Doctoral Dissertations

Date of Award

8-2024

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Chemistry

Major Professor

Frederick A. Heberle

Committee Members

Michael D. Best, Sharani Roy, Maxim O Lavrentovich

Abstract

This thesis explores the characterization of liquid-liquid phase separation in model lipid bilayers using fluorescence, optical microscopy, and cryo-electron microscopy (cryo-EM) integrated with machine learning (ML) analysis. The plasma membrane has a complex composition, lateral heterogeneity and dynamic structure which makes it challenging to study. Simplified model membranes containing three or four-component lipid mixtures, typically comprising low- and high-melting lipids along with cholesterol, form phase separated systems that mimic lateral heterogeneity/lipid rafts in biomembranes. In living cells, lipid rafts are thought to form nanoscopic domains smaller than 200 nm. These domains cannot be resolved by conventional optical microscopy. For a long time, these nanoscopic domains have been characterized using indirect techniques. Seeing is believing and cryo-EM is employed as the primary tool for visualizing these nanoscopic domains, leveraging its ability to analyze samples particle-by-particle. Chapter 3 introduces a novel application of ML for characterizing phase-separated vesicles in cryo-EM images. It presents a simulation-based study testing various supervised and unsupervised ML methods for classifying the phase state of liposomes. Chapter 4 transitions to an experimental study, applying the supervised ML pipeline developed in Chapter 3 to estimate phase fraction, domain size and domain number in a three-component mixture. Substantial heterogeneity is observed in experimental samples that was not present in simulated liposomes. Together, these studies successfully demonstrate cryo-EM's potential for studying nanoscopic domains in model membranes on vesicle-by-vesicle basis. Chapter 5 investigates the role of the membrane dipole potential in lipid phase separation, providing insights into the mechanisms driving domain formation in lipid bilayers. This comprehensive study also highlights the synergy between advanced microscopy, ML, and theoretical modeling in elucidating the complexities vi of lipid phase behavior. These studies underscore the importance of lipid composition in biological membranes as a mechanism for controlling lipid raft formation and function.

Download
Files over 3MB may be slow to open. For best results, right-click and select "save as..."

Share

COinS