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.
Overview
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
Verification
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?
The cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Flagged first, then by citations
Cited by
Sorted by citations
Items 201–300 of 504
Ono, H., Arai, Y., Furukawa, E., Narushima, D., Matsuura, T., Nakamura, H. K., Shiokawa, D., Nagai, M., Imai, T., Mimori, K., et al. (2021) · BMC Biology · doi:10.1186/s12915-021-01147-5 · Cited by 34
Zhang, Y., Devendran, C., Lupton, C. J., de Marco, A., & Neild, A. (2018) · Lab on a Chip · doi:10.1039/c8lc01117f · Cited by 33
Genomic heterogeneity in pancreatic cancer organoids and its stability with culture
Usman, O. H., Zhang, L., Xie, G., Kocher, H. M., Hwang, C., Wang, Y. J., Mallory, X., & Irianto, J. (2022) · npj Genomic Medicine · doi:10.1038/s41525-022-00342-9 · Cited by 36
Single-Cell Multi-Omics: Insights into Therapeutic Innovations to Advance Treatment in Cancer
Guan, A., & Quek, C. Y. J. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26062447 · Cited by 35
Highlighting the uniqueness in dielectrophoretic enrichment of circulating tumor cells
Iliescu, F. S., Sim, W. J., Heidari, H., Poenar, D. P., Miao, J., Taylor, H., & Iliescu, C. (2019) · Electrophoresis · doi:10.1002/elps.201800446 · Cited by 32
Spatial transcriptomics: recent developments and insights in respiratory research
Wang, W., Chu, L., He, L., Zhang, M., Dang, K., Gao, C., Ge, Q., Wang, Z., & Zhao, X. (2023) · Military Medical Research · doi:10.1186/s40779-023-00471-x · Cited by 33
Occult polyclonality of preclinical pancreatic cancer models drives in vitro evolution
Monberg, M. E., Geiger, H., Lee, J. J., Sharma, R., Semaan, A., Bernard, V., Wong, J., Wang, F., Liang, S., Swartzlander, D. B., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31376-3 · Cited by 30
The Systems Biology of Single-Cell Aging
Song, R., Sarnoski, E. A., & Açar, M. (2018) · iScience · doi:10.1016/j.isci.2018.08.023 · Cited by 31
Dhar, R., Missarova, A., Lehner, B., & Carey, L. B. (2019) · eLife · doi:10.7554/elife.38904 · Cited by 32
Miura, H., Takahashi, S., Shibata, T., Nagao, K., Obuse, C., Okumura, K., Ogata, M., Hiratani, I., & Takebayashi, S. (2020) · Nature Protocols · doi:10.1038/s41596-020-0378-5 · Cited by 34
Molecular analysis of circulating tumors cells: Biomarkers beyond enumeration
Hwang, W. L., Pleskow, H. M., & Miyamoto, D. T. (2018) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2018.01.003 · Cited by 30
Single-cell RNA-seq variant analysis for exploration of genetic heterogeneity in cancer
Fasterius, E., Uhlén, M., & Szigyarto, C. A. (2019) · Scientific Reports · doi:10.1038/s41598-019-45934-1 · Cited by 32
Zhang, B., Huang, K., Zhu, L., Luo, Y., & Xu, W. (2017) · Archives of Toxicology · doi:10.1007/s00204-017-1971-4 · Cited by 30
Xing, Q. R., Cipta, N. O., Hamashima, K., Liou, Y., Koh, C.-G., & Loh, Y. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00662 · Cited by 31
Exploring viral infection using single-cell sequencing
Rato, S., Golumbeanu, M., Telenti, A., & Ciuffi, A. (2016) · Virus Research · doi:10.1016/j.virusres.2016.10.016 · Cited by 29
Single-cell sequencing technology in tumor research
Bai, X., Li, Y., Zeng, X., Zhao, Q., & Zhang, Z. (2021) · Clinica Chimica Acta · doi:10.1016/j.cca.2021.03.013 · Cited by 30
Wang, Q., Yang, K., Zhang, Z., Wang, Z., Li, C., Li, L., Tian, J., Ye, Y., Wang, S., & Jiang, K. (2021) · Journal of Medical Internet Research · doi:10.2196/25789 · Cited by 27
Antigen-Specific Treg Therapy in Type 1 Diabetes – Challenges and Opportunities
Serr, I., Drost, F., Schubert, B., & Daniel, C. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.712870 · Cited by 31
Microfluidics-based single cell analysis: from transcriptomics to spatiotemporal multi-omics
Cao, J., Chen, X., Huang, S., Shi, W., Fan, Q., Gong, Y., Peng, Y., Wu, L., & Yang, C. J. (2022) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2022.116868 · Cited by 30
van den Hurk, M., & Bardy, C. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.108350 · Cited by 28
Bisulfite-free epigenomics and genomics of single cells through methylation-sensitive restriction
Niemöller, C., Wehrle, J., Riba, J., Claus, R., Renz, N., Rhein, J., Bleul, S., Stosch, J. M., Duyster, J., Plass, C., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-01661-w · Cited by 27
Kambey, P. A., Kanwore, K., Ayanlaja, A. A., Nadeem, I., Yinzhen, D., Buberwa, W., Liu, W., & Gao, D. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.645583 · Cited by 27
Exploring long non-coding RNA networks from single cell omics data
Zhao, X. F., Lan, Y., & Chen, D. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.08.003 · Cited by 27
Kong, S., Li, R., Tian, Y., Zhang, Y., Lu, Y., Ou, Q., Gao, P., Li, K., & Zhang, Y. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2022.1100016 · Cited by 28
Ren, L., Huang, D., Liu, H., Lin, N., Cai, P., Yu, X., Zhang, Y., Luo, N., Lin, H., Su, J., et al. (2024) · Oncology Letters · doi:10.3892/ol.2024.14285 · Cited by 28
Are we there yet? Exploring astrocyte heterogeneity one cell at a time
O’Dea, M. R., & Hasel, P. (2024) · Glia · doi:10.1002/glia.24621 · Cited by 27
Bowes, A. L., Tarabichi, M., Pillay, N., & Loo, P. V. (2022) · The Journal of Pathology · doi:10.1002/path.5914 · Cited by 29
Epigenetic Regulations in Mammalian Cells: Roles and Profiling Techniques
Kim, U., & Lee, D.-S. (2023) · Molecules and Cells · doi:10.14348/molcells.2023.0013 · Cited by 27
Goel, G. (2018) · Cancer Management and Research · doi:10.2147/cmar.s162967 · Cited by 26
Heterogeneity Studies of Mammalian Cells for Bioproduction: From Tools to Application
Schmitz, J., Noll, T., & Grünberger, A. (2019) · Trends in biotechnology · doi:10.1016/j.tibtech.2018.11.007 · Cited by 26
Single-cell genome sequencing of protozoan parasites
Dia, A., & Cheeseman, I. H. (2021) · Trends in Parasitology · doi:10.1016/j.pt.2021.05.013 · Cited by 25
Mahdipour–Shirayeh, A., Erdmann, N., Leung-Hagesteijn, C., & Tiedemann, R. E. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab413 · Cited by 25
Unbiased integration of single cell multi-omics dataPreprint
Dou, J., Liang, S., Mohanty, V., Cheng, X., Kim, S., Choi, J., Li, Y., Rezvani, K., Chen, R., & Chen, K. (2020) · Research Square · doi:10.1101/2020.12.11.422014 · Cited by 28
Applications of single-cell technologies in drug discovery for tumor treatment
Liu, B., Hu, S., & Wang, X. (2024) · iScience · doi:10.1016/j.isci.2024.110486 · Cited by 24
Fluidic Logic Used in a Systems Approach to Enable Integrated Single-Cell Functional Analysis
Ramalingam, N., Fowler, B., Szpankowski, L., Leyrat, A. A., Hukari, K. W., Maung, M. T., Yorza, W., Norris, M., Cesar, C., Shuga, J., et al. (2016) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2016.00070 · Cited by 22
Soil microbiome characterization and its future directions with biosensing
DeFord, L., & Yoon, J. (2024) · Journal of Biological Engineering · doi:10.1186/s13036-024-00444-1 · Cited by 28
Unravelling HIV-1 Latency, One Cell at a Time
Kok, Y. L., Ciuffi, A., & Metzner, K. J. (2017) · Trends in Microbiology · doi:10.1016/j.tim.2017.06.002 · Cited by 22
Liquid biopsy: state of reproductive medicine and beyond
Schobers, G., Koeck, R. M., Pellaers, D., Stevens, S. J. C., Macville, M. V. E., Paulussen, A. D. C., Coonen, E., van den Wijngaard, A., de Die-Smulders, C. E. M., de Wert, G. M. W. R., et al. (2021) · Human Reproduction · doi:10.1093/humrep/deab206 · Cited by 25
Evaluating microglial phenotypes using single-cell technologies
Sankowski, R., Monaco, G., & Prinz, M. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2021.11.001 · Cited by 25
Microfluidic single‐cell multiomics analysis
Xu, X., Zhang, Q., Li, M., Lin, S., Liang, S., Cai, L., Zhu, H., Su, R. J., & Yang, C. J. (2022) · Open Research (University of Surrey) · doi:10.1002/viw.20220034 · Cited by 24
Cellular identity at the single-cell level
Coskun, A. F., Eser, U., & Islam, S. (2016) · Molecular BioSystems · doi:10.1039/c6mb00388e · Cited by 24
Weizman, N. F., Wyse, B. A., Antes, R., Ibarrientos, Z., Sangaralingam, M., Motamedi, G., Kuznyetsov, V., Madjunkova, S., & Librach, C. L. (2019) · Scientific Reports · doi:10.1038/s41598-019-39111-7 · Cited by 23
Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects
Huo, L., Li, J. J., Chen, L., Yu, Z., Hutvágner, G., & Li, J. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab229 · Cited by 25
Andor, N., Lau, B. T., Catalanotti, C., Kumar, V., Sathe, A., Belhocine, K., Wheeler, T. D., Price, A. D., Song, M., Džakula, Ž., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/445932 · Cited by 23
Romeo, M., Dallio, M., Napolitano, C., Basile, C., Nardo, F. D., Vaia, P., Iodice, P., & Federico, A. (2025) · Diagnostics · doi:10.3390/diagnostics15030252 · Cited by 25
Functional phenotyping of genomic variants using joint multiomic single-cell DNA–RNA sequencing
Lindenhofer, D., Bauman, J., Hawkins, J. A., Fitzgerald, D., Yildiz, U., Jung, H., Korosteleva, A., Marttinen, M., Kueblbeck, M., Zaugg, J. B., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02805-0 · Cited by 29
Data mining for mutation-specific targets in acute myeloid leukemia
Benard, B. A., Gentles, A. J., Köhnke, T., Majeti, R., & Thomas, D. (2019) · Leukemia · doi:10.1038/s41375-019-0387-y · Cited by 23
Hematopoietic Reprogramming Entangles with Hematopoiesis
Wei, C., Yu, P., & Cheng, L. (2020) · Trends in Cell Biology · doi:10.1016/j.tcb.2020.07.006 · Cited by 22
Clustering single-cell multi-omics data with MoClust
Yuan, M., Chen, L., & Deng, M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac736 · Cited by 22
Strategies to identify natural antisense transcripts
Sun, Y., Li, D., Zhang, R., Shang, P., Zhang, G., Yang, T., & Qian, A. (2016) · Biochimie · doi:10.1016/j.biochi.2016.11.006 · Cited by 21
Applications of Single-Cell Sequencing for Multiomics
Xu, Y., & Zhou, X. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7717-8_19 · Cited by 22
Weng, J., Ju, F., Lyu, Z., Fan, N., Smit, D. J., Xu, W., Wu, X., Becker, P., Xu, Y., Schweiger, M. R., et al. (2025) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-025-03567-5 · Cited by 26
van Strijp, D., Vulders, R. C. M., Larsen, N. A., Schira, J., Bærlocher, L., van Driel, M. A., Pødenphant, M., Hansen, T. S., Kristensen, A. S., Mir, K. U., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-10704-4 · Cited by 21
Genetics and immunity in the era of single-cell genomics
Braga, F. A. V., Teichmann, S. A., & Chen, X. X. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw192 · Cited by 20
Mukherjee, P., Park, S. H., Pathak, N., Patino, C. A., Bao, G., & Espinosa, H. D. (2022) · ACS Nano · doi:10.1021/acsnano.2c05494 · Cited by 20
Shi, H., Williams, M. J., Satas, G., Weiner, A. C., McPherson, A., & Shah, S. P. (2024) · Nature Communications · doi:10.1038/s41467-024-46710-0 · Cited by 19
Bioinformatics approaches to single-cell analysis in developmental biology
Yalcin, D., Hakguder, Z., & Otu, H. H. (2015) · Molecular Human Reproduction · doi:10.1093/molehr/gav050 · Cited by 20
Xiong, X., Wang, X., Liu, C., Shao, Z.-M., & Yu, K. (2024) · Biomarker Research · doi:10.1186/s40364-024-00654-1 · Cited by 21
Kaur, H., Jha, P., Ochatt, S., & Kumar, V. (2023) · Critical Reviews in Biotechnology · doi:10.1080/07388551.2023.2165900 · Cited by 22
Joo, E. H., Kim, S., Park, D., Lee, T., Park, W., Han, K. Y., & Lee, J. E. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25115876 · Cited by 19
Review of GPU implementation to process of RNA sequence on cancer
Rahman, M. A., & Muniyandi, R. C. (2017) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2017.10.008 · Cited by 20
CNAPE: A Machine Learning Method for Copy Number Alteration Prediction from Gene Expression
Mu, Q., & Wang, J. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2944827 · Cited by 19
Single-Cell Sequencing in Cancer: Recent Applications to Immunogenomics and Multi-omics Tools
Sierant, M. C., & Choi, J. (2018) · Genomics & Informatics · doi:10.5808/gi.2018.16.4.e17 · Cited by 15
Chen, M., Jiang, J., & Hou, J. (2023) · Biomarker Research · doi:10.1186/s40364-023-00502-8 · Cited by 19
Tan, S. J., Yeo, T., Sukhatme, S. A., Kong, S. L., Lim, W., & Lim, C. T. (2017) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-55947-6_14 · Cited by 18
Single-cell genomic analysis of human cerebral organoids
Kanton, S., Treutlein, B., & Camp, J. G. (2020) · Methods in cell biology · doi:10.1016/bs.mcb.2020.03.013 · Cited by 18
SCeQTL: an R package for identifying eQTL from single-cell parallel sequencing data
Hu, Y., Xi, X., Yang, Q., & Zhang, X. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3534-6 · Cited by 18
Next Generation Imaging Techniques to Define Immune Topographies in Solid Tumors
Pietrobon, V., Cesano, A., Marincola, F. M., & Kather, J. N. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2020.604967 · Cited by 18
Discovering single-cell eQTLs from scRNA-seq data only
Ma, T., Li, H., & Zhang, X. (2022) · Gene · doi:10.1016/j.gene.2022.146520 · Cited by 18
Salvatore, J. E., Han, S., Farris, S. P., Mignogna, K. M., Miles, M. F., & Arpana Agrawal, A. (2018) · Addiction Biology · doi:10.1111/adb.12591 · Cited by 18
DNA Replication Timing Enters the Single-Cell Era
Hiratani, I., & Takahashi, S. (2019) · Genes · doi:10.3390/genes10030221 · Cited by 21
The Promise of Circulating Tumor Cells for Precision Cancer Therapy
Hwang, W. L., Hwang, K. L., & Miyamoto, D. T. (2016) · Biomarkers in Medicine · doi:10.2217/bmm-2016-0192 · Cited by 17
Single‐cell proteomics by mass spectrometry: Advances and implications in cancer research
Tan, Y. C., Low, T. Y., Lee, P. Y., & Lim, L. C. (2024) · PROTEOMICS · doi:10.1002/pmic.202300210 · Cited by 17
Anatomy of Mammalian Replication Domains
Takebayashi, S., Ogata, M., & Okumura, K. (2017) · Genes · doi:10.3390/genes8040110 · Cited by 17
Single cell transcriptomics of pluripotent stem cells: reprogramming and differentiation
Natarajan, K. N., Teichmann, S. A., & Kolodziejczyk, A. A. (2017) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2017.06.003 · Cited by 18
Studying X chromosome inactivation in the single-cell genomic era
Keniry, A., & Blewitt, M. E. (2018) · Biochemical Society Transactions · doi:10.1042/bst20170346 · Cited by 16
Multimodal single-cell approaches shed light on T cell heterogeneity
Nathan, A., Baglaenko, Y., Fonseka, C. Y., Beynor, J. I., & Raychaudhuri, S. (2019) · Current Opinion in Immunology · doi:10.1016/j.coi.2019.07.002 · Cited by 16
Non-coding antisense transcripts: fine regulation of gene expression in cancer
dos Santos, F., Capela, A. M., Mateus, F. C., Pereira, S. N., & de Jesus, B. B. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.10.009 · Cited by 19
Modeling the causal regulatory network by integrating chromatin accessibility and transcriptome data
Wang, Y., Jiang, R., & Wong, W. H. (2016) · National Science Review · doi:10.1093/nsr/nww025 · Cited by 17
Coalescing single-cell genomes and transcriptomes to decode breast cancer progression
Wang, K., Ye, R., Bai, S., Xiao, Z., Yang, L., Li, J., Tang, C., Sei, E., Peng, J., Casasent, T. D., et al. (2025) · Cell · doi:10.1016/j.cell.2025.08.012 · Cited by 19
Single-cell sequencing and its applications in bladder cancer
Wei, W., Rong, Y., Liu, S., Yang, C., Thokerunga, E., Cui, D., & Wang, F. (2022) · Expert Reviews in Molecular Medicine · doi:10.1017/erm.2021.23 · Cited by 17
Mao, X., Xu, M., Yan, X., Chen, L., Lin, N., & Zhang, Y. (2024) · hLife · doi:10.1016/j.hlife.2024.01.004 · Cited by 17
Making headway towards understanding how epigenetic mechanisms contribute to early-life effects
Vukić, M., Wu, H., & Daxinger, L. (2019) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2018.0126 · Cited by 16
Multi-Omics Profiling of the Tumor Microenvironment
Oekelen, O. V., & Laganà, A. (2022) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-91836-1_16 · Cited by 13
Highly multiplexed single-cell quantitative PCR
VanInsberghe, M. A., Zahn, H., White, A. K., Petriv, O. I., & Hansen, C. L. (2018) · PLoS ONE · doi:10.1371/journal.pone.0191601 · Cited by 16
Machine learning to dissect perturbations in complex cellular systems
Monfort‐Lanzas, P., Rungger, K., Madersbacher, L., & Hackl, H. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.02.028 · Cited by 20
Advances in Single-Cell Sequencing Technology and Its Application in Poultry Science
Liu, Y., Liang, S., Wang, B., Zhao, J., Zi, X., Yan, S., Dou, T., Jia, J., Wang, K., & Ge, C. (2022) · Genes · doi:10.3390/genes13122211 · Cited by 14
Zhang, H., Lu, X., Lu, B., Gullo, G., & Chen, L. (2024) · Future Oncology · doi:10.2217/fon-2023-0658 · Cited by 16
Single neuron transcriptome analysis can reveal more than cell type classification
Harbom, L. J., Chronister, W. D., & McConnell, M. J. (2016) · BioEssays · doi:10.1002/bies.201500097 · Cited by 16
Marie, R., Pødenphant, M., Koprowska, K., Bærlocher, L., Vulders, R. C. M., Wilding, J. L., Ashley, N., McGowan, S. J., van Strijp, D., van Hemert, F., et al. (2018) · Lab on a Chip · doi:10.1039/c8lc00169c · Cited by 15
Single-Cell Sequencing Applications in the Inner Ear
Wu, M., Xia, M., Li, W., & Li, H. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.637779 · Cited by 14
Single-cell technologies and analyses in hematopoiesis and hematological malignancies
Campillo‐Marcos, I., Álvarez‐Errico, D., Alandes, R. A., Mereu, E., & Esteller, M. (2021) · Experimental Hematology · doi:10.1016/j.exphem.2021.05.001 · Cited by 14
Reprogramming lineage identity through cell–cell fusion
Brown, K. E., & Fisher, A. G. (2021) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2021.04.004 · Cited by 16
Marks, J. R., Zawistowski, J. S., Salas-González, I., Tate, T. A., Morozova, T. V., Blackinton, J. G., Arvapalli, D. M., Velivela, S. D., Harton, G., Gawad, C., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.29.489440 · Cited by 17
Tn5 DNA Transposase in Multi-Omics Research
Penkov, D., Zubkova, E., & Parfyonova, Y. V. (2023) · Methods and Protocols · doi:10.3390/mps6020024 · Cited by 14
Phylogenetic inference from single-cell RNA-seq data
Liu, X., Griffiths, J. I., Bishara, I., Liu, J., Bild, A. H., & Chang, J. T. (2023) · Scientific Reports · doi:10.1038/s41598-023-39995-6 · Cited by 14
Edrisi, M., Huang, X., Ogilvie, H. A., & Nakhleh, L. K. (2023) · Nature Communications · doi:10.1038/s41467-023-44014-3 · Cited by 15
Integrated sequencing of exome and mRNA of large-sized single cells
Wang, L. Y., Guo, J., Cao, W., Zhang, M., He, J., & Li, Z. (2018) · Scientific Reports · doi:10.1038/s41598-017-18730-y · Cited by 15
Advances in single‐cell sequencing and its application to musculoskeletal system research
Zhang, Y., Wang, J., Yu, C., Xia, K., Yang, B., Zhang, Y., Ying, L., Wang, C., Huang, X., Chen, Q., et al. (2021) · Cell Proliferation · doi:10.1111/cpr.13161 · Cited by 15
Jiang, L., Sun, H., Guan, R., Shi, F., Zhao, F., & Liu, J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.880578 · Cited by 15
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
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
