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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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Related Experiment Video

Updated: Jan 18, 2026

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

Published on: March 29, 2024

haCCA: multi-module Integration of spot-based spatial transcriptomes and metabolomes.

Jing Xu^1,2,3, Xiao-Tian Shen^1,4, Chen Zhang^1,4

¹Department of General Surgery, Huashan Hospital, Fudan University, Shanghai, China.

Communications Biology

|January 16, 2026

Summary

This summary is machine-generated.

We developed haCCA, a novel workflow integrating spatial transcriptomics and MALDI-MSI data for simultaneous spatial profiling of mRNA and metabolites. This method enhances integration accuracy and enables new insights into tissue-level biological processes.

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Last Updated: Jan 18, 2026

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

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Published on: March 29, 2024

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

Area of Science:

Computational Biology
Bioinformatics
Spatial Omics

Background:

Spatial transcriptomics and MALDI-MSI provide high-resolution spatial data for mRNA and metabolites, respectively.
Integrating these datasets is challenging due to differing coordinate systems and feature spaces.
Existing methods lack robust approaches for accurate cross-platform spatial data integration.

Purpose of the Study:

To present haCCA, a workflow for integrating spatial transcriptomics and metabolomics data.
To enable simultaneous spatial profiling of mRNA and metabolites from adjacent tissue sections.
To improve the accuracy of spatial omics data integration.

Main Methods:

haCCA utilizes modified spatial registration techniques.
Canonical Correlation Analysis (CCA) is employed to construct a shared latent space.
High-correlated feature pairs are transferred between datasets for integration.

Main Results:

haCCA demonstrated improved integration accuracy compared to existing methods on both simulated and real data.
The workflow successfully enabled simultaneous spatial profiling of mRNA and metabolites.
Application to an Akt/Yap driven Padi4-/-ICC model revealed spatial distributions and metabolic effects of neutrophil extracellular traps (NETs).

Conclusions:

haCCA provides an effective solution for integrating spatial transcriptomics and metabolomics data.
The workflow facilitates in situ and in vivo exploration of spatial metabolic alterations.
A Python package is available to enhance the accessibility and usability of the haCCA workflow.