Application of high-content image analysis for quantitatively estimating lipid accumulation in oleaginous yeasts with potential for use in biodiesel production

Abstract

Biodiesel from oleaginous microorganisms is a viable substitute for a fossil fuel. Current methods for microorganism lipid productivity evaluation do not analyze lipid dynamics in single cells. Here, we described a high-content image analysis (HCA) as a promising strategy for screening oleaginous microorganisms for biodiesel production, while generating single-cell lipid dynamics data in large cell density. Rhodotorula slooffiae yeast were grown in standard (CTL) or lipid trigger medium (LTM), and lipid droplet (LD) accumulation was analyzed in deconvolved confocal microscopy images of cells stained with the lipophilic fluorescent Nile red (NR) dye using automated cell and LD segmentation. The 'vesicle segmentation' method yielded valid morphometric results for limited lipid accumulation in smaller LDs (CTL samples) and for high lipid accumulation in larger LDs (LTM samples), and detected LD localization changes. Thus, HCA can be used to analyze the lipid accumulation patterns likely to be encountered in screens for biodiesel production.

Keywords: Biodiesel; Biofuel; High-content image analysis; Lipid droplets; Oleaginous microorganisms.