Cited by 4,137Single-cell and spatial transcriptomicsGenomics and Phylogenetic StudiesMolecular Biology Techniques and Applications

Full-length RNA-seq from single cells using Smart-seq2

Picelli, Simone, Faridani, Omid R, Björklund, Åsa K, Winberg, Gösta, Sagasser, Sven, Sandberg, Rickard·2014·Nature Protocols

Abstract

Emerging methods for the accurate quantification of gene expression in individual cells hold promise for revealing the extent, function and origins of cell-to-cell variability. Different high-throughput methods for single-cell RNA-seq have been introduced that vary in coverage, sensitivity and multiplexing ability. We recently introduced Smart-seq for transcriptome analysis from single cells, and we subsequently optimized the method for improved sensitivity, accuracy and full-length coverage across transcripts. Here we present a detailed protocol for Smart-seq2 that allows the generation of full-length cDNA and sequencing libraries by using standard reagents. The entire protocol takes ∼2 d from cell picking to having a final library ready for sequencing; sequencing will require an additional 1-3 d depending on the strategy and sequencer. The current limitations are the lack of strand specificity and the inability to detect nonpolyadenylated (polyA(-)) RNA.

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Crossref · 10.1038/nprot.2014.006

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  • Inverse variance weighted meta-analysis identified genes with pan-cell type eQTLs, including MHC class I antigen presentation pathway genes ERAP2 and HLA-C.

    “We used an inverse variance weighted meta-analysis to identify genes with pan-cell type eQTLs, including those in the major histocompatibility complex (MHC) class I antigen presentation pathway including ERAP2 (P < 3.57×10−32, meta-analysis), encoding an aminopeptidase known to cleave viral peptides34, and HLA-C (P < 1.74×10−29, meta-analysis), which encodes the MHC class I heavy chain”
  • The single-cell transcriptomics field is divided into two major categories: microfluidics droplet-based and microwell plate-based.

    “The field has rapidly developed ever since and is divided into two major categories: microfluidics droplet-based transcriptomics7–10 and microwell plate-based11–13 transcriptomics (Box 1).”

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References 35 · Cited by 4,137

Authors
Picelli, S., Faridani, O. R., Björklund, Å. K., Winberg, G., Sagasser, S., & Sandberg, R.
Affiliations
  • Ludwig Cancer Research (SE)
  • Karolinska Institutet (SE)
Corresponding author
Sandberg, Rickard
Venue
Nature Protocols
Published
2014-01
Volume
9
Issue
1
Pages
171-181
ISSN
  • 1750-2799 (ISSN-L)
  • 1754-2189
OpenAlex
W2068767807
Cited by
4,137
References
35
Citations by year
2026: 277 · 2025: 433 · 2024: 438 · 2023: 459 · 2022: 525 · 2021: 552 · 2020: 479 · 2019: 394 · 2018: 275 · 2017: 135 · 2016: 103 · 2015: 42 · 2014: 23 · 2013: 1
FWCI
41.44
Type
journal article
Language
en
Open access
closed
Locations
  • Nature Protocols (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
Keywords
single-cell RNA sequencing, SmartSeq2, full-length cDNA, transcriptome sequencing, gene expression quantification, cell-to-cell variability, library preparation, polyadenylated RNA, strand specificity
MeSH terms
Gene Library, Sequence Analysis, RNA, Single-Cell Analysis

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