Enhancing the sensitivity of bacterial single-cell RNA sequencing using RamDA-seq and Cas9-based rRNA depletion
Bacterial populations exhibit heterogeneity in gene expression, which facilitates their survival and adaptation to unstable and unpredictable environments through the bet-hedging strategy. However, unraveling the rare subpopulations and heterogeneity in gene expression using population-level gene expression analysis remains a challenging task. Single-cell RNA sequencing (scRNA-seq) has the potential to identify rare subpopulations and capture heterogeneity in bacterial populations, but standard methods for scRNA-seq in bacteria are still under development, mainly due to differences in mRNA abundance and structure between eukaryotic and prokaryotic organisms. In this study, we present a hybrid approach that combines RamDA-seq with Cas9-based rRNA depletion for scRNA-seq in bacteria. This approach allows cDNA amplification and subsequent sequencing library preparation from low abundance bacterial RNAs. We evaluated its sequencing read rate, gene detection sensitivity, and gene expression patterns from the dilution series of total RNA or the sorted single Escherichia coli cells. Our results demonstrated the detection of more than 1,000 genes, about 24% of the genes in the E. coli genome, from single E. coli cells with less sequencing effort compared to conventional methods. We observed gene expression clusters between different cellular proliferation states or heat shock treatment. The approach demonstrated high detection sensitivity and reproducibility compared to current bacterial scRNA-seq methods and proved to be an invaluable tool for understanding the ecology of bacterial populations and capturing the heterogeneity of bacterial gene expression.