Abstract
Single-cell mRNA-seq is a valuable tool to dissect expression profiles and to understand the regulatory network of genes. Microfluidics is well suited for single-cell analysis owing both to the small volume of the reaction chambers and easiness of automation. Here we describe the workflow of single-cell mRNA-seq using C1 IFC, which can isolate and process up to 96 cells. Both on-chip procedure (lysis, reverse transcription, and preamplification PCR) and off-chip sequencing library preparation protocols are described. The workflow generates full-length mRNA information, which is more valuable compared to 3′ end counting method for many applications.
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References
Picelli S et al (2014) Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc 9:171–181
Kalisky T, Quake SR (2011) Single-cell genomics. Nat Methods 8:311–314
Taniguchi K, Kajiyama T, Kambara H (2009) Quantitative analysis of gene expression in a single cell by qPCR. Nat Methods 6:503–506
Dalerba P et al (2011) Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat Biotechnol 29:1120–1127
Tang F et al (2009) mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6:377–382
Pollen AA et al (2014) Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol 32:1053–1058
Grun D, van Oudenaarden A (2015) Design and analysis of single-cell sequencing experiments. Cell 163:799–810
Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA (2015) The technology and biology of single-cell RNA sequencing. Mol Cell 58:610–620
Ilicic T et al (2016) Classification of low quality cells from single-cell RNA-seq data. Genome Biol 17:29
Stubbington MJ et al (2016) T cell fate and clonality inference from single-cell transcriptomes. Nat Methods 13:329–332
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Gong, H., Do, D., Ramakrishnan, R. (2018). Single-Cell mRNA-Seq Using the Fluidigm C1 System and Integrated Fluidics Circuits. In: Raghavachari, N., Garcia-Reyero, N. (eds) Gene Expression Analysis. Methods in Molecular Biology, vol 1783. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7834-2_10
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DOI: https://doi.org/10.1007/978-1-4939-7834-2_10
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Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-7833-5
Online ISBN: 978-1-4939-7834-2
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