Machine Learning Enabled Stem Cell Lipidomics
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Van Grouw, Alexandria
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Abstract
Lipids form the life-defining barriers that separate a cell from its environment. They provide the cell with structure, allow it to move, hold the vital proteins that allow the influx of nutrients and outflux of waste, and are important members of metabolic and cell signaling pathways. Not only are there many thousands of different lipids, but lipid metabolism is also constantly changing in response to the state of the cell. In the face of overwhelming biological complexity, omics fields have emerged with the goal of profiling biological systems in a global fashion to create a snapshot of the system in a moment of time. Lipidomics enables the large-scale study of lipids by leveraging current developments in analytical techniques.
Stem cell therapies aim to utilize cellular functions to regenerate tissues, provide immunotherapy, and treat cancer. By taking advantage of the complexity of cellular machinery, these treatments can perform functions that traditional small molecule drugs and biologics are unable to perform. However, the complexity of these medicines requires additional tools for medicinal characterization in order to meet FDA guidelines for outlining mechanism of action (MoA) and defining safety and quality standards. Lipidomics approaches for cell characterization hold unique abilities to capture the complexity and heterogeneity of these therapies to help identify molecular markers of medicinal quality or critical quality attributes (CQAs).
Mass spectrometry’s analytical power is derived from the highly customizable nature of the available instrumentation. Custom mass spectrometry workflows and methodology can create advantages in terms of molecular coverage, specificity, sensitivity, and quantitative or statistical reliability that enable lipidomics for CQA discovery. In particular, developments that increase quantitative reliability and sensitivity can benefit the field of cellular lipidomics in order to capture individual cell diversity and improve batch harmonization. Lipidomics studies rely on the production of large datasets, from which useful biological information is extracted. Modern methods in machine learning (ML) are necessary to extract this information and identify molecular predictors of cellular properties such as potency.
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2025-05-16
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Dissertation