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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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MatriCom: a scRNA-Seq data mining tool to infer ECM-ECM and cell-ECM communication systems.

Rijuta Lamba¹, Asia M Paguntalan², Petar B Petrov³

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

Biorxiv : the Preprint Server for Biology

|January 7, 2025

Summary

MatriCom is a new tool that analyzes single-cell RNA sequencing data to map interactions within the extracellular matrix (ECM) and between cells and the ECM. It helps researchers understand ECM communication networks in various tissues and diseases.

Keywords:

Co-expression Extracellular Matrix Matrisome Network analysis Open access Protein-protein interactions Single-cell RNA sequencing

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Last Updated: Jun 3, 2025

Pattern-based Search of Epigenomic Data Using GeNemo

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Published on: October 8, 2017

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

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Published on: March 14, 2022

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

Area of Science:

Biochemistry
Bioinformatics
Cell Biology

Background:

The extracellular matrix (ECM) is a dynamic protein network crucial for multicellular organism structure and function.
Interactions within the ECM and between ECM proteins and cell surface receptors regulate cellular behavior and signal transduction.
Understanding these interactions is vital for deciphering tissue development, homeostasis, and disease pathogenesis.

Purpose of the Study:

To develop MatriCom, a web application and R package for inferring ECM-related communication networks from single-cell RNA sequencing (scRNA-Seq) data.
To provide a comprehensive resource for analyzing both ECM-ECM and cell-ECM interactions.
To facilitate the study of ECM communication in various biological contexts and diseases.

Main Methods:

Development of MatriCom, a tool integrating a curated database (MatriComDB) of over 25,000 matrisome interactions.
Utilizing scRNA-Seq datasets, including public repositories (Tabula Sapiens, Human Protein Atlas, HuBMAP) and user-generated data.
Incorporating specific rules for ECM protein interactions and offering customizable stringency filters for output analysis.

Main Results:

Demonstrated MatriCom's utility by analyzing the human kidney matrisome communication network.
Identified ubiquitous and tissue-specific ECM communication patterns through the integration of 46 scRNA-Seq datasets.
Validated the tool's capability to infer complex cellular and molecular interactions within the ECM.

Conclusions:

MatriCom serves as a powerful resource for dissecting ECM communication networks.
The tool aids in elucidating the roles of diverse cell populations in ECM dynamics.
MatriCom is expected to advance research into ECM dysregulation in diseases like cancer and fibrosis.