Targeted RNA sequencing enhances gene expression profiling of ultra-low input samples.
AuthorCurion, F; Handel, AE; Attar, M; Gallone, G; Bowden, R; Cader, MZ; Clark, MB
Source TitleRNA Biol
PublisherInforma UK Limited
University of Melbourne Author/sClark, Michael
Document TypeJournal Article
CitationsCurion, F., Handel, A. E., Attar, M., Gallone, G., Bowden, R., Cader, M. Z. & Clark, M. B. (2020). Targeted RNA sequencing enhances gene expression profiling of ultra-low input samples.. RNA Biol, pp.1-13. https://doi.org/10.1080/15476286.2020.1777768.
Access StatusAccess this item via the Open Access location
Open Access URLhttps://doi.org/10.1080/15476286.2020.1777768
NHMRC Grant codeNHMRC/1072662
RNA-seq is the standard method for profiling gene expression in many biological systems. Due to the wide dynamic range and complex nature of the transcriptome, RNA-seq provides an incomplete characterization, especially of lowly expressed genes and transcripts. Targeted RNA sequencing (RNA CaptureSeq) focuses sequencing on genes of interest, providing exquisite sensitivity for transcript detection and quantification. However, uses of CaptureSeq have focused on bulk samples and its performance on very small populations of cells is unknown. Here we show CaptureSeq greatly enhances transcriptomic profiling of target genes in ultra-low-input samples and provides equivalent performance to that on bulk samples. We validate the performance of CaptureSeq using multiple probe sets on samples of iPSC-derived cortical neurons. We demonstrate up to 275-fold enrichment for target genes, the detection of 10% additional genes and a greater than 5-fold increase in identified gene isoforms. Analysis of spike-in controls demonstrated CaptureSeq improved both detection sensitivity and expression quantification. Comparison to the CORTECON database of cerebral cortex development revealed CaptureSeq enhanced the identification of sample differentiation stage. CaptureSeq provides sensitive, reliable and quantitative expression measurements on hundreds-to-thousands of target genes from ultra-low-input samples and has the potential to greatly enhance transcriptomic profiling when samples are limiting.
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