Single-cell simultaneous metabolome and transcriptome profiling revealing metabolite-gene correlation networ

Metabolic studies at single cell level could directly define the cellular phenotype closest to physiological or disease states. However, the current single cell metabolome (SCM) study using mass spectroscopy has the difficulty to give a complete view of the metabolic activity in the cell, while the prediction of metabolism-phenotype relationship is limited by the potential inconsistency between transcriptomic and metabolic levels. Here, single-cell simultaneous metabolome and transcriptome profiling method (scMeT-seq) is developed, based on sub-picoliter sampling from the cell for the initial metabolome profiling followed by single cell transcriptome sequencing. This design does not only provide sufficient cytoplasm for SCM, but also nicely keeps the cellular viability for the accurate transcriptomic analysis in the same cell. Diverse relationships between the two omics are revealed in macrophages with the stimulation of lactate and lactate transporter (MCT1) antagonist. Moreover, we mapped metabolite states to the single-cell differentiation trajectory and gene correlation network of macrophages, which allows the unsupervised functional interpretation of metabolome. Thus, the established scMeT-seq should lead to a new perspective in metabolic research by transforming metabolomics from metabolite snapshot to functional approach.