1
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Peidli S, Green TD, Shen C, Gross T, Min J, Garda S, Yuan B, Schumacher LJ, Taylor-King JP, Marks DS, Luna A, Blüthgen N, Sander C. scPerturb: harmonized single-cell perturbation data. Nat Methods 2024; 21:531-540. [PMID: 38279009 DOI: 10.1038/s41592-023-02144-y] [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: 01/28/2023] [Accepted: 12/04/2023] [Indexed: 01/28/2024]
Abstract
Analysis across a growing number of single-cell perturbation datasets is hampered by poor data interoperability. To facilitate development and benchmarking of computational methods, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform quality control pipelines and harmonize feature annotations. The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations. The perturbation-response datasets and E-statistics computation software are publicly available at scperturb.org. This work provides an information resource for researchers working with single-cell perturbation data and recommendations for experimental design, including optimal cell counts and read depth.
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Affiliation(s)
- Stefan Peidli
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität, Berlin, Germany.
- Institute of Biology, Humboldt-Universität, Berlin, Germany.
| | - Tessa D Green
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Ciyue Shen
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | | | - Joseph Min
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Samuele Garda
- Institute of Biology, Humboldt-Universität, Berlin, Germany
- Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Bo Yuan
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Linus J Schumacher
- Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, UK
| | | | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Augustin Luna
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
- Computational Biology Branch, National Library of Medicine and Developmental Therapeutics Branch, National Cancer Institute, Bethesda, MD, USA.
| | - Nils Blüthgen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität, Berlin, Germany.
- Institute of Biology, Humboldt-Universität, Berlin, Germany.
| | - Chris Sander
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
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2
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Gottschlich A, Thomas M, Grünmeier R, Lesch S, Rohrbacher L, Igl V, Briukhovetska D, Benmebarek MR, Vick B, Dede S, Müller K, Xu T, Dhoqina D, Märkl F, Robinson S, Sendelhofert A, Schulz H, Umut Ö, Kavaka V, Tsiverioti CA, Carlini E, Nandi S, Strzalkowski T, Lorenzini T, Stock S, Müller PJ, Dörr J, Seifert M, Cadilha BL, Brabenec R, Röder N, Rataj F, Nüesch M, Modemann F, Wellbrock J, Fiedler W, Kellner C, Beltrán E, Herold T, Paquet D, Jeremias I, von Baumgarten L, Endres S, Subklewe M, Marr C, Kobold S. Single-cell transcriptomic atlas-guided development of CAR-T cells for the treatment of acute myeloid leukemia. Nat Biotechnol 2023; 41:1618-1632. [PMID: 36914885 PMCID: PMC7615296 DOI: 10.1038/s41587-023-01684-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 01/20/2023] [Indexed: 03/16/2023]
Abstract
Chimeric antigen receptor T cells (CAR-T cells) have emerged as a powerful treatment option for individuals with B cell malignancies but have yet to achieve success in treating acute myeloid leukemia (AML) due to a lack of safe targets. Here we leveraged an atlas of publicly available RNA-sequencing data of over 500,000 single cells from 15 individuals with AML and tissue from 9 healthy individuals for prediction of target antigens that are expressed on malignant cells but lacking on healthy cells, including T cells. Aided by this high-resolution, single-cell expression approach, we computationally identify colony-stimulating factor 1 receptor and cluster of differentiation 86 as targets for CAR-T cell therapy in AML. Functional validation of these established CAR-T cells shows robust in vitro and in vivo efficacy in cell line- and human-derived AML models with minimal off-target toxicity toward relevant healthy human tissues. This provides a strong rationale for further clinical development.
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Affiliation(s)
- Adrian Gottschlich
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
- Department of Medicine III, University Hospital, LMU Munich, Munich, Germany
| | - Moritz Thomas
- Institute of AI for Health, Helmholtz Munich, Neuherberg, Germany
- School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Ruth Grünmeier
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Stefanie Lesch
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Lisa Rohrbacher
- Department of Medicine III, University Hospital, LMU Munich, Munich, Germany
- Laboratory for Translational Cancer Immunology, Gene Center, LMU Munich, Munich, Germany
| | - Veronika Igl
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Daria Briukhovetska
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Mohamed-Reda Benmebarek
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Binje Vick
- Research Unit Apoptosis in Hematopoietic Stem Cells, Helmholtz Munich, German Research Center for Environmental Health (HMGU), Munich, Germany
- Department of Pediatrics, University Hospital, LMU Munich, Munich, Germany
| | - Sertac Dede
- Department of Neurology, University Hospital, LMU Munich, Munich, Germany
| | - Katharina Müller
- Department of Neurology, University Hospital, LMU Munich, Munich, Germany
| | - Tao Xu
- Department of Neurology, University Hospital, LMU Munich, Munich, Germany
| | - Dario Dhoqina
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Florian Märkl
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Sophie Robinson
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | | | - Heiko Schulz
- Institute of Pathology, LMU Munich, Munich, Germany
| | - Öykü Umut
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Vladyslav Kavaka
- Institute of Clinical Neuroimmunology, University Hospital, LMU Munich, Munich, Germany
- Biomedical Center (BMC), Faculty of Medicine, LMU Munich, Martinsried, Germany
| | - Christina Angeliki Tsiverioti
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Emanuele Carlini
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Sayantan Nandi
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Thaddäus Strzalkowski
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Theo Lorenzini
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Sophia Stock
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- Department of Medicine III, University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Philipp Jie Müller
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Janina Dörr
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Matthias Seifert
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Bruno L Cadilha
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Ruben Brabenec
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- Institute of AI for Health, Helmholtz Munich, Neuherberg, Germany
| | - Natalie Röder
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Felicitas Rataj
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Manuel Nüesch
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Franziska Modemann
- Department of Oncology, Hematology and Bone Marrow Transplantation with Section Pneumology, Hubertus Wald University Cancer Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Mildred Scheel Cancer Career Center, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf Hamburg, Hamburg, Germany
| | - Jasmin Wellbrock
- Department of Oncology, Hematology and Bone Marrow Transplantation with Section Pneumology, Hubertus Wald University Cancer Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Walter Fiedler
- Department of Oncology, Hematology and Bone Marrow Transplantation with Section Pneumology, Hubertus Wald University Cancer Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Kellner
- Division of Transfusion Medicine, Cell Therapeutics and Haemostaseology, University Hospital, LMU Munich, Munich, Germany
| | - Eduardo Beltrán
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Institute of Clinical Neuroimmunology, University Hospital, LMU Munich, Munich, Germany
- Biomedical Center (BMC), Faculty of Medicine, LMU Munich, Martinsried, Germany
| | - Tobias Herold
- Department of Medicine III, University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Dominik Paquet
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Irmela Jeremias
- Research Unit Apoptosis in Hematopoietic Stem Cells, Helmholtz Munich, German Research Center for Environmental Health (HMGU), Munich, Germany
- Department of Pediatrics, University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Louisa von Baumgarten
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
- Department of Neurosurgery, LMU Munich, Munich, Germany
| | - Stefan Endres
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
- Einheit für Klinische Pharmakologie (EKLiP), Helmholtz Munich, Research Center for Environmental Health (HMGU), Neuherberg, Germany
| | - Marion Subklewe
- Department of Medicine III, University Hospital, LMU Munich, Munich, Germany
- Laboratory for Translational Cancer Immunology, Gene Center, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Carsten Marr
- Institute of AI for Health, Helmholtz Munich, Neuherberg, Germany
| | - Sebastian Kobold
- Division of Clinical Pharmacology, University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany.
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.
- Einheit für Klinische Pharmakologie (EKLiP), Helmholtz Munich, Research Center for Environmental Health (HMGU), Neuherberg, Germany.
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3
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Fischer F, Fischer DS, Biederstedt E, Villani AC, Theis FJ. Scaling cross-tissue single-cell annotation models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.07.561331. [PMID: 37873298 PMCID: PMC10592700 DOI: 10.1101/2023.10.07.561331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Identifying cellular identities (both novel and well-studied) is one of the key use cases in single-cell transcriptomics. While supervised machine learning has been leveraged to automate cell annotation predictions for some time, there has been relatively little progress both in scaling neural networks to large data sets and in constructing models that generalize well across diverse tissues and biological contexts up to whole organisms. Here, we propose scTab, an automated, feature-attention-based cell type prediction model specific to tabular data, and train it using a novel data augmentation scheme across a large corpus of single-cell RNA-seq observations (22.2 million human cells in total). In addition, scTab leverages deep ensembles for uncertainty quantification. Moreover, we account for ontological relationships between labels in the model evaluation to accommodate for differences in annotation granularity across datasets. On this large-scale corpus, we show that cross-tissue annotation requires nonlinear models and that the performance of scTab scales in terms of training dataset size as well as model size - demonstrating the advantage of scTab over current state-of-the-art linear models in this context. Additionally, we show that the proposed data augmentation schema improves model generalization. In summary, we introduce a de novo cell type prediction model for single-cell RNA-seq data that can be trained across a large-scale collection of curated datasets from a diverse selection of human tissues and demonstrate the benefits of using deep learning methods in this paradigm. Our codebase, training data, and model checkpoints are publicly available at https://github.com/theislab/scTab to further enable rigorous benchmarks of foundation models for single-cell RNA-seq data.
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Affiliation(s)
- Felix Fischer
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
| | - David S. Fischer
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany
- Eric and Wendy Schmidt Center at the Broad Institute, Cambridge, MA, 02142, USA
| | - Evan Biederstedt
- Department of Biomedical Informatics, Harvard Medical School, Boston MA 02115
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Charlestown, MA 02129
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Boston, MA, 02114, USA
| | - Alexandra-Chloé Villani
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Charlestown, MA 02129
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Boston, MA, 02114, USA
- Department of Medicine, Harvard Medical School, Boston MA 02115
| | - Fabian J. Theis
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
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4
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Hrovatin K, Bastidas-Ponce A, Bakhti M, Zappia L, Büttner M, Salinno C, Sterr M, Böttcher A, Migliorini A, Lickert H, Theis FJ. Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas. Nat Metab 2023; 5:1615-1637. [PMID: 37697055 PMCID: PMC10513934 DOI: 10.1038/s42255-023-00876-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 07/26/2023] [Indexed: 09/13/2023]
Abstract
Although multiple pancreatic islet single-cell RNA-sequencing (scRNA-seq) datasets have been generated, a consensus on pancreatic cell states in development, homeostasis and diabetes as well as the value of preclinical animal models is missing. Here, we present an scRNA-seq cross-condition mouse islet atlas (MIA), a curated resource for interactive exploration and computational querying. We integrate over 300,000 cells from nine scRNA-seq datasets consisting of 56 samples, varying in age, sex and diabetes models, including an autoimmune type 1 diabetes model (NOD), a glucotoxicity/lipotoxicity type 2 diabetes model (db/db) and a chemical streptozotocin β-cell ablation model. The β-cell landscape of MIA reveals new cell states during disease progression and cross-publication differences between previously suggested marker genes. We show that β-cells in the streptozotocin model transcriptionally correlate with those in human type 2 diabetes and mouse db/db models, but are less similar to human type 1 diabetes and mouse NOD β-cells. We also report pathways that are shared between β-cells in immature, aged and diabetes models. MIA enables a comprehensive analysis of β-cell responses to different stressors, providing a roadmap for the understanding of β-cell plasticity, compensation and demise.
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Affiliation(s)
- Karin Hrovatin
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Aimée Bastidas-Ponce
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Medical Faculty, Technical University of Munich, Munich, Germany
| | - Mostafa Bakhti
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Luke Zappia
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, Garching, Germany
| | - Maren Büttner
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
| | - Ciro Salinno
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Medical Faculty, Technical University of Munich, Munich, Germany
| | - Michael Sterr
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Anika Böttcher
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Adriana Migliorini
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- McEwen Stem Cell Institute, University Health Network (UHN), Toronto, Ontario, Canada
| | - Heiko Lickert
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany.
- German Center for Diabetes Research (DZD), Neuherberg, Germany.
- Medical Faculty, Technical University of Munich, Munich, Germany.
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
- Department of Mathematics, Technical University of Munich, Garching, Germany.
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5
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Dudchenko O, Ordovas-Montanes J, Bingle CD. Respiratory epithelial cell types, states and fates in the era of single-cell RNA-sequencing. Biochem J 2023; 480:921-939. [PMID: 37410389 PMCID: PMC10422933 DOI: 10.1042/bcj20220572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/07/2023]
Abstract
Standalone and consortia-led single-cell atlases of healthy and diseased human airways generated with single-cell RNA-sequencing (scRNA-seq) have ushered in a new era in respiratory research. Numerous discoveries, including the pulmonary ionocyte, potentially novel cell fates, and a diversity of cell states among common and rare epithelial cell types have highlighted the extent of cellular heterogeneity and plasticity in the respiratory tract. scRNA-seq has also played a pivotal role in our understanding of host-virus interactions in coronavirus disease 2019 (COVID-19). However, as our ability to generate large quantities of scRNA-seq data increases, along with a growing number of scRNA-seq protocols and data analysis methods, new challenges related to the contextualisation and downstream applications of insights are arising. Here, we review the fundamental concept of cellular identity from the perspective of single-cell transcriptomics in the respiratory context, drawing attention to the need to generate reference annotations and to standardise the terminology used in literature. Findings about airway epithelial cell types, states and fates obtained from scRNA-seq experiments are compared and contrasted with information accumulated through the use of conventional methods. This review attempts to discuss major opportunities and to outline some of the key limitations of the modern-day scRNA-seq that need to be addressed to enable efficient and meaningful integration of scRNA-seq data from different platforms and studies, with each other as well as with data from other high-throughput sequencing-based genomic, transcriptomic and epigenetic analyses.
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Affiliation(s)
- Oleksandr Dudchenko
- Department of Infection, Immunity and Cardiovascular Disease, The Medical School, University of Sheffield, Sheffield, South Yorkshire, U.K
| | - Jose Ordovas-Montanes
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, MA, U.S.A
- Programme in Immunology, Harvard Medical School, Boston, MA, U.S.A
| | - Colin D. Bingle
- Department of Infection, Immunity and Cardiovascular Disease, The Medical School, University of Sheffield, Sheffield, South Yorkshire, U.K
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6
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Virshup I, Bredikhin D, Heumos L, Palla G, Sturm G, Gayoso A, Kats I, Koutrouli M, Berger B, Pe'er D, Regev A, Teichmann SA, Finotello F, Wolf FA, Yosef N, Stegle O, Theis FJ. The scverse project provides a computational ecosystem for single-cell omics data analysis. Nat Biotechnol 2023; 41:604-606. [PMID: 37037904 DOI: 10.1038/s41587-023-01733-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Affiliation(s)
- Isaac Virshup
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany.
| | - Danila Bredikhin
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany.
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences, Heidelberg, Germany.
| | - Lukas Heumos
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany.
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center with the CPC-M bioArchive; Helmholtz Zentrum Munich, Member of the German Center for Lung Research (DZL), Munich, Germany.
- School of Life Sciences, Technical University of Munich, Munich, Germany.
| | - Giovanni Palla
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany
- School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Gregor Sturm
- Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria
| | - Adam Gayoso
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Ilia Kats
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mikaela Koutrouli
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Harvard-MIT Health Sciences and Technology Program, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dana Pe'er
- Computational and Systems Biology Program, Sloan Kettering Institute, New York, NY, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Aviv Regev
- Genentech Research and Early Development, Genentech Inc, South San Francisco, CA, USA
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Cavendish Laboratory, University of Cambridge, Cambridge, UK
| | - Francesca Finotello
- Institute of Molecular Biology, University of Innsbruck, Innsbruck, Austria
- Digital Science Center (DiSC), University of Innsbruck, Innsbruck, Austria
| | - F Alexander Wolf
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany
- Lamin Labs, Munich, Germany
| | - Nir Yosef
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Oliver Stegle
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Fabian J Theis
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany
- School of Life Sciences, Technical University of Munich, Munich, Germany
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
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7
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Lotfollahi M, Rybakov S, Hrovatin K, Hediyeh-Zadeh S, Talavera-López C, Misharin AV, Theis FJ. Biologically informed deep learning to query gene programs in single-cell atlases. Nat Cell Biol 2023; 25:337-350. [PMID: 36732632 PMCID: PMC9928587 DOI: 10.1038/s41556-022-01072-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 12/08/2022] [Indexed: 02/04/2023]
Abstract
The increasing availability of large-scale single-cell atlases has enabled the detailed description of cell states. In parallel, advances in deep learning allow rapid analysis of newly generated query datasets by mapping them into reference atlases. However, existing data transformations learned to map query data are not easily explainable using biologically known concepts such as genes or pathways. Here we propose expiMap, a biologically informed deep-learning architecture that enables single-cell reference mapping. ExpiMap learns to map cells into biologically understandable components representing known 'gene programs'. The activity of each cell for a gene program is learned while simultaneously refining them and learning de novo programs. We show that expiMap compares favourably to existing methods while bringing an additional layer of interpretability to integrative single-cell analysis. Furthermore, we demonstrate its applicability to analyse single-cell perturbation responses in different tissues and species and resolve responses of patients who have coronavirus disease 2019 to different treatments across cell types.
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Affiliation(s)
- Mohammad Lotfollahi
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Wellcome Sanger Institute, Cambridge, UK
| | - Sergei Rybakov
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Karin Hrovatin
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Soroor Hediyeh-Zadeh
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Bioinformatics Division, WEHI, Melbourne, Victoria, Australia
| | - Carlos Talavera-López
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Division of Infectious Diseases and Tropical Medicine, Ludwig-Maximilian-Universität Klinikum, Munich, Germany
| | - Alexander V Misharin
- Division of Pulmonary and Critical Care Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- Wellcome Sanger Institute, Cambridge, UK.
- Department of Mathematics, Technical University of Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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8
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Miranda L, Bordes J, Gasperoni S, Lopez JP. Increasing resolution in stress neurobiology: from single cells to complex group behaviors. Stress 2023; 26:2186141. [PMID: 36855966 DOI: 10.1080/10253890.2023.2186141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/02/2023] Open
Abstract
Stress can have severe psychological and physiological consequences. Thus, inappropriate regulation of the stress response is linked to the etiology of mood and anxiety disorders. The generation and implementation of preclinical animal models represent valuable tools to explore and characterize the mechanisms underlying the pathophysiology of stress-related psychiatric disorders and the development of novel pharmacological strategies. In this commentary, we discuss the strengths and limitations of state-of-the-art molecular and computational advances employed in stress neurobiology research, with a focus on the ever-increasing spatiotemporal resolution in cell biology and behavioral science. Finally, we share our perspective on future directions in the fields of preclinical and human stress research.
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Affiliation(s)
- Lucas Miranda
- Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Joeri Bordes
- Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, Munich, Germany
| | - Serena Gasperoni
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Juan Pablo Lopez
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
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9
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Brendel M, Su C, Bai Z, Zhang H, Elemento O, Wang F. Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:814-835. [PMID: 36528240 PMCID: PMC10025684 DOI: 10.1016/j.gpb.2022.11.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 08/17/2022] [Accepted: 11/24/2022] [Indexed: 12/23/2022]
Abstract
Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously. Analysis of scRNA-seq data plays an important role in the study of cell states and phenotypes, and has helped elucidate biological processes, such as those occurring during the development of complex organisms, and improved our understanding of disease states, such as cancer, diabetes, and coronavirus disease 2019 (COVID-19). Deep learning, a recent advance of artificial intelligence that has been used to address many problems involving large datasets, has also emerged as a promising tool for scRNA-seq data analysis, as it has a capacity to extract informative and compact features from noisy, heterogeneous, and high-dimensional scRNA-seq data to improve downstream analysis. The present review aims at surveying recently developed deep learning techniques in scRNA-seq data analysis, identifying key steps within the scRNA-seq data analysis pipeline that have been advanced by deep learning, and explaining the benefits of deep learning over more conventional analytic tools. Finally, we summarize the challenges in current deep learning approaches faced within scRNA-seq data and discuss potential directions for improvements in deep learning algorithms for scRNA-seq data analysis.
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Affiliation(s)
- Matthew Brendel
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; Institute for Computational Biomedicine, Caryl and Israel Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA
| | - Chang Su
- Department of Health Service Administration and Policy, Temple University, Philadelphia, PA 19122, USA.
| | - Zilong Bai
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA
| | - Hao Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA
| | - Olivier Elemento
- Institute for Computational Biomedicine, Caryl and Israel Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA.
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10
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Dietrich A, Sturm G, Merotto L, Marini F, Finotello F, List M. SimBu: bias-aware simulation of bulk RNA-seq data with variable cell-type composition. Bioinformatics 2022; 38:ii141-ii147. [PMID: 36124800 DOI: 10.1093/bioinformatics/btac499] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION As complex tissues are typically composed of various cell types, deconvolution tools have been developed to computationally infer their cellular composition from bulk RNA sequencing (RNA-seq) data. To comprehensively assess deconvolution performance, gold-standard datasets are indispensable. Gold-standard, experimental techniques like flow cytometry or immunohistochemistry are resource-intensive and cannot be systematically applied to the numerous cell types and tissues profiled with high-throughput transcriptomics. The simulation of 'pseudo-bulk' data, generated by aggregating single-cell RNA-seq expression profiles in pre-defined proportions, offers a scalable and cost-effective alternative. This makes it feasible to create in silico gold standards that allow fine-grained control of cell-type fractions not conceivable in an experimental setup. However, at present, no simulation software for generating pseudo-bulk RNA-seq data exists. RESULTS We developed SimBu, an R package capable of simulating pseudo-bulk samples based on various simulation scenarios, designed to test specific features of deconvolution methods. A unique feature of SimBu is the modeling of cell-type-specific mRNA bias using experimentally derived or data-driven scaling factors. Here, we show that SimBu can generate realistic pseudo-bulk data, recapitulating the biological and statistical features of real RNA-seq data. Finally, we illustrate the impact of mRNA bias on the evaluation of deconvolution tools and provide recommendations for the selection of suitable methods for estimating mRNA content. SimBu is a user-friendly and flexible tool for simulating realistic pseudo-bulk RNA-seq datasets serving as in silico gold-standard for assessing cell-type deconvolution methods. AVAILABILITY AND IMPLEMENTATION SimBu is freely available at https://github.com/omnideconv/SimBu as an R package under the GPL-3 license. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alexander Dietrich
- Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - Gregor Sturm
- Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Lorenzo Merotto
- Institute of Molecular Biology, University of Innsbruck, 6020 Innsbruck, Austria.,Digital Science Center (DiSC), University of Innsbruck, 6020 Innsbruck, Austria
| | - Federico Marini
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany.,Research Center for Immunotherapy (FZI), University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Francesca Finotello
- Institute of Molecular Biology, University of Innsbruck, 6020 Innsbruck, Austria.,Digital Science Center (DiSC), University of Innsbruck, 6020 Innsbruck, Austria
| | - Markus List
- Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
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11
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Tritschler S, Thomas M, Böttcher A, Ludwig B, Schmid J, Schubert U, Kemter E, Wolf E, Lickert H, Theis FJ. A transcriptional cross species map of pancreatic islet cells. Mol Metab 2022; 66:101595. [PMID: 36113773 PMCID: PMC9526148 DOI: 10.1016/j.molmet.2022.101595] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 08/20/2022] [Accepted: 09/03/2022] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVE Pancreatic islets of Langerhans secrete hormones to regulate systemic glucose levels. Emerging evidence suggests that islet cells are functionally heterogeneous to allow a fine-tuned and efficient endocrine response to physiological changes. A precise description of the molecular basis of this heterogeneity, in particular linking animal models to human islets, is an important step towards identifying the factors critical for endocrine cell function in physiological and pathophysiological conditions. METHODS In this study, we used single-cell RNA sequencing to profile more than 50'000 endocrine cells isolated from healthy human, pig and mouse pancreatic islets and characterize transcriptional heterogeneity and evolutionary conservation of those cells across the three species. We systematically delineated endocrine cell types and α- and β-cell heterogeneity through prior knowledge- and data-driven gene sets shared across species, which altogether capture common and differential cellular properties, transcriptional dynamics and putative driving factors of state transitions. RESULTS We showed that global endocrine expression profiles correlate, and that critical identity and functional markers are shared between species, while only approximately 20% of cell type enriched expression is conserved. We resolved distinct human α- and β-cell states that form continuous transcriptional landscapes. These states differentially activate maturation and hormone secretion programs, which are related to regulatory hormone receptor expression, signaling pathways and different types of cellular stress responses. Finally, we mapped mouse and pig cells to the human reference and observed that the spectrum of human α- and β-cell heterogeneity and aspects of such functional gene expression are better recapitulated in the pig than mouse data. CONCLUSIONS Here, we provide a high-resolution transcriptional map of healthy human islet cells and their murine and porcine counterparts, which is easily queryable via an online interface. This comprehensive resource informs future efforts that focus on pancreatic endocrine function, failure and regeneration, and enables to assess molecular conservation in islet biology across species for translational purposes.
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Affiliation(s)
- Sophie Tritschler
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Technical University of Munich, School of Life Sciences Weihenstephan, 85354 Freising, Germany
| | - Moritz Thomas
- Technical University of Munich, School of Life Sciences Weihenstephan, 85354 Freising, Germany; Institute of AI for Health, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Anika Böttcher
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute of Stem Cell Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Barbara Ludwig
- Department of Medicine III, University Hospital Carl Gustav Carus, Technical University of Dresden, 01307 Dresden, Germany; Paul Langerhans Institute Dresden of Helmholtz Zentrum München, University Hospital Carl Gustav Carus, Technical University of Dresden, 01307 Dresden, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Janine Schmid
- Department of Medicine III, University Hospital Carl Gustav Carus, Technical University of Dresden, 01307 Dresden, Germany
| | - Undine Schubert
- Department of Medicine III, University Hospital Carl Gustav Carus, Technical University of Dresden, 01307 Dresden, Germany
| | - Elisabeth Kemter
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany; Chair for Molecular Animal Breeding and Biotechnology, Gene Center, LMU Munich, 81377 Munich, Germany; Center for Innovative Medical Models (CiMM), Department of Veterinary Sciences, LMU Munich, 85764 Oberschleißheim, Germany
| | - Eckhard Wolf
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany; Chair for Molecular Animal Breeding and Biotechnology, Gene Center, LMU Munich, 81377 Munich, Germany; Center for Innovative Medical Models (CiMM), Department of Veterinary Sciences, LMU Munich, 85764 Oberschleißheim, Germany
| | - Heiko Lickert
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute of Stem Cell Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany; Technical University of Munich, Medical Faculty, 81675 Munich, Germany.
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Technical University of Munich, Department of Mathematics, 85748 Garching b. Munich, Germany.
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12
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Multi-omics single-cell data integration and regulatory inference with graph-linked embedding. Nat Biotechnol 2022; 40:1458-1466. [PMID: 35501393 PMCID: PMC9546775 DOI: 10.1038/s41587-022-01284-4] [Citation(s) in RCA: 108] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 03/15/2022] [Indexed: 12/14/2022]
Abstract
Despite the emergence of experimental methods for simultaneous measurement of multiple omics modalities in single cells, most single-cell datasets include only one modality. A major obstacle in integrating omics data from multiple modalities is that different omics layers typically have distinct feature spaces. Here, we propose a computational framework called GLUE (graph-linked unified embedding), which bridges the gap by modeling regulatory interactions across omics layers explicitly. Systematic benchmarking demonstrated that GLUE is more accurate, robust and scalable than state-of-the-art tools for heterogeneous single-cell multi-omics data. We applied GLUE to various challenging tasks, including triple-omics integration, integrative regulatory inference and multi-omics human cell atlas construction over millions of cells, where GLUE was able to correct previous annotations. GLUE features a modular design that can be flexibly extended and enhanced for new analysis tasks. The full package is available online at https://github.com/gao-lab/GLUE. Different single-cell data modalities are integrated at atlas-scale by modeling regulatory interactions.
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13
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Shen S, Sun Y, Matsumoto M, Shim WJ, Sinniah E, Wilson SB, Werner T, Wu Z, Bradford ST, Hudson J, Little MH, Powell J, Nguyen Q, Palpant NJ. Integrating single-cell genomics pipelines to discover mechanisms of stem cell differentiation. Trends Mol Med 2021; 27:1135-1158. [PMID: 34657800 DOI: 10.1016/j.molmed.2021.09.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 09/19/2021] [Accepted: 09/22/2021] [Indexed: 12/12/2022]
Abstract
Pluripotent stem cells underpin a growing sector that leverages their differentiation potential for research, industry, and clinical applications. This review evaluates the landscape of methods in single-cell transcriptomics that are enabling accelerated discovery in stem cell science. We focus on strategies for scaling stem cell differentiation through multiplexed single-cell analyses, for evaluating molecular regulation of cell differentiation using new analysis algorithms, and methods for integration and projection analysis to classify and benchmark stem cell derivatives against in vivo cell types. By discussing the available methods, comparing their strengths, and illustrating strategies for developing integrated analysis pipelines, we provide user considerations to inform their implementation and interpretation.
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Affiliation(s)
- Sophie Shen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Yuliangzi Sun
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Maika Matsumoto
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Woo Jun Shim
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Enakshi Sinniah
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Sean B Wilson
- Murdoch Children's Research Institute, Melbourne, Australia
| | - Tessa Werner
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Zhixuan Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | | | - James Hudson
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Melissa H Little
- Murdoch Children's Research Institute, Melbourne, Australia; Department of Pediatrics, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia
| | - Joseph Powell
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, Australia; UNSW Cellular Genomics Futures Institute, UNSW, Sydney, Australia
| | - Quan Nguyen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Nathan J Palpant
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.
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