An Improved Bacterial Single-cell RNA-seq Reveals Biofilm Heterogeneity [scRNA-seq]
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1081799 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:In contrast to mammalian cells, bacterial cells lack mRNA polyadenylated tails, presenting a hurdle in isolating mRNA amidst the prevalent rRNA during single-cell RNA-seq. This study introduces a novel method, Ribosomal RNA-derived cDNA Depletion (RiboD), seamlessly integrated into the PETRI-seq technique, yielding RiboD-PETRI. This innovative approach offers a cost-effective, equipment-free, and high-throughput solution for bacterial single-cell RNA sequencing (scRNA-seq). By efficiently eliminating rRNA reads and substantially enhancing mRNA detection rates (up to 92%), our method enables precise exploration of bacterial population heterogeneity. Applying RiboD-PETRI to investigate biofilm heterogeneity, distinctive subpopulations marked by unique genes within biofilms were successfully identified. Notably, PdeI, a marker for the cell-surface attachment subpopulation, was observed to elevate cyclic diguanylate (c-di-GMP) levels, promoting persister cell formation. Thus, we address a persistent challenge in bacterial single-cell RNA-seq regarding rRNA abundance, exemplifying the utility of this method in exploring biofilm heterogeneity. Our method effectively tackles a long-standing issue in bacterial scRNA-seq: the overwhelming abundance of rRNA. This advancement significantly enhances our ability to investigate the intricate heterogeneity within biofilms at unprecedented resolution.
2024-12-10 | GSE260457 | GEO
Project description:In contrast to mammalian cells, bacterial cells lack mRNA polyadenylated tails, presenting a hurdle in isolating mRNA amidst the prevalent rRNA during single-cell RNA-seq. This study introduces a novel method, Ribosomal RNA-derived cDNA Depletion (RiboD), seamlessly integrated into the PETRI-seq technique, yielding RiboD-PETRI. This innovative approach offers a cost-effective, equipment-free, and high-throughput solution for bacterial single-cell RNA sequencing (scRNA-seq). By efficiently eliminating rRNA reads and substantially enhancing mRNA detection rates (up to 92%), our method enables precise exploration of bacterial population heterogeneity. Applying RiboD-PETRI to investigate biofilm heterogeneity, distinctive subpopulations marked by unique genes within biofilms were successfully identified. Notably, PdeI, a marker for the cell-surface attachment subpopulation, was observed to elevate cyclic diguanylate (c-di-GMP) levels, promoting persister cell formation. Thus, we address a persistent challenge in bacterial single-cell RNA-seq regarding rRNA abundance, exemplifying the utility of this method in exploring biofilm heterogeneity. Our method effectively tackles a long-standing issue in bacterial scRNA-seq: the overwhelming abundance of rRNA. This advancement significantly enhances our ability to investigate the intricate heterogeneity within biofilms at unprecedented resolution.
2024-12-10 | GSE259433 | GEO
Project description:An Improved Bacterial Single-cell RNA-seq Reveals Biofilm Heterogeneity
| PRJNA1081803 | ENA
Project description:An Improved Bacterial Single-cell RNA-seq Reveals Biofilm Heterogeneity [RNA-seq]
| PRJNA1081580 | ENA
Project description:Biofilm formation is an important mechanism of survival and persistence for many bacterial pathogens. These multicellular communities contain subpopulations of cells that display vast metabolic and transcriptional diversity along with high recalcitrance to antibiotics and host immune defenses. Investigating the complex heterogeneity within biofilm has been hindered by the lack of a sensitive and high-throughput method to assess stochastic transcriptional activity and regulation between bacterial subpopulations, which requires single-cell resolution. We have developed an optimized bacterial single-cell RNA sequencing method, BaSSSh-seq, to study Staphylococcus aureus diversity during biofilm growth and transcriptional adaptations following immune cell exposure.
2024-11-04 | GSE270986 | GEO
Project description:Improved bacterial single-cell RNA-seq through automated MATQ-seq [scRNA-seq]
| PRJNA904727 | ENA
Project description:This SuperSeries is composed of the SubSeries listed below.
2024-12-10 | GSE260458 | GEO
Project description:We developed a spatially resolved method to profile the spatial transcriptome of biofilm. In detail, we used fluorescent dyes to label the different regions of biofilm cultured in a microfluidic chip. After staining, the bacterial cells in biofilm were sorted into relevant bins according to their spatial information marked by the fluorescent pattern. Finally, miniBac-seq (RNA-seq) method was applied to capture the transcriptome of each bin.
2023-01-01 | GSE197541 | GEO
Project description:In this work, we have improved our previously published bacterial single-cell RNA-sequencing protocol (MATQ-seq), providing enhancements that achieve a higher cell throughput while also including integration of automation. We selected a more efficient reverse transcriptase which led to a lower drop-out rate and higher workflow robustness, and we also successfully implemented a Cas9-based ribosomal RNA depletion protocol into the MATQ-seq workflow. Applying this improved protocol on a large set of single Salmonella cells sampled over growth revealed improved gene coverage and a higher gene detection limit, allowing us to reveal the expression of small regulatory RNAs such as GcvB or CsrB at a single-cell level. In addition, we were able to confirm previously described phenotypic heterogeneity in Salmonella in regards to expression of pathogenicity-associated genes.
2023-02-09 | GSE218632 | GEO
Project description:In this work, we have improved our previously published bacterial single-cell RNA-sequencing protocol (MATQ-seq), providing enhancements that achieve a higher cell throughput while also including integration of automation. We selected a more efficient reverse transcriptase which led to a lower drop-out rate and higher workflow robustness, and we also successfully implemented a Cas9-based ribosomal RNA depletion protocol into the MATQ-seq workflow. Applying this improved protocol on a large set of single Salmonella cells sampled over growth revealed improved gene coverage and a higher gene detection limit, allowing us to reveal the expression of small regulatory RNAs such as GcvB or CsrB at a single-cell level. In addition, we were able to confirm previously described phenotypic heterogeneity in Salmonella in regards to expression of pathogenicity-associated genes.
2023-02-09 | GSE218631 | GEO