microSPLiT single-cell and bulk transcriptomes analysed with STAR - Pseudomonas putida KT2440/pKJK5

Description of the data and file structure

Data are displayed as 2 files

1. Bulk transcriptomics results (Bulk_STAR.csv)

STAR processed data combined in a gene x sample table

2. microSPLiT single-cell results (microSPLiT_STARsolo.xlsx)

STARsolo processed data combined as sublibraries’ gene associated transcript numbers (UMIs) per cell for the control (E1) and experiment (E2) sublibraries (F1-8) - (1 sublibrary per table).

Transcriptional heterogeneity in isogenic bacterial populations can play various roles in bacterial evolution, but its detection remains technically challenging. Here, we use microbial split-pool ligation transcriptomics to study the relationship between bacterial subpopulation formation and plasmid-host interactions at the single-cell level. We find that single-cell transcript abundances are influenced by bacterial growth state and plasmid carriage. Moreover, plasmid carriage constrains the formation of bacterial subpopulations. Plasmid genes, including those with core functions such as replication and maintenance, exhibit transcriptional heterogeneity associated with cell activity. Notably, we identify a cell subpopulation that does not transcribe conjugal plasmid transfer genes, which may help reduce plasmid burden on a subset of cells. Our study advances the understanding of plasmid-mediated subpopulation dynamics and provides insights into the plasmid-bacteria interplay.

After an overnight culture of 16 hours in 5 mL of selective LB (250 RPM), Pseudomonas putida KT2440 and Pseudomonas putida KT2440/pKJK5 were washed and diluted 500 times in 100 mL of LB. Flasks were incubated at 30°C (250 RPM), and cultures were sampled at turbidity (600 nm) of 0.5 and 1.5.

Population-level (bulk) RNA-seq

Samples (n=4 independent experiments) were centrifuged (3 minutes, 7000xg), RNA content was directly extracted using the Quick-RNA Fungal/Bacterial Miniprep Kit (Zymo Research, CA, USA) following the manufacturer's instructions, and libraries were prepared using the Zymo-Seq RiboFree Total RNA Library Kit (Zymo Research, CA, USA). Fragment integrity and size were assessed using a fragment analyzer (Agilent, CA, USA) and quantified with a Qubit dsDNA HS assay kit (Thermo Fisher Scientific, Waltham, MA, USA) before sequencing on a Novaseq 6000 (Illumina, CA, USA) at 2 ×150 bp performed by Novogene Co., Ltd. (Cambridge, United Kingdom). Sequence files can be found at NCBI under Bioproject ID PRJNA1019643.

microSPLiT scRNA-seq

The experiment was repeated 2 times (n= 2 independent experiments) following the microSPLiT protocol⁷.

Fragment integrity and size were assessed using a Fragment Analyzer (Agilent, CA, USA) using an NGS Fragment Kit (1-6000 bp) and were sequenced on a Novaseq 6000 (Illumina, CA, USA) at 2×150 bp performed by Novogene Co., Ltd. (Cambridge, United Kingdom). A list of primers and associated barcodes can be found in Kuchina et al, 2021⁷. Sequence files can be found at NCBI under Bioproject ID: PRJNA1019643. The number of cells and transcripts per cell were equivalent in the different generated sublibraries.

Computational method

The reference genome was generated and data processed using STAR v-2.7.9a (https://github.com/alexdobin/STAR/ ) combining the pKJK5 plasmid and Pseudomonas putida KT2440 genome using, respectively, GenBank record AM261282.1 and assembly GCA_000007565 from EnsemblBacteria). The reference genome was indexed for STAR using parameters (--genomeSAindexNbases 10) specific for small genomes using the formula min(14, log2 (Genome Length)/2 - 1). The created genome was used as a reference for both approaches (bulk & microSPLiT).

Population-level (bulk) RNA-seq

The sequenced reads were trimmed of the remaining adapter sequences and low quality based using bbduk (BBMap–Bushnell B. – sourceforge.net/projects/bbmap/  ), with right-side trimming using parameters {k=23 mink=11 hdist=1 tpe tbo trimq=10}. Trimmed reads were mapped against the reference genome, and the per-cell gene counts were quantified using STAR (https://github.com/alexdobin/STAR/ ). Transcripts identified as rRNA, tRNA and plasmid encoded were removed from the data. For heatmaps, feature counts from the generated contingency table (gene x sample) were normalized (divided by the total counts, multiplied by 10,000+1, and log10 transformed) and centered-scaled with the R scale function.

microSPLiT scRNA-seq

Sequenced reads were demultiplexed (barcode list can be found in Supplemental Data) and mapped against the reference genome, and the per-cell gene expression was quantified using STARsolo (https://github.com/alexdobin/STAR/  ). A matrix of unique molecular identifier (UMIs, unique gene transcript-associated barcode) counts for each cell (N-by-K matrix, with N cells and K genes) was generated by gathering cells from all sublibraries.