Phase Separation in Liposomes Determined by Ergosterol and Classified Using Machine Learning

Area of Science:

• Membrane biophysics
• Lipidomics
• Computational biology

Background:

• Ergosterol (Erg) is known to influence lipid membrane organization.
• Direct evidence of Erg's concentration-dependent effects on membrane phase behavior is lacking.
• Understanding sterol-lipid interactions is crucial for membrane function.

Purpose of the Study:

• To investigate the concentration-dependent phase behavior of lipid membranes with ergosterol (Erg).
• To compare the effects of Erg versus cholesterol (Chol) on membrane phase separation.
• To apply machine learning for analyzing liposome membrane structures.

Main Methods:

• Utilized cell-sized liposomes composed of 1,2-Dioleoyl-sn-glycero-3-phosphocholine (DOPC) and 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC).
• Investigated varying concentrations of Erg and Chol within the liposomes.
• Employed machine learning algorithms to analyze microscopic images of liposome phase separation.

Main Results:

• Ergosterol induced a significantly wider concentration range for membrane phase separation compared to cholesterol.
• Machine learning successfully identified homogeneous membranes.
• Machine learning performance for identifying complex phase-separated domains was limited by data availability.

Conclusions:

• Ergosterol exhibits distinct concentration-dependent effects on membrane phase behavior compared to cholesterol.
• Machine learning offers a promising automated approach for analyzing liposome structures, with potential for future improvements.
• This study provides direct evidence for Erg's influence on membrane domain formation.