1
|
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.
Collapse
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.
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
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] [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.
Collapse
|
4
|
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.
Collapse
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.
| |
Collapse
|
5
|
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.
Collapse
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.
| |
Collapse
|
6
|
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 .
Collapse
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.
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
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.
Collapse
Affiliation(s)
- Joseph Parker
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA;
| |
Collapse
|
9
|
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.
Collapse
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.
| |
Collapse
|
10
|
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] [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.
Collapse
|
11
|
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.
Collapse
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.
| |
Collapse
|
12
|
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 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.
Collapse
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.
| |
Collapse
|
13
|
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.
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
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.
Collapse
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.
| |
Collapse
|
16
|
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:e2400524. [PMID: 39236316 DOI: 10.1002/advs.202400524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [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.
Collapse
Affiliation(s)
- Liang Dong
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Clinical Research Center (CRC), Medical Pathology Center (MPC), Cancer Early Detection and Treatment Center (CEDTC), Chongqing University Three Gorges Hospital, Chongqing University, Chongqing, 404100, China
- Translational Medicine Research Center (TMRC), School of Medicine Chongqing University, Chongqing, 404100, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Shujun Hu
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Research Center of Oral and Maxillofacail Tumor, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Insititute of Oral Cancer and Precancerous Lesions, Central South University, Changsha, Hunan, 410008, China
| | - Xin Li
- Clinical Research Center (CRC), Medical Pathology Center (MPC), Cancer Early Detection and Treatment Center (CEDTC), Chongqing University Three Gorges Hospital, Chongqing University, Chongqing, 404100, China
- Translational Medicine Research Center (TMRC), School of Medicine Chongqing University, Chongqing, 404100, China
| | - Shiyao Pei
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Department of Dermatology, Third Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Liping Jin
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Lining Zhang
- Clinical Research Center (CRC), Medical Pathology Center (MPC), Cancer Early Detection and Treatment Center (CEDTC), Chongqing University Three Gorges Hospital, Chongqing University, Chongqing, 404100, China
- Translational Medicine Research Center (TMRC), School of Medicine Chongqing University, Chongqing, 404100, China
| | - Xiang Chen
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Anjie Min
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Research Center of Oral and Maxillofacail Tumor, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Insititute of Oral Cancer and Precancerous Lesions, Central South University, Changsha, Hunan, 410008, China
| | - Mingzhu Yin
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Clinical Research Center (CRC), Medical Pathology Center (MPC), Cancer Early Detection and Treatment Center (CEDTC), Chongqing University Three Gorges Hospital, Chongqing University, Chongqing, 404100, China
- Translational Medicine Research Center (TMRC), School of Medicine Chongqing University, Chongqing, 404100, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| |
Collapse
|
17
|
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 SC, Fayer S, Hamm A, James RG, Jarvik GP, McEwen AE, Moore N, Pendyala S, Popp NA, Post M, Rubin AF, Smith NT, Stone J, Tejura M, Wang ZR, Wheelock MK, Woo I, Zapp BD, Amgalan D, Aradhana A, Arana SM, Bassik MC, Bauman JR, Bhattacharya A, Cai XS, Chen Z, Conley S, Deshpande S, Doughty BR, Du PP, Galante JA, Gifford C, Greenleaf WJ, Guo K, Gupta R, Isobe S, Jagoda E, Jain N, Jones H, Kang HY, Kim SH, Kim Y, Klemm S, Kundu R, Kundu S, Lago-Docampo M, Lee-Yow YC, Levin-Konigsberg R, Li DY, Lindenhofer D, Ma XR, Marinov GK, Martyn GE, McCreery CV, Metzl-Raz E, Monteiro JP, Montgomery MT, Mualim KS, Munger C, Munson G, Nguyen TC, Nguyen T, Palmisano BT, Pampari A, Rabinovitch M, Ramste M, Ray J, Roy KR, Rubio OM, Schaepe JM, Schnitzler G, Schreiber J, Sharma D, Sheth MU, Shi H, Singh V, Sinha R, Steinmetz LM, Tan J, Tan A, Tycko J, Valbuena RC, Amiri VVP, van Kooten MJFM, Vaughan-Jackson A, Venida A, Weldy CS, Worssam MD, Xia F, Yao D, Zeng T, Zhao Q, Zhou R, Chen ZS, Cimini BA, Coppin G, Coté AG, Haghighi M, Hao T, Hill DE, Lacoste J, Laval F, Reno C, Roth FP, Singh S, Spirohn-Fitzgerald K, Taipale M, Teelucksingh T, Tixhon M, Yadav A, Yang Z, Kraus WL, Armendariz DA, Dederich AE, Gogate A, El Hayek L, Goetsch SC, Kaur K, Kim HB, McCoy MK, Nzima MZ, Pinzón-Arteaga CA, Posner BA, Schmitz DA, Sivakumar S, Sundarrajan A, Wang L, Wang Y, Wu J, Xu L, Xu J, Yu L, Zhang Y, Zhao H, Zhou Q, Won H, Bell JL, Broadaway KA, Degner KN, Etheridge AS, Koller BH, Mah W, Mu W, Ritola KD, Rosen JD, Schoenrock SA, Sharp RA, Bauer D, Lettre G, Sherwood R, Becerra B, Blaine LJ, Che E, Francoeur MJ, Gibbs EN, Kim N, King EM, Kleinstiver BP, Lecluze E, Li Z, Patel ZM, Phan QV, Ryu J, Starr ML, Wu T, Gersbach CA, Crawford GE, Allen AS, Majoros WH, Iglesias N, Rai R, Venukuttan R, Li B, Anglen T, Bounds LR, Hamilton MC, Liu S, McCutcheon SR, McRoberts Amador CD, Reisman SJ, ter Weele MA, Bodle JC, Streff HL, Siklenka K, Strouse K, Bernstein BE, Babu J, Corona GB, Dong K, Duarte FM, Durand NC, Epstein CB, Fan K, Gaskell E, Hall AW, Ham AM, Knudson MK, Shoresh N, Wekhande S, White CM, Xi W, Satpathy AT, Corces MR, Chang SH, Chin IM, Gardner JM, Gardell ZA, Gutierrez JC, Johnson AW, Kampman L, Kasowski M, Lareau CA, Liu V, Ludwig LS, McGinnis CS, Menon S, Qualls A, Sandor K, Turner AW, Ye CJ, Yin Y, Zhang W, Wold BJ, Carilli M, Cheong D, Filibam G, Green K, Kawauchi S, Kim C, Liang H, Loving R, Luebbert L, MacGregor G, Merchan AG, Rebboah E, Rezaie N, Sakr J, Sullivan DK, Swarna N, Trout D, Upchurch S, Weber R, Castro CP, Chou E, Feng F, Guerra A, Huang Y, Jiang L, Liu J, Mills RE, Qian W, Qin T, Sartor MA, Sherpa RN, Wang J, Wang Y, Welch JD, Zhang Z, Zhao N, Mukherjee S, Page CD, Clarke S, Doty RW, Duan Y, Gordan R, Ko KY, Li S, Li B, Thomson A, Raychaudhuri S, Price A, Ali TA, Dey KK, Durvasula A, Kellis M, Iakoucheva LM, Kakati T, Chen Y, Benazouz M, Jain S, Zeiberg D, De Paolis Kaluza MC, Velyunskiy M, Gasch A, Huang K, Jin Y, Lu Q, Miao J, Ohtake M, Scopel E, Steiner RD, 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.
Collapse
|
18
|
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.
Collapse
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
| |
Collapse
|
19
|
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.
Collapse
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
| |
Collapse
|
20
|
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.
Collapse
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
| |
Collapse
|
21
|
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.
Collapse
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
| |
Collapse
|
22
|
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.
Collapse
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
| |
Collapse
|
23
|
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.
Collapse
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
| |
Collapse
|
24
|
Pouyabahar D, Andrews T, Bader GD. Interpretable single-cell factor decomposition using sciRED. RESEARCH SQUARE 2024:rs.3.rs-4819117. [PMID: 39149508 PMCID: PMC11326389 DOI: 10.21203/rs.3.rs-4819117/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [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.
Collapse
Affiliation(s)
- Delaram Pouyabahar
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Tallulah Andrews
- Department of Biochemistry, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
- Department of Computer Science, University of Western Ontario, London, Ontario, Canada
| | - Gary D Bader
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
- Princess Margaret Research Institute, University Health Network, Toronto, Ontario, Canada
- CIFAR Multiscale Human Program, CIFAR, Toronto, Ontario, Canada
| |
Collapse
|
25
|
Pouyabahar D, Andrews T, Bader GD. 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.
Collapse
Affiliation(s)
- Delaram Pouyabahar
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Tallulah Andrews
- Department of Biochemistry, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
- Department of Computer Science, University of Western Ontario, London, Ontario, Canada
| | - Gary D Bader
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
- Princess Margaret Research Institute, University Health Network, Toronto, Ontario, Canada
- CIFAR Multiscale Human Program, CIFAR, Toronto, Ontario, Canada
| |
Collapse
|
26
|
Xu L, Zhang Y, Lin Z, Deng X, Ren X, Huang M, Li S, Zhou Q, Fang F, Yang Q, Zheng G, Chen Z, Wu Z, Sun X, Lin J, Shen J, Guo J, Li X, Xue T, Tan J, Lin X, Tan L, Peng H, Shen S, Peng S, Li S, Liang L, Cleary JM, Lai J, Xie Y, Kuang M. FASN-mediated fatty acid biosynthesis remodels immune environment in Clonorchis sinensis infection-related intrahepatic cholangiocarcinoma. J Hepatol 2024; 81:265-277. [PMID: 38508240 DOI: 10.1016/j.jhep.2024.03.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 03/03/2024] [Accepted: 03/04/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND & AIMS Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver cancer and is highly lethal. Clonorchis sinensis (C. sinensis) infection is an important risk factor for iCCA. Here we investigated the clinical impact and underlying molecular characteristics of C. sinensis infection-related iCCA. METHODS We performed single-cell RNA sequencing, whole-exome sequencing, RNA sequencing, metabolomics and spatial transcriptomics in 251 patients with iCCA from three medical centers. Alterations in metabolism and the immune microenvironment of C. sinensis-related iCCAs were validated through an in vitro co-culture system and in a mouse model of iCCA. RESULTS We revealed that C. sinensis infection was significantly associated with iCCA patients' overall survival and response to immunotherapy. Fatty acid biosynthesis and the expression of fatty acid synthase (FASN), a key enzyme catalyzing long-chain fatty acid synthesis, were significantly enriched in C. sinensis-related iCCAs. iCCA cell lines treated with excretory/secretory products of C. sinensis displayed elevated FASN and free fatty acids. The metabolic alteration of tumor cells was closely correlated with the enrichment of tumor-associated macrophage (TAM)-like macrophages and the impaired function of T cells, which led to formation of an immunosuppressive microenvironment and tumor progression. Spatial transcriptomics analysis revealed that malignant cells were in closer juxtaposition with TAM-like macrophages in C. sinensis-related iCCAs than non-C. sinensis-related iCCAs. Importantly, treatment with a FASN inhibitor significantly reversed the immunosuppressive microenvironment and enhanced anti-PD-1 efficacy in iCCA mouse models treated with excretory/secretory products from C. sinensis. CONCLUSIONS We provide novel insights into metabolic alterations and the immune microenvironment in C. sinensis infection-related iCCAs. We also demonstrate that the combination of a FASN inhibitor with immunotherapy could be a promising strategy for the treatment of C. sinensis-related iCCAs. IMPACT AND IMPLICATIONS Clonorchis sinensis (C. sinensis)-infected patients with intrahepatic cholangiocarcinoma (iCCA) have a worse prognosis and response to immunotherapy than non-C. sinensis-infected patients with iCCA. The underlying molecular characteristics of C. sinensis infection-related iCCAs remain unclear. Herein, we demonstrate that upregulation of FASN (fatty acid synthase) and free fatty acids in C. sinensis-related iCCAs leads to formation of an immunosuppressive microenvironment and tumor progression. Thus, administration of FASN inhibitors could significantly reverse the immunosuppressive microenvironment and further enhance the efficacy of anti-PD-1 against C. sinensis-related iCCAs.
Collapse
Affiliation(s)
- Lixia Xu
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
| | - Ying Zhang
- Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhilong Lin
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xinlang Deng
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiaoxue Ren
- Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Mingle Huang
- Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shangru Li
- Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Qianying Zhou
- Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Fei Fang
- Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Qingxia Yang
- Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Gaomin Zheng
- Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zebin Chen
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhongdao Wu
- Department of Parasitology of Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xi Sun
- Department of Parasitology of Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Jie Lin
- Second Department of General Surgery, Shunde Hospital, Southern Medical University, Foshan, China
| | - Jingxian Shen
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jianping Guo
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaoxing Li
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Tianchen Xue
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jing Tan
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China; Laboratory of Cancer Epigenome, Division of Medical Sciences, National Cancer Centre Singapore, Singapore
| | - Xiaoxuan Lin
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Li Tan
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Hong Peng
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shunli Shen
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Sui Peng
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shaoqiang Li
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Lijian Liang
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - James M Cleary
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, USA
| | - Jiaming Lai
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| | - Yubin Xie
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
| | - Ming Kuang
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
| |
Collapse
|
27
|
Mathys H, Boix CA, Akay LA, Xia Z, Davila-Velderrain J, Ng AP, Jiang X, Abdelhady G, Galani K, Mantero J, Band N, James BT, Babu S, Galiana-Melendez F, Louderback K, Prokopenko D, Tanzi RE, Bennett DA, Tsai LH, Kellis M. Single-cell multiregion dissection of Alzheimer's disease. Nature 2024; 632:858-868. [PMID: 39048816 PMCID: PMC11338834 DOI: 10.1038/s41586-024-07606-7] [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: 06/20/2022] [Accepted: 05/24/2024] [Indexed: 07/27/2024]
Abstract
Alzheimer's disease is the leading cause of dementia worldwide, but the cellular pathways that underlie its pathological progression across brain regions remain poorly understood1-3. Here we report a single-cell transcriptomic atlas of six different brain regions in the aged human brain, covering 1.3 million cells from 283 post-mortem human brain samples across 48 individuals with and without Alzheimer's disease. We identify 76 cell types, including region-specific subtypes of astrocytes and excitatory neurons and an inhibitory interneuron population unique to the thalamus and distinct from canonical inhibitory subclasses. We identify vulnerable populations of excitatory and inhibitory neurons that are depleted in specific brain regions in Alzheimer's disease, and provide evidence that the Reelin signalling pathway is involved in modulating the vulnerability of these neurons. We develop a scalable method for discovering gene modules, which we use to identify cell-type-specific and region-specific modules that are altered in Alzheimer's disease and to annotate transcriptomic differences associated with diverse pathological variables. We identify an astrocyte program that is associated with cognitive resilience to Alzheimer's disease pathology, tying choline metabolism and polyamine biosynthesis in astrocytes to preserved cognitive function late in life. Together, our study develops a regional atlas of the ageing human brain and provides insights into cellular vulnerability, response and resilience to Alzheimer's disease pathology.
Collapse
Affiliation(s)
- Hansruedi Mathys
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- University of Pittsburgh Brain Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Carles A Boix
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Computational and Systems Biology Program, MIT, Cambridge, MA, USA
| | - Leyla Anne Akay
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Ziting Xia
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Harvard-MIT Health Sciences and Technology Program, MIT, Cambridge, MA, USA
| | | | - Ayesha P Ng
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Xueqiao Jiang
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Ghada Abdelhady
- University of Pittsburgh Brain Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Kyriaki Galani
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Julio Mantero
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Neil Band
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Benjamin T James
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sudhagar Babu
- University of Pittsburgh Brain Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Fabiola Galiana-Melendez
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Kate Louderback
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Dmitry Prokopenko
- Genetics and Aging Research Unit, McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Rudolph E Tanzi
- Genetics and Aging Research Unit, McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Li-Huei Tsai
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA.
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| |
Collapse
|
28
|
Ozturk K, Panwala R, Sheen J, Ford K, Jayne N, Portell A, Zhang DE, Hutter S, Haferlach T, Ideker T, Mali P, Carter H. Interface-guided phenotyping of coding variants in the transcription factor RUNX1. Cell Rep 2024; 43:114436. [PMID: 38968069 PMCID: PMC11345852 DOI: 10.1016/j.celrep.2024.114436] [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: 12/08/2023] [Revised: 05/15/2024] [Accepted: 06/19/2024] [Indexed: 07/07/2024] Open
Abstract
Single-gene missense mutations remain challenging to interpret. Here, we deploy scalable functional screening by sequencing (SEUSS), a Perturb-seq method, to generate mutations at protein interfaces of RUNX1 and quantify their effect on activities of downstream cellular programs. We evaluate single-cell RNA profiles of 115 mutations in myelogenous leukemia cells and categorize them into three functionally distinct groups, wild-type (WT)-like, loss-of-function (LoF)-like, and hypomorphic, that we validate in orthogonal assays. LoF-like variants dominate the DNA-binding site and are recurrent in cancer; however, recurrence alone does not predict functional impact. Hypomorphic variants share characteristics with LoF-like but favor protein interactions, promoting gene expression indicative of nerve growth factor (NGF) response and cytokine recruitment of neutrophils. Accessible DNA near differentially expressed genes frequently contains RUNX1-binding motifs. Finally, we reclassify 16 variants of uncertain significance and train a classifier to predict 103 more. Our work demonstrates the potential of targeting protein interactions to better define the landscape of phenotypes reachable by missense mutations.
Collapse
Affiliation(s)
- Kivilcim Ozturk
- Division of Medical Genetics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Rebecca Panwala
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Jeanna Sheen
- School of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Kyle Ford
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Nathan Jayne
- School of Biological Sciences, University of California, San Diego, La Jolla, CA, USA; Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA
| | - Andrew Portell
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Dong-Er Zhang
- Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA
| | - Stephan Hutter
- MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 Munich, Germany
| | - Torsten Haferlach
- MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 Munich, Germany
| | - Trey Ideker
- Division of Medical Genetics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA; Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA
| | - Prashant Mali
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
| | - Hannah Carter
- Division of Medical Genetics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA; Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA.
| |
Collapse
|
29
|
Hou Y, Lin B, Xu T, Jiang J, Luo S, Chen W, Chen X, Wang Y, Liao G, Wang J, Zhang J, Li X, Xiang X, Xie Y, Wang J, Peng S, Lv W, Liu Y, Xiao H. The neurotransmitter calcitonin gene-related peptide shapes an immunosuppressive microenvironment in medullary thyroid cancer. Nat Commun 2024; 15:5555. [PMID: 39030177 PMCID: PMC11271530 DOI: 10.1038/s41467-024-49824-7] [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: 11/13/2023] [Accepted: 06/20/2024] [Indexed: 07/21/2024] Open
Abstract
Neurotransmitters are key modulators in neuro-immune circuits and have been linked to tumor progression. Medullary thyroid cancer (MTC), an aggressive neuroendocrine tumor, expresses neurotransmitter calcitonin gene-related peptide (CGRP), is insensitive to chemo- and radiotherapies, and the effectiveness of immunotherapies remains unknown. Thus, a comprehensive analysis of the tumor microenvironment would facilitate effective therapies and provide evidence on CGRP's function outside the nervous system. Here, we compare the single-cell landscape of MTC and papillary thyroid cancer (PTC) and find that expression of CGRP in MTC is associated with dendritic cell (DC) abnormal development characterized by activation of cAMP related pathways and high levels of Kruppel Like Factor 2 (KLF2), correlated with an impaired activity of tumor infiltrating T cells. A CGRP receptor antagonist could offset CGRP detrimental impact on DC development in vitro. Our study provides insights of the MTC immunosuppressive microenvironment, and proposes CGRP receptor as a potential therapeutic target.
Collapse
MESH Headings
- Tumor Microenvironment/immunology
- Humans
- Thyroid Neoplasms/genetics
- Thyroid Neoplasms/metabolism
- Thyroid Neoplasms/immunology
- Thyroid Neoplasms/pathology
- Calcitonin Gene-Related Peptide/metabolism
- Carcinoma, Neuroendocrine/genetics
- Carcinoma, Neuroendocrine/metabolism
- Carcinoma, Neuroendocrine/pathology
- Carcinoma, Neuroendocrine/immunology
- Dendritic Cells/immunology
- Dendritic Cells/metabolism
- Thyroid Cancer, Papillary/metabolism
- Thyroid Cancer, Papillary/immunology
- Thyroid Cancer, Papillary/genetics
- Thyroid Cancer, Papillary/pathology
- Receptors, Calcitonin Gene-Related Peptide/metabolism
- Cyclic AMP/metabolism
- Lymphocytes, Tumor-Infiltrating/immunology
- Lymphocytes, Tumor-Infiltrating/metabolism
- Neurotransmitter Agents/metabolism
- Gene Expression Regulation, Neoplastic
- Cell Line, Tumor
- Calcitonin Gene-Related Peptide Receptor Antagonists/pharmacology
- Single-Cell Analysis
Collapse
Affiliation(s)
- Yingtong Hou
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Bo Lin
- Department of Thyroid Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Tianyi Xu
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Juan Jiang
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shuli Luo
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Wanna Chen
- Department of Thyroid Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xinwen Chen
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yuanqi Wang
- Department of Liver Surgery, Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Guanrui Liao
- Department of Liver Surgery, Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jianping Wang
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jiayuan Zhang
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xuyang Li
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xiao Xiang
- Department of Liver Surgery, Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yubin Xie
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ji Wang
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Sui Peng
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Clinical Trials Unit, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Weiming Lv
- Department of Thyroid Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yihao Liu
- Clinical Trials Unit, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
| | - Haipeng Xiao
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
| |
Collapse
|
30
|
Xu Y, Wang Y, Ma S. SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer. CELL REPORTS METHODS 2024; 4:100813. [PMID: 38971150 PMCID: PMC11294836 DOI: 10.1016/j.crmeth.2024.100813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 04/23/2024] [Accepted: 06/13/2024] [Indexed: 07/08/2024]
Abstract
Gene co-expression analysis of single-cell transcriptomes, aiming to define functional relationships between genes, is challenging due to excessive dropout values. Here, we developed a single-cell graphical Gaussian model (SingleCellGGM) algorithm to conduct single-cell gene co-expression network analysis. When applied to mouse single-cell datasets, SingleCellGGM constructed networks from which gene co-expression modules with highly significant functional enrichment were identified. We considered the modules as gene expression programs (GEPs). These GEPs enable direct cell-type annotation of individual cells without cell clustering, and they are enriched with genes required for the functions of the corresponding cells, sometimes at levels greater than 10-fold. The GEPs are conserved across datasets and enable universal cell-type label transfer across different studies. We also proposed a dimension-reduction method through averaging by GEPs for single-cell analysis, enhancing the interpretability of results. Thus, SingleCellGGM offers a unique GEP-based perspective to analyze single-cell transcriptomes and reveals biological insights shared by different single-cell datasets.
Collapse
Affiliation(s)
- Yupu Xu
- MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China
| | - Yuzhou Wang
- MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China; The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Shisong Ma
- MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China; School of Data Science, University of Science and Technology of China, Hefei, China.
| |
Collapse
|
31
|
Verhey TB, Seo H, Gillmor A, Thoppey-Manoharan V, Schriemer D, Morrissy S. mosaicMPI: a framework for modular data integration across cohorts and -omics modalities. Nucleic Acids Res 2024; 52:e53. [PMID: 38813827 PMCID: PMC11229337 DOI: 10.1093/nar/gkae442] [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: 09/02/2023] [Revised: 04/26/2024] [Accepted: 05/10/2024] [Indexed: 05/31/2024] Open
Abstract
Advances in molecular profiling have facilitated generation of large multi-modal datasets that can potentially reveal critical axes of biological variation underlying complex diseases. Distilling biological meaning, however, requires computational strategies that can perform mosaic integration across diverse cohorts and datatypes. Here, we present mosaicMPI, a framework for discovery of low to high-resolution molecular programs representing both cell types and states, and integration within and across datasets into a network representing biological themes. Using existing datasets in glioblastoma, we demonstrate that this approach robustly integrates single cell and bulk programs across multiple platforms. Clinical and molecular annotations from cohorts are statistically propagated onto this network of programs, yielding a richly characterized landscape of biological themes. This enables deep understanding of individual tumor samples, systematic exploration of relationships between modalities, and generation of a reference map onto which new datasets can rapidly be mapped. mosaicMPI is available at https://github.com/MorrissyLab/mosaicMPI.
Collapse
Affiliation(s)
- Theodore B Verhey
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Heewon Seo
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
| | - Aaron Gillmor
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
| | - Varsha Thoppey-Manoharan
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
| | - David Schriemer
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
| | - Sorana Morrissy
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| |
Collapse
|
32
|
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.
Collapse
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
| |
Collapse
|
33
|
Boe RH, Triandafillou CG, Lazcano R, Wargo JA, Raj A. Spatial transcriptomics reveals influence of microenvironment on intrinsic fates in melanoma therapy resistance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.30.601416. [PMID: 39005406 PMCID: PMC11244927 DOI: 10.1101/2024.06.30.601416] [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
Resistance to cancer therapy is driven by both cell-intrinsic and microenvironmental factors. Previous work has revealed that multiple resistant cell fates emerge in melanoma following treatment with targeted therapy and that, in vitro, these resistant fates are determined by the transcriptional state of individual cells prior to exposure to treatment. What remains unclear is whether these resistant fates are shared across different genetic backgrounds and how, if at all, these resistant fates interact with the tumor microenvironment. Through spatial transcriptomics and single-cell RNA sequencing, we uncovered distinct resistance programs in melanoma cells shaped by both intrinsic cellular states and the tumor microenvironment. Consensus non-negative matrix factorization revealed shared intrinsic resistance programs across different cell lines, highlighting the presence of universal and unique resistance pathways. In patient samples, we demonstrated that these resistance programs coexist within individual tumors and associate with diverse immune signatures, suggesting that the tumor microenvironment and distribution of resistant fates are closely connected. Single-cell resolution spatial transcriptomics in xenograft models revealed both intrinsically determined and extrinsically influenced resistant fates. Overall, this work demonstrates that each therapy resistant fate coexists with a distinct immune microenvironment in tumors and that, in vivo, tissue features, such as regions of necrosis, can influence which resistant fate is adopted.
Collapse
Affiliation(s)
- Ryan H. Boe
- Genetics and Epigenetics Program, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Catherine G. Triandafillou
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA
| | - Rossana Lazcano
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Jennifer A. Wargo
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Arjun Raj
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| |
Collapse
|
34
|
Chen X, Zhao J, Yue S, Li Z, Duan X, Lin Y, Yang Y, He J, Gao L, Pan Z, Yang X, Su X, Huang M, Li X, Zhao Y, Zhang X, Li Z, Hu L, Tang J, Hao Y, Tian Q, Wang Y, Xu L, Huang Q, Cao Y, Chen Y, Zhu B, Li Y, Bai F, Zhang G, Ye L. An oncolytic virus delivering tumor-irrelevant bystander T cell epitopes induces anti-tumor immunity and potentiates cancer immunotherapy. NATURE CANCER 2024; 5:1063-1081. [PMID: 38609488 PMCID: PMC11286533 DOI: 10.1038/s43018-024-00760-x] [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: 01/17/2023] [Accepted: 03/15/2024] [Indexed: 04/14/2024]
Abstract
Tumor-specific T cells are crucial in anti-tumor immunity and act as targets for cancer immunotherapies. However, these cells are numerically scarce and functionally exhausted in the tumor microenvironment (TME), leading to inefficacious immunotherapies in most patients with cancer. By contrast, emerging evidence suggested that tumor-irrelevant bystander T (TBYS) cells are abundant and preserve functional memory properties in the TME. To leverage TBYS cells in the TME to eliminate tumor cells, we engineered oncolytic virus (OV) encoding TBYS epitopes (OV-BYTE) to redirect the antigen specificity of tumor cells to pre-existing TBYS cells, leading to effective tumor inhibition in multiple preclinical models. Mechanistically, OV-BYTE induced epitope spreading of tumor antigens to elicit more diverse tumor-specific T cell responses. Remarkably, the OV-BYTE strategy targeting human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific T cell memory efficiently inhibited tumor progression in a human tumor cell-derived xenograft model, providing important insights into the improvement of cancer immunotherapies in a large population with a history of SARS-CoV-2 infection or coronavirus disease 2019 (COVID-19) vaccination.
Collapse
Affiliation(s)
- Xiangyu Chen
- Institute of Immunological Innovation and Translation, Chongqing Medical University, Chongqing, China
- Changping Laboratory, Beijing, China
| | - Jing Zhao
- Key Laboratory of Animal Epidemiology of the Ministry of Agriculture, College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Shuai Yue
- Institute of Immunology, Third Military Medical University, Chongqing, China
- Cancer Center, Daping Hospital and Army Medical Center of PLA, Third Military Medical University, Chongqing, China
| | - Ziyu Li
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China
| | - Xiang Duan
- The State Key Laboratory of Pharmaceutical Biotechnology, National Resource Center for Mutant Mice, MOE Key Laboratory of Model Animals for Disease Study, MOE Engineering Research Center of Protein and Peptide Medicine, Chemistry and Biomedicine Innovation Center, Model Animal Research Center, Medical School of Nanjing University, Nanjing, China
| | - Yao Lin
- Institute of Immunology, Third Military Medical University, Chongqing, China
| | - Yang Yang
- Guangdong Provincial Key Laboratory of Immune Regulation and Immunotherapy, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China
| | - Junjian He
- Institute of Immunology, Third Military Medical University, Chongqing, China
| | - Leiqiong Gao
- Institute of Immunology, Third Military Medical University, Chongqing, China
| | - Zhiwei Pan
- Institute of Immunology, Third Military Medical University, Chongqing, China
| | - Xiaofan Yang
- Dermatology Hospital, Southern Medical University, Guangzhou, China
| | - Xingxing Su
- Department of Hepatobiliary Surgery, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Min Huang
- Key Laboratory of Animal Epidemiology of the Ministry of Agriculture, College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Xiao Li
- Key Laboratory of Animal Epidemiology of the Ministry of Agriculture, College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Ye Zhao
- Key Laboratory of Animal Epidemiology of the Ministry of Agriculture, College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Xuehui Zhang
- Key Laboratory of Animal Epidemiology of the Ministry of Agriculture, College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Zhirong Li
- Institute of Immunology, Third Military Medical University, Chongqing, China
| | - Li Hu
- Institute of Immunology, Third Military Medical University, Chongqing, China
| | - Jianfang Tang
- Institute of Immunology, Third Military Medical University, Chongqing, China
| | - Yaxing Hao
- Institute of Immunology, Third Military Medical University, Chongqing, China
| | - Qin Tian
- Dermatology Hospital, Southern Medical University, Guangzhou, China
| | - Yifei Wang
- Institute of Immunological Innovation and Translation, Chongqing Medical University, Chongqing, China
| | - Lifan Xu
- Institute of Immunology, Third Military Medical University, Chongqing, China
| | - Qizhao Huang
- Institute of Immunological Innovation and Translation, Chongqing Medical University, Chongqing, China
| | - Yingjiao Cao
- Guangdong Provincial Key Laboratory of Immune Regulation and Immunotherapy, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China
| | - Yaokai Chen
- Department of Infectious Diseases, Chongqing Public Health Medical Center, Chongqing, China
| | - Bo Zhu
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, China
| | - Yan Li
- The State Key Laboratory of Pharmaceutical Biotechnology, National Resource Center for Mutant Mice, MOE Key Laboratory of Model Animals for Disease Study, MOE Engineering Research Center of Protein and Peptide Medicine, Chemistry and Biomedicine Innovation Center, Model Animal Research Center, Medical School of Nanjing University, Nanjing, China.
| | - Fan Bai
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China.
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China.
| | - Guozhong Zhang
- Key Laboratory of Animal Epidemiology of the Ministry of Agriculture, College of Veterinary Medicine, China Agricultural University, Beijing, China.
| | - Lilin Ye
- Changping Laboratory, Beijing, China.
- Institute of Immunology, Third Military Medical University, Chongqing, China.
| |
Collapse
|
35
|
Zheng J, Lu W, Wang C, Chen S, Zhang Q, Su C. Unfolding the mysteries of heterogeneity from a high-resolution perspective: integration analysis of single-cell multi-omics and spatial omics revealed functionally heterogeneous cancer cells in ccRCC. Aging (Albany NY) 2024; 16:10943-10971. [PMID: 38944814 PMCID: PMC11272124 DOI: 10.18632/aging.205974] [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: 09/19/2023] [Accepted: 05/16/2024] [Indexed: 07/01/2024]
Abstract
The genomic landscape of clear cell renal cell carcinoma (ccRCC) has a considerable intra-tumor heterogeneity, which is a significant obstacle in the field of precision oncology and plays a pivotal role in metastasis, recurrence, and therapeutic resistance of cancer. The mechanisms of intra-tumor heterogeneity in ccRCC have yet to be fully established. We integrated single-cell RNA sequencing (scRNA-seq) and transposase-accessible chromatin sequencing (scATAC-seq) data from a single-cell multi-omics perspective. Based on consensus non-negative matrix factorization (cNMF) algorithm, functionally heterogeneous cancer cells were classified into metabolism, inflammatory, and EMT meta programs, with spatial transcriptomics sequencing (stRNA-seq) providing spatial information of such disparate meta programs of cancer cells. The bulk RNA sequencing (RNA-seq) data revealed high clinical prognostic values of functionally heterogeneous cancer cells of three meta programs, with transcription factor regulatory network and motif activities revealing the key transcription factors that regulate functionally heterogeneous ccRCC cells. The interactions between varying meta programs and other cell subpopulations in the microenvironment were investigated. Finally, we assessed the sensitivity of cancer cells of disparate meta programs to different anti-cancer agents. Our findings inform on the intra-tumor heterogeneity of ccRCC and its regulatory networks and offers new perspectives to facilitate the designs of rational therapeutic strategies.
Collapse
Affiliation(s)
- Jie Zheng
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
| | - Wenhao Lu
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
| | - Chengbang Wang
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
| | - Shaohua Chen
- Department of Urology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
- Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
| | - Qingyun Zhang
- Department of Urology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
- Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
| | - Cheng Su
- Department of Pediatric Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
| |
Collapse
|
36
|
Goodspeed A, Bodlak A, Duffy AB, Nelson-Taylor S, Oike N, Porfilio T, Shirai R, Walker D, Treece A, Black J, Donaldson N, Cost C, Garrington T, Greffe B, Luna-Fineman S, Demedis J, Lake J, Danis E, Verneris M, Adams DL, Hayashi M. Characterization of transcriptional heterogeneity and novel therapeutic targets using single cell RNA-sequencing of primary and circulating Ewing sarcoma cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.18.576251. [PMID: 38293103 PMCID: PMC10827204 DOI: 10.1101/2024.01.18.576251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Ewing sarcoma is the second most common bone cancer in children, accounting for 2% of pediatric cancer diagnoses. Patients who present with metastatic disease at the time of diagnosis have a dismal prognosis, compared to the >70% 5-year survival of those with localized disease. Here, we utilized single cell RNA-sequencing to characterize the transcriptional landscape of primary Ewing sarcoma tumors and surrounding tumor microenvironment (TME). Copy-number analysis identified subclonal evolution within patients prior to treatment. Primary tumor samples demonstrate a heterogenous transcriptional landscape with several conserved gene expression programs, including those composed of genes related to proliferation and EWS targets. Single cell RNA-sequencing and immunofluorescence of circulating tumor cells at the time of diagnosis identified TSPAN8 as a novel therapeutic target.
Collapse
|
37
|
Chapple RH, Liu X, Natarajan S, Alexander MIM, Kim Y, Patel AG, LaFlamme CW, Pan M, Wright WC, Lee HM, Zhang Y, Lu M, Koo SC, Long C, Harper J, Savage C, Johnson MD, Confer T, Akers WJ, Dyer MA, Sheppard H, Easton J, Geeleher P. An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs. Genome Biol 2024; 25:161. [PMID: 38898465 PMCID: PMC11186099 DOI: 10.1186/s13059-024-03309-4] [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: 11/26/2023] [Accepted: 06/14/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Neuroblastoma is a common pediatric cancer, where preclinical studies suggest that a mesenchymal-like gene expression program contributes to chemotherapy resistance. However, clinical outcomes remain poor, implying we need a better understanding of the relationship between patient tumor heterogeneity and preclinical models. RESULTS Here, we generate single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We develop an unsupervised machine learning approach ("automatic consensus nonnegative matrix factorization" (acNMF)) to compare the gene expression programs found in preclinical models to a large cohort of patient tumors. We confirm a weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some pre-treated high-risk patient tumors, but this appears distinct from the presumptive drug-resistance mesenchymal programs evident in cell lines. Surprisingly, however, this weak-mesenchymal-like program is maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 h, suggesting an uncharacterized therapy-escape mechanism. CONCLUSIONS Collectively, our findings improve the understanding of how neuroblastoma patient tumor heterogeneity is reflected in preclinical models, provides a comprehensive integrated resource, and a generalizable set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell RNA-seq datasets.
Collapse
Affiliation(s)
- Richard H Chapple
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Xueying Liu
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Sivaraman Natarajan
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Margaret I M Alexander
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Yuna Kim
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Anand G Patel
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Christy W LaFlamme
- Department of Cell and Molecular Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Graduate School of Biomedical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Min Pan
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - William C Wright
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Hyeong-Min Lee
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Yinwen Zhang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Meifen Lu
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Selene C Koo
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Courtney Long
- Animal Resources Center, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - John Harper
- Animal Resources Center, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Chandra Savage
- Animal Resources Center, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Melissa D Johnson
- Center for In Vivo Imaging and Therapeutics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Thomas Confer
- Center for In Vivo Imaging and Therapeutics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Walter J Akers
- Department of Biomedical Engineering, University of Texas Southwestern Medical School, Dallas, TX, 75390, USA
| | - Michael A Dyer
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, 20815, USA
| | - Heather Sheppard
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - John Easton
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Paul Geeleher
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.
| |
Collapse
|
38
|
Danino-Levi M, Goldberg T, Keter M, Akselrod N, Shprach-Buaron N, Safra M, Singer G, Alon S. Computational analysis of super-resolved in situ sequencing data reveals genes modified by immune-tumor contact events. RNA (NEW YORK, N.Y.) 2024; 30:749-759. [PMID: 38575346 PMCID: PMC11182005 DOI: 10.1261/rna.079801.123] [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: 08/14/2023] [Accepted: 03/18/2024] [Indexed: 04/06/2024]
Abstract
Cancer cells can manipulate immune cells and escape from the immune system response. Quantifying the molecular changes that occur when an immune cell touches a tumor cell can increase our understanding of the underlying mechanisms. Recently, it became possible to perform such measurements in situ-for example, using expansion sequencing, which enabled in situ sequencing of genes with super-resolution. We systematically examined whether individual immune cells from specific cell types express genes differently when in physical proximity to individual tumor cells. First, we demonstrated that a dense mapping of genes in situ can be used for the segmentation of cell bodies in 3D, thus improving our ability to detect likely touching cells. Next, we used three different computational approaches to detect the molecular changes that are triggered by proximity: differential expression analysis, tree-based machine learning classifiers, and matrix factorization analysis. This systematic analysis revealed tens of genes, in specific cell types, whose expression separates immune cells that are proximal to tumor cells from those that are not proximal, with a significant overlap between the different detection methods. Remarkably, an order of magnitude more genes are triggered by proximity to tumor cells in CD8 T cells compared to CD4 T cells, in line with the ability of CD8 T cells to directly bind major histocompatibility complex (MHC) class I on tumor cells. Thus, in situ sequencing of an individual biopsy can be used to detect genes likely involved in immune-tumor cell-cell interactions. The data used in this manuscript and the code of the InSituSeg, machine learning, cNMF, and Moran's I methods are publicly available at doi:10.5281/zenodo.7497981.
Collapse
Affiliation(s)
- Michal Danino-Levi
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
- Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Tal Goldberg
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
- Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Maya Keter
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Nikol Akselrod
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Noa Shprach-Buaron
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
- Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Modi Safra
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
- Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Gonen Singer
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Shahar Alon
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
- Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| |
Collapse
|
39
|
Cheng J, Xiao M, Meng Q, Zhang M, Zhang D, Liu L, Jin Q, Fu Z, Li Y, Chen X, Xie H. Decoding temporal heterogeneity in NSCLC through machine learning and prognostic model construction. World J Surg Oncol 2024; 22:156. [PMID: 38872167 PMCID: PMC11170806 DOI: 10.1186/s12957-024-03435-0] [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: 03/11/2024] [Accepted: 06/01/2024] [Indexed: 06/15/2024] Open
Abstract
BACKGROUND Non-small cell lung cancer (NSCLC) is a prevalent and heterogeneous disease with significant genomic variations between the early and advanced stages. The identification of key genes and pathways driving NSCLC tumor progression is critical for improving the diagnosis and treatment outcomes of this disease. METHODS In this study, we conducted single-cell transcriptome analysis on 93,406 cells from 22 NSCLC patients to characterize malignant NSCLC cancer cells. Utilizing cNMF, we classified these cells into distinct modules, thus identifying the diverse molecular profiles within NSCLC. Through pseudotime analysis, we delineated temporal gene expression changes during NSCLC evolution, thus demonstrating genes associated with disease progression. Using the XGBoost model, we assessed the significance of these genes in the pseudotime trajectory. Our findings were validated by using transcriptome sequencing data from The Cancer Genome Atlas (TCGA), supplemented via LASSO regression to refine the selection of characteristic genes. Subsequently, we established a risk score model based on these genes, thus providing a potential tool for cancer risk assessment and personalized treatment strategies. RESULTS We used cNMF to classify malignant NSCLC cells into three functional modules, including the metabolic reprogramming module, cell cycle module, and cell stemness module, which can be used for the functional classification of malignant tumor cells in NSCLC. These findings also indicate that metabolism, the cell cycle, and tumor stemness play important driving roles in the malignant evolution of NSCLC. We integrated cNMF and XGBoost to select marker genes that are indicative of both early and advanced NSCLC stages. The expression of genes such as CHCHD2, GAPDH, and CD24 was strongly correlated with the malignant evolution of NSCLC at the single-cell data level. These genes have been validated via histological data. The risk score model that we established (represented by eight genes) was ultimately validated with GEO data. CONCLUSION In summary, our study contributes to the identification of temporal heterogeneous biomarkers in NSCLC, thus offering insights into disease progression mechanisms and potential therapeutic targets. The developed workflow demonstrates promise for future applications in clinical practice.
Collapse
Affiliation(s)
- Junpeng Cheng
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China
| | - Meizhu Xiao
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China
| | - Qingkang Meng
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China
| | - Min Zhang
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China
| | - Denan Zhang
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China
| | - Lei Liu
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China
| | - Qing Jin
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China
| | - Zhijin Fu
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China
| | - Yanjiao Li
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China
| | - Xiujie Chen
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China.
| | - Hongbo Xie
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China.
| |
Collapse
|
40
|
Winter PS, Ramseier ML, Navia AW, Saksena S, Strouf H, Senhaji N, DenAdel A, Mirza M, An HH, Bilal L, Dennis P, Leahy CS, Shigemori K, Galves-Reyes J, Zhang Y, Powers F, Mulugeta N, Gupta AJ, Calistri N, Van Scoyk A, Jones K, Liu H, Stevenson KE, Ren S, Luskin MR, Couturier CP, Amini AP, Raghavan S, Kimmerling RJ, Stevens MM, Crawford L, Weinstock DM, Manalis SR, Shalek AK, Murakami MA. Mutation and cell state compatibility is required and targetable in Ph+ acute lymphoblastic leukemia minimal residual disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.597767. [PMID: 38915726 PMCID: PMC11195125 DOI: 10.1101/2024.06.06.597767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Efforts to cure BCR::ABL1 B cell acute lymphoblastic leukemia (Ph+ ALL) solely through inhibition of ABL1 kinase activity have thus far been insufficient despite the availability of tyrosine kinase inhibitors (TKIs) with broad activity against resistance mutants. The mechanisms that drive persistence within minimal residual disease (MRD) remain poorly understood and therefore untargeted. Utilizing 13 patient-derived xenograft (PDX) models and clinical trial specimens of Ph+ ALL, we examined how genetic and transcriptional features co-evolve to drive progression during prolonged TKI response. Our work reveals a landscape of cooperative mutational and transcriptional escape mechanisms that differ from those causing resistance to first generation TKIs. By analyzing MRD during remission, we show that the same resistance mutation can either increase or decrease cellular fitness depending on transcriptional state. We further demonstrate that directly targeting transcriptional state-associated vulnerabilities at MRD can overcome BCR::ABL1 independence, suggesting a new paradigm for rationally eradicating MRD prior to relapse. Finally, we illustrate how cell mass measurements of leukemia cells can be used to rapidly monitor dominant transcriptional features of Ph+ ALL to help rationally guide therapeutic selection from low-input samples.
Collapse
Affiliation(s)
- Peter S. Winter
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Michelle L. Ramseier
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Andrew W. Navia
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Sachit Saksena
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Computational and Systems Biology Program, MIT, Cambridge, MA, USA
| | - Haley Strouf
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Nezha Senhaji
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Alan DenAdel
- Center for Computational Molecular Biology, Brown University, Providence, RI, USA
- Department of Biostatistics, Brown University, Providence, RI, USA
| | - Mahnoor Mirza
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Hyun Hwan An
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Laura Bilal
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Peter Dennis
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Catharine S. Leahy
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kay Shigemori
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jennyfer Galves-Reyes
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Ye Zhang
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Foster Powers
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nolawit Mulugeta
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | | | - Nicholas Calistri
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Alex Van Scoyk
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kristen Jones
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Huiyun Liu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Siyang Ren
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA USA
| | - Marlise R. Luskin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Charles P. Couturier
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | | | - Srivatsan Raghavan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | - Mark M. Stevens
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Lorin Crawford
- Center for Computational Molecular Biology, Brown University, Providence, RI, USA
- Department of Biostatistics, Brown University, Providence, RI, USA
- Microsoft Research, Cambridge, MA, USA
| | - David M. Weinstock
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Current Address: Merck and Co., Rahway, NJ, USA
| | - Scott R. Manalis
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Alex K. Shalek
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Mark A. Murakami
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| |
Collapse
|
41
|
Liu S, Zhang S, Dong H, Jin X, Sun J, Zhou H, Jin Y, Li Y, Wu G. CD63 + tumor-associated macrophages drive the progression of hepatocellular carcinoma through the induction of epithelial-mesenchymal transition and lipid reprogramming. BMC Cancer 2024; 24:698. [PMID: 38849760 PMCID: PMC11157766 DOI: 10.1186/s12885-024-12472-7] [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: 11/01/2023] [Accepted: 06/03/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND Tumor-associated macrophages (TAMs) constitute a substantial part of human hepatocellular carcinoma (HCC). The present study was devised to explore TAM diversity and their roles in HCC progression. METHODS Through the integration of multiple 10 × single-cell transcriptomic data derived from HCC samples and the use of consensus nonnegative matrix factorization (an unsupervised clustering algorithm), TAM molecular subtypes and expression programs were evaluated in detail. The roles played by these TAM subtypes in HCC were further probed through pseudotime, enrichment, and intercellular communication analyses. Lastly, vitro experiments were performed to validate the relationship between CD63, which is an inflammatory TAM expression program marker, and tumor cell lines. RESULTS We found that the inflammatory expression program in TAMs had a more obvious interaction with HCC cells, and CD63, as a marker gene of the inflammatory expression program, was associated with poor prognosis of HCC patients. Both bulk RNA-seq and vitro experiments confirmed that higher TAM CD63 expression was associated with the growth of HCC cells as well as their epithelial-mesenchymal transition, metastasis, invasion, and the reprogramming of lipid metabolism. CONCLUSIONS These analyses revealed that the TAM inflammatory expression program in HCC is closely associated with malignant tumor cells, with the hub gene CD63 thus representing an ideal target for therapeutic intervention in this cancer type.
Collapse
Affiliation(s)
- Shiqi Liu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of China Medical University, Shenyang, People's Republic of China
| | - Shuairan Zhang
- Department of Gastroenterology, The First Affiliated Hospital of China Medical University, Shenyang, People's Republic of China
| | - Hang Dong
- Phase I Clinical Trails Center, The People's Hospital of China Medical University, Shenyang, People's Republic of China
| | - Xiuli Jin
- Department of Gastroenterology, The First Affiliated Hospital of China Medical University, Shenyang, People's Republic of China
| | - Jing Sun
- Department of Gastroenterology, The First Affiliated Hospital of China Medical University, Shenyang, People's Republic of China
| | - Haonan Zhou
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of China Medical University, Shenyang, People's Republic of China
| | - Yifan Jin
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of China Medical University, Shenyang, People's Republic of China
| | - Yiling Li
- Department of Gastroenterology, The First Affiliated Hospital of China Medical University, Shenyang, People's Republic of China.
| | - Gang Wu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of China Medical University, Shenyang, People's Republic of China.
| |
Collapse
|
42
|
Huycke TR, Häkkinen TJ, Miyazaki H, Srivastava V, Barruet E, McGinnis CS, Kalantari A, Cornwall-Scoones J, Vaka D, Zhu Q, Jo H, Oria R, Weaver VM, DeGrado WF, Thomson M, Garikipati K, Boffelli D, Klein OD, Gartner ZJ. Patterning and folding of intestinal villi by active mesenchymal dewetting. Cell 2024; 187:3072-3089.e20. [PMID: 38781967 PMCID: PMC11166531 DOI: 10.1016/j.cell.2024.04.039] [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: 06/12/2023] [Revised: 12/30/2023] [Accepted: 04/26/2024] [Indexed: 05/25/2024]
Abstract
Tissue folds are structural motifs critical to organ function. In the intestine, bending of a flat epithelium into a periodic pattern of folds gives rise to villi, finger-like protrusions that enable nutrient absorption. However, the molecular and mechanical processes driving villus morphogenesis remain unclear. Here, we identify an active mechanical mechanism that simultaneously patterns and folds the intestinal epithelium to initiate villus formation. At the cellular level, we find that PDGFRA+ subepithelial mesenchymal cells generate myosin II-dependent forces sufficient to produce patterned curvature in neighboring tissue interfaces. This symmetry-breaking process requires altered cell and extracellular matrix interactions that are enabled by matrix metalloproteinase-mediated tissue fluidization. Computational models, together with in vitro and in vivo experiments, revealed that these cellular features manifest at the tissue level as differences in interfacial tensions that promote mesenchymal aggregation and interface bending through a process analogous to the active dewetting of a thin liquid film.
Collapse
Affiliation(s)
- Tyler R Huycke
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA; Program in Craniofacial Biology and Department of Orofacial Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Teemu J Häkkinen
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA; Program in Craniofacial Biology and Department of Orofacial Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Hikaru Miyazaki
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA; Program in Craniofacial Biology and Department of Orofacial Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Vasudha Srivastava
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Emilie Barruet
- Department of Pediatrics, Cedars-Sinai Guerin Children's, Los Angeles, CA, USA
| | - Christopher S McGinnis
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Ali Kalantari
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA; Program in Craniofacial Biology and Department of Orofacial Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jake Cornwall-Scoones
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Dedeepya Vaka
- Department of Pediatrics, Cedars-Sinai Guerin Children's, Los Angeles, CA, USA
| | - Qin Zhu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Hyunil Jo
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Roger Oria
- Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA; Comprehensive Cancer Center, Helen Diller Family Cancer Research Center, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Bioengineering and Therapeutic Sciences, Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Valerie M Weaver
- Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA; Comprehensive Cancer Center, Helen Diller Family Cancer Research Center, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Bioengineering and Therapeutic Sciences, Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - William F DeGrado
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Matt Thomson
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Krishna Garikipati
- Departments of Mechanical Engineering, and Mathematics, University of Michigan, Ann Arbor, MI, USA
| | - Dario Boffelli
- Department of Pediatrics, Cedars-Sinai Guerin Children's, Los Angeles, CA, USA
| | - Ophir D Klein
- Program in Craniofacial Biology and Department of Orofacial Sciences, University of California, San Francisco, San Francisco, CA, USA; Department of Pediatrics, Cedars-Sinai Guerin Children's, Los Angeles, CA, USA.
| | - Zev J Gartner
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA.
| |
Collapse
|
43
|
Lim J, Rodriguez R, Williams K, Silva J, Gutierrez AG, Tyler P, Baharom F, Sun T, Lin E, Martin S, Kayser BD, Johnston RJ, Mellman I, Delamarre L, West NR, Müller S, Qu Y, Heger K. The Exonuclease TREX1 Constitutes an Innate Immune Checkpoint Limiting cGAS/STING-Mediated Antitumor Immunity. Cancer Immunol Res 2024; 12:663-672. [PMID: 38489753 PMCID: PMC11148535 DOI: 10.1158/2326-6066.cir-23-1078] [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: 12/19/2023] [Revised: 02/15/2024] [Accepted: 03/15/2024] [Indexed: 03/17/2024]
Abstract
The DNA exonuclease three-prime repair exonuclease 1 (TREX1) is critical for preventing autoimmunity in mice and humans by degrading endogenous cytosolic DNA, which otherwise triggers activation of the innate cGAS/STING pathway leading to the production of type I IFNs. As tumor cells are prone to aberrant cytosolic DNA accumulation, we hypothesized that they are critically dependent on TREX1 activity to limit their immunogenicity. Here, we show that in tumor cells, TREX1 restricts spontaneous activation of the cGAS/STING pathway, and the subsequent induction of a type I IFN response. As a result, TREX1 deficiency compromised in vivo tumor growth in mice. This delay in tumor growth depended on a functional immune system, systemic type I IFN signaling, and tumor-intrinsic cGAS expression. Mechanistically, we show that tumor TREX1 loss drove activation of CD8+ T cells and NK cells, prevented CD8+ T-cell exhaustion, and remodeled an immunosuppressive myeloid compartment. Consequently, TREX1 deficiency combined with T-cell-directed immune checkpoint blockade. Collectively, we conclude that TREX1 is essential to limit tumor immunogenicity, and that targeting this innate immune checkpoint remodels the tumor microenvironment and enhances antitumor immunity by itself and in combination with T-cell-targeted therapies. See related article by Toufektchan et al., p. 673.
Collapse
Affiliation(s)
| | | | | | - John Silva
- Genentech Inc., South San Francisco, California
| | | | - Paul Tyler
- Genentech Inc., South San Francisco, California
| | | | - Tao Sun
- Genentech Inc., South San Francisco, California
| | - Eva Lin
- Genentech Inc., South San Francisco, California
| | | | | | | | - Ira Mellman
- Genentech Inc., South San Francisco, California
| | | | | | | | - Yan Qu
- Genentech Inc., South San Francisco, California
| | - Klaus Heger
- Genentech Inc., South San Francisco, California
| |
Collapse
|
44
|
Nussbaum YI, Hossain KSMT, Kaifi J, Warren WC, Shyu CR, Mitchem JB. Identifying gene expression programs in single-cell RNA-seq data using linear correlation explanation. J Biomed Inform 2024; 154:104644. [PMID: 38631462 DOI: 10.1016/j.jbi.2024.104644] [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: 10/05/2023] [Revised: 02/29/2024] [Accepted: 04/14/2024] [Indexed: 04/19/2024]
Abstract
OBJECTIVE Gene expression analysis through single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of gene regulation in diverse cell types, tissues, and organisms. While existing methods primarily focus on identifying cell type-specific gene expression programs (GEPs), the characterization of GEPs associated with biological processes and stimuli responses remains limited. In this study, we aim to infer biologically meaningful GEPs that are associated with both cellular phenotypes and activity programs directly from scRNA-seq data. METHODS We applied linear CorEx, a machine-learning-based approach, to infer GEPs by grouping genes based on total correlation optimization function in simulated and real-world scRNA-seq datasets. Additionally, we utilized a transfer learning approach to project CorEx-inferred GEPs to other scRNA-seq datasets. RESULTS By leveraging total correlation optimization, linear CorEx groups genes and demonstrates superior performance in identifying cell types and activity programs compared to similar methods using simulated data. Furthermore, we apply this same approach to real-world scRNA-seq data from the mouse dentate gyrus and embryonic colon development, uncovering biologically relevant GEPs related to cell types, developmental ages, and cell cycle programs. We also demonstrate the potential for transfer learning by evaluating similar datasets, showcasing the cross-species sensitivity of linear CorEx. CONCLUSION Our findings validate linear CorEx as a valuable tool for comprehensively analyzing complex signals in scRNA-seq data, leading to deeper insights into gene expression dynamics, cellular heterogeneity, and regulatory mechanisms.
Collapse
Affiliation(s)
- Yulia I Nussbaum
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65201, USA
| | - K S M Tozammel Hossain
- Department of Information Science, University of North Texas, 3940 N Elm St, Denton, TX 76203, USA
| | - Jussuf Kaifi
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65201, USA; Department of Surgery, University of Missouri Hospital, 1 Hospital Dr., Columbia, MO 65212, USA; Harry S. Truman Memorial Veterans' Hospital, 800 Hospital Dr., Columbia, MO 65201, USA; Siteman Cancer Center, Washington University School of Medicine, 4921 Parkview Pl, St. Louis, MO 63110, USA
| | - Wesley C Warren
- Department of Surgery, University of Missouri Hospital, 1 Hospital Dr., Columbia, MO 65212, USA; Bond Life Sciences Center, University of Missouri, 1201 Rollin St., Columbia, MO 65211, USA
| | - Chi-Ren Shyu
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65201, USA
| | - Jonathan B Mitchem
- VA Northeast Ohio Healthcare System, 10701 East Boulevard, Cleveland, OH 44106, USA; Department of Colon and Rectal Surgery, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA; Department of Inflammation and Immunity, Lerner Research Institute, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
| |
Collapse
|
45
|
Prakash A, Dion E, Banerjee TD, Monteiro A. The molecular basis of scale development highlighted by a single-cell atlas of Bicyclus anynana butterfly pupal forewings. Cell Rep 2024; 43:114147. [PMID: 38662541 DOI: 10.1016/j.celrep.2024.114147] [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: 12/18/2023] [Revised: 02/26/2024] [Accepted: 04/09/2024] [Indexed: 06/01/2024] Open
Abstract
Butterfly wings display a diversity of cell types, including large polyploid scale cells, yet the molecular basis of such diversity is poorly understood. To explore scale cell diversity at a transcriptomic level, we employ single-cell RNA sequencing of ∼5,200 large cells (>6 μm) from 22.5- to 25-h male pupal forewings of the butterfly Bicyclus anynana. Using unsupervised clustering, followed by in situ hybridization, immunofluorescence, and CRISPR-Cas9 editing of candidate genes, we annotate various cell types on the wing. We identify genes marking non-innervated scale cells, pheromone-producing glandular cells, and innervated sensory cell types. We show that senseless, a zinc-finger transcription factor, and HR38, a hormone receptor, determine the identity, size, and color of different scale cell types and are important regulators of scale cell differentiation. This dataset and the identification of various wing cell-type markers provide a foundation to compare and explore scale cell-type diversification across arthropod species.
Collapse
Affiliation(s)
- Anupama Prakash
- Department of Biological Sciences, National University of Singapore, Singapore, Singapore.
| | - Emilie Dion
- Department of Biological Sciences, National University of Singapore, Singapore, Singapore
| | - Tirtha Das Banerjee
- Department of Biological Sciences, National University of Singapore, Singapore, Singapore
| | - Antónia Monteiro
- Department of Biological Sciences, National University of Singapore, Singapore, Singapore.
| |
Collapse
|
46
|
Liu Y, Carbonetto P, Willwerscheid J, Oakes SA, Macleod KF, Stephens M. Dissecting tumor transcriptional heterogeneity from single-cell RNA-seq data by generalized binary covariance decomposition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.15.553436. [PMID: 37645713 PMCID: PMC10462040 DOI: 10.1101/2023.08.15.553436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Profiling tumors with single-cell RNA sequencing (scRNA-seq) has the potential to identify recurrent patterns of transcription variation related to cancer progression, and produce new therapeutically relevant insights. However, the presence of strong inter-tumor heterogeneity often obscures more subtle patterns that are shared across tumors, some of which may characterize clinically relevant disease subtypes. Here we introduce a new statistical method, generalized binary covariance decomposition (GBCD), to address this problem. We show that GBCD can help decompose transcriptional heterogeneity into interpretable components - including patient-specific, dataset-specific and shared components relevant to disease subtypes - and that, in the presence of strong inter-tumor heterogeneity, it can produce more interpretable results than existing methods. Applied to data from three studies on pancreatic cancer adenocarcinoma (PDAC), GBCD produces a refined characterization of existing tumor subtypes (e.g., classical vs. basal), and identifies a new gene expression program (GEP) that is prognostic of poor survival independent of established prognostic factors such as tumor stage and subtype. The new GEP is enriched for genes involved in a variety of stress responses, and suggests a potentially important role for the integrated stress response in PDAC development and prognosis.
Collapse
|
47
|
Rabadam G, Wibrand C, Flynn E, Hartoularos GC, Sun Y, Madubata C, Fragiadakis GK, Ye CJ, Kim S, Gartner ZJ, Sirota M, Neely J. Coordinated immune dysregulation in juvenile dermatomyositis revealed by single-cell genomics. JCI Insight 2024; 9:e176963. [PMID: 38743491 PMCID: PMC11383589 DOI: 10.1172/jci.insight.176963] [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: 11/07/2023] [Accepted: 05/08/2024] [Indexed: 05/16/2024] Open
Abstract
Juvenile dermatomyositis (JDM) is one of several childhood-onset autoimmune disorders characterized by a type I IFN response and autoantibodies. Treatment options are limited due to an incomplete understanding of how the disease emerges from dysregulated cell states across the immune system. We therefore investigated the blood of patients with JDM at different stages of disease activity using single-cell transcriptomics paired with surface protein expression. By immunophenotyping peripheral blood mononuclear cells, we observed skewing of the B cell compartment toward an immature naive state as a hallmark of JDM at diagnosis. Furthermore, we find that these changes in B cells are paralleled by T cell signatures suggestive of Th2-mediated inflammation that persist despite disease quiescence. We applied network analysis to reveal that hyperactivation of the type I IFN response in all immune populations is coordinated with previously masked cell states including dysfunctional protein processing in CD4+ T cells and regulation of cell death programming in NK cells, CD8+ T cells, and γδ T cells. Together, these findings unveil the coordinated immune dysregulation underpinning JDM and provide insight into strategies for restoring balance in immune function.
Collapse
Affiliation(s)
- Gabrielle Rabadam
- UC Berkeley-UC San Francisco Graduate Program in Bioengineering, and
- Department of Pharmaceutical Chemistry, UCSF, San Francisco, California, USA
| | - Camilla Wibrand
- Aarhus University, Aarhus, Denmark
- Division of Pediatric Rheumatology, Department of Pediatrics
| | | | - George C Hartoularos
- Graduate Program in Biological and Medical Informatics
- Division of Rheumatology, Department of Medicine
- Institute for Human Genetics
| | - Yang Sun
- Division of Rheumatology, Department of Medicine
| | - Chioma Madubata
- Division of Pediatric Rheumatology, Department of Pediatrics
- CoLabs
| | | | - Chun Jimmie Ye
- Division of Rheumatology, Department of Medicine
- Institute for Human Genetics
- Department of Epidemiology and Biostatistics, and
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, California, USA
- Chan Zuckerberg Biohub, San Francisco, California, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, California, USA
| | - Susan Kim
- Division of Pediatric Rheumatology, Department of Pediatrics
| | - Zev J Gartner
- Department of Pharmaceutical Chemistry, UCSF, San Francisco, California, USA
- Chan Zuckerberg Biohub, San Francisco, California, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, California, USA
- Department of Pediatrics, UCSF, San Francisco, California, USA
| | - Jessica Neely
- Division of Pediatric Rheumatology, Department of Pediatrics
| |
Collapse
|
48
|
Zhu B, Gao S, Chen S, Yeung J, Bai Y, Huang AY, Yeo YY, Liao G, Mao S, Jiang ZG, Rodig SJ, Shalek AK, Nolan GP, Jiang S, Ma Z. Cross-domain information fusion for enhanced cell population delineation in single-cell spatial-omics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.12.593710. [PMID: 38798592 PMCID: PMC11118457 DOI: 10.1101/2024.05.12.593710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Cell population delineation and identification is an essential step in single-cell and spatial-omics studies. Spatial-omics technologies can simultaneously measure information from three complementary domains related to this task: expression levels of a panel of molecular biomarkers at single-cell resolution, relative positions of cells, and images of tissue sections, but existing computational methods for performing this task on single-cell spatial-omics datasets often relinquish information from one or more domains. The additional reliance on the availability of "atlas" training or reference datasets limits cell type discovery to well-defined but limited cell population labels, thus posing major challenges for using these methods in practice. Successful integration of all three domains presents an opportunity for uncovering cell populations that are functionally stratified by their spatial contexts at cellular and tissue levels: the key motivation for employing spatial-omics technologies in the first place. In this work, we introduce Cell Spatio- and Neighborhood-informed Annotation and Patterning (CellSNAP), a self-supervised computational method that learns a representation vector for each cell in tissue samples measured by spatial-omics technologies at the single-cell or finer resolution. The learned representation vector fuses information about the corresponding cell across all three aforementioned domains. By applying CellSNAP to datasets spanning both spatial proteomic and spatial transcriptomic modalities, and across different tissue types and disease settings, we show that CellSNAP markedly enhances de novo discovery of biologically relevant cell populations at fine granularity, beyond current approaches, by fully integrating cells' molecular profiles with cellular neighborhood and tissue image information.
Collapse
Affiliation(s)
- Bokai Zhu
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sheng Gao
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, PA, United States
| | - Shuxiao Chen
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, PA, United States
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Yunhao Bai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amy Y Huang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Guanrui Liao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Shulin Mao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
| | - Zhenghui G Jiang
- Division of Gastroenterology/Liver Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Alex K Shalek
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Sizun Jiang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Zongming Ma
- Department of Statistics and Data Science, Yale University, New Haven, CT, United States
| |
Collapse
|
49
|
Wang J, Sun H, Mou L, Lu Y, Wu Z, Pu Z, Yang MM. Unveiling the molecular complexity of proliferative diabetic retinopathy through scRNA-seq, AlphaFold 2, and machine learning. Front Endocrinol (Lausanne) 2024; 15:1382896. [PMID: 38800474 PMCID: PMC11116564 DOI: 10.3389/fendo.2024.1382896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 04/25/2024] [Indexed: 05/29/2024] Open
Abstract
Background Proliferative diabetic retinopathy (PDR), a major cause of blindness, is characterized by complex pathogenesis. This study integrates single-cell RNA sequencing (scRNA-seq), Non-negative Matrix Factorization (NMF), machine learning, and AlphaFold 2 methods to explore the molecular level of PDR. Methods We analyzed scRNA-seq data from PDR patients and healthy controls to identify distinct cellular subtypes and gene expression patterns. NMF was used to define specific transcriptional programs in PDR. The oxidative stress-related genes (ORGs) identified within Meta-Program 1 were utilized to construct a predictive model using twelve machine learning algorithms. Furthermore, we employed AlphaFold 2 for the prediction of protein structures, complementing this with molecular docking to validate the structural foundation of potential therapeutic targets. We also analyzed protein-protein interaction (PPI) networks and the interplay among key ORGs. Results Our scRNA-seq analysis revealed five major cell types and 14 subcell types in PDR patients, with significant differences in gene expression compared to those in controls. We identified three key meta-programs underscoring the role of microglia in the pathogenesis of PDR. Three critical ORGs (ALKBH1, PSIP1, and ATP13A2) were identified, with the best-performing predictive model demonstrating high accuracy (AUC of 0.989 in the training cohort and 0.833 in the validation cohort). Moreover, AlphaFold 2 predictions combined with molecular docking revealed that resveratrol has a strong affinity for ALKBH1, indicating its potential as a targeted therapeutic agent. PPI network analysis, revealed a complex network of interactions among the hub ORGs and other genes, suggesting a collective role in PDR pathogenesis. Conclusion This study provides insights into the cellular and molecular aspects of PDR, identifying potential biomarkers and therapeutic targets using advanced technological approaches.
Collapse
Affiliation(s)
- Jun Wang
- Department of Endocrinology, Shenzhen People’s Hospital (The Second Clinical Medical College of Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| | - Hongyan Sun
- Department of Ophthalmology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| | - Lisha Mou
- Imaging Department, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
- MetaLife Center, Shenzhen Institute of Translational Medicine, Guangdong, Shenzhen, China
| | - Ying Lu
- Imaging Department, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
- MetaLife Center, Shenzhen Institute of Translational Medicine, Guangdong, Shenzhen, China
| | - Zijing Wu
- Imaging Department, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
- MetaLife Center, Shenzhen Institute of Translational Medicine, Guangdong, Shenzhen, China
| | - Zuhui Pu
- Imaging Department, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
- MetaLife Center, Shenzhen Institute of Translational Medicine, Guangdong, Shenzhen, China
| | - Ming-ming Yang
- Department of Ophthalmology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| |
Collapse
|
50
|
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 2024:10.1038/s41551-024-01209-3. [PMID: 38710838 DOI: 10.1038/s41551-024-01209-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [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.
Collapse
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.
| |
Collapse
|