# Using STRAINS, a big data method that analyzes the spatiotemporal distribution of cell phenotypes, to investigate mechanotransduction pathways in injured cartilage

> **NIH NIH R21** · CORNELL UNIVERSITY · 2024 · $206,023

## Abstract

PROJECT SUMMARY
Osteoarthritis is a leading cause of disability in the US affecting almost 30 million people at an annual cost of
$128 billion. Initiation of osteoarthritis is tied to genetic predisposition, chronic overload, or acute injury due to
joint trauma. The development of osteoarthritis after acute injury is particularly prevalent with more than 50% of
injury developing full blown symptomatic osteoarthritis 10-20 years after injury. Despite the importance of this
topic and decades of research, the local mechanical events that occur in cartilage during tissue injury and how
they affect chondrocyte phenotypes and ultimately cell fate are still poorly understood. Importantly, developing
an understanding of the mechanotransduction response in cartilage tissue is confounded by the heterogeneity
of cellular responses arising from spatially complex strain fields induced during impact and the heterogeneity of
cartilage tissue itself. These factors indicate that to understand the coordination of mechanotransduction
throughout the tissue it will be critical to develop a framework capable of simultaneously analyzing the real time
response of multiple signaling pathways for thousands of cells distributed in locations throughout the tissue.
Recently, we developed a novel SpatioTemporal Response Analysis IN Situ (STRAINS) tool that combines in
situ real time measurements of chondrocyte behavior with big data machine learning analysis techniques to
provide a spatiotemporal map of cellular behavior throughout a tissue explant. In this proposal we take advantage
of this newly developed microscopy and image analysis techniques to determine the location dependent
distribution of cell phenotypes and how they change after an impact. Our aim is to use these techniques to probe
the peracute response of chondrocytes to impact trauma to more fully understand the processes that occur
during the very early disease process, and, more specifically, the effects that manipulating Ca2+ signaling or
protecting MT bioenergetics have on cell fate after joint injury. Such studies have the potential to identify a
window of opportunity for intervening in the disease process of post traumatic osteoarthritis, when disease
modification is still possible. The specific aims of the proposal are to: 1) Determine whether local peak strain
magnitude affects the distribution of cellular phenotypes similarly in the superficial and middle zones. 2)
Determine how altering known calcium dependent mechanotransduction pathways alters distribution of
phenotypes after impact. 3) Determine how altering mitochondria related cellular responses affects the
distribution of phenotypes after impact. The proposed work will develop an understanding of the mechanisms
that govern cell fate after traumatic injury. Identifying the specific cellular behaviors that accompany
hyperphysiologic loading will provide new targets for future therapies in post-traumatic osteoarthritis. Consistent
with an R21 mechan...

## Key facts

- **NIH application ID:** 10810985
- **Project number:** 1R21AR083064-01A1
- **Recipient organization:** CORNELL UNIVERSITY
- **Principal Investigator:** Lawrence J. BONASSAR
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $206,023
- **Award type:** 1
- **Project period:** 2024-09-05 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10810985

## Citation

> US National Institutes of Health, RePORTER application 10810985, Using STRAINS, a big data method that analyzes the spatiotemporal distribution of cell phenotypes, to investigate mechanotransduction pathways in injured cartilage (1R21AR083064-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10810985. Licensed CC0.

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