1
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Armingol E, Baghdassarian HM, Lewis NE. The diversification of methods for studying cell-cell interactions and communication. Nat Rev Genet 2024; 25:381-400. [PMID: 38238518 PMCID: PMC11139546 DOI: 10.1038/s41576-023-00685-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/01/2023] [Indexed: 05/20/2024]
Abstract
No cell lives in a vacuum, and the molecular interactions between cells define most phenotypes. Transcriptomics provides rich information to infer cell-cell interactions and communication, thus accelerating the discovery of the roles of cells within their communities. Such research relies heavily on algorithms that infer which cells are interacting and the ligands and receptors involved. Specific pressures on different research niches are driving the evolution of next-generation computational tools, enabling new conceptual opportunities and technological advances. More sophisticated algorithms now account for the heterogeneity and spatial organization of cells, multiple ligand types and intracellular signalling events, and enable the use of larger and more complex datasets, including single-cell and spatial transcriptomics. Similarly, new high-throughput experimental methods are increasing the number and resolution of interactions that can be analysed simultaneously. Here, we explore recent progress in cell-cell interaction research and highlight the diversification of the next generation of tools, which have yielded a rich ecosystem of tools for different applications and are enabling invaluable discoveries.
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Affiliation(s)
- Erick Armingol
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA.
- Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA.
| | - Hratch M Baghdassarian
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
- Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Nathan E Lewis
- Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
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2
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Pekayvaz K, Losert C, Knottenberg V, Gold C, van Blokland IV, Oelen R, Groot HE, Benjamins JW, Brambs S, Kaiser R, Gottschlich A, Hoffmann GV, Eivers L, Martinez-Navarro A, Bruns N, Stiller S, Akgöl S, Yue K, Polewka V, Escaig R, Joppich M, Janjic A, Popp O, Kobold S, Petzold T, Zimmer R, Enard W, Saar K, Mertins P, Huebner N, van der Harst P, Franke LH, van der Wijst MGP, Massberg S, Heinig M, Nicolai L, Stark K. Multiomic analyses uncover immunological signatures in acute and chronic coronary syndromes. Nat Med 2024; 30:1696-1710. [PMID: 38773340 PMCID: PMC11186793 DOI: 10.1038/s41591-024-02953-4] [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: 05/02/2023] [Accepted: 03/26/2024] [Indexed: 05/23/2024]
Abstract
Acute and chronic coronary syndromes (ACS and CCS) are leading causes of mortality. Inflammation is considered a key pathogenic driver of these diseases, but the underlying immune states and their clinical implications remain poorly understood. Multiomic factor analysis (MOFA) allows unsupervised data exploration across multiple data types, identifying major axes of variation and associating these with underlying molecular processes. We hypothesized that applying MOFA to multiomic data obtained from blood might uncover hidden sources of variance and provide pathophysiological insights linked to clinical needs. Here we compile a longitudinal multiomic dataset of the systemic immune landscape in both ACS and CCS (n = 62 patients in total, n = 15 women and n = 47 men) and validate this in an external cohort (n = 55 patients in total, n = 11 women and n = 44 men). MOFA reveals multicellular immune signatures characterized by distinct monocyte, natural killer and T cell substates and immune-communication pathways that explain a large proportion of inter-patient variance. We also identify specific factors that reflect disease state or associate with treatment outcome in ACS as measured using left ventricular ejection fraction. Hence, this study provides proof-of-concept evidence for the ability of MOFA to uncover multicellular immune programs in cardiovascular disease, opening new directions for mechanistic, biomarker and therapeutic studies.
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Grants
- Deutsche Forschungsgemeinschaft (German Research Foundation)
- Deutsches Zentrum fr Herz-Kreislaufforschung (Deutsches Zentrum fr Herz-Kreislaufforschung e.V.)
- Deutsche Herzstiftung e.V., Frankfurt a.M. Institutional Strategy LMUexcellent of LMU Munich Else-Krner-Fresenius Stiftung DFG Clinician Scientist Programme PRIME DZHK Sule B Antrag DZHK B 21-014 SE
- Was supported by the Helmholtz Association under the joint research school ;Munich School for Data Science MUDS
- DFG GO 3823/1-1, grant number: 510821390 Frderprogramm fr Forschung und Lehre der Medizinischen Fakultt der LMU the Bavarian Cancer Research Center (BZKF) Else Kroner-Fresenius-Stiftung
- Was supported by a grant from the Frderprogramm fur Forschung und Lehre (FFoLe) of the Ludwig Maximilian University (LMU) of Munich.
- DFG SFB 1123, Z02
- DFG EN 1093/2-1
- DFG KO5055-2-1 and KO5055/3-1 the Bavarian Cancer Research Center (BZKF) the international doctoral program i-Target: immunotargeting of cancer the Melanoma Research Alliance (grant number 409510), Marie Sklodowska-Curie Training Network for Optimizing Adoptive T Cell Therapy of Cancer (funded by the Horizon 2020 programme of the European Union; grant 955575), Else Kroner-Fresenius-Stiftung (IOLIN), German Cancer Aid (AvantCAR.de), the Wilhelm-Sander-Stiftung, Ernst Jung Stiftung, Institutional Strategy LMUexcellent of LMU Munich (within the framework of the German Excellence Initiative), the Go-Bio-Initiative, the m4-Award of the Bavarian Ministry for Economical Affairs, Bundesministerium fur Bildung und Forschung, European Research Council (Starting Grant 756017 and PoC Grant 101100460, by the SFB-TRR 338/1 2021452881907, Fritz-Bender Foundation, Deutsche Jose#x0301; Carreras Leuk#x00E4;mie Stiftung, Hector Foundation, the Bavarian Research Foundation, the Bruno and Helene J#x00F6;ster Foundation (360#x00B0; CAR)
- T.P. from the DFG (PE 2704/3-1)
- DFG SFB1243, A14 DFG EN 1093/2-1,
- DZHK Säule B Antrag DZHK B 21-014 SE
- DZHK Säule B Antrag DZHK B 21-014 SE DFG SFB-1470-B03 the Chan Zuckerberg Foundation ERC Advanced Grant under the European Union Horizon 2020 Research and Innovation Program (AdG788970)
- Deutsche Forschungsgemeinschaft (DFG) SFB 914, B02 and Z01 DFG SFB 1123, B06 DFG SFB1321, P10 DFG FOR 2033 ERC-2018-ADG German Centre for Cardiovascular Research (DZHK) MHA 1.4VD
- DZHK project 81Z0600106 Supported by the Chan Zuckerberg Foundation
- DZHK S#x00E4;ule B Antrag DZHK B 21-014 SE Deutsche Herzstiftung e.V., Frankfurt a.M. DFG SFB 1123, B06 DFG NI 2219/2-1 Corona Foundation German Centre for Cardiovascular Research (DZHK) Clinician Scientist Programme the Ernst und Berta Grimmke Stiftung the GTH Junior research grant
- DZHK partner site project Deutsche Forschungsgemeinschaft (DFG) SFB 914, B02 DFG SFB 1123, A07 DFG SFB 359, A03 ERC grant 947611
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Affiliation(s)
- Kami Pekayvaz
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany.
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany.
| | - Corinna Losert
- Institute of Computational Biology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Computer Science, TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | | | - Christoph Gold
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Irene V van Blokland
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Roy Oelen
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Hilde E Groot
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jan Walter Benjamins
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Sophia Brambs
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
| | - Rainer Kaiser
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Adrian Gottschlich
- Department of Medicine III, LMU University Hospital, Munich, Germany
- Division of Clinical Pharmacology, LMU University Hospital, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Gordon Victor Hoffmann
- Division of Clinical Pharmacology, LMU University Hospital, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Luke Eivers
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
| | | | - Nils Bruns
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
| | - Susanne Stiller
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
| | - Sezer Akgöl
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
| | - Keyang Yue
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
| | - Vivien Polewka
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
| | - Raphael Escaig
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
| | - Markus Joppich
- Department of Informatics, Ludwig-Maximilian University, Munich, Germany
| | - Aleksandar Janjic
- Anthropology and Human Genomics, Faculty of Biology, Ludwig-Maximilian University, Munich, Germany
| | - Oliver Popp
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Sebastian Kobold
- Division of Clinical Pharmacology, LMU University Hospital, Member of the German Center for Lung Research (DZL), Munich, Germany
- German Cancer Consortium (DKTK), a partnership between DKFZ and LMU University Hospital, Partner Site Munich, Munich, Germany
- Einheit für Klinische Pharmakologie (EKLiP), Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Tobias Petzold
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité (DHZC), Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
- Charite-Universitätsmedizin Berlin, Berlin, Germany
| | - Ralf Zimmer
- Department of Informatics, Ludwig-Maximilian University, Munich, Germany
| | - Wolfgang Enard
- Anthropology and Human Genomics, Faculty of Biology, Ludwig-Maximilian University, Munich, Germany
| | - Kathrin Saar
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
| | - Philipp Mertins
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Norbert Huebner
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
- Charite-Universitätsmedizin Berlin, Berlin, Germany
| | - Pim van der Harst
- Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lude H Franke
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Monique G P van der Wijst
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Steffen Massberg
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Matthias Heinig
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany.
- Institute of Computational Biology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany.
- Department of Computer Science, TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
| | - Leo Nicolai
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany.
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany.
| | - Konstantin Stark
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany.
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany.
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3
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Barlow GL, Schürch CM, Bhate SS, Phillips D, Young A, Dong S, Martinez HA, Kaber G, Nagy N, Ramachandran S, Meng J, Korpos E, Bluestone JA, Nolan GP, Bollyky PL. The Extra-Islet Pancreas Supports Autoimmunity in Human Type 1 Diabetes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.03.15.23287145. [PMID: 36993739 PMCID: PMC10055577 DOI: 10.1101/2023.03.15.23287145] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
In autoimmune Type 1 diabetes (T1D), immune cells infiltrate and destroy the islets of Langerhans - islands of endocrine tissue dispersed throughout the pancreas. However, the contribution of cellular programs outside islets to insulitis is unclear. Here, using CO-Detection by indEXing (CODEX) tissue imaging and cadaveric pancreas samples, we simultaneously examine islet and extra-islet inflammation in human T1D. We identify four sub-states of inflamed islets characterized by the activation profiles of CD8 + T cells enriched in islets relative to the surrounding tissue. We further find that the extra-islet space of lobules with extensive islet-infiltration differs from the extra-islet space of less infiltrated areas within the same tissue section. Finally, we identify lymphoid structures away from islets enriched in CD45RA + T cells - a population also enriched in one of the inflamed islet sub-states. Together, these data help define the coordination between islets and the extra-islet pancreas in the pathogenesis of human T1D.
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4
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Ramirez Flores RO, Schäfer PSL, Küchenhoff L, Saez-Rodriguez J. Complementing Cell Taxonomies with a Multicellular Analysis of Tissues. Physiology (Bethesda) 2024; 39:0. [PMID: 38319138 DOI: 10.1152/physiol.00001.2024] [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: 01/03/2024] [Accepted: 01/31/2024] [Indexed: 02/07/2024] Open
Abstract
The application of single-cell molecular profiling coupled with spatial technologies has enabled charting of cellular heterogeneity in reference tissues and in disease. This new wave of molecular data has highlighted the expected diversity of single-cell dynamics upon shared external queues and spatial organizations. However, little is known about the relationship between single-cell heterogeneity and the emergence and maintenance of robust multicellular processes in developed tissues and its role in (patho)physiology. Here, we present emerging computational modeling strategies that use increasingly available large-scale cross-condition single-cell and spatial datasets to study multicellular organization in tissues and complement cell taxonomies. This perspective should enable us to better understand how cells within tissues collectively process information and adapt synchronized responses in disease contexts and to bridge the gap between structural changes and functions in tissues.
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Affiliation(s)
- Ricardo Omar Ramirez Flores
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Sven Lars Schäfer
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Leonie Küchenhoff
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
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5
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Daly AC, Cambuli F, Äijö T, Lötstedt B, Marjanovic N, Kuksenko O, Smith-Erb M, Fernandez S, Domovic D, Van Wittenberghe N, Drokhlyansky E, Griffin GK, Phatnani H, Bonneau R, Regev A, Vickovic S. Tissue and cellular spatiotemporal dynamics in colon aging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590125. [PMID: 38712088 PMCID: PMC11071407 DOI: 10.1101/2024.04.22.590125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Tissue structure and molecular circuitry in the colon can be profoundly impacted by systemic age-related effects, but many of the underlying molecular cues remain unclear. Here, we built a cellular and spatial atlas of the colon across three anatomical regions and 11 age groups, encompassing ~1,500 mouse gut tissues profiled by spatial transcriptomics and ~400,000 single nucleus RNA-seq profiles. We developed a new computational framework, cSplotch, which learns a hierarchical Bayesian model of spatially resolved cellular expression associated with age, tissue region, and sex, by leveraging histological features to share information across tissue samples and data modalities. Using this model, we identified cellular and molecular gradients along the adult colonic tract and across the main crypt axis, and multicellular programs associated with aging in the large intestine. Our multi-modal framework for the investigation of cell and tissue organization can aid in the understanding of cellular roles in tissue-level pathology.
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Affiliation(s)
- Aidan C. Daly
- New York Genome Center, New York, NY, USA
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
| | | | - Tarmo Äijö
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
| | - Britta Lötstedt
- New York Genome Center, New York, NY, USA
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Nemanja Marjanovic
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Olena Kuksenko
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | | | | | | | | | - Eugene Drokhlyansky
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gabriel K Griffin
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Hemali Phatnani
- New York Genome Center, New York, NY, USA
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
- Center for Data Science, New York University, New York, NY, USA
- Current address: Genentech, 1 DNA Way, South San Francisco, CA, USA
| | - Aviv Regev
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Current address: Genentech, 1 DNA Way, South San Francisco, CA, USA
| | - Sanja Vickovic
- New York Genome Center, New York, NY, USA
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Engineering and Herbert Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Beijer Laboratory for Gene and Neuro Research, Uppsala University, Uppsala, Sweden
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6
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Baghdassarian HM, Dimitrov D, Armingol E, Saez-Rodriguez J, Lewis NE. Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples. CELL REPORTS METHODS 2024; 4:100758. [PMID: 38631346 PMCID: PMC11046036 DOI: 10.1016/j.crmeth.2024.100758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 12/22/2023] [Accepted: 03/22/2024] [Indexed: 04/19/2024]
Abstract
In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. Here, we integrate two tools, LIANA and Tensor-cell2cell, which, when combined, can deploy multiple existing methods and resources to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this work, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step by step in both Python and R and provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This workflow typically takes ∼1.5 h to complete from installation to downstream visualizations on a graphics processing unit-enabled computer for a dataset of ∼63,000 cells, 10 cell types, and 12 samples.
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Affiliation(s)
- Hratch M Baghdassarian
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Daniel Dimitrov
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, 69120 Heidelberg, Germany
| | - Erick Armingol
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, 69120 Heidelberg, Germany.
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
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7
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Li R, Chen X, Yang X. Navigating the landscapes of spatial transcriptomics: How computational methods guide the way. WILEY INTERDISCIPLINARY REVIEWS. RNA 2024; 15:e1839. [PMID: 38527900 DOI: 10.1002/wrna.1839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/24/2024] [Accepted: 03/04/2024] [Indexed: 03/27/2024]
Abstract
Spatially resolved transcriptomics has been dramatically transforming biological and medical research in various fields. It enables transcriptome profiling at single-cell, multi-cellular, or sub-cellular resolution, while retaining the information of geometric localizations of cells in complex tissues. The coupling of cell spatial information and its molecular characteristics generates a novel multi-modal high-throughput data source, which poses new challenges for the development of analytical methods for data-mining. Spatial transcriptomic data are often highly complex, noisy, and biased, presenting a series of difficulties, many unresolved, for data analysis and generation of biological insights. In addition, to keep pace with the ever-evolving spatial transcriptomic experimental technologies, the existing analytical theories and tools need to be updated and reformed accordingly. In this review, we provide an overview and discussion of the current computational approaches for mining of spatial transcriptomics data. Future directions and perspectives of methodology design are proposed to stimulate further discussions and advances in new analytical models and algorithms. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Evolution and Genomics > Computational Analyses of RNA RNA Export and Localization > RNA Localization.
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Affiliation(s)
- Runze Li
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Xu Chen
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Xuerui Yang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
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8
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Baghdassarian HM, Lewis NE. Resource allocation in mammalian systems. Biotechnol Adv 2024; 71:108305. [PMID: 38215956 PMCID: PMC11182366 DOI: 10.1016/j.biotechadv.2023.108305] [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: 08/03/2023] [Revised: 12/17/2023] [Accepted: 12/18/2023] [Indexed: 01/14/2024]
Abstract
Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.
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Affiliation(s)
- Hratch M Baghdassarian
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
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9
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Schäfer PSL, Dimitrov D, Villablanca EJ, Saez-Rodriguez J. Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system. Nat Immunol 2024; 25:405-417. [PMID: 38413722 DOI: 10.1038/s41590-024-01768-2] [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: 05/31/2023] [Accepted: 01/16/2024] [Indexed: 02/29/2024]
Abstract
The immune system comprises diverse specialized cell types that cooperate to defend the host against a wide range of pathogenic threats. Recent advancements in single-cell and spatial multi-omics technologies provide rich information about the molecular state of immune cells. Here, we review how the integration of single-cell and spatial multi-omics data with prior knowledge-gathered from decades of detailed biochemical studies-allows us to obtain functional insights, focusing on gene regulatory processes and cell-cell interactions. We present diverse applications in immunology and critically assess underlying assumptions and limitations. Finally, we offer a perspective on the ongoing technological and algorithmic developments that promise to get us closer to a systemic mechanistic understanding of the immune system.
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Affiliation(s)
- Philipp Sven Lars Schäfer
- Institute for Computational Bioscience, Faculty of Medicine and Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Daniel Dimitrov
- Institute for Computational Bioscience, Faculty of Medicine and Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Eduardo J Villablanca
- Division of Immunology and Allergy, Department of Medicine Solna, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
- Center of Molecular Medicine, Stockholm, Sweden
| | - Julio Saez-Rodriguez
- Institute for Computational Bioscience, Faculty of Medicine and Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany.
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10
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Lin S, Zhao F, Wu Z, Yao J, Zhao Y, Yuan Z. Streamlining spatial omics data analysis with Pysodb. Nat Protoc 2024; 19:831-895. [PMID: 38135744 DOI: 10.1038/s41596-023-00925-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 10/02/2023] [Indexed: 12/24/2023]
Abstract
Advances in spatial omics technologies have improved the understanding of cellular organization in tissues, leading to the generation of complex and heterogeneous data and prompting the development of specialized tools for managing, loading and visualizing spatial omics data. The Spatial Omics Database (SODB) was established to offer a unified format for data storage and interactive visualization modules. Here we detail the use of Pysodb, a Python-based tool designed to enable the efficient exploration and loading of spatial datasets from SODB within a Python environment. We present seven case studies using Pysodb, detailing the interaction with various computational methods, ensuring reproducibility of experimental data and facilitating the integration of new data and alternative applications in SODB. The approach offers a reference for method developers by outlining label and metadata availability in representative spatial data that can be loaded by Pysodb. The tool is supplemented by a website ( https://protocols-pysodb.readthedocs.io/ ) with detailed information for benchmarking analysis, and allows method developers to focus on computational models by facilitating data processing. This protocol is designed for researchers with limited experience in computational biology. Depending on the dataset complexity, the protocol typically requires ~12 h to complete.
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Affiliation(s)
- Senlin Lin
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Fangyuan Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | | | - Yi Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
- University of Chinese Academy of Sciences, Beijing, China.
| | - Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China.
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11
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Pascual-Reguant A, Kroh S, Hauser AE. Tissue niches and immunopathology through the lens of spatial tissue profiling techniques. Eur J Immunol 2024; 54:e2350484. [PMID: 37985207 DOI: 10.1002/eji.202350484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/22/2023]
Abstract
Spatial organization plays a fundamental role in biology, influencing the function of biological structures at various levels. The immune system, in particular, relies on the orchestrated interactions of immune cells with their microenvironment to mount protective or pathogenic immune responses. The COVID-19 pandemic has underscored the significance of studying immunity within target organs to understand disease progression and severity. To achieve this, multiplex histology and spatial transcriptomics have proven indispensable in providing a spatial context to protein and gene expression patterns. By combining these techniques, researchers gain a more comprehensive understanding of the complex interactions at the cellular and molecular level in distinct tissue niches, key functional units modulating health and disease. In this review, we discuss recent advances in spatial tissue profiling techniques, highlighting their advantages over traditional histopathology studies. The insights gained from these approaches have the potential to revolutionize the diagnosis and treatment of various diseases including cancer, autoimmune disorders, and infectious diseases. However, we also acknowledge their challenges and limitations. Despite these, spatial tissue profiling offers promising opportunities to improve our understanding of how tissue niches direct regional immunity, and their relevance in tissue immunopathology, as a basis for novel therapeutic strategies and personalized medicine.
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Affiliation(s)
- Anna Pascual-Reguant
- Department of Rheumatology and Clinical Immunology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Immune Dynamics, Deutsches Rheuma-Forschungszentrum (DRFZ), Leibniz Institute, Berlin, Germany
- Spatial Genomics, Centre Nacional d'Anàlisi Genòmica, Barcelona, 08028, Spain
| | - Sandy Kroh
- Immune Dynamics, Deutsches Rheuma-Forschungszentrum (DRFZ), Leibniz Institute, Berlin, Germany
| | - Anja E Hauser
- Department of Rheumatology and Clinical Immunology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Immune Dynamics, Deutsches Rheuma-Forschungszentrum (DRFZ), Leibniz Institute, Berlin, Germany
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12
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Yao S, Harder A, Darki F, Chang YW, Li A, Nikouei K, Volpe G, Lundström JN, Zeng J, Wray N, Lu Y, Sullivan PF, Leffler JH. Connecting genomic results for psychiatric disorders to human brain cell types and regions reveals convergence with functional connectivity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.18.24301478. [PMID: 38410450 PMCID: PMC10896415 DOI: 10.1101/2024.01.18.24301478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Understanding the temporal and spatial brain locations etiological for psychiatric disorders is essential for targeted neurobiological research. Integration of genomic insights from genome-wide association studies with single-cell transcriptomics is a powerful approach although past efforts have necessarily relied on mouse atlases. Leveraging a comprehensive atlas of the adult human brain, we prioritized cell types via the enrichment of SNP-heritabilities for brain diseases, disorders, and traits, progressing from individual cell types to brain regions. Our findings highlight specific neuronal clusters significantly enriched for the SNP-heritabilities for schizophrenia, bipolar disorder, and major depressive disorder along with intelligence, education, and neuroticism. Extrapolation of cell-type results to brain regions reveals important patterns for schizophrenia with distinct subregions in the hippocampus and amygdala exhibiting the highest significance. Cerebral cortical regions display similar enrichments despite the known prefrontal dysfunction in those with schizophrenia highlighting the importance of subcortical connectivity. Using functional MRI connectivity from cases with schizophrenia and neurotypical controls, we identified brain networks that distinguished cases from controls that also confirmed involvement of the central and lateral amygdala, hippocampal body, and prefrontal cortex. Our findings underscore the value of single-cell transcriptomics in decoding the polygenicity of psychiatric disorders and offer a promising convergence of genomic, transcriptomic, and brain imaging modalities toward common biological targets.
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Affiliation(s)
- Shuyang Yao
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Arvid Harder
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Fahimeh Darki
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Yu-Wei Chang
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Ang Li
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Kasra Nikouei
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Johan N Lundström
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Monell Chemical Senses Center, Philadelphia, PA, USA
| | - Jian Zeng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Naomi Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Jens Hjerling Leffler
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
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13
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Mason K, Sathe A, Hess PR, Rong J, Wu CY, Furth E, Susztak K, Levinsohn J, Ji HP, Zhang N. Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions. Genome Biol 2024; 25:14. [PMID: 38217002 PMCID: PMC10785550 DOI: 10.1186/s13059-023-03159-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 12/22/2023] [Indexed: 01/14/2024] Open
Abstract
Existing methods for analysis of spatial transcriptomic data focus on delineating the global gene expression variations of cell types across the tissue, rather than local gene expression changes driven by cell-cell interactions. We propose a new statistical procedure called niche-differential expression (niche-DE) analysis that identifies cell-type-specific niche-associated genes, which are differentially expressed within a specific cell type in the context of specific spatial niches. We further develop niche-LR, a method to reveal ligand-receptor signaling mechanisms that underlie niche-differential gene expression patterns. Niche-DE and niche-LR are applicable to low-resolution spot-based spatial transcriptomics data and data that is single-cell or subcellular in resolution.
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Affiliation(s)
- Kaishu Mason
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, USA
| | - Anuja Sathe
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Paul R Hess
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, USA
| | - Jiazhen Rong
- Genomics and Computational Biology Graduate Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Chi-Yun Wu
- The Gladstone Institute, San Francisco, USA
| | - Emma Furth
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Katalin Susztak
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jonathan Levinsohn
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hanlee P Ji
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nancy Zhang
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, USA.
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14
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Liang Q, Huang Y, He S, Chen K. Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity. Nat Commun 2023; 14:8416. [PMID: 38110427 PMCID: PMC10728201 DOI: 10.1038/s41467-023-44206-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 12/04/2023] [Indexed: 12/20/2023] Open
Abstract
Advances in single-cell technology have enabled molecular dissection of heterogeneous biospecimens at unprecedented scales and resolutions. Cluster-centric approaches are widely applied in analyzing single-cell data, however they have limited power in dissecting and interpreting highly heterogenous, dynamically evolving data. Here, we present GSDensity, a graph-modeling approach that allows users to obtain pathway-centric interpretation and dissection of single-cell and spatial transcriptomics (ST) data without performing clustering. Using pathway gene sets, we show that GSDensity can accurately detect biologically distinct cells and reveal novel cell-pathway associations ignored by existing methods. Moreover, GSDensity, combined with trajectory analysis can identify curated pathways that are active at various stages of mouse brain development. Finally, GSDensity can identify spatially relevant pathways in mouse brains and human tumors including those following high-order organizational patterns in the ST data. Particularly, we create a pan-cancer ST map revealing spatially relevant and recurrently active pathways across six different tumor types.
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Affiliation(s)
- Qingnan Liang
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Yuefan Huang
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Shan He
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA.
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15
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Pham D, Tan X, Balderson B, Xu J, Grice LF, Yoon S, Willis EF, Tran M, Lam PY, Raghubar A, Kalita-de Croft P, Lakhani S, Vukovic J, Ruitenberg MJ, Nguyen QH. Robust mapping of spatiotemporal trajectories and cell-cell interactions in healthy and diseased tissues. Nat Commun 2023; 14:7739. [PMID: 38007580 PMCID: PMC10676408 DOI: 10.1038/s41467-023-43120-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 11/01/2023] [Indexed: 11/27/2023] Open
Abstract
Spatial transcriptomics (ST) technologies generate multiple data types from biological samples, namely gene expression, physical distance between data points, and/or tissue morphology. Here we developed three computational-statistical algorithms that integrate all three data types to advance understanding of cellular processes. First, we present a spatial graph-based method, pseudo-time-space (PSTS), to model and uncover relationships between transcriptional states of cells across tissues undergoing dynamic change (e.g. neurodevelopment, brain injury and/or microglia activation, and cancer progression). We further developed a spatially-constrained two-level permutation (SCTP) test to study cell-cell interaction, finding highly interactive tissue regions across thousands of ligand-receptor pairs with markedly reduced false discovery rates. Finally, we present a spatial graph-based imputation method with neural network (stSME), to correct for technical noise/dropout and increase ST data coverage. Together, the algorithms that we developed, implemented in the comprehensive and fast stLearn software, allow for robust interrogation of biological processes within healthy and diseased tissues.
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Affiliation(s)
- Duy Pham
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Xiao Tan
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Brad Balderson
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Jun Xu
- Genome Innovation Hub, The University of Queensland, Brisbane, Australia
| | - Laura F Grice
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Sohye Yoon
- Genome Innovation Hub, The University of Queensland, Brisbane, Australia
| | - Emily F Willis
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Minh Tran
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Pui Yeng Lam
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Arti Raghubar
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | | | - Sunil Lakhani
- UQ Centre for Clinical Research, The University of Queensland, Brisbane, Australia
| | - Jana Vukovic
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Marc J Ruitenberg
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Australia.
| | - Quan H Nguyen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.
- QIMR Berghofer Medical Research Institute, Herston, Australia.
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16
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Ramirez Flores RO, Lanzer JD, Dimitrov D, Velten B, Saez-Rodriguez J. Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease. eLife 2023; 12:e93161. [PMID: 37991480 PMCID: PMC10718529 DOI: 10.7554/elife.93161] [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] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 11/14/2023] [Indexed: 11/23/2023] Open
Abstract
Biomedical single-cell atlases describe disease at the cellular level. However, analysis of this data commonly focuses on cell-type-centric pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes. Here, we propose multicellular factor analysis for the unsupervised analysis of samples from cross-condition single-cell atlases and the identification of multicellular programs associated with disease. Our strategy, which repurposes group factor analysis as implemented in multi-omics factor analysis, incorporates the variation of patient samples across cell-types or other tissue-centric features, such as cell compositions or spatial relationships, and enables the joint analysis of multiple patient cohorts, facilitating the integration of atlases. We applied our framework to a collection of acute and chronic human heart failure atlases and described multicellular processes of cardiac remodeling, independent to cellular compositions and their local organization, that were conserved in independent spatial and bulk transcriptomics datasets. In sum, our framework serves as an exploratory tool for unsupervised analysis of cross-condition single-cell atlases and allows for the integration of the measurements of patient cohorts across distinct data modalities.
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Affiliation(s)
- Ricardo Omar Ramirez Flores
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuantHeidelbergGermany
| | - Jan David Lanzer
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuantHeidelbergGermany
| | - Daniel Dimitrov
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuantHeidelbergGermany
| | - Britta Velten
- Heidelberg University, Centre for Organismal Studies, Centre for Scientific ComputingHeidelbergGermany
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuantHeidelbergGermany
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17
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Dong M, Wang B, Wei J, de O Fonseca AH, Perry CJ, Frey A, Ouerghi F, Foxman EF, Ishizuka JJ, Dhodapkar RM, van Dijk D. Causal identification of single-cell experimental perturbation effects with CINEMA-OT. Nat Methods 2023; 20:1769-1779. [PMID: 37919419 PMCID: PMC10630139 DOI: 10.1038/s41592-023-02040-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 09/08/2023] [Indexed: 11/04/2023]
Abstract
Recent advancements in single-cell technologies allow characterization of experimental perturbations at single-cell resolution. While methods have been developed to analyze such experiments, the application of a strict causal framework has not yet been explored for the inference of treatment effects at the single-cell level. Here we present a causal-inference-based approach to single-cell perturbation analysis, termed CINEMA-OT (causal independent effect module attribution + optimal transport). CINEMA-OT separates confounding sources of variation from perturbation effects to obtain an optimal transport matching that reflects counterfactual cell pairs. These cell pairs represent causal perturbation responses permitting a number of novel analyses, such as individual treatment-effect analysis, response clustering, attribution analysis, and synergy analysis. We benchmark CINEMA-OT on an array of treatment-effect estimation tasks for several simulated and real datasets and show that it outperforms other single-cell perturbation analysis methods. Finally, we perform CINEMA-OT analysis of two newly generated datasets: (1) rhinovirus and cigarette-smoke-exposed airway organoids, and (2) combinatorial cytokine stimulation of immune cells. In these experiments, CINEMA-OT reveals potential mechanisms by which cigarette-smoke exposure dulls the airway antiviral response, as well as the logic that governs chemokine secretion and peripheral immune cell recruitment.
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Affiliation(s)
- Mingze Dong
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Bao Wang
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Jessica Wei
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA
| | | | - Curtis J Perry
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Alexander Frey
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA
- Department of Surgery, Yale School of Medicine, New Haven, CT, USA
| | - Feriel Ouerghi
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Ellen F Foxman
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA.
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA.
| | - Jeffrey J Ishizuka
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA.
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA.
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA.
| | - Rahul M Dhodapkar
- Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - David van Dijk
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
- Department of Internal Medicine (Cardiology), Yale School of Medicine, New Haven, CT, USA.
- Department of Computer Science, Yale University, New Haven, CT, USA.
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18
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Walker BL, Nie Q. NeST: nested hierarchical structure identification in spatial transcriptomic data. Nat Commun 2023; 14:6554. [PMID: 37848426 PMCID: PMC10582109 DOI: 10.1038/s41467-023-42343-x] [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: 06/13/2023] [Accepted: 10/06/2023] [Indexed: 10/19/2023] Open
Abstract
Spatial gene expression in tissue is characterized by regions in which particular genes are enriched or depleted. Frequently, these regions contain nested inside them subregions with distinct expression patterns. Segmentation methods in spatial transcriptomic (ST) data extract disjoint regions maximizing similarity over the greatest number of genes, typically on a particular spatial scale, thus lacking the ability to find region-within-region structure. We present NeST, which extracts spatial structure through coexpression hotspots-regions exhibiting localized spatial coexpression of some set of genes. Coexpression hotspots identify structure on any spatial scale, over any possible subset of genes, and are highly explainable. NeST also performs spatial analysis of cell-cell interactions via ligand-receptor, identifying active areas de novo without restriction of cell type or other groupings, in both two and three dimensions. Through application on ST datasets of varying type and resolution, we demonstrate the ability of NeST to reveal a new level of biological structure.
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Affiliation(s)
- Benjamin L Walker
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA, 92627, USA
- Department of Mathematics, University of California Irvine, Irvine, CA, 92627, USA
| | - Qing Nie
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA, 92627, USA.
- Department of Mathematics, University of California Irvine, Irvine, CA, 92627, USA.
- Department of Developmental and Cell Biology, University of California Irvine, Irvine, CA, 92627, USA.
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19
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Shi Q, Chen X, Zhang Z. Decoding Human Biology and Disease Using Single-cell Omics Technologies. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:926-949. [PMID: 37739168 PMCID: PMC10928380 DOI: 10.1016/j.gpb.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/22/2023] [Accepted: 06/08/2023] [Indexed: 09/24/2023]
Abstract
Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.
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Affiliation(s)
- Qiang Shi
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Xueyan Chen
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Zemin Zhang
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Changping Laboratory, Beijing 102206, China.
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20
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Cheng M, Jiang Y, Xu J, Mentis AFA, Wang S, Zheng H, Sahu SK, Liu L, Xu X. Spatially resolved transcriptomics: a comprehensive review of their technological advances, applications, and challenges. J Genet Genomics 2023; 50:625-640. [PMID: 36990426 DOI: 10.1016/j.jgg.2023.03.011] [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: 10/05/2022] [Revised: 03/11/2023] [Accepted: 03/16/2023] [Indexed: 03/29/2023]
Abstract
The ability to explore life kingdoms is largely driven by innovations and breakthroughs in technology, from the invention of the microscope 350 years ago to the recent emergence of single-cell sequencing, by which the scientific community has been able to visualize life at an unprecedented resolution. Most recently, the Spatially Resolved Transcriptomics (SRT) technologies have filled the gap in probing the spatial or even three-dimensional organization of the molecular foundation behind the molecular mysteries of life, including the origin of different cellular populations developed from totipotent cells and human diseases. In this review, we introduce recent progresses and challenges on SRT from the perspectives of technologies and bioinformatic tools, as well as the representative SRT applications. With the currently fast-moving progress of the SRT technologies and promising results from early adopted research projects, we can foresee the bright future of such new tools in understanding life at the most profound analytical level.
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Affiliation(s)
| | - Yujia Jiang
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
| | | | | | - Shuai Wang
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | | | - Sunil Kumar Sahu
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China; State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, Guangdong 518083, China
| | - Longqi Liu
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Xun Xu
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China; BGI-Shenzhen, Shenzhen, Guangdong 518103, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen, Guangdong 518120, China.
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21
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Bailin SS, Kropski JA, Gangula RD, Hannah L, Simmons JD, Mashayekhi M, Ye F, Fan R, Mallal S, Warren CM, Kalams SA, Gabriel CL, Wanjalla CN, Koethe JR. Changes in subcutaneous white adipose tissue cellular composition and molecular programs underlie glucose intolerance in persons with HIV. Front Immunol 2023; 14:1152003. [PMID: 37711619 PMCID: PMC10499182 DOI: 10.3389/fimmu.2023.1152003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 08/07/2023] [Indexed: 09/16/2023] Open
Abstract
Introduction Subcutaneous adipose tissue (SAT) is a critical regulator of systemic metabolic homeostasis. Persons with HIV (PWH) have an increased risk of metabolic diseases and significant alterations in the SAT immune environment compared with the general population. Methods We generated a comprehensive single-cell multi-omic SAT atlas to characterize cellular compositional and transcriptional changes in 59 PWH across a spectrum of metabolic health. Results Glucose intolerance was associated with increased lipid-associated macrophages, CD4+ and CD8+ T effector memory cells, and decreased perivascular macrophages. We observed a coordinated intercellular regulatory program which enriched for genes related to inflammation and lipid-processing across multiple cell types as glucose intolerance increased. Increased CD4+ effector memory tissue-resident cells most strongly associated with altered expression of adipocyte genes critical for lipid metabolism and cellular regulation. Intercellular communication analysis demonstrated enhanced pro-inflammatory and pro-fibrotic signaling between immune cells and stromal cells in PWH with glucose intolerance compared with non-diabetic PWH. Lastly, while cell type-specific gene expression among PWH with diabetes was globally similar to HIV-negative individuals with diabetes, we observed substantially divergent intercellular communication pathways. Discussion These findings suggest a central role of tissue-resident immune cells in regulating SAT inflammation among PWH with metabolic disease, and underscore unique mechanisms that may converge to promote metabolic disease.
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Affiliation(s)
- Samuel S. Bailin
- Department of Medicine, Division of Infectious Diseases, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Jonathan A. Kropski
- Department of Medicine, Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN, United States
- Deparment of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, United States
| | - Rama D. Gangula
- Tennessee Center for AIDS Research, Vanderbilt University Medical Center, Nashville, TN, United States
| | - LaToya Hannah
- Department of Medicine, Division of Diabetes, Endocrinology, and Metabolism, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Joshua D. Simmons
- Tennessee Center for AIDS Research, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Mona Mashayekhi
- Department of Medicine, Division of Diabetes, Endocrinology, and Metabolism, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Fei Ye
- Department of Biostatics, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Run Fan
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Simon Mallal
- Department of Medicine, Division of Infectious Diseases, Vanderbilt University Medical Center, Nashville, TN, United States
- Tennessee Center for AIDS Research, Vanderbilt University Medical Center, Nashville, TN, United States
- Insitute for Immunology and Infectious Diseases, Murdoch University, Perth, WA, Australia
- Vanderbilt Technologies for Advanced Genomics, Vanderbilt University Medical Center, Nashville, TN, United States
- Center for Translational Immunology and Infectious Diseases, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Christian M. Warren
- Tennessee Center for AIDS Research, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Spyros A. Kalams
- Department of Medicine, Division of Infectious Diseases, Vanderbilt University Medical Center, Nashville, TN, United States
- Tennessee Center for AIDS Research, Vanderbilt University Medical Center, Nashville, TN, United States
- Center for Translational Immunology and Infectious Diseases, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Curtis L. Gabriel
- Department of Medicine, Division of Gastroenterology, Hepatology, and Nutrition, Nashville, TN, United States
| | - Celestine N. Wanjalla
- Department of Medicine, Division of Infectious Diseases, Vanderbilt University Medical Center, Nashville, TN, United States
- Center for Translational Immunology and Infectious Diseases, Vanderbilt University Medical Center, Nashville, TN, United States
| | - John R. Koethe
- Department of Medicine, Division of Infectious Diseases, Vanderbilt University Medical Center, Nashville, TN, United States
- Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN, United States
- Center for Translational Immunology and Infectious Diseases, Vanderbilt University Medical Center, Nashville, TN, United States
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22
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Grody EI, Abraham A, Shukla V, Goyal Y. Toward a systems-level probing of tumor clonality. iScience 2023; 26:106574. [PMID: 37192968 PMCID: PMC10182304 DOI: 10.1016/j.isci.2023.106574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023] Open
Abstract
Cancer has been described as a genetic disease that clonally evolves in the face of selective pressures imposed by cell-intrinsic and extrinsic factors. Although classical models based on genetic data predominantly propose Darwinian mechanisms of cancer evolution, recent single-cell profiling of cancers has described unprecedented heterogeneity in tumors providing support for alternative models of branched and neutral evolution through both genetic and non-genetic mechanisms. Emerging evidence points to a complex interplay between genetic, non-genetic, and extrinsic environmental factors in shaping the evolution of tumors. In this perspective, we briefly discuss the role of cell-intrinsic and extrinsic factors that shape clonal behaviors during tumor progression, metastasis, and drug resistance. Taking examples of pre-malignant states associated with hematological malignancies and esophageal cancer, we discuss recent paradigms of tumor evolution and prospective approaches to further enhance our understanding of this spatiotemporally regulated process.
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Affiliation(s)
- Emanuelle I. Grody
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL 60208, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Ajay Abraham
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Human Immunobiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Vipul Shukla
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Human Immunobiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Corresponding author
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL 60208, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Corresponding author
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23
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Murdaugh RL, Anastas JN. Applying single cell multi-omic analyses to understand treatment resistance in pediatric high grade glioma. Front Pharmacol 2023; 14:1002296. [PMID: 37205910 PMCID: PMC10191214 DOI: 10.3389/fphar.2023.1002296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 04/20/2023] [Indexed: 05/21/2023] Open
Abstract
Despite improvements in cancer patient outcomes seen in the past decade, tumor resistance to therapy remains a major impediment to achieving durable clinical responses. Intratumoral heterogeneity related to genetic, epigenetic, transcriptomic, proteomic, and metabolic differences between individual cancer cells has emerged as a driver of therapeutic resistance. This cell to cell heterogeneity can be assessed using single cell profiling technologies that enable the identification of tumor cell clones that exhibit similar defining features like specific mutations or patterns of DNA methylation. Single cell profiling of tumors before and after treatment can generate new insights into the cancer cell characteristics that confer therapeutic resistance by identifying intrinsically resistant sub-populations that survive treatment and by describing new cellular features that emerge post-treatment due to tumor cell evolution. Integrative, single cell analytical approaches have already proven advantageous in studies characterizing treatment-resistant clones in cancers where pre- and post-treatment patient samples are readily available, such as leukemia. In contrast, little is known about other cancer subtypes like pediatric high grade glioma, a class of heterogeneous, malignant brain tumors in children that rapidly develop resistance to multiple therapeutic modalities, including chemotherapy, immunotherapy, and radiation. Leveraging single cell multi-omic technologies to analyze naïve and therapy-resistant glioma may lead to the discovery of novel strategies to overcome treatment resistance in brain tumors with dismal clinical outcomes. In this review, we explore the potential for single cell multi-omic analyses to reveal mechanisms of glioma resistance to therapy and discuss opportunities to apply these approaches to improve long-term therapeutic response in pediatric high grade glioma and other brain tumors with limited treatment options.
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Affiliation(s)
- Rebecca L. Murdaugh
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
- Program in Cell and Gene Therapy, Baylor College of Medicine, Houston, TX, United States
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States
| | - Jamie N. Anastas
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
- Program in Cell and Gene Therapy, Baylor College of Medicine, Houston, TX, United States
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States
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24
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Baghdassarian H, Dimitrov D, Armingol E, Saez-Rodriguez J, Lewis NE. Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.28.538731. [PMID: 37162916 PMCID: PMC10168343 DOI: 10.1101/2023.04.28.538731] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. While multiple cell-cell communication tools exist, results are specific to the tool of choice, due to the diverse assumptions made across computational frameworks. Moreover, tools are often limited to analyzing single samples or to performing pairwise comparisons. As experimental design complexity and sample numbers continue to increase in single-cell datasets, so does the need for generalizable methods to decipher cell-cell communication in such scenarios. Here, we integrate two tools, LIANA and Tensor-cell2cell, which combined can deploy multiple existing methods and resources, to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this protocol, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step-by-step in both Python and R, and we provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This protocol typically takes ~1.5h to complete from installation to downstream visualizations on a GPU-enabled computer, for a dataset of ~63k cells, 10 cell types, and 12 samples.
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Affiliation(s)
- Hratch Baghdassarian
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Daniel Dimitrov
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, 69120, Heidelberg, Germany
| | - Erick Armingol
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, 69120, Heidelberg, Germany
| | - Nathan E. Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
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25
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Benhar I, Ding J, Yan W, Whitney IE, Jacobi A, Sud M, Burgin G, Shekhar K, Tran NM, Wang C, He Z, Sanes JR, Regev A. Temporal single-cell atlas of non-neuronal retinal cells reveals dynamic, coordinated multicellular responses to central nervous system injury. Nat Immunol 2023; 24:700-713. [PMID: 36807640 DOI: 10.1038/s41590-023-01437-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 01/13/2023] [Indexed: 02/22/2023]
Abstract
Non-neuronal cells are key to the complex cellular interplay that follows central nervous system insult. To understand this interplay, we generated a single-cell atlas of immune, glial and retinal pigment epithelial cells from adult mouse retina before and at multiple time points after axonal transection. We identified rare subsets in naive retina, including interferon (IFN)-response glia and border-associated macrophages, and delineated injury-induced changes in cell composition, expression programs and interactions. Computational analysis charted a three-phase multicellular inflammatory cascade after injury. In the early phase, retinal macroglia and microglia were reactivated, providing chemotactic signals concurrent with infiltration of CCR2+ monocytes from the circulation. These cells differentiated into macrophages in the intermediate phase, while an IFN-response program, likely driven by microglia-derived type I IFN, was activated across resident glia. The late phase indicated inflammatory resolution. Our findings provide a framework to decipher cellular circuitry, spatial relationships and molecular interactions following tissue injury.
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Affiliation(s)
- Inbal Benhar
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel.
| | - Jiarui Ding
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Computer Science, University of British Columbia, Vancouver, British Columbia, Canada
| | - Wenjun Yan
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Irene E Whitney
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Anne Jacobi
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Malika Sud
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Grace Burgin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Karthik Shekhar
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemical and Biomolecular Engineering, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Nicholas M Tran
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Chen Wang
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Zhigang He
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Joshua R Sanes
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Genentech, South San Francisco, CA, USA.
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26
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Comiter C, Vaishnav ED, Ciampricotti M, Li B, Yang Y, Rodig SJ, Turner M, Pfaff KL, Jané-Valbuena J, Slyper M, Waldman J, Vigneau S, Wu J, Blosser TR, Segerstolpe Å, Abravanel D, Wagle N, Zhuang X, Rudin CM, Klughammer J, Rozenblatt-Rosen O, Kobayash-Kirschvink KJ, Shu J, Regev A. Inference of single cell profiles from histology stains with the Single-Cell omics from Histology Analysis Framework (SCHAF). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.21.533680. [PMID: 36993643 PMCID: PMC10055250 DOI: 10.1101/2023.03.21.533680] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Tissue biology involves an intricate balance between cell-intrinsic processes and interactions between cells organized in specific spatial patterns, which can be respectively captured by single-cell profiling methods, such as single-cell RNA-seq (scRNA-seq), and histology imaging data, such as Hematoxylin-and-Eosin (H&E) stains. While single-cell profiles provide rich molecular information, they can be challenging to collect routinely and do not have spatial resolution. Conversely, histological H&E assays have been a cornerstone of tissue pathology for decades, but do not directly report on molecular details, although the observed structure they capture arises from molecules and cells. Here, we leverage adversarial machine learning to develop SCHAF (Single-Cell omics from Histology Analysis Framework), to generate a tissue sample's spatially-resolved single-cell omics dataset from its H&E histology image. We demonstrate SCHAF on two types of human tumors-from lung and metastatic breast cancer-training with matched samples analyzed by both sc/snRNA-seq and by H&E staining. SCHAF generated appropriate single-cell profiles from histology images in test data, related them spatially, and compared well to ground-truth scRNA-Seq, expert pathologist annotations, or direct MERFISH measurements. SCHAF opens the way to next-generation H&E2.0 analyses and an integrated understanding of cell and tissue biology in health and disease.
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27
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Spatial RNA sequencing methods show high resolution of single cell in cancer metastasis and the formation of tumor microenvironment. Biosci Rep 2023; 43:232194. [PMID: 36459212 PMCID: PMC9950536 DOI: 10.1042/bsr20221680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 11/30/2022] [Accepted: 12/02/2022] [Indexed: 12/03/2022] Open
Abstract
Cancer metastasis often leads to death and therapeutic resistance. This process involves the participation of a variety of cell components, especially cellular and intercellular communications in the tumor microenvironment (TME). Using genetic sequencing technology to comprehensively characterize the tumor and TME is therefore key to understanding metastasis and therapeutic resistance. The use of spatial transcriptome sequencing enables the localization of gene expressions and cell activities in tissue sections. By examining the localization change as well as gene expression of these cells, it is possible to characterize the progress of tumor metastasis and TME formation. With improvements of this technology, spatial transcriptome sequencing technology has been extended from local regions to whole tissues, and from single sequencing technology to multimodal analysis combined with a variety of datasets. This has enabled the detection of every single cell in tissue slides, with high resolution, to provide more accurate predictive information for tumor treatments. In this review, we summarize the results of recent studies dealing with new multimodal methods and spatial transcriptome sequencing methods in tumors to illustrate recent developments in the imaging resolution of micro-tissues.
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28
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Abstract
The theory that cancer-associated fibroblasts (CAFs) are immunosuppressive cells has prevailed throughout the past decade. However, recent high-throughput, high-resolution mesenchyme-directed single-cell studies have harnessed computational advances to functionally characterize cell states, highlighting the existence of immunostimulatory CAFs. Our group and others have uncovered and experimentally substantiated key functions of cancer antigen-presenting CAFs in T cell immunity, both in vitro and in vivo, refuting the conventional assumption that CAFs impede adaptive immune rejection of tumours. In this Perspective, I unify the follicular and non-follicular, non-endothelial stroma of tumours under the 'peripheral adaptive immune mesenchyme' framework and position subsets of CAFs as direct positive regulators of the adaptive immune system. Building on the understanding of cancer antigen presentation by CAFs and the second touch hypothesis, which postulates that full T cell polarization requires interaction with antigen-presenting cells in the non-lymphoid tissue where the antigen resides, I re-design the 'cancer-immunity cycle' to incorporate intratumoural activation of cancer-specific CD4+ T cells. Lastly, a road map to therapeutic harnessing of immunostimulatory CAF states is proposed.
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Affiliation(s)
- Maria Tsoumakidou
- Institute of Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece.
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29
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Danan CH, Katada K, Parham LR, Hamilton KE. Spatial transcriptomics add a new dimension to our understanding of the gut. Am J Physiol Gastrointest Liver Physiol 2023; 324:G91-G98. [PMID: 36472345 PMCID: PMC9870576 DOI: 10.1152/ajpgi.00191.2022] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 01/19/2023]
Abstract
The profound complexity of the intestinal mucosa demands a spatial approach to the study of gut transcriptomics. Although single-cell RNA sequencing has revolutionized our ability to survey the diverse cell types of the intestine, knowledge of cell type alone cannot fully describe the cells that make up the intestinal mucosa. During homeostasis and disease, dramatic gradients of oxygen, nutrients, extracellular matrix proteins, morphogens, and microbiota collectively dictate intestinal cell state, and only spatial techniques can articulate differences in cellular transcriptomes at this level. Spatial transcriptomic techniques assign transcriptomic data to precise regions in a tissue of interest. In recent years, these protocols have become increasingly accessible, and their application in the intestinal mucosa has exploded in popularity. In the gut, spatial transcriptomics typically involve the application of tissue sections to spatially barcoded RNA sequencing or laser capture microdissection followed by RNA sequencing. In combination with single-cell RNA sequencing, these spatial sequencing approaches allow for the construction of spatial transcriptional maps at pseudosingle-cell resolution. In this review, we describe the spatial transcriptomic technologies recently applied in the gut and the previously unattainable discoveries that they have produced.
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Affiliation(s)
- Charles H Danan
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Kay Katada
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Louis R Parham
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Kathryn E Hamilton
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
- Institute for Regenerative Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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30
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Haghverdi L, Ludwig LS. Single-cell multi-omics and lineage tracing to dissect cell fate decision-making. Stem Cell Reports 2023; 18:13-25. [PMID: 36630900 PMCID: PMC9860164 DOI: 10.1016/j.stemcr.2022.12.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 12/07/2022] [Accepted: 12/07/2022] [Indexed: 01/12/2023] Open
Abstract
The concept of cell fate relates to the future identity of a cell, and its daughters, which is obtained via cell differentiation and division. Understanding, predicting, and manipulating cell fate has been a long-sought goal of developmental and regenerative biology. Recent insights obtained from single-cell genomic and integrative lineage-tracing approaches have further aided to identify molecular features predictive of cell fate. In this perspective, we discuss these approaches with a focus on theoretical concepts and future directions of the field to dissect molecular mechanisms underlying cell fate.
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Affiliation(s)
- Laleh Haghverdi
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany.
| | - Leif S. Ludwig
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany,Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany,Corresponding author
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31
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Ospina O, Soupir A, Fridley BL. A Primer on Preprocessing, Visualization, Clustering, and Phenotyping of Barcode-Based Spatial Transcriptomics Data. Methods Mol Biol 2023; 2629:115-140. [PMID: 36929076 DOI: 10.1007/978-1-0716-2986-4_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Recent developments in spatially resolved transcriptomics (ST) have resulted in a large number of studies characterizing the architecture of tissues, the spatial distribution of cell types, and their interactions. Furthermore, ST promises to enable the discovery of more accurate drug targets while also providing a better understanding of the etiology and evolution of complex diseases. The analysis of ST brings similar challenges as seen in other gene expression assays such as scRNA-seq; however, there is the additional spatial information that warrants the development of suitable algorithms for the quality control, preprocessing, visualization, and other discovery-enabling approaches (e.g., clustering, cell phenotyping). In this chapter, we review some of the existing algorithms to perform these analytical tasks and highlight some of the unmet analytical challenges in the analysis of ST data. Given the diversity of available ST technologies, we focus this chapter on the analysis of barcode-based RNA quantitation techniques.
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Affiliation(s)
- Oscar Ospina
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Alex Soupir
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Brooke L Fridley
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
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32
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SPICEMIX enables integrative single-cell spatial modeling of cell identity. Nat Genet 2023; 55:78-88. [PMID: 36624346 PMCID: PMC9840703 DOI: 10.1038/s41588-022-01256-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 11/01/2022] [Indexed: 01/11/2023]
Abstract
Spatial transcriptomics can reveal spatially resolved gene expression of diverse cells in complex tissues. However, the development of computational methods that can use the unique properties of spatial transcriptome data to unveil cell identities remains a challenge. Here we introduce SPICEMIX, an interpretable method based on probabilistic, latent variable modeling for joint analysis of spatial information and gene expression from spatial transcriptome data. Both simulation and real data evaluations demonstrate that SPICEMIX markedly improves on the inference of cell types and their spatial patterns compared with existing approaches. By applying to spatial transcriptome data of brain regions in human and mouse acquired by seqFISH+, STARmap and Visium, we show that SPICEMIX can enhance the inference of complex cell identities, reveal interpretable spatial metagenes and uncover differentiation trajectories. SPICEMIX is a generalizable analysis framework for spatial transcriptome data to investigate cell-type composition and spatial organization of cells in complex tissues.
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33
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Rood JE, Maartens A, Hupalowska A, Teichmann SA, Regev A. Impact of the Human Cell Atlas on medicine. Nat Med 2022; 28:2486-2496. [PMID: 36482102 DOI: 10.1038/s41591-022-02104-7] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 10/24/2022] [Indexed: 12/13/2022]
Abstract
Single-cell atlases promise to provide a 'missing link' between genes, diseases and therapies. By identifying the specific cell types, states, programs and contexts where disease-implicated genes act, we will understand the mechanisms of disease at the cellular and tissue levels and can use this understanding to develop powerful disease diagnostics; identify promising new drug targets; predict their efficacy, toxicity and resistance mechanisms; and empower new kinds of therapies, from cancer therapies to regenerative medicine. Here, we lay out a vision for the potential of cell atlases to impact the future of medicine, and describe how advances over the past decade have begun to realize this potential in common complex diseases, infectious diseases (including COVID-19), rare diseases and cancer.
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Affiliation(s)
| | - Aidan Maartens
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - Sarah A Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
- Theory of Condensed Matter, Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, UK.
| | - Aviv Regev
- Genentech, South San Francisco, CA, USA.
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