Intra-Ramanome Correlation Analysis Unveils Metabolite Conversion Network from an Isogenic Cellular Population

Revealing dynamic features of cellular systems, such as links among metabolic phenotypes, typically requires a time- or condition-series set of samples. Here Intra-Ramanome Correlation analysis (IRCA) was proposed to achieve this goal from just one snapshot of an isogenic population, by pairwise correlating among cells all the thousands of Raman bands from Single-cell Raman Spectra (SCRS), i.e., based on the intrinsic inter-cellular metabolic heterogeneity. IRCA of Chlamydomonas reinhardtii under nitrogen depletion revealed a metabolite conversion network at each time point and its temporal dynamics that feature protein-to-starch conversion followed by starch-to-TAG conversion (plus conversion of membrane lipids to TAG). Such correlation patterns in IRCA were abrased by knocking out the starch-biosynthesis pathway yet fully restored by genetic complementation. Extension to 64 ramanomes from microalgae, fungi and bacteria under various conditions suggests IRCA-derived metabolite conversion network as an intrinsic, reliable, species-resolved and state-sensitive metabolic signature of isogenic cellular population. The high throughput, low cost, excellent scalability and broad extendibility of IRCA suggest its broad application in cellular systems.