Engineering Center for Translational Medicine of Precision Cancer Immunodiagnosis and Therapy, YuceBio Technology Co Ltd, Shenzhen, China
| | - Dongfang Wu
- Engineering Center for Translational Medicine of Precision Cancer Immunodiagnosis and Therapy, YuceBio Technology Co Ltd, Shenzhen, China
- Key Laboratory of Gene Engineering of the Ministry of Education, Institute of Healthy Aging Research, School of Life Sciences, Sun-Yat-sen University, Guangzhou, China
| | - Hongyang Wang
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- International Cooperation Laboratory on Signal Transduction, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wen Wen
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- Department of Laboratory Diagnosis, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
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