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The Extracellular Matrix

The Extracellular Matrix

Overview
In order to maintain tissue organization, many animal cells are surrounded by structural molecules that make up the extracellular matrix (ECM). Together, the molecules in the ECM maintain the structural integrity of tissue as well as the remarkable specific properties of certain tissues.
Composition of the Extracellular Matrix
The extracellular matrix (ECM) is commonly composed of ground substance, a gel-like fluid, fibrous components, and many structurally and functionally diverse...

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Updated: Jun 4, 2025

Author Spotlight: Dissection and Isolation of Region-Specific Decellularized Lung Tissue

Author Spotlight: Dissection and Isolation of Region-Specific Decellularized Lung Tissue

Published on: September 29, 2023

Machine learning identifies remodeling patterns in human lung extracellular matrix.

Monica J Emerson¹, Oliver Willacy², Chris D Madsen³

¹Section for Visual Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark. Kongens Lyngby, Denmark; Currently at Digital Science and Innovation, Novo Nordisk A/S, Måløv, Denmark.

Acta Biomaterialia

|January 2, 2025

Summary

This summary is machine-generated.

We developed a novel 3D mapping and machine learning method to analyze extracellular matrix (ECM) structure in lung diseases. This approach reveals disease-specific ECM alterations, improving diagnostic objectivity.

Keywords:

Extracellular matrix structure High Resolution 3D Imaging Human Lungs Machine learning Pulmonary emphysema pulmonary fibrosis

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Last Updated: Jun 4, 2025

Author Spotlight: Dissection and Isolation of Region-Specific Decellularized Lung Tissue

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Published on: September 29, 2023

Engineered Lung Tissues Prepared from Decellularized Lung Slices

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Tunable Hydrogels from Pulmonary Extracellular Matrix for 3D Cell Culture

Tunable Hydrogels from Pulmonary Extracellular Matrix for 3D Cell Culture

Published on: January 17, 2017

Area of Science:

Biomaterials Science
Computational Biology
Pathology

Background:

Organ function relies on the precise 3D structure of the extracellular matrix (ECM).
Detailed analysis of ECM architecture in health and disease remains limited.
ECM structure critically regulates cell behavior and tissue homeostasis.

Purpose of the Study:

To develop and deploy a high-resolution 3D ECM mapping method combined with machine learning.
To characterize ECM architecture in various human lung diseases.
To identify disease-specific ECM remodeling signatures.

Main Methods:

A high-resolution 3D ECM mapping technique was utilized.
A machine learning pipeline was designed for ECM detection and characterization.
Analysis was performed on human lung tissue from healthy individuals and patients with emphysema, usual interstitial pneumonia, sarcoidosis, and COVID-19.

Main Results:

Disease-specific ECM remodeling signatures were generated.
A health/disease probability map was created, inferring healthy and diseased ECM architecture.
Fibrosis was shown to be a series of disease-specific alterations, not a single phenomenon.

Conclusions:

The developed method provides objective insights into ECM structure in pulmonary diseases.
ECM alterations are disease-specific, impacting tissue structure and cell regulation.
This approach may enable more confident and potentially less invasive diagnosis of lung diseases.