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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 7, 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

A signal-diffusion-based unsupervised contrastive representation learning for spatial transcriptomics analysis.

Nan Chen¹, Xiao Yu¹, Weimin Li¹

¹School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.

Bioinformatics (Oxford, England)

|November 15, 2024

Summary

This summary is machine-generated.

We developed a new method, SDUCL, to better analyze spatial transcriptomics data by integrating gene expression, spatial information, and images. This approach improves understanding of tissue heterogeneity and tumor microenvironments.

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Spatial transcriptomics enables high-throughput gene expression measurement with preserved tissue spatial structure.
Integrating gene expression, spatial, and image data is crucial for dissecting tissue heterogeneity and biological functions.
Existing methods struggle to effectively utilize spatial information and high-resolution histological images.

Purpose of the Study:

To propose a novel signal-diffusion-based unsupervised contrast learning method (SDUCL) for learning low-dimensional latent embeddings of cells/spots.
To improve the integration of spatial information and histological images in spatial transcriptomics analysis.
To enhance the understanding of tissue heterogeneity and biological functions through improved data integration.

Main Methods:

SDUCL integrates image features, spatial relationships, and gene expression data.
A signal diffusion microenvironment discovery algorithm simulates biological signal diffusion to capture cellular microenvironment interactions.
Mutual information maximization between local and microenvironment representations learns discriminative embeddings.

Main Results:

SDUCL effectively integrates multi-modal data, including image features, spatial relationships, and gene expression.
The signal diffusion algorithm captures cellular microenvironment interactions.
SDUCL learns more discriminative representations by maximizing mutual information.
Analysis of diverse spatial transcriptomics datasets (multi-species, normal, tumor) demonstrated SDUCL's effectiveness.

Conclusions:

SDUCL enhances downstream tasks like clustering, visualization, trajectory inference, and differential gene analysis.
The method improves the understanding of tissue structure and tumor microenvironments.
SDUCL offers a powerful new tool for spatial transcriptomics data analysis.