Transcriptomics

Dataset Information

0

Efficient and cost-effective bacterial mRNA sequencing from low input samples through ribosomal RNA depletion


ABSTRACT: RNA sequencing is a powerful approach to quantify the genome-wide distribution of mRNA molecules in a population to gain deeper understanding of cellular functions and phenotypes. However, unlike eukaryotic cells, mRNA sequencing of bacterial samples is more challenging due to the absence of a poly-A tail that typically enables efficient capture and enrichment of mRNA from the abundant rRNA molecules in a cell. Moreover, bacterial cells frequently contain 100-fold lower quantities of RNA compared to mammalian cells, which further complicates mRNA sequencing from non-cultivable and non-model bacterial species which are often present in low abundance. To overcome these limitations, we report EMBR-seq (Enrichment of mRNA by Blocked rRNA), a method that efficiently depletes 5S, 16S and 23S rRNA using blocking primers to prevent their amplification. EMBR-seq results in greater than 80% of the sequenced RNA molecules deriving from mRNA. We demonstrate that this increased efficiency provides a deeper view of the transcriptome without introducing technical amplification-induced biases. Moreover, compared to recent methods that employ a large array of oligonucleotides to deplete rRNA, EMBR-seq employs a single oligonucleotide per rRNA, thereby making this new technology significantly more cost-effective, especially when applied to varied bacterial species. Finally, compared to existing commercial kits, we show that EMBR-seq can be used to successfully quantify the transcriptome from more than 500-fold lower starting total RNA. Thus, EMBR-seq provides an efficient and cost-effective approach to quantify global gene expression profiles from low input bacterial samples.

ORGANISM(S): Escherichia coli str. K-12 substr. MG1655

PROVIDER: GSE149666 | GEO | 2020/10/22

REPOSITORIES: GEO

Dataset's files

Source:
Action DRS
Other
Items per page:
1 - 1 of 1

Similar Datasets

| PRJNA760801 | ENA
2019-12-28 | GSE142656 | GEO
2020-05-11 | GSE147155 | GEO
2023-09-29 | GSE223404 | GEO
2021-10-15 | E-MTAB-9686 | biostudies-arrayexpress
2022-08-04 | GSE210198 | GEO
2020-09-30 | GSE120327 | GEO
2014-01-07 | E-GEOD-51403 | biostudies-arrayexpress
| 2623816 | ecrin-mdr-crc
2015-05-29 | GSE65189 | GEO