Integrated workflow of down-scaled Smart-seq2 and absolute quantitative, single-cell digital PCR validates lack of representability of low-abundant transcripts

Abstract

Single-cell RNA sequencing (scRNA-seq) can unmask transcriptional heterogeneity facilitating the detection of rare subpopulations at unprecedented resolution. In response to challenges related to coverage and quantity of transcriptome analysis, the lack of unbiased and absolutely quantitative validation methods hampers further improvements. Digital PCR (dPCR) represents such a method as we could show that the inherent partitioning enhances molecular detections by increasing effective mRNA concentrations. We developed a scRT-ddPCR method and validated it using two breast cancer cell lines, MCF7 and BT-474, and bulk methods. ErbB2, a low-abundant transcript in MCF7 cells, suffers from dropouts in scRNA-seq and thus calculated fold changes are biased. Using our scRT-ddPCR, we could improve the detection of ErbB2 and based on the absolute counts obtained we could validate the scRNA-seq fold change. We think this workflow is a valuable addition to the single-cell transcriptomic research toolbox and could even become a new standard in fold change validation because of its reliability, ease of use and increased sensitivity.