![]() ![]() Over the last decade, the development of advanced analysis techniques has enabled the study of complex biological systems and phenomena at single-cell resolution. Single-cell analysis techniques can also be applied to the detection of rare cells within heterogeneous cell populations, which would be useful in both basic research and clinical applications 1, 2. Single-cell analysis techniques are critical to distinguish differences between individual cells within seemingly homogeneous populations, such as divergent cell cycle status, cell lineage bias, or other cellular processes. This low cost, sensitive, and adaptable technique will provide an accessible platform for high throughput single-cell analysis and enable a wide range of research and clinical applications. Using mixtures of in vitro transcripts and murine cell lines, we demonstrated the detection of single RNA molecules and rare cell populations at a frequency of 0.1%. We also developed a simulation model to guide experimental design and optimize the accuracy and precision of the assay. Fluorescent signals indicative of gene expressions are analyzed using a probabilistic deconvolution method to account for ambient RNA and cell doublets and produce single-cell gene signature profiles, as well as predict cell frequencies within heterogeneous samples. Our customized reagent cocktail incorporates the bacteriophage T7 gene 2.5 protein to overcome cell lysate-mediated inhibition and allows for one-step RT-PCR of single cells encapsulated in nanoliter droplets. Here, we present a novel microdroplet-based, one-step reverse-transcriptase polymerase chain reaction (RT-PCR) platform and demonstrate the detection of three targets simultaneously in over 100,000 single cells in a single experiment with a rapid read-out. Gene expression analysis of individual cells enables characterization of heterogeneous and rare cell populations, yet widespread implementation of existing single-cell gene analysis techniques has been hindered due to limitations in scale, ease, and cost. ![]()
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