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CRISPR/Cas9-based depletion of 16S ribosomal RNA improves library complexity of single-cell RNA-sequencing in planarians

Authors :
Kuang-Tse Wang
Carolyn E. Adler
Source :
BMC Genomics, Vol 24, Iss 1, Pp 1-15 (2023)
Publication Year :
2023
Publisher :
BMC, 2023.

Abstract

Abstract Background Single-cell RNA-sequencing (scRNA-seq) relies on PCR amplification to retrieve information from vanishingly small amounts of starting material. To selectively enrich mRNA from abundant non-polyadenylated transcripts, poly(A) selection is a key step during library preparation. However, some transcripts, such as mitochondrial genes, can escape this elimination and overwhelm libraries. Often, these transcripts are removed in silico, but whether physical depletion improves detection of rare transcripts in single cells is unclear. Results We find that a single 16S ribosomal RNA is widely enriched in planarian scRNA-seq datasets, independent of the library preparation method. To deplete this transcript from scRNA-seq libraries, we design 30 single-guide RNAs spanning its length. To evaluate the effects of depletion, we perform a side-by-side comparison of the effects of eliminating the 16S transcript and find a substantial increase in the number of genes detected per cell, coupled with virtually complete loss of the 16S RNA. Moreover, we systematically determine that library complexity increases with a limited number of PCR cycles following CRISPR treatment. When compared to in silico depletion of 16S, physically removing it reduces dropout rates, retrieves more clusters, and reveals more differentially expressed genes. Conclusions Our results show that abundant transcripts reduce the retrieval of informative transcripts in scRNA-seq and distort the analysis. Physical removal of these contaminants enables the detection of rare transcripts at lower sequencing depth, and also outperforms in silico depletion. Importantly, this method can be easily customized to deplete any abundant transcript from scRNA-seq libraries.

Details

Language :
English
ISSN :
14712164
Volume :
24
Issue :
1
Database :
Directory of Open Access Journals
Journal :
BMC Genomics
Publication Type :
Academic Journal
Accession number :
edsdoj.8d3040bcc31f455eb61973eb21594879
Document Type :
article
Full Text :
https://doi.org/10.1186/s12864-023-09724-4