Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity
Open accessAbstract
We report scM&T-seq, a method for parallel single-cell genome-wide methylome and transcriptome sequencing that allows for the discovery of associations between transcriptional and epigenetic variation. Profiling of 61 mouse embryonic stem cells confirmed known links between DNA methylation and transcription. Notably, the method revealed previously unrecognized associations between heterogeneously methylated distal regulatory elements and transcription of key pluripotency genes.
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?
scM&T-seq is the first single-cell multi-omics technology achieving co-profile of the DNA methylome and transcriptome.
“The first single-cell multi-omics technology achieving co-profile of the DNA methylome and transcriptome is single-cell methylome and transcriptome sequencing (scM&T-seq) [103]”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [103]
scM&T-seq measures DNA methylome and transcriptome via a similar protocol as G&T-seq for physically separating mRNA and DNA, but instead of performing WGS on the DNA fraction, it undergoes scBS-seq to profile the methylome.
“scM&T-seq [82] measures DNA methylome and transcriptome via a similar protocol as G&T-seq for physically separating mRNA and DNA, but instead of performing WGS on the DNA fraction, it undergoes scBS-seq to profile the methylome.”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [82]
Several other methods have been published to simultaneously measure several omics features from one cell, all including DNA methylation.
“Several other methods have been published to simultaneously measure several omics features from one cell (all of them including DNA methylation).”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [82]
Angermueller et al. studied associations between changes in methylation and expression of individual genes and detected 1493 associations, both positive and negative, highlighting the complexity of the methylome-transcriptome interaction.
“For instance, Angermueller et al. [82] studied associations between changes in methylation and expression of individual genes. They detected 1493 associations, both positive and negative, highlighting the complexity of the methylome–transcriptome interaction.”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [82]
Multiomic methods can measure both the transcriptome and DNA methylome in one cell.
“Multiomic sequencing can also assay the transcriptome and DNA methylome from the same cell (Angermueller et al., 2016; Hou et al., 2016; Hu et al., 2016; Luo et al., 2018b).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Angermueller et al., 2016
M&T-seq captures DNA methylation and transcriptome data.
“M&T‐seq (Angermueller et al, 2016) captures DNA methylation and transcriptome data, with NMT‐seq (preprint: Clark et al, 2018) further adding chromatin‐accessibility information using a GpC methyltransferase (Kelly et al, 2012).”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Angermueller et al, 2016
scM&T-seq was the first WGBS-based multi-omics method; it used G&T-seq and scBS-seq to assess methylation heterogeneity and Smart-seq2 to obtain RNA data together with genome-wide methylation.
“The first method of WGBS multi-omics, single-cell genome-wide methylome and transcriptome sequencing (scM&T-seq)28, borrowed already-established single-omics techniques from G&T-seq21 and scBS-seq26 to measure DNA methylation heterogeneity in single cells, combined with Smart-seq2 to generate transcriptional data concurrently with genome-wide methylation data.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [28]
Multiple teams have created methods that jointly profile DNA methylation and RNA.
“Many groups have built from this foundation to develop technologies that co-profile the DNA methylome and transcriptome28–31.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [28]
scTRIO-seq and scNMT-seq can simultaneously sample genomic CNVs, DNA methylome, nucleosome occupancy and transcriptome of single cells.
“Methods discussed above such as scTRIO-seq28 and scNMT-seq26 can be used to simultaneously sample genomic copy number variations, the DNA methylome, nucleosome occupancy and the transcriptome of single cells to reveal new cell types and their roles in a studied lineage.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [28]
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
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · see citing works
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556 · see citing works
Functions of DNA methylation: islands, start sites, gene bodies and beyond
Jones, P. A. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3230 · Cited by 6,061 · see citing works
Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications
Krueger, F., & Andrews, S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr167 · Cited by 5,161 · see citing works
Self-citationMaster Transcription Factors and Mediator Establish Super-Enhancers at Key Cell Identity Genes
Whyte, W. A., Orlando, D. A., Hnisz, D., Abraham, B. J., Lin, C. Y., Kagey, M. H., Rahl, P. B., Lee, T. I., & Young, R. A. (2013) · Cell · doi:10.1016/j.cell.2013.03.035 · Cited by 4,143 · see citing works
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356 · see citing works
Function and information content of DNA methylation
Schübeler, D. (2015) · Nature · doi:10.1038/nature14192 · Cited by 2,060 · see citing works
Fast and SNP-tolerant detection of complex variants and splicing in short reads
Wu, T. D., & Nacu, S. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq057 · Cited by 1,894 · see citing works
Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells
Yan, L., Yang, M., Guo, H., Yang, L., Wu, J., Li, R., Liu, P., Lian, Y., Zheng, X., Yan, J., et al. (2013) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2660 · Cited by 1,740 · see citing works
Nanog safeguards pluripotency and mediates germline development
Chambers, I., Silva, J. C. R., Colby, D., Nichols, J., Nijmeijer, B., Robertson, M., Vrána, J., Jones, K., Grotewold, L., & Smith, A. G. (2007) · Nature · doi:10.1038/nature06403 · Cited by 1,635 · see citing works
DNA-binding factors shape the mouse methylome at distal regulatory regions
Stadler, M. B., Murr, R., Burger, L., Ivanek, R., Lienert, F., Schöler, A., Nimwegen, E. V., Wirbelauer, C., Oakeley, E. J., Gaidatzis, D., et al. (2011) · Nature · doi:10.1038/nature10716 · Cited by 1,383 · 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
Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity
Smallwood, S. A., Lee, H. J., Angermueller, C., Krueger, F., Saadeh, H., Peat, J., Andrews, S. R., Stegle, O., Reik, W., & Kelsey, G. (2014) · Nature Methods · doi:10.1038/nmeth.3035 · Cited by 1,125 · see citing works
Self-citationG&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
Self-citationIndependent filtering increases detection power for high-throughput experiments
Bourgon, R., Gentleman, R. C., & Huber, W. K. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914005107 · Cited by 731 · see citing works
Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation
Kolodziejczyk, A. A., Kim, J. K., Tsang, J. C. H., Ilicic, T., Henriksson, J., Natarajan, K. N., Tuck, A. C., Gao, X., Bühler, M., Liu, P., et al. (2015) · Cell Stem Cell · doi:10.1016/j.stem.2015.09.011 · Cited by 570 · see citing works
Regulated Fluctuations in Nanog Expression Mediate Cell Fate Decisions in Embryonic Stem Cells
Kalmár, T., Lim, C. L., Hayward, P. C., Descalzo, S. M., Nichols, J., García‐Ojalvo, J., & Arias, A. M. (2009) · PLoS Biology · doi:10.1371/journal.pbio.1000149 · Cited by 541 · see citing works
Single-Cell Gene Expression Profiling
Levsky, J. M., Shenoy, S. M., Pezo, R. C., & Singer, R. H. (2002) · Science · doi:10.1126/science.1072241 · Cited by 526 · see citing works
Guo, H., Zhu, P., Wu, X., Li, X., Wen, L., & Tang, F. (2013) · Genome Research · doi:10.1101/gr.161679.113 · Cited by 518 · see citing works
Integrated genome and transcriptome sequencing of the same cell
Dey, S. S., Kester, L., Spanjaard, B., Bienko, M., & van Oudenaarden, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3129 · Cited by 502 · see citing works
Habibi, E., Brinkman, A. B., Arand, J., Kroeze, L. I., Kerstens, H. H. D., Matarese, F., Lepikhov, K., Gut, M., Heath, I. B., Hubner, N. C., et al. (2013) · Cell stem cell · doi:10.1016/j.stem.2013.06.002 · Cited by 470 · see citing works
Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics
Farlik, M., Sheffield, N. C., Nuzzo, A., Datlinger, P., Schönegger, A., Klughammer, J., & Bock, C. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.02.001 · Cited by 422 · see citing works
Ficz, G., Hore, T. A., Santos, F., Lee, H. J., Dean, W., Arand, J., Krueger, F., Oxley, D., Paul, Y., Walter, J., et al. (2013) · Cell stem cell · doi:10.1016/j.stem.2013.06.004 · Cited by 387 · see citing works
Self-citationReprogramming the Methylome: Erasing Memory and Creating Diversity
Lee, H. J., Hore, T. A., & Reik, W. (2014) · Cell stem cell · doi:10.1016/j.stem.2014.05.008 · Cited by 382 · see citing works
Self-citationA Heterogeneous Expression Pattern for Nanog in Embryonic Stem Cells
Singh, A. M., Hamazaki, T., Hankowski, K. E., & Terada, N. (2007) · Stem Cells · doi:10.1634/stemcells.2007-0126 · Cited by 353 · see citing works
Dynamic Heterogeneity and DNA Methylation in Embryonic Stem Cells
Singer, Z. S., Yong, J., Tischler, J., Hackett, J. A., Altınok, A., Surani, M. A., Cai, L., & Elowitz, M. B. (2014) · Molecular Cell · doi:10.1016/j.molcel.2014.06.029 · Cited by 275 · see citing works
Transcription factor heterogeneity in pluripotent stem cells: a stochastic advantage
Torres‐Padilla, M., & Chambers, I. (2014) · Development · doi:10.1242/dev.102624 · Cited by 198 · see citing works
Ficz, G., Hore, T. A., Santos, F., Lee, H. J., Dean, W., Arand, J., Krueger, F., Oxley, D., Paul, Y., Walter, J., et al. (2013) · Clinical Epigenetics · doi:10.1186/1868-7083-5-s1-s2 · Cited by 164 · see citing works
Self-citationPluripotency re‐centered around Esrrb
Papp, B., & Plath, K. (2012) · The EMBO Journal · doi:10.1038/emboj.2012.285 · Cited by 22 · 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
Self-citationEleven 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
Self-citationMulti-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets
Argelaguet, R., Velten, B., Arnol, D., Dietrich, S., Zenz, T., Marioni, J. C., Buettner, F., Huber, W., & Stegle, O. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178124 · Cited by 1,510 · see citing works
Self-citationIntegrative 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
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
Self-citationMOFA+: 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
Self-citationThe 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
Joint profiling of chromatin accessibility and gene expression in thousands of single cells
Cao, J., Cusanovich, D. A., Ramani, V., Aghamirzaie, D., Pliner, H. A., Hill, A. J., Daza, R. M., McFaline-Figueroa, J. L., Packer, J. S., Christiansen, L., et al. (2018) · Science · doi:10.1126/science.aau0730 · Cited by 938 · 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
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
Clark, S. J., Argelaguet, R., Kapourani, C.-A., Stubbs, T. M., Lee, H. J., Alda-Catalinas, C., Krueger, F., Sanguinetti, G., Kelsey, G., Marioni, J. C., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03149-4 · Cited by 657 · see citing works
Self-citationZitnik, M., Nguyen, F., Wang, B., Leskovec, J., Goldenberg, A., & Hoffman, M. M. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.09.012 · Cited by 675 · 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
Epigenetic inheritance of acquired traits through sperm RNAs and sperm RNA modifications
Chen, Q., Yan, W., & Duan, E. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.106 · Cited by 576 · see citing works
Mimitou, E. P., Cheng, A., Montalbano, A., Hao, S., Stoeckius, M., Legut, M., Roush, T., Herrera, A., Papalexi, E., Ouyang, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0392-0 · Cited by 520 · see citing works
DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning
Angermueller, C., Lee, H. J., Reik, W., & Stegle, O. (2017) · Genome Biology · doi:10.1186/s13059-017-1189-z · Cited by 536 · see citing works
Self-citationSingle-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex
Luo, C., Keown, C. L., Kurihara, L., Zhou, J., He, Y., Li, J., Castanon, R., Lucero, J., Nery, J. R., Sandoval, J. P., et al. (2017) · Science · doi:10.1126/science.aan3351 · 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
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
Self-citationComputational principles and challenges in single-cell data integration
Argelaguet, R., Cuomo, A. S. E., Stegle, O., & Marioni, J. C. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00895-7 · Cited by 472 · see citing works
Self-citationHigh-throughput single-cell ChIP-seq identifies heterogeneity of chromatin states in breast cancer
Grosselin, K., Durand, A., Marsolier, J., Poitou, A., Marangoni, E., Nemati, F., Dahmani, A., Lameiras, S., Reyal, F., Frenoy, O., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0424-9 · Cited by 466 · see citing works
Dynamics of the epigenetic landscape during the maternal-to-zygotic transition
Eckersley-Maslin, M. A., Alda-Catalinas, C., & Reik, W. (2018) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-018-0008-z · Cited by 431 · see citing works
Self-citationSingle-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
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
Self-citationAn 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
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
Highly scalable generation of DNA methylation profiles in single cells
Mulqueen, R. M., Pokholok, D., Norberg, S. J., Torkenczy, K. A., Fields, A. J., Sun, D., Sinnamon, J. R., Shendure, J., Trapnell, C., O'Roak, B. J., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4112 · Cited by 289 · 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
Self-citationInnovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review
Hassan, M., Awan, F. M., Naz, A., deAndrés-Galiana, E. J., Alvarez, O., Cernea, A., Fernández-Brillet, L., Fernández-Martínez, J. L., & Kloczkowski, A. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23094645 · Cited by 317 · see citing works
“There are new ways for measuring the single cell genome and transcriptome sequencing (G&T-seq) allowing us to simultaneously obtain both transcriptomic and genomic information from a single cell [90], which can provide clearer information that may be helpful in designing precision medicine [9].”
BackgroundSimultaneous profiling of transcriptome and DNA methylome from a single cell
Hu, Y., Huang, K., An, Q., Du, G., Hu, G., Xue, J., Zhu, X., Wang, C.-Y., Xue, Z., & Fan, G. (2016) · Genome Biology · doi:10.1186/s13059-016-0950-z · Cited by 300 · 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
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
Using single-cell genomics to understand developmental processes and cell fate decisions
Griffiths, J. A., Scialdone, A., & Marioni, J. C. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178046 · Cited by 250 · see citing works
Clark, S. J., Smallwood, S. A., Lee, H. J., Krueger, F., Reik, W., & Kelsey, G. (2017) · Nature Protocols · doi:10.1038/nprot.2016.187 · Cited by 270 · see citing works
Self-citationHotspot identifies informative gene modules across modalities of single-cell genomics
DeTomaso, D., & Yosef, N. (2021) · Cell Systems · doi:10.1016/j.cels.2021.04.005 · Cited by 267 · see citing works
Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes
Armand, E. J., Li, J., Xie, F., Luo, C., & Mukamel, E. A. (2021) · Neuron · doi:10.1016/j.neuron.2020.12.010 · Cited by 241 · 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
Pott, S. (2017) · eLife · doi:10.7554/elife.23203 · Cited by 230 · see citing works
Hernando-Herraez, I., Evano, B., Stubbs, T., Commere, P.-H., Jan Bonder, M., Clark, S., Andrews, S., Tajbakhsh, S., & Reik, W. (2019) · Nature Communications · doi:10.1038/s41467-019-12293-4 · Cited by 236 · see citing works
Self-citationTutorial: guidelines for the experimental design of single-cell RNA sequencing studies
Lafzi, A., Moutinho, C., Picelli, S., & Heyn, H. (2018) · Nature Protocols · doi:10.1038/s41596-018-0073-y · Cited by 218 · 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
“…technology achieving co-profile of the DNA methylome and transcriptome is single-cell methylome and transcriptome sequencing (scM&T-seq) [103], which utilizes G&T-seq to separate and amplify genomic DNA and mRNA from the same single cell and applies scBS-seq [104] to generate DNA…”
MethodSingle-cell DNA methylation profiling: technologies and biological applications
Karemaker, I. D., & Vermeulen, M. (2018) · Trends in Biotechnology · doi:10.1016/j.tibtech.2018.04.002 · Cited by 170 · see citing works
Multi-omics of single cells: strategies and applications
Bock, C., Farlik, M., & Sheffield, N. C. (2016) · Trends in Biotechnology · doi:10.1016/j.tibtech.2016.04.004 · Cited by 146 · 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
Epigenetic Research in Neuropsychiatric Disorders: the “Tissue Issue”
Bakulski, K. M., Halladay, A., Hu, V. W., Mill, J., & Fallin, M. D. (2016) · Current Behavioral Neuroscience Reports · doi:10.1007/s40473-016-0083-4 · Cited by 127 · 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
Zhu, T., Liu, J., Beck, S., Pan, S., Capper, D., Lechner, M., Thirlwell, C., Breeze, C. E., & Teschendorff, A. E. (2022) · Nature Methods · doi:10.1038/s41592-022-01412-7 · Cited by 103 · see citing works
Single cell transcriptomics in neuroscience: cell classification and beyond
Tasic, B. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.04.021 · Cited by 83 · 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
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
Self-citationTime-resolved single-cell RNA-seq using metabolic RNA labelling
Erhard, F., Saliba, A.-E., Lusser, A., Toussaint, C., Hennig, T., Prusty, B. K., Kirschenbaum, D., Abadie, K., Miska, E. A., Friedel, C. C., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00157-z · Cited by 73 · see citing works
Computational methods for the integrative analysis of single-cell data
Forcato, M., Romano, O., & Bicciato, S. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa042 · Cited by 71 · see citing works
Perspectives on defining cell types in the brain
Mukamel, E. A., & Ngai, J. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.11.007 · Cited by 56 · see citing works
Brown, A. J., Snapkov, I., Akbar, R., Pavlović, M., Miho, E., Sandve, G. K., & Greiff, V. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00071b · Cited by 59 · see citing works
Lopez‐Atalaya, J. P., Askew, K. E., Sierra, A., & Gomez‐Nicola, D. (2018) · Developmental Neurobiology · doi:10.1002/dneu.22545 · Cited by 49 · see citing works
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 · see citing works
Graph neural networks for single-cell omics data: a review of approaches and applications
Li, S., Hua, H., & Chen, S. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf109 · Cited by 43 · see citing works
Sample-multiplexing approaches for single-cell sequencing
Zhang, Y., Xu, S., Wen, Z., Gao, J., Li, S., Weissman, S. M., & Pan, X. (2022) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-022-04482-0 · Cited by 40 · see citing works
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 · see citing works
Genome to Phenome: A Systems Biology Approach to PTSD Using an Animal Model
Chakraborty, N., Meyerhoff, J., Jett, M., & Hammamieh, R. (2017) · Methods in Molecular Biology · doi:10.1007/978-1-4939-6952-4_6 · Cited by 14 · see citing works
- Authors
- 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., Kelsey, G., Stegle, O., & Reik, W.
- Affiliations
- European Bioinformatics Institute (GB)
- Babraham Institute (GB)
- Wellcome Sanger Institute (GB)
- Corresponding author
- Voet, Thierry
- Kelsey, Gavin
- Stegle, Oliver
- Reik, Wolf
- ORCID
- Venue
- Nature Methods
- Published
- 2016-01-11
- Volume
- 13
- Issue
- 3
- Pages
- 229-232
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC4770512
- OpenAlex
- W2222000795
- Cited by
- 717
- References
- 29
- Citations by year
- 2026: 37 · 2025: 44 · 2024: 53 · 2023: 77 · 2022: 78 · 2021: 82 · 2020: 90 · 2019: 71 · 2018: 80 · 2017: 60 · 2016: 42 · 2015: 1
- FWCI
- 43.82
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Lirias (submittedVersion)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- Funders
- Wellcome Trust (105031, 105031REIK, 105031/Z/14/Z, 105045, 095645)
- Medical Research Council (MC_PC_15075, MR/K011332/1)
- Biotechnology and Biological Sciences Research Council (BBS/E/B/000C0400, BBS/E/T/000PR5885, BBS/E/T/000PR6193)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- 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 Chromatin Dynamics
- Keywords
- single-cell DNA methylation, single-cell transcriptome, epigenetic heterogeneity, transcriptional heterogeneity, distal regulatory elements, pluripotency genes, mouse embryonic stem cells, parallel sequencing
- MeSH terms
- Animals, Base Sequence, Cells, Cultured, Molecular Sequence Data, Transcription Factors, Epigenesis, Genetic, Regulatory Elements, Transcriptional, Mice, Embryonic Stem Cells, High-Throughput Nucleotide Sequencing
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
