Single-cell and spatial sequencing application in pathology
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Traditionally, diagnostic pathology uses histology representing structural alterations in a disease’s cells and tissues. In many cases, however, it is supplemented by other morphology-based methods such as immunohistochemistry and fluorescent in situ hybridization. Single-cell RNA sequencing (scRNA-seq) is one of the strategies that may help tackle the heterogeneous cells in a disease, but it does not usually provide histologic information. Spatial sequencing is designed to assign cell types, subtypes, or states according to the mRNA expression on a histological section by RNA sequencing. It can provide mRNA expressions not only of diseased cells, such as cancer cells but also of stromal cells, such as immune cells, fibroblasts, and vascular cells. In this review, we studied current methods of spatial transcriptome sequencing based on their technical backgrounds, tissue preparation, and analytic procedures. With the pathology examples, useful recommendations for pathologists who are just getting started to use spatial sequencing analysis in research are provided here. In addition, leveraging spatial sequencing by integration with scRNA-seq is reviewed. With the advantages of simultaneous histologic and single-cell information, spatial sequencing may give a molecular basis for pathological diagnosis, improve our understanding of diseases,
Abstract
Traditionally, diagnostic pathology uses histology representing structural alterations in a disease’s cells and tissues. In many cases, however, it is supplemented by other morphology-based methods such as immunohistochemistry and fluorescent in situ hybridization. Single-cell RNA sequencing (scRNA-seq) is one of the strategies that may help tackle the heterogeneous cells in a disease, but it does not usually provide histologic information. Spatial sequencing is designed to assign cell types, subtypes, or states according to the mRNA expression on a histological section by RNA sequencing. It can provide mRNA expressions not only of diseased cells, such as cancer cells but also of stromal cells, such as immune cells, fibroblasts, and vascular cells. In this review, we studied current methods of spatial transcriptome sequencing based on their technical backgrounds, tissue preparation, and analytic procedures. With the pathology examples, useful recommendations for pathologists who are just getting started to use spatial sequencing analysis in research are provided here. In addition, leveraging spatial sequencing by integration with scRNA-seq is reviewed. With the advantages of simultaneous histologic and single-cell information, spatial sequencing may give a molecular basis for pathological diagnosis, improve our understanding of diseases,
참고문헌 (43)
- Stewart BJ, "Applying single-cell technologies to clinical pathology : progress in nephropathology", J Pathol, 250, pp.693~704 (2020)
- 황병진, "Single-cell RNA sequencing technologies and bioinformatics pipelines", Experimental and Molecular Medicine, 50, pp.1~14 (2018), 생화학분자생물학회
- Liu B, "Analysis and visualization of spatial transcriptomic data", Front Genet, 12, pp.785290~ (2021)
- Longo SK, "Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics", Nat Rev Genet, 22, pp.627~644 (2021)
- Tang F, "mRNA-Seq whole-transcriptome analysis of a single cell", Nat Methods, 6, pp.377~382 (2009)
- Zheng GX, "Massively parallel digital transcriptional profiling of single cells", Nat Commun, 8, pp.14049~ (2017)
- Chen G, "Single-cell RNA-seq technologies and related computational data analysis", Front Genet, 10, pp.317~ (2019)
- Luecken MD, "Current best practices in single-cell RNAseq analysis: a tutorial", Mol Syst Biol, 15, pp.e8746~ (2019)
- Andrews TS, "Tutorial : guidelines for the computational analysis of single-cell RNA sequencing data", Nat Protoc, 16, pp.1~9 (2021)
- Yukie Kashima, "Single-cell sequencing techniques from individual to multiomics analyses", Experimental and Molecular Medicine, 52, pp.1~9 (2020), 생화학분자생물학회[DOI]
- Papalexi E, "Single-cell RNA sequencing to explore immune cell heterogeneity", Nat Rev Immunol, 18, pp.35~45 (2018)
- Ziegenhain C, "Comparative analysis of single-cell RNA sequencing methods", Mol Cell, 65, pp.631~643 (2017)
- Rao A, "Exploring tissue architecture using spatial transcriptomics", Nature, 596, pp.211~220 (2021)
- Lewis SM, "Spatial omics and multiplexed imaging to explore cancer biology", Nat Methods, 18, pp.997~1012 (2021)
- Williams CG, "An introduction to spatial transcriptomics for biomedical research", Genome Med, 14, pp.68~ (2022)
- Moor AE, "Spatial transcriptomics : paving the way for tissue-level systems biology", Curr Opin Biotechnol, 46, pp.126~133 (2017)
- Jovic D, "Single-cell RNA sequencing technologies and applications : a brief overview", Clin Transl Med, 12, pp.e694~ (2022)
- Haque A, "A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications", Genome Med, 9, pp.75~ (2017)
- Denisenko E, "Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows", Genome Biol, 21, pp.130~ (2020)
- Lafzi A, "Tutorial : guidelines for the experimental design of single-cell RNA sequencing studies", Nat Protoc, 13, pp.2742~2757 (2018)
- Slyper M, "A single-cell and singlenucleus RNA-Seq toolbox for fresh and frozen human tumors", Nat Med, 26, pp.792~802 (2020)
- Hao Y, "Integrated analysis of multimodal single-cell data", Cell, 184, pp.3573~3587 (2021)
- Aran D, "Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage", Nat Immunol, 20, pp.163~172 (2019)
- Trapnell C, "The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells", Nat Biotechnol, 32, pp.381~386 (2014)
- La Manno G, "RNA velocity of single cells", Nature, 560, pp.494~498 (2018)
- Pratapa A, "Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data", Nat Methods, 17, pp.147~154 (2020)
- Skok Gibbs C, "High-performance single-cell gene regulatory network inference at scale : the Inferelator 3. 0", Bioinformatics, 38, pp.2519~2528 (2022)
- Zhuang X, "Spatially resolved single-cell genomics and transcriptomics by imaging", Nat Methods, 18, pp.18~22 (2021)
- Larsson L, "Spatially resolved transcriptomics adds a new dimension to genomics", Nat Methods, 18, pp.15~18 (2021)
- Villacampa EG, "Genome-wide spatial expression profiling in formalin-fixed tissues", Cell Genomics, 1, pp.100065~ (2021)
- Elosua-Bayes M, "SPOTlight : seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes", Nucleic Acids Res, 49, pp.e50~ (2021)
- Bergenstrahle L, "Super-resolved spatial transcriptomics by deep data fusion", Nat Biotechnol, 40, pp.476~479 (2022)
- Qian X, "Probabilistic cell typing enables fine mapping of closely related cell types in situ", Nat Methods, 17, pp.101~106 (2020)
- Arora R, "Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response", Nature Communications (2022)[DOI]
- Armingol E, "Deciphering cellcell interactions and communication from gene expression", Nat Rev Genet, 22, pp.71~88 (2021)
- Efremova M, "CellPhoneDB : inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes", Nat Protoc, 15, pp.1484~1506 (2020)
- Dries R, "Giotto : a toolbox for integrative analysis and visualization of spatial expression data", Genome Biol, 22, pp.78~ (2021)
- Kim YS, "Different molecular features of epithelioid and giant cells in foreign body reaction identified by singlecell RNA sequencing", J Invest Dermatol, 142, pp.3232~3242 (2022)
- Ji AL, "Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma", Cell, 182, pp.497~514 (2020)
- Moncada R, "Integrating microarraybased spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas", Nat Biotechnol, 38, pp.333~342 (2020)
- Argelaguet R, "Multi-omics profiling of mouse gastrulation at single-cell resolution", Nature, 576, pp.487~491 (2019)
- Abascal F, "Somatic mutation landscapes at single-molecule resolution", Nature, 593, pp.405~410 (2021)
- Jeongwoo Lee, "Single-cell multiomics: technologies and data analysis methods", Experimental and Molecular Medicine, 52, pp.1~15 (2020), 생화학분자생물학회[DOI]