Quantitative single-cell RNA-seq with unique molecular identifiers
Open accessAbstract
Single-cell RNA sequencing (RNA-seq) is a powerful tool to reveal cellular heterogeneity, discover new cell types and characterize tumor microevolution. However, losses in cDNA synthesis and bias in cDNA amplification lead to severe quantitative errors. We show that molecular labels--random sequences that label individual molecules--can nearly eliminate amplification noise, and that microfluidic sample preparation and optimized reagents produce a fivefold improvement in mRNA capture efficiency.
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Venus protein expression correlates mostly with RNA findings, except weak acinar expression around E18.5 which appears underestimated in scRNA sequencing data.
“These patterns correlate mostly with the RNA findings, except the weak but robust acinar expression around E18.5 which appears underestimated in scRNA sequencing data potentially attributed to technical limitations for weakly expressed genes [50–54].”
From TBX3 is dynamically expressed in pancreatic organogenesis and fine-tunes regeneration (2023) · cites this paper as [53]
By counting molecules rather than the number of amplified sequencing reads, one can account for biases related to amplification and obtain more accurate estimates of gene expression levels.
“The rationale is that by counting molecules rather than the number of amplified sequencing reads, one can account for biases related to amplification, and obtain more accurate estimates of gene expression levels71220.”
From Batch effects and the effective design of single-cell gene expression studies (2017) · cites this paper as [20]
These observations underscore the importance of using UMIs in single cell gene expression studies.
“These observations concur with previous studies1220 as they underscore the importance of using UMIs in single cell gene expression studies.”
From Batch effects and the effective design of single-cell gene expression studies (2017) · cites this paper as [20]
Both endogenous and ERCC spike-in control CV patterns approximately followed an over-dispersed Poisson distribution.
“Both endogenous and ERCC spike-in control CV patterns approximately followed an over-dispersed Poisson distribution (Supplementary Fig. S11), which is consistent with previous studies1120.”
From Batch effects and the effective design of single-cell gene expression studies (2017) · cites this paper as [20]
The UMI protocol maintains strand information.
“Because the UMI protocol maintains strand information, we required that reads map to a gene in the correct orientation (featureCounts flag -s 1).”
From Batch effects and the effective design of single-cell gene expression studies (2017) · cites this paper as [20]
Inclusion of added reference transcripts and the use of unique molecule identifiers (UMIs) have been applied to overcome amplification bias and to improve gene quantification.
“The inclusion of added reference transcripts and the use of unique molecule identifiers (UMIs) have been applied to overcome amplification bias and to improve gene quantification [177, 178].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [177]
The BackSPIN algorithm has been extensively validated using computational and biological validation tools.
“The algorithm has by now been extensively validated using both computational (including comparisons to PCA, and independent-component analysis, tSNE, and GP-LVM) biological validation tools 9, 63–66.”
From Molecular interrogation of hypothalamic organization reveals distinct dopamine neuronal subtypes (2017) · cites this paper as [64]
The UMI on each tag should allow removal of amplification biases because it is added before amplification.
“The UMI on each tag should allow one to remove these biases, as it is added prior to amplification9.”
From Power analysis of single-cell RNA-sequencing experiments (2017) · cites this paper as [9]
UMI information improves quantification of mRNA molecules.
“Utilization of UMI information improves quantification of mRNA molecules (Gr € un et al., 2014; Islam et al., 2014)”
From Comparative Analysis of Single-Cell RNA Sequencing Methods (2017) · cites this paper as Islam et al., 2014
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Self-citation
- Authors
- Islam, S., Zeisel, A., Joost, S., La Manno, G., Zajac, P., Kasper, M., Lönnerberg, P., & Linnarsson, S.
- Affiliations
- Karolinska Institutet (SE)
- Corresponding author
- Linnarsson, Sten
- Venue
- Nature Methods
- Published
- 2013-12-22
- Volume
- 11
- Issue
- 2
- Pages
- 163-166
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2084942072
- Cited by
- 1,183
- References
- 24
- Citations by year
- 2026: 33 · 2025: 72 · 2024: 57 · 2023: 91 · 2022: 102 · 2021: 139 · 2020: 129 · 2019: 150 · 2018: 121 · 2017: 111 · 2016: 87 · 2015: 59 · 2014: 31 · 2013: 1
- FWCI
- 26.60
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Licence
- cc-by-nc-nd
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- 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, unique molecular identifiers, amplification bias, microfluidic sample preparation, cellular heterogeneity
- MeSH terms
- Animals, Cells, Cultured, RNA, Messenger, Gene Library, Sequence Analysis, RNA, Gene Expression Profiling, Systems Biology, Mice, Embryonic Stem Cells, High-Throughput Nucleotide Sequencing
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