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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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“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]
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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]
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“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]
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“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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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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