microSPLiT – microbial single-cell RNA sequencing by split-pool barcoding

Single-cell RNA-sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes but current methods are incompatible with bacteria. Researchers from the University of Washington have developed microSPLiT, a high-throughput scRNA-seq method for gram-negative and gram-positive bacteria that can resolve heterogeneous transcriptional states. The researchers applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. They retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction, and also identified novel and unexpected gene expression states including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities otherwise not amenable to single-cell analysis such as natural microbiota.

microSPLiT development and validation

(A) microSPLiT method summary. Fixed gram-positive and gramnegative bacterial cells are permeabilized with a mild detergent Tween-20 and their cell walls are degraded by lysozyme. The mRNA is then polyadenylated in-cell with E.coli Poly(A) Polymerase I (PAP). Inset: comparison of mRNA read counts (normalized) obtained from PAP-treated cells compared with cells treated with Terminator 5’ phosphate-dependent exonuclease (TEX) and both methods consecutively (T+P). The cellular RNA then undergoes three rounds of combinatorial barcoding including in-cell reverse transcription (RT) and two in-cell ligation reactions, followed by lysis and library preparation. (B) Barnyard plot for the E. coli and B. subtilis species-mixing experiment. Each dot corresponds to a putative single-cell transcriptome. UMI – unique molecular identifier. (C) Total (in thousands) and mRNA UMI counts per cell for both species. (D) t-stochastic neighbor embedding (t-SNE) of the data from heat shock experiment showing distinct clusters. “HS” – heat shocked.