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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Oct 1, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

STRIDE: accurately decomposing and integrating spatial transcriptomics using single-cell RNA sequencing.

Dongqing Sun¹, Zhaoyang Liu¹, Taiwen Li²

¹Department of Urology, Tongji Hospital, Frontier Science Center for Stem Cells, School of Life Science and Technology, Tongji University, Shanghai 200092, China.

Nucleic Acids Research

|March 7, 2022

Summary

This summary is machine-generated.

Spatial transcriptomics deconvolution by topic modeling (STRIDE) accurately identifies cell types in tissue samples. This computational method enhances understanding of cellular heterogeneity and spatial gene expression, improving tissue architecture reconstruction.

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Spatial transcriptomics offers insights into cellular heterogeneity within tissues.
Current spatial technologies face limitations in single-cell resolution for localization and interaction studies.

Purpose of the Study:

To introduce STRIDE, a computational method for spatial transcriptomics deconvolution.
To leverage topic modeling and single-cell transcriptomics data for accurate cell-type decomposition.

Main Methods:

STRIDE utilizes topic profiles trained from single-cell transcriptomics to decompose spatial mixtures.
The method employs topic modeling for deconvolution of cell types from spatial transcriptomic data.

Main Results:

STRIDE accurately estimates cell-type proportions with balanced specificity and sensitivity.
The method successfully maps rare cell types, identifies spatially localized genes, and defines domains.
Discovered topics correlate with cell-type-specific functions and aid in 3D tissue reconstruction.

Conclusions:

STRIDE is a versatile computational tool for spatial transcriptomics deconvolution.
The method enhances the analysis of cellular heterogeneity and spatial gene expression.
STRIDE facilitates integrated analysis of spatial and single-cell transcriptomics data.