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Welcome to

Version 1.0.1 Sept 2022

or see tutorials here

MatriNet is an interactive database developed and maintained by the Izzilab at the University of Oulu (Finland) in collaboration with AutomicsML© to study the connectome and the network profiles of the extracellular matrix (ECM) in healthy and neoplastic tissues and cells.

The extracellular matrix (ECM) is a three-dimensional network of proteins of diverse nature, whose interactions are essential to provide tissues with the correct mechanical and biochemical cues they need for proper development and homeostasis. Changes in the quantity of extracellular matrix (ECM) components and their balance within the tumor microenvironment (TME) accompany and fuel all steps of tumor development, growth and metastasis. A deeper and more systematic understanding of these processes is fundamental for the development of future therapeutic approaches.

The wealth of “big data” from numerous sources has enabled gigantic steps forward in the comprehension of the oncogenic process, also impacting on our understanding of ECM changes in the TME. Most of the available studies, however, have not considered the network nature of ECM and the possibility that even moderate changes in one or few components might significantly “rebound” on several others due to network connections, fundamentally altering the structure of the whole network. To investigate these changes, MatriNet makes use of different data sources such as immunohistochemisty staining intensity profiles from tissue microarrays from The Human Protein Atlas (THPA,, confidence score data from MatrisomeDB (, gene expression data from The Cancer Genome Atlas (TCGA, and the Genotype-Tissue Expression (GTEx, project, and single-cell data from the Tabula Sapiens single-cell atlas ( and THPA. Tumor-specific ECM networks are estimated from the interactions curated by the MatrixDB database (

To facilitate the exploration of network-scale changes in tumor ECM, we have implemented MatriNet, a database enabling the study of topological changes in ECM networks as a consequence of the different quantity of their components across different tumors, tissues and cell types. The use of MatriNet is intuitive and offers new insights into local and global features of ECM networks, facilitating the identification of similarities and differences between samples as well as the visualization of the most salient characteristics of targets' connectomes which can be prioritized for experimental investigations.

In its current implementation (v1.0.1), MatriNet is still in early development, and many more tools and data are expected to be integrated in the near future. If you are interested in collaborating with us, provide data or ideas/feedback, please contact us.

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