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

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, so...

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Detalles Bibliográficos
Autores principales: Wang, Kuang-Tse, Adler, Carolyn E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10588366/
https://www.ncbi.nlm.nih.gov/pubmed/37864134
http://dx.doi.org/10.1186/s12864-023-09724-4
Descripción
Sumario: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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09724-4.