Experimental and analytical techniques for studying mechanotransduction in articular cartilage

Tissues are often complex heterogeneous systems, where individual cells coordinate responses to internal and external stimuli. To fully understand cell function in such environments, it is critical to connect the behavior of individual cells with the full tissue-scale response. Articular cartilage is one such system where the complex extracellular matrix and heterogeneous cell responses make it difficult to understand how chondrocytes respond to injury-inducing strain. In this thesis I will explore the methods I developed to map the spatiotemporal behaviors of chondrocytes in articular cartilage after impact. These methods combine microscopy techniques with large scale data analysis, making use of a custom-programmed decision tree algorithm, supervised time series classifiers, and unsupervised clustering via a variational autoencoder to identify and categorize cell phenotypes. Time series data collected from thousands of chondrocytes \textit{in situ} during and after impact allow me to probe responses through the lenses of calcium signaling, mitochondrial polarity, and nuclear membrane permeability. This thesis serves to outline the experimental and analytical methods developed to probe cellular response to external stimuli. While the focus is on method development, I will touch on some of the biological implications of impact on articular cartilage, and identify questions and hypotheses generated through the experiments conducted for this work.

Name thesis:degree_name

Ph. D., Physics

Level thesis:degree_level

Doctor of Philosophy

Discipline thesis:degree_discipline

Physics

Grantor

Cornell University

Year dc:date.issued

2023

Author dc:creator

• Zheng, Jingyang

Committee members dc:contributor.committeemember

• Bonassar, Lawrence
• Myers, Christopher
• Delco, Michelle

• Calcium signaling
• Cartilage
• Machine learning
• Mechanotransduction

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language.iso

en

DOI

https://doi.org/10.7298/wrtz-q992

Dc Identifier Other

ProQuest Submission ID: 13767
ProQuest Publication ID: 30568234

OAI identifier oai:identifier

oai:ecommons.cornell.edu:1813/114822