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Mayr CH, Santacruz D, Jarosch S, Bleck M, Dalton J, McNabola A, Lempp C, Neubert L, Rath B, Kamp JC, Jonigk D, Kühnel M, Schlüter H, Klimowicz A, Doerr J, Dick A, Ramirez F, Thomas MJ. Spatial transcriptomic characterization of pathologic niches in IPF. SCIENCE ADVANCES 2024; 10:eadl5473. [PMID: 39121212 PMCID: PMC11313858 DOI: 10.1126/sciadv.adl5473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 07/08/2024] [Indexed: 08/11/2024]
Abstract
Despite advancements in antifibrotic therapy, idiopathic pulmonary fibrosis (IPF) remains a medical condition with unmet needs. Single-cell RNA sequencing (scRNA-seq) has enhanced our understanding of IPF but lacks the cellular tissue context and gene expression localization that spatial transcriptomics provides. To bridge this gap, we profiled IPF and control patient lung tissue using spatial transcriptomics, integrating the data with an IPF scRNA-seq atlas. We identified three disease-associated niches with unique cellular compositions and localizations. These include a fibrotic niche, consisting of myofibroblasts and aberrant basaloid cells, located around airways and adjacent to an airway macrophage niche in the lumen, containing SPP1+ macrophages. In addition, we identified an immune niche, characterized by distinct lymphoid cell foci in fibrotic tissue, surrounded by remodeled endothelial vessels. This spatial characterization of IPF niches will facilitate the identification of drug targets that disrupt disease-driving niches and aid in the development of disease relevant in vitro models.
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Affiliation(s)
- Christoph H. Mayr
- Boehringer Ingelheim Pharma GmbH & Co. KG, Department Immunology and Respiratory Disease research, Birkendorfer Straße 65, 88397 Biberach an der Riß, Germany
| | - Diana Santacruz
- Boehringer Ingelheim Pharma GmbH & Co. KG, Global Computational Biology and Digital Sciences, Birkendorfer Straße 65, 88397 Biberach an der Riß, Germany
| | - Sebastian Jarosch
- Boehringer Ingelheim Pharma GmbH & Co. KG, Department Drug Discovery Sciences, Birkendorfer Straße 65, 88397 Biberach an der Riß, Germany
| | - Marina Bleck
- Boehringer Ingelheim Pharmaceuticals Inc., Department of Immunology and Respiratory Diseases Research, 900 Ridgebury Road, Ridgefield, CT 06877 USA
| | - John Dalton
- Boehringer Ingelheim Pharmaceuticals Inc., Department of Immunology and Respiratory Diseases Research, 900 Ridgebury Road, Ridgefield, CT 06877 USA
| | - Angela McNabola
- Boehringer Ingelheim Pharmaceuticals Inc., Department of Immunology and Respiratory Diseases Research, 900 Ridgebury Road, Ridgefield, CT 06877 USA
| | - Charlotte Lempp
- Boehringer Ingelheim Pharma GmbH & Co. KG, Department Drug Discovery Sciences, Birkendorfer Straße 65, 88397 Biberach an der Riß, Germany
| | - Lavinia Neubert
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), German Center for Lung Research (DZL), Hannover, Germany
- Institute of Pathology, Hannover Medical School, Hannover, Germany
| | - Berenice Rath
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), German Center for Lung Research (DZL), Hannover, Germany
- Institute of Pathology, Hannover Medical School, Hannover, Germany
| | - Jan C. Kamp
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), German Center for Lung Research (DZL), Hannover, Germany
- Department of Respiratory Medicine and Infectious Diseases, Hannover Medical School, Hannover, Germany
| | - Danny Jonigk
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), German Center for Lung Research (DZL), Hannover, Germany
- Institute of Pathology, Hannover Medical School, Hannover, Germany
- Institute of Pathology, University Medical Center RWTH University of Aachen, Aachen, Germany
| | - Mark Kühnel
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), German Center for Lung Research (DZL), Hannover, Germany
- Institute of Pathology, University Medical Center RWTH University of Aachen, Aachen, Germany
| | - Holger Schlüter
- Boehringer Ingelheim Pharma GmbH & Co. KG, Department Immunology and Respiratory Disease research, Birkendorfer Straße 65, 88397 Biberach an der Riß, Germany
| | - Alexander Klimowicz
- Boehringer Ingelheim Pharmaceuticals Inc., Department of Immunology and Respiratory Diseases Research, 900 Ridgebury Road, Ridgefield, CT 06877 USA
| | - Jonas Doerr
- Boehringer Ingelheim Pharma GmbH & Co. KG, Department Drug Discovery Sciences, Birkendorfer Straße 65, 88397 Biberach an der Riß, Germany
| | - Alec Dick
- Boehringer Ingelheim Pharma GmbH & Co. KG, Global Computational Biology and Digital Sciences, Birkendorfer Straße 65, 88397 Biberach an der Riß, Germany
| | - Fidel Ramirez
- Boehringer Ingelheim Pharma GmbH & Co. KG, Global Computational Biology and Digital Sciences, Birkendorfer Straße 65, 88397 Biberach an der Riß, Germany
| | - Matthew J. Thomas
- Boehringer Ingelheim Pharma GmbH & Co. KG, Department Immunology and Respiratory Disease research, Birkendorfer Straße 65, 88397 Biberach an der Riß, Germany
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Ding J, Li L, Lu Q, Venegas J, Wang Y, Wu L, Jin W, Wen H, Liu R, Tang W, Dai X, Li Z, Zuo W, Chang Y, Lei YL, Shang L, Danaher P, Xie Y, Tang J. SpatialCTD: A Large-Scale Tumor Microenvironment Spatial Transcriptomic Dataset to Evaluate Cell Type Deconvolution for Immuno-Oncology. J Comput Biol 2024. [PMID: 39117342 DOI: 10.1089/cmb.2024.0532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2024] Open
Abstract
Recent technological advancements have enabled spatially resolved transcriptomic profiling but at a multicellular resolution that is more cost-effective. The task of cell type deconvolution has been introduced to disentangle discrete cell types from such multicellular spots. However, existing benchmark datasets for cell type deconvolution are either generated from simulation or limited in scale, predominantly encompassing data on mice and are not designed for human immuno-oncology. To overcome these limitations and promote comprehensive investigation of cell type deconvolution for human immuno-oncology, we introduce a large-scale spatial transcriptomic deconvolution benchmark dataset named SpatialCTD, encompassing 1.8 million cells and 12,900 pseudo spots from the human tumor microenvironment across the lung, kidney, and liver. In addition, SpatialCTD provides more realistic reference than those generated from single-cell RNA sequencing (scRNA-seq) data for most reference-based deconvolution methods. To utilize the location-aware SpatialCTD reference, we propose a graph neural network-based deconvolution method (i.e., GNNDeconvolver). Extensive experiments show that GNNDeconvolver often outperforms existing state-of-the-art methods by a substantial margin, without requiring scRNA-seq data. To enable comprehensive evaluations of spatial transcriptomics data from flexible protocols, we provide an online tool capable of converting spatial transcriptomic data from various platforms (e.g., 10× Visium, MERFISH, and sci-Space) into pseudo spots, featuring adjustable spot size. The SpatialCTD dataset and GNNDeconvolver implementation are available at https://github.com/OmicsML/SpatialCTD, and the online converter tool can be accessed at https://omicsml.github.io/SpatialCTD/.
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Affiliation(s)
- Jiayuan Ding
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Lingxiao Li
- Boston University, Boston, Massachusetts, USA
| | - Qiaolin Lu
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Julian Venegas
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Yixin Wang
- Department of Bioengineering, Stanford University, Palo Alto, California, USA
| | - Lidan Wu
- NanoString Technologies, Seattle, USA
| | - Wei Jin
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Hongzhi Wen
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Renming Liu
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Wenzhuo Tang
- Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, USA
| | - Xinnan Dai
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Zhaoheng Li
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Wangyang Zuo
- Department of Computer Science, Zhejiang University of Technology, Hangzhou, China
| | - Yi Chang
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Yu Leo Lei
- Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, USA
- University of Michigan Rogel Cancer Center, Ann Arbor, USA
| | - Lulu Shang
- Department of Biostatistics, MD Anderson Cancer Center, Houston, USA
| | | | - Yuying Xie
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, USA
- Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, USA
| | - Jiliang Tang
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
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3
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He R, Lu J, Feng J, Lu Z, Shen K, Xu K, Luo H, Yang G, Chi H, Huang S. Advancing immunotherapy for melanoma: the critical role of single-cell analysis in identifying predictive biomarkers. Front Immunol 2024; 15:1435187. [PMID: 39026661 PMCID: PMC11254669 DOI: 10.3389/fimmu.2024.1435187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2024] [Accepted: 06/21/2024] [Indexed: 07/20/2024] Open
Abstract
Melanoma, a malignant skin cancer arising from melanocytes, exhibits rapid metastasis and a high mortality rate, especially in advanced stages. Current treatment modalities, including surgery, radiation, and immunotherapy, offer limited success, with immunotherapy using immune checkpoint inhibitors (ICIs) being the most promising. However, the high mortality rate underscores the urgent need for robust, non-invasive biomarkers to predict patient response to adjuvant therapies. The immune microenvironment of melanoma comprises various immune cells, which influence tumor growth and immune response. Melanoma cells employ multiple mechanisms for immune escape, including defects in immune recognition and epithelial-mesenchymal transition (EMT), which collectively impact treatment efficacy. Single-cell analysis technologies, such as single-cell RNA sequencing (scRNA-seq), have revolutionized the understanding of tumor heterogeneity and immune microenvironment dynamics. These technologies facilitate the identification of rare cell populations, co-expression patterns, and regulatory networks, offering deep insights into tumor progression, immune response, and therapy resistance. In the realm of biomarker discovery for melanoma, single-cell analysis has demonstrated significant potential. It aids in uncovering cellular composition, gene profiles, and novel markers, thus advancing diagnosis, treatment, and prognosis. Additionally, tumor-associated antibodies and specific genetic and cellular markers identified through single-cell analysis hold promise as predictive biomarkers. Despite these advancements, challenges such as RNA-protein expression discrepancies and tumor heterogeneity persist, necessitating further research. Nonetheless, single-cell analysis remains a powerful tool in elucidating the mechanisms underlying therapy response and resistance, ultimately contributing to the development of personalized melanoma therapies and improved patient outcomes.
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Affiliation(s)
- Ru He
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Jiaan Lu
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Jianglong Feng
- Department of Pathology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Ziqing Lu
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Kaixin Shen
- Department of Art and Design, Shanghai Institute of Technology, Shanghai, China
| | - Ke Xu
- Department of Oncology, Chongqing General Hospital, Chongqing University, Chongqing, China
| | - Huiyan Luo
- Department of Oncology, Chongqing General Hospital, Chongqing University, Chongqing, China
| | - Guanhu Yang
- Department of Specialty Medicine, Ohio University, Athens, OH, United States
| | - Hao Chi
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Shangke Huang
- Department of Oncology, The Affiliated Hospital, Southwest Medical University, Luzhou, Sichuan, China
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4
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Valihrach L, Zucha D, Abaffy P, Kubista M. A practical guide to spatial transcriptomics. Mol Aspects Med 2024; 97:101276. [PMID: 38776574 DOI: 10.1016/j.mam.2024.101276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 04/30/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024]
Abstract
Spatial transcriptomics is revolutionizing modern biology, offering researchers an unprecedented ability to unravel intricate gene expression patterns within tissues. From pioneering techniques to newly commercialized platforms, the field of spatial transcriptomics has evolved rapidly, ushering in a new era of understanding across various disciplines, from developmental biology to disease research. This dynamic expansion is reflected in the rapidly growing number of technologies and data analysis techniques developed and introduced. However, the expanding landscape presents a considerable challenge for researchers, especially newcomers to the field, as staying informed about these advancements becomes increasingly complex. To address this challenge, we have prepared an updated review with a particular focus on technologies that have reached commercialization and are, therefore, accessible to a broad spectrum of potential new users. In this review, we present the fundamental principles of spatial transcriptomic methods, discuss the challenges in data analysis, provide insights into experimental considerations, offer information about available resources for spatial transcriptomics, and conclude with a guide for method selection and a forward-looking perspective. Our aim is to serve as a guiding resource for both experienced users and newcomers navigating the complex realm of spatial transcriptomics in this era of rapid development. We intend to equip researchers with the necessary knowledge to make informed decisions and contribute to the cutting-edge research that spatial transcriptomics offers.
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Affiliation(s)
- Lukas Valihrach
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic; Department of Cellular Neurophysiology, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic.
| | - Daniel Zucha
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic; Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, Czech Republic
| | - Pavel Abaffy
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic
| | - Mikael Kubista
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic.
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5
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Huuki-Myers LA, Spangler A, Eagles NJ, Montgomery KD, Kwon SH, Guo B, Grant-Peters M, Divecha HR, Tippani M, Sriworarat C, Nguyen AB, Ravichandran P, Tran MN, Seyedian A, Hyde TM, Kleinman JE, Battle A, Page SC, Ryten M, Hicks SC, Martinowich K, Collado-Torres L, Maynard KR. A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex. Science 2024; 384:eadh1938. [PMID: 38781370 DOI: 10.1126/science.adh1938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 12/06/2023] [Indexed: 05/25/2024]
Abstract
The molecular organization of the human neocortex historically has been studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally defined spatial domains that move beyond classic cytoarchitecture. We used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex. Integration with paired single-nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we mapped the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains.
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Affiliation(s)
- Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Abby Spangler
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Nicholas J Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Kelsey D Montgomery
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Boyi Guo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Melissa Grant-Peters
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - Heena R Divecha
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Madhavi Tippani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Chaichontat Sriworarat
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Annie B Nguyen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Prashanthi Ravichandran
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA
| | - Matthew N Tran
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Arta Seyedian
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Stephanie C Page
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Mina Ryten
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London WC1N 1EH, UK
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Keri Martinowich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Johns Hopkins Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
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Sang-aram C, Browaeys R, Seurinck R, Saeys Y. Spotless, a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics. eLife 2024; 12:RP88431. [PMID: 38787371 PMCID: PMC11126312 DOI: 10.7554/elife.88431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024] Open
Abstract
Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called synthspot to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).
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Affiliation(s)
- Chananchida Sang-aram
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation ResearchGhentBelgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent UniversityGhentBelgium
| | - Robin Browaeys
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation ResearchGhentBelgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent UniversityGhentBelgium
| | - Ruth Seurinck
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation ResearchGhentBelgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent UniversityGhentBelgium
| | - Yvan Saeys
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation ResearchGhentBelgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent UniversityGhentBelgium
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7
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Fujiwara N, Kimura G, Nakagawa H. Emerging Roles of Spatial Transcriptomics in Liver Research. Semin Liver Dis 2024; 44:115-132. [PMID: 38574750 DOI: 10.1055/a-2299-7880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
Spatial transcriptomics, leveraging sequencing- and imaging-based techniques, has emerged as a groundbreaking technology for mapping gene expression within the complex architectures of tissues. This approach provides an in-depth understanding of cellular and molecular dynamics across various states of healthy and diseased livers. Through the integration of sophisticated bioinformatics strategies, it enables detailed exploration of cellular heterogeneity, transitions in cell states, and intricate cell-cell interactions with remarkable precision. In liver research, spatial transcriptomics has been particularly revelatory, identifying distinct zonated functions of hepatocytes that are crucial for understanding the metabolic and detoxification processes of the liver. Moreover, this technology has unveiled new insights into the pathogenesis of liver diseases, such as the role of lipid-associated macrophages in steatosis and endothelial cell signals in liver regeneration and repair. In the domain of liver cancer, spatial transcriptomics has proven instrumental in delineating intratumor heterogeneity, identifying supportive microenvironmental niches and revealing the complex interplay between tumor cells and the immune system as well as susceptibility to immune checkpoint inhibitors. In conclusion, spatial transcriptomics represents a significant advance in hepatology, promising to enhance our understanding and treatment of liver diseases.
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Affiliation(s)
- Naoto Fujiwara
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Mie University, Mie, Japan
| | - Genki Kimura
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Mie University, Mie, Japan
| | - Hayato Nakagawa
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Mie University, Mie, Japan
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8
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Yin W, Wan Y, Zhou Y. SpatialcoGCN: deconvolution and spatial information-aware simulation of spatial transcriptomics data via deep graph co-embedding. Brief Bioinform 2024; 25:bbae130. [PMID: 38557675 PMCID: PMC10982953 DOI: 10.1093/bib/bbae130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 02/22/2024] [Accepted: 03/08/2024] [Indexed: 04/04/2024] Open
Abstract
Spatial transcriptomics (ST) data have emerged as a pivotal approach to comprehending the function and interplay of cells within intricate tissues. Nonetheless, analyses of ST data are restricted by the low spatial resolution and limited number of ribonucleic acid transcripts that can be detected with several popular ST techniques. In this study, we propose that both of the above issues can be significantly improved by introducing a deep graph co-embedding framework. First, we establish a self-supervised, co-graph convolution network-based deep learning model termed SpatialcoGCN, which leverages single-cell data to deconvolve the cell mixtures in spatial data. Evaluations of SpatialcoGCN on a series of simulated ST data and real ST datasets from human ductal carcinoma in situ, developing human heart and mouse brain suggest that SpatialcoGCN could outperform other state-of-the-art cell type deconvolution methods in estimating per-spot cell composition. Moreover, with competitive accuracy, SpatialcoGCN could also recover the spatial distribution of transcripts that are not detected by raw ST data. With a similar co-embedding framework, we further established a spatial information-aware ST data simulation method, SpatialcoGCN-Sim. SpatialcoGCN-Sim could generate simulated ST data with high similarity to real datasets. Together, our approaches provide efficient tools for studying the spatial organization of heterogeneous cells within complex tissues.
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Affiliation(s)
- Wang Yin
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, 38 Xueyuan Road, Beijing 100191, China
- Department of Neurobiology, School of Basic Medical Sciences, Neuroscience Research Institute, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - You Wan
- Department of Neurobiology, School of Basic Medical Sciences, Neuroscience Research Institute, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Yuan Zhou
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, 38 Xueyuan Road, Beijing 100191, China
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9
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Slabowska AO, Pyke C, Hvid H, Jessen LE, Baumgart S, Das V. A systematic evaluation of state-of-the-art deconvolution methods in spatial transcriptomics: insights from cardiovascular disease and chronic kidney disease. FRONTIERS IN BIOINFORMATICS 2024; 4:1352594. [PMID: 38601476 PMCID: PMC11004278 DOI: 10.3389/fbinf.2024.1352594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/11/2024] [Indexed: 04/12/2024] Open
Abstract
A major challenge in sequencing-based spatial transcriptomics (ST) is resolution limitations. Tissue sections are divided into hundreds of thousands of spots, where each spot invariably contains a mixture of cell types. Methods have been developed to deconvolute the mixed transcriptional signal into its constituents. Although ST is becoming essential for drug discovery, especially in cardiometabolic diseases, to date, no deconvolution benchmark has been performed on these types of tissues and diseases. However, the three methods, Cell2location, RCTD, and spatialDWLS, have previously been shown to perform well in brain tissue and simulated data. Here, we compare these methods to assess the best performance when using human data from cardiovascular disease (CVD) and chronic kidney disease (CKD) from patients in different pathological states, evaluated using expert annotation. In this study, we found that all three methods performed comparably well in deconvoluting verifiable cell types, including smooth muscle cells and macrophages in vascular samples and podocytes in kidney samples. RCTD shows the best performance accuracy scores in CVD samples, while Cell2location, on average, achieved the highest performance across all test experiments. Although all three methods had similar accuracies, Cell2location needed less reference data to converge at the expense of higher computational intensity. Finally, we also report that RCTD has the fastest computational time and the simplest workflow, requiring fewer computational dependencies. In conclusion, we find that each method has particular advantages, and the optimal choice depends on the use case.
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Affiliation(s)
- Alban Obel Slabowska
- Digital Science and Innovation, Computational Biology—AI and Digital Research, Novo Nordisk A/S, Måløv, Denmark
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, Kgs Lyngby, Denmark
| | - Charles Pyke
- Pathology and Imaging, Global Drug Development, Novo Nordisk A/S, Måløv, Denmark
| | - Henning Hvid
- Pathology and Imaging, Global Drug Development, Novo Nordisk A/S, Måløv, Denmark
| | - Leon Eyrich Jessen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, Kgs Lyngby, Denmark
| | - Simon Baumgart
- Digital Science and Innovation, Computational Biology—AI and Digital Research, Novo Nordisk A/S, Måløv, Denmark
| | - Vivek Das
- Digital Science and Innovation, Computational Biology—AI and Digital Research, Novo Nordisk A/S, Måløv, Denmark
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10
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Garmire LX, Li Y, Huang Q, Xu C, Teichmann SA, Kaminski N, Pellegrini M, Nguyen Q, Teschendorff AE. Challenges and perspectives in computational deconvolution of genomics data. Nat Methods 2024; 21:391-400. [PMID: 38374264 DOI: 10.1038/s41592-023-02166-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 12/26/2023] [Indexed: 02/21/2024]
Abstract
Deciphering cell-type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach for estimating cell-type abundances from a variety of omics data. Despite substantial methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four important challenges related to computational deconvolution: the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies, and strategies to promote rigorous benchmarking.
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Affiliation(s)
- Lana X Garmire
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
| | - Yijun Li
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Qianhui Huang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Chuan Xu
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - Naftali Kaminski
- Pulmonary, Critical Care & Sleep Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Matteo Pellegrini
- Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Quan Nguyen
- Institute for Molecular Bioscience, The University of Queensland and QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- UCL Cancer Institute, University College London, London, UK
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11
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Mañanes D, Rivero-García I, Relaño C, Torres M, Sancho D, Jimenez-Carretero D, Torroja C, Sánchez-Cabo F. SpatialDDLS: an R package to deconvolute spatial transcriptomics data using neural networks. Bioinformatics 2024; 40:btae072. [PMID: 38366652 PMCID: PMC10881086 DOI: 10.1093/bioinformatics/btae072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 01/10/2024] [Accepted: 02/13/2024] [Indexed: 02/18/2024] Open
Abstract
SUMMARY Spatial transcriptomics has changed our way to study tissue structure and cellular organization. However, there are still limitations in its resolution, and most available platforms do not reach a single cell resolution. To address this issue, we introduce SpatialDDLS, a fast neural network-based algorithm for cell type deconvolution of spatial transcriptomics data. SpatialDDLS leverages single-cell RNA sequencing data to simulate mixed transcriptional profiles with predefined cellular composition, which are subsequently used to train a fully connected neural network to uncover cell type diversity within each spot. By comparing it with two state-of-the-art spatial deconvolution methods, we demonstrate that SpatialDDLS is an accurate and fast alternative to the available state-of-the art tools. AVAILABILITY AND IMPLEMENTATION The R package SpatialDDLS is available via CRAN-The Comprehensive R Archive Network: https://CRAN.R-project.org/package=SpatialDDLS. A detailed manual of the main functionalities implemented in the package can be found at https://diegommcc.github.io/SpatialDDLS.
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Affiliation(s)
- Diego Mañanes
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
| | - Inés Rivero-García
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
- Departamento de Ingeniería Biomédica, ETSI de Telecomunicaciones, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Carlos Relaño
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
| | - Miguel Torres
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
| | - David Sancho
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
| | | | - Carlos Torroja
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
| | - Fátima Sánchez-Cabo
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
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12
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Zahedi R, Ghamsari R, Argha A, Macphillamy C, Beheshti A, Alizadehsani R, Lovell NH, Lotfollahi M, Alinejad-Rokny H. Deep learning in spatially resolved transcriptfomics: a comprehensive technical view. Brief Bioinform 2024; 25:bbae082. [PMID: 38483255 PMCID: PMC10939360 DOI: 10.1093/bib/bbae082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/22/2024] [Accepted: 02/13/2024] [Indexed: 03/17/2024] Open
Abstract
Spatially resolved transcriptomics (SRT) is a pioneering method for simultaneously studying morphological contexts and gene expression at single-cell precision. Data emerging from SRT are multifaceted, presenting researchers with intricate gene expression matrices, precise spatial details and comprehensive histology visuals. Such rich and intricate datasets, unfortunately, render many conventional methods like traditional machine learning and statistical models ineffective. The unique challenges posed by the specialized nature of SRT data have led the scientific community to explore more sophisticated analytical avenues. Recent trends indicate an increasing reliance on deep learning algorithms, especially in areas such as spatial clustering, identification of spatially variable genes and data alignment tasks. In this manuscript, we provide a rigorous critique of these advanced deep learning methodologies, probing into their merits, limitations and avenues for further refinement. Our in-depth analysis underscores that while the recent innovations in deep learning tailored for SRT have been promising, there remains a substantial potential for enhancement. A crucial area that demands attention is the development of models that can incorporate intricate biological nuances, such as phylogeny-aware processing or in-depth analysis of minuscule histology image segments. Furthermore, addressing challenges like the elimination of batch effects, perfecting data normalization techniques and countering the overdispersion and zero inflation patterns seen in gene expression is pivotal. To support the broader scientific community in their SRT endeavors, we have meticulously assembled a comprehensive directory of readily accessible SRT databases, hoping to serve as a foundation for future research initiatives.
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Affiliation(s)
- Roxana Zahedi
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
| | - Reza Ghamsari
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
| | - Ahmadreza Argha
- The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
- Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, 2052, NSW, Australia
| | - Callum Macphillamy
- School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, 5371, Australia
| | - Amin Beheshti
- School of Computing, Macquarie University, Sydney, 2109, Australia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, Melbourne, VIC, 3216, Australia
| | - Nigel H Lovell
- The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
- Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, 2052, NSW, Australia
| | - Mohammad Lotfollahi
- Computational Health Center, Helmholtz Munich, Germany
- Wellcome Sanger Institute, Cambridge, UK
| | - Hamid Alinejad-Rokny
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
- Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, 2052, NSW, Australia
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13
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Mason K, Sathe A, Hess PR, Rong J, Wu CY, Furth E, Susztak K, Levinsohn J, Ji HP, Zhang N. Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions. Genome Biol 2024; 25:14. [PMID: 38217002 PMCID: PMC10785550 DOI: 10.1186/s13059-023-03159-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 12/22/2023] [Indexed: 01/14/2024] Open
Abstract
Existing methods for analysis of spatial transcriptomic data focus on delineating the global gene expression variations of cell types across the tissue, rather than local gene expression changes driven by cell-cell interactions. We propose a new statistical procedure called niche-differential expression (niche-DE) analysis that identifies cell-type-specific niche-associated genes, which are differentially expressed within a specific cell type in the context of specific spatial niches. We further develop niche-LR, a method to reveal ligand-receptor signaling mechanisms that underlie niche-differential gene expression patterns. Niche-DE and niche-LR are applicable to low-resolution spot-based spatial transcriptomics data and data that is single-cell or subcellular in resolution.
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Affiliation(s)
- Kaishu Mason
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, USA
| | - Anuja Sathe
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Paul R Hess
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, USA
| | - Jiazhen Rong
- Genomics and Computational Biology Graduate Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Chi-Yun Wu
- The Gladstone Institute, San Francisco, USA
| | - Emma Furth
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Katalin Susztak
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jonathan Levinsohn
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hanlee P Ji
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nancy Zhang
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, USA.
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14
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Luo J, Deng M, Zhang X, Sun X. ESICCC as a systematic computational framework for evaluation, selection, and integration of cell-cell communication inference methods. Genome Res 2023; 33:1788-1805. [PMID: 37827697 PMCID: PMC10691505 DOI: 10.1101/gr.278001.123] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 09/21/2023] [Indexed: 10/14/2023]
Abstract
Cell-cell communication (CCC) is critical for determining cell fates and functions in multicellular organisms. With the advent of single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), an increasing number of CCC inference methods have been developed. Nevertheless, a thorough comparison of their performances is yet to be conducted. To fill this gap, we developed a systematic benchmark framework called ESICCC to evaluate 18 ligand-receptor (LR) inference methods and five ligand/receptor-target inference methods using a total of 116 data sets, including 15 ST data sets, 15 sets of cell line perturbation data, two sets of cell type-specific expression/proteomics data, and 84 sets of sampled or unsampled scRNA-seq data. We evaluated and compared the agreement, accuracy, robustness, and usability of these methods. Regarding accuracy evaluation, RNAMagnet, CellChat, and scSeqComm emerge as the three best-performing methods for intercellular ligand-receptor inference based on scRNA-seq data, whereas stMLnet and HoloNet are the best methods for predicting ligand/receptor-target regulation using ST data. To facilitate the practical applications, we provide a decision-tree-style guideline for users to easily choose best tools for their specific research concerns in CCC inference, and develop an ensemble pipeline CCCbank that enables versatile combinations of methods and databases. Moreover, our comparative results also uncover several critical influential factors for CCC inference, such as prior interaction information, ligand-receptor scoring algorithm, intracellular signaling complexity, and spatial relationship, which may be considered in the future studies to advance the development of new methodologies.
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Affiliation(s)
- Jiaxin Luo
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
| | - Minghua Deng
- School of Mathematical Sciences, Peking University, Beijing, 100871, China
| | - Xuegong Zhang
- Bioinformatics Division of BNRIST and Department of Automation, MOE Key Lab of Bioinformatics, Tsinghua University, Beijing, 100084, China
| | - Xiaoqiang Sun
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China;
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15
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Liu Y, Li N, Qi J, Xu G, Zhao J, Wang N, Huang X, Jiang W, Justet A, Adams TS, Homer R, Amei A, Rosas IO, Kaminski N, Wang Z, Yan X. A hybrid machine learning and regression method for cell type deconvolution of spatial barcoding-based transcriptomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.24.554722. [PMID: 37662370 PMCID: PMC10473707 DOI: 10.1101/2023.08.24.554722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Spatial barcoding-based transcriptomic (ST) data require cell type deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method, to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER uses a machine learning approach to remove the systematic difference between ST and scRNA-seq data (platform effects) explicitly and efficiently to ensure the linear relationship between ST data and cell type-specific expression profile. It also considers sparsity of cell types per capture spot and across-spots spatial correlation in cell type compositions. Based on the estimated cell type proportions, SDePER imputes cell type compositions and gene expression at unmeasured locations in a tissue map with enhanced resolution. Applications to coarse-grained simulated data and four real datasets showed that SDePER achieved more accurate and robust results than existing methods, suggesting the importance of considering platform effects, sparsity and spatial correlation in cell type deconvolution.
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16
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Heumos L, Schaar AC, Lance C, Litinetskaya A, Drost F, Zappia L, Lücken MD, Strobl DC, Henao J, Curion F, Schiller HB, Theis FJ. Best practices for single-cell analysis across modalities. Nat Rev Genet 2023; 24:550-572. [PMID: 37002403 PMCID: PMC10066026 DOI: 10.1038/s41576-023-00586-w] [Citation(s) in RCA: 161] [Impact Index Per Article: 161.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2023] [Indexed: 04/03/2023]
Abstract
Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
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Affiliation(s)
- Lukas Heumos
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Anna C Schaar
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany
| | - Christopher Lance
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Paediatrics, Dr von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Anastasia Litinetskaya
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Felix Drost
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Luke Zappia
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Malte D Lücken
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity, Helmholtz Munich, Munich, Germany
| | - Daniel C Strobl
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Juan Henao
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
| | - Fabiola Curion
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Herbert B Schiller
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany.
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