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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
G&T-seq and DR-seq are two representative multi-omics technologies for integrative analysis of genome and transcriptome.
“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]
The first methods developed for integrated multi-omics allow parallel interrogation of the genome and transcriptome of the same cell (G&T-seq, DR-seq and single-cell transcriptogenomics).
“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]
Many genes display copy-number gene-dosage effects on transcriptional expression.
“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]
Two methods, G&T-seq and DR-seq, appeared the following year.
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 1,709 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells
Shalek, A. K., Satija, R., Adiconis, X., Gertner, R. S., Gaublomme, J. T., Raychowdhury, R., Schwartz, S., Yosef, N., Malboeuf, C., Lu, D., et al. (2013) · Nature · doi:10.1038/nature12172 · Cited by 1,190 · see citing works
Quantitative single-cell RNA-seq with unique molecular identifiers
Islam, S., Zeisel, A., Joost, S., La Manno, G., Zajac, P., Kasper, M., Lönnerberg, P., & Linnarsson, S. (2014) · Nature Methods · doi:10.1038/nmeth.2772 · Cited by 1,183 · see citing works
Single-cell sequencing-based technologies will revolutionize whole-organism science
Shapiro, E., Biezuner, T., & Linnarsson, S. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3542 · Cited by 1,177 · see citing works
Genome-Wide Detection of Single-Nucleotide and Copy-Number Variations of a Single Human Cell
Zong, C., Lu, S., Chapman, A. R., & Xie, X. S. (2012) · Science · doi:10.1126/science.1229164 · Cited by 1,154 · see citing works
Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing
Xue, Z., Huang, K., Cai, C., Cai, L., Jiang, C.-Y., Feng, Y., Liu, Z., Zeng, Q., Cheng, L., Sun, Y. E., et al. (2013) · Nature · doi:10.1038/nature12364 · Cited by 963 · see citing works
A faster circular binary segmentation algorithm for the analysis of array CGH data
Venkatraman, E. S., & Olshen, A. B. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btl646 · Cited by 963 · see citing works
Validation of noise models for single-cell transcriptomics
Grün, D., Kester, L., & van Oudenaarden, A. (2014) · Nature Methods · doi:10.1038/nmeth.2930 · Cited by 721 · see citing works
Self-citationQuantitative assessment of single-cell RNA-sequencing methods
Wu, A. R., Neff, N. F., Kalisky, T., Dalerba, P., Treutlein, B., Rothenberg, M. E., Mburu, F. M., Mantalas, G. L., Sim, S., Clarke, M. F., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2694 · Cited by 696 · see citing works
Mosaic Copy Number Variation in Human Neurons
McConnell, M. J., Lindberg, M. R., Brennand, K. J., Piper, J. C., Voet, T., Cowing-Zitron, C., Shumilina, S., Lasken, R. S., Vermeesch, J. R., Hall, I. M., et al. (2013) · Science · doi:10.1126/science.1243472 · Cited by 551 · see citing works
Single-Neuron Sequencing Analysis of L1 Retrotransposition and Somatic Mutation in the Human Brain
Evrony, G. D., Cai, X., Lee, E., Hills, L. B., Elhosary, P. C., Lehmann, H. S., Parker, J. J., Atabay, K. D., Gilmore, E. C., Poduri, A., et al. (2012) · Cell · doi:10.1016/j.cell.2012.09.035 · Cited by 548 · see citing works
The External RNA Controls Consortium: a progress report
The External RNA Controls Consortium (2005) · Nature Methods · doi:10.1038/nmeth1005-731 · Cited by 373 · see citing works
Genome analyses of single human oocytes
Hou, Y., Fan, W., Yan, L., Li, R., Lian, Y., Huang, J., Li, J., Xu, L., Tang, F., Xie, X. S., et al. (2013) · Cell · doi:10.1016/j.cell.2013.11.040 · Cited by 302 · see citing works
Transcriptional consequences of aneuploidy
Sheltzer, J. M., Torres, E. M., Dunham, M. J., & Amon, A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1209227109 · Cited by 290 · see citing works
Every Cell Is Special: Genome-wide Studies Add a New Dimension to Single-Cell Biology
Junker, J. P., & van Oudenaarden, A. (2014) · Cell · doi:10.1016/j.cell.2014.02.010 · Cited by 219 · see citing works
DNA template strand sequencing of single-cells maps genomic rearrangements at high resolution
Falconer, E., Hills, M., Naumann, U., Poon, S. S. S., Chavez, E. A., Sanders, A. D., Zhao, Y., Hirst, M., & Lansdorp, P. M. (2012) · Nature Methods · doi:10.1038/nmeth.2206 · Cited by 199 · see citing works
A versatile genome-scale PCR-based pipeline for high-definition DNA FISH
Bienko, M., Crosetto, N., Teytelman, L., Klemm, S. L., Itzkovitz, S., & van Oudenaarden, A. (2012) · Nature Methods · doi:10.1038/nmeth.2306 · Cited by 75 · see citing works
Self-citationZhang, C., Zhang, C., Chen, S., Yin, X., Pan, X., Lin, G., Tan, Y., Tan, K., Xu, Z., Hu, P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0054236 · Cited by 72 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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 · see citing works
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 · see citing works
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 · see citing works
Integrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460 · see citing works
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 · see citing works
Single-cell genome sequencing: current state of the science
Gawad, C., Koh, W., & Quake, S. R. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2015.16 · Cited by 1,334 · see citing works
Eleven grand challenges in single-cell data science
Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1926-6 · Cited by 1,327 · see citing works
Methods and applications for single-cell and spatial multi-omics
Vandereyken, K., Sifrim, A., Thienpont, B., & Voet, T. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00580-2 · Cited by 1,244 · see citing works
MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data
Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J. C., & Stegle, O. (2020) · Genome Biology · doi:10.1186/s13059-020-02015-1 · Cited by 1,091 · see citing works
The technological landscape and applications of single-cell multi-omics
Baysoy, A., Bai, Z., Satija, R., & Fan, R. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00615-w · Cited by 1,037 · see citing works
Orchestrating single-cell analysis with Bioconductor
Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0654-x · Cited by 945 · see citing works
Defining cell types and states with single-cell genomics
Trapnell, C. (2015) · Genome Research · doi:10.1101/gr.190595.115 · Cited by 813 · see citing works
Scaling single-cell genomics from phenomenology to mechanism
Tanay, A., & Regev, A. (2017) · Nature · doi:10.1038/nature21350 · Cited by 792 · see citing works
Toward understanding and exploiting tumor heterogeneity
Alizadeh, A. A., Aranda, V., Bardelli, A., Blanpain, C., Bock, C., Borowski, C., Caldas, C., Califano, A., Doherty, M., Elsner, M., et al. (2015) · Nature Medicine · doi:10.1038/nm.3915 · Cited by 742 · see citing works
G&T-seq: parallel sequencing of single-cell genomes and transcriptomes
Macaulay, I. C., Haerty, W., Kumar, P., Li, Y. I., Hu, T. X., Teng, M. J., Goolam, M., Saurat, N., Coupland, P., Shirley, L. M., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3370 · Cited by 750 · see citing works
Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity
Angermueller, C., Clark, S. J., Lee, H. J., Macaulay, I. C., Teng, M. J., Hu, T. X., Krueger, F., Smallwood, S. A., Ponting, C. P., Voet, T., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3728 · Cited by 717 · see citing works
Hou, Y., Guo, H., Cao, C., Li, X., Hu, B., Zhu, P., Wu, X., Wen, L., Tang, F., Huang, Y., et al. (2016) · Cell Research · doi:10.1038/cr.2016.23 · Cited by 647 · see citing works
Revealing the vectors of cellular identity with single-cell genomics
Wagner, A., Regev, A., & Yosef, N. (2016) · Nature Biotechnology · doi:10.1038/nbt.3711 · Cited by 632 · see citing works
Advances and Applications of Single-Cell Sequencing Technologies
Wang, Y., & Navin, N. E. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.05.005 · Cited by 610 · see citing works
Tumour heterogeneity and metastasis at single-cell resolution
Lawson, D. A., Kessenbrock, K., Davis, R. T., Pervolarakis, N., & Werb, Z. (2018) · Nature Cell Biology · doi:10.1038/s41556-018-0236-7 · Cited by 606 · see citing works
Lineage tracing meets single-cell omics: opportunities and challenges
Wagner, D. E., & Klein, A. M. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0223-2 · Cited by 594 · see citing works
Applications of multi‐omics analysis in human diseases
Chen, C., Wang, J., Pan, D., Wang, X., Xu, Y., Yan, J., Wang, L., Yang, X., Yang, M., & Liu, G. (2023) · MedComm · doi:10.1002/mco2.315 · Cited by 545 · see citing works
Single-cell multiomics: technologies and data analysis methods
Lee, J., Hyeon, D. Y., & Hwang, D. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0420-2 · Cited by 524 · see citing works
Multi-omics profiling of mouse gastrulation at single-cell resolution
Argelaguet, R., Clark, S. J., Mohammed, H., Stapel, L. C., Krueger, C., Kapourani, C.-A., Imaz-Rosshandler, I., Lohoff, T., Xiang, Y., Hanna, C. W., et al. (2019) · Nature · doi:10.1038/s41586-019-1825-8 · Cited by 450 · see citing works
Single-cell multiomics: multiple measurements from single cells
Macaulay, I. C., Ponting, C. P., & Voet, T. (2017) · Trends in Genetics · doi:10.1016/j.tig.2016.12.003 · Cited by 451 · see citing works
Single-cell epigenomics: Recording the past and predicting the future
Kelsey, G., Stegle, O., & Reik, W. (2017) · Science · doi:10.1126/science.aan6826 · Cited by 415 · see citing works
Single-cell RNA sequencing: Technical advancements and biological applications
Hedlund, E., & Deng, Q. (2017) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2017.07.003 · Cited by 396 · see citing works
Single-cell multimodal omics: the power of many
Zhu, C., Preissl, S., & Ren, B. (2020) · Nature Methods · doi:10.1038/s41592-019-0691-5 · Cited by 386 · see citing works
Unravelling biology and shifting paradigms in cancer with single-cell sequencing
Baslan, T., & Hicks, J. B. (2017) · Nature reviews. Cancer · doi:10.1038/nrc.2017.58 · Cited by 377 · see citing works
Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells
Guo, F., Li, L., Li, J., Wu, X., Hu, B., Zhu, P., Wen, L., & Tang, F. (2017) · Cell Research · doi:10.1038/cr.2017.82 · Cited by 374 · see citing works
The first five years of single-cell cancer genomics and beyondPreprint
Navin, N. E. (2015) · Genome Research · doi:10.1101/gr.191098.115 · Cited by 368 · see citing works
Tracking cancer progression: from circulating tumor cells to metastasis
Castro-Giner, F., & Aceto, N. (2020) · Genome Medicine · doi:10.1186/s13073-020-00728-3 · Cited by 367 · see citing works
An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome
Zhu, C., Yu, M., Huang, H., Juric, I., Abnousi, A., Hu, R., Lucero, J., Behrens, M. M., Hu, M., & Ren, B. (2019) · Nature Structural & Molecular Biology · doi:10.1038/s41594-019-0323-x · Cited by 344 · see citing works
Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma
Caldéraro, J., Seraphin, T. P., Luedde, T., & Simon, T. G. (2022) · Journal of Hepatology · doi:10.1016/j.jhep.2022.01.014 · Cited by 353 · see citing works
Challenges and emerging directions in single-cell analysis
Yuan, G., Cai, L., Elowitz, M. B., Enver, T., Fan, G., Guo, G., Irizarry, R. A., Kharchenko, P. V., Kim, J., Orkin, S. H., et al. (2017) · Genome biology · doi:10.1186/s13059-017-1218-y · Cited by 339 · see citing works
Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity
Clark, S. J., Lee, H. J., Smallwood, S. A., Kelsey, G., & Reik, W. (2016) · Genome Biology · doi:10.1186/s13059-016-0944-x · Cited by 310 · see citing works
Rodriguez-Meira, A., Buck, G., Clark, S.-A., Povinelli, B. J., Alcolea, V., Louka, E., McGowan, S., Hamblin, A., Sousos, N., Barkas, N., et al. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.01.009 · Cited by 305 · see citing works
Advancing Cancer Research and Medicine with Single-Cell Genomics
Lim, B., Lin, Y., & Navin, N. E. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.03.008 · Cited by 290 · see citing works
Disentangling neural cell diversity using single-cell transcriptomics
Poulin, J.-F., Tasic, B., Hjerling-Leffler, J., Trimarchi, J. M., & Awatramani, R. (2016) · Nature Neuroscience · doi:10.1038/nn.4366 · Cited by 293 · see citing works
Single-cell epigenomics: techniques and emerging applications
Schwartzman, O., & Tanay, A. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3980 · Cited by 282 · see citing works
Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets
Leung, M. K. K., Delong, A., Alipanahi, B., & Frey, B. J. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2494198 · Cited by 276 · see citing works
Integrative Methods and Practical Challenges for Single-Cell Multi-omics
Ma, A., McDermaid, A., Xu, J., Chang, Y., & Ma, Q. (2020) · Trends in biotechnology · doi:10.1016/j.tibtech.2020.02.013 · Cited by 272 · see citing works
Single-cell sequencing techniques from individual to multiomics analyses
Kashima, Y., Sakamoto, Y., Kaneko, K., Seki, M., Suzuki, Y., & Suzuki, A. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-00499-2 · Cited by 270 · see citing works
Challenges in measuring and understanding biological noise
Eling, N., Morgan, M. D., & Marioni, J. C. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0130-6 · Cited by 247 · see citing works
To NMD or Not To NMD: Nonsense-Mediated mRNA Decay in Cancer and Other Genetic Diseases
Supek, F., Lehner, B., & Lindeboom, R. G. H. (2020) · Trends in Genetics · doi:10.1016/j.tig.2020.11.002 · Cited by 246 · see citing works
Somatic mosaicism and neurodevelopmental disease
D’Gama, A. M., & Walsh, C. A. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0257-3 · Cited by 236 · see citing works
Single-cell RNA-sequencing: The future of genome biology is now
Picelli, S. (2016) · RNA Biology · doi:10.1080/15476286.2016.1201618 · Cited by 237 · see citing works
Revolutionizing immunology with single-cell RNA sequencing
Chen, H., Ye, F., & Guo, G. (2019) · Cellular and Molecular Immunology · doi:10.1038/s41423-019-0214-4 · Cited by 237 · see citing works
Pathogenic Mechanisms of Somatic Mutation and Genome Mosaicism in Aging
Vijg, J., & Dong, X. (2020) · Cell · doi:10.1016/j.cell.2020.06.024 · Cited by 230 · see citing works
Fan, J., Lee, H., Lee, S., Ryu, D., Lee, S., Xue, C., Kim, S. J., Kım, K., Barkas, N., Park, P. J., et al. (2018) · Genome Research · doi:10.1101/gr.228080.117 · Cited by 237 · see citing works
Mapping gene regulatory networks from single-cell omics data
Fiers, M. W. E. J., Minnoye, L., Aibar, S., Bravo González-Blas, C., Kalender Atak, Z., & Aerts, S. (2018) · Briefings in Functional Genomics · doi:10.1093/bfgp/elx046 · Cited by 235 · see citing works
Single-cell transcriptome sequencing: recent advances and remaining challenges
Liu, S., & Trapnell, C. (2016) · F1000Research · doi:10.12688/f1000research.7223.1 · Cited by 230 · see citing works
OncoNEM: inferring tumor evolution from single-cell sequencing data
Ross, E. M., & Markowetz, F. M. (2016) · Genome biology · doi:10.1186/s13059-016-0929-9 · Cited by 226 · see citing works
Single-Cell Analysis in Cancer Genomics
Saadatpour, A., Lai, S., Guo, G., & Yuan, G. (2015) · Trends in Genetics · doi:10.1016/j.tig.2015.07.003 · Cited by 222 · see citing works
Understanding tumor ecosystems by single-cell sequencing: promises and limitations
Ren, X., Kang, B., & Zhang, Z. (2018) · Genome biology · doi:10.1186/s13059-018-1593-z · Cited by 214 · see citing works
Single-Cell (Multi)omics Technologies
Chappell, L., Russell, A. J. C., & Voet, T. (2018) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-091416-035324 · Cited by 206 · see citing works
Tumour Heterogeneity: The Key Advantages of Single-Cell Analysis
Tellez-Gabriel, M., Ory, B., Lamoureux, F., Heymann, M., & Heymann, D. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17122142 · Cited by 193 · see citing works
Single-cell sequencing to multi-omics: technologies and applications
Wu, X., Yang, X., Dai, Y., Zhao, Z., Zhu, J., Guo, H., & Yang, R. (2024) · Biomarker Research · doi:10.1186/s40364-024-00643-4 · Cited by 195 · see citing works
“In addition, 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.”
BackgroundAdvances in single-cell omics and multiomics for high-resolution molecular profiling
Lim, J., Park, C., Kim, M., Kim, H., Kim, J., & Lee, D.-S. (2024) · Experimental & Molecular Medicine · doi:10.1038/s12276-024-01186-2 · Cited by 193 · see citing works
Single Cell Multi-Omics Technology: Methodology and Application
Hu, Y., An, Q., Sheu, K. M., Trejo, B. M., Fan, S., & Guo, Y. Q. (2018) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2018.00028 · Cited by 192 · see citing works
A Single-Cell Sequencing Guide for Immunologists
See, P., Lum, J., Chen, J., & Ginhoux, F. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.02425 · Cited by 187 · see citing works
Independent origins of neurons and synapses: insights from ctenophores
Moroz, L. L., & Kohn, A. B. (2016) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2015.0041 · Cited by 171 · see citing works
Separation and parallel sequencing of the genomes and transcriptomes of single cells using G&T-seq
Macaulay, I. C., Teng, M. J., Haerty, W., Kumar, P., Ponting, C. P., & Voet, T. (2016) · Nature Protocols · doi:10.1038/nprot.2016.138 · Cited by 171 · see citing works
Emerging Principles in Myelopoiesis at Homeostasis and during Infection and Inflammation
Schultze, J. L., Mass, E., & Schlitzer, A. (2019) · Immunity · doi:10.1016/j.immuni.2019.01.019 · Cited by 167 · see citing works
Single-Cell Genomics: A Stepping Stone for Future Immunology Discoveries
Giladi, A., & Amit, I. (2018) · Cell · doi:10.1016/j.cell.2017.11.011 · Cited by 168 · see citing works
Zhu, C., Gao, Y., Guo, H., Xia, B., Song, J., Wu, X., Zeng, H., Kee, K., Tang, F., & Yi, C. (2017) · Cell stem cell · doi:10.1016/j.stem.2017.02.013 · Cited by 159 · see citing works
Harmancı, A. S., Harmanci, A. O., & Zhou, X. (2020) · Nature Communications · doi:10.1038/s41467-019-13779-x · Cited by 156 · see citing works
Massive parallel sequencing in forensics: advantages, issues, technicalities, and prospects
Ballard, D., Winkler-Galicki, J., & Wesoły, J. (2020) · International Journal of Legal Medicine · doi:10.1007/s00414-020-02294-0 · Cited by 148 · see citing works
SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells
Han, K. Y., Kim, K.-T., Joung, J.-G., Son, D.-S., Kim, Y. J., Jo, A., Jeon, H.-J., Moon, H.-S., Yoo, C. E., Chung, W., et al. (2018) · Genome Research · doi:10.1101/gr.223263.117 · Cited by 143 · see citing works
Welch, J. D., Hartemink, A. J., & Prins, J. F. (2017) · Genome Biology · doi:10.1186/s13059-017-1269-0 · Cited by 140 · see citing works
Cancer Diagnosis Using a Liquid Biopsy: Challenges and Expectations
Castro-Giner, F., Gkountela, S., Donato, C., Alborelli, I., Quagliata, L., Ng, C. K. Y., Piscuoglio, S., & Aceto, N. (2018) · Diagnostics · doi:10.3390/diagnostics8020031 · Cited by 138 · see citing works
Future of Digital Assays to Resolve Clinical Heterogeneity of Single Extracellular Vesicles
Morales, R. T., & Ko, J. (2022) · ACS Nano · doi:10.1021/acsnano.2c04337 · Cited by 135 · see citing works
Velten, L., Story, B. A., Hernández-Malmierca, P., Raffel, S., Leonce, D. R., Milbank, J. H., Paulsen, M. S., Demir, A., Szu‐Tu, C., Frömel, R., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21650-1 · Cited by 135 · see citing works
Scalable microfluidics for single-cell RNA printing and sequencing
Bose, S., Wan, Z., Carr, A., Rizvi, A. H., Vieira, G., Pe’er, D., & Sims, P. A. (2015) · Genome biology · doi:10.1186/s13059-015-0684-3 · Cited by 131 · see citing works
Single-Cell Transcriptomics Bioinformatics and Computational Challenges
Poirion, O., Zhu, X., Ching, T. H., & Garmire, L. X. (2016) · Frontiers in Genetics · doi:10.3389/fgene.2016.00163 · Cited by 125 · see citing works
Microfluidic enrichment for the single cell analysis of circulating tumor cells
Yeo, T., Tan, S. J., Lim, C. L., Lau, D. P. X., Chua, Y. W., Krisna, S. S., Iyer, G., Tan, G. S., Lim, T. K. H., Tan, D. S. W., et al. (2016) · Scientific Reports · doi:10.1038/srep22076 · Cited by 123 · see citing works
Understanding development and stem cells using single cell-based analyses of gene expression
Kumar, P., Tan, Y., & Cahan, P. (2017) · Development · doi:10.1242/dev.133058 · Cited by 117 · see citing works
Single-Cell Sequencing for Precise Cancer Research: Progress and Prospects
Zhang, X., Marjani, S. L., Hu, Z., Weissman, S. M., Pan, X., & Wu, S. (2016) · Cancer Research · doi:10.1158/0008-5472.can-15-1907 · Cited by 122 · see citing works
Campbell, K. R., Steif, A., Laks, E., Zahn, H., Lai, D., McPherson, A., Farahani, H., Kabeer, F., O’Flanagan, C. H., Biele, J., et al. (2019) · Genome biology · doi:10.1186/s13059-019-1645-z · Cited by 112 · see citing works
Dissecting Cellular Heterogeneity Using Single-Cell RNA Sequencing.
Choi, Y. H., & Kim, J. K. (2019) · Europe PMC (PubMed Central) · doi:10.14348/molcells.2019.2446 · Cited by 120 · see citing works
Single-cell genomics: coming of age
Linnarsson, S., & Teichmann, S. A. (2016) · Genome biology · doi:10.1186/s13059-016-0960-x · Cited by 121 · see citing works
Deconstructing the pluripotency gene regulatory network
Li, M., & Belmonte, J. C. I. (2018) · Nature Cell Biology · doi:10.1038/s41556-018-0067-6 · Cited by 116 · see citing works
Single-cell technologies: From research to application
Wen, L., Li, G., Huang, T., Geng, W., Pei, H., Yang, J., Zhu, M., Zhang, P., Hou, R., Tian, G., et al. (2022) · The Innovation · doi:10.1016/j.xinn.2022.100342 · Cited by 118 · see citing works
Töhönen, V., Katayama, S., Vesterlund, L., Jouhilahti, E., Sheikhi, M., Madissoon, E., Filippini-Cattaneo, G., Jaconi, M. E., Johnsson, A., Bürglin, T. R., et al. (2015) · Nature Communications · doi:10.1038/ncomms9207 · Cited by 117 · see citing works
Survey on Multi-omics, and Multi-omics Data Analysis, Integration and Application
Shahrajabian, M. H., & Sun, W. (2023) · Current Pharmaceutical Analysis · doi:10.2174/1573412919666230406100948 · Cited by 116 · see citing works
Computational strategies for single-cell multi-omics integration
Adossa, N. A., Khan, S., Rytkönen, K. T., & Elo, L. L. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.04.060 · Cited by 106 · see citing works
Simultaneous quantification of protein–DNA contacts and transcriptomes in single cells
Rooijers, K., Markodimitraki, C. M., Rang, F. J., de Vries, S. S., Chialastri, A., de Luca, K. L., Mooijman, D., Dey, S. S., & Kind, J. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0150-y · Cited by 110 · see citing works
Self-citationGenome-wide stability of the DNA replication program in single mammalian cells
Takahashi, S., Miura, H., Shibata, T., Nagao, K., Okumura, K., Ogata, M., Obuse, C., Takebayashi, S., & Hiratani, I. (2019) · Nature Genetics · doi:10.1038/s41588-019-0347-5 · Cited by 101 · see citing works
Gravina, S., Dong, X., Yu, B., & Vijg, J. (2016) · Genome biology · doi:10.1186/s13059-016-1011-3 · Cited by 109 · see citing works
Computational approaches for interpreting sc<scp>RNA</scp>‐seq data
Rostom, R., Svensson, V., Teichmann, S. A., & Kar, G. (2017) · FEBS Letters · doi:10.1002/1873-3468.12684 · Cited by 107 · see citing works
Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine
Li, Y., Ma, L., Wu, D., & Chen, G. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab024 · Cited by 101 · see citing works
Wang, M., Song, W., Chen, M., Wang, Q., Zhou, X., Xu, P., Krek, A., Yoon, Y., Ho, L., Orr, M. E., et al. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00517-z · Cited by 100 · see citing works
Zachariadis, V., Cheng, H., Andrews, N., & Enge, M. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.09.025 · Cited by 90 · see citing works
Andor, N., Lau, B. T., Catalanotti, C., Sathe, A., Kubit, M. A., Chen, J., Blaj, C., Cherry, A. M., Bangs, C. D., Grimes, S. M., et al. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa016 · Cited by 92 · see citing works
Diversity of Reactive Astrogliosis in CNS Pathology: Heterogeneity or Plasticity?
Moulson, A. J., Squair, J. W., Franklin, R. J. M., Tetzlaff, W., & Assinck, P. (2021) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2021.703810 · Cited by 90 · see citing works
Moudgil, A., Wilkinson, M., Chen, X., He, J., Cammack, A. J., Vasek, M. J., Lagunas, T., Qi, Z., Lalli, M. A., Guo, C., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.037 · Cited by 80 · see citing works
Translating insights into tumor evolution to clinical practice: promises and challenges
Fittall, M. W., & Loo, P. V. (2019) · Genome Medicine · doi:10.1186/s13073-019-0632-z · Cited by 88 · see citing works
Systems Immunology: Learning the Rules of the Immune System
Villani, A., Sarkizova, S., & Hacohen, N. (2018) · Annual Review of Immunology · doi:10.1146/annurev-immunol-042617-053035 · Cited by 84 · see citing works
Into the multiverse: advances in single-cell multiomic profiling
Ogbeide, S., Giannese, F., Mincarelli, L., & Macaulay, I. C. (2022) · Trends in Genetics · doi:10.1016/j.tig.2022.03.015 · Cited by 82 · see citing works
Using single‐cell multiple omics approaches to resolve tumor heterogeneity
Ortega, M. A., Poirion, O., Zhu, X., Huang, S., Wolfgruber, T. K., Sebra, R., & Garmire, L. X. (2017) · Clinical and Translational Medicine · doi:10.1186/s40169-017-0177-y · Cited by 81 · see citing works
- 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 · 2014: 1
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review