The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells
Open accessAbstract
Genome-wide transcriptome analyses are routinely used to monitor tissue-, disease- and cell type–specific gene expression, but it has been technically challenging to generate expression profiles from single cells. Here we describe a robust mRNA-Seq protocol (Smart-Seq) that is applicable down to single cell levels. Compared with existing methods, Smart-Seq has improved read coverage across transcripts, which enhances detailed analyses of alternative transcript isoforms and identification of single-nucleotide polymorphisms. We determined the sensitivity and quantitative accuracy of Smart-Seq for single-cell transcriptomics by evaluating it on total RNA dilution series. We found that although gene expression estimates from single cells have increased noise, hundreds of differentially expressed genes could be identified using few cells per cell type. Applying Smart-Seq to circulating tumor cells from melanomas, we identified distinct gene expression patterns, including candidate biomarkers for melanoma circulating tumor cells. Our protocol will be useful for addressing fundamental biological problems requiring genome-wide transcriptome profiling in rare cells.
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“Newer protocols such as Smart-seq [173] and Smart-seq2 [174] have enabled us to work from very small amounts of starting mRNA that, with proper amplification, can be obtained from just a single cell.”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [173]
In the citing paper
“rapid technological advances have enabled genome-wide profiling of RNA7–10, DNA11–17, protein18–21, epigenetic modifications22–26, chromatin accessibility27,28, and other molecular events29 in single cells.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [8]
In the citing paper
“At the other end of the spectrum, the limited efficiency of RNA capture and conversion into cDNA leads to prevalent dropout events (false negatives): transcripts that are expressed in the cell but are entirely undetected in its mRNA profile8 (Fig. 3c).”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [8]
In the citing paper
“Several scRNA-seq methods developed in the recent decade have become essential for capturing intercellular variation and its impact on cell functions at the population scale15–19.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [18]
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Cites
The Sequence Alignment/Map format and SAMtools
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Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
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Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
Alternative isoform regulation in human tissue transcriptomes
Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S. F., Schroth, G. P., & Burge, C. B. (2008) · Nature · doi:10.1038/nature07509 · Cited by 5,181
Self-citationmRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969
Self-citationPan, Q., Shai, O., Lee, L. J., Frey, B. J., & Blencowe, B. J. (2008) · Nature Genetics · doi:10.1038/ng.259 · Cited by 3,951
The NIH Roadmap Epigenomics Mapping Consortium
Bernstein, B. E., Stamatoyannopoulos, J. A., Costello, J. F., Ren, B., Milosavljevic, A., Meissner, A., Kellis, M., Marra, M. A., Beaudet, A. L., Ecker, J. R., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt1010-1045 · Cited by 1,892
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Analyzing Circulating Tumor Cells One at a Time
Ortiz, V., & Yu, M. (2018) · Trends in Cell Biology · doi:10.1016/j.tcb.2018.05.004 · Cited by 48
Single-cell transcriptogenomics reveals transcriptional exclusion of ENU-mutated alleles
Li, W., Calder, R. B., Mar, J. C., & Vijg, J. (2015) · Mutation Research - Fundamental and Molecular Mechanisms of Mutagenesis · doi:10.1016/j.mrfmmm.2015.01.002 · Cited by 34
Single-Cell RNA Sequencing for Studying Human Cancers
Aran, D. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020722-091857 · Cited by 31
Simultaneous Profiling of mRNA Transcriptome and DNA Methylome from a Single Cell
Hu, Y., An, Q., Guo, Y., Zhong, J., Fan, S., Rao, P., Liu, X., Liu, Y., & Fan, G. (2019) · Methods in Molecular Biology · doi:10.1007/978-1-4939-9240-9_21 · Cited by 26
Shangguan, S., Ehrenberg, P. K., Geretz, A., Yum, L., Kundu, G., May, K., Fourati, S., Nganou-Makamdop, K., Williams, L. D., Sawant, S., et al. (2021) · eLife · doi:10.7554/elife.69577 · Cited by 26
Single-Cell Semiconductor Sequencing
Kohn, A. B., Moroz, T. P., Barnes, J. P., Netherton, M., & Moroz, L. L. (2013) · Methods in Molecular Biology · doi:10.1007/978-1-62703-556-9_18 · Cited by 22
Blasi, T., Buettner, F., Strasser, M. K., Marr, C., & Theis, F. J. (2017) · Physical Biology · doi:10.1088/1478-3975/aa609a · Cited by 18
Metadata
- Authors
- Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., Schroth, G. P., & Sandberg, R.
- Affiliations
- Ludwig Cancer Research (SE)
- Illumina (United States) (US)
- Scripps Research Institute (US)
- Moores Cancer Center (US)
- University of California San Diego (US)
- Karolinska Institutet (SE)
- Corresponding author
- Sandberg, Rickard
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2012-07-22
- Volume
- 30
- Issue
- 8
- Pages
- 777-782
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC3467340
- OpenAlex
- W2121690903
- Cited by
- 1,609
- References
- 35
- Citations by year
- 2026: 63 · 2025: 106 · 2024: 103 · 2023: 108 · 2022: 138 · 2021: 134 · 2020: 153 · 2019: 145 · 2018: 127 · 2017: 121 · 2016: 113 · 2015: 105 · 2014: 110 · 2013: 63 · 2012: 17
- FWCI
- 37.69
- Type
- journal article
- Language
- en
- Update notices
- correction (2020-02-03)
- Open access
- green
- europepmc.org
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (R33MH087925)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (K12HD001259)
- 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, SMART-seq, alternative splicing, single nucleotide polymorphisms, stochastic gene expression, circulating tumor cells, melanoma, biomarkers
- MeSH terms
- Animals, Female, Humans, Melanoma, Neoplastic Cells, Circulating, RNA, Messenger, Sensitivity and Specificity, Gene Library, Cluster Analysis, Sequence Analysis, RNA, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Mice
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