Advances in spatial transcriptomics and related data analysis strategies

Spatial transcriptomics technologies developed in recent years can provide various information including tissue heterogeneity, which is fundamental in biological and medical research, and have been making significant breakthroughs. Single-cell RNA sequencing (scRNA-seq) cannot provide spatial inform...

Full description

Saved in:
Bibliographic Details
Published in:Journal of translational medicine 2023-05, Vol.21 (1), p.330-330, Article 330
Main Authors: Du, Jun, Yang, Yu-Chen, An, Zhi-Jie, Zhang, Ming-Hui, Fu, Xue-Hang, Huang, Zou-Fang, Yuan, Ye, Hou, Jian
Format: Article
Language:English
Subjects:
Analysis
Bar codes
Biomedical Research
Cell cycle
Computational biology
Data Analysis
Efficiency
Embryos
Gene expression
Gene Expression Profiling
Histogenesis
Hybridization
Lasers
Medical research
Methodology
Methods
Microenvironments
Protocol
Review
RNA sequencing
Sequence Analysis, RNA
Single-Cell Analysis
Spatial analysis (Statistics)
Spatial discrimination
Spatial transcriptomics
Tissue heterogeneity
Transcriptome - genetics
Transcriptomics
Zebrafish
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Spatial transcriptomics technologies developed in recent years can provide various information including tissue heterogeneity, which is fundamental in biological and medical research, and have been making significant breakthroughs. Single-cell RNA sequencing (scRNA-seq) cannot provide spatial information, while spatial transcriptomics technologies allow gene expression information to be obtained from intact tissue sections in the original physiological context at a spatial resolution. Various biological insights can be generated into tissue architecture and further the elucidation of the interaction between cells and the microenvironment. Thus, we can gain a general understanding of histogenesis processes and disease pathogenesis, etc. Furthermore, in silico methods involving the widely distributed R and Python packages for data analysis play essential roles in deriving indispensable bioinformation and eliminating technological limitations. In this review, we summarize available technologies of spatial transcriptomics, probe into several applications, discuss the computational strategies and raise future perspectives, highlighting the developmental potential.
ISSN:1479-5876
1479-5876
DOI:10.1186/s12967-023-04150-2