J Pathol Transl Med.  2023 Jan;57(1):43-51. 10.4132/jptm.2022.12.12.

Single-cell and spatial sequencing application in pathology

• Kim YS
• Choi J
• Lee SH

• KMID: 2539035
• DOI: http://doi.org/10.4132/jptm.2022.12.12

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, and have potential clinical applications in prognostics and diagnostic pathology.

Keyword

Single-cell sequencing; Spatial sequencing; Pathology; Histology; Transcriptome; Diseases

Figure

• 

  Fig. 1 Overview of strengths and limitations of clinical and experimental methods for gene expression. Conventional methods of histopathology (A) and bulk RNA sequencing (RNA-seq) (B). FISH, fluorescence in situ hybridization. Recent methods of single-cell RNA sequencing (scRNA-seq) (C) and next-generation sequencing (NGS)-based spatial transcriptomics (ST) (D).

• 

  Fig. 2 Overview of spatial transcriptomics (ST) analysis. (A) Example of tissue slide (Visium technology) for ST. Original tissue image, detected area, spot clustering of ST data by unsupervised clustering (spatial spots colored by spot clusters), and magnified view of spatial spots are shown. Distance between spatial spots were 100-μm, and each spot has a diameter of 55-μm. (B) ST can measure genome-wide expression profiles in each 55-μm spatial spots. (C) Analysis strategies for ST data.

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  Fig. 3 Schematic view of integrated analysis of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST). (A) Strengths and limitations of integrated analysis of scRNA-seq and ST. (B) Analysis strategies for integrated scRNA-seq and ST data.

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  Fig. 4 Example study of integrated analysis [38]. Findings from single-cell RNA sequencing (scRNA-seq) (A), spatial transcriptomics (ST) (B), and integrated analysis of scRNA-seq and ST (C) are summarized. FBR, foreign body reaction.

Reference

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