1
|
Wang L, Izadmehr S, Sfakianos JP, Tran M, Beaumont KG, Brody R, Cordon-Cardo C, Horowitz A, Sebra R, Oh WK, Bhardwaj N, Galsky MD, Zhu J. Single-cell transcriptomic-informed deconvolution of bulk data identifies immune checkpoint blockade resistance in urothelial cancer. iScience 2024; 27:109928. [PMID: 38812546 PMCID: PMC11133924 DOI: 10.1016/j.isci.2024.109928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 11/23/2023] [Accepted: 05/03/2024] [Indexed: 05/31/2024] Open
Abstract
Interactions within the tumor microenvironment (TME) significantly influence tumor progression and treatment responses. While single-cell RNA sequencing (scRNA-seq) and spatial genomics facilitate TME exploration, many clinical cohorts are assessed at the bulk tissue level. Integrating scRNA-seq and bulk tissue RNA-seq data through computational deconvolution is essential for obtaining clinically relevant insights. Our method, ProM, enables the examination of major and minor cell types. Through evaluation against existing methods using paired single-cell and bulk RNA sequencing of human urothelial cancer (UC) samples, ProM demonstrates superiority. Application to UC cohorts treated with immune checkpoint inhibitors reveals pre-treatment cellular features associated with poor outcomes, such as elevated SPP1 expression in macrophage/monocytes (MM). Our deconvolution method and paired single-cell and bulk tissue RNA-seq dataset contribute novel insights into TME heterogeneity and resistance to immune checkpoint blockade.
Collapse
Affiliation(s)
- Li Wang
- Department of Precision Medicine, Aitia, Somerville, MA 02143, USA
- Department of Medicine, Division of Hematology Oncology, Icahn School of Medicine at Mount Sinai, Tisch Cancer Institute, New York, NY 10029, USA
| | - Sudeh Izadmehr
- Department of Medicine, Division of Hematology Oncology, Icahn School of Medicine at Mount Sinai, Tisch Cancer Institute, New York, NY 10029, USA
| | - John P. Sfakianos
- Department of Urology; Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Michelle Tran
- Department of Medicine, Division of Hematology Oncology, Icahn School of Medicine at Mount Sinai, Tisch Cancer Institute, New York, NY 10029, USA
- The Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kristin G. Beaumont
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Rachel Brody
- Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Carlos Cordon-Cardo
- Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Amir Horowitz
- The Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Robert Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - William K. Oh
- Department of Medicine, Division of Hematology Oncology, Icahn School of Medicine at Mount Sinai, Tisch Cancer Institute, New York, NY 10029, USA
| | - Nina Bhardwaj
- Department of Medicine, Division of Hematology Oncology, Icahn School of Medicine at Mount Sinai, Tisch Cancer Institute, New York, NY 10029, USA
- The Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Matthew D. Galsky
- Department of Medicine, Division of Hematology Oncology, Icahn School of Medicine at Mount Sinai, Tisch Cancer Institute, New York, NY 10029, USA
| | - Jun Zhu
- Department of Medicine, Division of Hematology Oncology, Icahn School of Medicine at Mount Sinai, Tisch Cancer Institute, New York, NY 10029, USA
| |
Collapse
|
2
|
Li X, Turaga D, Li RG, Tsai CR, Quinn JN, Zhao Y, Wilson R, Carlson K, Wang J, Spinner JA, Hickey EJ, Adachi I, Martin JF. The Macrophage Landscape Across the Lifespan of a Human Cardiac Allograft. Circulation 2024; 149:1650-1666. [PMID: 38344825 PMCID: PMC11105989 DOI: 10.1161/circulationaha.123.065294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 01/16/2024] [Indexed: 05/22/2024]
Abstract
BACKGROUND Much of our knowledge of organ rejection after transplantation is derived from rodent models. METHODS We used single-nucleus RNA sequencing to investigate the inflammatory myocardial microenvironment in human pediatric cardiac allografts at different stages after transplantation. We distinguished donor- from recipient-derived cells using naturally occurring genetic variants embedded in single-nucleus RNA sequencing data. RESULTS Donor-derived tissue resident macrophages, which accompany the allograft into the recipient, are lost over time after transplantation. In contrast, monocyte-derived macrophages from the recipient populate the heart within days after transplantation and form 2 macrophage populations: recipient MP1 and recipient MP2. Recipient MP2s have cell signatures similar to donor-derived resident macrophages; however, they lack signatures of pro-reparative phagocytic activity typical of donor-derived resident macrophages and instead express profibrotic genes. In contrast, recipient MP1s express genes consistent with hallmarks of cellular rejection. Our data suggest that recipient MP1s activate a subset of natural killer cells, turning them into a cytotoxic cell population through feed-forward signaling between recipient MP1s and natural killer cells. CONCLUSIONS Our findings reveal an imbalance of donor-derived and recipient-derived macrophages in the pediatric cardiac allograft that contributes to allograft failure.
Collapse
Affiliation(s)
- Xiao Li
- The Texas Heart Institute, Houston, TX, USA
| | - Diwakar Turaga
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA
- Division of Critical Care Medicine, Texas Children’s Hospital, Houston TX, USA
| | - Rich G. Li
- The Texas Heart Institute, Houston, TX, USA
| | - Chang-Ru Tsai
- Department of Integrative Physiology, Baylor College of Medicine, Houston, TX, USA
| | - Julianna N. Quinn
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, TX, USA
| | - Yi Zhao
- The Texas Heart Institute, Houston, TX, USA
| | | | - Katherine Carlson
- Department of Integrative Physiology, Baylor College of Medicine, Houston, TX, USA
| | - Jun Wang
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, TX, USA
| | - Joseph A. Spinner
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA
- Division of Cardiology, Texas Children’s Hospital, Houston, TX, USA
| | - Edward J. Hickey
- Department of Surgery, Baylor College of Medicine, Houston, Texas, USA
- Division of Congenital Heart Surgery, Texas Children’s Hospital, Houston, TX, USA
| | - Iki Adachi
- Department of Surgery, Baylor College of Medicine, Houston, Texas, USA
- Division of Congenital Heart Surgery, Texas Children’s Hospital, Houston, TX, USA
| | - James F. Martin
- The Texas Heart Institute, Houston, TX, USA
- Department of Integrative Physiology, Baylor College of Medicine, Houston, TX, USA
- Center for Organ Repair and Renewal, Baylor College of Medicine, Houston, TX, USA
| |
Collapse
|
3
|
Waichman TV, Vercesi ML, Berardino AA, Beckel MS, Giacomini D, Rasetto NB, Herrero M, Di Bella DJ, Arlotta P, Schinder AF, Chernomoretz A. scX: a user-friendly tool for scRNAseq exploration. BIOINFORMATICS ADVANCES 2024; 4:vbae062. [PMID: 38779177 PMCID: PMC11109472 DOI: 10.1093/bioadv/vbae062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 03/06/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024]
Abstract
Motivation Single-cell RNA sequencing (scRNAseq) has transformed our ability to explore biological systems. Nevertheless, proficient expertise is essential for handling and interpreting the data. Results In this article, we present scX, an R package built on the Shiny framework that streamlines the analysis, exploration, and visualization of single-cell experiments. With an interactive graphic interface, implemented as a web application, scX provides easy access to key scRNAseq analyses, including marker identification, gene expression profiling, and differential gene expression analysis. Additionally, scX seamlessly integrates with commonly used single-cell Seurat and SingleCellExperiment R objects, resulting in efficient processing and visualization of varied datasets. Overall, scX serves as a valuable and user-friendly tool for effortless exploration and sharing of single-cell data, simplifying some of the complexities inherent in scRNAseq analysis. Availability and implementation Source code can be downloaded from https://github.com/chernolabs/scX. A docker image is available from dockerhub as chernolabs/scx.
Collapse
Affiliation(s)
- Tomás V Waichman
- Integrative Systems Biology Lab, Leloir Institute, Buenos Aires, CP1405, Argentina
| | - M L Vercesi
- Integrative Systems Biology Lab, Leloir Institute, Buenos Aires, CP1405, Argentina
| | - Ariel A Berardino
- Integrative Systems Biology Lab, Leloir Institute, Buenos Aires, CP1405, Argentina
- Instituto de Investigaciones Bioquímicas de Buenos Aires, CONICET, Buenos Aires, CP1405, Argentina
| | - Maximiliano S Beckel
- Integrative Systems Biology Lab, Leloir Institute, Buenos Aires, CP1405, Argentina
- Instituto de Investigaciones Bioquímicas de Buenos Aires, CONICET, Buenos Aires, CP1405, Argentina
| | - Damiana Giacomini
- Instituto de Investigaciones Bioquímicas de Buenos Aires, CONICET, Buenos Aires, CP1405, Argentina
- Laboratory of Neuronal Plasticity, Leloir Institute, Buenos Aires, CP1405, Argentina
| | - Natalí B Rasetto
- Instituto de Investigaciones Bioquímicas de Buenos Aires, CONICET, Buenos Aires, CP1405, Argentina
- Laboratory of Neuronal Plasticity, Leloir Institute, Buenos Aires, CP1405, Argentina
| | - Magalí Herrero
- Instituto de Investigaciones Bioquímicas de Buenos Aires, CONICET, Buenos Aires, CP1405, Argentina
- Laboratory of Neuronal Plasticity, Leloir Institute, Buenos Aires, CP1405, Argentina
| | - Daniela J Di Bella
- Department of Stem Cells and Regenerative Biology, Harvard University, Cambridge, MA 02138, United States
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02138, United States
| | - Paola Arlotta
- Department of Stem Cells and Regenerative Biology, Harvard University, Cambridge, MA 02138, United States
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02138, United States
| | - Alejandro F Schinder
- Instituto de Investigaciones Bioquímicas de Buenos Aires, CONICET, Buenos Aires, CP1405, Argentina
- Laboratory of Neuronal Plasticity, Leloir Institute, Buenos Aires, CP1405, Argentina
| | - Ariel Chernomoretz
- Integrative Systems Biology Lab, Leloir Institute, Buenos Aires, CP1405, Argentina
- Departamento de Física, FCEN, Universidad de Buenos Aires, Buenos Aires, CP1428, Argentina
- INFINA, UBA-CONICET, Buenos Aires, CP 1428, Argentina
| |
Collapse
|
4
|
Tian A, Baidouri H, Kim S, Li J, Cheng X, Li Y, Chen R, Raghunathan V. To be or not to be - Decoding the Trabecular Meshwork Cell Identity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.591346. [PMID: 38746421 PMCID: PMC11092480 DOI: 10.1101/2024.04.26.591346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The trabecular meshwork within the conventional outflow apparatus is critical in maintaining intraocular pressure homeostasis. In vitro studies employing primary cell cultures of the human trabecular meshwork (hTM) have conventionally served as surrogates for investigating the pathobiology of TM dysfunction. Despite its abundant use, translation of outcomes from in vitro studies to ex vivo and/or in vivo studies remains a challenge. Given the cell heterogeneity, performing single-cell RNA sequencing comparing primary hTM cell cultures to hTM tissue may provide important insights on cellular identity and translatability, as such an approach has not been reported before. In this study, we assembled a total of 14 primary hTM in vitro samples across passages 1-4, including 4 samples from individuals diagnosed with glaucoma. This dataset offers a comprehensive transcriptomic resource of primary hTM in vitro scRNA-seq data to study global changes in gene expression in comparison to cells in tissue in situ. We have performed extensive preprocessing and quality control, allowing the research community to access and utilize this public resource.
Collapse
Affiliation(s)
- Alice Tian
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Hasna Baidouri
- University of Houston, College of Optomtery, Houston, TX, 77204, USA
| | - Sangbae Kim
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Jin Li
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Xuesen Cheng
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Yumei Li
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Rui Chen
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
| | | |
Collapse
|
5
|
Zhang X, Song B, Carlino MJ, Li G, Ferchen K, Chen M, Thompson EN, Kain BN, Schnell D, Thakkar K, Kouril M, Jin K, Hay SB, Sen S, Bernardicius D, Ma S, Bennett SN, Croteau J, Salvatori O, Lye MH, Gillen AE, Jordan CT, Singh H, Krause DS, Salomonis N, Grimes HL. An immunophenotype-coupled transcriptomic atlas of human hematopoietic progenitors. Nat Immunol 2024; 25:703-715. [PMID: 38514887 PMCID: PMC11003869 DOI: 10.1038/s41590-024-01782-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 02/07/2024] [Indexed: 03/23/2024]
Abstract
Analysis of the human hematopoietic progenitor compartment is being transformed by single-cell multimodal approaches. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) enables coupled surface protein and transcriptome profiling, thereby revealing genomic programs underlying progenitor states. To perform CITE-seq systematically on primary human bone marrow cells, we used titrations with 266 CITE-seq antibodies (antibody-derived tags) and machine learning to optimize a panel of 132 antibodies. Multimodal analysis resolved >80 stem, progenitor, immune, stromal and transitional cells defined by distinctive surface markers and transcriptomes. This dataset enables flow cytometry solutions for in silico-predicted cell states and identifies dozens of cell surface markers consistently detected across donors spanning race and sex. Finally, aligning annotations from this atlas, we nominate normal marrow equivalents for acute myeloid leukemia stem cell populations that differ in clinical response. This atlas serves as an advanced digital resource for hematopoietic progenitor analyses in human health and disease.
Collapse
Affiliation(s)
- Xuan Zhang
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Baobao Song
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Immunology Graduate Program, University of Cincinnati, Cincinnati, OH, USA
| | - Maximillian J Carlino
- Yale Stem Cell Center, Yale School of Medicine, New Haven, CT, USA
- Department of Laboratory Medicine, Yale University, New Haven, CT, USA
| | - Guangyuan Li
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Kyle Ferchen
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Mi Chen
- Yale Stem Cell Center, Yale School of Medicine, New Haven, CT, USA
- Department of Laboratory Medicine, Yale University, New Haven, CT, USA
| | - Evrett N Thompson
- Yale Stem Cell Center, Yale School of Medicine, New Haven, CT, USA
- Department of Laboratory Medicine, Yale University, New Haven, CT, USA
- Department of Cell Biology, Yale School of Medicine, New Haven, CT, USA
| | - Bailee N Kain
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Dan Schnell
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Kairavee Thakkar
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Michal Kouril
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Kang Jin
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Stuart B Hay
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Sidharth Sen
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - David Bernardicius
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Siyuan Ma
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Sierra N Bennett
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | | | | | | | - Austin E Gillen
- Division of Hematology, University of Colorado School of Medicine, Aurora, CO, USA
- Rocky Mountain Regional VA Medical Center, Aurora, CO, USA
| | - Craig T Jordan
- Division of Hematology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Harinder Singh
- Departments of Immunology and Computational and Systems Biology, Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Diane S Krause
- Yale Stem Cell Center, Yale School of Medicine, New Haven, CT, USA
- Department of Laboratory Medicine, Yale University, New Haven, CT, USA
- Department of Cell Biology, Yale School of Medicine, New Haven, CT, USA
| | - Nathan Salomonis
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA.
| | - H Leighton Grimes
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA.
- Division of Experimental Hematology and Cancer Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
| |
Collapse
|
6
|
Ren J, Lyu X, Guo J, Shi X, Zhou Y, Li Q. CDSKNN XMBD: a novel clustering framework for large-scale single-cell data based on a stable graph structure. J Transl Med 2024; 22:233. [PMID: 38433205 PMCID: PMC10910752 DOI: 10.1186/s12967-024-05009-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 02/19/2024] [Indexed: 03/05/2024] Open
Abstract
BACKGROUND Accurate and efficient cell grouping is essential for analyzing single-cell transcriptome sequencing (scRNA-seq) data. However, the existing clustering techniques often struggle to provide timely and accurate cell type groupings when dealing with datasets with large-scale or imbalanced cell types. Therefore, there is a need for improved methods that can handle the increasing size of scRNA-seq datasets while maintaining high accuracy and efficiency. METHODS We propose CDSKNNXMBD (Community Detection based on a Stable K-Nearest Neighbor Graph Structure), a novel single-cell clustering framework integrating partition clustering algorithm and community detection algorithm, which achieves accurate and fast cell type grouping by finding a stable graph structure. RESULTS We evaluated the effectiveness of our approach by analyzing 15 tissues from the human fetal atlas. Compared to existing methods, CDSKNN effectively counteracts the high imbalance in single-cell data, enabling effective clustering. Furthermore, we conducted comparisons across multiple single-cell datasets from different studies and sequencing techniques. CDSKNN is of high applicability and robustness, and capable of balancing the complexities of across diverse types of data. Most importantly, CDSKNN exhibits higher operational efficiency on datasets at the million-cell scale, requiring an average of only 6.33 min for clustering 1.46 million single cells, saving 33.3% to 99% of running time compared to those of existing methods. CONCLUSIONS The CDSKNN is a flexible, resilient, and promising clustering tool that is particularly suitable for clustering imbalanced data and demonstrates high efficiency on large-scale scRNA-seq datasets.
Collapse
Affiliation(s)
- Jun Ren
- School of Informatics, Xiamen University, Xiamen, 361105, China
- Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361102, China
- National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen, 361102, China
| | - Xuejing Lyu
- National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen, 361102, China
| | - Jintao Guo
- National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen, 361102, China
| | - Xiaodong Shi
- School of Informatics, Xiamen University, Xiamen, 361105, China
| | - Ying Zhou
- Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361102, China.
- National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen, 361102, China.
| | - Qiyuan Li
- Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361102, China.
- National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen, 361102, China.
| |
Collapse
|
7
|
Farah EN, Hu RK, Kern C, Zhang Q, Lu TY, Ma Q, Tran S, Zhang B, Carlin D, Monell A, Blair AP, Wang Z, Eschbach J, Li B, Destici E, Ren B, Evans SM, Chen S, Zhu Q, Chi NC. Spatially organized cellular communities form the developing human heart. Nature 2024; 627:854-864. [PMID: 38480880 PMCID: PMC10972757 DOI: 10.1038/s41586-024-07171-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/07/2024] [Indexed: 03/18/2024]
Abstract
The heart, which is the first organ to develop, is highly dependent on its form to function1,2. However, how diverse cardiac cell types spatially coordinate to create the complex morphological structures that are crucial for heart function remains unclear. Here we integrated single-cell RNA-sequencing with high-resolution multiplexed error-robust fluorescence in situ hybridization to resolve the identity of the cardiac cell types that develop the human heart. This approach also provided a spatial mapping of individual cells that enables illumination of their organization into cellular communities that form distinct cardiac structures. We discovered that many of these cardiac cell types further specified into subpopulations exclusive to specific communities, which support their specialization according to the cellular ecosystem and anatomical region. In particular, ventricular cardiomyocyte subpopulations displayed an unexpected complex laminar organization across the ventricular wall and formed, with other cell subpopulations, several cellular communities. Interrogating cell-cell interactions within these communities using in vivo conditional genetic mouse models and in vitro human pluripotent stem cell systems revealed multicellular signalling pathways that orchestrate the spatial organization of cardiac cell subpopulations during ventricular wall morphogenesis. These detailed findings into the cellular social interactions and specialization of cardiac cell types constructing and remodelling the human heart offer new insights into structural heart diseases and the engineering of complex multicellular tissues for human heart repair.
Collapse
Affiliation(s)
- Elie N Farah
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Robert K Hu
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Colin Kern
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Qingquan Zhang
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Ting-Yu Lu
- Materials Science and Engineering Program, University of California San Diego, La Jolla, CA, USA
| | - Qixuan Ma
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Shaina Tran
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Bo Zhang
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Daniel Carlin
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Alexander Monell
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Andrew P Blair
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Zilu Wang
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Jacqueline Eschbach
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Bin Li
- Department of Cellular and Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Eugin Destici
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Bing Ren
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Sylvia M Evans
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
- Department of Pharmacology, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Shaochen Chen
- Materials Science and Engineering Program, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Department of NanoEngineering, University of California San Diego, La Jolla, CA, USA
- Institute of Engineering in Medicine, University of California San Diego, La Jolla, CA, USA
| | - Quan Zhu
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.
| | - Neil C Chi
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA.
- Institute of Engineering in Medicine, University of California San Diego, La Jolla, CA, USA.
| |
Collapse
|
8
|
Petersen C, Mucke L, Corces MR. CHOIR improves significance-based detection of cell types and states from single-cell data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.18.576317. [PMID: 38328105 PMCID: PMC10849522 DOI: 10.1101/2024.01.18.576317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Clustering is a critical step in the analysis of single-cell data, as it enables the discovery and characterization of putative cell types and states. However, most popular clustering tools do not subject clustering results to statistical inference testing, leading to risks of overclustering or underclustering data and often resulting in ineffective identification of cell types with widely differing prevalence. To address these challenges, we present CHOIR (clustering hierarchy optimization by iterative random forests), which applies a framework of random forest classifiers and permutation tests across a hierarchical clustering tree to statistically determine which clusters represent distinct populations. We demonstrate the enhanced performance of CHOIR through extensive benchmarking against 14 existing clustering methods across 100 simulated and 4 real single-cell RNA-seq, ATAC-seq, spatial transcriptomic, and multi-omic datasets. CHOIR can be applied to any single-cell data type and provides a flexible, scalable, and robust solution to the important challenge of identifying biologically relevant cell groupings within heterogeneous single-cell data.
Collapse
Affiliation(s)
- Cathrine Petersen
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA
- Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Lennart Mucke
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA
- Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - M. Ryan Corces
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA
- Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| |
Collapse
|
9
|
Xiong YX, Zhang XF. scDOT: enhancing single-cell RNA-Seq data annotation and uncovering novel cell types through multi-reference integration. Brief Bioinform 2024; 25:bbae072. [PMID: 38436563 PMCID: PMC10939303 DOI: 10.1093/bib/bbae072] [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: 10/26/2023] [Revised: 01/12/2024] [Accepted: 02/07/2024] [Indexed: 03/05/2024] Open
Abstract
The proliferation of single-cell RNA-seq data has greatly enhanced our ability to comprehend the intricate nature of diverse tissues. However, accurately annotating cell types in such data, especially when handling multiple reference datasets and identifying novel cell types, remains a significant challenge. To address these issues, we introduce Single Cell annotation based on Distance metric learning and Optimal Transport (scDOT), an innovative cell-type annotation method adept at integrating multiple reference datasets and uncovering previously unseen cell types. scDOT introduces two key innovations. First, by incorporating distance metric learning and optimal transport, it presents a novel optimization framework. This framework effectively learns the predictive power of each reference dataset for new query data and simultaneously establishes a probabilistic mapping between cells in the query data and reference-defined cell types. Secondly, scDOT develops an interpretable scoring system based on the acquired probabilistic mapping, enabling the precise identification of previously unseen cell types within the data. To rigorously assess scDOT's capabilities, we systematically evaluate its performance using two diverse collections of benchmark datasets encompassing various tissues, sequencing technologies and diverse cell types. Our experimental results consistently affirm the superior performance of scDOT in cell-type annotation and the identification of previously unseen cell types. These advancements provide researchers with a potent tool for precise cell-type annotation, ultimately enriching our understanding of complex biological tissues.
Collapse
Affiliation(s)
- Yi-Xuan Xiong
- School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China
- Key Laboratory of Nonlinear Analysis & Applications (Ministry of Education), Central China Normal University, Wuhan 430079, China
| | - Xiao-Fei Zhang
- School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China
- Key Laboratory of Nonlinear Analysis & Applications (Ministry of Education), Central China Normal University, Wuhan 430079, China
| |
Collapse
|
10
|
Zhang Y, Sun H, Zhang W, Fu T, Huang S, Mou M, Zhang J, Gao J, Ge Y, Yang Q, Zhu F. CellSTAR: a comprehensive resource for single-cell transcriptomic annotation. Nucleic Acids Res 2024; 52:D859-D870. [PMID: 37855686 PMCID: PMC10767908 DOI: 10.1093/nar/gkad874] [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: 08/15/2023] [Revised: 09/12/2023] [Accepted: 09/27/2023] [Indexed: 10/20/2023] Open
Abstract
Large-scale studies of single-cell sequencing and biological experiments have successfully revealed expression patterns that distinguish different cell types in tissues, emphasizing the importance of studying cellular heterogeneity and accurately annotating cell types. Analysis of gene expression profiles in these experiments provides two essential types of data for cell type annotation: annotated references and canonical markers. In this study, the first comprehensive database of single-cell transcriptomic annotation resource (CellSTAR) was thus developed. It is unique in (a) offering the comprehensive expertly annotated reference data for annotating hundreds of cell types for the first time and (b) enabling the collective consideration of reference data and marker genes by incorporating tens of thousands of markers. Given its unique features, CellSTAR is expected to attract broad research interests from the technological innovations in single-cell transcriptomics, the studies of cellular heterogeneity & dynamics, and so on. It is now publicly accessible without any login requirement at: https://idrblab.org/cellstar.
Collapse
Affiliation(s)
- Ying Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Huaicheng Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Tingting Fu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shijie Huang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jinsong Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yichao Ge
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Qingxia Yang
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| |
Collapse
|
11
|
Addala V, Newell F, Pearson JV, Redwood A, Robinson BW, Creaney J, Waddell N. Computational immunogenomic approaches to predict response to cancer immunotherapies. Nat Rev Clin Oncol 2024; 21:28-46. [PMID: 37907723 DOI: 10.1038/s41571-023-00830-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/03/2023] [Indexed: 11/02/2023]
Abstract
Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.
Collapse
Affiliation(s)
- Venkateswar Addala
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
- Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
| | - Felicity Newell
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - John V Pearson
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Alec Redwood
- National Centre for Asbestos Related Diseases, University of Western Australia, Perth, Western Australia, Australia
- Institute of Respiratory Health, Perth, Western Australia, Australia
- School of Biomedical Science, University of Western Australia, Perth, Western Australia, Australia
| | - Bruce W Robinson
- National Centre for Asbestos Related Diseases, University of Western Australia, Perth, Western Australia, Australia
- Institute of Respiratory Health, Perth, Western Australia, Australia
- Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
- Medical School, University of Western Australia, Perth, Western Australia, Australia
| | - Jenette Creaney
- National Centre for Asbestos Related Diseases, University of Western Australia, Perth, Western Australia, Australia
- Institute of Respiratory Health, Perth, Western Australia, Australia
- School of Biomedical Science, University of Western Australia, Perth, Western Australia, Australia
- Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
| | - Nicola Waddell
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
- Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
| |
Collapse
|
12
|
Li C, Ye G, Jiang Y, Wang Z, Yu H, Yang M. Artificial Intelligence in battling infectious diseases: A transformative role. J Med Virol 2024; 96:e29355. [PMID: 38179882 DOI: 10.1002/jmv.29355] [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: 10/16/2023] [Revised: 12/01/2023] [Accepted: 12/17/2023] [Indexed: 01/06/2024]
Abstract
It is widely acknowledged that infectious diseases have wrought immense havoc on human society, being regarded as adversaries from which humanity cannot elude. In recent years, the advancement of Artificial Intelligence (AI) technology has ushered in a revolutionary era in the realm of infectious disease prevention and control. This evolution encompasses early warning of outbreaks, contact tracing, infection diagnosis, drug discovery, and the facilitation of drug design, alongside other facets of epidemic management. This article presents an overview of the utilization of AI systems in the field of infectious diseases, with a specific focus on their role during the COVID-19 pandemic. The article also highlights the contemporary challenges that AI confronts within this domain and posits strategies for their mitigation. There exists an imperative to further harness the potential applications of AI across multiple domains to augment its capacity in effectively addressing future disease outbreaks.
Collapse
Affiliation(s)
- Chunhui Li
- School of Life Science, Advanced Research Institute of Multidisciplinary Science, Key Laboratory of Molecular Medicine and Biotherapy, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Guoguo Ye
- Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for Infectious Disease, The Third People's Hospital of Shenzhen, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
| | - Yinghan Jiang
- School of Life Science, Advanced Research Institute of Multidisciplinary Science, Key Laboratory of Molecular Medicine and Biotherapy, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Zhiming Wang
- School of Life Science, Advanced Research Institute of Multidisciplinary Science, Key Laboratory of Molecular Medicine and Biotherapy, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Haiyang Yu
- Hangzhou Yalla Information Technology Service Co., Ltd., Hangzhou, People's Republic of China
| | - Minghui Yang
- School of Life Science, Advanced Research Institute of Multidisciplinary Science, Key Laboratory of Molecular Medicine and Biotherapy, Beijing Institute of Technology, Beijing, People's Republic of China
| |
Collapse
|
13
|
Liu H, Zeng Q, Zhou J, Bartlett A, Wang BA, Berube P, Tian W, Kenworthy M, Altshul J, Nery JR, Chen H, Castanon RG, Zu S, Li YE, Lucero J, Osteen JK, Pinto-Duarte A, Lee J, Rink J, Cho S, Emerson N, Nunn M, O'Connor C, Wu Z, Stoica I, Yao Z, Smith KA, Tasic B, Luo C, Dixon JR, Zeng H, Ren B, Behrens MM, Ecker JR. Single-cell DNA methylome and 3D multi-omic atlas of the adult mouse brain. Nature 2023; 624:366-377. [PMID: 38092913 PMCID: PMC10719113 DOI: 10.1038/s41586-023-06805-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 10/31/2023] [Indexed: 12/17/2023]
Abstract
Cytosine DNA methylation is essential in brain development and is implicated in various neurological disorders. Understanding DNA methylation diversity across the entire brain in a spatial context is fundamental for a complete molecular atlas of brain cell types and their gene regulatory landscapes. Here we used single-nucleus methylome sequencing (snmC-seq3) and multi-omic sequencing (snm3C-seq)1 technologies to generate 301,626 methylomes and 176,003 chromatin conformation-methylome joint profiles from 117 dissected regions throughout the adult mouse brain. Using iterative clustering and integrating with companion whole-brain transcriptome and chromatin accessibility datasets, we constructed a methylation-based cell taxonomy with 4,673 cell groups and 274 cross-modality-annotated subclasses. We identified 2.6 million differentially methylated regions across the genome that represent potential gene regulation elements. Notably, we observed spatial cytosine methylation patterns on both genes and regulatory elements in cell types within and across brain regions. Brain-wide spatial transcriptomics data validated the association of spatial epigenetic diversity with transcription and improved the anatomical mapping of our epigenetic datasets. Furthermore, chromatin conformation diversities occurred in important neuronal genes and were highly associated with DNA methylation and transcription changes. Brain-wide cell-type comparisons enabled the construction of regulatory networks that incorporate transcription factors, regulatory elements and their potential downstream gene targets. Finally, intragenic DNA methylation and chromatin conformation patterns predicted alternative gene isoform expression observed in a whole-brain SMART-seq2 dataset. Our study establishes a brain-wide, single-cell DNA methylome and 3D multi-omic atlas and provides a valuable resource for comprehending the cellular-spatial and regulatory genome diversity of the mouse brain.
Collapse
Affiliation(s)
- Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Qiurui Zeng
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Anna Bartlett
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Bang-An Wang
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Peter Berube
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Wei Tian
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Mia Kenworthy
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jordan Altshul
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Joseph R Nery
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Huaming Chen
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Rosa G Castanon
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Songpeng Zu
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Yang Eric Li
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Jacinta Lucero
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Julia K Osteen
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Antonio Pinto-Duarte
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jasper Lee
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jon Rink
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Silvia Cho
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Nora Emerson
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Michael Nunn
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Carolyn O'Connor
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Zhanghao Wu
- Sky Computing Lab, University of California, Berkeley, Berkeley, CA, USA
| | - Ion Stoica
- Sky Computing Lab, University of California, Berkeley, Berkeley, CA, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Chongyuan Luo
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jesse R Dixon
- Peptide Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Bing Ren
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Institute of Genomic Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - M Margarita Behrens
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Joseph R Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA.
| |
Collapse
|
14
|
Zhang Z, Chen X, Tang R, Zhu Y, Guo H, Qu Y, Xie P, Lian IY, Wang Y, Lo YH. Interpretable unsupervised learning enables accurate clustering with high-throughput imaging flow cytometry. Sci Rep 2023; 13:20533. [PMID: 37996496 PMCID: PMC10667244 DOI: 10.1038/s41598-023-46782-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 11/04/2023] [Indexed: 11/25/2023] Open
Abstract
A primary challenge of high-throughput imaging flow cytometry (IFC) is to analyze the vast amount of imaging data, especially in applications where ground truth labels are unavailable or hard to obtain. We present an unsupervised deep embedding algorithm, the Deep Convolutional Autoencoder-based Clustering (DCAEC) model, to cluster label-free IFC images without any prior knowledge of input labels. The DCAEC model first encodes the input images into the latent representations and then clusters based on the latent representations. Using the DCAEC model, we achieve a balanced accuracy of 91.9% for human white blood cell (WBC) clustering and 97.9% for WBC/leukemia clustering using the 3D IFC images and 3D DCAEC model. Above all, although no human recognizable features can separate the clusters of cells with protein localization, we demonstrate the fused DCAEC model can achieve a cluster balanced accuracy of 85.3% from the label-free 2D transmission and 3D side scattering images. To reveal how the neural network recognizes features beyond human ability, we use the gradient-weighted class activation mapping method to discover the cluster-specific visual patterns automatically. Evaluation results show that the automatically identified salient image regions have strong cluster-specific visual patterns for different clusters, which we believe is a stride for the interpretable neural network for cell analysis with high-throughput IFCs.
Collapse
Affiliation(s)
- Zunming Zhang
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Xinyu Chen
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Rui Tang
- NanoCellect Biomedical, Inc., San Diego, CA, 92121, USA
| | - Yuxuan Zhu
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Han Guo
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Yunjia Qu
- Department of Bioengineering, Institute of Engineering in Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0435, USA
| | - Pengtao Xie
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Ian Y Lian
- Department of Biology, Lamar University, Beaumont, TX, 77710, USA
| | - Yingxiao Wang
- Department of Bioengineering, Institute of Engineering in Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0435, USA
| | - Yu-Hwa Lo
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA.
| |
Collapse
|
15
|
Liang X, Cao L, Chen H, Wang L, Wang Y, Fu L, Tan X, Chen E, Ding Y, Tang J. A critical assessment of clustering algorithms to improve cell clustering and identification in single-cell transcriptome study. Brief Bioinform 2023; 25:bbad497. [PMID: 38168839 PMCID: PMC10782910 DOI: 10.1093/bib/bbad497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/13/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
Cell clustering is typically the initial step in single-cell RNA sequencing (scRNA-seq) analyses. The performance of clustering considerably impacts the validity and reproducibility of cell identification. A variety of clustering algorithms have been developed for scRNA-seq data. These algorithms generate cell label sets that assign each cell to a cluster. However, different algorithms usually yield different label sets, which can introduce variations in cell-type identification based on the generated label sets. Currently, the performance of these algorithms has not been systematically evaluated in single-cell transcriptome studies. Herein, we performed a critical assessment of seven state-of-the-art clustering algorithms including four deep learning-based clustering algorithms and commonly used methods Seurat, Cosine-based Tanimoto similarity-refined graph for community detection using Leiden's algorithm (CosTaL) and Single-cell consensus clustering (SC3). We used diverse evaluation indices based on 10 different scRNA-seq benchmarks to systematically evaluate their clustering performance. Our results show that CosTaL, Seurat, Deep Embedding for Single-cell Clustering (DESC) and SC3 consistently outperformed Single-Cell Clustering Assessment Framework and scDeepCluster based on nine effectiveness scores. Notably, CosTaL and DESC demonstrated superior performance in clustering specific cell types. The performance of the single-cell Variational Inference tools varied across different datasets, suggesting its sensitivity to certain dataset characteristics. Notably, DESC exhibited promising results for cell subtype identification and capturing cellular heterogeneity. In addition, SC3 requires more memory and exhibits slower computation speed compared to other algorithms for the same dataset. In sum, this study provides useful guidance for selecting appropriate clustering methods in scRNA-seq data analysis.
Collapse
Affiliation(s)
- Xiao Liang
- Department of Obstetrics and Gynecology, Women and Children’s Hospital of Chongqing Medical University, Chongqing 401147, China
- School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Lijie Cao
- School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Hao Chen
- School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Lidan Wang
- School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Yangyun Wang
- School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Lijuan Fu
- Joint International Research Laboratory of Reproduction and Development of the Ministry of Education of China, School of Public Health, Chongqing Medical University, Chongqing 400016, China
- Department of Pharmacology, Academician Workstation, Changsha Medical University, Changsha 410219, China
| | - Xiaqin Tan
- The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Enxiang Chen
- School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
- Joint International Research Laboratory of Reproduction and Development of the Ministry of Education of China, School of Public Health, Chongqing Medical University, Chongqing 400016, China
| | - Yubin Ding
- Department of Obstetrics and Gynecology, Women and Children’s Hospital of Chongqing Medical University, Chongqing 401147, China
- Joint International Research Laboratory of Reproduction and Development of the Ministry of Education of China, School of Public Health, Chongqing Medical University, Chongqing 400016, China
| | - Jing Tang
- Department of Obstetrics and Gynecology, Women and Children’s Hospital of Chongqing Medical University, Chongqing 401147, China
- School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| |
Collapse
|
16
|
Tang L, Xu N, Huang M, Yi W, Sang X, Shao M, Li Y, Hao ZZ, Liu R, Shen Y, Yue F, Liu X, Xu C, Liu S. A primate nigrostriatal atlas of neuronal vulnerability and resilience in a model of Parkinson's disease. Nat Commun 2023; 14:7497. [PMID: 37980356 PMCID: PMC10657376 DOI: 10.1038/s41467-023-43213-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 11/02/2023] [Indexed: 11/20/2023] Open
Abstract
The degenerative process in Parkinson's disease (PD) causes a progressive loss of dopaminergic neurons (DaNs) in the nigrostriatal system. Resolving the differences in neuronal susceptibility warrants an amenable PD model that, in comparison to post-mortem human specimens, controls for environmental and genetic differences in PD pathogenesis. Here we generated high-quality profiles for 250,173 cells from the substantia nigra (SN) and putamen (PT) of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced parkinsonian macaques and matched controls. Our primate model of parkinsonism recapitulates important pathologic features in nature PD and provides an unbiased view of the axis of neuronal vulnerability and resistance. We identified seven molecularly defined subtypes of nigral DaNs which manifested a gradient of vulnerability and were confirmed by fluorescence-activated nuclei sorting. Neuronal resilience was associated with a FOXP2-centered regulatory pathway shared between PD-resistant DaNs and glutamatergic excitatory neurons, as well as between humans and nonhuman primates. We also discovered activation of immune response common to glial cells of SN and PT, indicating concurrently activated pathways in the nigrostriatal system. Our study provides a unique resource to understand the mechanistic connections between neuronal susceptibility and PD pathophysiology, and to facilitate future biomarker discovery and targeted cell therapy.
Collapse
Affiliation(s)
- Lei Tang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Nana Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Mengyao Huang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Wei Yi
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Xuan Sang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Mingting Shao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Ye Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Zhao-Zhe Hao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Ruifeng Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Yuhui Shen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Feng Yue
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Haikou, 570228, China
- Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Haikou, 570228, China
| | - Xialin Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.
| | - Chuan Xu
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
| | - Sheng Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.
- Guangdong Province Key Laboratory of Brain Function and Disease, Guangzhou, China.
| |
Collapse
|
17
|
Zelig A, Kariti H, Kaplan N. KMD clustering: robust general-purpose clustering of biological data. Commun Biol 2023; 6:1110. [PMID: 37919399 PMCID: PMC10622433 DOI: 10.1038/s42003-023-05480-z] [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: 08/02/2023] [Accepted: 10/18/2023] [Indexed: 11/04/2023] Open
Abstract
The noisy and high-dimensional nature of biological data has spawned advanced clustering algorithms that are tailored for specific biological datatypes. However, the performance of such methods varies greatly between datasets and they require post hoc tuning of cryptic hyperparameters. We present k minimal distance (KMD) clustering, a general-purpose method based on a generalization of single and average linkage hierarchical clustering. We introduce a generalized silhouette-like function to eliminate the cryptic hyperparameter k, and use sampling to enable application to million-object datasets. Rigorous comparisons to general and specialized clustering methods on simulated, mass cytometry and scRNA-seq datasets show consistent high performance of KMD clustering across all datasets.
Collapse
Affiliation(s)
- Aviv Zelig
- Data Science & Engineering Program, Faculty of Industrial Engineering & Management, Technion - Israel Institute of Technology, Haifa, Israel
- Department of Physiology, Biophysics & Systems Biology, Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel
| | - Hagai Kariti
- Department of Physiology, Biophysics & Systems Biology, Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel
| | - Noam Kaplan
- Department of Physiology, Biophysics & Systems Biology, Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel.
| |
Collapse
|
18
|
Song Y, Miao Z, Brazma A, Papatheodorou I. Benchmarking strategies for cross-species integration of single-cell RNA sequencing data. Nat Commun 2023; 14:6495. [PMID: 37838716 PMCID: PMC10576752 DOI: 10.1038/s41467-023-41855-w] [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: 11/11/2022] [Accepted: 09/21/2023] [Indexed: 10/16/2023] Open
Abstract
The growing number of available single-cell gene expression datasets from different species creates opportunities to explore evolutionary relationships between cell types across species. Cross-species integration of single-cell RNA-sequencing data has been particularly informative in this context. However, in order to do so robustly it is essential to have rigorous benchmarking and appropriate guidelines to ensure that integration results truly reflect biology. Here, we benchmark 28 combinations of gene homology mapping methods and data integration algorithms in a variety of biological settings. We examine the capability of each strategy to perform species-mixing of known homologous cell types and to preserve biological heterogeneity using 9 established metrics. We also develop a new biology conservation metric to address the maintenance of cell type distinguishability. Overall, scANVI, scVI and SeuratV4 methods achieve a balance between species-mixing and biology conservation. For evolutionarily distant species, including in-paralogs is beneficial. SAMap outperforms when integrating whole-body atlases between species with challenging gene homology annotation. We provide our freely available cross-species integration and assessment pipeline to help analyse new data and develop new algorithms.
Collapse
Affiliation(s)
- Yuyao Song
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SA, United Kingdom.
| | - Zhichao Miao
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SA, United Kingdom
- Guangzhou Laboratory, Guangzhou International Bio Island, Guangzhou, 510005, China
| | - Alvis Brazma
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SA, United Kingdom
| | - Irene Papatheodorou
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SA, United Kingdom.
| |
Collapse
|
19
|
Gao MY, Wang JQ, He J, Gao R, Zhang Y, Li X. Single-Cell RNA-Sequencing in Astrocyte Development, Heterogeneity, and Disease. Cell Mol Neurobiol 2023; 43:3449-3464. [PMID: 37552355 DOI: 10.1007/s10571-023-01397-7] [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: 01/07/2023] [Accepted: 07/29/2023] [Indexed: 08/09/2023]
Abstract
Astrocytes are the most plentiful cell type in the central nervous system (CNS) and perform complicated functions in health and disease. It is obvious that different astrocyte subpopulations, or activation states, are relevant with specific genomic programs and functions. In recent years, the emergence of new technologies such as single-cell RNA sequencing (scRNA-seq) has made substantial advance in the characterization of astrocyte heterogeneity, astrocyte developmental trajectory, and its role in CNS diseases which has had a significant impact on neuroscience. In this review, we present an overview of astrocyte development, heterogeneity, and its essential role in the physiological and pathological environments of the CNS. We focused on the critical role of single-cell sequencing in revealing astrocyte development, heterogeneity, and its role in different CNS diseases.
Collapse
Affiliation(s)
- Meng-Yuan Gao
- A National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest China, Key Laboratory of Medicinal Resources and Natural Pharmaceutical Chemistry (Shaanxi Normal University), The Ministry of Education, College of Life Sciences, Shaanxi Normal University, Xi'an, 710119, Shaanxi, China
| | - Jia-Qi Wang
- A National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest China, Key Laboratory of Medicinal Resources and Natural Pharmaceutical Chemistry (Shaanxi Normal University), The Ministry of Education, College of Life Sciences, Shaanxi Normal University, Xi'an, 710119, Shaanxi, China
| | - Jin He
- A National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest China, Key Laboratory of Medicinal Resources and Natural Pharmaceutical Chemistry (Shaanxi Normal University), The Ministry of Education, College of Life Sciences, Shaanxi Normal University, Xi'an, 710119, Shaanxi, China
| | - Rui Gao
- A National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest China, Key Laboratory of Medicinal Resources and Natural Pharmaceutical Chemistry (Shaanxi Normal University), The Ministry of Education, College of Life Sciences, Shaanxi Normal University, Xi'an, 710119, Shaanxi, China
| | - Yuan Zhang
- A National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest China, Key Laboratory of Medicinal Resources and Natural Pharmaceutical Chemistry (Shaanxi Normal University), The Ministry of Education, College of Life Sciences, Shaanxi Normal University, Xi'an, 710119, Shaanxi, China
| | - Xing Li
- A National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest China, Key Laboratory of Medicinal Resources and Natural Pharmaceutical Chemistry (Shaanxi Normal University), The Ministry of Education, College of Life Sciences, Shaanxi Normal University, Xi'an, 710119, Shaanxi, China.
| |
Collapse
|
20
|
He X, Qian K, Wang Z, Zeng S, Li H, Li WV. scAce: an adaptive embedding and clustering method for single-cell gene expression data. Bioinformatics 2023; 39:btad546. [PMID: 37672035 PMCID: PMC10500084 DOI: 10.1093/bioinformatics/btad546] [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: 05/04/2023] [Revised: 08/01/2023] [Accepted: 09/05/2023] [Indexed: 09/07/2023] Open
Abstract
MOTIVATION Since the development of single-cell RNA sequencing (scRNA-seq) technologies, clustering analysis of single-cell gene expression data has been an essential tool for distinguishing cell types and identifying novel cell types. Even though many methods have been available for scRNA-seq clustering analysis, the majority of them are constrained by the requirement on predetermined cluster numbers or the dependence on selected initial cluster assignment. RESULTS In this article, we propose an adaptive embedding and clustering method named scAce, which constructs a variational autoencoder to simultaneously learn cell embeddings and cluster assignments. In the scAce method, we develop an adaptive cluster merging approach which achieves improved clustering results without the need to estimate the number of clusters in advance. In addition, scAce provides an option to perform clustering enhancement, which can update and enhance cluster assignments based on previous clustering results from other methods. Based on computational analysis of both simulated and real datasets, we demonstrate that scAce outperforms state-of-the-art clustering methods for scRNA-seq data, and achieves better clustering accuracy and robustness. AVAILABILITY AND IMPLEMENTATION The scAce package is implemented in python 3.8 and is freely available from https://github.com/sldyns/scAce.
Collapse
Affiliation(s)
- Xinwei He
- School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China
| | - Kun Qian
- School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China
| | - Ziqian Wang
- School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China
| | - Shirou Zeng
- School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China
| | - Hongwei Li
- School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China
| | - Wei Vivian Li
- Department of Statistics, University of California, Riverside, Riverside 92521, United States
| |
Collapse
|
21
|
Madadi Y, Monavarfeshani A, Chen H, Stamer WD, Williams RW, Yousefi S. Artificial Intelligence Models for Cell Type and Subtype Identification Based on Single-Cell RNA Sequencing Data in Vision Science. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2837-2852. [PMID: 37294649 PMCID: PMC10631573 DOI: 10.1109/tcbb.2023.3284795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) provides a high throughput, quantitative and unbiased framework for scientists in many research fields to identify and characterize cell types within heterogeneous cell populations from various tissues. However, scRNA-seq based identification of discrete cell-types is still labor intensive and depends on prior molecular knowledge. Artificial intelligence has provided faster, more accurate, and user-friendly approaches for cell-type identification. In this review, we discuss recent advances in cell-type identification methods using artificial intelligence techniques based on single-cell and single-nucleus RNA sequencing data in vision science. The main purpose of this review paper is to assist vision scientists not only to select suitable datasets for their problems, but also to be aware of the appropriate computational tools to perform their analysis. Developing novel methods for scRNA-seq data analysis remains to be addressed in future studies.
Collapse
|
22
|
Guo M, Morley MP, Jiang C, Wu Y, Li G, Du Y, Zhao S, Wagner A, Cakar AC, Kouril M, Jin K, Gaddis N, Kitzmiller JA, Stewart K, Basil MC, Lin SM, Ying Y, Babu A, Wikenheiser-Brokamp KA, Mun KS, Naren AP, Clair G, Adkins JN, Pryhuber GS, Misra RS, Aronow BJ, Tickle TL, Salomonis N, Sun X, Morrisey EE, Whitsett JA, Xu Y. Guided construction of single cell reference for human and mouse lung. Nat Commun 2023; 14:4566. [PMID: 37516747 PMCID: PMC10387117 DOI: 10.1038/s41467-023-40173-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 07/13/2023] [Indexed: 07/31/2023] Open
Abstract
Accurate cell type identification is a key and rate-limiting step in single-cell data analysis. Single-cell references with comprehensive cell types, reproducible and functionally validated cell identities, and common nomenclatures are much needed by the research community for automated cell type annotation, data integration, and data sharing. Here, we develop a computational pipeline utilizing the LungMAP CellCards as a dictionary to consolidate single-cell transcriptomic datasets of 104 human lungs and 17 mouse lung samples to construct LungMAP single-cell reference (CellRef) for both normal human and mouse lungs. CellRefs define 48 human and 40 mouse lung cell types catalogued from diverse anatomic locations and developmental time points. We demonstrate the accuracy and stability of LungMAP CellRefs and their utility for automated cell type annotation of both normal and diseased lungs using multiple independent methods and testing data. We develop user-friendly web interfaces for easy access and maximal utilization of the LungMAP CellRefs.
Collapse
Affiliation(s)
- Minzhe Guo
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH, 45267, USA.
| | - Michael P Morley
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Cheng Jiang
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Yixin Wu
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Guangyuan Li
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Yina Du
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Shuyang Zhao
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Andrew Wagner
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Adnan Cihan Cakar
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Michal Kouril
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Kang Jin
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | | | - Joseph A Kitzmiller
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Kathleen Stewart
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Maria C Basil
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Susan M Lin
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yun Ying
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Apoorva Babu
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kathryn A Wikenheiser-Brokamp
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
- Division of Pathology and Laboratory Medicine, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
- Department of Pathology & Laboratory Medicine, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH, 45267, USA
| | - Kyu Shik Mun
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
- Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Anjaparavanda P Naren
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Geremy Clair
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Joshua N Adkins
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Gloria S Pryhuber
- Department of Pediatrics Division of Neonatology, University of Rochester Medical Center, Rochester, NY, 14642, USA
| | - Ravi S Misra
- Department of Pediatrics Division of Neonatology, University of Rochester Medical Center, Rochester, NY, 14642, USA
| | - Bruce J Aronow
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH, 45267, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Timothy L Tickle
- Data Sciences Platform, The Broad Institute, Cambridge, MA, 02142, USA
| | - Nathan Salomonis
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH, 45267, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Xin Sun
- Department of Pediatrics, University of California at San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA
- Department of Biological Sciences, University of California at San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Edward E Morrisey
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jeffrey A Whitsett
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH, 45267, USA
| | - Yan Xu
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH, 45267, USA.
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA.
| |
Collapse
|
23
|
Xiong YX, Wang MG, Chen L, Zhang XF. Cell-type annotation with accurate unseen cell-type identification using multiple references. PLoS Comput Biol 2023; 19:e1011261. [PMID: 37379341 PMCID: PMC10335708 DOI: 10.1371/journal.pcbi.1011261] [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: 01/20/2023] [Revised: 07/11/2023] [Accepted: 06/11/2023] [Indexed: 06/30/2023] Open
Abstract
The recent advances in single-cell RNA sequencing (scRNA-seq) techniques have stimulated efforts to identify and characterize the cellular composition of complex tissues. With the advent of various sequencing techniques, automated cell-type annotation using a well-annotated scRNA-seq reference becomes popular. But it relies on the diversity of cell types in the reference, which may not capture all the cell types present in the query data of interest. There are generally unseen cell types in the query data of interest because most data atlases are obtained for different purposes and techniques. Identifying previously unseen cell types is essential for improving annotation accuracy and uncovering novel biological discoveries. To address this challenge, we propose mtANN (multiple-reference-based scRNA-seq data annotation), a new method to automatically annotate query data while accurately identifying unseen cell types with the aid of multiple references. Key innovations of mtANN include the integration of deep learning and ensemble learning to improve prediction accuracy, and the introduction of a new metric that considers three complementary aspects to distinguish between unseen cell types and shared cell types. Additionally, we provide a data-driven method to adaptively select a threshold for identifying previously unseen cell types. We demonstrate the advantages of mtANN over state-of-the-art methods for unseen cell-type identification and cell-type annotation on two benchmark dataset collections, as well as its predictive power on a collection of COVID-19 datasets. The source code and tutorial are available at https://github.com/Zhangxf-ccnu/mtANN.
Collapse
Affiliation(s)
- Yi-Xuan Xiong
- School of Mathematics and Statistics, Central China Normal University, Wuhan, China
- Key Laboratory of Nonlinear Analysis & Applications (Ministry of Education), Central China Normal University, Wuhan, China
| | - Meng-Guo Wang
- School of Mathematics and Statistics, Central China Normal University, Wuhan, China
- Key Laboratory of Nonlinear Analysis & Applications (Ministry of Education), Central China Normal University, Wuhan, China
| | - Luonan Chen
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China
- Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai, Guangdong, China
| | - Xiao-Fei Zhang
- School of Mathematics and Statistics, Central China Normal University, Wuhan, China
- Key Laboratory of Nonlinear Analysis & Applications (Ministry of Education), Central China Normal University, Wuhan, China
| |
Collapse
|
24
|
Miranda AMA, Janbandhu V, Maatz H, Kanemaru K, Cranley J, Teichmann SA, Hübner N, Schneider MD, Harvey RP, Noseda M. Single-cell transcriptomics for the assessment of cardiac disease. Nat Rev Cardiol 2023; 20:289-308. [PMID: 36539452 DOI: 10.1038/s41569-022-00805-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/03/2022] [Indexed: 12/24/2022]
Abstract
Cardiovascular disease is the leading cause of death globally. An advanced understanding of cardiovascular disease mechanisms is required to improve therapeutic strategies and patient risk stratification. State-of-the-art, large-scale, single-cell and single-nucleus transcriptomics facilitate the exploration of the cardiac cellular landscape at an unprecedented level, beyond its descriptive features, and can further our understanding of the mechanisms of disease and guide functional studies. In this Review, we provide an overview of the technical challenges in the experimental design of single-cell and single-nucleus transcriptomics studies, as well as a discussion of the type of inferences that can be made from the data derived from these studies. Furthermore, we describe novel findings derived from transcriptomics studies for each major cardiac cell type in both health and disease, and from development to adulthood. This Review also provides a guide to interpreting the exhaustive list of newly identified cardiac cell types and states, and highlights the consensus and discordances in annotation, indicating an urgent need for standardization. We describe advanced applications such as integration of single-cell data with spatial transcriptomics to map genes and cells on tissue and define cellular microenvironments that regulate homeostasis and disease progression. Finally, we discuss current and future translational and clinical implications of novel transcriptomics approaches, and provide an outlook of how these technologies will change the way we diagnose and treat heart disease.
Collapse
Affiliation(s)
| | - Vaibhao Janbandhu
- Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
- School of Clinical Medicine, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia
| | - Henrike Maatz
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Kazumasa Kanemaru
- Cellular Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - James Cranley
- Cellular Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Sarah A Teichmann
- Cellular Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Deptartment of Physics, Cavendish Laboratory, University of Cambridge, Cambridge, UK
| | - Norbert Hübner
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Charite-Universitätsmedizin Berlin, Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
| | | | - Richard P Harvey
- Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
- School of Clinical Medicine, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia
- School of Biotechnology and Biomolecular Sciences, UNSW Sydney, Sydney, NSW, Australia
| | - Michela Noseda
- National Heart and Lung Institute, Imperial College London, London, UK.
| |
Collapse
|
25
|
Xu Y, Kramann R, McCord RP, Hayat S. MASI enables fast model-free standardization and integration of single-cell transcriptomics data. Commun Biol 2023; 6:465. [PMID: 37117305 PMCID: PMC10144903 DOI: 10.1038/s42003-023-04820-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 04/06/2023] [Indexed: 04/30/2023] Open
Abstract
Single-cell transcriptomics datasets from the same anatomical sites generated by different research labs are becoming increasingly common. However, fast and computationally inexpensive tools for standardization of cell-type annotation and data integration are still needed in order to increase research inclusivity. To standardize cell-type annotation and integrate single-cell transcriptomics datasets, we have built a fast model-free integration method, named MASI (Marker-Assisted Standardization and Integration). We benchmark MASI with other well-established methods and demonstrate that MASI outperforms other methods, in terms of integration, annotation, and speed. To harness knowledge from single-cell atlases, we demonstrate three case studies that cover integration across biological conditions, surveyed participants, and research groups, respectively. Finally, we show MASI can annotate approximately one million cells on a personal laptop, making large-scale single-cell data integration more accessible. We envision that MASI can serve as a cheap computational alternative for the single-cell research community.
Collapse
Affiliation(s)
- Yang Xu
- UT-ORNL Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996, USA
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Rafael Kramann
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - Rachel Patton McCord
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN, 37996, USA.
| | - Sikander Hayat
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany.
| |
Collapse
|
26
|
Liu H, Zeng Q, Zhou J, Bartlett A, Wang BA, Berube P, Tian W, Kenworthy M, Altshul J, Nery JR, Chen H, Castanon RG, Zu S, Li YE, Lucero J, Osteen JK, Pinto-Duarte A, Lee J, Rink J, Cho S, Emerson N, Nunn M, O'Connor C, Yao Z, Smith KA, Tasic B, Zeng H, Luo C, Dixon JR, Ren B, Behrens MM, Ecker JR. Single-cell DNA Methylome and 3D Multi-omic Atlas of the Adult Mouse Brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.16.536509. [PMID: 37131654 PMCID: PMC10153407 DOI: 10.1101/2023.04.16.536509] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Cytosine DNA methylation is essential in brain development and has been implicated in various neurological disorders. A comprehensive understanding of DNA methylation diversity across the entire brain in the context of the brain's 3D spatial organization is essential for building a complete molecular atlas of brain cell types and understanding their gene regulatory landscapes. To this end, we employed optimized single-nucleus methylome (snmC-seq3) and multi-omic (snm3C-seq1) sequencing technologies to generate 301,626 methylomes and 176,003 chromatin conformation/methylome joint profiles from 117 dissected regions throughout the adult mouse brain. Using iterative clustering and integrating with companion whole-brain transcriptome and chromatin accessibility datasets, we constructed a methylation-based cell type taxonomy that contains 4,673 cell groups and 261 cross-modality-annotated subclasses. We identified millions of differentially methylated regions (DMRs) across the genome, representing potential gene regulation elements. Notably, we observed spatial cytosine methylation patterns on both genes and regulatory elements in cell types within and across brain regions. Brain-wide multiplexed error-robust fluorescence in situ hybridization (MERFISH2) data validated the association of this spatial epigenetic diversity with transcription and allowed the mapping of the DNA methylation and topology information into anatomical structures more precisely than our dissections. Furthermore, multi-scale chromatin conformation diversities occur in important neuronal genes, highly associated with DNA methylation and transcription changes. Brain-wide cell type comparison allowed us to build a regulatory model for each gene, linking transcription factors, DMRs, chromatin contacts, and downstream genes to establish regulatory networks. Finally, intragenic DNA methylation and chromatin conformation patterns predicted alternative gene isoform expression observed in a companion whole-brain SMART-seq3 dataset. Our study establishes the first brain-wide, single-cell resolution DNA methylome and 3D multi-omic atlas, providing an unparalleled resource for comprehending the mouse brain's cellular-spatial and regulatory genome diversity.
Collapse
Affiliation(s)
- Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Qiurui Zeng
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Anna Bartlett
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Bang-An Wang
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Peter Berube
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Wei Tian
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Mia Kenworthy
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jordan Altshul
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Joseph R Nery
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Huaming Chen
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Rosa G Castanon
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Songpeng Zu
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Yang Eric Li
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Jacinta Lucero
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Julia K Osteen
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Antonio Pinto-Duarte
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jasper Lee
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jon Rink
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Silvia Cho
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Nora Emerson
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Michael Nunn
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Carolyn O'Connor
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Chongyuan Luo
- Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA
| | - Jesse R Dixon
- Peptide Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Bing Ren
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Institute of Genomic Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - M Margarita Behrens
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Joseph R Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| |
Collapse
|
27
|
Li H, Long C, Hong Y, Luo L, Zuo Y. Characterizing Cellular Differentiation Potency and Waddington Landscape via Energy Indicator. RESEARCH (WASHINGTON, D.C.) 2023; 6:0118. [PMID: 37223479 PMCID: PMC10202187 DOI: 10.34133/research.0118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 03/20/2023] [Indexed: 05/25/2023]
Abstract
The precise characterization of cellular differentiation potency remains an open question, which is fundamentally important for deciphering the dynamics mechanism related to cell fate transition. We quantitatively evaluated the differentiation potency of different stem cells based on the Hopfield neural network (HNN). The results emphasized that cellular differentiation potency can be approximated by Hopfield energy values. We then profiled the Waddington energy landscape of embryogenesis and cell reprogramming processes. The energy landscape at single-cell resolution further confirmed that cell fate decision is progressively specified in a continuous process. Moreover, the transition of cells from one steady state to another in embryogenesis and cell reprogramming processes was dynamically simulated on the energy ladder. These two processes can be metaphorized as the motion of descending and ascending ladders, respectively. We further deciphered the dynamics of the gene regulatory network (GRN) for driving cell fate transition. Our study proposes a new energy indicator to quantitatively characterize cellular differentiation potency without prior knowledge, facilitating the further exploration of the potential mechanism of cellular plasticity.
Collapse
Affiliation(s)
- Hanshuang Li
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences,
Inner Mongolia University, Hohhot 010070, China
| | - Chunshen Long
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences,
Inner Mongolia University, Hohhot 010070, China
| | - Yan Hong
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences,
Inner Mongolia University, Hohhot 010070, China
| | - Liaofu Luo
- Department of Physics,
Inner Mongolia University, Hohhot 010070, China
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences,
Inner Mongolia University, Hohhot 010070, China
| |
Collapse
|
28
|
Li X, Lin Y, Xie C, Li Z, Chen M, Wang P, Zhou J. A Clustering Method Unifying Cell-Type Recognition and Subtype Identification for Tumor Heterogeneity Analysis. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:822-832. [PMID: 36044493 DOI: 10.1109/tcbb.2022.3203185] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The rapid development of single-cell technology has opened up a whole new perspective for identifying cell types in multicellular organisms and understanding the relationships between them. Distinguishing different cell types and subtypes can identify the components of different immune cells and different tumor clones in the tumor microenvironment, which is the basic work of tumor heterogeneity analysis and can help researchers understand the mechanism of tumor immune escape. Existing algorithms treat both cell types and subtypes as populations of cells with specific gene expression patterns, which is not conducive to accurate cell typing. For that, we proposed a cell similarity metric that unifies cell type recognition and subtype identification (UCRSI), with the assumption that selectively expressed genes represent differences in underlying cell type with on/off manner, while differences in expression level represent different cell subtype with more/less manner. Our method calculates these two kinds of differences separately, and then combines them using a consensus adjacency matrix, and finally cell typing is completed using spectral clustering algorithm. The results show that UCRSI can reconstruct expert annotation of single-cell RNA sequencing datasets more robustly than existing methods. And, UCRSI is useful for analyzing tumor heterogeneity and improving visualization of large-scale cell clustering.
Collapse
|
29
|
Frank M, Reid D. Dissecting symbiosis cell by cell. MOLECULAR PLANT 2023; 16:308-309. [PMID: 36588344 DOI: 10.1016/j.molp.2022.12.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 12/30/2022] [Accepted: 12/31/2022] [Indexed: 06/17/2023]
Affiliation(s)
- Manuel Frank
- Department of Molecular Biology and Genetics, Aarhus University, Universitetsbyen 81, 8000 Aarhus C, Denmark
| | - Dugald Reid
- Department of Animal, Plant and Soil Sciences, School of Agriculture, Biomedicine and Environment, La Trobe University, Melbourne, VIC 3086, Australia.
| |
Collapse
|
30
|
Li G, Song B, Singh H, Surya Prasath VB, Leighton Grimes H, Salomonis N. Decision level integration of unimodal and multimodal single cell data with scTriangulate. Nat Commun 2023; 14:406. [PMID: 36697445 PMCID: PMC9876931 DOI: 10.1038/s41467-023-36016-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 01/10/2023] [Indexed: 01/27/2023] Open
Abstract
Decisively delineating cell identities from uni- and multimodal single-cell datasets is complicated by diverse modalities, clustering methods, and reference atlases. We describe scTriangulate, a computational framework to mix-and-match multiple clustering results, modalities, associated algorithms, and resolutions to achieve an optimal solution. Rather than ensemble approaches which select the "consensus", scTriangulate picks the most stable solution through coalitional iteration. When evaluated on diverse multimodal technologies, scTriangulate outperforms alternative approaches to identify high-confidence cell-populations and modality-specific subtypes. Unlike existing integration strategies that rely on modality-specific joint embedding or geometric graphs, scTriangulate makes no assumption about the distributions of raw underlying values. As a result, this approach can solve unprecedented integration challenges, including the ability to automate reference cell-atlas construction, resolve clonal architecture within molecularly defined cell-populations and subdivide clusters to discover splicing-defined disease subtypes. scTriangulate is a flexible strategy for unified integration of single-cell or multimodal clustering solutions, from nearly unlimited sources.
Collapse
Affiliation(s)
- Guangyuan Li
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH, 45267, USA
| | - Baobao Song
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Immunology Graduate Program, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA
| | - Harinder Singh
- Center for Systems Immunology and the Department of Immunology, University of Pittsburgh, Pittsburgh, PA, USA
| | - V B Surya Prasath
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH, 45267, USA.,Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, OH, USA.,Department of Computer Science, University of Cincinnati, Cincinnati, OH 45221, USA
| | - H Leighton Grimes
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. .,Immunology Graduate Program, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA. .,Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, OH, USA.
| | - Nathan Salomonis
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. .,Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH, 45267, USA. .,Immunology Graduate Program, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA. .,Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, OH, USA. .,Department of Computer Science, University of Cincinnati, Cincinnati, OH 45221, USA.
| |
Collapse
|
31
|
Schaafsma E, Croteau W, Mohamed E, Nowak EC, Smits NC, Deng J, Sarde A, Webber CA, Rabadi D, Cheng C, Noelle R, Lines JL. VISTA Targeting of T-cell Quiescence and Myeloid Suppression Overcomes Adaptive Resistance. Cancer Immunol Res 2023; 11:38-55. [PMID: 36260656 PMCID: PMC10544831 DOI: 10.1158/2326-6066.cir-22-0116] [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: 02/15/2022] [Revised: 07/18/2022] [Accepted: 10/14/2022] [Indexed: 01/05/2023]
Abstract
V domain immunoglobulin suppressor of T-cell activation (VISTA) is a premier target for cancer treatment due to its broad expression in many cancer types and enhanced expression upon development of adaptive immune checkpoint resistance. In the CT26 colorectal cancer model, monotherapy of small tumors with anti-VISTA resulted in slowed tumor growth. In a combination therapy setting, large CT26 tumors showed complete adaptive resistance to anti-PD-1/CTLA-4, but inclusion of anti-VISTA led to rejection of half the tumors. Mechanisms of enhanced antitumor immunity were investigated using single-cell RNA sequencing (scRNA-seq), multiplex image analysis, and flow cytometry of the tumor immune infiltrate. In both treatment models, anti-VISTA upregulated stimulated antigen presentation pathways and reduced myeloid-mediated suppression. Imaging revealed an anti-VISTA stimulated increase in contacts between T cells and myeloid cells, further supporting the notion of increased antigen presentation. scRNA-seq of tumor-specific CD8+ T cells revealed that anti-VISTA therapy induced T-cell pathways highly distinct from and complementary to those induced by anti-PD-1 therapy. Whereas anti-CTLA-4/PD-1 expanded progenitor exhausted CD8+ T-cell subsets, anti-VISTA promoted costimulatory genes and reduced regulators of T-cell quiescence. Notably, this is the first report of a checkpoint regulator impacting CD8+ T-cell quiescence, and the first indication that quiescence may be a target in the context of T-cell exhaustion and in cancer. This study builds a foundation for all future studies on the role of anti-VISTA in the development of antitumor immunity and provides important mechanistic insights that strongly support use of anti-VISTA to overcome the adaptive resistance seen in contemporary treatments involving PD-1 and/or CTLA-4. See related Spotlight by Wei, p. 3.
Collapse
Affiliation(s)
- Evelien Schaafsma
- Department of Microbiology and Immunology, Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Walburga Croteau
- Department of Microbiology and Immunology, Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
| | - ElTanbouly Mohamed
- Department of Microbiology and Immunology, Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
- Laboratory of Molecular Immunology, The Rockefeller University, New York, NY, 10065, USA
| | - Elizabeth C. Nowak
- Department of Microbiology and Immunology, Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
| | - Nicole C. Smits
- Department of Microbiology and Immunology, Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
| | - Jie Deng
- University of California, Los Angeles. Department of Radiation Oncology
| | - Aurelien Sarde
- Department of Microbiology and Immunology, Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
| | | | - Dina Rabadi
- Department of Biological Sciences, Dartmouth College, Hanover, NH, USA
| | - Chao Cheng
- Department of Microbiology and Immunology, Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Randolph Noelle
- Department of Microbiology and Immunology, Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
| | - J. Louise Lines
- Department of Microbiology and Immunology, Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
| |
Collapse
|
32
|
Liu Y, Li HD, Xu Y, Liu YW, Peng X, Wang J. IsoCell: An Approach to Enhance Single Cell Clustering by Integrating Isoform-Level Expression Through Orthogonal Projection. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:465-475. [PMID: 35100120 DOI: 10.1109/tcbb.2022.3147193] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Single cell RNA sequencing (scRNA-seq) provides a powerful approach for profiling transcriptomes at single cell resolution. An essential application of scRNA-seq is the discovery of cell types with the aid of clustering analysis. Currently, existing single cell clustering methods are exclusively based on gene-level expression data, without considering alternative splicing information. It has been shown that alternative splicing has an important influence on biological processes such as cell differentiation and cell cycle. We therefore hypothesize that adding information about alternative splicing may help enhance single cell clustering. This motivates us to develop a way to integrate isoform-level expression and gene-level expression. We report an approach to enhance single cell clustering by integrating isoform-level expression through orthogonal projection. First, we construct an orthogonal projection matrix based on gene expression data. Second, isoforms are projected to the gene space to remove the redundant information between them. Third, isoform selection is performed based on the residual of the projected expression and the selected isoforms are combined with gene expression data for subsequent clustering. We applied our method to sixteen scRNA-seq datasets. We find that alternative splicing contains differential information among cell types and can be integrated to enhance single cell clustering. Compared with using only gene-level expression data, the integration of isoform-level expression leads to better clustering performances for most of the datasets. The integration of isoform-level expression also has potential in the detection of novel cell subgroups. Our study shows that integrating isoform and gene-level expression is a promising way to improve single cell clustering. The IsoCell R package is freely available at both Github (https://github.com/genemine/IsoCell) and Zenodo (https://zenodo.org/record/4395707).
Collapse
|
33
|
Single-cell microglial transcriptomics during demyelination defines a microglial state required for lytic carcass clearance. Mol Neurodegener 2022; 17:82. [PMID: 36514132 PMCID: PMC9746011 DOI: 10.1186/s13024-022-00584-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 11/21/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Microglia regulate the response to injury and disease in the brain and spinal cord. In white matter diseases microglia may cause demyelination. However, how microglia respond and regulate demyelination is not fully understood. METHODS To understand how microglia respond during demyelination, we fed mice cuprizone-a potent demyelinating agent-and assessed the dynamics of genetically fate-mapped microglia. We then used single-cell RNA sequencing to identify and track the microglial subpopulations that arise during demyelination. To understand how microglia contribute to the clearance of dead oligodendrocytes, we ablated microglia starting at the peak of cuprizone-induced cell death and used the viability dye acridine orange to monitor apoptotic and lytic cell morphologies after microglial ablation. Lastly, we treated serum-free primary microglial cultures to model distinct aspects of cuprizone-induced demyelination and assessed the response. RESULTS The cuprizone diet generated a robust microglial response by week 4 of the diet. Single-cell RNA sequencing at this time point revealed the presence of several cuprizone-associated microglia (CAM) clusters. These clusters expressed a transcriptomic signature indicative of cytokine regulation and reactive oxygen species production with altered lysosomal and metabolic changes consistent with ongoing phagocytosis. Using acridine orange to monitor apoptotic and lytic cell death after microglial ablation, we found that microglia preferentially phagocytose lytic carcasses. In culture, microglia exposed to lytic carcasses partially recapitulated the CAM state, suggesting that phagocytosis contributes to this distinct microglial state during cuprizone demyelination. CONCLUSIONS Microglia serve multiple roles during demyelination, yet their transcriptomic state resembles other neurodegenerative conditions. The phagocytosis of cellular debris is likely a universal cause for a common neurodegenerative microglial state.
Collapse
|
34
|
Quah FX, Hemberg M. SC3s: efficient scaling of single cell consensus clustering to millions of cells. BMC Bioinformatics 2022; 23:536. [PMID: 36503522 PMCID: PMC9743492 DOI: 10.1186/s12859-022-05085-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 11/25/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Today it is possible to profile the transcriptome of individual cells, and a key step in the analysis of these datasets is unsupervised clustering. For very large datasets, efficient algorithms are required to ensure that analyses can be conducted with reasonable time and memory requirements. RESULTS Here, we present a highly efficient k-means based approach, and we demonstrate that it scales favorably with the number of cells with regards to time and memory. CONCLUSIONS We have demonstrated that our streaming k-means clustering algorithm gives state-of-the-art performance while resource requirements scale favorably for up to 2 million cells.
Collapse
Affiliation(s)
- Fu Xiang Quah
- grid.10306.340000 0004 0606 5382Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA UK ,grid.5335.00000000121885934The Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN UK
| | - Martin Hemberg
- grid.10306.340000 0004 0606 5382Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA UK ,grid.38142.3c000000041936754XPresent Address: Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women’s Hospital, 75 Francis Street, Boston, MA 02115 USA
| |
Collapse
|
35
|
Single-cell genomics identifies distinct B1 cell developmental pathways and reveals aging-related changes in the B-cell receptor repertoire. Cell Biosci 2022; 12:57. [PMID: 35526067 PMCID: PMC9080186 DOI: 10.1186/s13578-022-00795-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 04/20/2022] [Indexed: 11/30/2022] Open
Abstract
Background B1 cells are self-renewing innate-like B lymphocytes that provide the first line of defense against pathogens. B1 cells primarily reside in the peritoneal cavity and are known to originate from various fetal tissues, yet their developmental pathways and the mechanisms underlying maintenance of B1 cells throughout adulthood remain unclear. Results We performed high-throughput single-cell analysis of the transcriptomes and B-cell receptor repertoires of peritoneal B cells of neonates, young adults, and elderly mice. Gene expression analysis of 31,718 peritoneal B cells showed that the neonate peritoneal cavity contained many B1 progenitors, and neonate B cell specific clustering revealed two trajectories of peritoneal B1 cell development, including pre-BCR dependent and pre-BCR independent pathways. We also detected profound age-related changes in B1 cell transcriptomes: clear difference in senescence genetic program was evident in differentially aged B1 cells, and we found an example that a B1 subset only present in the oldest mice was marked by expression of the fatty-acid receptor CD36. We also performed antibody gene sequencing of 15,967 peritoneal B cells from the three age groups and discovered that B1 cell aging was associated with clonal expansion and two B1 cell clones expanded in the aged mice had the same CDR-H3 sequence (AGDYDGYWYFDV) as a pathogenically linked cell type from a recent study of an atherosclerosis mouse model. Conclusions Beyond offering an unprecedent data resource to explore the cell-to-cell variation in B cells, our study has revealed that B1 precursor subsets are present in the neonate peritoneal cavity and dissected the developmental pathway of the precursor cells. Besides, this study has found the expression of CD36 on the B1 cells in the aged mice. And the single-cell B-cell receptor sequencing reveals B1 cell aging is associated with clonal expansion. Supplementary Information The online version contains supplementary material available at 10.1186/s13578-022-00795-6.
Collapse
|
36
|
Jafari E, Johnson T, Wang Y, Liu Y, Huang K, Wang Y. AIscEA: unsupervised integration of single-cell gene expression and chromatin accessibility via their biological consistency. Bioinformatics 2022; 38:5236-5244. [PMID: 36250795 PMCID: PMC9710555 DOI: 10.1093/bioinformatics/btac683] [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: 03/06/2022] [Revised: 10/07/2022] [Accepted: 10/14/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION The integrative analysis of single-cell gene expression and chromatin accessibility measurements is essential for revealing gene regulation, but it is one of the key challenges in computational biology. Gene expression and chromatin accessibility are measurements from different modalities, and no common features can be directly used to guide integration. Current state-of-the-art methods lack practical solutions for finding heterogeneous clusters. However, previous methods might not generate reliable results when cluster heterogeneity exists. More importantly, current methods lack an effective way to select hyper-parameters under an unsupervised setting. Therefore, applying computational methods to integrate single-cell gene expression and chromatin accessibility measurements remains difficult. RESULTS We introduce AIscEA-Alignment-based Integration of single-cell gene Expression and chromatin Accessibility-a computational method that integrates single-cell gene expression and chromatin accessibility measurements using their biological consistency. AIscEA first defines a ranked similarity score to quantify the biological consistency between cell clusters across measurements. AIscEA then uses the ranked similarity score and a novel permutation test to identify cluster alignment across measurements. AIscEA further utilizes graph alignment for the aligned cell clusters to align the cells across measurements. We compared AIscEA with the competing methods on several benchmark datasets and demonstrated that AIscEA is highly robust to the choice of hyper-parameters and can better handle the cluster heterogeneity problem. Furthermore, AIscEA significantly outperforms the state-of-the-art methods when integrating real-world SNARE-seq and scMultiome-seq datasets in terms of integration accuracy. AVAILABILITY AND IMPLEMENTATION AIscEA is available at https://figshare.com/articles/software/AIscEA_zip/21291135 on FigShare as well as {https://github.com/elhaam/AIscEA} onGitHub. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Elham Jafari
- Computer Science Department, Indiana University, Bloomington, IN 47408, USA
| | - Travis Johnson
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Yue Wang
- Department of Medical & Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Yunlong Liu
- Department of Medical & Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Kun Huang
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Yijie Wang
- Computer Science Department, Indiana University, Bloomington, IN 47408, USA
| |
Collapse
|
37
|
Xu L, Xue T, Ding W, Shen L. Comparison of scRNA-seq data analysis method combinations. Brief Funct Genomics 2022; 21:433-440. [PMID: 36124658 DOI: 10.1093/bfgp/elac027] [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: 04/13/2022] [Revised: 07/29/2022] [Accepted: 07/29/2022] [Indexed: 12/14/2022] Open
Abstract
Single-cell ribonucleic acid (RNA)-sequencing (scRNA-seq) data analysis refers to the use of appropriate methods to analyze the dataset generated by RNA-sequencing performed on the single-cell transcriptome. It usually contains three steps: normalization to eliminate the technical noise, dimensionality reduction to facilitate visual understanding and data compression and clustering to divide the data into several similarity-based clusters. In addition, the gene expression data contain a large number of zero counts. These zero counts are considered relevant to random dropout events induced by multiple factors in the sequencing experiments, such as low RNA input, and the stochastic nature of the gene expression pattern at the single-cell level. The zero counts can be eliminated only through the analysis of the scRNA-seq data, and although many methods have been proposed to this end, there is still a lack of research on the combined effect of existing methods. In this paper, we summarize the two kinds of normalization, two kinds of dimension reduction and three kinds of clustering methods widely used in the current mainstream scRNA-seq data analysis. Furthermore, we propose to combine these methods into 12 technology combinations, each with a whole set of scRNA-seq data analysis processes. We evaluated the proposed combinations using Goolam, a publicly available scRNA-seq, by comparing the final clustering results and found the most suitable collection scheme of these classic methods. Our results showed that using appropriate technology combinations can improve the efficiency and accuracy of the scRNA-seq data analysis. The combinations not only satisfy the basic requirements of noise reduction, dimension reduction and cell clustering but also ensure preserving the heterogeneity of cells in downstream analysis. The dataset, Goolam, used in the study can be obtained from the ArrayExpress database under the accession number E-MTAB-3321.
Collapse
|
38
|
Wei JR, Hao ZZ, Xu C, Huang M, Tang L, Xu N, Liu R, Shen Y, Teichmann SA, Miao Z, Liu S. Identification of visual cortex cell types and species differences using single-cell RNA sequencing. Nat Commun 2022; 13:6902. [PMID: 36371428 PMCID: PMC9653448 DOI: 10.1038/s41467-022-34590-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 10/31/2022] [Indexed: 11/13/2022] Open
Abstract
The primate neocortex exerts high cognitive ability and strong information processing capacity. Here, we establish a single-cell RNA sequencing dataset of 133,454 macaque visual cortical cells. It covers major cortical cell classes including 25 excitatory neuron types, 37 inhibitory neuron types and all glial cell types. We identified layer-specific markers including HPCAL1 and NXPH4, and also identified two cell types, an NPY-expressing excitatory neuron type that expresses the dopamine receptor D3 gene; and a primate specific activity-dependent OSTN + sensory neuron type. Comparisons of our dataset with humans and mice show that the gene expression profiles differ between species in relation to genes that are implicated in the synaptic plasticity and neuromodulation of excitatory neurons. The comparisons also revealed that glutamatergic neurons may be more diverse across species than GABAergic neurons and non-neuronal cells. These findings pave the way for understanding how the primary cortex fulfills the high-cognitive functions.
Collapse
Affiliation(s)
- Jia-Ru Wei
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Zhao-Zhe Hao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Chuan Xu
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Mengyao Huang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Lei Tang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Nana Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Ruifeng Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Yuhui Shen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
- Department of Physics, Cavendish Laboratory, University of Cambridge, Cambridge, UK.
| | - Zhichao Miao
- GMU-GIBH Joint School of Life Sciences, Guangzhou Laboratory, Guangzhou Medical University, Guangzhou, China.
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Cambridge, UK.
| | - Sheng Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.
- Guangdong Province Key Laboratory of Brain Function and Disease, Guangzhou, China.
| |
Collapse
|
39
|
Abstract
High dimensional single-cell analysis such as single cell and single nucleus RNA sequencing (sc/snRNAseq) are currently being widely applied to explore microglia diversity. The use of sc/snRNAseq provides a powerful and unbiased approach to deconvolve heterogeneous cellular populations. However, sc/snRNAseq and analyses pipelines are designed to find heterogeneity. Indeed, cellular heterogeneity is often the most frequently reported finding. In this Perspective, we consider the ubiquitous concept of heterogeneity focusing on its application to microglia research and its influence on the field of neuroimmunology. We suggest that a clear understanding of the semantic and biological implications of microglia heterogeneity is essential for mitigating confusion among researchers. Microglia “heterogeneity” is often described in the literature, but a clear understanding of what “heterogeneity” entails is essential to avoid confusion among researchers.
Collapse
|
40
|
Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity. Nat Commun 2022; 13:5455. [PMID: 36114209 PMCID: PMC9481560 DOI: 10.1038/s41467-022-33136-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 09/05/2022] [Indexed: 11/30/2022] Open
Abstract
Clustering is a powerful machine learning method for discovering similar patterns according to the proximity of elements in feature space. It is widely used in computer science, bioscience, geoscience, and economics. Although the state-of-the-art partition-based and connectivity-based clustering methods have been developed, weak connectivity and heterogeneous density in data impede their effectiveness. In this work, we propose a boundary-seeking Clustering algorithm using the local Direction Centrality (CDC). It adopts a density-independent metric based on the distribution of K-nearest neighbors (KNNs) to distinguish between internal and boundary points. The boundary points generate enclosed cages to bind the connections of internal points, thereby preventing cross-cluster connections and separating weakly-connected clusters. We demonstrate the validity of CDC by detecting complex structured clusters in challenging synthetic datasets, identifying cell types from single-cell RNA sequencing (scRNA-seq) and mass cytometry (CyTOF) data, recognizing speakers on voice corpuses, and testifying on various types of real-world benchmarks. Clustering is a powerful machine learning method for discovering similar patterns according to the proximity of elements in feature space. Here the authors propose a local direction centrality clustering algorithm that copes with heterogeneous density and weak connectivity issues.
Collapse
|
41
|
Huang W, Xu Q, Su J, Tang L, Hao ZZ, Xu C, Liu R, Shen Y, Sang X, Xu N, Tie X, Miao Z, Liu X, Xu Y, Liu F, Liu Y, Liu S. Linking transcriptomes with morphological and functional phenotypes in mammalian retinal ganglion cells. Cell Rep 2022; 40:111322. [PMID: 36103830 DOI: 10.1016/j.celrep.2022.111322] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 05/19/2022] [Accepted: 08/17/2022] [Indexed: 11/03/2022] Open
Abstract
Retinal ganglion cells (RGCs) are the brain's gateway to the visual world. They can be classified into different types on the basis of their electrophysiological, transcriptomic, or morphological characteristics. Here, we characterize the transcriptomic, morphological, and functional features of 472 high-quality RGCs using Patch sequencing (Patch-seq), providing functional and morphological annotation of many transcriptomic-defined cell types of a previously established RGC atlas. We show a convergence of different modalities in defining the RGC identity and reveal the degree of correspondence for well-characterized cell types across multimodal data. Moreover, we complement some RGC types with detailed morphological and functional properties. We also identify differentially expressed genes among ON, OFF, and ON-OFF RGCs such as Vat1l, Slitrk6, and Lmo7, providing candidate marker genes for functional studies. Our research suggests that the molecularly distinct clusters may also differ in their roles of encoding visual information.
Collapse
Affiliation(s)
- Wanjing Huang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, China
| | - Qiang Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, China
| | - Jing Su
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, China
| | - Lei Tang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, China
| | - Zhao-Zhe Hao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, China
| | - Chuan Xu
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Ruifeng Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, China
| | - Yuhui Shen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, China
| | - Xuan Sang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, China
| | - Nana Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, China
| | - Xiaoxiu Tie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, China
| | - Zhichao Miao
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Cambridge CB10 1SD, UK
| | - Xialin Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, China
| | - Ying Xu
- Guangdong-Hongkong-Macau Institute of CNS Regeneration, Jinan University, Guangzhou, 510632, China; Key Laboratory of CNS Regeneration (Jinan University), Ministry of Education, Guangzhou, 510632, China
| | - Feng Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, China.
| | - Yizhi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, China; Research Unit of Ocular Development and Regeneration, Chinese Academy of Medical Sciences, Beijing 100085, China.
| | - Sheng Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, China; Guangdong Province Key Laboratory of Brain Function and Disease, Guangzhou 510080, China.
| |
Collapse
|
42
|
Doyle JJ. Cell types as species: Exploring a metaphor. FRONTIERS IN PLANT SCIENCE 2022; 13:868565. [PMID: 36072310 PMCID: PMC9444152 DOI: 10.3389/fpls.2022.868565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 07/29/2022] [Indexed: 06/05/2023]
Abstract
The concept of "cell type," though fundamental to cell biology, is controversial. Cells have historically been classified into types based on morphology, physiology, or location. More recently, single cell transcriptomic studies have revealed fine-scale differences among cells with similar gross phenotypes. Transcriptomic snapshots of cells at various stages of differentiation, and of cells under different physiological conditions, have shown that in many cases variation is more continuous than discrete, raising questions about the relationship between cell type and cell state. Some researchers have rejected the notion of fixed types altogether. Throughout the history of discussions on cell type, cell biologists have compared the problem of defining cell type with the interminable and often contentious debate over the definition of arguably the most important concept in systematics and evolutionary biology, "species." In the last decades, systematics, like cell biology, has been transformed by the increasing availability of molecular data, and the fine-grained resolution of genetic relationships have generated new ideas about how that variation should be classified. There are numerous parallels between the two fields that make exploration of the "cell types as species" metaphor timely. These parallels begin with philosophy, with discussion of both cell types and species as being either individuals, groups, or something in between (e.g., homeostatic property clusters). In each field there are various different types of lineages that form trees or networks that can (and in some cases do) provide criteria for grouping. Developing and refining models for evolutionary divergence of species and for cell type differentiation are parallel goals of the two fields. The goal of this essay is to highlight such parallels with the hope of inspiring biologists in both fields to look for new solutions to similar problems outside of their own field.
Collapse
|
43
|
Li D, Ding J, Bar-Joseph Z. Unsupervised cell functional annotation for single-cell RNA-seq. Genome Res 2022; 32:gr.276609.122. [PMID: 35764397 PMCID: PMC9528981 DOI: 10.1101/gr.276609.122] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 06/10/2022] [Indexed: 11/25/2022]
Abstract
One of the first steps in the analysis of single-cell RNA sequencing (scRNA-seq) data is the assignment of cell types. Although a number of supervised methods have been developed for this, in most cases such assignment is performed by first clustering cells in low-dimensional space and then assigning cell types to different clusters. To overcome noise and to improve cell type assignments, we developed UNIFAN, a neural network method that simultaneously clusters and annotates cells using known gene sets. UNIFAN combines both low-dimensional representation for all genes and cell-specific gene set activity scores to determine the clustering. We applied UNIFAN to human and mouse scRNA-seq data sets from several different organs. We show, by using knowledge about gene sets, that UNIFAN greatly outperforms prior methods developed for clustering scRNA-seq data. The gene sets assigned by UNIFAN to different clusters provide strong evidence for the cell type that is represented by this cluster, making annotations easier.
Collapse
Affiliation(s)
- Dongshunyi Li
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Jun Ding
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, Quebec, H4A 3J1, Canada
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| |
Collapse
|
44
|
Single-cell transcriptomics of adult macaque hippocampus reveals neural precursor cell populations. Nat Neurosci 2022; 25:805-817. [PMID: 35637371 DOI: 10.1038/s41593-022-01073-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 04/07/2022] [Indexed: 12/13/2022]
Abstract
The extent to which neurogenesis occurs in adult primates remains controversial. In this study, using an optimized single-cell RNA sequencing pipeline, we profiled 207,785 cells from the adult macaque hippocampus and identified 34 cell populations comprising all major hippocampal cell types. Analysis of their gene expression, specification trajectories and gene regulatory networks revealed the presence of all key neurogenic precursor cell populations, including a heterogeneous pool of radial glia-like cells (RGLs), intermediate progenitor cells (IPCs) and neuroblasts. We identified HMGB2 as a novel IPC marker. Comparison with mouse single-cell transcriptomic data revealed differences in neurogenic processes between species. We confirmed that neurogenesis is recapitulated in ex vivo neurosphere cultures from adult primates, further supporting the existence of neural precursor cells (NPCs) that are able to proliferate and differentiate. Our large-scale dataset provides a comprehensive adult neurogenesis atlas for primates.
Collapse
|
45
|
Ren J, Zhang Q, Zhou Y, Hu Y, Lyu X, Fang H, Yang J, Yu R, Shi X, Li Q. A downsampling Method Enables Robust Clustering and Integration of Single-Cell Transcriptome Data. J Biomed Inform 2022; 130:104093. [DOI: 10.1016/j.jbi.2022.104093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 04/06/2022] [Accepted: 05/03/2022] [Indexed: 11/27/2022]
|
46
|
He S, Dou L, Li X, Zhang Y. Review of bioinformatics in Azheimer's Disease Research. Comput Biol Med 2022; 143:105269. [PMID: 35158118 DOI: 10.1016/j.compbiomed.2022.105269] [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: 01/06/2022] [Revised: 01/21/2022] [Accepted: 01/23/2022] [Indexed: 01/05/2023]
Abstract
Alzheimer's disease (AD) is a severe neurodegenerative disease with slow course of onset and deterioration with time. With the speedup of global aging, AD has become a disease that seriously threatens the physical health of the elderly; therefore, the effective prevention and treatments of AD is an extremely important area of study for researchers and clinicians. Rapid technological developments have promoted the analysis of various kinds of complex data sets using machine learning methods. The common machine learning algorithms, such as Lasso, SVM and Random Forest, are very important in AD research. To help accelerate AD-related research, we review some recent research progress on Alzheimer's disease, including database, image analysis, gene expression, etc., which can provide AD researchers with more comprehensive research methods.
Collapse
Affiliation(s)
- Shida He
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China; Department of Computer Science, University of Tsukuba, Japan
| | - Lijun Dou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China; School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Xuehong Li
- Beidahuang Industry Group General Hospital, Harbin, China.
| | - Ying Zhang
- Department of Anesthesiology, Hospital (T.C.M) Affiliated To Southwest Medical University, Luzhou, China.
| |
Collapse
|
47
|
Xu Y, Baumgart SJ, Stegmann CM, Hayat S. MACA: marker-based automatic cell-type annotation for single-cell expression data. Bioinformatics 2022; 38:1756-1760. [PMID: 34935911 DOI: 10.1093/bioinformatics/btab840] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 10/07/2021] [Accepted: 12/17/2021] [Indexed: 02/03/2023] Open
Abstract
SUMMARY Accurately identifying cell types is a critical step in single-cell sequencing analyses. Here, we present marker-based automatic cell-type annotation (MACA), a new tool for annotating single-cell transcriptomics datasets. We developed MACA by testing four cell-type scoring methods with two public cell-marker databases as reference in six single-cell studies. MACA compares favorably to four existing marker-based cell-type annotation methods in terms of accuracy and speed. We show that MACA can annotate a large single-nuclei RNA-seq study in minutes on human hearts with ∼290K cells. MACA scales easily to large datasets and can broadly help experts to annotate cell types in single-cell transcriptomics datasets, and we envision MACA provides a new opportunity for integration and standardization of cell-type annotation across multiple datasets. AVAILABILITY AND IMPLEMENTATION MACA is written in python and released under GNU General Public License v3.0. The source code is available at https://github.com/ImXman/MACA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Yang Xu
- Bayer-Broad Joint Precision Cardiology Lab, 75 Ames Street, Cambridge, MA 02142, USA
| | - Simon J Baumgart
- Bayer-Broad Joint Precision Cardiology Lab, 75 Ames Street, Cambridge, MA 02142, USA
| | - Christian M Stegmann
- Bayer-Broad Joint Precision Cardiology Lab, 75 Ames Street, Cambridge, MA 02142, USA
| | - Sikander Hayat
- Bayer-Broad Joint Precision Cardiology Lab, 75 Ames Street, Cambridge, MA 02142, USA
| |
Collapse
|
48
|
Tang H, Yu X, Liu R, Zeng T. Vec2image: an explainable artificial intelligence model for the feature representation and classification of high-dimensional biological data by vector-to-image conversion. Brief Bioinform 2022; 23:6518046. [PMID: 35106553 PMCID: PMC8921615 DOI: 10.1093/bib/bbab584] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/06/2021] [Accepted: 12/20/2021] [Indexed: 01/05/2023] Open
Abstract
Feature representation and discriminative learning are proven models and technologies in artificial intelligence fields; however, major challenges for machine learning on large biological datasets are learning an effective model with mechanistical explanation on the model determination and prediction. To satisfy such demands, we developed Vec2image, an explainable convolutional neural network framework for characterizing the feature engineering, feature selection and classifier training that is mainly based on the collaboration of principal component coordinate conversion, deep residual neural networks and embedded k-nearest neighbor representation on pseudo images of high-dimensional biological data, where the pseudo images represent feature measurements and feature associations simultaneously. Vec2image has achieved better performance compared with other popular methods and illustrated its efficiency on feature selection in cell marker identification from tissue-specific single-cell datasets. In particular, in a case study on type 2 diabetes (T2D) by multiple human islet scRNA-seq datasets, Vec2image first displayed robust performance on T2D classification model building across different datasets, then a specific Vec2image model was trained to accurately recognize the cell state and efficiently rank feature genes relevant to T2D which uncovered potential T2D cellular pathogenesis; and next the cell activity changes, cell composition imbalances and cell–cell communication dysfunctions were associated to our finding T2D feature genes from both population-shared and individual-specific perspectives. Collectively, Vec2image is a new and efficient explainable artificial intelligence methodology that can be widely applied in human-readable classification and prediction on the basis of pseudo image representation of biological deep sequencing data.
Collapse
Affiliation(s)
- Hui Tang
- School of Mathematics, South China University of Technology, Guangzhou, 510640, China
| | - Xiangtian Yu
- Clinical Research Center, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China
| | - Rui Liu
- School of Mathematics, South China University of Technology, Guangzhou, 510640, China.,Pazhou Lab, Guangzhou 510330, China
| | - Tao Zeng
- Guangzhou Laboratory, Guangzhou, China.,Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| |
Collapse
|
49
|
Li X, Garg M, Jia T, Liao Q, Yuan L, Li M, Wu Z, Wu W, Bi Y, George N, Papatheodorou I, Brazma A, Luo H, Fang S, Miao Z, Shu Y. Single-Cell Analysis Reveals the Immune Characteristics of Myeloid Cells and Memory T Cells in Recovered COVID-19 Patients With Different Severities. Front Immunol 2022; 12:781432. [PMID: 35046942 PMCID: PMC8762286 DOI: 10.3389/fimmu.2021.781432] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 12/03/2021] [Indexed: 12/20/2022] Open
Abstract
Despite many studies on the immune characteristics of Coronavirus disease 2019 (COVID-19) patients in the progression stage, a detailed understanding of pertinent immune cells in recovered patients is lacking. We performed single-cell RNA sequencing on samples from recovered COVID-19 patients and healthy controls. We created a comprehensive immune landscape with more than 260,000 peripheral blood mononuclear cells (PBMCs) from 41 samples by integrating our dataset with previously reported datasets, which included samples collected between 27 and 47 days after symptom onset. According to our large-scale single-cell analysis, recovered patients, who had severe symptoms (severe/critical recovered), still exhibited peripheral immune disorders 1-2 months after symptom onset. Specifically, in these severe/critical recovered patients, human leukocyte antigen (HLA) class II and antigen processing pathways were downregulated in both CD14 monocytes and dendritic cells compared to healthy controls, while the proportion of CD14 monocytes increased. These may lead to the downregulation of T-cell differentiation pathways in memory T cells. However, in the mild/moderate recovered patients, the proportion of plasmacytoid dendritic cells increased compared to healthy controls, accompanied by the upregulation of HLA-DRA and HLA-DRB1 in both CD14 monocytes and dendritic cells. In addition, T-cell differentiation regulation and memory T cell-related genes FOS, JUN, CD69, CXCR4, and CD83 were upregulated in the mild/moderate recovered patients. Further, the immunoglobulin heavy chain V3-21 (IGHV3-21) gene segment was preferred in B-cell immune repertoires in severe/critical recovered patients. Collectively, we provide a large-scale single-cell atlas of the peripheral immune response in recovered COVID-19 patients.
Collapse
Affiliation(s)
- Xu Li
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Manik Garg
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Tingting Jia
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Qijun Liao
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Lifang Yuan
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Mao Li
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Zhengyu Wu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Weihua Wu
- Major Infectious Disease Control Key Laboratory, Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Yalan Bi
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Nancy George
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Irene Papatheodorou
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Huanle Luo
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Shisong Fang
- Major Infectious Disease Control Key Laboratory, Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Zhichao Miao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom.,Translational Research Institute of Brain and Brain-Like Intelligence and Department of Anesthesiology, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, China
| | - Yuelong Shu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| |
Collapse
|
50
|
He J, Kleyman M, Chen J, Alikaya A, Rothenhoefer KM, Ozturk BE, Wirthlin M, Bostan AC, Fish K, Byrne LC, Pfenning AR, Stauffer WR. Transcriptional and anatomical diversity of medium spiny neurons in the primate striatum. Curr Biol 2021; 31:5473-5486.e6. [PMID: 34727523 PMCID: PMC9359438 DOI: 10.1016/j.cub.2021.10.015] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 09/17/2021] [Accepted: 10/06/2021] [Indexed: 10/20/2022]
Abstract
Medium spiny neurons (MSNs) constitute the vast majority of striatal neurons and the principal interface between dopamine reward signals and functionally diverse cortico-basal ganglia circuits. Information processing in these circuits is dependent on distinct MSN types: cell types that are traditionally defined according to their projection targets or dopamine receptor expression. Single-cell transcriptional studies have revealed greater MSN heterogeneity than predicted by traditional circuit models, but the transcriptional landscape in the primate striatum remains unknown. Here, we set out to establish molecular definitions for MSN subtypes in Rhesus monkeys and to explore the relationships between transcriptionally defined subtypes and anatomical subdivisions of the striatum. Our results suggest at least nine MSN subtypes, including dorsal striatum subtypes associated with striosome and matrix compartments, ventral striatum subtypes associated with the nucleus accumbens shell and olfactory tubercle, and an MSN-like cell type restricted to μ-opioid receptor rich islands in the ventral striatum. Although each subtype was demarcated by discontinuities in gene expression, continuous variation within subtypes defined gradients corresponding to anatomical locations and, potentially, functional specializations. These results lay the foundation for achieving cell-type-specific transgenesis in the primate striatum and provide a blueprint for investigating circuit-specific information processing.
Collapse
Affiliation(s)
- Jing He
- Department of Neurobiology, Systems Neuroscience Center, Brain Institute, Center for Neuroscience, Center for the Neural Basis of Cognition, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15213, USA
| | - Michael Kleyman
- Department of Computational Biology, School of Computer Science, Neuroscience Institute, Center for the Neural Basis of Cognition, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
| | - Jianjiao Chen
- Department of Neurobiology, Systems Neuroscience Center, Brain Institute, Center for Neuroscience, Center for the Neural Basis of Cognition, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15213, USA
| | - Aydin Alikaya
- Department of Neurobiology, Systems Neuroscience Center, Brain Institute, Center for Neuroscience, Center for the Neural Basis of Cognition, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15213, USA
| | - Kathryn M Rothenhoefer
- Department of Neurobiology, Systems Neuroscience Center, Brain Institute, Center for Neuroscience, Center for the Neural Basis of Cognition, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15213, USA
| | - Bilge Esin Ozturk
- Department of Ophthalmology, Brain Institute, Center for Neuroscience, Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Morgan Wirthlin
- Department of Computational Biology, School of Computer Science, Neuroscience Institute, Center for the Neural Basis of Cognition, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
| | - Andreea C Bostan
- Department of Neurobiology, Systems Neuroscience Center, Brain Institute, Center for Neuroscience, Center for the Neural Basis of Cognition, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15213, USA
| | - Kenneth Fish
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Leah C Byrne
- Department of Ophthalmology, Brain Institute, Center for Neuroscience, Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Andreas R Pfenning
- Department of Computational Biology, School of Computer Science, Neuroscience Institute, Center for the Neural Basis of Cognition, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.
| | - William R Stauffer
- Department of Neurobiology, Systems Neuroscience Center, Brain Institute, Center for Neuroscience, Center for the Neural Basis of Cognition, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15213, USA.
| |
Collapse
|