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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: Jun 15, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

ReSort enhances reference-based cell type deconvolution for spatial transcriptomics through regional information

Linhua Wang¹, Ling Wu², Guantong Qi³

¹Graduate School of Biomedical Sciences, Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, United States.

Bioinformatics Advances

|June 13, 2025

Summary

This summary is machine-generated.

We developed Region-based Cell Sorting (ReSort) to improve spatial transcriptomics (ST) cell type deconvolution by reducing reliance on reference data. ReSort enhances accuracy and reveals immune cell enrichment in a mouse breast cancer model.

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Last Updated: Jun 15, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

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Published on: July 6, 2022

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

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Published on: March 14, 2022

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Spatial transcriptomics (ST) provides gene expression data with tissue localization but lacks single-cell resolution.
Reference-based cell type deconvolution methods are used to infer cell type composition in ST data.
Technical variations between reference datasets and ST data limit the accuracy of current deconvolution methods.

Purpose of the Study:

To introduce Region-based Cell Sorting (ReSort) as a novel method to improve cell type deconvolution in spatial transcriptomics.
To reduce the dependency on external reference data for deconvolution.
To address technical discrepancies affecting the accuracy of spatial transcriptomics analysis.

Main Methods:

Region-based Cell Sorting (ReSort) leverages region-level information from spatial transcriptomics data.
The method is designed to mitigate issues arising from batch and platform differences between reference and spatial transcriptomics datasets.
Performance was evaluated using simulation studies and applied to a mouse breast cancer model.

Main Results:

ReSort demonstrated improved performance in enhancing reference-based deconvolution methods in simulations.
Application to a mouse breast cancer model identified enrichment of M0 and M2 macrophages within the epithelial clone.
These findings offer new insights into epithelial-mesenchymal transition and immune infiltration dynamics.

Conclusions:

ReSort offers a robust approach to cell type deconvolution for spatial transcriptomics data.
The method effectively handles technical variations, improving analytical accuracy.
ReSort provides valuable insights into complex biological processes like tumor microenvironment composition and dynamics.