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Gaspard-Boulinc LC, Gortana L, Walter T, Barillot E, Cavalli FMG. Cell-type deconvolution methods for spatial transcriptomics. Nat Rev Genet 2025:10.1038/s41576-025-00845-y. [PMID: 40369312 DOI: 10.1038/s41576-025-00845-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/14/2025] [Indexed: 05/16/2025]
Abstract
Spatial transcriptomics is a powerful method for studying the spatial organization of cells, which is a critical feature in the development, function and evolution of multicellular life. However, sequencing-based spatial transcriptomics has not yet achieved cellular-level resolution, so advanced deconvolution methods are needed to infer cell-type contributions at each location in the data. Recent progress has led to diverse tools for cell-type deconvolution that are helping to describe tissue architectures in health and disease. In this Review, we describe the varied types of cell-type deconvolution methods for spatial transcriptomics, contrast their capabilities and summarize them in a web-based, interactive table to enable more efficient method selection.
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Affiliation(s)
- Lucie C Gaspard-Boulinc
- Institut Curie, PSL University, Paris, France
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1331, Paris, France
- Mines Paris, PSL University, CBIO - Centre for Computational Biology, Paris, France
| | - Luca Gortana
- Institut Curie, PSL University, Paris, France
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1331, Paris, France
- Mines Paris, PSL University, CBIO - Centre for Computational Biology, Paris, France
| | - Thomas Walter
- Institut Curie, PSL University, Paris, France
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1331, Paris, France
- Mines Paris, PSL University, CBIO - Centre for Computational Biology, Paris, France
| | - Emmanuel Barillot
- Institut Curie, PSL University, Paris, France
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1331, Paris, France
- Mines Paris, PSL University, CBIO - Centre for Computational Biology, Paris, France
| | - Florence M G Cavalli
- Institut Curie, PSL University, Paris, France.
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1331, Paris, France.
- Mines Paris, PSL University, CBIO - Centre for Computational Biology, Paris, France.
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Wang Q, Zhu H, Deng L, Xu S, Xie W, Li M, Wang R, Tie L, Zhan L, Yu G. Spatial Transcriptomics: Biotechnologies, Computational Tools, and Neuroscience Applications. SMALL METHODS 2025; 9:e2401107. [PMID: 39760243 DOI: 10.1002/smtd.202401107] [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: 07/19/2024] [Revised: 12/22/2024] [Indexed: 01/07/2025]
Abstract
Spatial transcriptomics (ST) represents a revolutionary approach in molecular biology, providing unprecedented insights into the spatial organization of gene expression within tissues. This review aims to elucidate advancements in ST technologies, their computational tools, and their pivotal applications in neuroscience. It is begun with a historical overview, tracing the evolution from early image-based techniques to contemporary sequence-based methods. Subsequently, the computational methods essential for ST data analysis, including preprocessing, cell type annotation, spatial clustering, detection of spatially variable genes, cell-cell interaction analysis, and 3D multi-slices integration are discussed. The central focus of this review is the application of ST in neuroscience, where it has significantly contributed to understanding the brain's complexity. Through ST, researchers advance brain atlas projects, gain insights into brain development, and explore neuroimmune dysfunctions, particularly in brain tumors. Additionally, ST enhances understanding of neuronal vulnerability in neurodegenerative diseases like Alzheimer's and neuropsychiatric disorders such as schizophrenia. In conclusion, while ST has already profoundly impacted neuroscience, challenges remain issues such as enhancing sequencing technologies and developing robust computational tools. This review underscores the transformative potential of ST in neuroscience, paving the way for new therapeutic insights and advancements in brain research.
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Affiliation(s)
- Qianwen Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Hongyuan Zhu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Lin Deng
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Shuangbin Xu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Wenqin Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Ming Li
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Rui Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Liang Tie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Li Zhan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Guangchuang Yu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
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Yang CX, Sin DD, Ng RT. SMART: spatial transcriptomics deconvolution using marker-gene-assisted topic model. Genome Biol 2024; 25:304. [PMID: 39623485 PMCID: PMC11610197 DOI: 10.1186/s13059-024-03441-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 11/20/2024] [Indexed: 12/06/2024] Open
Abstract
While spatial transcriptomics offer valuable insights into gene expression patterns within the spatial context of tissue, many technologies do not have a single-cell resolution. Here, we present SMART, a marker gene-assisted deconvolution method that simultaneously infers the cell type-specific gene expression profile and the cellular composition at each spot. Using multiple datasets, we show that SMART outperforms the existing methods in realistic settings. It also provides a two-stage approach to enhance its performance on cell subtypes. The covariate model of SMART enables the identification of cell type-specific differentially expressed genes across conditions, elucidating biological changes at a single-cell-type resolution.
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Affiliation(s)
- Chen Xi Yang
- Centre for Heart Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, BC, Canada.
- Department of Bioinformatics, Faculty of Science, University of British Columbia, Vancouver, BC, Canada.
| | - Don D Sin
- Centre for Heart Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, BC, Canada
- Division of Respiratory Medicine, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Raymond T Ng
- Centre for Heart Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, BC, Canada
- Department of Bioinformatics, Faculty of Science, University of British Columbia, Vancouver, BC, Canada
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
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