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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 13, 2025

Pattern-based Search of Epigenomic Data Using GeNemo

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

MatriCom, a single-cell RNA-sequencing data mining tool to infer cell-extracellular matrix interactions.

Rijuta Lamba^1,2, Asia M Paguntalan³, Petar B Petrov⁴

¹Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu FI-90014, Finland.

Journal of Cell Science

|June 12, 2025

Summary

This summary is machine-generated.

MatriCom infers cell-extracellular matrix (ECM) interactions from single-cell RNA sequencing data using a curated database. This tool reveals communication networks, aiding in understanding tissue development and disease.

Keywords:

Co-expression Extracellular matrix Matrisome Network analysis Protein–protein interactions

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Last Updated: Jun 13, 2025

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Area of Science:

Biochemistry
Bioinformatics
Cell Biology

Background:

The extracellular matrix (ECM) provides structural support and mediates cell signaling in multicellular organisms.
Protein interactions within the ECM and with cell receptors are crucial for tissue development and function.
Understanding these interactions is key to deciphering cellular communication and tissue remodeling.

Purpose of the Study:

To introduce MatriCom, a novel web application and R package for inferring ECM and cell-ECM communications.
To leverage a comprehensive database (MatriComDB) of over 25,000 curated interactions for accurate imputation.
To provide a tool for analyzing user-generated and public scRNA-Seq datasets.

Main Methods:

Development of MatriCom, a web application and R package.
Utilizing the MatriComDB, a curated database of matrisome component interactions.
Applying interaction imputation algorithms to single-cell RNA sequencing (scRNA-Seq) data.
Incorporating specific rules governing ECM protein interactions.

Main Results:

Construction of the first human kidney matrisome communication network.
Analysis of 46 scRNA-Seq datasets from healthy adult tissues.
Identification of conserved and diverse ECM assemblies and cell/ECM interaction mechanisms.
Demonstration of MatriCom's capability to generate novel biological insights.

Conclusions:

MatriCom provides a powerful platform for dissecting ECM and cell-ECM communication networks.
The tool facilitates the study of tissue-specific ECM organization and signaling.
MatriCom aids in understanding the evolutionary conservation and diversification of ECM interactions across tissues.