1
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Schrod S, Lück N, Lohmayer R, Solbrig S, Völkl D, Wipfler T, Shutta KH, Ben Guebila M, Schäfer A, Beißbarth T, Zacharias HU, Oefner PJ, Quackenbush J, Altenbuchinger M. Spatial Cellular Networks from omics data with SpaCeNet. Genome Res 2024; 34:1371-1383. [PMID: 39231609 DOI: 10.1101/gr.279125.124] [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: 02/15/2024] [Accepted: 08/27/2024] [Indexed: 09/06/2024]
Abstract
Advances in omics technologies have allowed spatially resolved molecular profiling of single cells, providing a window not only into the diversity and distribution of cell types within a tissue, but also into the effects of interactions between cells in shaping the transcriptional landscape. Cells send chemical and mechanical signals which are received by other cells, where they can subsequently initiate context-specific gene regulatory responses. These interactions and their responses shape the individual molecular phenotype of a cell in a given microenvironment. RNAs or proteins measured in individual cells, together with the cells' spatial distribution, provide invaluable information about these mechanisms and the regulation of genes beyond processes occurring independently in each individual cell. "SpaCeNet" is a method designed to elucidate both the intracellular molecular networks (how molecular variables affect each other within the cell) and the intercellular molecular networks (how cells affect molecular variables in their neighbors). This is achieved by estimating conditional independence (CI) relations between captured variables within individual cells and by disentangling these from CI relations between variables of different cells.
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Affiliation(s)
- Stefan Schrod
- Department of Medical Bioinformatics, University Medical Center Göttingen, 37077 Göttingen, Germany
| | - Niklas Lück
- Department of Medical Bioinformatics, University Medical Center Göttingen, 37077 Göttingen, Germany
| | - Robert Lohmayer
- Leibniz Institute for Immunotherapy, 93053 Regensburg, Germany
| | - Stefan Solbrig
- Institute of Theoretical Physics, University of Regensburg, 93053 Regensburg, Germany
| | - Dennis Völkl
- Institute of Theoretical Physics, University of Regensburg, 93053 Regensburg, Germany
| | - Tina Wipfler
- Institute of Theoretical Physics, University of Regensburg, 93053 Regensburg, Germany
| | - Katherine H Shutta
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA
| | - Marouen Ben Guebila
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
| | - Andreas Schäfer
- Institute of Theoretical Physics, University of Regensburg, 93053 Regensburg, Germany
| | - Tim Beißbarth
- Department of Medical Bioinformatics, University Medical Center Göttingen, 37077 Göttingen, Germany
- Campus Institute Data Science (CIDAS), University of Göttingen, 37077 Göttingen, Germany
| | - Helena U Zacharias
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover Medical School, 30625 Hannover, Germany
| | - Peter J Oefner
- Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany
| | - John Quackenbush
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA
| | - Michael Altenbuchinger
- Department of Medical Bioinformatics, University Medical Center Göttingen, 37077 Göttingen, Germany;
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2
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Kim H, Kim KE, Madan E, Martin P, Gogna R, Rhee HW, Won KJ. Unveiling contact-mediated cellular crosstalk. Trends Genet 2024; 40:868-879. [PMID: 38906738 DOI: 10.1016/j.tig.2024.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/28/2024] [Accepted: 05/29/2024] [Indexed: 06/23/2024]
Abstract
Cell-cell interactions orchestrate complex functions in multicellular organisms, forming a regulatory network for diverse biological processes. Their disruption leads to disease states. Recent advancements - including single-cell sequencing and spatial transcriptomics, coupled with powerful bioengineering and molecular tools - have revolutionized our understanding of how cells respond to each other. Notably, spatial transcriptomics allows us to analyze gene expression changes based on cell proximity, offering a unique window into the impact of cell-cell contact. Additionally, computational approaches are being developed to decipher how cell contact governs the symphony of cellular responses. This review explores these cutting-edge approaches, providing valuable insights into deciphering the intricate cellular changes influenced by cell-cell communication.
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Affiliation(s)
- Hyobin Kim
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West, Hollywood, CA, USA
| | - Kwang-Eun Kim
- Department of Convergence Medicine, Yonsei University Wonju College of Medicine, Wonju, South Korea; Department of Chemistry, Seoul National University, Seoul, South Korea
| | - Esha Madan
- Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, USA; School of Medicine, Institute of Molecular Medicine, Virginia Commonwealth University, Richmond, VA, USA; Department of Human and Molecular Genetics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Patrick Martin
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West, Hollywood, CA, USA
| | - Rajan Gogna
- Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, USA; School of Medicine, Institute of Molecular Medicine, Virginia Commonwealth University, Richmond, VA, USA; Department of Human and Molecular Genetics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Hyun-Woo Rhee
- Department of Chemistry, Seoul National University, Seoul, South Korea.
| | - Kyoung-Jae Won
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West, Hollywood, CA, USA.
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3
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Kumar A, Schrader AW, Aggarwal B, Boroojeny AE, Asadian M, Lee J, Song YJ, Zhao SD, Han HS, Sinha S. Intracellular spatial transcriptomic analysis toolkit (InSTAnT). Nat Commun 2024; 15:7794. [PMID: 39242579 PMCID: PMC11379969 DOI: 10.1038/s41467-024-49457-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 06/04/2024] [Indexed: 09/09/2024] Open
Abstract
Imaging-based spatial transcriptomics technologies such as Multiplexed error-robust fluorescence in situ hybridization (MERFISH) can capture cellular processes in unparalleled detail. However, rigorous and robust analytical tools are needed to unlock their full potential for discovering subcellular biological patterns. We present Intracellular Spatial Transcriptomic Analysis Toolkit (InSTAnT), a computational toolkit for extracting molecular relationships from spatial transcriptomics data at single molecule resolution. InSTAnT employs specialized statistical tests and algorithms to detect gene pairs and modules exhibiting intriguing patterns of co-localization, both within individual cells and across the cellular landscape. We showcase the toolkit on five different datasets representing two different cell lines, two brain structures, two species, and three different technologies. We perform rigorous statistical assessment of discovered co-localization patterns, find supporting evidence from databases and RNA interactions, and identify associated subcellular domains. We uncover several cell type and region-specific gene co-localizations within the brain. Intra-cellular spatial patterns discovered by InSTAnT mirror diverse molecular relationships, including RNA interactions and shared sub-cellular localization or function, providing a rich compendium of testable hypotheses regarding molecular functions.
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Affiliation(s)
- Anurendra Kumar
- College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Alex W Schrader
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Bhavay Aggarwal
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | | | - Marisa Asadian
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - JuYeon Lee
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - You Jin Song
- Department of Cell and Developmental Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Sihai Dave Zhao
- Department of Statistics, University of Illinois Urbana-Champaign, Urbana, IL, 61820, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
| | - Hee-Sun Han
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
| | - Saurabh Sinha
- H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30318, USA.
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
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4
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Su J, Song Y, Zhu Z, Huang X, Fan J, Qiao J, Mao F. Cell-cell communication: new insights and clinical implications. Signal Transduct Target Ther 2024; 9:196. [PMID: 39107318 PMCID: PMC11382761 DOI: 10.1038/s41392-024-01888-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 05/09/2024] [Accepted: 06/02/2024] [Indexed: 09/11/2024] Open
Abstract
Multicellular organisms are composed of diverse cell types that must coordinate their behaviors through communication. Cell-cell communication (CCC) is essential for growth, development, differentiation, tissue and organ formation, maintenance, and physiological regulation. Cells communicate through direct contact or at a distance using ligand-receptor interactions. So cellular communication encompasses two essential processes: cell signal conduction for generation and intercellular transmission of signals, and cell signal transduction for reception and procession of signals. Deciphering intercellular communication networks is critical for understanding cell differentiation, development, and metabolism. First, we comprehensively review the historical milestones in CCC studies, followed by a detailed description of the mechanisms of signal molecule transmission and the importance of the main signaling pathways they mediate in maintaining biological functions. Then we systematically introduce a series of human diseases caused by abnormalities in cell communication and their progress in clinical applications. Finally, we summarize various methods for monitoring cell interactions, including cell imaging, proximity-based chemical labeling, mechanical force analysis, downstream analysis strategies, and single-cell technologies. These methods aim to illustrate how biological functions depend on these interactions and the complexity of their regulatory signaling pathways to regulate crucial physiological processes, including tissue homeostasis, cell development, and immune responses in diseases. In addition, this review enhances our understanding of the biological processes that occur after cell-cell binding, highlighting its application in discovering new therapeutic targets and biomarkers related to precision medicine. This collective understanding provides a foundation for developing new targeted drugs and personalized treatments.
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Affiliation(s)
- Jimeng Su
- Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, China
- Cancer Center, Peking University Third Hospital, Beijing, China
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China
| | - Ying Song
- Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, China
- Cancer Center, Peking University Third Hospital, Beijing, China
| | - Zhipeng Zhu
- Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, China
- Cancer Center, Peking University Third Hospital, Beijing, China
| | - Xinyue Huang
- Biomedical Research Institute, Shenzhen Peking University-the Hong Kong University of Science and Technology Medical Center, Shenzhen, China
| | - Jibiao Fan
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China
| | - Jie Qiao
- State Key Laboratory of Female Fertility Promotion, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China.
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, China.
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, China.
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, China.
| | - Fengbiao Mao
- Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, China.
- Cancer Center, Peking University Third Hospital, Beijing, China.
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5
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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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6
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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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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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Sears RG, Lenaghan SC, Stewart CN. AI to enable plant cell metabolic engineering. TRENDS IN PLANT SCIENCE 2024; 29:126-129. [PMID: 37778886 DOI: 10.1016/j.tplants.2023.09.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/30/2023] [Accepted: 09/08/2023] [Indexed: 10/03/2023]
Abstract
Plant metabolic engineering must take into consideration the heterogeneous cell types that play a role in metabolite production; cells do not participate equally. We posit that artificial intelligence (AI) developed for biomedical purposes can be applied to plant cell characterization to accelerate the development of metabolic engineering strategies in plants.
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Affiliation(s)
- Robert G Sears
- Department of Plant Sciences, The University of Tennessee, Knoxville, Knoxville, TN, USA; Center for Agricultural Synthetic Biology, The University of Tennessee, Knoxville, Knoxville, TN, USA
| | - Scott C Lenaghan
- Center for Agricultural Synthetic Biology, The University of Tennessee, Knoxville, Knoxville, TN, USA; Department of Food Science, The University of Tennessee, Knoxville, Knoxville, TN, USA
| | - C Neal Stewart
- Department of Plant Sciences, The University of Tennessee, Knoxville, Knoxville, TN, USA; Center for Agricultural Synthetic Biology, The University of Tennessee, Knoxville, Knoxville, TN, USA.
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9
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Kim H, Kumar A, Lövkvist C, Palma AM, Martin P, Kim J, Bhoopathi P, Trevino J, Fisher P, Madan E, Gogna R, Won KJ. CellNeighborEX: deciphering neighbor-dependent gene expression from spatial transcriptomics data. Mol Syst Biol 2023; 19:e11670. [PMID: 37815040 PMCID: PMC10632736 DOI: 10.15252/msb.202311670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 09/14/2023] [Accepted: 09/18/2023] [Indexed: 10/11/2023] Open
Abstract
Cells have evolved their communication methods to sense their microenvironments and send biological signals. In addition to communication using ligands and receptors, cells use diverse channels including gap junctions to communicate with their immediate neighbors. Current approaches, however, cannot effectively capture the influence of various microenvironments. Here, we propose a novel approach to investigate cell neighbor-dependent gene expression (CellNeighborEX) in spatial transcriptomics (ST) data. To categorize cells based on their microenvironment, CellNeighborEX uses direct cell location or the mixture of transcriptome from multiple cells depending on ST technologies. For each cell type, CellNeighborEX identifies diverse gene sets associated with partnering cell types, providing further insight. We found that cells express different genes depending on their neighboring cell types in various tissues including mouse embryos, brain, and liver cancer. Those genes are associated with critical biological processes such as development or metastases. We further validated that gene expression is induced by neighboring partners via spatial visualization. The neighbor-dependent gene expression suggests new potential genes involved in cell-cell interactions beyond what ligand-receptor co-expression can discover.
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Affiliation(s)
- Hyobin Kim
- Department of Computational BiomedicineCedars‐Sinai Medical CenterHollywoodCAUSA
- Biotech Research and Innovation Centre (BRIC)University of CopenhagenCopenhagenDenmark
| | - Amit Kumar
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- School of Medicine, Institute of Molecular MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Human and Molecular Genetics, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
| | - Cecilia Lövkvist
- Novo Nordisk Foundation Center for Stem Cell Medicine, reNEWUniversity of CopenhagenCopenhagenDenmark
| | - António M Palma
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- School of Medicine, Institute of Molecular MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Instituto Superior TecnicoUniversidade de LisboaLisboaPortugal
| | - Patrick Martin
- Department of Computational BiomedicineCedars‐Sinai Medical CenterHollywoodCAUSA
- Biotech Research and Innovation Centre (BRIC)University of CopenhagenCopenhagenDenmark
| | - Junil Kim
- School of Systems Biomedical ScienceSoongsil UniversitySeoulKorea
| | - Praveen Bhoopathi
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- School of Medicine, Institute of Molecular MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Human and Molecular Genetics, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
| | - Jose Trevino
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- Department of Surgery, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
| | - Paul Fisher
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- School of Medicine, Institute of Molecular MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Human and Molecular Genetics, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
| | - Esha Madan
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- School of Medicine, Institute of Molecular MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Human and Molecular Genetics, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Surgery, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
| | - Rajan Gogna
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- School of Medicine, Institute of Molecular MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Human and Molecular Genetics, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Surgery, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
| | - Kyoung Jae Won
- Department of Computational BiomedicineCedars‐Sinai Medical CenterHollywoodCAUSA
- Biotech Research and Innovation Centre (BRIC)University of CopenhagenCopenhagenDenmark
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10
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Chen H, Li D, Bar-Joseph Z. SCS: cell segmentation for high-resolution spatial transcriptomics. Nat Methods 2023; 20:1237-1243. [PMID: 37429992 DOI: 10.1038/s41592-023-01939-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 06/02/2023] [Indexed: 07/12/2023]
Abstract
Spatial transcriptomics promises to greatly improve our understanding of tissue organization and cell-cell interactions. While most current platforms for spatial transcriptomics only offer multi-cellular resolution, with 10-15 cells per spot, recent technologies provide a much denser spot placement leading to subcellular resolution. A key challenge for these newer methods is cell segmentation and the assignment of spots to cells. Traditional image-based segmentation methods are limited and do not make full use of the information profiled by spatial transcriptomics. Here we present subcellular spatial transcriptomics cell segmentation (SCS), which combines imaging data with sequencing data to improve cell segmentation accuracy. SCS assigns spots to cells by adaptively learning the position of each spot relative to the center of its cell using a transformer neural network. SCS was tested on two new subcellular spatial transcriptomics technologies and outperformed traditional image-based segmentation methods. SCS achieved better accuracy, identified more cells and provided more realistic cell size estimation. Subcellular analysis of RNAs using SCS spot assignments provides information on RNA localization and further supports the segmentation results.
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Affiliation(s)
- Hao Chen
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Dongshunyi Li
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
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11
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Gurkar AU, Gerencser AA, Mora AL, Nelson AC, Zhang AR, Lagnado AB, Enninful A, Benz C, Furman D, Beaulieu D, Jurk D, Thompson EL, Wu F, Rodriguez F, Barthel G, Chen H, Phatnani H, Heckenbach I, Chuang JH, Horrell J, Petrescu J, Alder JK, Lee JH, Niedernhofer LJ, Kumar M, Königshoff M, Bueno M, Sokka M, Scheibye-Knudsen M, Neretti N, Eickelberg O, Adams PD, Hu Q, Zhu Q, Porritt RA, Dong R, Peters S, Victorelli S, Pengo T, Khaliullin T, Suryadevara V, Fu X, Bar-Joseph Z, Ji Z, Passos JF. Spatial mapping of cellular senescence: emerging challenges and opportunities. NATURE AGING 2023; 3:776-790. [PMID: 37400722 PMCID: PMC10505496 DOI: 10.1038/s43587-023-00446-6] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 05/30/2023] [Indexed: 07/05/2023]
Abstract
Cellular senescence is a well-established driver of aging and age-related diseases. There are many challenges to mapping senescent cells in tissues such as the absence of specific markers and their relatively low abundance and vast heterogeneity. Single-cell technologies have allowed unprecedented characterization of senescence; however, many methodologies fail to provide spatial insights. The spatial component is essential, as senescent cells communicate with neighboring cells, impacting their function and the composition of extracellular space. The Cellular Senescence Network (SenNet), a National Institutes of Health (NIH) Common Fund initiative, aims to map senescent cells across the lifespan of humans and mice. Here, we provide a comprehensive review of the existing and emerging methodologies for spatial imaging and their application toward mapping senescent cells. Moreover, we discuss the limitations and challenges inherent to each technology. We argue that the development of spatially resolved methods is essential toward the goal of attaining an atlas of senescent cells.
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Affiliation(s)
- Aditi U Gurkar
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Ana L Mora
- Dorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, the Ohio State University, Columbus, OH, USA
| | - Andrew C Nelson
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Anru R Zhang
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine and Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Anthony B Lagnado
- Department of Physiology and Biomedical Engineering, Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA
| | - Archibald Enninful
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | | | - David Furman
- Buck Institute for Research on Aging, Novato, CA, USA
- Stanford 1000 Immunomes Project, Stanford School of Medicine, Stanford University, Stanford, CA, USA
- Instituto de Investigaciones en Medicina Traslacional (IIMT), Universidad Austral, Pilar, Argentina
| | - Delphine Beaulieu
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Diana Jurk
- Department of Physiology and Biomedical Engineering, Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA
| | - Elizabeth L Thompson
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Fei Wu
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Fernanda Rodriguez
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Grant Barthel
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Hao Chen
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Hemali Phatnani
- Columbia University Irving Medical Center and New York Genome Center, Columbia University, New York, NY, USA
| | | | - Jeffrey H Chuang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Jeremy Horrell
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA
| | - Joana Petrescu
- Columbia University Irving Medical Center and New York Genome Center, Columbia University, New York, NY, USA
| | - Jonathan K Alder
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jun Hee Lee
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Laura J Niedernhofer
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Manoj Kumar
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, Stanford, CA, USA
| | - Melanie Königshoff
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Marta Bueno
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Miiko Sokka
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA
| | | | - Nicola Neretti
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA
| | - Oliver Eickelberg
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Peter D Adams
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Qianjiang Hu
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Quan Zhu
- University of California, San Diego, CA, USA
| | - Rebecca A Porritt
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Runze Dong
- Department of Biochemistry, Institute for Protein Design and Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
| | - Samuel Peters
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Stella Victorelli
- Department of Physiology and Biomedical Engineering, Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA
| | - Thomas Pengo
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Timur Khaliullin
- Dorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, the Ohio State University, Columbus, OH, USA
| | - Vidyani Suryadevara
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, Stanford, CA, USA
| | - Xiaonan Fu
- Department of Biochemistry, Institute for Protein Design and Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Zhicheng Ji
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine and Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - João F Passos
- Department of Physiology and Biomedical Engineering, Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
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12
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Chen H, Li D, Bar-Joseph Z. SCS: cell segmentation for high-resolution spatial transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.11.523658. [PMID: 37398213 PMCID: PMC10312435 DOI: 10.1101/2023.01.11.523658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Spatial transcriptomics promises to greatly improve our understanding of tissue organization and cell-cell interactions. While most current platforms for spatial transcriptomics only offer multi-cellular resolution, with 10-15 cells per spot, recent technologies provide a much denser spot placement leading to sub-cellular resolution. A key challenge for these newer methods is cell segmentation and the assignment of spots to cells. Traditional image-based segmentation methods are limited and do not make full use of the information profiled by spatial transcrip-tomics. Here we present SCS, which combines imaging data with sequencing data to improve cell segmentation accuracy. SCS assigns spots to cells by adaptively learning the position of each spot relative to the center of its cell using a transformer neural network. SCS was tested on two new sub-cellular spatial transcriptomics technologies and outperformed traditional image-based segmentation methods. SCS achieved better accuracy, identified more cells, and provided more realistic cell size estimation. Sub-cellular analysis of RNAs using SCS spots assignments provides information on RNA localization and further supports the segmentation results.
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13
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Deshpande A, Loth M, Sidiropoulos DN, Zhang S, Yuan L, Bell ATF, Zhu Q, Ho WJ, Santa-Maria C, Gilkes DM, Williams SR, Uytingco CR, Chew J, Hartnett A, Bent ZW, Favorov AV, Popel AS, Yarchoan M, Kiemen A, Wu PH, Fujikura K, Wirtz D, Wood LD, Zheng L, Jaffee EM, Anders RA, Danilova L, Stein-O'Brien G, Kagohara LT, Fertig EJ. Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces. Cell Syst 2023; 14:285-301.e4. [PMID: 37080163 PMCID: PMC10236356 DOI: 10.1016/j.cels.2023.03.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/26/2022] [Accepted: 03/20/2023] [Indexed: 04/22/2023]
Abstract
Recent advances in spatial transcriptomics (STs) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor as well as the tumor microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as it occurs in tumor-immune interactions. We present SpaceMarkers, a bioinformatics algorithm to infer molecular changes from cell-cell interactions from latent space analysis of ST data. We apply this approach to infer the molecular changes from tumor-immune interactions in Visium spatial transcriptomics data of metastasis, invasive and precursor lesions, and immunotherapy treatment. Further transfer learning in matched scRNA-seq data enabled further quantification of the specific cell types in which SpaceMarkers are enriched. Altogether, SpaceMarkers can identify the location and context-specific molecular interactions within the TME from ST data.
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Affiliation(s)
- Atul Deshpande
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Melanie Loth
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Dimitrios N Sidiropoulos
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Cellular and Molecular Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Shuming Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Long Yuan
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Immunology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alexander T F Bell
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Qingfeng Zhu
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Won Jin Ho
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Cesar Santa-Maria
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniele M Gilkes
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | | | | | | | | | - Alexander V Favorov
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Aleksander S Popel
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Mark Yarchoan
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ashley Kiemen
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Pei-Hsun Wu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Kohei Fujikura
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Denis Wirtz
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Chemical and Biomolecular Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA; Johns Hopkins Physical Sciences - Oncology Center and Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA
| | - Laura D Wood
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Lei Zheng
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Elizabeth M Jaffee
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Robert A Anders
- Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ludmila Danilova
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Genevieve Stein-O'Brien
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Luciane T Kagohara
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Elana J Fertig
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Applied Mathematics and Statistics, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
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14
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Lee AJ, Cahill R, Abbasi-Asl R. Machine Learning for Uncovering Biological Insights in Spatial Transcriptomics Data. ARXIV 2023:arXiv:2303.16725v1. [PMID: 37033464 PMCID: PMC10081350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Development and homeostasis in multicellular systems both require exquisite control over spatial molecular pattern formation and maintenance. Advances in spatially-resolved and high-throughput molecular imaging methods such as multiplexed immunofluorescence and spatial transcriptomics (ST) provide exciting new opportunities to augment our fundamental understanding of these processes in health and disease. The large and complex datasets resulting from these techniques, particularly ST, have led to rapid development of innovative machine learning (ML) tools primarily based on deep learning techniques. These ML tools are now increasingly featured in integrated experimental and computational workflows to disentangle signals from noise in complex biological systems. However, it can be difficult to understand and balance the different implicit assumptions and methodologies of a rapidly expanding toolbox of analytical tools in ST. To address this, we summarize major ST analysis goals that ML can help address and current analysis trends. We also describe four major data science concepts and related heuristics that can help guide practitioners in their choices of the right tools for the right biological questions.
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15
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Zhu J, Shang L, Zhou X. SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics. Genome Biol 2023; 24:39. [PMID: 36869394 PMCID: PMC9983268 DOI: 10.1186/s13059-023-02879-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 02/16/2023] [Indexed: 03/05/2023] Open
Abstract
Spatially resolved transcriptomics (SRT)-specific computational methods are often developed, tested, validated, and evaluated in silico using simulated data. Unfortunately, existing simulated SRT data are often poorly documented, hard to reproduce, or unrealistic. Single-cell simulators are not directly applicable for SRT simulation as they cannot incorporate spatial information. We present SRTsim, an SRT-specific simulator for scalable, reproducible, and realistic SRT simulations. SRTsim not only maintains various expression characteristics of SRT data but also preserves spatial patterns. We illustrate the benefits of SRTsim in benchmarking methods for spatial clustering, spatial expression pattern detection, and cell-cell communication identification.
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Affiliation(s)
- Jiaqiang Zhu
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Lulu Shang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA.
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16
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Kumar A, Schrader AW, Boroojeny AE, Asadian M, Lee J, Song YJ, Zhao SD, Han HS, Sinha S. Intracellular Spatial Transcriptomic Analysis Toolkit (InSTAnT). RESEARCH SQUARE 2023:rs.3.rs-2481749. [PMID: 36747718 PMCID: PMC9901031 DOI: 10.21203/rs.3.rs-2481749/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Imaging-based spatial transcriptomics technologies such as MERFISH offer snapshots of cellular processes in unprecedented detail, but new analytic tools are needed to realize their full potential. We present InSTAnT, a computational toolkit for extracting molecular relationships from spatial transcriptomics data at the intra-cellular resolution. InSTAnT detects gene pairs and modules with interesting patterns of mutual co-localization within and across cells, using specialized statistical tests and graph mining. We showcase the toolkit on datasets profiling a human cancer cell line and hypothalamic preoptic region of mouse brain. We performed rigorous statistical assessment of discovered co-localization patterns, found supporting evidence from databases and RNA interactions, and identified subcellular domains associated with RNA-colocalization. We identified several novel cell type-specific gene co-localizations in the brain. Intra-cellular spatial patterns discovered by InSTAnT mirror diverse molecular relationships, including RNA interactions and shared sub-cellular localization or function, providing a rich compendium of testable hypotheses regarding molecular functions.
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Affiliation(s)
- Anurendra Kumar
- College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Alex W. Schrader
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | | | - Marisa Asadian
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Juyeon Lee
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - You Jin Song
- Department of Cell and Developmental Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Sihai Dave Zhao
- Department of Statistics, University of Illinois Urbana-Champaign, Urbana, IL, 61820, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Hee-Sun Han
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Saurabh Sinha
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
- H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30318, USA
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17
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Yue L, Liu F, Hu J, Yang P, Wang Y, Dong J, Shu W, Huang X, Wang S. A guidebook of spatial transcriptomic technologies, data resources and analysis approaches. Comput Struct Biotechnol J 2023; 21:940-955. [PMID: 38213887 PMCID: PMC10781722 DOI: 10.1016/j.csbj.2023.01.016] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 01/13/2023] [Accepted: 01/14/2023] [Indexed: 01/18/2023] Open
Abstract
Advances in transcriptomic technologies have deepened our understanding of the cellular gene expression programs of multicellular organisms and provided a theoretical basis for disease diagnosis and therapy. However, both bulk and single-cell RNA sequencing approaches lose the spatial context of cells within the tissue microenvironment, and the development of spatial transcriptomics has made overall bias-free access to both transcriptional information and spatial information possible. Here, we elaborate development of spatial transcriptomic technologies to help researchers select the best-suited technology for their goals and integrate the vast amounts of data to facilitate data accessibility and availability. Then, we marshal various computational approaches to analyze spatial transcriptomic data for various purposes and describe the spatial multimodal omics and its potential for application in tumor tissue. Finally, we provide a detailed discussion and outlook of the spatial transcriptomic technologies, data resources and analysis approaches to guide current and future research on spatial transcriptomics.
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Affiliation(s)
- Liangchen Yue
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Feng Liu
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China
| | - Jiongsong Hu
- University of South China, Hengyang, Hunan 421001, China
| | - Pin Yang
- Anhui Medical University, Hefei 230022, Anhui, China
| | - Yuxiang Wang
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Junguo Dong
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Wenjie Shu
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Xingxu Huang
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, the First Affiliated Hospital, and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310029, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Shengqi Wang
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
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18
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Ma C, Chitra U, Zhang S, Raphael BJ. Belayer: Modeling discrete and continuous spatial variation in gene expression from spatially resolved transcriptomics. Cell Syst 2022; 13:786-797.e13. [PMID: 36265465 PMCID: PMC9814896 DOI: 10.1016/j.cels.2022.09.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 07/13/2022] [Accepted: 09/06/2022] [Indexed: 01/26/2023]
Abstract
Spatially resolved transcriptomics (SRT) technologies measure gene expression at known locations in a tissue slice, enabling the identification of spatially varying genes or cell types. Current approaches for these tasks assume either that gene expression varies continuously across a tissue or that a tissue contains a small number of regions with distinct cellular composition. We propose a model for SRT data from layered tissues that includes both continuous and discrete spatial variation in expression and an algorithm, Belayer, to learn the parameters of this model. Belayer models gene expression as a piecewise linear function of the relative depth of a tissue layer with possible discontinuities at layer boundaries. We use conformal maps to model relative depth and derive a dynamic programming algorithm to infer layer boundaries and gene expression functions. Belayer accurately identifies tissue layers and biologically meaningful spatially varying genes in SRT data from the brain and skin.
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Affiliation(s)
- Cong Ma
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08540, USA
| | - Uthsav Chitra
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08540, USA
| | - Shirley Zhang
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08540, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08540, USA.
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19
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Peng L, Wang F, Wang Z, Tan J, Huang L, Tian X, Liu G, Zhou L. Cell-cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies. Brief Bioinform 2022; 23:6618236. [PMID: 35753695 DOI: 10.1093/bib/bbac234] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/15/2022] [Accepted: 05/19/2022] [Indexed: 12/12/2022] Open
Abstract
Carcinomas are complex ecosystems composed of cancer, stromal and immune cells. Communication between these cells and their microenvironments induces cancer progression and causes therapy resistance. In order to improve the treatment of cancers, it is essential to quantify crosstalk between and within various cell types in a tumour microenvironment. Focusing on the coordinated expression patterns of ligands and cognate receptors, cell-cell communication can be inferred through ligand-receptor interactions (LRIs). In this manuscript, we carry out the following work: (i) introduce pipeline for ligand-receptor-mediated intercellular communication estimation from single-cell transcriptomics and list a few available LRI-related databases and visualization tools; (ii) demonstrate seven classical intercellular communication scoring strategies, highlight four types of representative intercellular communication inference methods, including network-based approaches, machine learning-based approaches, spatial information-based approaches and other approaches; (iii) summarize the evaluation and validation avenues for intercellular communication inference and analyze the advantages and limitations for the above four types of cell-cell communication methods; (iv) comment several major challenges while provide further research directions for intercellular communication analysis in the tumour microenvironments. We anticipate that this work helps to better understand intercellular crosstalk and to further develop powerful cell-cell communication estimation tools for tumor-targeted therapy.
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Affiliation(s)
- Lihong Peng
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China.,College of Life Sciences and Chemistry, Hunan University of Technology, 412007, Hunan, China
| | - Feixiang Wang
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China
| | - Zhao Wang
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China
| | - Jingwei Tan
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China
| | - Li Huang
- Academy of Arts and Design, Tsinghua University, 10084, Beijing, China.,The Future Laboratory, Tsinghua University, 10084, Beijing, China
| | - Xiongfei Tian
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China
| | - Guangyi Liu
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China
| | - Liqian Zhou
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China
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20
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Prybutok AN, Cain JY, Leonard JN, Bagheri N. Fighting fire with fire: deploying complexity in computational modeling to effectively characterize complex biological systems. Curr Opin Biotechnol 2022; 75:102704. [DOI: 10.1016/j.copbio.2022.102704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 01/27/2022] [Accepted: 02/06/2022] [Indexed: 11/03/2022]
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21
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Membrane marker selection for segmenting single cell spatial proteomics data. Nat Commun 2022; 13:1999. [PMID: 35422106 PMCID: PMC9010440 DOI: 10.1038/s41467-022-29667-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 03/25/2022] [Indexed: 12/21/2022] Open
Abstract
The ability to profile spatial proteomics at the single cell level enables the study of cell types, their spatial distribution, and interactions in several tissues and conditions. Current methods for cell segmentation in such studies rely on known membrane or cell boundary markers. However, for many tissues, an optimal set of markers is not known, and even within a tissue, different cell types may express different markers. Here we present RAMCES, a method that uses a convolutional neural network to learn the optimal markers for a new sample and outputs a weighted combination of the selected markers for segmentation. Testing RAMCES on several existing datasets indicates that it correctly identifies cell boundary markers, improving on methods that rely on a single marker or those that extend nuclei segmentations. Application to new spatial proteomics data demonstrates its usefulness for accurately assigning cell types based on the proteins expressed in segmented cells. Cell segmentation of single-cell spatial proteomics data remains a challenge and often relies on the selection of a membrane marker, which is not always known. Here, the authors introduce RAMCES, a method that selects the optimal membrane markers to use for more accurate cell segmentation.
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22
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Tanevski J, Flores ROR, Gabor A, Schapiro D, Saez-Rodriguez J. Explainable multiview framework for dissecting spatial relationships from highly multiplexed data. Genome Biol 2022; 23:97. [PMID: 35422018 PMCID: PMC9011939 DOI: 10.1186/s13059-022-02663-5] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 04/01/2022] [Indexed: 12/12/2022] Open
Abstract
The advancement of highly multiplexed spatial technologies requires scalable methods that can leverage spatial information. We present MISTy, a flexible, scalable, and explainable machine learning framework for extracting relationships from any spatial omics data, from dozens to thousands of measured markers. MISTy builds multiple views focusing on different spatial or functional contexts to dissect different effects. We evaluated MISTy on in silico and breast cancer datasets measured by imaging mass cytometry and spatial transcriptomics. We estimated structural and functional interactions coming from different spatial contexts in breast cancer and demonstrated how to relate MISTy's results to clinical features.
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Affiliation(s)
- Jovan Tanevski
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, Heidelberg, Germany
- Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia
| | - Ricardo Omar Ramirez Flores
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, Heidelberg, Germany
| | - Attila Gabor
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, Heidelberg, Germany
| | - Denis Schapiro
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, Heidelberg, Germany
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute of Pathology, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, Heidelberg, Germany
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, Heidelberg, Germany.
- Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, Aachen, Germany.
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23
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Jin S, Ramos R. Computational exploration of cellular communication in skin from emerging single-cell and spatial transcriptomic data. Biochem Soc Trans 2022; 50:297-308. [PMID: 35191953 PMCID: PMC9022991 DOI: 10.1042/bst20210863] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 01/29/2022] [Accepted: 02/01/2022] [Indexed: 12/28/2022]
Abstract
Tissue development and homeostasis require coordinated cell-cell communication. Recent advances in single-cell sequencing technologies have emerged as a revolutionary method to reveal cellular heterogeneity with unprecedented resolution. This offers a great opportunity to explore cell-cell communication in tissues systematically and comprehensively, and to further identify signaling mechanisms driving cell fate decisions and shaping tissue phenotypes. Using gene expression information from single-cell transcriptomics, several computational tools have been developed for inferring cell-cell communication, greatly facilitating analysis and interpretation. However, in single-cell transcriptomics, spatial information of cells is inherently lost. Given that most cell signaling events occur within a limited distance in tissues, incorporating spatial information into cell-cell communication analysis is critical for understanding tissue organization and function. Spatial transcriptomics provides spatial location of cell subsets along with their gene expression, leading to new directions for leveraging spatial information to develop computational approaches for cell-cell communication inference and analysis. These computational approaches have been successfully applied to uncover previously unrecognized mechanisms of intercellular communication within various contexts and across organ systems, including the skin, a formidable model to study mechanisms of cell-cell communication due to the complex interactions between the different cell populations that comprise it. Here, we review emergent cell-cell communication inference tools using single-cell transcriptomics and spatial transcriptomics, and highlight the biological insights gained by applying these computational tools to exploring cellular communication in skin development, homeostasis, disease and aging, as well as discuss future potential research avenues.
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Affiliation(s)
- Suoqin Jin
- School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China
| | - Raul Ramos
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92697, U.S.A
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24
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Spatial components of molecular tissue biology. Nat Biotechnol 2022; 40:308-318. [PMID: 35132261 DOI: 10.1038/s41587-021-01182-1] [Citation(s) in RCA: 109] [Impact Index Per Article: 54.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 12/03/2021] [Indexed: 02/06/2023]
Abstract
Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly evolving, making it possible to comprehensively characterize cells and tissues in health and disease. To maximize the biological insights obtained using these techniques, it is critical to both clearly articulate the key biological questions in spatial analysis of tissues and develop the requisite computational tools to address them. Developers of analytical tools need to decide on the intrinsic molecular features of each cell that need to be considered, and how cell shape and morphological features are incorporated into the analysis. Also, optimal ways to compare different tissue samples at various length scales are still being sought. Grouping these biological problems and related computational algorithms into classes across length scales, thus characterizing common issues that need to be addressed, will facilitate further progress in spatial transcriptomics and proteomics.
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Teng H, Yuan Y, Bar-Joseph Z. Clustering spatial transcriptomics data. Bioinformatics 2022; 38:997-1004. [PMID: 34623423 PMCID: PMC8796363 DOI: 10.1093/bioinformatics/btab704] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 08/28/2021] [Accepted: 10/06/2021] [Indexed: 02/04/2023] Open
Abstract
MOTIVATION Recent advancements in fluorescence in situ hybridization (FISH) techniques enable them to concurrently obtain information on the location and gene expression of single cells. A key question in the initial analysis of such spatial transcriptomics data is the assignment of cell types. To date, most studies used methods that only rely on the expression levels of the genes in each cell for such assignments. To fully utilize the data and to improve the ability to identify novel sub-types, we developed a new method, FICT, which combines both expression and neighborhood information when assigning cell types. RESULTS FICT optimizes a probabilistic function that we formalize and for which we provide learning and inference algorithms. We used FICT to analyze both simulated and several real spatial transcriptomics data. As we show, FICT can accurately identify cell types and sub-types, improving on expression only methods and other methods proposed for clustering spatial transcriptomics data. Some of the spatial sub-types identified by FICT provide novel hypotheses about the new functions for excitatory and inhibitory neurons. AVAILABILITY AND IMPLEMENTATION FICT is available at: https://github.com/haotianteng/FICT. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Haotian Teng
- Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Ye Yuan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Ziv Bar-Joseph
- Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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26
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Noel T, Wang QS, Greka A, Marshall JL. Principles of Spatial Transcriptomics Analysis: A Practical Walk-Through in Kidney Tissue. Front Physiol 2022; 12:809346. [PMID: 35069263 PMCID: PMC8770822 DOI: 10.3389/fphys.2021.809346] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 11/26/2021] [Indexed: 12/26/2022] Open
Abstract
Spatial transcriptomic technologies capture genome-wide readouts across biological tissue space. Moreover, recent advances in this technology, including Slide-seqV2, have achieved spatial transcriptomic data collection at a near-single cell resolution. To-date, a repertoire of computational tools has been developed to discern cell type classes given the transcriptomic profiles of tissue coordinates. Upon applying these tools, we can explore the spatial patterns of distinct cell types and characterize how genes are spatially expressed within different cell type contexts. The kidney is one organ whose function relies upon spatially defined structures consisting of distinct cellular makeup. Thus, the application of Slide-seqV2 to kidney tissue has enabled us to elucidate spatially characteristic cellular and genetic profiles at a scale that remains largely unexplored. Here, we review spatial transcriptomic technologies, as well as computational approaches for cell type mapping and spatial cell type and transcriptomic characterizations. We take kidney tissue as an example to demonstrate how the technologies are applied, while considering the nuances of this architecturally complex tissue.
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Affiliation(s)
- Teia Noel
- Kidney Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Qingbo S. Wang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, United States
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, United States
- Department of Statistical Genetics, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Anna Greka
- Kidney Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Jamie L. Marshall
- Kidney Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, United States
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Chen H, Murray E, Sinha A, Laumas A, Li J, Lesman D, Nie X, Hotaling J, Guo J, Cairns BR, Macosko EZ, Cheng CY, Chen F. Dissecting mammalian spermatogenesis using spatial transcriptomics. Cell Rep 2021; 37:109915. [PMID: 34731600 PMCID: PMC8606188 DOI: 10.1016/j.celrep.2021.109915] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 07/20/2021] [Accepted: 10/11/2021] [Indexed: 12/13/2022] Open
Abstract
Single-cell RNA sequencing has revealed extensive molecular diversity in gene programs governing mammalian spermatogenesis but fails to delineate their dynamics in the native context of seminiferous tubules, the spatially confined functional units of spermatogenesis. Here, we use Slide-seq, a spatial transcriptomics technology, to generate an atlas that captures the spatial gene expression patterns at near-single-cell resolution in the mouse and human testis. Using Slide-seq data, we devise a computational framework that accurately localizes testicular cell types in individual seminiferous tubules. Unbiased analysis systematically identifies spatially patterned genes and gene programs. Combining Slide-seq with targeted in situ RNA sequencing, we demonstrate significant differences in the cellular compositions of spermatogonial microenvironment between mouse and human testes. Finally, a comparison of the spatial atlas generated from the wild-type and diabetic mouse testis reveals a disruption in the spatial cellular organization of seminiferous tubules as a potential mechanism of diabetes-induced male infertility. Chen et al. generate a spatial transcriptome atlas of the mammalian testis at near-single-cell resolution that recapitulates spermatogenesis by accurately localizing testicular cell types and reconstructing tissue structures. The atlas is used to reveal the spatial organization of testicular microenvironment and profile its changes under diabetic conditions.
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Affiliation(s)
- Haiqi Chen
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Evan Murray
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Anubhav Sinha
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; McGovern Institute, MIT, Cambridge, MA 02139, USA; Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA 02142, USA
| | | | - Jilong Li
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Daniel Lesman
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Xichen Nie
- Department of Oncological Sciences and Huntsman Cancer Institute, Howard Hughes Medical Institute, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - Jim Hotaling
- Department of Oncological Sciences and Huntsman Cancer Institute, Howard Hughes Medical Institute, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - Jingtao Guo
- Department of Oncological Sciences and Huntsman Cancer Institute, Howard Hughes Medical Institute, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - Bradley R Cairns
- Department of Oncological Sciences and Huntsman Cancer Institute, Howard Hughes Medical Institute, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - Evan Z Macosko
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - C Yan Cheng
- The Mary M. Wohlford Laboratory for Male Contraceptive Research, Center for Biomedical Research, Population Council, New York, NY, 10065, USA
| | - Fei Chen
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA.
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Cell-Cell Communication Networks in Tissue: Toward Quantitatively Linking Structure with Function. ACTA ACUST UNITED AC 2021; 27. [PMID: 34693081 DOI: 10.1016/j.coisb.2021.05.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Forefront techniques for molecular interrogation of mammalian tissues, such as multiplexed tissue imaging, intravital microscopy, and single-cell RNA sequencing (scRNAseq), can combine to quantify cell-type abundance, co-localization, and global levels of receptors and their ligands. Nonetheless, it remains challenging to translate these various quantities into a more comprehensive understanding of how cell-cell communication networks dynamically operate. Therefore, construction of computational models for network-level functions - including niche-dependent actions, homeostasis, and multi-scale coordination - will be valuable for productively integrating the battery of experimental approaches. Here, we review recent progress in understanding cell-cell communication networks in tissue. Featured examples include ligand-receptor dissection of immunosuppressive and mitogenic signaling in the tumor microenvironment. As a future direction, we highlight an unmet potential to bridge high-level statistical approaches with low-level physicochemical mechanisms.
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Ding J, Alavi A, Ebrahimkhani MR, Bar-Joseph Z. Computational tools for analyzing single-cell data in pluripotent cell differentiation studies. CELL REPORTS METHODS 2021; 1:100087. [PMID: 35474899 PMCID: PMC9017169 DOI: 10.1016/j.crmeth.2021.100087] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Single-cell technologies are revolutionizing the ability of researchers to infer the causes and results of biological processes. Although several studies of pluripotent cell differentiation have recently utilized single-cell sequencing data, other aspects related to the optimization of differentiation protocols, their validation, robustness, and usage are still not taking full advantage of single-cell technologies. In this review, we focus on computational approaches for the analysis of single-cell omics and imaging data and discuss their use to address many of the major challenges involved in the development, validation, and use of cells obtained from pluripotent cell differentiation.
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Affiliation(s)
- Jun Ding
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, 1001 Decarie Boulevard, Montreal QC H4A 3J1, Canada
| | - Amir Alavi
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
| | - Mo R. Ebrahimkhani
- Department of Pathology, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261, USA
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
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Abstract
Spatial transcriptomics is a rapidly growing field that promises to comprehensively characterize tissue organization and architecture at the single-cell or subcellular resolution. Such information provides a solid foundation for mechanistic understanding of many biological processes in both health and disease that cannot be obtained by using traditional technologies. The development of computational methods plays important roles in extracting biological signals from raw data. Various approaches have been developed to overcome technology-specific limitations such as spatial resolution, gene coverage, sensitivity, and technical biases. Downstream analysis tools formulate spatial organization and cell-cell communications as quantifiable properties, and provide algorithms to derive such properties. Integrative pipelines further assemble multiple tools in one package, allowing biologists to conveniently analyze data from beginning to end. In this review, we summarize the state of the art of spatial transcriptomic data analysis methods and pipelines, and discuss how they operate on different technological platforms.
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Affiliation(s)
- Ruben Dries
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
- Bioinformatics Graduate Program, Boston University, Boston, Massachusetts 02215, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
| | - Jiaji Chen
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
| | - Natalie Del Rossi
- Department of Genetics and Genomic Sciences, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Mohammed Muzamil Khan
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
- Bioinformatics Graduate Program, Boston University, Boston, Massachusetts 02215, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
| | - Adriana Sistig
- Department of Genetics and Genomic Sciences, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
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31
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Yu J, Luo X. Recovering Spatially-Varying Cell-Specific Gene Co-expression Networks for Single-Cell Spatial Expression Data. Front Genet 2021; 12:656637. [PMID: 33981332 PMCID: PMC8107398 DOI: 10.3389/fgene.2021.656637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 03/18/2021] [Indexed: 11/23/2022] Open
Abstract
Recent advances in single-cell technologies enable spatial expression profiling at the cell level, making it possible to elucidate spatial changes of cell-specific genomic features. The gene co-expression network is an important feature that encodes the gene-gene marginal dependence structure and allows for the functional annotation of highly connected genes. In this paper, we design a simple and computationally efficient two-step algorithm to recover spatially-varying cell-specific gene co-expression networks for single-cell spatial expression data. The algorithm first estimates the gene expression covariance matrix for each cell type and then leverages the spatial locations of cells to construct cell-specific networks. The second step uses expression covariance matrices estimated in step one and label information from neighboring cells as an empirical prior to obtain thresholded Bayesian posterior estimates. After completing estimates for each cell, this algorithm can further predict or interpolate gene co-expression networks on tissue positions where cells are not captured. In the simulation study, the comparison against the traditional cell-type-specific network algorithms and the cell-specific network method but without incorporating spatial information highlights the advantages of the proposed algorithm in estimation accuracy. We also applied our algorithm to real-world datasets and found some meaningful biological results. The accompanied software is available on https://github.com/jingeyu/CSSN.
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Affiliation(s)
- Jinge Yu
- Institute of Statistics and Big Data, Renmin University of China, Beijing, China
| | - Xiangyu Luo
- Institute of Statistics and Big Data, Renmin University of China, Beijing, China
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