GSE119888
Single-cell RNA-seq reports growth condition-specific global transcriptomes of individual bacteria
Bacteria respond to changes in their environment with specific transcriptional programmes, but even within genetically identical populations these programmes are not homogenously expressed. Such transcriptional heterogeneity between individual bacteria allows genetically clonal communities to develop a complex array of phenotypes, examples of which include persisters that resist antibiotic treatment and metabolically specialized cells that emerge under nutrient-limiting conditions. Fluorescent reporter constructs have played a pivotal role in deciphering heterogeneous gene expression within bacterial populations but have been limited to recording the activity of single genes in a few genetically tractable model species, whereas the vast majority of bacteria remain difficult to engineer and/or even to cultivate. Single-cell transcriptomics is revolutionizing the analysis of phenotypic cell-to-cell variation in eukaryotes, but technical hurdles have prevented its robust application to prokaryotes. Here, using the improved poly(A)-independent single-cell RNA-sequencing protocol MATQ-seq, we report the faithful capture of growth-dependent gene expression patterns in individual Salmonella and Pseudomonas bacteria across all RNA classes and genomic regions. These transcriptomes provide important reference points for single-cell RNA-sequencing of other bacterial species, mixed microbial communities and host–pathogen interactions. Overall design: In three culture conditions (Late Stationary phase, NaCl shock and Anaerobic shock), we systematically sorted a pool of ten Salmonella (n=60) and single Salmonella (n=71). For Pseudomonas aeruginosa only anaerobic shock condition was applied and systematically sorted as a pool of ten bacteria (n=5) and single bacteria (n=13). After sorting bacteria were lysed immediately and RNA was reverse transcribed following the MATQ-seq protocol. Libraries were prepared unsing Nextera XT (Illumina) library preparation kit. Pooled libraries were sequenced using and Illumina Nextseq 500 (2 × 75 base pairs (bp)), mid-output (1×) and high output (1×) for Pseudomonas, and an Illumina Novaseq 6000 (2 × S1 and 1 × S2 full cartridges, 2 × 50 bp PE) for Salmonella. After demultiplexing, data quality was examined using FastQC (v.0.11.7). Illumina and MATQ-seq adaptors were removed using cutadapt (v.1.9). Trimmed reads were mapped to the S. enterica SL1344 (NCBI ASM21085v2) and P. aeruginosa strain PAO1 genomes using STAR aligner (v.2.5.4b) with default settings. Read counts for each gene were determined using the featureCounts programme. Genes with more than five aligned reads were considered detected. Data were then subjected to PCA, where the 300 genes with the highest variance were selected to perform dimension reduction. Five libraries (M2G3, M3C4, M2H7, M2B7 and M2C10) that were clear outliers on the PCA plot were excluded from further analysis.