The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Integrated genome and transcriptome sequencing of the same cell
Open accessAbstract
Single-cell genomics and single-cell transcriptomics have emerged as powerful tools to study the biology of single cells at a genome-wide scale. However, a major challenge is to sequence both genomic DNA and mRNA from the same cell, which would allow direct comparison of genomic variation and transcriptome heterogeneity. We describe a quasilinear amplification strategy to quantify genomic DNA and mRNA from the same cell without physically separating the nucleic acids before amplification. We show that the efficiency of our integrated approach is similar to existing methods for single-cell sequencing of either genomic DNA or mRNA. Further, we find that genes with high cell-to-cell variability in transcript numbers generally have lower genomic copy numbers, and vice versa, suggesting that copy number variations may drive variability in gene expression among individual cells. Applications of our integrated sequencing approach could range from gaining insights into cancer evolution and heterogeneity to understanding the transcriptional consequences of copy number variations in healthy and diseased tissues.
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“multiple multi-omics technologies for integrative analysis of genome and transcriptome have been developed, among which genome and transcriptome sequencing (G&T-seq) [96] and gDNA-mRNA sequencing (DR-seq) [97] stand out as two representative technologies.”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [97]
In the citing paper
“The first methods developed for integrated multi-omics allow parallel interrogation of the genome and transcriptome of the same cell (G&T-seq [79], DR-seq [80] and single-cell transcriptogenomics [81]).”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [80]
In the citing paper
“Previous work in which DNA and RNA were assayed in the same cells has shown that many genes display copy-number gene-dosage effects on transcriptional expression28–30.”
From Occult polyclonality of preclinical pancreatic cancer models drives in vitro evolution (2022) · cites this paper as [29]
In the citing paper
“The next year, genome and transcriptome sequencing (G&T-seq)21 and gDNA–mRNA sequencing (DR-seq)22 were reported.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [22]
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Citations
Cites
mRNA-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
Tumour evolution inferred by single-cell sequencing
Navin, N., Kendall, J., Troge, J., Andrews, P., Rodgers, L., McIndoo, J., Cook, K., Stepansky, A., Levy, D., Esposito, D., et al. (2011) · Nature · doi:10.1038/nature09807 · Cited by 2,598
Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences
Raj, A., & van Oudenaarden, A. (2008) · Cell · doi:10.1016/j.cell.2008.09.050 · Cited by 2,593
Smart-seq2 for sensitive full-length transcriptome profiling in single cells
Picelli, S., Björklund, Å. K., Faridani, O. R., Sagasser, S., Winberg, G., & Sandberg, R. (2013) · Nature Methods · doi:10.1038/nmeth.2639 · Cited by 2,358
Origins and functional impact of copy number variation in the human genome
The Wellcome Trust Case Control Consortium, Conrad, D. F., Pinto, D., Redon, R., Feuk, L., Gokcumen, O., Zhang, Y., Aerts, J., Andrews, T. D., Barnes, C., et al. (2010) · Nature · doi:10.1038/nature08516 · Cited by 1,970
Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types
Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Elefant, N., Paul, F., Zaretsky, I., Mildner, A., Cohen, N., Jung, S., Tanay, A., et al. (2014) · Science · doi:10.1126/science.1247651 · Cited by 1,795
Relative Impact of Nucleotide and Copy Number Variation on Gene Expression Phenotypes
Stranger, B. E., Forrest, M. S., Dunning, M., Ingle, C., Beazley, C., Thorne, N., Redon, R., Bird, C., Grassi, A., Lee, C., et al. (2007) · Science · doi:10.1126/science.1136678 · Cited by 1,743
Mouse genomic variation and its effect on phenotypes and gene regulation.
Keane, T. M., Goodstadt, L., Danecek, P., White, M. A., Wong, K., Yalcin, B., Heger, A., Agam, A., Slater, G., Goodson, M., et al. (2011) · Nature · doi:10.1038/nature10413 · Cited by 1,580
Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells
Deng, Q., Ramsköld, D., Reinius, B., & Sandberg, R. (2014) · Science · doi:10.1126/science.1245316 · Cited by 1,345
CEL-seq: single-cell RNA-seq by multiplexed linear amplification
Hashimshony, T., Wagner, F., Sher, N., & Yanai, I. (2012) · Cell Reports · doi:10.1016/j.celrep.2012.08.003 · Cited by 1,231
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Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232
Single-cell RNA sequencing to explore immune cell heterogeneity
Papalexi, E., & Satija, R. (2018) · Nature Reviews Immunology · doi:10.1038/nri.2017.76 · Cited by 1,806
The Technology and Biology of Single-Cell RNA Sequencing
Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C., & Teichmann, S. A. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.04.005 · Cited by 1,487
Integrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., & al, E. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-162597 · Cited by 1,457
Metadata
- Authors
- Dey, S. S., Kester, L., Spanjaard, B., Bienko, M., & van Oudenaarden, A.
- Affiliations
- Royal Netherlands Academy of Arts and Sciences (NL)
- Venue
- Nature Biotechnology
- Published
- 2015-01-19
- Volume
- 33
- Issue
- 3
- Pages
- 285-289
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC4374170
- OpenAlex
- W2011637823
- Cited by
- 502
- References
- 27
- Citations by year
- 2026: 14 · 2025: 33 · 2024: 36 · 2023: 41 · 2022: 51 · 2021: 46 · 2020: 60 · 2019: 48 · 2018: 47 · 2017: 46 · 2016: 49 · 2015: 30
- FWCI
- 24.88
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- KNAW research portal (Royal Academy of Art and Sciences (KNAW)) (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion)
- 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 › Genetics › Genomic variations and chromosomal abnormalities
- Keywords
- single-cell genomics, single-cell transcriptomics, copy number variation, gene expression heterogeneity, tumor heterogeneity, tumor evolution
- MeSH terms
- DNA, Gene Expression Regulation, Humans, RNA, Messenger, Genome, Cell Line, Tumor, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Transcriptome
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