The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Joint profiling of chromatin accessibility and gene expression in thousands of single cells
Open accessAbstract
Single-cell chromatin and RNA analysis: Single-cell analyses have begun to provide insight into the differences among and within the individual cells that make up a tissue or organism. However, technological barriers owing to the small amount of material present in each single cell have prevented parallel analyses. Cao et al. present sci-CAR, a pooled barcode method that jointly analyzes both the RNA transcripts and chromatin profiles of single cells. By applying sci-CAR to lung adenocarcinoma cells and mouse kidney tissue, the authors demonstrate precision in assessing expression and genome accessibility at a genome-wide scale. The approach provides an improvement over bulk analysis, which can be confounded by differing cellular subgroups. Science , this issue p. 1380
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?
In the citing paper
“Milo may also be applicable to single-cell assays other than scRNA-seq, including multi-omic assays [35–39].”
From Differential abundance testing on single-cell data using k-nearest neighbor graphs (2022) · cites this paper as [36]
In the citing paper
“Furthermore, multimodal single-cell approaches are swiftly gaining traction within hepatology, allowing ‘combination readouts’ from the same cell, for example simultaneous acquisition of transcriptomic and epigenomic information, again maximising our ability to identify the key therapeutic targets in pathological cells.165,166”
From Understanding the cellular interactome of non-alcoholic fatty liver disease (2022) · cites this paper as [165]
In the citing paper
“Novel therapeutics developed for one fibrotic disorder may be applicable to a wide range of fibrotic diseases due to shared pathways across organs that are uncovered by this work.”
From Fibrosis: from mechanisms to medicines (2020) · cites this paper as [182]
In the citing paper
“The cardinal identity of a cell is probably already instilled at the postmitotic stage during development9,61,62,63,64, and its subsequent phenotypic differentiation and expression are regulated by extrinsic signals such as cell interactions and neural activity.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [62]
In the citing paper
“To achieve higher throughput in single-cell analysis, Sci-CAR-seq combined single-cell combinatorial indexing55 with ATAC-seq to provide deep insights into chromatin states jointly with gene expression in over 11,200 nuclei56.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [56]
In the citing paper
“Single-cell multiomics allows uncovering of the mechanisms of gene regulation across different omics layers6–9 but lacks spatial information, which is crucial to understanding cellular function in tissue.”
From Spatial epigenome–transcriptome co-profiling of mammalian tissues (2023) · cites this paper as [8]
In the citing paper
“Single-cell measurements of transcriptional states, chromatin accessibility, and DNA methylation, have also provided a better understanding of the heterogeneity that exists in such regulatory mechanisms during hematopoiesis (Buenrostro et al., 2018; Cao et al., 2018; Ludwig et al., 2019b; Ulirsch et al., 2019).”
From Unraveling Hematopoiesis through the Lens of Genomics (2020) · cites this paper as Cao et al., 2018
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
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
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Flagged first, then by citations
Research works printed but not linked: 3
- 10.1126/science.aam8940 · DOI · Paper page
- 10.1016/j.cell.2018.02.001 · DOI · Paper page
- 10.18637/jss.v033.i01 · DOI · Paper page
Cited by
Sorted by citations
Items 201–300 of 948
Single-Cell Multiomics Techniques: From Conception to Applications
Dimitriu, M. A., Lazar-Contes, I., Roszkowski, M., & Mansuy, I. M. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.854317 · Cited by 55
A single-cell multiomic analysis of kidney organoid differentiation
Yoshimura, Y., Muto, Y., Ledru, N., Wu, H., Omachi, K., Miner, J. H., & Humphreys, B. D. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2219699120 · Cited by 56
van Kuijk, K., Kuppe, C., Betsholtz, C., Vanlandewijck, M., Kramann, R., & Sluimer, J. C. (2019) · Cardiovascular Research · doi:10.1093/cvr/cvz185 · Cited by 52
Applications of Single-Cell Omics in Tumor Immunology
Liu, J., Qu, S., Zhang, T., Gao, Y., Shi, H., Song, K., Chen, W., & Yin, W. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.697412 · Cited by 53
Reversible, tunable epigenetic silencing of TCF1 generates flexibility in the T cell memory decision
Abadie, K., Clark, E. C., Valanparambil, R. M., Ukogu, O., Yang, W., Daza, R. M., Ng, K. K. H., Fathima, J., Wang, A. L., Lee, J., et al. (2024) · Immunity · doi:10.1016/j.immuni.2023.12.006 · Cited by 53
Self-citationPiferrer, F., & Wang, H. (2023) · Wiley eBooks · doi:10.1002/9781119821946 · Cited by 55
Simultaneous single-cell analysis of 5mC and 5hmC with SIMPLE-seq
Bai, D., Zhang, X., Xiang, H., Guo, Z., Zhu, C., & Yi, C. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02148-9 · Cited by 54
Ovarian cancer and the evolution of subtype classifications using transcriptional profiling†
Cook, D. P., & Vanderhyden, B. C. (2019) · Biology of Reproduction · doi:10.1093/biolre/ioz099 · Cited by 50
Deciphering Cell Fate Decision by Integrated Single-Cell Sequencing Analysis
Sagar, & Grün, D. (2020) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-111419-091750 · Cited by 48
How to Get Started with Single Cell RNA Sequencing Data Analysis
Balzer, M. S., Ma, Z., Zhou, J., Abedini, A., & Suszták, K. (2021) · Journal of the American Society of Nephrology · doi:10.1681/asn.2020121742 · Cited by 51
Tracking cell-type-specific temporal dynamics in human and mouse brains
Lu, Z., Zhang, M., Lee, J., Sziraki, A., Anderson, S., Zhang, Z., Xu, Z., Jiang, W., Ge, S., Nelson, P. T., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.042 · Cited by 51
Self-citationMapping cells through time and space with moscotPreprint
Klein, D., Palla, G., Lange, M., Klein, M., Piran, Z., Gander, M., Meng-Papaxanthos, L., Sterr, M., Bastidas-Ponce, A., Tarquis-Medina, M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.11.540374 · Cited by 49
Plongthongkum, N., Diep, D. H., Chen, S., Lake, B. B., & Zhang, K. (2021) · Nature Protocols · doi:10.1038/s41596-021-00507-3 · Cited by 47
Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data
Xu, J., Lu, C., Jin, S., Meng, Y., Fu, X., Zeng, X., Nussinov, R., & Cheng, F. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf138 · Cited by 54
Simultaneous Profiling of Gene Expression and Chromatin Accessibility in Single Cells
Reyes, M., Billman, K., Hacohen, N., & Blainey, P. C. (2019) · Advanced Biosystems · doi:10.1002/adbi.201900065 · Cited by 49
Intricacies of single-cell multi-omics data integration
Rautenstrauch, P., Vlot, A. H. C., Saran, S., & Ohler, U. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.08.012 · Cited by 48
Profiling Chromatin Accessibility at Single-Cell Resolution
Sinha, S., Satpathy, A. T., Zhou, W., Ji, H., Stratton, J. A., Jaffer, A., Bahlis, N. J., Morrissy, A. S., & Biernaskie, J. A. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.06.010 · Cited by 49
Benchmarking multi-omics integration algorithms across single-cell RNA and ATAC data
Xiao, C., Chen, Y., Meng, Q., Wei, L., & Zhang, X. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae095 · Cited by 49
Single-cell ATAC-seq signal extraction and enhancement with SCATE
Ji, Z., Zhou, W., Hou, W., & Ji, H. (2020) · Genome biology · doi:10.1186/s13059-020-02075-3 · Cited by 49
Lineage Tracing: Computational Reconstruction Goes Beyond the Limit of Imaging.
Wu, S.-H. S., Lee, J., & Koo, B. (2019) · Europe PMC (PubMed Central) · doi:10.14348/molcells.2019.0006 · Cited by 46
The emergence and promise of single-cell temporal-omics approaches
Lederer, A. R., & Manno, G. L. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2019.12.005 · Cited by 44
Deleersnijder, D., Callemeyn, J., Arijs, I., Naesens, M., Craenenbroeck, A. H. V., Lambrechts, D., & Sprangers, B. (2021) · Journal of the American Society of Nephrology · doi:10.1681/asn.2021020157 · Cited by 49
Abedini, A., Sánchez-Navaro, A., Wu, J., Klötzer, K. A., Ma, Z., Poudel, B., Doke, T., Balzer, M. S., Frederick, J., Černecká, H., et al. (2023) · Journal of Clinical Investigation · doi:10.1172/jci157165 · Cited by 45
Iqbal, F., Lupieri, A., Aikawa, M., & Aïkawa, E. (2020) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.120.314776 · Cited by 45
Worssam, M. D., & Jørgensen, H. F. (2021) · Biochemical Society Transactions · doi:10.1042/bst20210138 · Cited by 46
Complex Analysis of Single-Cell RNA Sequencing Data
Khozyainova, A. A., Valyaeva, A. A., Arbatsky, M. S., Isaev, S. V., Iamshchikov, P. S., Volchkov, E., Sabirov, M. S., Zainullina, V. R., Chechekhin, V. I., Vorobev, R. S., et al. (2023) · Biochemistry (Moscow) · doi:10.1134/s0006297923020074 · Cited by 47
Li, H., Zhang, Z., Squires, M., Chen, X. X., & Zhang, X. (2025) · Nature Methods · doi:10.1038/s41592-025-02651-0 · Cited by 68
The Unmixing Problem: A Guide to Applying Single-Cell RNA Sequencing to Bone
Greenblatt, M. B., Ono, N., Ayturk, U. M., Debnath, S., & Lalani, S. (2019) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.3802 · Cited by 43
Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities
Singh, R., Hie, B., Narayan, A., & Berger, B. (2021) · Genome biology · doi:10.1186/s13059-021-02313-2 · Cited by 41
The cell type composition of the adult mouse brain revealed by single cell and spatial genomicsPreprint
Langlieb, J., Sachdev, N., Balderrama, K. S., Nadaf, N. M., Raj, M., Murray, E. M., Webber, J. T., Vanderburg, C. R., Gazestani, V., Tward, D. J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.06.531307 · Cited by 44
Progress in single-cell multimodal sequencing and multi-omics data integration
Wang, X. J., Wu, X., Hong, N., & Jin, W. (2023) · Biophysical Reviews · doi:10.1007/s12551-023-01092-3 · Cited by 45
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
Cesaro, G., Nagai, J. S., Nicolò, G., Chiodi, A., Tussardi, G., Klöker, V., Musumarra, C. V., Mosca, E., Costa, I. G., Camillo, B. D., et al. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf280 · Cited by 48
Single‐Cell Multi‐Omics and Its Prospective Application in Cancer Biology
Peng, A., Mao, X., Zhong, J., Fan, S., & Hu, Y. (2020) · PROTEOMICS · doi:10.1002/pmic.201900271 · Cited by 44
Exploiting Single-Cell Tools in Gene and Cell Therapy
Bode, D., Cull, A. H., Rubio-Lara, J. A., & Kent, D. G. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.702636 · Cited by 44
Johnson, A., Laterra, J., & López-Bertoni, H. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.995498 · Cited by 45
Simultaneous deep transcriptome and proteome profiling in a single mouse oocyte
Jiang, Y., Zhu, L., Cao, L., Wu, Q., Chen, J., Wang, Y., Wu, J., Zhang, T., Wang, Z., Guan, Z.-Y., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113455 · Cited by 44
Metzner, E., Southard, K. M., & Norman, T. M. (2024) · Cell Systems · doi:10.1016/j.cels.2024.12.002 · Cited by 45
Single Cell Omics: From Assay Design to Biomedical Application
Chen, W.-Y., Li, S., Kulkarni, A. S., Huang, L., Cao, J., Qian, K., & Wan, J. (2019) · Biotechnology Journal · doi:10.1002/biot.201900262 · Cited by 42
Self-citationSingle‐cell omics: Overview, analysis, and application in biomedical science
Stein, C. M., Weiskirchen, R., Damm, F., & Strzelecka, P. M. (2021) · Journal of Cellular Biochemistry · doi:10.1002/jcb.30134 · Cited by 43
Computational Methods for Single-Cell Imaging and Omics Data Integration
Watson, E. R., Fard, A. T., & Mar, J. C. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.768106 · Cited by 46
Duren, Z., Chang, F., Naqing, F., Xin, J., Liu, Q., & Wong, W. H. (2022) · Genome biology · doi:10.1186/s13059-022-02682-2 · Cited by 37
Yan, Y., Liu, H., Abedini, A., Sheng, X., Palmer, M., Li, H., & Suszták, K. (2024) · Nature Communications · doi:10.1038/s41467-024-45295-y · Cited by 40
Unique and assay specific features of NOMe-, ATAC- and DNase I-seq data
Nordström, K. J. V., Schmidt, F., Gasparoni, N., Salhab, A., Gasparoni, G., Kattler, K., Müller, F., Ebert, P., Costa, I. G., Pfeifer, N., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz799 · Cited by 40
Single-cell analysis of cis-regulatory elements
Marand, A. P., & Schmitz, R. J. (2021) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2021.102094 · Cited by 41
JSNMF enables effective and accurate integrative analysis of single-cell multiomics data
Ma, Y., Sun, Z., Zeng, P., Zhang, W., & Lin, Z. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac105 · Cited by 40
scBridge embraces cell heterogeneity in single-cell RNA-seq and ATAC-seq data integration
Li, Y., Zhang, D., Yang, M., Peng, D., Yu, J., Liu, Y., Lv, J., Chen, L., & Peng, X. (2023) · Nature Communications · doi:10.1038/s41467-023-41795-5 · Cited by 39
The Advancement and Application of the Single-Cell Transcriptome in Biological and Medical Research
Huang, K., Xu, Y., Feng, T., Lan, H., Ling, F., Xiang, H., & Liu, Q. (2024) · Biology · doi:10.3390/biology13060451 · Cited by 44
Wang, X., Yan, J., Shen, B., & Wei, G. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.708066 · Cited by 39
Single-cell technologies to decipher cardiovascular diseases
Abplanalp, W. T., Tucker, N. R., & Dimmeler, S. (2022) · European Heart Journal · doi:10.1093/eurheartj/ehac095 · Cited by 42
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
Lattke, M., & Guillemot, F. (2022) · WIREs Mechanisms of Disease · doi:10.1002/wsbm.1557 · Cited by 45
Simultaneous profiling of chromatin architecture and transcription in single cells
Qu, J., Sun, J., Zhao, C., Liu, X., Zhang, X., Jiang, S., Wei, C., Yu, H., Zeng, X., Fan, L., et al. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-023-01066-9 · Cited by 39
Miyahira, A. K., Sharp, A., Ellis, L., Jones, J. C., Kaochar, S., Larman, H. B., Quigley, D. A., Ye, H., Simons, J. W., Pienta, K. J., et al. (2019) · The Prostate · doi:10.1002/pros.23934 · Cited by 40
Jiang, Y., Harigaya, Y., Zhang, Z., Zhang, H., Zang, C., & Zhang, N. R. (2022) · Cell Systems · doi:10.1016/j.cels.2022.08.004 · Cited by 41
Novella-Rausell, C., Grudniewska, M., Peters, D. J. M., & Mahfouz, A. E. (2023) · iScience · doi:10.1016/j.isci.2023.106877 · Cited by 38
Deep learning-based advances and applications for single-cell RNA-sequencing data analysis
Bao, S., Li, K., Yan, C., Zhang, Z., Qu, J., & Zhou, M. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab473 · Cited by 40
BFF and cellhashR: analysis tools for accurate demultiplexing of cell hashing data
Boggy, G. J., McElfresh, G., Mahyari, E., Ventura, A. B., Hansen, S. G., Picker, L. J., & Bimber, B. N. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac213 · Cited by 36
A universal framework for single-cell multi-omics data integration with graph convolutional networks
Gao, H., Zhang, B., Liu, L., Li, S., Gao, X., & Yu, B. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad081 · Cited by 38
Epigenetic control of microglial immune responses
Scholz, R., Brösamle, D., Yuan, X., Beyer, M., & Neher, J. J. (2024) · Immunological Reviews · doi:10.1111/imr.13317 · Cited by 39
Devall, M. A. M., Jennelle, L. T., Bryant, J., Bien, S. A., Peters, U., Powell, S. M., & Casey, G. (2020) · PLoS ONE · doi:10.1371/journal.pone.0227116 · Cited by 36
Revisiting 3D chromatin architecture in cancer development and progression
Feng, Y., & Pauklin, S. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa747 · Cited by 36
Looking at neurodevelopment through a big data lens
Briscoe, J., & Marín, Ó. (2020) · Science · doi:10.1126/science.aaz8627 · Cited by 38
Advances in single-cell multi-omics profiling
Bai, D., Peng, J., & Yi, C. (2021) · RSC Chemical Biology · doi:10.1039/d0cb00163e · Cited by 36
Tanemoto, F., & Mimura, I. (2022) · Pharmaceuticals · doi:10.3390/ph15020123 · Cited by 35
Single-Cell Analysis in Immuno-Oncology
Christodoulou, M., & Zaravinos, A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24098422 · Cited by 35
Liu, Y., Altreuter, J., Bodapati, S., Cristea, S., Wong, C. J., Wu, C. J., & Michor, F. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100444 · Cited by 39
Zhao, J., & Pei, L. (2020) · JACC Basic to Translational Science · doi:10.1016/j.jacbts.2020.05.007 · Cited by 36
Kim, E., Dorrity, M. W., Fitzgerald, B. A., Seo, H., Sepuru, K. M., Queitsch, C., Mitsuda, N., Han, S., & Torii, K. U. (2022) · Nature Plants · doi:10.1038/s41477-022-01304-w · Cited by 36
Transcriptional Regulation in the Immune System: One Cell at a Time
Roy, A. L. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.01355 · Cited by 34
Vlachavas, E., Bohn, J. R., Ückert, F., & Nürnberg, S. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22062822 · Cited by 37
Hu, J., Zhong, Y., & Shang, X. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab400 · Cited by 33
Single-cell multi-omics and lineage tracing to dissect cell fate decision-making
Haghverdi, L., & Ludwig, L. S. (2023) · Stem Cell Reports · doi:10.1016/j.stemcr.2022.12.003 · Cited by 37
Direct transposition of native DNA for sensitive multimodal single-molecule sequencing
Nanda, A. S., Wu, K., Irkliyenko, I., Woo, B., Ostrowski, M. S., Clugston, A. S., Sayles, L. C., Xu, L., Satpathy, A. T., Nguyen, H. G., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01748-0 · Cited by 36
Self-citationSingle-cell transcriptomics as a framework and roadmap for understanding the brain
Cembrowski, M. S. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.108353 · Cited by 33
Global prediction of chromatin accessibility using small-cell-number and single-cell RNA-seq
Zhou, W., Ji, Z., Fang, W., & Ji, H. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz716 · Cited by 32
Are Parallel Proliferation Pathways Redundant?
Nussinov, R., Tsai, C., & Jang, H. (2020) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2020.03.013 · Cited by 33
Tagmentation-based single-cell genomicsPreprint
Adey, A. (2021) · Genome Research · doi:10.1101/gr.275223.121 · Cited by 33
Self-citationA community-based transcriptomics classification and nomenclature of neocortical cell types
Yuste, R., Hawrylycz, M., Aalling, N., Aguilar-Valles, A., Arendt, D., Armañanzas, R., Ascoli, G. A., Bielza, C., Bokharaie, V., Bergmann, T. B., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0685-8 · Cited by 32
Graybuck (2019) · doi:10.1101/525014 · Cited by 31
Machine learning integrative approaches to advance computational immunology
Curion, F., & Theis, F. J. (2024) · Genome Medicine · doi:10.1186/s13073-024-01350-3 · Cited by 36
Vascular Homeostasis and Inflammation in Health and Disease—Lessons from Single Cell Technologies
Bondareva, O., & Sheikh, B. N. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21134688 · Cited by 31
Applications of Single-Cell Omics to Dissect Tumor Microenvironment
Guo, T., Li, W., & Cai, X. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.548719 · Cited by 33
The developmental hourglass model and recapitulation: An attempt to integrate the two models
Uesaka, M., Kuratani, S., & Irie, N. (2021) · Journal of Experimental Zoology Part B Molecular and Developmental Evolution · doi:10.1002/jez.b.23027 · Cited by 30
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
Advancing bioinformatics with language models: components, applications, and perspectives
Liu, J., Yang, M., Yu, Y., Xu, H., Wang, T., Li, K., & Zhou, X. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag367 · Cited by 66
Eiers, A.-K., Vettorazzi, S., & Tuckermann, J. P. (2024) · Annals of the Rheumatic Diseases · doi:10.1136/ard-2023-225371 · Cited by 31
Machine learning methods to model multicellular complexity and tissue specificity
Sealfon, R. S. G., Wong, A. K., & Troyanskaya, O. G. (2021) · Nature Reviews Materials · doi:10.1038/s41578-021-00339-3 · Cited by 30
Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data
Chen, X. X., Wang, Y., Cappuccio, A., Cheng, W.-S., Ruf-Zamojski, F. M., Nair, V. D., Miller, C. M., Rubenstein, A. B., Nudelman, G., Tadych, A., et al. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00476-5 · Cited by 31
Cell identity specification in plants: lessons from flower development
Xu, X., Smaczniak, C., Muiño, J. M., & Kaufmann, K. (2021) · Journal of Experimental Botany · doi:10.1093/jxb/erab110 · Cited by 32
Role of epigenetics in unicellular to multicellular transition in Dictyostelium
Wang, S. Y., Pollina, E. A., Wang, I., Pino, L. K., Bushnell, H. L., Takashima, K., Fritsche, C. M., Sabin, G., Garcia, B. A., Greer, E. L., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02360-9 · Cited by 27
Lobato-Moreno, S., Yildiz, U., Claringbould, A., Servaas, N. H., Vlachou, E. P., Arnold, C., Bauersachs, H. G., Campos-Fornés, V., Kim, M., Berest, I., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02700-8 · Cited by 35
Enhancer Dysfunction in 3D Genome and Disease
Xia, J., & Wei, G. (2019) · Cells · doi:10.3390/cells8101281 · Cited by 26
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
Lung Cell Atlases in Health and Disease
Adams, T. S., Marlier, A., & Kaminski, N. (2022) · Annual Review of Physiology · doi:10.1146/annurev-physiol-032922-082826 · Cited by 34
Application of Single-Cell and Spatial Omics in Musculoskeletal Disorder Research
Feng, S., Li, J., Tian, J., Lu, S., & Zhao, Y. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032271 · Cited by 29
Deep Learning in Single-cell Analysis
Molho, D., Ding, J., Tang, W., Li, Z., Wen, H., Wang, Y., Venegas, J., Jin, W., Liu, R., Su, R., et al. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3641284 · Cited by 38
Das, D., Fletcher, R. B., & Ngai, J. (2019) · Wiley Interdisciplinary Reviews Developmental Biology · doi:10.1002/wdev.361 · Cited by 29
Single-cell transcriptomics: background, technologies, applications, and challenges
Duhan, L., Kumari, D., Naime, M., Parmar, V. S., Chhillar, A. K., Dangi, M., & Pasrija, R. (2024) · Molecular Biology Reports · doi:10.1007/s11033-024-09553-y · Cited by 29
Advances and applications of environmental stress adaptation research
Hawkins, L. J., & Storey, K. B. (2019) · Comparative Biochemistry and Physiology Part A Molecular & Integrative Physiology · doi:10.1016/j.cbpa.2019.110623 · Cited by 35
Metadata
- Authors
- 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., Steemers, F. J., Adey, A. C., Trapnell, C., & Shendure, J.
- Affiliations
- University of Washington (US)
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Illumina (United States) (US)
- Oregon Health & Science University (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Cusanovich, Darren A.
- Ramani, Vijay
- Trapnell, Cole
- Shendure, Jay
- ORCID
- Venue
- Science
- Published
- 2018-09-28
- Volume
- 361
- Issue
- 6409
- Pages
- 1380-1385
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC6571013
- OpenAlex
- W2889236955
- Cited by
- 938
- References
- 42
- Citations by year
- 2026: 102 · 2025: 109 · 2024: 113 · 2023: 132 · 2022: 125 · 2021: 156 · 2020: 117 · 2019: 74 · 2018: 9
- FWCI
- 31.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (DGE-1256082)
- National Institutes of Health (DP1HG007811, R01HG006283, DP2 HD088158, R35GM124704)
- Paul G. Allen Frontiers Foundation
- Howard Hughes Medical Institute
- W. M. Keck Foundation
- Dale. F. Frey Award for Breakthrough Scientists
- Alfred P. Sloan Foundation Research Fellowship
- National Heart, Lung, and Blood Institute (T32HL007828)
- 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 Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- chromatin accessibility, single-cell transcriptomics, combinatorial indexing, dexamethasone treatment, mouse kidney, cis-regulatory elements, gene expression regulation, single-cell multi-omics
- MeSH terms
- A549 Cells, Animals, Chromatin, Dexamethasone, Gene Expression Regulation, Humans, Kidney, Transcription, Genetic, Gene Expression Profiling, Genomics, NIH 3T3 Cells, Regulatory Elements, Transcriptional, Mice, HEK293 Cells, Single-Cell Analysis
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