CEL-seq2: sensitive highly-multiplexed single-cell RNA-Seq
Open accessAbstract
Single-cell transcriptomics requires a method that is sensitive, accurate, and reproducible. Here, we present CEL-Seq2, a modified version of our CEL-Seq method, with threefold higher sensitivity, lower costs, and less hands-on time. We implemented CEL-Seq2 on Fluidigm's C1 system, providing its first single-cell, on-chip barcoding method, and we detected gene expression changes accompanying the progression through the cell cycle in mouse fibroblast cells. We also compare with Smart-Seq to demonstrate CEL-Seq2's increased sensitivity relative to other available methods. Collectively, the improvements make CEL-Seq2 uniquely suited to single-cell RNA-Seq analysis in terms of economics, resolution, and ease of use.
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scRNA-seq conducted in the small volumes of the Fluidigm C1 platform outperforms CEL-seq2, Smart-seq, or other commercially available kits in microliter volumes.
“For example, it has previously been shown that scRNA-seq conducted in the small volumes available in the automated microfluidic platform from Fluidigm (C1 platform) outperforms CEL-seq2, Smart-seq, or other commercially available kits in microliter volumes (Hashimshony et al., 2016; Wu et al., 2014).”
From Comparative Analysis of Single-Cell RNA Sequencing Methods (2017) · cites this paper as Hashimshony et al., 2016
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Probing the Tumor Suppressor Function of BAP1 in CRISPR-Engineered Human Liver Organoids
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Troy+ brain stem cells cycle through quiescence and regulate their number by sensing niche occupancy
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From tissues to cell types and back: single-cell gene expression analysis of tissue architecture
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Droplet-based single cell RNAseq tools: a practical guide
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A plate-based single-cell ATAC-seq workflow for fast and robust profiling of chromatin accessibility
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Cardiac differentiation roadmap for analysis of plasticity and balanced lineage commitment
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- Authors
- Hashimshony, T., Senderovich, N., Avital, G., Klochendler, A., de Leeuw, Y., Anavy, L., Gennert, D., Li, S., Livak, K. J., Rozenblatt-Rosen, O., Dor, Y., Regev, A., & Yanai, I.
- Affiliations
- Technion – Israel Institute of Technology (IL)
- Hebrew University of Jerusalem (IL)
- Standard BioTools (United States) (US)
- Broad Institute (US)
- Corresponding author
- Yanai, Itai
- ORCID
- Venue
- Genome Biology
- Published
- 2016-04-28
- Volume
- 17
- Issue
- 1
- Pages
- 77-77
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC4848782
- OpenAlex
- W2342688693
- Cited by
- 1,125
- References
- 66
- Citations by year
- 2026: 59 · 2025: 114 · 2024: 125 · 2023: 111 · 2022: 151 · 2021: 161 · 2020: 129 · 2019: 119 · 2018: 97 · 2017: 44 · 2016: 14 · 2015: 1
- FWCI
- 47.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Genome biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- Seventh Framework Programme (310927)
- 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, single-cell transcriptomics, sequencing sensitivity, cell cycle progression, mouse fibroblasts, cost reduction
- MeSH terms
- Algorithms, Animals, Cell Cycle, Cells, Cultured, Fibroblasts, Sensitivity and Specificity, Sequence Analysis, RNA, Gene Expression Profiling, Mice, Single-Cell Analysis
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