1
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Li L, Sun J, Fu Y, Changrob S, McGrath JJC, Wilson PC. A hybrid demultiplexing strategy that improves performance and robustness of cell hashing. Brief Bioinform 2024; 25:bbae254. [PMID: 38828640 PMCID: PMC11145454 DOI: 10.1093/bib/bbae254] [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: 10/12/2023] [Revised: 04/27/2024] [Accepted: 05/08/2024] [Indexed: 06/05/2024] Open
Abstract
Cell hashing, a nucleotide barcode-based method that allows users to pool multiple samples and demultiplex in downstream analysis, has gained widespread popularity in single-cell sequencing due to its compatibility, simplicity, and cost-effectiveness. Despite these advantages, the performance of this method remains unsatisfactory under certain circumstances, especially in experiments that have imbalanced sample sizes or use many hashtag antibodies. Here, we introduce a hybrid demultiplexing strategy that increases accuracy and cell recovery in multi-sample single-cell experiments. This approach correlates the results of cell hashing and genetic variant clustering, enabling precise and efficient cell identity determination without additional experimental costs or efforts. In addition, we developed HTOreader, a demultiplexing tool for cell hashing that improves the accuracy of cut-off calling by avoiding the dominance of negative signals in experiments with many hashtags or imbalanced sample sizes. When compared to existing methods using real-world datasets, this hybrid approach and HTOreader consistently generate reliable results with increased accuracy and cell recovery.
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Affiliation(s)
- Lei Li
- Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, 413 E. 69th Street, New York, NY 10021, United States
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, United States
| | - Jiayi Sun
- Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, 413 E. 69th Street, New York, NY 10021, United States
| | - Yanbin Fu
- Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, 413 E. 69th Street, New York, NY 10021, United States
| | - Siriruk Changrob
- Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, 413 E. 69th Street, New York, NY 10021, United States
| | - Joshua J C McGrath
- Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, 413 E. 69th Street, New York, NY 10021, United States
| | - Patrick C Wilson
- Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, 413 E. 69th Street, New York, NY 10021, United States
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2
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Wang HQ, Wu XL, Zhang J, Wang ST, Sang YJ, Li K, Yang CF, Sun F, Li CJ. Meiotic transcriptional reprogramming mediated by cell-cell communications in humans and mice revealed by scATAC-seq and scRNA-seq. Zool Res 2024; 45:601-616. [PMID: 38766744 PMCID: PMC11188612 DOI: 10.24272/j.issn.2095-8137.2023.414] [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: 02/17/2024] [Accepted: 04/27/2024] [Indexed: 05/22/2024] Open
Abstract
Meiosis is a highly complex process significantly influenced by transcriptional regulation. However, studies on the mechanisms that govern transcriptomic changes during meiosis, especially in prophase I, are limited. Here, we performed single-cell ATAC-seq of human testis tissues and observed reprogramming during the transition from zygotene to pachytene spermatocytes. This event, conserved in mice, involved the deactivation of genes associated with meiosis after reprogramming and the activation of those related to spermatogenesis before their functional onset. Furthermore, we identified 282 transcriptional regulators (TRs) that underwent activation or deactivation subsequent to this process. Evidence suggested that physical contact signals from Sertoli cells may regulate these TRs in spermatocytes, while secreted ENHO signals may alter metabolic patterns in these cells. Our results further indicated that defective transcriptional reprogramming may be associated with non-obstructive azoospermia (NOA). This study revealed the importance of both physical contact and secreted signals between Sertoli cells and germ cells in meiotic progression.
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Affiliation(s)
- Hai-Quan Wang
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
- Medical School, Nanjing University, Nanjing, Jiangsu 210093, China
| | - Xiao-Long Wu
- Department of Urology and Andrology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310000, China
| | - Jing Zhang
- Medical School, Nanjing University, Nanjing, Jiangsu 210093, China
| | - Si-Ting Wang
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Yong-Juan Sang
- Model Animal Research Center of Medical School, Nanjing University, Nanjing, Nanjing, Jiangsu 210093, China
| | - Kang Li
- Medical School, Nanjing University, Nanjing, Jiangsu 210093, China
| | - Chao-Fan Yang
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang 311121, China
| | - Fei Sun
- Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, China. E-mail:
| | - Chao-Jun Li
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China. E-mail:
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3
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Cuevas-Diaz Duran R, Wei H, Wu J. Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets. BMC Genomics 2024; 25:444. [PMID: 38711017 PMCID: PMC11073985 DOI: 10.1186/s12864-024-10364-5] [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: 09/02/2023] [Accepted: 04/29/2024] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND Normalization is a critical step in the analysis of single-cell RNA-sequencing (scRNA-seq) datasets. Its main goal is to make gene counts comparable within and between cells. To do so, normalization methods must account for technical and biological variability. Numerous normalization methods have been developed addressing different sources of dispersion and making specific assumptions about the count data. MAIN BODY The selection of a normalization method has a direct impact on downstream analysis, for example differential gene expression and cluster identification. Thus, the objective of this review is to guide the reader in making an informed decision on the most appropriate normalization method to use. To this aim, we first give an overview of the different single cell sequencing platforms and methods commonly used including isolation and library preparation protocols. Next, we discuss the inherent sources of variability of scRNA-seq datasets. We describe the categories of normalization methods and include examples of each. We also delineate imputation and batch-effect correction methods. Furthermore, we describe data-driven metrics commonly used to evaluate the performance of normalization methods. We also discuss common scRNA-seq methods and toolkits used for integrated data analysis. CONCLUSIONS According to the correction performed, normalization methods can be broadly classified as within and between-sample algorithms. Moreover, with respect to the mathematical model used, normalization methods can further be classified into: global scaling methods, generalized linear models, mixed methods, and machine learning-based methods. Each of these methods depict pros and cons and make different statistical assumptions. However, there is no better performing normalization method. Instead, metrics such as silhouette width, K-nearest neighbor batch-effect test, or Highly Variable Genes are recommended to assess the performance of normalization methods.
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Affiliation(s)
- Raquel Cuevas-Diaz Duran
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Nuevo Leon, 64710, Mexico.
| | - Haichao Wei
- The Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
- Center for Stem Cell and Regenerative Medicine, UT Brown Foundation Institute of Molecular Medicine, Houston, TX, 77030, USA
| | - Jiaqian Wu
- The Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
- Center for Stem Cell and Regenerative Medicine, UT Brown Foundation Institute of Molecular Medicine, Houston, TX, 77030, USA.
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, 77030, USA.
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4
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Frost HR. Reconstruction Set Test (RESET): A computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error. PLoS Comput Biol 2024; 20:e1012084. [PMID: 38683883 PMCID: PMC11081506 DOI: 10.1371/journal.pcbi.1012084] [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: 09/29/2023] [Revised: 05/09/2024] [Accepted: 04/17/2024] [Indexed: 05/02/2024] Open
Abstract
We have developed a new, and analytically novel, single sample gene set testing method called Reconstruction Set Test (RESET). RESET quantifies gene set importance based on the ability of set genes to reconstruct values for all measured genes. RESET is realized using a computationally efficient randomized reduced rank reconstruction algorithm (available via the RESET R package on CRAN) that can effectively detect patterns of differential abundance and differential correlation for self-contained and competitive scenarios. As demonstrated using real and simulated scRNA-seq data, RESET provides superior performance at a lower computational cost relative to other single sample approaches.
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Affiliation(s)
- H. Robert Frost
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, United States of America
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5
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Zhou X, Seow WY, Ha N, Cheng TH, Jiang L, Boonruangkan J, Goh JJL, Prabhakar S, Chou N, Chen KH. Highly sensitive spatial transcriptomics using FISHnCHIPs of multiple co-expressed genes. Nat Commun 2024; 15:2342. [PMID: 38491027 PMCID: PMC10943009 DOI: 10.1038/s41467-024-46669-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 03/04/2024] [Indexed: 03/18/2024] Open
Abstract
High-dimensional, spatially resolved analysis of intact tissue samples promises to transform biomedical research and diagnostics, but existing spatial omics technologies are costly and labor-intensive. We present Fluorescence In Situ Hybridization of Cellular HeterogeneIty and gene expression Programs (FISHnCHIPs) for highly sensitive in situ profiling of cell types and gene expression programs. FISHnCHIPs achieves this by simultaneously imaging ~2-35 co-expressed genes (clustered into modules) that are spatially co-localized in tissues, resulting in similar spatial information as single-gene Fluorescence In Situ Hybridization (FISH), but with ~2-20-fold higher sensitivity. Using FISHnCHIPs, we image up to 53 modules from the mouse kidney and mouse brain, and demonstrate high-speed, large field-of-view profiling of a whole tissue section. FISHnCHIPs also reveals spatially restricted localizations of cancer-associated fibroblasts in a human colorectal cancer biopsy. Overall, FISHnCHIPs enables fast, robust, and scalable cell typing of tissues with normal physiology or undergoing pathogenesis.
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Affiliation(s)
- Xinrui Zhou
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Wan Yi Seow
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Norbert Ha
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Teh How Cheng
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Lingfan Jiang
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Jeeranan Boonruangkan
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Jolene Jie Lin Goh
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Shyam Prabhakar
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Nigel Chou
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore.
| | - Kok Hao Chen
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore.
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6
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Mei S, Alchahin AM, Tsea I, Kfoury Y, Hirz T, Jeffries NE, Zhao T, Xu Y, Zhang H, Sarkar H, Wu S, Subtelny AO, Johnsen JI, Zhang Y, Salari K, Wu CL, Randolph MA, Scadden DT, Dahl DM, Shin J, Kharchenko PV, Saylor PJ, Sykes DB, Baryawno N. Single-cell analysis of immune and stroma cell remodeling in clear cell renal cell carcinoma primary tumors and bone metastatic lesions. Genome Med 2024; 16:1. [PMID: 38281962 PMCID: PMC10823713 DOI: 10.1186/s13073-023-01272-6] [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: 10/10/2022] [Accepted: 12/11/2023] [Indexed: 01/30/2024] Open
Abstract
BACKGROUND Despite therapeutic advances, once a cancer has metastasized to the bone, it represents a highly morbid and lethal disease. One third of patients with advanced clear cell renal cell carcinoma (ccRCC) present with bone metastasis at the time of diagnosis. However, the bone metastatic niche in humans, including the immune and stromal microenvironments, has not been well-defined, hindering progress towards identification of therapeutic targets. METHODS We collected fresh patient samples and performed single-cell transcriptomic profiling of solid metastatic tissue (Bone Met), liquid bone marrow at the vertebral level of spinal cord compression (Involved), and liquid bone marrow from a different vertebral body distant from the tumor site but within the surgical field (Distal), as well as bone marrow from patients undergoing hip replacement surgery (Benign). In addition, we incorporated single-cell data from primary ccRCC tumors (ccRCC Primary) for comparative analysis. RESULTS The bone marrow of metastatic patients is immune-suppressive, featuring increased, exhausted CD8 + cytotoxic T cells, T regulatory cells, and tumor-associated macrophages (TAM) with distinct transcriptional states in metastatic lesions. Bone marrow stroma from tumor samples demonstrated a tumor-associated mesenchymal stromal cell population (TA-MSC) that appears to be supportive of epithelial-to mesenchymal transition (EMT), bone remodeling, and a cancer-associated fibroblast (CAFs) phenotype. This stromal subset is associated with poor progression-free and overall survival and also markedly upregulates bone remodeling through the dysregulation of RANK/RANKL/OPG signaling activity in bone cells, ultimately leading to bone resorption. CONCLUSIONS These results provide a comprehensive analysis of the bone marrow niche in the setting of human metastatic cancer and highlight potential therapeutic targets for both cell populations and communication channels.
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Affiliation(s)
- Shenglin Mei
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA.
| | - Adele M Alchahin
- Childhood Cancer Research Unit, Department of Women's and Children's Health, Karolinska Institutet, 17176, Stockholm, Sweden
| | - Ioanna Tsea
- Childhood Cancer Research Unit, Department of Women's and Children's Health, Karolinska Institutet, 17176, Stockholm, Sweden
| | - Youmna Kfoury
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Taghreed Hirz
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Nathan Elias Jeffries
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Ting Zhao
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Yanxin Xu
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
| | - Hanyu Zhang
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Hirak Sarkar
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Shulin Wu
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Alexander O Subtelny
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - John Inge Johnsen
- Childhood Cancer Research Unit, Department of Women's and Children's Health, Karolinska Institutet, 17176, Stockholm, Sweden
| | - Yida Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Keyan Salari
- Department of Urology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Chin-Lee Wu
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Mark A Randolph
- Division of Plastic and Reconstructive Surgery, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - David T Scadden
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Douglas M Dahl
- Department of Urology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - John Shin
- Department of Neurosurgery, Harvard Medical School, Boston, MA, 02115, USA.
| | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA.
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA.
- Present: Altos Labs, San Diego, CA, 92121, USA.
| | - Philip J Saylor
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, 02114, USA.
| | - David B Sykes
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA.
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA.
| | - Ninib Baryawno
- Childhood Cancer Research Unit, Department of Women's and Children's Health, Karolinska Institutet, 17176, Stockholm, Sweden.
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7
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Najm M, Cornet M, Albergante L, Zinovyev A, Sermet-Gaudelus I, Stoven V, Calzone L, Martignetti L. Representation and quantification of module activity from omics data with rROMA. NPJ Syst Biol Appl 2024; 10:8. [PMID: 38242871 PMCID: PMC10799004 DOI: 10.1038/s41540-024-00331-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 01/03/2024] [Indexed: 01/21/2024] Open
Abstract
The efficiency of analyzing high-throughput data in systems biology has been demonstrated in numerous studies, where molecular data, such as transcriptomics and proteomics, offers great opportunities for understanding the complexity of biological processes. One important aspect of data analysis in systems biology is the shift from a reductionist approach that focuses on individual components to a more integrative perspective that considers the system as a whole, where the emphasis shifted from differential expression of individual genes to determining the activity of gene sets. Here, we present the rROMA software package for fast and accurate computation of the activity of gene sets with coordinated expression. The rROMA package incorporates significant improvements in the calculation algorithm, along with the implementation of several functions for statistical analysis and visualizing results. These additions greatly expand the package's capabilities and offer valuable tools for data analysis and interpretation. It is an open-source package available on github at: www.github.com/sysbio-curie/rROMA . Based on publicly available transcriptomic datasets, we applied rROMA to cystic fibrosis, highlighting biological mechanisms potentially involved in the establishment and progression of the disease and the associated genes. Results indicate that rROMA can detect disease-related active signaling pathways using transcriptomic and proteomic data. The results notably identified a significant mechanism relevant to cystic fibrosis, raised awareness of a possible bias related to cell culture, and uncovered an intriguing gene that warrants further investigation.
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Affiliation(s)
- Matthieu Najm
- INSERM U900, 75428, Paris, France
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France
- Institut Curie, PSL Research University, 75248, Paris, France
| | - Matthieu Cornet
- INSERM U900, 75428, Paris, France
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France
- Institut Curie, PSL Research University, 75248, Paris, France
| | - Luca Albergante
- INSERM U900, 75428, Paris, France
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France
- Institut Curie, PSL Research University, 75248, Paris, France
| | - Andrei Zinovyev
- INSERM U900, 75428, Paris, France
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France
- Institut Curie, PSL Research University, 75248, Paris, France
| | - Isabelle Sermet-Gaudelus
- Faculté de Médecine, Université de Paris, Paris, France
- Institut Necker Enfants Malades, INSERM U1151, Paris, France
- AP-HP. Centre - Université Paris Cité; Hôpital Necker Enfants Malades, Centre de Référence Maladie Rare - Mucoviscidose, Paris, France
| | - Véronique Stoven
- INSERM U900, 75428, Paris, France
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France
- Institut Curie, PSL Research University, 75248, Paris, France
| | - Laurence Calzone
- INSERM U900, 75428, Paris, France
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France
- Institut Curie, PSL Research University, 75248, Paris, France
| | - Loredana Martignetti
- INSERM U900, 75428, Paris, France.
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France.
- Institut Curie, PSL Research University, 75248, Paris, France.
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8
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Sur A, Wang Y, Capar P, Margolin G, Prochaska MK, Farrell JA. Single-cell analysis of shared signatures and transcriptional diversity during zebrafish development. Dev Cell 2023; 58:3028-3047.e12. [PMID: 37995681 PMCID: PMC11181902 DOI: 10.1016/j.devcel.2023.11.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 08/24/2023] [Accepted: 11/01/2023] [Indexed: 11/25/2023]
Abstract
During development, animals generate distinct cell populations with specific identities, functions, and morphologies. We mapped transcriptionally distinct populations across 489,686 cells from 62 stages during wild-type zebrafish embryogenesis and early larval development (3-120 h post-fertilization). Using these data, we identified the limited catalog of gene expression programs reused across multiple tissues and their cell-type-specific adaptations. We also determined the duration each transcriptional state is present during development and identify unexpected long-term cycling populations. Focused clustering and transcriptional trajectory analyses of non-skeletal muscle and endoderm identified transcriptional profiles and candidate transcriptional regulators of understudied cell types and subpopulations, including the pneumatic duct, individual intestinal smooth muscle layers, spatially distinct pericyte subpopulations, and recently discovered best4+ cells. To enable additional discoveries, we make this comprehensive transcriptional atlas of early zebrafish development available through our website, Daniocell.
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Affiliation(s)
- Abhinav Sur
- Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20814, USA
| | - Yiqun Wang
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Paulina Capar
- Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20814, USA
| | - Gennady Margolin
- Bioinformatics and Scientific Programming Core, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20814, USA
| | - Morgan Kathleen Prochaska
- Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20814, USA
| | - Jeffrey A Farrell
- Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20814, USA.
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9
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Du ZH, Hu WL, Li JQ, Shang X, You ZH, Chen ZZ, Huang YA. scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data. Commun Biol 2023; 6:1268. [PMID: 38097699 PMCID: PMC10721875 DOI: 10.1038/s42003-023-05634-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 11/24/2023] [Indexed: 12/17/2023] Open
Abstract
Recent developments in single-cell technology have enabled the exploration of cellular heterogeneity at an unprecedented level, providing invaluable insights into various fields, including medicine and disease research. Cell type annotation is an essential step in its omics research. The mainstream approach is to utilize well-annotated single-cell data to supervised learning for cell type annotation of new singlecell data. However, existing methods lack good generalization and robustness in cell annotation tasks, partially due to difficulties in dealing with technical differences between datasets, as well as not considering the heterogeneous associations of genes in regulatory mechanism levels. Here, we propose the scPML model, which utilizes various gene signaling pathway data to partition the genetic features of cells, thus characterizing different interaction maps between cells. Extensive experiments demonstrate that scPML performs better in cell type annotation and detection of unknown cell types from different species, platforms, and tissues.
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Affiliation(s)
- Zhi-Hua Du
- College of Computer Science and Software Engineering, ShenZhen University, 3688 Nanhai Avenue, Shenzhen, China
| | - Wei-Lin Hu
- College of Computer Science and Software Engineering, ShenZhen University, 3688 Nanhai Avenue, Shenzhen, China
| | - Jian-Qiang Li
- College of Computer Science and Software Engineering, ShenZhen University, 3688 Nanhai Avenue, Shenzhen, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Zhuang-Zhuang Chen
- College of Computer Science and Software Engineering, ShenZhen University, 3688 Nanhai Avenue, Shenzhen, China
| | - Yu-An Huang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
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10
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Johnson MR, Li S, Guerrero-Juarez CF, Miller P, Brack BJ, Mereby SA, Moreno JA, Feigin CY, Gaska J, Rivera-Perez JA, Nie Q, Ploss A, Shvartsman SY, Mallarino R. A multifunctional Wnt regulator underlies the evolution of rodent stripe patterns. Nat Ecol Evol 2023; 7:2143-2159. [PMID: 37813945 PMCID: PMC10839778 DOI: 10.1038/s41559-023-02213-7] [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: 03/29/2023] [Accepted: 08/27/2023] [Indexed: 10/11/2023]
Abstract
Animal pigment patterns are excellent models to elucidate mechanisms of biological organization. Although theoretical simulations, such as Turing reaction-diffusion systems, recapitulate many animal patterns, they are insufficient to account for those showing a high degree of spatial organization and reproducibility. Here, we study the coat of the African striped mouse (Rhabdomys pumilio) to uncover how periodic stripes form. Combining transcriptomics, mathematical modelling and mouse transgenics, we show that the Wnt modulator Sfrp2 regulates the distribution of hair follicles and establishes an embryonic prepattern that foreshadows pigment stripes. Moreover, by developing in vivo gene editing in striped mice, we find that Sfrp2 knockout is sufficient to alter the stripe pattern. Strikingly, mutants exhibited changes in pigmentation, revealing that Sfrp2 also regulates hair colour. Lastly, through evolutionary analyses, we find that striped mice have evolved lineage-specific changes in regulatory elements surrounding Sfrp2, many of which may be implicated in modulating the expression of this gene. Altogether, our results show that a single factor controls coat pattern formation by acting both as an orienting signalling mechanism and a modulator of pigmentation. More broadly, our work provides insights into how spatial patterns are established in developing embryos and the mechanisms by which phenotypic novelty originates.
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Affiliation(s)
- Matthew R Johnson
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Sha Li
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Christian F Guerrero-Juarez
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Developmental and Cell Biology, University of California, Irvine, CA, USA
- Department of Mathematics, University of California, Irvine, CA, USA
- Center for Complex Biological Systems, University of California, Irvine, CA, USA
- NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA, USA
| | - Pearson Miller
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
| | - Benjamin J Brack
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Sarah A Mereby
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Jorge A Moreno
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Charles Y Feigin
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Jenna Gaska
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | | | - Qing Nie
- Department of Developmental and Cell Biology, University of California, Irvine, CA, USA
- Department of Mathematics, University of California, Irvine, CA, USA
- Center for Complex Biological Systems, University of California, Irvine, CA, USA
- NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA, USA
| | - Alexander Ploss
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Stanislav Y Shvartsman
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
- The Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Ricardo Mallarino
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
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11
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Li L, Sun J, Fu Y, Changrob S, McGrath JJ, Wilson PC. A hybrid demultiplexing strategy that improves performance and robustness of cell hashing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.02.535299. [PMID: 37066221 PMCID: PMC10104010 DOI: 10.1101/2023.04.02.535299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Recent advances in single cell RNA sequencing allow users to pool multiple samples and demultiplex in downstream analysis, which greatly increase experimental efficiency and cost-effectiveness. Among all the demultiplexing methods, nucleotide barcode-based cell hashing has gained widespread popularity due to its compatibility and simplicity. Despite these advantages, certain issues of this technic remain to be solved, such as challenges in distinguishing true positive from background, high reagent cost for samples with large cell numbers, and unpredictable false negative and false doublet rates. Here, we propose a hybrid demultiplexing strategy that increases calling accuracy and cell recovery of cell hashing without adding experimental cost. In this approach, we computationally cluster all single cells based on their natural genetic variations and assign donor identity by finding the dominant hashtag in each genotype cluster. This hybrid strategy assigns donor identity to any cell that is identified as singlet by either genotype clustering or cell hashing, which allows us to demultiplex most majority of cells even if only a small fraction of cells are labeled with hashtags. When comparing its performance with cell hashing on multiple real-world datasets, this hybrid approach consistently generates reliable demultiplexing results with increased cell recovery and accuracy.
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Affiliation(s)
- Lei Li
- Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Jiayi Sun
- Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Yanbin Fu
- Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Siriruk Changrob
- Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Joshua J.C. McGrath
- Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Patrick C. Wilson
- Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, New York, NY, 10065, USA
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12
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Li P, Wei J, Zhu Y. CellGO: a novel deep learning-based framework and webserver for cell-type-specific gene function interpretation. Brief Bioinform 2023; 25:bbad417. [PMID: 37995133 PMCID: PMC10790717 DOI: 10.1093/bib/bbad417] [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: 08/07/2023] [Revised: 10/09/2023] [Accepted: 10/29/2023] [Indexed: 11/25/2023] Open
Abstract
Interpreting the function of genes and gene sets identified from omics experiments remains a challenge, as current pathway analysis tools often fail to consider the critical biological context, such as tissue or cell-type specificity. To address this limitation, we introduced CellGO. CellGO tackles this challenge by leveraging the visible neural network (VNN) and single-cell gene expressions to mimic cell-type-specific signaling propagation along the Gene Ontology tree within a cell. This design enables a novel scoring system to calculate the cell-type-specific gene-pathway paired active scores, based on which, CellGO is able to identify cell-type-specific active pathways associated with single genes. In addition, by aggregating the activities of single genes, CellGO extends its capability to identify cell-type-specific active pathways for a given gene set. To enhance biological interpretation, CellGO offers additional features, including the identification of significantly active cell types and driver genes and community analysis of pathways. To validate its performance, CellGO was assessed using a gene set comprising mixed cell-type markers, confirming its ability to discern active pathways across distinct cell types. Subsequent benchmarking analyses demonstrated CellGO's superiority in effectively identifying cell types and their corresponding cell-type-specific pathways affected by gene knockouts, using either single genes or sets of genes differentially expressed between knockout and control samples. Moreover, CellGO demonstrated its ability to infer cell-type-specific pathogenesis for disease risk genes. Accessible as a Python package, CellGO also provides a user-friendly web interface, making it a versatile and accessible tool for researchers in the field.
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Affiliation(s)
- Peilong Li
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200032, China
| | - Junfeng Wei
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200032, China
| | - Ying Zhu
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200032, China
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13
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Paas-Oliveros E, Hernández-Lemus E, de Anda-Jáuregui G. Computational single cell oncology: state of the art. Front Genet 2023; 14:1256991. [PMID: 38028624 PMCID: PMC10663273 DOI: 10.3389/fgene.2023.1256991] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.
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Affiliation(s)
- Ernesto Paas-Oliveros
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Guillermo de Anda-Jáuregui
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Investigadores por Mexico, Conahcyt, Mexico City, Mexico
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14
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Gao T, Kastriti ME, Ljungström V, Heinzel A, Tischler AS, Oberbauer R, Loh PR, Adameyko I, Park PJ, Kharchenko PV. A pan-tissue survey of mosaic chromosomal alterations in 948 individuals. Nat Genet 2023; 55:1901-1911. [PMID: 37904053 PMCID: PMC10838621 DOI: 10.1038/s41588-023-01537-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 09/18/2023] [Indexed: 11/01/2023]
Abstract
Genetic mutations accumulate in an organism's body throughout its lifetime. While somatic single-nucleotide variants have been well characterized in the human body, the patterns and consequences of large chromosomal alterations in normal tissues remain largely unknown. Here, we present a pan-tissue survey of mosaic chromosomal alterations (mCAs) in 948 healthy individuals from the Genotype-Tissue Expression project, augmenting RNA-based allelic imbalance estimation with haplotype phasing. We found that approximately a quarter of the individuals carry a clonally-expanded mCA in at least one tissue, with incidence strongly correlated with age. The prevalence and genome-wide patterns of mCAs vary considerably across tissue types, suggesting tissue-specific mutagenic exposure and selection pressures. The mCA landscapes in normal adrenal and pituitary glands resemble those in tumors arising from these tissues, whereas the same is not true for the esophagus and skin. Together, our findings show a widespread age-dependent emergence of mCAs across normal human tissues with intricate connections to tumorigenesis.
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Affiliation(s)
- Teng Gao
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Maria Eleni Kastriti
- Department of Neuroimmunology, Center for Brain Research, Medical University of Vienna, Vienna, Austria
- Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Sweden
| | - Viktor Ljungström
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Andreas Heinzel
- Department of Nephrology, Internal Medicine III, Medical University of Vienna, Vienna, Austria
| | - Arthur S Tischler
- Department of Pathology and Laboratory Medicine, Tufts Medical Center, Boston, MA, USA
| | - Rainer Oberbauer
- Department of Nephrology, Internal Medicine III, Medical University of Vienna, Vienna, Austria
| | - Po-Ru Loh
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Igor Adameyko
- Department of Neuroimmunology, Center for Brain Research, Medical University of Vienna, Vienna, Austria
- Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Sweden
| | - Peter J Park
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA.
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15
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Parry EM, Lemvigh CK, Deng S, Dangle N, Ruthen N, Knisbacher BA, Broséus J, Hergalant S, Guièze R, Li S, Zhang W, Johnson C, Long JM, Yin S, Werner L, Anandappa A, Purroy N, Gohil S, Oliveira G, Bachireddy P, Shukla SA, Huang T, Khoury JD, Thakral B, Dickinson M, Tam C, Livak KJ, Getz G, Neuberg D, Feugier P, Kharchenko P, Wierda W, Olsen LR, Jain N, Wu CJ. ZNF683 marks a CD8 + T cell population associated with anti-tumor immunity following anti-PD-1 therapy for Richter syndrome. Cancer Cell 2023; 41:1803-1816.e8. [PMID: 37738974 PMCID: PMC10618915 DOI: 10.1016/j.ccell.2023.08.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 05/30/2023] [Accepted: 08/30/2023] [Indexed: 09/24/2023]
Abstract
Unlike many other hematologic malignancies, Richter syndrome (RS), an aggressive B cell lymphoma originating from indolent chronic lymphocytic leukemia, is responsive to PD-1 blockade. To discover the determinants of response, we analyze single-cell transcriptome data generated from 17 bone marrow samples longitudinally collected from 6 patients with RS. Response is associated with intermediate exhausted CD8 effector/effector memory T cells marked by high expression of the transcription factor ZNF683, determined to be evolving from stem-like memory cells and divergent from terminally exhausted cells. This signature overlaps with that of tumor-infiltrating populations from anti-PD-1 responsive solid tumors. ZNF683 is found to directly target key T cell genes (TCF7, LMO2, CD69) and impact pathways of T cell cytotoxicity and activation. Analysis of pre-treatment peripheral blood from 10 independent patients with RS treated with anti-PD-1, as well as patients with solid tumors treated with anti-PD-1, supports an association of ZNF683high T cells with response.
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Affiliation(s)
- Erin M Parry
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Camilla K Lemvigh
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Health Technology, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Stephanie Deng
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Nathan Dangle
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Neil Ruthen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | | | - Julien Broséus
- Inserm UMRS1256 Nutrition-Génétique et Exposition Aux Risques Environnementaux (N-GERE), Université de Lorraine, 54000 Nancy, France; Université de Lorraine, CHRU-Nancy, Service d'hématologie Biologique, Pôle Laboratoires, 54000 Nancy, France
| | - Sébastien Hergalant
- Inserm UMRS1256 Nutrition-Génétique et Exposition Aux Risques Environnementaux (N-GERE), Université de Lorraine, 54000 Nancy, France
| | - Romain Guièze
- CHU Clermont-Ferrand, 63000 Clermont-Ferrand, France; EA 7453 (CHELTER), Université Clermont Auvergne, 63001 Clermont-Ferrand, France
| | - Shuqiang Li
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Wandi Zhang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Connor Johnson
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jaclyn M Long
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA; Division of Gastroenterology, Hepatology, and Nutrition, Boston Children's Hospital, Boston, MA 02115, USA
| | - Shanye Yin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Lillian Werner
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Annabelle Anandappa
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Noelia Purroy
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Satyen Gohil
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Giacomo Oliveira
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Pavan Bachireddy
- Department of Hematopoietic Biology and Malignancy, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Sachet A Shukla
- Department of Hematopoietic Biology and Malignancy, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Teddy Huang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Joseph D Khoury
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Beenu Thakral
- Department of Hematopathology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Michael Dickinson
- Peter MacCallum Cancer Centre, Royal Melbourne Hospital, Melbourne, VIC, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Constantine Tam
- Alfred Health, Melbourne, VIC, Australia; Monash University, Melbourne, VIC, Australia
| | - Kenneth J Livak
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Gad Getz
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Donna Neuberg
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Pierre Feugier
- Inserm UMRS1256 Nutrition-Génétique et Exposition Aux Risques Environnementaux (N-GERE), Université de Lorraine, 54000 Nancy, France; Université de Lorraine, CHRU Nancy, service d'hématologie clinique, Nancy, France
| | - Peter Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02215, USA
| | - William Wierda
- Department of Leukemia, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Lars Rønn Olsen
- Department of Health Technology, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Nitin Jain
- Department of Leukemia, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Catherine J Wu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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16
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Tseng KC, Crump JG. Craniofacial developmental biology in the single-cell era. Development 2023; 150:dev202077. [PMID: 37812056 PMCID: PMC10617621 DOI: 10.1242/dev.202077] [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: 10/10/2023]
Abstract
The evolution of a unique craniofacial complex in vertebrates made possible new ways of breathing, eating, communicating and sensing the environment. The head and face develop through interactions of all three germ layers, the endoderm, ectoderm and mesoderm, as well as the so-called fourth germ layer, the cranial neural crest. Over a century of experimental embryology and genetics have revealed an incredible diversity of cell types derived from each germ layer, signaling pathways and genes that coordinate craniofacial development, and how changes to these underlie human disease and vertebrate evolution. Yet for many diseases and congenital anomalies, we have an incomplete picture of the causative genomic changes, in particular how alterations to the non-coding genome might affect craniofacial gene expression. Emerging genomics and single-cell technologies provide an opportunity to obtain a more holistic view of the genes and gene regulatory elements orchestrating craniofacial development across vertebrates. These single-cell studies generate novel hypotheses that can be experimentally validated in vivo. In this Review, we highlight recent advances in single-cell studies of diverse craniofacial structures, as well as potential pitfalls and the need for extensive in vivo validation. We discuss how these studies inform the developmental sources and regulation of head structures, bringing new insights into the etiology of structural birth anomalies that affect the vertebrate head.
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Affiliation(s)
- Kuo-Chang Tseng
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA 90033, USA
| | - J. Gage Crump
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA 90033, USA
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17
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Naderi Yeganeh P, Teo YY, Karagkouni D, Pita-Juárez Y, Morgan SL, Slack FJ, Vlachos IS, Hide WA. PanomiR: a systems biology framework for analysis of multi-pathway targeting by miRNAs. Brief Bioinform 2023; 24:bbad418. [PMID: 37985452 PMCID: PMC10661971 DOI: 10.1093/bib/bbad418] [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: 08/08/2023] [Revised: 10/16/2023] [Accepted: 10/20/2023] [Indexed: 11/22/2023] Open
Abstract
Charting microRNA (miRNA) regulation across pathways is key to characterizing their function. Yet, no method currently exists that can quantify how miRNAs regulate multiple interconnected pathways or prioritize them for their ability to regulate coordinate transcriptional programs. Existing methods primarily infer one-to-one relationships between miRNAs and pathways using differentially expressed genes. We introduce PanomiR, an in silico framework for studying the interplay of miRNAs and disease functions. PanomiR integrates gene expression, mRNA-miRNA interactions and known biological pathways to reveal coordinated multi-pathway targeting by miRNAs. PanomiR utilizes pathway-activity profiling approaches, a pathway co-expression network and network clustering algorithms to prioritize miRNAs that target broad-scale transcriptional disease phenotypes. It directly resolves differential regulation of pathways, irrespective of their differential gene expression, and captures co-activity to establish functional pathway groupings and the miRNAs that may regulate them. PanomiR uses a systems biology approach to provide broad but precise insights into miRNA-regulated functional programs. It is available at https://bioconductor.org/packages/PanomiR.
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Affiliation(s)
- Pourya Naderi Yeganeh
- Harvard Medical School, Boston, MA, USA
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School Initiative for RNA Medicine, Boston, MA, USA
| | - Yue Y Teo
- National University of Singapore, Singapore
- École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Dimitra Karagkouni
- Harvard Medical School, Boston, MA, USA
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School Initiative for RNA Medicine, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yered Pita-Juárez
- Harvard Medical School, Boston, MA, USA
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School Initiative for RNA Medicine, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sarah L Morgan
- Harvard Medical School, Boston, MA, USA
- Centre for Neuroscience, Surgery and Trauma, Blizard Institute, Queen Mary University of London, London E1 2AT, UK
| | - Frank J Slack
- Harvard Medical School, Boston, MA, USA
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School Initiative for RNA Medicine, Boston, MA, USA
| | - Ioannis S Vlachos
- Harvard Medical School, Boston, MA, USA
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School Initiative for RNA Medicine, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Winston A Hide
- Harvard Medical School, Boston, MA, USA
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School Initiative for RNA Medicine, Boston, MA, USA
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18
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Yi D, Nam JW, Jeong H. Toward the functional interpretation of somatic structural variations: bulk- and single-cell approaches. Brief Bioinform 2023; 24:bbad297. [PMID: 37587831 PMCID: PMC10516374 DOI: 10.1093/bib/bbad297] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/05/2023] [Accepted: 07/23/2023] [Indexed: 08/18/2023] Open
Abstract
Structural variants (SVs) are genomic rearrangements that can take many different forms such as copy number alterations, inversions and translocations. During cell development and aging, somatic SVs accumulate in the genome with potentially neutral, deleterious or pathological effects. Generation of somatic SVs is a key mutational process in cancer development and progression. Despite their importance, the detection of somatic SVs is challenging, making them less studied than somatic single-nucleotide variants. In this review, we summarize recent advances in whole-genome sequencing (WGS)-based approaches for detecting somatic SVs at the tissue and single-cell levels and discuss their advantages and limitations. First, we describe the state-of-the-art computational algorithms for somatic SV calling using bulk WGS data and compare the performance of somatic SV detectors in the presence or absence of a matched-normal control. We then discuss the unique features of cutting-edge single-cell-based techniques for analyzing somatic SVs. The advantages and disadvantages of bulk and single-cell approaches are highlighted, along with a discussion of their sensitivity to copy-neutral SVs, usefulness for functional inferences and experimental and computational costs. Finally, computational approaches for linking somatic SVs to their functional readouts, such as those obtained from single-cell transcriptome and epigenome analyses, are illustrated, with a discussion of the promise of these approaches in health and diseases.
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Affiliation(s)
- Dohun Yi
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Jin-Wu Nam
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Research Institute for Convergence of Basic Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Bio-BigData Center, Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Hanyang Institute of Advanced BioConvergence, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Hyobin Jeong
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Bio-BigData Center, Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Hanyang Institute of Advanced BioConvergence, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
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19
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Guerrero-Juarez CF, Schilf P, Li J, Zappia MP, Bao L, Patel PM, Gieseler-Tillmann J, Murthy S, Cole C, Sverdlov M, Frolov MV, Hashimoto T, Ishii N, Rülicke T, Bieber K, Ludwig RJ, Sadik CD, Amber KT. C-type lectin receptor expression is a hallmark of neutrophils infiltrating the skin in epidermolysis bullosa acquisita. Front Immunol 2023; 14:1266359. [PMID: 37799716 PMCID: PMC10548123 DOI: 10.3389/fimmu.2023.1266359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 08/31/2023] [Indexed: 10/07/2023] Open
Abstract
Introduction Inflammatory epidermolysis bullosa acquisita (EBA) is characterized by a neutrophilic response to anti-type VII collagen (COL7) antibodies resulting in the development of skin inflammation and blistering. The antibody transfer model of EBA closely mirrors this EBA phenotype. Methods To better understand the changes induced in neutrophils upon recruitment from peripheral blood into lesional skin in EBA, we performed single-cell RNA-sequencing of whole blood and skin dissociate to capture minimally perturbed neutrophils and characterize their transcriptome. Results Through this approach, we identified clear distinctions between circulating activated neutrophils and intradermal neutrophils. Most strikingly, the gene expression of multiple C-type lectin receptors, which have previously been reported to orchestrate host defense against fungi and select bacteria, were markedly dysregulated. After confirming the upregulation of Clec4n, Clec4d, and Clec4e in experimental EBA as well as in lesional skin from patients with inflammatory EBA, we performed functional studies in globally deficient Clec4e-/- and Clec4d-/- mice as well as in neutrophil-specific Clec4n-/- mice. Deficiency in these genes did not reduce disease in the EBA model. Discussion Collectively, our results suggest that while the upregulation of Clec4n, Clec4d, and Clec4e is a hallmark of activated dermal neutrophil populations, their individual contribution to the pathogenesis of EBA is dispensable.
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Affiliation(s)
- Christian F. Guerrero-Juarez
- Carle Illinois College of Medicine, University of Illinois, Urbana-Champaign, Urbana, IL, United States
- Department of Dermatology, Rush University Medical Center, Chicago, IL, United States
| | - Paul Schilf
- Department of Dermatology, Allergy, and Venereology, University of Lübeck, Lübeck, Germany
| | - Jing Li
- Department of Dermatology, Rush University Medical Center, Chicago, IL, United States
| | - Maria Paula Zappia
- Department of Biochemistry and Molecular Genetics, University of Illinois at Chicago, Chicago, IL, United States
| | - Lei Bao
- Department of Dermatology, Allergy, and Venereology, University of Lübeck, Lübeck, Germany
| | - Payal M. Patel
- Department of Dermatology, Rush University Medical Center, Chicago, IL, United States
- Department of Dermatology, Massachusetts General Hospital, Boston, MA, United States
| | | | - Sripriya Murthy
- Department of Dermatology, Allergy, and Venereology, University of Lübeck, Lübeck, Germany
| | - Connor Cole
- Department of Dermatology, Rush University Medical Center, Chicago, IL, United States
| | - Maria Sverdlov
- Research Histology Core, University of Illinois at Chicago, Chicago, IL, United States
| | - Maxim V. Frolov
- Department of Biochemistry and Molecular Genetics, University of Illinois at Chicago, Chicago, IL, United States
| | - Takashi Hashimoto
- Department of Dermatology, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Norito Ishii
- Department of Dermatology, Kurume University School of Medicine, and Kurume University Institute of Cutaneous Cell Biology, Kurume, Japan
| | - Thomas Rülicke
- Department of Biomedical Sciences and Ludwig Boltzmann Institute for Hematology and Oncology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Katja Bieber
- Department of Dermatology, Allergy, and Venereology, University of Lübeck, Lübeck, Germany
- Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany
| | - Ralf J. Ludwig
- Department of Dermatology, Allergy, and Venereology, University of Lübeck, Lübeck, Germany
- Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany
| | - Christian D. Sadik
- Department of Dermatology, Allergy, and Venereology, University of Lübeck, Lübeck, Germany
| | - Kyle T. Amber
- Department of Dermatology, Rush University Medical Center, Chicago, IL, United States
- Department of Internal Medicine, Rush University Medical Center, Chicago, IL, United States
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20
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Yuan Y, Alzrigat M, Rodriguez-Garcia A, Wang X, Bexelius TS, Johnsen JI, Arsenian-Henriksson M, Liaño-Pons J, Bedoya-Reina OC. Target Genes of c-MYC and MYCN with Prognostic Power in Neuroblastoma Exhibit Different Expressions during Sympathoadrenal Development. Cancers (Basel) 2023; 15:4599. [PMID: 37760568 PMCID: PMC10527308 DOI: 10.3390/cancers15184599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/06/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Deregulation of the MYC family of transcription factors c-MYC (encoded by MYC), MYCN, and MYCL is prevalent in most human cancers, with an impact on tumor initiation and progression, as well as response to therapy. In neuroblastoma (NB), amplification of the MYCN oncogene and over-expression of MYC characterize approximately 40% and 10% of all high-risk NB cases, respectively. However, the mechanism and stage of neural crest development in which MYCN and c-MYC contribute to the onset and/or progression of NB are not yet fully understood. Here, we hypothesized that subtle differences in the expression of MYCN and/or c-MYC targets could more accurately stratify NB patients in different risk groups rather than using the expression of either MYC gene alone. We employed an integrative approach using the transcriptome of 498 NB patients from the SEQC cohort and previously defined c-MYC and MYCN target genes to model a multigene transcriptional risk score. Our findings demonstrate that defined sets of c-MYC and MYCN targets with significant prognostic value, effectively stratify NB patients into different groups with varying overall survival probabilities. In particular, patients exhibiting a high-risk signature score present unfavorable clinical parameters, including increased clinical risk, higher INSS stage, MYCN amplification, and disease progression. Notably, target genes with prognostic value differ between c-MYC and MYCN, exhibiting distinct expression patterns in the developing sympathoadrenal system. Genes associated with poor outcomes are mainly found in sympathoblasts rather than in chromaffin cells during the sympathoadrenal development.
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Affiliation(s)
- Ye Yuan
- Department of Microbiology, Tumor and Cell Biology (MTC), Biomedicum, Karolinska Institutet, SE-171 65 Stockholm, Sweden
| | - Mohammad Alzrigat
- Department of Microbiology, Tumor and Cell Biology (MTC), Biomedicum, Karolinska Institutet, SE-171 65 Stockholm, Sweden
| | - Aida Rodriguez-Garcia
- Department of Microbiology, Tumor and Cell Biology (MTC), Biomedicum, Karolinska Institutet, SE-171 65 Stockholm, Sweden
| | - Xueyao Wang
- Department of Microbiology, Tumor and Cell Biology (MTC), Biomedicum, Karolinska Institutet, SE-171 65 Stockholm, Sweden
| | - Tomas Sjöberg Bexelius
- Paediatric Oncology Unit, Astrid Lindgren’s Children Hospital, SE-171 64 Solna, Sweden
- Department of Women’s and Children’s Health, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - John Inge Johnsen
- Department of Women’s and Children’s Health, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Marie Arsenian-Henriksson
- Department of Microbiology, Tumor and Cell Biology (MTC), Biomedicum, Karolinska Institutet, SE-171 65 Stockholm, Sweden
| | - Judit Liaño-Pons
- Department of Microbiology, Tumor and Cell Biology (MTC), Biomedicum, Karolinska Institutet, SE-171 65 Stockholm, Sweden
| | - Oscar C. Bedoya-Reina
- Department of Microbiology, Tumor and Cell Biology (MTC), Biomedicum, Karolinska Institutet, SE-171 65 Stockholm, Sweden
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21
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Ma Y, Deng C, Zhou Y, Zhang Y, Qiu F, Jiang D, Zheng G, Li J, Shuai J, Zhang Y, Yang J, Su J. Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data. CELL GENOMICS 2023; 3:100383. [PMID: 37719150 PMCID: PMC10504677 DOI: 10.1016/j.xgen.2023.100383] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/26/2023] [Accepted: 07/25/2023] [Indexed: 09/19/2023]
Abstract
Advances in single-cell RNA sequencing (scRNA-seq) techniques have accelerated functional interpretation of disease-associated variants discovered from genome-wide association studies (GWASs). However, identification of trait-relevant cell populations is often impeded by inherent technical noise and high sparsity in scRNA-seq data. Here, we developed scPagwas, a computational approach that uncovers trait-relevant cellular context by integrating pathway activation transformation of scRNA-seq data and GWAS summary statistics. scPagwas effectively prioritizes trait-relevant genes, which facilitates identification of trait-relevant cell types/populations with high accuracy in extensive simulated and real datasets. Cellular-level association results identified a novel subpopulation of naive CD8+ T cells related to COVID-19 severity and oligodendrocyte progenitor cell and microglia subsets with critical pathways by which genetic variants influence Alzheimer's disease. Overall, our approach provides new insights for the discovery of trait-relevant cell types and improves the mechanistic understanding of disease variants from a pathway perspective.
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Affiliation(s)
- Yunlong Ma
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Chunyu Deng
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150080, China
| | - Yijun Zhou
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Yaru Zhang
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Fei Qiu
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Dingping Jiang
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Gongwei Zheng
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Jingjing Li
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Jianwei Shuai
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Yan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150080, China
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310012, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Jianzhong Su
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
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22
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Belova T, Biondi N, Hsieh PH, Lutsik P, Chudasama P, Kuijjer M. Heterogeneity in the gene regulatory landscape of leiomyosarcoma. NAR Cancer 2023; 5:zcad037. [PMID: 37492373 PMCID: PMC10365024 DOI: 10.1093/narcan/zcad037] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 07/06/2023] [Accepted: 07/18/2023] [Indexed: 07/27/2023] Open
Abstract
Characterizing inter-tumor heterogeneity is crucial for selecting suitable cancer therapy, as the presence of diverse molecular subgroups of patients can be associated with disease outcome or response to treatment. While cancer subtypes are often characterized by differences in gene expression, the mechanisms driving these differences are generally unknown. We set out to model the regulatory mechanisms driving sarcoma heterogeneity based on patient-specific, genome-wide gene regulatory networks. We developed a new computational framework, PORCUPINE, which combines knowledge on biological pathways with permutation-based network analysis to identify pathways that exhibit significant regulatory heterogeneity across a patient population. We applied PORCUPINE to patient-specific leiomyosarcoma networks modeled on data from The Cancer Genome Atlas and validated our results in an independent dataset from the German Cancer Research Center. PORCUPINE identified 37 heterogeneously regulated pathways, including pathways representing potential targets for treatment of subgroups of leiomyosarcoma patients, such as FGFR and CTLA4 inhibitory signaling. We validated the detected regulatory heterogeneity through analysis of networks and chromatin states in leiomyosarcoma cell lines. We showed that the heterogeneity identified with PORCUPINE is not associated with methylation profiles or clinical features, thereby suggesting an independent mechanism of patient heterogeneity driven by the complex landscape of gene regulatory interactions.
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Affiliation(s)
- Tatiana Belova
- Computational Biology and Systems Medicine Group, Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
| | - Nicola Biondi
- Precision Sarcoma Research Group, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases, Heidelberg, Germany
| | - Ping-Han Hsieh
- Computational Biology and Systems Medicine Group, Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
| | - Pavlo Lutsik
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Oncology, Catholic University (KU) Leuven, Leuven, Belgium
| | - Priya Chudasama
- Precision Sarcoma Research Group, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases, Heidelberg, Germany
| | - Marieke L Kuijjer
- Computational Biology and Systems Medicine Group, Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
- Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, the Netherlands
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23
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Wiggins BG, Wang YF, Burke A, Grunberg N, Vlachaki Walker JM, Dore M, Chahrour C, Pennycook BR, Sanchez-Garrido J, Vernia S, Barr AR, Frankel G, Birdsey GM, Randi AM, Schiering C. Endothelial sensing of AHR ligands regulates intestinal homeostasis. Nature 2023; 621:821-829. [PMID: 37586410 PMCID: PMC10533400 DOI: 10.1038/s41586-023-06508-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 08/02/2023] [Indexed: 08/18/2023]
Abstract
Endothelial cells line the blood and lymphatic vasculature, and act as an essential physical barrier, control nutrient transport, facilitate tissue immunosurveillance and coordinate angiogenesis and lymphangiogenesis1,2. In the intestine, dietary and microbial cues are particularly important in the regulation of organ homeostasis. However, whether enteric endothelial cells actively sense and integrate such signals is currently unknown. Here we show that the aryl hydrocarbon receptor (AHR) acts as a critical node for endothelial cell sensing of dietary metabolites in adult mice and human primary endothelial cells. We first established a comprehensive single-cell endothelial atlas of the mouse small intestine, uncovering the cellular complexity and functional heterogeneity of blood and lymphatic endothelial cells. Analyses of AHR-mediated responses at single-cell resolution identified tissue-protective transcriptional signatures and regulatory networks promoting cellular quiescence and vascular normalcy at steady state. Endothelial AHR deficiency in adult mice resulted in dysregulated inflammatory responses and the initiation of proliferative pathways. Furthermore, endothelial sensing of dietary AHR ligands was required for optimal protection against enteric infection. In human endothelial cells, AHR signalling promoted quiescence and restrained activation by inflammatory mediators. Together, our data provide a comprehensive dissection of the effect of environmental sensing across the spectrum of enteric endothelia, demonstrating that endothelial AHR signalling integrates dietary cues to maintain tissue homeostasis by promoting endothelial cell quiescence and vascular normalcy.
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Affiliation(s)
- Benjamin G Wiggins
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK.
- MRC London Institute of Medical Sciences, London, UK.
| | - Yi-Fang Wang
- MRC London Institute of Medical Sciences, London, UK
| | - Alice Burke
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, London, UK
| | - Nil Grunberg
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, London, UK
| | - Julia M Vlachaki Walker
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, London, UK
| | - Marian Dore
- MRC London Institute of Medical Sciences, London, UK
| | | | - Betheney R Pennycook
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, London, UK
| | | | - Santiago Vernia
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, London, UK
| | - Alexis R Barr
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, London, UK
| | - Gad Frankel
- Department of Life Sciences, Imperial College London, London, UK
| | - Graeme M Birdsey
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Anna M Randi
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Chris Schiering
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK.
- MRC London Institute of Medical Sciences, London, UK.
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24
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Flynn E, Almonte-Loya A, Fragiadakis GK. Single-Cell Multiomics. Annu Rev Biomed Data Sci 2023; 6:313-337. [PMID: 37159875 PMCID: PMC11146013 DOI: 10.1146/annurev-biodatasci-020422-050645] [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: 05/11/2023]
Abstract
Single-cell RNA sequencing methods have led to improved understanding of the heterogeneity and transcriptomic states present in complex biological systems. Recently, the development of novel single-cell technologies for assaying additional modalities, specifically genomic, epigenomic, proteomic, and spatial data, allows for unprecedented insight into cellular biology. While certain technologies collect multiple measurements from the same cells simultaneously, even when modalities are separately assayed in different cells, we can apply novel computational methods to integrate these data. The application of computational integration methods to multimodal paired and unpaired data results in rich information about the identities of the cells present and the interactions between different levels of biology, such as between genetic variation and transcription. In this review, we both discuss the single-cell technologies for measuring these modalities and describe and characterize a variety of computational integration methods for combining the resulting data to leverage multimodal information toward greater biological insight.
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Affiliation(s)
- Emily Flynn
- CoLabs, University of California, San Francisco, California, USA;
| | - Ana Almonte-Loya
- CoLabs, University of California, San Francisco, California, USA;
- Biomedical Informatics Program, University of California, San Francisco, California, USA
| | - Gabriela K Fragiadakis
- CoLabs, University of California, San Francisco, California, USA;
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, California, USA
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25
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Hile GA, Coit P, Xu B, Victory AM, Gharaee-Kermani M, Estadt SN, Maz MP, Martens JWS, Wasikowski R, Dobry C, Tsoi LC, Iglesias-Bartolome R, Berthier CC, Billi AC, Gudjonsson JE, Sawalha AH, Kahlenberg JM. Regulation of Photosensitivity by the Hippo Pathway in Lupus Skin. Arthritis Rheumatol 2023; 75:1216-1228. [PMID: 36704840 PMCID: PMC10313771 DOI: 10.1002/art.42460] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 12/05/2022] [Accepted: 01/24/2023] [Indexed: 01/28/2023]
Abstract
OBJECTIVE Photosensitivity is one of the most common manifestations of systemic lupus erythematosus (SLE), yet its pathogenesis is not well understood. The normal-appearing epidermis of patients with SLE exhibits increased ultraviolet B (UVB)-driven cell death that persists in cell culture. Here, we investigated the role of epigenetic modification and Hippo signaling in enhanced UVB-induced apoptosis seen in SLE keratinocytes. METHODS We analyzed DNA methylation in cultured keratinocytes from SLE patients compared to keratinocytes from healthy controls (n = 6/group). Protein expression was validated in cultured keratinocytes using immunoblotting and immunofluorescence. An immortalized keratinocyte line overexpressing WWC1 was generated via lentiviral vector. WWC1-driven changes were inhibited using a large tumor suppressor kinase 1/2 (LATS1/2) inhibitor (TRULI) and small interfering RNA (siRNA). The interaction between the Yes-associated protein (YAP) and the transcriptional enhancer associate domain (TEAD) was inhibited by overexpression of an N/TERT cell line expressing a tetracycline-inducible green fluorescent protein-tagged protein that inhibits YAP-TEAD binding (TEADi). Apoptosis was assessed using cleaved caspase 3/7 and TUNEL staining. RESULTS Hippo signaling was the top differentially methylated pathway in SLE versus control keratinocytes. SLE keratinocytes (n = 6) showed significant hypomethylation (Δβ = -0.153) and thus overexpression of the Hippo regulator WWC1 (P = 0.002). WWC1 overexpression increased LATS1/2 kinase activation, leading to YAP cytoplasmic retention and altered proapoptotic transcription in SLE keratinocytes. Accordingly, UVB-mediated apoptosis in keratinocytes could be enhanced by WWC1 overexpression or YAP-TEAD inhibition, mimicking SLE keratinocytes. Importantly, inhibition of LATS1/2 with either the chemical inhibitor TRULI or siRNA effectively eliminated enhanced UVB-apoptosis in SLE keratinocytes. CONCLUSION Our work unravels a novel driver of photosensitivity in SLE: overactive Hippo signaling in SLE keratinocytes restricts YAP transcriptional activity, leading to shifts that promote UVB apoptosis.
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Affiliation(s)
- Grace A. Hile
- Department of Dermatology, University of Michigan; Ann Arbor, USA
| | - Patrick Coit
- Division of Rheumatology, University of Michigan; Ann Arbor, USA
- Graduate Program in Immunology, University of Michigan; Ann Arbor, USA
- Departments of Pediatrics, Medicine, and Immunology, and Lupus Center of Excellence, University of Pittsburgh; Pittsburgh, USA
| | - Bin Xu
- Division of Rheumatology, University of Michigan; Ann Arbor, USA
| | | | - Mehrnaz Gharaee-Kermani
- Department of Dermatology, University of Michigan; Ann Arbor, USA
- Division of Rheumatology, University of Michigan; Ann Arbor, USA
| | - Shannon N. Estadt
- Graduate Program in Immunology, University of Michigan; Ann Arbor, USA
| | - Mitra P. Maz
- Graduate Program in Immunology, University of Michigan; Ann Arbor, USA
| | | | - Rachael Wasikowski
- Department of Dermatology, University of Michigan; Ann Arbor, USA
- Department of Computational Medicine & Bioinformatics, University of Michigan; Ann Arbor, USA
| | - Craig Dobry
- Department of Dermatology, University of Michigan; Ann Arbor, USA
| | - Lam C. Tsoi
- Department of Dermatology, University of Michigan; Ann Arbor, USA
- Department of Computational Medicine & Bioinformatics, University of Michigan; Ann Arbor, USA
- Department of Biostatistics, University of Michigan; Ann Arbor, MI 48109, USA
| | - Ramiro Iglesias-Bartolome
- Laboratory of Cellular and Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health; Bethesda, USA
| | - Celine C. Berthier
- Department of Computational Medicine & Bioinformatics, University of Michigan; Ann Arbor, USA
- Department of Biostatistics, University of Michigan; Ann Arbor, MI 48109, USA
| | - Allison C. Billi
- Department of Dermatology, University of Michigan; Ann Arbor, USA
| | | | - Amr H. Sawalha
- Departments of Pediatrics, Medicine, and Immunology, and Lupus Center of Excellence, University of Pittsburgh; Pittsburgh, USA
| | - J. Michelle Kahlenberg
- Department of Dermatology, University of Michigan; Ann Arbor, USA
- Division of Rheumatology, University of Michigan; Ann Arbor, USA
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26
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Lee S, Deng L, Wang Y, Wang K, Sartor MA, Wang XS. IndepthPathway: an integrated tool for in-depth pathway enrichment analysis based on single-cell sequencing data. Bioinformatics 2023; 39:btad325. [PMID: 37243667 PMCID: PMC10275909 DOI: 10.1093/bioinformatics/btad325] [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: 07/30/2022] [Revised: 04/29/2023] [Accepted: 05/26/2023] [Indexed: 05/29/2023] Open
Abstract
MOTIVATION Single-cell sequencing enables exploring the pathways and processes of cells, and cell populations. However, there is a paucity of pathway enrichment methods designed to tolerate the high noise and low gene coverage of this technology. When gene expression data are noisy and signals are sparse, testing pathway enrichment based on the genes expression may not yield statistically significant results, which is particularly problematic when detecting the pathways enriched in less abundant cells that are vulnerable to disturbances. RESULTS In this project, we developed a Weighted Concept Signature Enrichment Analysis specialized for pathway enrichment analysis from single-cell transcriptomics (scRNA-seq). Weighted Concept Signature Enrichment Analysis took a broader approach for assessing the functional relations of pathway gene sets to differentially expressed genes, and leverage the cumulative signature of molecular concepts characteristic of the highly differentially expressed genes, which we termed as the universal concept signature, to tolerate the high noise and low coverage of this technology. We then incorporated Weighted Concept Signature Enrichment Analysis into an R package called "IndepthPathway" for biologists to broadly leverage this method for pathway analysis based on bulk and single-cell sequencing data. Through simulating technical variability and dropouts in gene expression characteristic of scRNA-seq as well as benchmarking on a real dataset of matched single-cell and bulk RNAseq data, we demonstrate that IndepthPathway presents outstanding stability and depth in pathway enrichment results under stochasticity of the data, thus will substantially improve the scientific rigor of the pathway analysis for single-cell sequencing data. AVAILABILITY AND IMPLEMENTATION The IndepthPathway R package is available through: https://github.com/wangxlab/IndepthPathway.
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Affiliation(s)
- Sanghoon Lee
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA 15232, United States
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA 15232, United States
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, United States
| | - Letian Deng
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA 15232, United States
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA 15232, United States
| | - Yue Wang
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA 15232, United States
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA 15232, United States
| | - Kai Wang
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Maureen A Sartor
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Xiao-Song Wang
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA 15232, United States
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA 15232, United States
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, United States
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27
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Resztak JA, Wei J, Zilioli S, Sendler E, Alazizi A, Mair-Meijers HE, Wu P, Wen X, Slatcher RB, Zhou X, Luca F, Pique-Regi R. Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single-cell resolution. Genome Res 2023; 33:839-856. [PMID: 37442575 PMCID: PMC10519413 DOI: 10.1101/gr.276765.122] [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: 03/17/2022] [Accepted: 06/08/2023] [Indexed: 07/15/2023]
Abstract
Synthetic glucocorticoids, such as dexamethasone, have been used as a treatment for many immune conditions, such as asthma and, more recently, severe COVID-19. Single-cell data can capture more fine-grained details on transcriptional variability and dynamics to gain a better understanding of the molecular underpinnings of inter-individual variation in drug response. Here, we used single-cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated peripheral blood mononuclear cells from 96 African American children. We used novel statistical approaches to calculate a mean-independent measure of gene expression variability and a measure of transcriptional response pseudotime. Using these approaches, we showed that glucocorticoids reverse the effects of immune stimulation on both gene expression mean and variability. Our novel measure of gene expression response dynamics, based on the diagonal linear discriminant analysis, separated individual cells by response status on the basis of their transcriptional profiles and allowed us to identify different dynamic patterns of gene expression along the response pseudotime. We identified genetic variants regulating gene expression mean and variability, including treatment-specific effects, and showed widespread genetic regulation of the transcriptional dynamics of the gene expression response.
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Affiliation(s)
- Justyna A Resztak
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA
| | - Julong Wei
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA
| | - Samuele Zilioli
- Department of Psychology, Wayne State University, Detroit, Michigan 48201, USA
- Department of Family Medicine and Public Health Sciences, Wayne State University, Detroit, Michigan 48201, USA
| | - Edward Sendler
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA
| | - Adnan Alazizi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA
| | - Henriette E Mair-Meijers
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA
| | - Peijun Wu
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Richard B Slatcher
- Department of Psychology, University of Georgia, Athens, Georgia 30602, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA;
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan 48201, USA
- Department of Biology, University of Rome "Tor Vergata," 00133 Rome, Italy
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA;
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan 48201, USA
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28
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Van de Sande B, Lee JS, Mutasa-Gottgens E, Naughton B, Bacon W, Manning J, Wang Y, Pollard J, Mendez M, Hill J, Kumar N, Cao X, Chen X, Khaladkar M, Wen J, Leach A, Ferran E. Applications of single-cell RNA sequencing in drug discovery and development. Nat Rev Drug Discov 2023; 22:496-520. [PMID: 37117846 PMCID: PMC10141847 DOI: 10.1038/s41573-023-00688-4] [Citation(s) in RCA: 52] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/10/2023] [Indexed: 04/30/2023]
Abstract
Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.
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Affiliation(s)
| | | | | | - Bart Naughton
- Computational Neurobiology, Eisai, Cambridge, MA, USA
| | - Wendi Bacon
- EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
- The Open University, Milton Keynes, UK
| | | | - Yong Wang
- Precision Bioinformatics, Prometheus Biosciences, San Diego, CA, USA
| | | | - Melissa Mendez
- Genomic Sciences, GlaxoSmithKline, Collegeville, PA, USA
| | - Jon Hill
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, USA
| | - Namit Kumar
- Informatics & Predictive Sciences, Bristol Myers Squibb, San Diego, CA, USA
| | - Xiaohong Cao
- Genomic Research Center, AbbVie Inc., Cambridge, MA, USA
| | - Xiao Chen
- Magnet Biomedicine, Cambridge, MA, USA
| | - Mugdha Khaladkar
- Human Genetics and Computational Biology, GlaxoSmithKline, Collegeville, PA, USA
| | - Ji Wen
- Oncology Research and Development Unit, Pfizer, La Jolla, CA, USA
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29
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Sunadome K, Erickson AG, Kah D, Fabry B, Adori C, Kameneva P, Faure L, Kanatani S, Kaucka M, Dehnisch Ellström I, Tesarova M, Zikmund T, Kaiser J, Edwards S, Maki K, Adachi T, Yamamoto T, Fried K, Adameyko I. Directionality of developing skeletal muscles is set by mechanical forces. Nat Commun 2023; 14:3060. [PMID: 37244931 PMCID: PMC10224984 DOI: 10.1038/s41467-023-38647-7] [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: 10/16/2020] [Accepted: 05/05/2023] [Indexed: 05/29/2023] Open
Abstract
Formation of oriented myofibrils is a key event in musculoskeletal development. However, the mechanisms that drive myocyte orientation and fusion to control muscle directionality in adults remain enigmatic. Here, we demonstrate that the developing skeleton instructs the directional outgrowth of skeletal muscle and other soft tissues during limb and facial morphogenesis in zebrafish and mouse. Time-lapse live imaging reveals that during early craniofacial development, myoblasts condense into round clusters corresponding to future muscle groups. These clusters undergo oriented stretch and alignment during embryonic growth. Genetic perturbation of cartilage patterning or size disrupts the directionality and number of myofibrils in vivo. Laser ablation of musculoskeletal attachment points reveals tension imposed by cartilage expansion on the forming myofibers. Application of continuous tension using artificial attachment points, or stretchable membrane substrates, is sufficient to drive polarization of myocyte populations in vitro. Overall, this work outlines a biomechanical guidance mechanism that is potentially useful for engineering functional skeletal muscle.
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Affiliation(s)
- Kazunori Sunadome
- Department of Physiology and Pharmacology, Karolinska Institutet, 17177, Stockholm, Sweden
| | - Alek G Erickson
- Department of Physiology and Pharmacology, Karolinska Institutet, 17177, Stockholm, Sweden
| | - Delf Kah
- Department of Physics, University of Erlangen-Nuremberg, 91052, Erlangen, Germany
| | - Ben Fabry
- Department of Physics, University of Erlangen-Nuremberg, 91052, Erlangen, Germany
| | - Csaba Adori
- Department of Neuroscience, Karolinska Institutet, 17177, Stockholm, Sweden
- Department of Molecular Biosciences, Wenner Gren Institute, Stockholm University, 10691, Stockholm, Sweden
| | - Polina Kameneva
- Department of Neuroimmunology, Center for Brain Research, Medical University Vienna, 1090, Vienna, Austria
| | - Louis Faure
- Department of Neuroimmunology, Center for Brain Research, Medical University Vienna, 1090, Vienna, Austria
| | - Shigeaki Kanatani
- Department of Medical Biochemistry and Biophysics, Division of Molecular Neurobiology, Karolinska Institutet, 17177, Stockholm, Sweden
| | - Marketa Kaucka
- Max Planck Institute for Evolutionary Biology, August-Thienemann-Str.2, 24306, Plön, Germany
| | | | - Marketa Tesarova
- Central European Institute of Technology, Brno University of Technology, Brno, Czech Republic
| | - Tomas Zikmund
- Central European Institute of Technology, Brno University of Technology, Brno, Czech Republic
| | - Jozef Kaiser
- Central European Institute of Technology, Brno University of Technology, Brno, Czech Republic
| | - Steven Edwards
- KTH Royal Institute of Technology, SE-100 44, Stockholm, Sweden
| | - Koichiro Maki
- Laboratory of Biomechanics, Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Taiji Adachi
- Laboratory of Biomechanics, Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Takuya Yamamoto
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto, 606-8501, Japan
- Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, 606-8507, Japan
| | - Kaj Fried
- Department of Neuroscience, Karolinska Institutet, 17177, Stockholm, Sweden.
| | - Igor Adameyko
- Department of Physiology and Pharmacology, Karolinska Institutet, 17177, Stockholm, Sweden.
- Department of Neuroimmunology, Center for Brain Research, Medical University Vienna, 1090, Vienna, Austria.
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30
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Zhang S, Pyne S, Pietrzak S, Halberg S, McCalla SG, Siahpirani AF, Sridharan R, Roy S. Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets. Nat Commun 2023; 14:3064. [PMID: 37244909 PMCID: PMC10224950 DOI: 10.1038/s41467-023-38637-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 05/10/2023] [Indexed: 05/29/2023] Open
Abstract
Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation.
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Affiliation(s)
- Shilu Zhang
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
| | - Saptarshi Pyne
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
| | - Stefan Pietrzak
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
- Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, WI, USA
| | - Spencer Halberg
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Sunnie Grace McCalla
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Alireza Fotuhi Siahpirani
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Rupa Sridharan
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
- Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, WI, USA
| | - Sushmita Roy
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
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31
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Frost HR. Reconstruction Set Test (RESET): a computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.03.535366. [PMID: 37066315 PMCID: PMC10104009 DOI: 10.1101/2023.04.03.535366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
We have developed a new, and analytically novel, single sample gene set testing method called Reconstruction Set Test (RESET). RESET quantifies gene set importance at both the sample-level and for the entire dataset based on the ability of set genes to reconstruct values for all measured genes. RESET addresses four important limitations of current techniques: 1) existing single sample methods are designed to detect mean differences and struggle to identify differential correlation patterns, 2) computationally efficient techniques are self-contained methods and cannot directly detect competitive scenarios where set genes differ from non-set genes in the same sample, 3) the scores generated by current methods can only be accurately compared across samples for a single set and not between sets, and 4) the computational performance of even the fastest existing methods be significant on very large datasets. RESET is realized using a computationally efficient randomized reduced rank reconstruction algorithm (available via the RESET R package on CRAN) that can effectively detect patterns of differential abundance and differential correlation for self-contained and competitive scenarios. As demonstrated using real and simulated scRNA-seq data, RESET provides superior accuracy at a lower computational cost relative to other single sample approaches.
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Affiliation(s)
- H. Robert Frost
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755
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32
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Sur A, Wang Y, Capar P, Margolin G, Farrell JA. Single-cell analysis of shared signatures and transcriptional diversity during zebrafish development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.20.533545. [PMID: 36993555 PMCID: PMC10055256 DOI: 10.1101/2023.03.20.533545] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
During development, animals generate distinct cell populations with specific identities, functions, and morphologies. We mapped transcriptionally distinct populations across 489,686 cells from 62 stages during wild-type zebrafish embryogenesis and early larval development (3-120 hours post-fertilization). Using these data, we identified the limited catalog of gene expression programs reused across multiple tissues and their cell-type-specific adaptations. We also determined the duration each transcriptional state is present during development and suggest new long-term cycling populations. Focused analyses of non-skeletal muscle and the endoderm identified transcriptional profiles of understudied cell types and subpopulations, including the pneumatic duct, individual intestinal smooth muscle layers, spatially distinct pericyte subpopulations, and homologs of recently discovered human best4+ enterocytes. The transcriptional regulators of these populations remain unknown, so we reconstructed gene expression trajectories to suggest candidates. To enable additional discoveries, we make this comprehensive transcriptional atlas of early zebrafish development available through our website, Daniocell.
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Affiliation(s)
- Abhinav Sur
- Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20814
| | - Yiqun Wang
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138
| | - Paulina Capar
- Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20814
| | - Gennady Margolin
- Bioinformatics and Scientific Programming Core, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, Maryland 20814
| | - Jeffrey A. Farrell
- Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20814
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33
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Landais Y, Vallot C. Multi-modal quantification of pathway activity with MAYA. Nat Commun 2023; 14:1668. [PMID: 36966153 PMCID: PMC10039856 DOI: 10.1038/s41467-023-37410-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 03/16/2023] [Indexed: 03/27/2023] Open
Abstract
Signaling pathways can be activated through various cascades of genes depending on cell identity and biological context. Single-cell atlases now provide the opportunity to inspect such complexity in health and disease. Yet, existing reference tools for pathway scoring resume activity of each pathway to one unique common metric across cell types. Here, we present MAYA, a computational method that enables the automatic detection and scoring of the diverse modes of activation of biological pathways across cell populations. MAYA improves the granularity of pathway analysis by detecting subgroups of genes within reference pathways, each characteristic of a cell population and how it activates a pathway. Using multiple single-cell datasets, we demonstrate the biological relevance of identified modes of activation, the robustness of MAYA to noisy pathway lists and batch effect. MAYA can also predict cell types starting from lists of reference markers in a cluster-free manner. Finally, we show that MAYA reveals common modes of pathway activation in tumor cells across patients, opening the perspective to discover shared therapeutic vulnerabilities.
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Affiliation(s)
| | - Céline Vallot
- CNRS UMR3244, Institut Curie, PSL University, Paris, France.
- Translational Research Department, Institut Curie, PSL University, Paris, France.
- Single Cell Initiative, Institut Curie, PSL University, Paris, France.
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34
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Gao T, Soldatov R, Sarkar H, Kurkiewicz A, Biederstedt E, Loh PR, Kharchenko PV. Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes. Nat Biotechnol 2023; 41:417-426. [PMID: 36163550 PMCID: PMC10289836 DOI: 10.1038/s41587-022-01468-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 08/11/2022] [Indexed: 11/09/2022]
Abstract
Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and nongenetic sources of tumor heterogeneity in a single assay. Here we present a computational method, Numbat, that integrates haplotype information obtained from population-based phasing with allele and expression signals to enhance detection of copy number variations from scRNA-seq. Numbat exploits the evolutionary relationships between subclones to iteratively infer single-cell copy number profiles and tumor clonal phylogeny. Analysis of 22 tumor samples, including multiple myeloma, gastric, breast and thyroid cancers, shows that Numbat can reconstruct the tumor copy number profile and precisely identify malignant cells in the tumor microenvironment. We identify genetic subpopulations with transcriptional signatures relevant to tumor progression and therapy resistance. Numbat requires neither sample-matched DNA data nor a priori genotyping, and is applicable to a wide range of experimental settings and cancer types.
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Affiliation(s)
- Teng Gao
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Ruslan Soldatov
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Hirak Sarkar
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Adam Kurkiewicz
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Evan Biederstedt
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Po-Ru Loh
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Stem Cell Institute, Cambridge, MA, USA.
- Altos Labs, San Diego, CA, USA.
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35
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Dong Y, Weng J, Zhu Y, Sun D, He W, Chen Q, Cheng J, Zhu Y, Jiang Y. Transcriptomic profiling of the developing brain revealed cell-type and brain-region specificity in a mouse model of prenatal stress. BMC Genomics 2023; 24:86. [PMID: 36829105 PMCID: PMC9951484 DOI: 10.1186/s12864-023-09186-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 02/14/2023] [Indexed: 02/26/2023] Open
Abstract
BACKGROUND Prenatal stress (PS) is considered as a risk factor for many mental disorders. PS-induced transcriptomic alterations may contribute to the functional dysregulation during brain development. Here, we used RNA-seq to explore changes of gene expression in the mouse fetal brain after prenatal exposure to chronic unpredictable mild stress (CUMS). RESULTS We compared the stressed brains to the controls and identified groups of significantly differentially expressed genes (DEGs). GO analysis on up-regulated DEGs revealed enrichment for the cell cycle pathways, while down-regulated DEGs were mostly enriched in the neuronal pathways related to synaptic transmission. We further performed cell-type enrichment analysis using published scRNA-seq data from the fetal mouse brain and revealed cell-type-specificity for up- and down-regulated DEGs, respectively. The up-regulated DEGs were highly enriched in the radial glia, while down-regulated DEGs were enriched in different types of neurons. Cell deconvolution analysis further showed altered cell fractions in the stressed brain, indicating accumulation of neuroblast and impaired neurogenesis. Moreover, we also observed distinct brain-region expression pattern when mapping DEGs onto the developing Allen brain atlas. The up-regulated DEGs were primarily enriched in the dorsal forebrain regions including the cortical plate and hippocampal formation. Surprisingly, down-regulated DEGs were found excluded from the cortical region, but highly expressed on various regions in the ventral forebrain, midbrain and hindbrain. CONCLUSION Taken together, we provided an unbiased data source for transcriptomic alterations of the whole fetal brain after chronic PS, and reported differential cell-type and brain-region vulnerability of the developing brain in response to environmental insults during the pregnancy.
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Affiliation(s)
- Yuhao Dong
- grid.8547.e0000 0001 0125 2443Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, 200032 Shanghai, China
| | - Jie Weng
- grid.8547.e0000 0001 0125 2443Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, 200032 Shanghai, China
| | - Yueyan Zhu
- grid.8547.e0000 0001 0125 2443Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, 200032 Shanghai, China
| | - Daijing Sun
- grid.8547.e0000 0001 0125 2443Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, 200032 Shanghai, China
| | - Wei He
- grid.8547.e0000 0001 0125 2443Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, 200032 Shanghai, China
| | - Qi Chen
- grid.8547.e0000 0001 0125 2443Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, 200032 Shanghai, China
| | - Jin Cheng
- grid.8547.e0000 0001 0125 2443Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, 200032 Shanghai, China
| | - Ying Zhu
- grid.8547.e0000 0001 0125 2443Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, 200032 Shanghai, China
| | - Yan Jiang
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, 200032, Shanghai, China.
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36
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Leng J, Xing Z, Li X, Bao X, Zhu J, Zhao Y, Wu S, Yang J. Assessment of Diagnosis, Prognosis and Immune Infiltration Response to the Expression of the Ferroptosis-Related Molecule HAMP in Clear Cell Renal Cell Carcinoma. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:913. [PMID: 36673667 PMCID: PMC9858726 DOI: 10.3390/ijerph20020913] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/20/2022] [Accepted: 12/31/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Hepcidin antimicrobial peptide (HAMP) is a key factor in maintaining iron metabolism, which may induce ferroptosis when upregulated. However, its prognostic value and relation to immune infiltrating cells remains unclear. METHODS This study analyzed the expression levels of HAMP in the Oncomine, Timer and Ualcan databases, and examined its prognostic potential in KIRC with R programming. The Timer and GEPIA databases were used to estimate the correlations between HAMP and immune infiltration and the markers of immune cells. The intersection genes and the co-expression PPI network were constructed via STRING, R programming and GeneMANIA, and the hub genes were selected with Cytoscape. In addition, we analyzed the gene set enrichment and GO/KEGG pathways by GSEA. RESULTS Our study revealed higher HAMP expression levels in tumor tissues including KIRC, which were related to poor prognosis in terms of OS, DSS and PFI. The expression of HAMP was positively related to the immune infiltration level of macrophages, Tregs, etc., corresponding with the immune biomarkers. Based on the intersection genes, we constructed the PPI network and used the 10 top hub genes. Further, we performed a pathway enrichment analysis of the gene sets, including Huntington's disease, the JAK-STAT signaling pathway, ammonium ion metabolic process, and so on. CONCLUSION In summary, our study gave an insight into the potential prognosis of HAMP, which may act as a diagnostic biomarker and therapeutic target related to immune infiltration in KIRC.
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Affiliation(s)
- Jing Leng
- Department of Medical Oncology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Zixuan Xing
- Department of Infectious Diseases, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Xiang Li
- Department of Medical Oncology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Xinyue Bao
- Department of Medical Oncology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Junzheya Zhu
- Department of Medical Oncology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Yunhan Zhao
- Department of Medical Oncology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Shaobo Wu
- Department of Medical Oncology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Jiao Yang
- Department of Medical Oncology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
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37
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Chatterjee D, Deng WM. Standardization of Single-Cell RNA-Sequencing Analysis Workflow to Study Drosophila Ovary. Methods Mol Biol 2023; 2677:151-171. [PMID: 37464241 DOI: 10.1007/978-1-0716-3259-8_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
Developments in single-cell technology have considerably changed the way we study biology. Significant efforts have been made over the last few years to build comprehensive cell-type-specific transcriptomic atlases for a wide range of tissues in several model organisms in order to discover cell-type-specific markers and drivers of gene expression. One such tissue is the ovary of the fruit-fly Drosophila melanogaster, which is a popular model system with wide-ranging applications in the study of both development and disease. Three independent studies have recently produced comprehensive maps of cell-type-specific gene expression that describe both spatiotemporal regulation of the process of oogenesis and unique transcriptomic profiles of different cell types that constitute the ovary. In this chapter, we outlined the wet-lab protocol that was followed in our recent study for sample preparation and reanalyze the resultant dataset to discuss the benchmarks in data analysis, which are fundamental to comprehensive curation of the single-cell dataset representing the fly ovary.
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Affiliation(s)
- Deeptiman Chatterjee
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, Tulane Cancer Center, New Orleans, LA, USA.
- Current address: Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Wu-Min Deng
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, Tulane Cancer Center, New Orleans, LA, USA.
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38
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Chen Y, Zhang H, Sun X. Improving the performance of single-cell RNA-seq data mining based on relative expression orderings. Brief Bioinform 2022; 24:6931720. [PMID: 36528803 PMCID: PMC9851298 DOI: 10.1093/bib/bbac556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/10/2022] [Accepted: 11/16/2022] [Indexed: 12/23/2022] Open
Abstract
The advent of single-cell RNA-sequencing (scRNA-seq) provides an unprecedented opportunity to explore gene expression profiles at the single-cell level. However, gene expression values vary over time and under different conditions even within the same cell. There is an urgent need for more stable and reliable feature variables at the single-cell level to depict cell heterogeneity. Thus, we construct a new feature matrix called the delta rank matrix (DRM) from scRNA-seq data by integrating an a priori gene interaction network, which transforms the unreliable gene expression value into a stable gene interaction/edge value on a single-cell basis. This is the first time that a gene-level feature has been transformed into an interaction/edge-level for scRNA-seq data analysis based on relative expression orderings. Experiments on various scRNA-seq datasets have demonstrated that DRM performs better than the original gene expression matrix in cell clustering, cell identification and pseudo-trajectory reconstruction. More importantly, the DRM really achieves the fusion of gene expressions and gene interactions and provides a method of measuring gene interactions at the single-cell level. Thus, the DRM can be used to find changes in gene interactions among different cell types, which may open up a new way to analyze scRNA-seq data from an interaction perspective. In addition, DRM provides a new method to construct a cell-specific network for each single cell instead of a group of cells as in traditional network construction methods. DRM's exceptional performance is due to its extraction of rich gene-association information on biological systems and stable characterization of cells.
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Affiliation(s)
- Yuanyuan Chen
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China,College of Science, Nanjing Agricultural University, Nanjing 210095, China
| | - Hao Zhang
- College of Science, Nanjing Agricultural University, Nanjing 210095, China
| | - Xiao Sun
- Corresponding author: Xiao Sun, State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China. Tel: +8613951989906; E-mail:
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Su M, Pan T, Chen QZ, Zhou WW, Gong Y, Xu G, Yan HY, Li S, Shi QZ, Zhang Y, He X, Jiang CJ, Fan SC, Li X, Cairns MJ, Wang X, Li YS. Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications. Mil Med Res 2022; 9:68. [PMID: 36461064 PMCID: PMC9716519 DOI: 10.1186/s40779-022-00434-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/18/2022] [Indexed: 12/03/2022] Open
Abstract
The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.
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Affiliation(s)
- Min Su
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Tao Pan
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, Hainan, China
| | - Qiu-Zhen Chen
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Wei-Wei Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Yi Gong
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.,Department of Immunology, Nanjing Medical University, Nanjing, 211166, China
| | - Gang Xu
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, Hainan, China
| | - Huan-Yu Yan
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Si Li
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, Hainan, China
| | - Qiao-Zhen Shi
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Ya Zhang
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, Hainan, China
| | - Xiao He
- Department of Laboratory Medicine, Women and Children's Hospital of Chongqing Medical University, Chongqing, 401174, China
| | | | - Shi-Cai Fan
- Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, 518110, Guangdong, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, Faculty of Health and Medicine, the University of Newcastle, University Drive, Callaghan, NSW, 2308, Australia. .,Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW, 2305, Australia.
| | - Xi Wang
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.
| | - Yong-Sheng Li
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, Hainan, China.
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40
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Liu I, Jiang L, Samuelsson ER, Marco Salas S, Beck A, Hack OA, Jeong D, Shaw ML, Englinger B, LaBelle J, Mire HM, Madlener S, Mayr L, Quezada MA, Trissal M, Panditharatna E, Ernst KJ, Vogelzang J, Gatesman TA, Halbert ME, Palova H, Pokorna P, Sterba J, Slaby O, Geyeregger R, Diaz A, Findlay IJ, Dun MD, Resnick A, Suvà ML, Jones DTW, Agnihotri S, Svedlund J, Koschmann C, Haberler C, Czech T, Slavc I, Cotter JA, Ligon KL, Alexandrescu S, Yung WKA, Arrillaga-Romany I, Gojo J, Monje M, Nilsson M, Filbin MG. The landscape of tumor cell states and spatial organization in H3-K27M mutant diffuse midline glioma across age and location. Nat Genet 2022; 54:1881-1894. [PMID: 36471067 PMCID: PMC9729116 DOI: 10.1038/s41588-022-01236-3] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 10/20/2022] [Indexed: 12/12/2022]
Abstract
Histone 3 lysine27-to-methionine (H3-K27M) mutations most frequently occur in diffuse midline gliomas (DMGs) of the childhood pons but are also increasingly recognized in adults. Their potential heterogeneity at different ages and midline locations is vastly understudied. Here, through dissecting the single-cell transcriptomic, epigenomic and spatial architectures of a comprehensive cohort of patient H3-K27M DMGs, we delineate how age and anatomical location shape glioma cell-intrinsic and -extrinsic features in light of the shared driver mutation. We show that stem-like oligodendroglial precursor-like cells, present across all clinico-anatomical groups, display varying levels of maturation dependent on location. We reveal a previously underappreciated relationship between mesenchymal cancer cell states and age, linked to age-dependent differences in the immune microenvironment. Further, we resolve the spatial organization of H3-K27M DMG cell populations and identify a mitotic oligodendroglial-lineage niche. Collectively, our study provides a powerful framework for rational modeling and therapeutic interventions.
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Affiliation(s)
- Ilon Liu
- grid.511177.4Department of Pediatric Oncology, Dana-Farber Boston Children’s Cancer and Blood Disorders Center, Boston, MA USA ,grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Li Jiang
- grid.511177.4Department of Pediatric Oncology, Dana-Farber Boston Children’s Cancer and Blood Disorders Center, Boston, MA USA ,grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Erik R. Samuelsson
- grid.10548.380000 0004 1936 9377Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Sergio Marco Salas
- grid.10548.380000 0004 1936 9377Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Alexander Beck
- grid.5252.00000 0004 1936 973XCenter for Neuropathology, Ludwig-Maximilians-University, Munich, Germany
| | - Olivia A. Hack
- grid.511177.4Department of Pediatric Oncology, Dana-Farber Boston Children’s Cancer and Blood Disorders Center, Boston, MA USA ,grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Daeun Jeong
- grid.511177.4Department of Pediatric Oncology, Dana-Farber Boston Children’s Cancer and Blood Disorders Center, Boston, MA USA ,grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - McKenzie L. Shaw
- grid.511177.4Department of Pediatric Oncology, Dana-Farber Boston Children’s Cancer and Blood Disorders Center, Boston, MA USA ,grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Bernhard Englinger
- grid.511177.4Department of Pediatric Oncology, Dana-Farber Boston Children’s Cancer and Blood Disorders Center, Boston, MA USA ,grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA ,grid.22937.3d0000 0000 9259 8492Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Jenna LaBelle
- grid.511177.4Department of Pediatric Oncology, Dana-Farber Boston Children’s Cancer and Blood Disorders Center, Boston, MA USA ,grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Hafsa M. Mire
- grid.511177.4Department of Pediatric Oncology, Dana-Farber Boston Children’s Cancer and Blood Disorders Center, Boston, MA USA ,grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Sibylle Madlener
- grid.22937.3d0000 0000 9259 8492Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Lisa Mayr
- grid.22937.3d0000 0000 9259 8492Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Michael A. Quezada
- grid.168010.e0000000419368956Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA USA
| | - Maria Trissal
- grid.511177.4Department of Pediatric Oncology, Dana-Farber Boston Children’s Cancer and Blood Disorders Center, Boston, MA USA ,grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Eshini Panditharatna
- grid.511177.4Department of Pediatric Oncology, Dana-Farber Boston Children’s Cancer and Blood Disorders Center, Boston, MA USA ,grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Kati J. Ernst
- grid.7497.d0000 0004 0492 0584Hopp Children’s Cancer Center Heidelberg (KiTZ), Division of Pediatric Glioma Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jayne Vogelzang
- grid.65499.370000 0001 2106 9910Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA USA
| | - Taylor A. Gatesman
- grid.21925.3d0000 0004 1936 9000Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA USA ,grid.239553.b0000 0000 9753 0008John G. Rangos Sr. Research Center, Children’s Hospital of Pittsburgh, Pittsburgh, PA USA
| | - Matthew E. Halbert
- grid.21925.3d0000 0004 1936 9000Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA USA ,grid.239553.b0000 0000 9753 0008John G. Rangos Sr. Research Center, Children’s Hospital of Pittsburgh, Pittsburgh, PA USA
| | - Hana Palova
- grid.10267.320000 0001 2194 0956Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Petra Pokorna
- grid.10267.320000 0001 2194 0956Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Jaroslav Sterba
- Pediatric Oncology Department, University Hospital Brno, Faculty of Medicine, Masaryk University, ICRC, Brno, Czech Republic
| | - Ondrej Slaby
- grid.10267.320000 0001 2194 0956Central European Institute of Technology, Masaryk University, Brno, Czech Republic ,grid.10267.320000 0001 2194 0956Department of Biology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Rene Geyeregger
- grid.22937.3d0000 0000 9259 8492Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria ,grid.416346.2Department of Clinical Cell Biology and FACS Core Unit, St. Anna Children’s Cancer Research Institute (CCRI), Vienna, Austria
| | - Aaron Diaz
- grid.266102.10000 0001 2297 6811Department of Neurological Surgery, University of California San Francisco, San Francisco, CA USA
| | - Izac J. Findlay
- grid.266842.c0000 0000 8831 109XCancer Signalling Research Group, School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, New South Wales Australia ,grid.413648.cPrecision Medicine Program, Hunter Medical Research Institute, New Lambton Heights, New South Wales Australia
| | - Matthew D. Dun
- grid.266842.c0000 0000 8831 109XCancer Signalling Research Group, School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, New South Wales Australia ,grid.413648.cPrecision Medicine Program, Hunter Medical Research Institute, New Lambton Heights, New South Wales Australia
| | - Adam Resnick
- grid.239552.a0000 0001 0680 8770Center for Data Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA USA
| | - Mario L. Suvà
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA ,grid.32224.350000 0004 0386 9924Department of Pathology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA USA
| | - David T. W. Jones
- grid.7497.d0000 0004 0492 0584Hopp Children’s Cancer Center Heidelberg (KiTZ), Division of Pediatric Glioma Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sameer Agnihotri
- grid.21925.3d0000 0004 1936 9000Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA USA ,grid.239553.b0000 0000 9753 0008John G. Rangos Sr. Research Center, Children’s Hospital of Pittsburgh, Pittsburgh, PA USA
| | - Jessica Svedlund
- grid.10548.380000 0004 1936 9377Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Carl Koschmann
- grid.412590.b0000 0000 9081 2336Division of Pediatric Hematology/Oncology, Department of Pediatrics, Michigan Medicine, Ann Arbor, MI USA
| | - Christine Haberler
- grid.22937.3d0000 0000 9259 8492Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Thomas Czech
- grid.22937.3d0000 0000 9259 8492Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Irene Slavc
- grid.22937.3d0000 0000 9259 8492Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Jennifer A. Cotter
- grid.239546.f0000 0001 2153 6013Department of Pathology and Laboratory Medicine, Children’s Hospital Los Angeles, Keck School of Medicine of University of Southern California, Los Angeles, CA USA
| | - Keith L. Ligon
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA ,grid.65499.370000 0001 2106 9910Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA USA ,grid.62560.370000 0004 0378 8294Department of Pathology, Brigham and Women’s Hospital, Boston, MA USA ,grid.2515.30000 0004 0378 8438Department of Pathology, Boston Children’s Hospital, Boston, MA USA
| | - Sanda Alexandrescu
- grid.2515.30000 0004 0378 8438Department of Pathology, Boston Children’s Hospital, Boston, MA USA
| | - W. K. Alfred Yung
- grid.240145.60000 0001 2291 4776Department of Neuro-Oncology, Brain Tumor Center, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Isabel Arrillaga-Romany
- grid.32224.350000 0004 0386 9924Massachusetts General Hospital, Cancer Center, Boston, MA USA
| | - Johannes Gojo
- grid.511177.4Department of Pediatric Oncology, Dana-Farber Boston Children’s Cancer and Blood Disorders Center, Boston, MA USA ,grid.22937.3d0000 0000 9259 8492Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Michelle Monje
- grid.168010.e0000000419368956Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA USA ,grid.413575.10000 0001 2167 1581Howard Hughes Medical Institute, Stanford, CA USA
| | - Mats Nilsson
- grid.10548.380000 0004 1936 9377Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Mariella G. Filbin
- grid.511177.4Department of Pediatric Oncology, Dana-Farber Boston Children’s Cancer and Blood Disorders Center, Boston, MA USA ,grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
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Miller M, Tward D, Trouvé A. Molecular Computational Anatomy: Unifying the Particle to Tissue Continuum via Measure Representations of the Brain. BME FRONTIERS 2022; 2022:9868673. [PMID: 37206893 PMCID: PMC10193958 DOI: 10.34133/2022/9868673] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 04/11/2022] [Indexed: 12/24/2023] Open
Abstract
OBJECTIVE The objective of this research is to unify the molecular representations of spatial transcriptomics and cellular scale histology with the tissue scales of computational anatomy for brain mapping. IMPACT STATEMENT We present a unified representation theory for brain mapping based on geometric varifold measures of the microscale deterministic structure and function with the statistical ensembles of the spatially aggregated tissue scales. INTRODUCTION Mapping across coordinate systems in computational anatomy allows us to understand structural and functional properties of the brain at the millimeter scale. New measurement technologies in digital pathology and spatial transcriptomics allow us to measure the brain molecule by molecule and cell by cell based on protein and transcriptomic functional identity. We currently have no mathematical representations for integrating consistently the tissue limits with the molecular particle descriptions. The formalism derived here demonstrates the methodology for transitioning consistently from the molecular scale of quantized particles-using mathematical structures as first introduced by Dirac as the class of generalized functions-to the tissue scales with methods originally introduced by Euler for fluids. METHODS We introduce two mathematical methods based on notions of generalized functions and statistical mechanics. We use geometric varifolds, a product measure on space and function, to represent functional states at the micro-scales-electrophysiology, molecular histology-integrated with a Boltzmann-like program to pass from deterministic particle descriptions to empirical probabilities on the functional states at the tissue scales. RESULTS Our space-function varifold representation provides a recipe for traversing from molecular to tissue scales in terms of a cascade of linear space scaling composed with nonlinear functional feature mapping. Following the cascade implies every scale is a geometric measure so that a universal family of measure norms can be introduced which quantifies the geodesic connection between brains in the orbit independent of the probing technology, whether it be RNA identities, Tau or amyloid histology, spike trains, or dense MR imagery. CONCLUSIONS We demonstrate a unified brain mapping theory for molecular and tissue scales based on geometric measure representations. We call the consistent aggregation of tissue scales from particle and cellular scales, molecular computational anatomy.
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Affiliation(s)
- Michael Miller
- Department of Biomedical Engineering & Kavli Neuroscience Discovery Institute & Center for Imaging Science, Johns Hopkins University, Baltimore, USA
| | - Daniel Tward
- Departments of Computational Medicine & Neurology, University of California Los Angeles, Los Angeles, USA
| | - Alain Trouvé
- Centre Giovanni Borelli (UMR 9010), Ecole Normale Supérieure Paris-Saclay, Université Paris-Saclay, Gif-sur-Yvette, France
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Muhl L, Mocci G, Pietilä R, Liu J, He L, Genové G, Leptidis S, Gustafsson S, Buyandelger B, Raschperger E, Hansson EM, Björkegren JL, Vanlandewijck M, Lendahl U, Betsholtz C. A single-cell transcriptomic inventory of murine smooth muscle cells. Dev Cell 2022; 57:2426-2443.e6. [DOI: 10.1016/j.devcel.2022.09.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 09/12/2022] [Accepted: 09/27/2022] [Indexed: 11/28/2022]
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43
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Zhang MJ, Hou K, Dey KK, Sakaue S, Jagadeesh KA, Weinand K, Taychameekiatchai A, Rao P, Pisco AO, Zou J, Wang B, Gandal M, Raychaudhuri S, Pasaniuc B, Price AL. Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data. Nat Genet 2022; 54:1572-1580. [PMID: 36050550 PMCID: PMC9891382 DOI: 10.1038/s41588-022-01167-z] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 07/19/2022] [Indexed: 02/03/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) provides unique insights into the pathology and cellular origin of disease. We introduce single-cell disease relevance score (scDRS), an approach that links scRNA-seq with polygenic disease risk at single-cell resolution, independent of annotated cell types. scDRS identifies cells exhibiting excess expression across disease-associated genes implicated by genome-wide association studies (GWASs). We applied scDRS to 74 diseases/traits and 1.3 million single-cell gene-expression profiles across 31 tissues/organs. Cell-type-level results broadly recapitulated known cell-type-disease associations. Individual-cell-level results identified subpopulations of disease-associated cells not captured by existing cell-type labels, including T cell subpopulations associated with inflammatory bowel disease, partially characterized by their effector-like states; neuron subpopulations associated with schizophrenia, partially characterized by their spatial locations; and hepatocyte subpopulations associated with triglyceride levels, partially characterized by their higher ploidy levels. Genes whose expression was correlated with the scDRS score across cells (reflecting coexpression with GWAS disease-associated genes) were strongly enriched for gold-standard drug target and Mendelian disease genes.
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Affiliation(s)
- Martin Jinye Zhang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Kushal K Dey
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Saori Sakaue
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Karthik A Jagadeesh
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathryn Weinand
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Aris Taychameekiatchai
- Department of Medicine and Liver Center, University of California, San Francisco, San Francisco, CA, USA
- Developmental and Stem Cell Biology Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Poorvi Rao
- Department of Medicine and Liver Center, University of California, San Francisco, San Francisco, CA, USA
| | | | - James Zou
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- Department of Electrical Engineering, Stanford University, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA
| | - Bruce Wang
- Department of Medicine and Liver Center, University of California, San Francisco, San Francisco, CA, USA
| | - Michael Gandal
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Soumya Raychaudhuri
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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44
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Le H, Peng B, Uy J, Carrillo D, Zhang Y, Aevermann BD, Scheuermann RH. Machine learning for cell type classification from single nucleus RNA sequencing data. PLoS One 2022; 17:e0275070. [PMID: 36149937 PMCID: PMC9506651 DOI: 10.1371/journal.pone.0275070] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 09/09/2022] [Indexed: 11/18/2022] Open
Abstract
With the advent of single cell/nucleus RNA sequencing (sc/snRNA-seq), the field of cell phenotyping is now a data-driven exercise providing statistical evidence to support cell type/state categorization. However, the task of classifying cells into specific, well-defined categories with the empirical data provided by sc/snRNA-seq remains nontrivial due to the difficulty in determining specific differences between related cell types with close transcriptional similarities, resulting in challenges with matching cell types identified in separate experiments. To investigate possible approaches to overcome these obstacles, we explored the use of supervised machine learning methods-logistic regression, support vector machines, random forests, neural networks, and light gradient boosting machine (LightGBM)-as approaches to classify cell types using snRNA-seq datasets from human brain middle temporal gyrus (MTG) and human kidney. Classification accuracy was evaluated using an F-beta score weighted in favor of precision to account for technical artifacts of gene expression dropout. We examined the impact of hyperparameter optimization and feature selection methods on F-beta score performance. We found that the best performing model for granular cell type classification in both datasets is a multinomial logistic regression classifier and that an effective feature selection step was the most influential factor in optimizing the performance of the machine learning pipelines.
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Affiliation(s)
- Huy Le
- Department of Bioengineering, University of California, San Diego, CA, United States of America
| | - Beverly Peng
- Department of Bioengineering, University of California, San Diego, CA, United States of America
| | - Janelle Uy
- Department of Bioengineering, University of California, San Diego, CA, United States of America
| | - Daniel Carrillo
- Department of Bioengineering, University of California, San Diego, CA, United States of America
| | - Yun Zhang
- Department of informatics, J. Craig Venter Institute, La Jolla, CA, United States of America
| | - Brian D. Aevermann
- Department of informatics, J. Craig Venter Institute, La Jolla, CA, United States of America
| | - Richard H. Scheuermann
- Department of informatics, J. Craig Venter Institute, La Jolla, CA, United States of America
- Department of Pathology, University of California, San Diego, CA, United States of America
- La Jolla Institute for Immunology, San Diego, CA, United States of America
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45
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Tritschler S, Thomas M, Böttcher A, Ludwig B, Schmid J, Schubert U, Kemter E, Wolf E, Lickert H, Theis FJ. A transcriptional cross species map of pancreatic islet cells. Mol Metab 2022; 66:101595. [PMID: 36113773 PMCID: PMC9526148 DOI: 10.1016/j.molmet.2022.101595] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 08/20/2022] [Accepted: 09/03/2022] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVE Pancreatic islets of Langerhans secrete hormones to regulate systemic glucose levels. Emerging evidence suggests that islet cells are functionally heterogeneous to allow a fine-tuned and efficient endocrine response to physiological changes. A precise description of the molecular basis of this heterogeneity, in particular linking animal models to human islets, is an important step towards identifying the factors critical for endocrine cell function in physiological and pathophysiological conditions. METHODS In this study, we used single-cell RNA sequencing to profile more than 50'000 endocrine cells isolated from healthy human, pig and mouse pancreatic islets and characterize transcriptional heterogeneity and evolutionary conservation of those cells across the three species. We systematically delineated endocrine cell types and α- and β-cell heterogeneity through prior knowledge- and data-driven gene sets shared across species, which altogether capture common and differential cellular properties, transcriptional dynamics and putative driving factors of state transitions. RESULTS We showed that global endocrine expression profiles correlate, and that critical identity and functional markers are shared between species, while only approximately 20% of cell type enriched expression is conserved. We resolved distinct human α- and β-cell states that form continuous transcriptional landscapes. These states differentially activate maturation and hormone secretion programs, which are related to regulatory hormone receptor expression, signaling pathways and different types of cellular stress responses. Finally, we mapped mouse and pig cells to the human reference and observed that the spectrum of human α- and β-cell heterogeneity and aspects of such functional gene expression are better recapitulated in the pig than mouse data. CONCLUSIONS Here, we provide a high-resolution transcriptional map of healthy human islet cells and their murine and porcine counterparts, which is easily queryable via an online interface. This comprehensive resource informs future efforts that focus on pancreatic endocrine function, failure and regeneration, and enables to assess molecular conservation in islet biology across species for translational purposes.
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Affiliation(s)
- Sophie Tritschler
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Technical University of Munich, School of Life Sciences Weihenstephan, 85354 Freising, Germany
| | - Moritz Thomas
- Technical University of Munich, School of Life Sciences Weihenstephan, 85354 Freising, Germany; Institute of AI for Health, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Anika Böttcher
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute of Stem Cell Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Barbara Ludwig
- Department of Medicine III, University Hospital Carl Gustav Carus, Technical University of Dresden, 01307 Dresden, Germany; Paul Langerhans Institute Dresden of Helmholtz Zentrum München, University Hospital Carl Gustav Carus, Technical University of Dresden, 01307 Dresden, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Janine Schmid
- Department of Medicine III, University Hospital Carl Gustav Carus, Technical University of Dresden, 01307 Dresden, Germany
| | - Undine Schubert
- Department of Medicine III, University Hospital Carl Gustav Carus, Technical University of Dresden, 01307 Dresden, Germany
| | - Elisabeth Kemter
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany; Chair for Molecular Animal Breeding and Biotechnology, Gene Center, LMU Munich, 81377 Munich, Germany; Center for Innovative Medical Models (CiMM), Department of Veterinary Sciences, LMU Munich, 85764 Oberschleißheim, Germany
| | - Eckhard Wolf
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany; Chair for Molecular Animal Breeding and Biotechnology, Gene Center, LMU Munich, 81377 Munich, Germany; Center for Innovative Medical Models (CiMM), Department of Veterinary Sciences, LMU Munich, 85764 Oberschleißheim, Germany
| | - Heiko Lickert
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute of Stem Cell Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany; Technical University of Munich, Medical Faculty, 81675 Munich, Germany.
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Technical University of Munich, Department of Mathematics, 85748 Garching b. Munich, Germany.
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46
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Wu X, Bhatia N, Grozinger CM, Yi SV. Comparative studies of genomic and epigenetic factors influencing transcriptional variation in two insect species. G3 GENES|GENOMES|GENETICS 2022; 12:6693626. [PMID: 36137211 PMCID: PMC9635643 DOI: 10.1093/g3journal/jkac230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 08/05/2022] [Indexed: 11/16/2022]
Abstract
Different genes show different levels of expression variability. For example, highly expressed genes tend to exhibit less expression variability. Genes whose promoters have TATA box and initiator motifs tend to have increased expression variability. On the other hand, DNA methylation of transcriptional units, or gene body DNA methylation, is associated with reduced gene expression variability in many species. Interestingly, some insect lineages, most notably Diptera including the canonical model insect Drosophila melanogaster, have lost DNA methylation. Therefore, it is of interest to determine whether genomic features similarly influence gene expression variability in lineages with and without DNA methylation. We analyzed recently generated large-scale data sets in D. melanogaster and honey bee (Apis mellifera) to investigate these questions. Our analysis shows that increased gene expression levels are consistently associated with reduced expression variability in both species, while the presence of TATA box is consistently associated with increased gene expression variability. In contrast, initiator motifs and gene lengths have weak effects limited to some data sets. Importantly, we show that a sequence characteristics indicative of gene body DNA methylation is strongly and negatively associate with gene expression variability in honey bees, while it shows no such association in D. melanogaster. These results suggest the evolutionary loss of DNA methylation in some insect lineages has reshaped the molecular mechanisms concerning the regulation of gene expression variability.
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Affiliation(s)
| | - Neharika Bhatia
- School of Biological Sciences, Institute for Bioengineering and Bioscience, Georgia Institute of Technology , Atlanta, GA 30332, USA
| | - Christina M Grozinger
- Department of Entomology, Center for Pollinator Research, Huck Institutes of the Life Sciences, Pennsylvania State University , University Park, PA 16801, USA
| | - Soojin V Yi
- School of Biological Sciences, Institute for Bioengineering and Bioscience, Georgia Institute of Technology , Atlanta, GA 30332, USA
- Department of Ecology, Evolution and Marine Biology, University of California Santa Barbara , Santa Barbara, CA 93106, USA
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47
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Kastriti ME, Faure L, Von Ahsen D, Bouderlique TG, Boström J, Solovieva T, Jackson C, Bronner M, Meijer D, Hadjab S, Lallemend F, Erickson A, Kaucka M, Dyachuk V, Perlmann T, Lahti L, Krivanek J, Brunet J, Fried K, Adameyko I. Schwann cell precursors represent a neural crest-like state with biased multipotency. EMBO J 2022; 41:e108780. [PMID: 35815410 PMCID: PMC9434083 DOI: 10.15252/embj.2021108780] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/14/2022] [Accepted: 06/15/2022] [Indexed: 12/29/2022] Open
Abstract
Schwann cell precursors (SCPs) are nerve-associated progenitors that can generate myelinating and non-myelinating Schwann cells but also are multipotent like the neural crest cells from which they originate. SCPs are omnipresent along outgrowing peripheral nerves throughout the body of vertebrate embryos. By using single-cell transcriptomics to generate a gene expression atlas of the entire neural crest lineage, we show that early SCPs and late migratory crest cells have similar transcriptional profiles characterised by a multipotent "hub" state containing cells biased towards traditional neural crest fates. SCPs keep diverging from the neural crest after being primed towards terminal Schwann cells and other fates, with different subtypes residing in distinct anatomical locations. Functional experiments using CRISPR-Cas9 loss-of-function further show that knockout of the common "hub" gene Sox8 causes defects in neural crest-derived cells along peripheral nerves by facilitating differentiation of SCPs towards sympathoadrenal fates. Finally, specific tumour populations found in melanoma, neurofibroma and neuroblastoma map to different stages of SCP/Schwann cell development. Overall, SCPs resemble migrating neural crest cells that maintain multipotency and become transcriptionally primed towards distinct lineages.
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Affiliation(s)
- Maria Eleni Kastriti
- Department of Molecular Neuroscience, Center for Brain ResearchMedical University ViennaViennaAustria
- Department of Physiology and PharmacologyKarolinska InstitutetStockholmSweden
- Department of Neuroimmunology, Center for Brain ResearchMedical University ViennaViennaAustria
| | - Louis Faure
- Department of Neuroimmunology, Center for Brain ResearchMedical University ViennaViennaAustria
| | - Dorothea Von Ahsen
- Department of Neuroimmunology, Center for Brain ResearchMedical University ViennaViennaAustria
| | | | - Johan Boström
- Department of Neuroimmunology, Center for Brain ResearchMedical University ViennaViennaAustria
| | - Tatiana Solovieva
- Division of Biology and Biological EngineeringCalifornia Institute of TechnologyPasadenaCAUSA
| | - Cameron Jackson
- Division of Biology and Biological EngineeringCalifornia Institute of TechnologyPasadenaCAUSA
| | - Marianne Bronner
- Division of Biology and Biological EngineeringCalifornia Institute of TechnologyPasadenaCAUSA
| | - Dies Meijer
- Centre for Discovery Brain SciencesUniversity of EdinburghEdinburghUK
| | - Saida Hadjab
- Department of NeuroscienceKarolinska InstitutetStockholmSweden
| | | | - Alek Erickson
- Department of Physiology and PharmacologyKarolinska InstitutetStockholmSweden
| | - Marketa Kaucka
- Max Planck Institute for Evolutionary BiologyPlönGermany
| | | | - Thomas Perlmann
- Department of Cell and Molecular BiologyKarolinska InstitutetStockholmSweden
| | - Laura Lahti
- Department of Cell and Molecular BiologyKarolinska InstitutetStockholmSweden
| | - Jan Krivanek
- Department of Histology and Embryology, Faculty of MedicineMasaryk UniversityBrnoCzech Republic
| | - Jean‐Francois Brunet
- Institut de Biologie de l'ENS (IBENS), INSERM, CNRS, École Normale SupérieurePSL Research UniversityParisFrance
| | - Kaj Fried
- Department of NeuroscienceKarolinska InstitutetStockholmSweden
| | - Igor Adameyko
- Department of Physiology and PharmacologyKarolinska InstitutetStockholmSweden
- Department of Neuroimmunology, Center for Brain ResearchMedical University ViennaViennaAustria
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48
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Im NG, Guillaumet-Adkins A, Wal M, Rogers AJ, Frede J, Havig CC, Yang J, Anand P, Stegmann SK, Waldschmidt JM, Sotudeh N, Niu L, Voisine J, Schweiger MR, Grassberger C, Lohr JG, Knoechel B. Regulatory Programs of B-cell Activation and Germinal Center Reaction Allow B-ALL Escape from CD19 CAR T-cell Therapy. Cancer Immunol Res 2022; 10:1055-1068. [PMID: 35759797 PMCID: PMC9444959 DOI: 10.1158/2326-6066.cir-21-0626] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 04/13/2022] [Accepted: 06/23/2022] [Indexed: 11/16/2022]
Abstract
Chimeric antigen receptor (CAR) T-cell therapy has led to tremendous successes in the treatment of B-cell malignancies. However, a large fraction of treated patients relapse, often with disease expressing reduced levels of the target antigen. Here, we report that exposing CD19+ B-cell acute lymphoblastic leukemia (B-ALL) cells to CD19 CAR T cells reduced CD19 expression within hours. Initially, CD19 CAR T cells caused clustering of CD19 at the T cell-leukemia cell interface followed by CD19 internalization and decreased CD19 surface expression on the B-ALL cells. CD19 expression was then repressed by transcriptional rewiring. Using single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin using sequencing, we demonstrated that a subset of refractory CD19low cells sustained decreased CD19 expression through transcriptional programs of physiologic B-cell activation and germinal center reaction. Inhibiting B-cell activation programs with the Bruton's tyrosine kinase inhibitor ibrutinib increased the cytotoxicity of CD19 CAR T cells without affecting CAR T-cell viability. These results demonstrate transcriptional plasticity as an underlying mechanism of escape from CAR T cells and highlight the importance of combining CAR T-cell therapy with targeted therapies that aim to overcome this plasticity. See related Spotlight by Zhao and Melenhorst, p. 1040.
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Affiliation(s)
- Nam Gyu Im
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Institute for Translational Epigenetics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Center for Molecular Medicine Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Amy Guillaumet-Adkins
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Megha Wal
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anna J. Rogers
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Julia Frede
- Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Claire C. Havig
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jing Yang
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Praveen Anand
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Johannes M. Waldschmidt
- Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Noori Sotudeh
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Leili Niu
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jordan Voisine
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Michal R. Schweiger
- Institute for Translational Epigenetics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Center for Molecular Medicine Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Clemens Grassberger
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jens G. Lohr
- Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Corresponding Authors: Birgit Knoechel, MD, PhD, Department of Pediatric Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA; Phone: 617-632-2072; ., Jens G. Lohr, MD, PhD, Division of Hematologic Neoplasia, Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA; Phone: 617-632-2069;
| | - Birgit Knoechel
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Division of Hematology/Oncology, Department of Medicine, Boston Children’s Hospital, MA, USA.,Corresponding Authors: Birgit Knoechel, MD, PhD, Department of Pediatric Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA; Phone: 617-632-2072; ., Jens G. Lohr, MD, PhD, Division of Hematologic Neoplasia, Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA; Phone: 617-632-2069;
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49
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Zemek RM, Chin WL, Fear VS, Wylie B, Casey TH, Forbes C, Tilsed CM, Boon L, Guo BB, Bosco A, Forrest ARR, Millward MJ, Nowak AK, Lake RA, Lassmann T, Joost Lesterhuis W. Temporally restricted activation of IFNβ signaling underlies response to immune checkpoint therapy in mice. Nat Commun 2022; 13:4895. [PMID: 35986006 PMCID: PMC9390963 DOI: 10.1038/s41467-022-32567-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 08/06/2022] [Indexed: 02/08/2023] Open
Abstract
The biological determinants of the response to immune checkpoint blockade (ICB) in cancer remain incompletely understood. Little is known about dynamic biological events that underpin therapeutic efficacy due to the inability to frequently sample tumours in patients. Here, we map the transcriptional profiles of 144 responding and non-responding tumours within two mouse models at four time points during ICB. We find that responding tumours display on/fast-off kinetics of type-I-interferon (IFN) signaling. Phenocopying of this kinetics using time-dependent sequential dosing of recombinant IFNs and neutralizing antibodies markedly improves ICB efficacy, but only when IFNβ is targeted, not IFNα. We identify Ly6C+/CD11b+ inflammatory monocytes as the primary source of IFNβ and find that active type-I-IFN signaling in tumour-infiltrating inflammatory monocytes is associated with T cell expansion in patients treated with ICB. Together, our results suggest that on/fast-off modulation of IFNβ signaling is critical to the therapeutic response to ICB, which can be exploited to drive clinical outcomes towards response. Immune checkpoint blockade (ICB) is partially successful as a cancer therapy. Here using mouse models, the authors transcriptionally monitor responding and non-responding tumours showing that responding tumours were associated with transient IFN-β signalling which could promote the anti-tumour response.
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50
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Iida K, Kondo J, Wibisana JN, Inoue M, Okada M. ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes. Bioinformatics 2022; 38:4330-4336. [PMID: 35924984 PMCID: PMC9477531 DOI: 10.1093/bioinformatics/btac541] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 07/04/2022] [Accepted: 08/01/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Single-cell RNA sequencing (scRNA-seq) analysis reveals heterogeneity and dynamic cell transitions. However, conventional gene-based analyses require intensive manual curation to interpret biological implications of computational results. Hence, a theory for efficiently annotating individual cells remains warranted. RESULTS We present ASURAT, a computational tool for simultaneously performing unsupervised clustering and functional annotation of disease, cell type, biological process and signaling pathway activity for single-cell transcriptomic data, using a correlation graph decomposition for genes in database-derived functional terms. We validated the usability and clustering performance of ASURAT using scRNA-seq datasets for human peripheral blood mononuclear cells, which required fewer manual curations than existing methods. Moreover, we applied ASURAT to scRNA-seq and spatial transcriptome datasets for human small cell lung cancer and pancreatic ductal adenocarcinoma, respectively, identifying previously overlooked subpopulations and differentially expressed genes. ASURAT is a powerful tool for dissecting cell subpopulations and improving biological interpretability of complex and noisy transcriptomic data. AVAILABILITY AND IMPLEMENTATION ASURAT is published on Bioconductor (https://doi.org/10.18129/B9.bioc.ASURAT). The codes for analyzing data in this article are available at Github (https://github.com/keita-iida/ASURATBI) and figshare (https://doi.org/10.6084/m9.figshare.19200254.v4). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Keita Iida
- To whom correspondence should be addressed.
| | - Jumpei Kondo
- Division of Health Sciences, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan,Department of Clinical Bio-Resource Research and Development, Graduate School of Medicine Kyoto University, Kyoto 606-8501, Japan
| | | | - Masahiro Inoue
- Department of Clinical Bio-Resource Research and Development, Graduate School of Medicine Kyoto University, Kyoto 606-8501, Japan
| | - Mariko Okada
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan,Center for Drug Design and Research, National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Osaka 567-0085, Japan
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