Source identification for exosomal communication via protein language models

Exosomes are extracellular vesicles that propagate in the body as a form of cell-to-cell communication, implicated in many diseases such as cancer and neurodegeneration. To understand the impacts of exosomal messages, it is important to determine the message source: the organ system that initially secreted them. To do so, we develop a new technique based on protein language models (PLMs); PLMs with Transformer neural architecture now learn powerful protein representations in a self-supervised manner. Learned protein representations can be used to estimate the source organs of a protein. Using a pre-trained Transformer-based PLM as a feature extractor and fine-tuning a prediction model over the extracted features to predict source organs, yields reasonable predictive accuracy. We apply this new analysis tool to bulk exosomal proteomics data to understand the differences between healthy aging and neurodegenerative disease.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Wu, Xinbo

Contributors dc:contributor

• Varshney, Lav R

• Exosome
• Protein Language Model

Statement dc:rights

• Copyright 2024 Xinbo Wu

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/125838

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F125838