Single-cell analysis of gene expression offers the possibility of exploring cellular and molecular heterogeneity in stem and developmental cell systems, including cancer, to infer routes of cellular specification and their respective gene regulatory modules. PCR-based technologies, although limited to the analysis of a predefined set of genes, afford a cost-effective balance of throughput and biological information and have become a method of choice in stem cell laboratories. Here we describe an experimental and analytical protocol based on the Fluidigm microfluidics platform for the simultaneous expression analysis of 48 or 96 genes in multiples of 48 or 96 cells. We detail wet laboratory procedures and describe clustering, principal component analysis, correlation, and classification tools for the inference of cellular pathways and gene networks.
Single-cell quantitative RT-PCR Microfluidics Hierarchical clustering Principal component analysis Machine learning Random forests Logistic regression Correlation-based gene networks
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We are grateful to Elizabeth Kruse and Mattias Ohlsson for critical reading of this chapter. We acknowledge Swedish Foundation for Strategic Research for funding to José Teles and Leukaemia and Lymphoma Research and Cancer Research, UK, for Programme Grants to Tariq Enver.
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