Comparison of Nile Red and Cell Size Analysis for High‐Throughput Lipid Estimation Within Oleaginous Yeast

Abstract With growing interest in oleaginous yeast as producers of future fuels and bulk chemicals, a robust, high‐throughput method for estimating lipid production is required. Although the lipophilic dye Nile red is frequently used to assay large samples of yeast and microalgae, inconsistent stain permeability between species and strains limits its effectiveness for some microorganisms. In this study, the oleaginous yeast Metschnikowia pulcherrima is used to develop a fluorescence‐free, cell‐size‐based image analysis method for estimating lipid production, which is then compared with an optimized Nile red method across several experimental scenarios. Cell size analysis (CSA) outperforms Nile red in all scenarios, correlating well with lipid extraction data when screening multiple strains, screening a subset of strains grown in different conditions, and tracking the lipid accumulation of a culture over time. Stain permeability is shown to vary significantly among the strains trialled, with lipid droplet size and cell wall thickness having a deleterious effect in the permeability of high‐lipid‐accumulating cells. CSA can also allow culture population dynamics to be monitored, providing key process information of cell size distribution in response to changing media compositions. Practical Applications : Nile red is currently the go‐to method for high‐ throughput lipid screening; however, staining inconsistencies in some organisms caused by varying cell morphology makes it challenging to optimize a robust protocol. Although fluorescence‐free methods exist (Raman spectroscopy, Fourier‐transform infrared spectroscopy, GCMS), the need for extensive sample preparation and specialist equipment restricts their widespread adoption. The CSA method presented here offers an accurate, robust, and cheap alternative for the study of microorganisms where fluorescence‐based avenues are not feasible. Furthermore, the population dynamics collected during CSA can easily be applied to bioreactor style processing, where tracking size distributions can provide real time information of culture status. This additional information is valuable even if fluorescence screening is a possibility.