Quantitative Detection of Low-Abundance Transcripts at Single-Cell Level in Human Epidermal Keratinocytes by Digital...

Genetic and epigenetic characterization of the large cellular diversity observed within tissues is essential to understanding the molecular networks that ensure the regulation of homeostasis, repair, and regeneration, but also pathophysiological processes. Skin is composed of multiple cell lineages and is therefore fully concerned by this complexity. Even within one particular lineage, such as epidermal keratinocytes, different immaturity statuses or differentiation stages are represented, which are still incompletely characterized. Accordingly, there is presently great demand for methods and technologies enabling molecular investigation at single-cell level. Also, most current methods used to analyze gene expression at RNA level, such as RT-qPCR, do not directly provide quantitative data, but rather comparative ratios between two conditions. A second important need in skin biology is thus to determine the number of RNA molecules in a given cell sample. Here, we describe a workflow that we have set up to meet these specific needs, by means of transcript quantification in cellular micro-samples using flow cytometry sorting and reverse transcription-digital droplet polymerase chain reaction. As a proof-of-principle, the workflow was tested for the detection of transcription factor transcripts expressed at low levels in keratinocyte precursor cells. A linear correlation was found between quantification values and keratinocyte input numbers in a low quantity range from 40 cells to 1 cell. Interpretable signals were repeatedly obtained from single-cell samples corresponding to estimated expression levels as low as 10-20 transcript copies per keratinocyte or less. The present workflow may have broad applications for the detection and quantification of low-abundance nucleic acid species in single cells, opening up perspectives for the study of cell-to-cell genetic and molecular heterogeneity. Interestingly, the process described here does not require internal references such as house-keeping gene expression, as it is initiated with defined cell numbers, precisely sorted by flow cytometry.