A head-to-head comparison of ribodepletion and polyA selection approaches for Caenorhabditis elegans low input RNA-sequencing libraries. Issue 7 (15th April 2021)

• Record Type: Journal Article
• Authors: Barrett, Alec; McWhirter, Rebecca; Taylor, Seth R; Weinreb, Alexis; Miller, David M; Hammarlund, Marc
• Editors: Ward, J
• Is Part Of: G3. Volume 11:Issue 7(2021)
• Publication Date: 2021-04-15
• Subjects: C. elegans -- transcriptomics -- RNAseq -- low input RNAseq -- Ribodepletion -- Poly-adenylated -- library preparation; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1093/g3journal/jkab121
• Languages: English
• ISSNs: 2160-1836

Abstract

Abstract: A recent and powerful technique is to obtain transcriptomes from rare cell populations, such as single neurons in Caenorhabditis elegans, by enriching dissociated cells using fluorescent sorting. However, these cell samples often have low yields of RNA that present challenges in library preparation. This can lead to PCR duplicates, noisy gene expression for lowly expressed genes, and other issues that limit endpoint analysis. Furthermore, some common resources, such as sequence-specific kits for removing ribosomal RNA, are not optimized for nonmammalian samples. To advance library construction for such challenging samples, we compared two approaches for building RNAseq libraries from less than 10 nanograms of C. elegans RNA: SMARTSeq V4 (Takara), a widely used kit for selecting poly-adenylated transcripts; and SoLo Ovation (Tecan Genomics), a newly developed ribodepletion-based approach. For ribodepletion, we used a custom kit of 200 probes designed to match C. elegans rRNA gene sequences. We found that SoLo Ovation, in combination with our custom C. elegans probe set for rRNA depletion, detects an expanded set of noncoding RNAs, shows reduced noise in lowly expressed genes, and more accurately counts expression of long genes. The approach described here should be broadly useful for similar efforts to analyze transcriptomics when RNA is limiting.