Abstract
In the last few years single-cell RNA sequencing (scRNA-seq) has enabled the investigation of cellular heterogeneity at the transcriptional level, the characterization of rare cell types as well as the detailed analysis of the stochastic nature of gene expression. A large number of methods have been developed, varying in their throughput, sensitivity, and scalability. A major distinction is whether they profile only 5′- or 3′-terminal part of the transcripts or allow for the characterization of the entire length of the transcripts. Among the latter, Smart-seq2 is still considered the “gold standard” due to its sensitivity, precision, lower cost, scalability and for being easy to set up on automated platforms. In this chapter I describe how to efficiently generate sequencing-ready libraries, highlight common issues and pitfalls, and offer solutions for generating high-quality data.
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Picelli, S. (2019). Full-Length Single-Cell RNA Sequencing with Smart-seq2. In: Proserpio, V. (eds) Single Cell Methods. Methods in Molecular Biology, vol 1979. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9240-9_3
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DOI: https://doi.org/10.1007/978-1-4939-9240-9_3
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