# Role of extracellular matrix in age-related declines of muscle regeneration

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2021 · $281,359

## Abstract

PROJECT SUMMARY
 The capacity for muscle regeneration decreases markedly with aging. While regeneration is led by muscle
stem cells (MuSC), complex age-related changes in the skeletal muscle extracellular matrix (ECM) provide
potent signals that drive aberrant lineage specification. The complexity of the interactions between aging
MuSC and their environmental niche defined by biomechanical, architectural, and dynamic changes in the
ECM suggests a data-driven analysis can elucidate underlying mechanisms, increase our fundamental
understanding of aging and stem cell biology, and point to novel therapeutic strategies. In this research, -omics
data (i.e., single cell RNA-seq and imaging flow cytometry assessments of myogenic markers) obtained from
cells cultured onto substrates of varying elasticity and cell-adhesion will be used to probe signaling pathways
including mitochondrial/metabolic signaling pathways in cultured MuSCs. We propose that the implementation
of machine learning/artificial intelligence (ML/AI) paradigms represents a critical next step for integrating multi-
layer -omics datasets and building predictive models that will more comprehensively elucidate stem cell
responses to the extrinsic biophysical environment.
 The overarching goal of this Supplement is to test the central hypothesis that Biological data and domain
knowledge relating to muscle aging can be embedded in a framework of Bayesian optimization will allow for
elucidating mechanisms and accurately predicting regenerative responses. This central hypothesis will be
tested by conducting three specific aims: Specific Aim 1. To prepare -omics data for ML models: Curate
datasets, identify and impute missing data, compile metadata, and pre-process data to quantify descriptors
used in model building. Adopt data management protocols associated with best practices. Specific Aim 2. To
perform benchmark ML modeling with Bayesian optimization: Identify environmental variables (ECM stiffness
and composition, signaling molecules) and cellular characteristics (age, expression markers) that correlate with
epigenetic signatures and myogenicity, then develop mechanistic ML models and estimate posterior
distributions. Specific Aim 3. To broaden approaches to ML modeling and broaden researcher engagement in
the biology of aging: CMU will host a hackathon with teams that combine students and researchers from
regional universities and HBCU partners.

## Key facts

- **NIH application ID:** 10410777
- **Project number:** 3R01AG061005-03S1
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Fabrisia Ambrosio
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $281,359
- **Award type:** 3
- **Project period:** 2019-08-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10410777

## Citation

> US National Institutes of Health, RePORTER application 10410777, Role of extracellular matrix in age-related declines of muscle regeneration (3R01AG061005-03S1). Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nih/10410777. Licensed CC0.

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