Cellular innate fluorescence (IF) is a natural fluorescence derived from cellular metabolites and biomolecules within microbial cells. Although IF is suggested to be a promising tool for probing the physiology of cells in a non-invasive manner, the link between single-cell IF and heterogeneity in material production remains largely unexplored. This study aimed to examine the link between cellular IF in oleaginous yeasts and their lipid-producing capabilities at multiple taxonomic levels: intra-species, inter-species, and inter-genus. Briefly, we developed a novel microscopic method that can directly link cellular IF (a single-cell IF signature) and the lipid-producing capability of cells at single-cell resolution, thereby enabling the recognition of high heterogeneity in single-cell IF and lipid production in cells. At the intra-species level, the time-course analysis revealed a parallelism between the shifts in single-cell IF signatures and lipid production by Lipomyces starkeyi. The regression model constructed based on single-cell IF signatures could predict intracellular lipid contents. The link between IF and lipid production was also observed across the inter-strain, inter-species, and inter-genus levels, where the regression model constructed with single-cell IF signatures of different strains, species, and genera could predict lipid production. Machine learning models established a computational link to predict lipid productivity by relying on the single-cell IF signatures. These results indicate that the single-cell IF signature is a promising tool for lipid production analysis and prediction in oleaginous yeast species across taxa.
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