Full-length RNA-seq from single cells using Smart-seq2
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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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”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [34]
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).”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [13]
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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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