(307b) Efficient and Cost-Effective Bacterial mRNA Sequencing from Low Input Samples through Ribosomal RNA Depletion (Industry Candidate) | AIChE

(307b) Efficient and Cost-Effective Bacterial mRNA Sequencing from Low Input Samples through Ribosomal RNA Depletion (Industry Candidate)

Authors 

Heom, K., UCSB
Liu, E., UCSB
O'Malley, M., University of California-Santa Barbara
Dey, S., University of California, Santa Barbara
RNA sequencing provides 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 prevents efficient capture and enrichment of mRNA from the abundant rRNA molecules in a cell. Further, bacterial cells frequently contain 100-fold lower RNA than mammalian cells. Therefore, samples derived from non-cultivable and non-model bacterial species can have limiting starting amounts of total RNA, necessitating efficient amplification of small quantities of RNA. To overcome these limitations, we report EMBR-Seq (Enrichment of mRNA by Blocked rRNA Sequencing), a method that efficiently depletes 5S, 16S and 23S rRNA using blocking primers to prevent their amplification, resulting in greater than 80% of the sequenced 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 commercial kits, we show that EMBR-Seq successfully quantifies the transcriptome from 500-fold lower starting total RNA. Thus, EMBR-Seq provides an efficient and cost-effective approach to measure global gene expression profiles from low input bacterial samples.

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