Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin

R: A Language and Environment for Statistical Computing

R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works

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 · see citing works

Fast gapped-read alignment with Bowtie 2

Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400 · see citing works

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 · see citing works

featureCounts: an efficient general purpose program for assigning sequence reads to genomic features

Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219 · see citing works

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 · see citing works

Comprehensive Integration of Single-Cell Data

Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453 · see citing works

Simple Combinations of Lineage-Determining Transcription Factors Prime cis-Regulatory Elements Required for Macrophage and B Cell Identities

Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y. C., Laslo, P., Cheng, J. X., Murre, C., Singh, H., & Glass, C. K. (2010) · Molecular Cell · doi:10.1016/j.molcel.2010.05.004 · Cited by 13,382 · see citing works

UMAP: Uniform Manifold Approximation and Projection

McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826 · see citing works

Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position

Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013) · Nature Methods · doi:10.1038/nmeth.2688 · Cited by 6,422 · see citing works

Wnt/β-Catenin Signaling in Development and Disease

Clevers, H. (2006) · Cell · doi:10.1016/j.cell.2006.10.018 · Cited by 5,568 · see citing works

A bivalent chromatin structure marks key developmental genes in embryonic stem cells

Bernstein, B. E., Mikkelsen, T. S., Xie, X., Kamal, M., Huebert, D. J., Cuff, J., Fry, B., Meissner, A., Wernig, M., Plath, K., et al. (2006) · Cell · doi:10.1016/j.cell.2006.02.041 · Cited by 5,361 · see citing works

Master 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

RNA velocity of single cells

La Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., Lidschreiber, K., Kastriti, M. E., Lönnerberg, P., Furlan, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0414-6 · Cited by 4,118 · see citing works

Super-enhancers in the control of cell identity and disease

Hnisz, D., Abraham, B. J., Lee, T. I., Lau, A., Saint-André, V., Sigova, A. A., Hoke, H. A., & Young, R. A. (2013) · Cell · doi:10.1016/j.cell.2013.09.053 · Cited by 3,662 · see citing works

Simultaneous epitope and transcriptome measurement in single cells

Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., Satija, R., & Smibert, P. (2017) · Nature Methods · doi:10.1038/nmeth.4380 · Cited by 3,098 · see citing works

Molecular Architecture of the Mouse Nervous System

Zeisel, A., Hochgerner, H., Lönnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Häring, M., Braun, E., Borm, L. E., La Manno, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.021 · Cited by 2,995 · see citing works

An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues

Corces, M. R., Trevino, A. E., Hamilton, E. G., Greenside, P. G., Sinnott-Armstrong, N. A., Vesuna, S., Satpathy, A. T., Rubin, A. J., Montine, K. S., Wu, B., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4396 · Cited by 2,496 · see citing works

Single-cell chromatin accessibility reveals principles of regulatory variation

Buenrostro, J. D., Wu, B., Litzenburger, U. M., Ruff, D., Gonzales, M. L., Snyder, M. P., Chang, H. Y., & Greenleaf, W. J. (2015) · Nature · doi:10.1038/nature14590 · Cited by 2,308 · see citing works

A unique chromatin signature uncovers early developmental enhancers in humans

Rada-Iglesias, A., Bajpai, R., Swigut, T., Brugmann, S. A., Flynn, R. A., & Wysocka, J. (2011) · Nature · doi:10.1038/nature09692 · Cited by 2,233 · see citing works

Transcription factors: from enhancer binding to developmental control

Spitz, F., & Furlong, E. E. M. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3207 · Cited by 2,227 · see citing works

UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracyPreprint

Smith, T. S., Heger, A., & Sudbery, I. M. (2017) · Genome Research · doi:10.1101/gr.209601.116 · Cited by 2,072 · see citing works

Similarity network fusion for aggregating data types on a genomic scale

Wang, B., Mezlini, A. M., Demir, F., Fiume, M., Tu, Z., Brudno, M., Haibe-Kains, B., & Goldenberg, A. (2014) · Nature Methods · doi:10.1038/nmeth.2810 · Cited by 2,036 · see citing works

Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens

Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C. P., Jerby-Arnon, L., Marjanovic, N. D., Dionne, D., Burks, T., Raychowdhury, R., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.038 · Cited by 1,863 · see citing works

Molecular diversity and specializations among the cells of the adult mouse brain

Saunders, A., Macosko, E. Z., Wysoker, A., Goldman, M., Krienen, F. M., de Rivera, H., Bien, E., Baum, M., Bortolin, L., Wang, S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.07.028 · Cited by 1,847 · see citing works

A Phase Separation Model for Transcriptional Control

Hnisz, D., Shrinivas, K., Young, R. A., Chakraborty, A. K., & Sharp, P. A. (2017) · Cell · doi:10.1016/j.cell.2017.02.007 · Cited by 1,753 · see citing works

chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data

Schep, A. N., Wu, B., Buenrostro, J. D., & Greenleaf, W. J. (2017) · Nature Methods · doi:10.1038/nmeth.4401 · Cited by 1,680 · see citing works

Self-Renewal, Multipotency, and the Existence of Two Cell Populations within an Epithelial Stem Cell Niche

Blanpain, C., Lowry, W. E., Geoghegan, A., Polak, L., & Fuchs, E. (2004) · Cell · doi:10.1016/j.cell.2004.08.012 · Cited by 1,428 · see citing works

Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding

Rosenberg, A. B., Roco, C. M., Muscat, R. A., Kuchina, A., Sample, P., Yao, Z., Graybuck, L. T., Peeler, D. J., Mukherjee, S., Chen, W., et al. (2018) · Science · doi:10.1126/science.aam8999 · Cited by 1,422 · see citing works

Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing

Cusanovich, D. A., Daza, R., Adey, A., Pliner, H. A., Christiansen, L., Gunderson, K. L., Steemers, F. J., Trapnell, C., & Shendure, J. (2015) · Science · doi:10.1126/science.aab1601 · Cited by 1,248 · see citing works

Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma

Tirosh, I., Venteicher, A. S., Hebert, C. M., Escalante, L. E., Patel, A. P., Yizhak, K., Fisher, J. M., Rodman, C., Mount, C., Filbin, M. G., et al. (2016) · Nature · doi:10.1038/nature20123 · Cited by 1,216 · see citing works

Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity

Welch, J. D., Kozareva, V., Ferreira, A., Vanderburg, C., Martin, C., & Macosko, E. Z. (2019) · Cell · doi:10.1016/j.cell.2019.05.006 · Cited by 1,193 · see citing works

Long-range enhancer–promoter contacts in gene expression control

Schoenfelder, S., & Fraser, P. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0128-0 · Cited by 1,102 · see citing works

Massively parallel single-nucleus RNA-seq with DroNc-seq

Habib, N., Avraham-Davidi, I., Basu, A., Burks, T., Shekhar, K., Hofree, M., Choudhury, S. R., Aguet, F., Gelfand, E., Ardlie, K., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4407 · Cited by 1,069 · see citing works

Densely Interconnected Transcriptional Circuits Control Cell States in Human Hematopoiesis

Novershtern, N., Subramanian, A., Lawton, L. N., Mak, R. H., Haining, W. N., McConkey, M. E., Habib, N., Yosef, N., Chang, C. Y., Shay, T., et al. (2011) · Cell · doi:10.1016/j.cell.2011.01.004 · Cited by 942 · 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

Latent Enhancers Activated by Stimulation in Differentiated Cells

Ostuni, R., Piccolo, V., Barozzi, I., Polletti, S., Termanini, A., Bonifacio, S., Curina, A., Prosperini, E., Ghisletti, S., & Natoli, G. (2013) · Cell · doi:10.1016/j.cell.2012.12.018 · Cited by 867 · see citing works

Characterization of cell fate probabilities in single-cell data with Palantir

Setty, M., Kiseliovas, V., Levine, J., Gayoso, A., Mazutis, L., & Pe’er, D. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0068-4 · Cited by 833 · see citing works

Tn5 transposase and tagmentation procedures for massively scaled sequencing projectsPreprint

Picelli, S., Björklund, Å. K., Reinius, B., Sagasser, S., Winberg, G., & Sandberg, R. (2014) · Genome Research · doi:10.1101/gr.177881.114 · Cited by 829 · see citing works

High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell

Chen, S., Lake, B. B., & Zhang, K. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0290-0 · Cited by 810 · see citing works

Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data

Pliner, H. A., Packer, J. S., McFaline-Figueroa, J. L., Cusanovich, D. A., Daza, R. M., Aghamirzaie, D., Srivatsan, S., Qiu, X., Jackson, D., Minkina, A., et al. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.06.044 · Cited by 789 · see citing works

Chromatin state dynamics during blood formation

Lara-Astiaso, D., Weiner, A., Lorenzo-Vivas, E., Zaretsky, I., Jaitin, D. A., David, E., Keren-Shaul, H., Mildner, A., Winter, D., Jung, S., et al. (2014) · Science · doi:10.1126/science.1256271 · Cited by 776 · see citing works

Mouse Gli1 mutants are viable but have defects in SHH signaling in combination with a Gli2 mutation

Park, H. L., Bai, C., Platt, K. A., Matise, M. P., Beeghly, A., Hui, C., Nakashima, M., & Joyner, A. L. (2000) · Development · doi:10.1242/dev.127.8.1593 · Cited by 717 · see citing works

A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens

Gasperini, M., Hill, A. J., McFaline-Figueroa, J. L., Martin, B., Kim, S., Zhang, M. D., Jackson, D., Leith, A., Schreiber, J., Noble, W. S., et al. (2019) · Cell · doi:10.1016/j.cell.2018.11.029 · Cited by 613 · see citing works

Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons

Habib, N., Li, Y., Heidenreich, M., Swiech, L., Avraham-Davidi, I., Trombetta, J. J., Hession, C., Zhang, F., & Regev, A. (2016) · Science · doi:10.1126/science.aad7038 · Cited by 601 · see citing works

Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells

Rabani, M., Levin, J. Z., Fan, L., Adiconis, X., Raychowdhury, R., Garber, M., Gnirke, A., Nusbaum, C., Hacohen, N., Friedman, N., et al. (2011) · Nature Biotechnology · doi:10.1038/nbt.1861 · Cited by 583 · see citing works

Emerging interactions between skin stem cells and their niches

Hsu, Y., Li, L., & Fuchs, E. V. (2014) · Nature Medicine · doi:10.1038/nm.3643 · Cited by 563 · see citing works

Tcf3 and Lef1 regulate lineage differentiation of multipotent stem cells in skinPreprint

Merrill, B. J., Gat, U., DasGupta, R., & Fuchs, E. V. (2001) · Genes & Development · doi:10.1101/gad.891401 · Cited by 559 · see citing works

Hair follicle stem cells are specified and function in early skin morphogenesis

Nowak, J. A., Polak, L., Pasolli, H. A., & Fuchs, E. (2008) · Cell Stem Cell · doi:10.1016/j.stem.2008.05.009 · Cited by 554 · see citing works

Genomic encoding of transcriptional burst kinetics

Larsson, A. J. M., Johnsson, P., Hagemann-Jensen, M., Hartmanis, L., Faridani, O. R., Reinius, B., Segerstolpe, Å., Rivera, C. M., Ren, B., & Sandberg, R. (2019) · Nature · doi:10.1038/s41586-018-0836-1 · Cited by 534 · see citing works

The Transcriptional Repressor ZEB1 Promotes Metastasis and Loss of Cell Polarity in Cancer

Spaderna, S., Schmalhofer, O., Wahlbuhl, M., Dimmler, A. W., Bauer, K., Sultan, A., Hlubek, F., Jung, A., Strand, D. J., Eger, A., et al. (2008) · Cancer Research · doi:10.1158/0008-5472.can-07-5682 · Cited by 532 · see citing works

Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements

Mumbach, M. R., Satpathy, A. T., Boyle, E. A., Dai, C., Gowen, B. G., Cho, S. W., Nguyen, M. L., Rubin, A. J., Granja, J. M., Kazane, K. R., et al. (2017) · Nature Genetics · doi:10.1038/ng.3963 · Cited by 490 · see citing works

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice

Olsson, A., Venkatasubramanian, M., Chaudhri, V. K., Aronow, B. J., Salomonis, N., Singh, H., & Grimes, H. L. (2016) · Nature · doi:10.1038/nature19348 · Cited by 476 · see citing works

From single-cell to cell-pool transcriptomes: Stochasticity in gene expression and RNA splicing

Marinov, G. K., Williams, B. A., McCue, K., Schroth, G. P., Gertz, J., Myers, R. M., & Wold, B. J. (2014) · Genome Research · doi:10.1101/gr.161034.113 · Cited by 468 · see citing works

Locus control regions

Li, Q., Peterson, K. R., Fang, X., & Stamatoyannopoulos, G. (2002) · Blood · doi:10.1182/blood-2002-04-1104 · Cited by 456 · see citing works

cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data

Bravo González-Blas, C., Minnoye, L., Papasokrati, D., Aibar, S., Hulselmans, G., Christiaens, V., Davie, K., Wouters, J., & Aerts, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0367-1 · Cited by 456 · see citing works

Comprehensive analysis of single cell ATAC-seq data with SnapATAC

Fang, R., Preissl, S., Li, Y., Hou, X., Lucero, J., Wang, X., Motamedi, A., Shiau, A. K., Zhou, X., Xie, F., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21583-9 · Cited by 455 · see citing works

Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility

Lareau, C. A., Duarte, F. M., Chew, J. G., Kartha, V. K., Burkett, Z. D., Kohlway, A. S., Pokholok, D., Aryee, M. J., Steemers, F. J., Lebofsky, R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0147-6 · Cited by 444 · see citing works

Single-Cell Transcriptomics Reveals that Differentiation and Spatial Signatures Shape Epidermal and Hair Follicle Heterogeneity

Joost, S., Zeisel, A., Jacob, T., Sun, X., La Manno, G., Lönnerberg, P., Linnarsson, S., & Kasper, M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.08.010 · Cited by 429 · 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

Spatial organization within a niche as a determinant of stem-cell fate

Rompolas, P., Mesa, K. R., & Greco, V. (2013) · Nature · doi:10.1038/nature12602 · Cited by 398 · see citing works

Live imaging of stem cell and progeny behaviour in physiological hair-follicle regeneration

Rompolas, P., Deschene, E. R., Zito, G., Gonzalez, D. G., Saotome, I., Haberman, A. M., & Greco, V. (2012) · Nature · doi:10.1038/nature11218 · Cited by 379 · see citing works

Pioneer factors govern super-enhancer dynamics in stem cell plasticity and lineage choice

Adam, R. C., Yang, H., Rockowitz, S., Larsen, S. B., Nikolova, M., Oristian, D., Polak, L., Kadaja, M., Asare, A., Zheng, D., et al. (2015) · Nature · doi:10.1038/nature14289 · Cited by 376 · 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

Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation

Preissl, S., Fang, R., Huang, H., Zhao, Y., Raviram, R., Gorkin, D. U., Zhang, Y., Sos, B. C., Afzal, V., Dickel, D. E., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0079-3 · Cited by 332 · see citing works

γ-Secretase Functions through Notch Signaling to Maintain Skin Appendages but Is Not Required for Their Patterning or Initial Morphogenesis

Pan, Y., Lin, M., Tian, X., Cheng, H., Gridley, T., Shen, J., & Kopan, R. (2004) · Developmental Cell · doi:10.1016/j.devcel.2004.09.014 · Cited by 326 · see citing works

Sonic hedgehog-dependent activation of Gli2 is essential for embryonic hair follicle developmentPreprint

Mill, P., Mo, R., Fu, H. E., Grachtchouk, M. A., Kim, P. C. W., Dlugosz, A. A., & Hui, C. (2003) · Genes & Development · doi:10.1101/gad.1038103 · Cited by 326 · see citing works

The Molecular Anatomy of Mouse Skin during Hair Growth and Rest

Joost, S., Annusver, K., Jacob, T., Sun, X., Dalessandri, T., Sivan, U., Sequeira, I., Sandberg, R., & Kasper, M. (2020) · Cell stem cell · doi:10.1016/j.stem.2020.01.012 · Cited by 312 · see citing works

Sequencing thousands of single-cell genomes with combinatorial indexing

Vitak, S. A., Torkenczy, K. A., Rosenkrantz, J. L., Fields, A. J., Christiansen, L., Wong, M. H., Carbone, L., Steemers, F. J., & Adey, A. (2017) · Nature Methods · doi:10.1038/nmeth.4154 · Cited by 294 · see citing works

WNT Signaling in the Control of Hair Growth and Structure

Millar, S. E., Willert, K., Salinas, P. C., Roelink, H., Nusse, R., Sussman, D. J., & Barsh, G. S. (1999) · Developmental Biology · doi:10.1006/dbio.1998.9140 · Cited by 292 · see citing works

Hoxc13 mutant mice lack external hairPreprint

Godwin, A. R. F., & Capecchi, M. R. (1998) · Genes & Development · doi:10.1101/gad.12.1.11 · Cited by 252 · see citing works

Epithelial-Mesenchymal Micro-niches Govern Stem Cell Lineage Choices

Yang, H., Adam, R. C., Ge, Y., Hua, Z. L., & Fuchs, E. V. (2017) · Cell · doi:10.1016/j.cell.2017.03.038 · Cited by 249 · see citing works

Identity Noise and Adipogenic Traits Characterize Dermal Fibroblast Aging

Salzer, M. C., Lafzi, A., Berenguer, A., Youssif, C., Castellanos‐Martín, A., Solanas, G., Peixoto, F. O., Attolini, C. S., Prats, N., Aguilera, M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.10.012 · Cited by 243 · see citing works

Epigenomic State Transitions Characterize Tumor Progression in Mouse Lung Adenocarcinoma

LaFave, L. M., Kartha, V. K., Ma, S., Meli, K., Priore, I. S. D., Lareau, C. A., Naranjo, S., Westcott, P. M. K., Duarte, F. M., Sankar, V., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.06.006 · Cited by 207 · see citing works

Genome-wide Maps of Histone Modifications Unwind In Vivo Chromatin States of the Hair Follicle Lineage

Lien, W.-H., Guo, X., Polak, L., Lawton, L. N., Young, R. A., Zheng, D., & Fuchs, E. (2011) · Cell Stem Cell · doi:10.1016/j.stem.2011.07.015 · Cited by 199 · see citing works

Single-Cell Transcriptomics of Traced Epidermal and Hair Follicle Stem Cells Reveals Rapid Adaptations during Wound Healing

Joost, S., Jacob, T., Sun, X., Annusver, K., Manno, G. L., Sur, I., & Kasper, M. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.09.059 · Cited by 196 · see citing works

Homeostasis of protein and mRNA concentrations in growing cells

Lin, J., & Amir, A. (2018) · Nature Communications · doi:10.1038/s41467-018-06714-z · Cited by 180 · see citing works

BMP Signaling and Its pSMAD1/5 Target Genes Differentially Regulate Hair Follicle Stem Cell Lineages

Genander, M., Cook, P. J., Ramsköld, D., Keyes, B. E., Mertz, A. F., Sandberg, R., & Fuchs, E. V. (2014) · Cell stem cell · doi:10.1016/j.stem.2014.09.009 · Cited by 174 · see citing works

Hair follicle renewal: organization of stem cells in the matrix and the role of stereotyped lineages and behaviors

Legué, E., & Nicolas, J.-F. (2005) · Development · doi:10.1242/dev.01975 · Cited by 169 · see citing works

Single-cell and single-molecule epigenomics to uncover genome regulation at unprecedented resolution

Shema, E., Bernstein, B., & Buenrostro, J. D. (2018) · Nature Genetics · doi:10.1038/s41588-018-0290-x · Cited by 164 · see citing works

A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens

Gasperini, M. J., Hill, A. J., McFaline‐Figueroa, J. L., Martin, B., Kim, S., Zhang, M. D., Jackson, D., Leith, A., Schreiber, J. M., Noble, W. S., et al. (2019) · Cell · doi:10.1016/j.cell.2019.02.027 · Cited by 150 · see citing works

High-throughput chromatin accessibility profiling at single-cell resolution

Mezger, A., Klemm, S. L., Mann, I. K., Brower, K. K., Mir, A. A., Bostick, M. L., Farmer, A. A., Fordyce, P. M., Linnarsson, S., & Greenleaf, W. J. (2018) · Nature Communications · doi:10.1038/s41467-018-05887-x · Cited by 149 · see citing works

PRC1 Fine-tunes Gene Repression and Activation to Safeguard Skin Development and Stem Cell Specification

Cohen, I., Zhao, D., Bar, C., Valdés, V. J., Dauber-Decker, K. L., Nguyen, M. B., Nakayama, M., Rendl, M., Bickmore, W. A., Koseki, H., et al. (2018) · Cell stem cell · doi:10.1016/j.stem.2018.04.005 · Cited by 126 · see citing works

Systematic comparative analysis of single cell RNA-sequencing methodsPreprint

Ding, J., Adiconis, X., Simmons, S., Kowalczyk, M. S., Hession, C. C., Marjanovic, N. D., Hughes, T. K., Wadsworth, M. H., Burks, T., Nguyễn, L., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/632216 · Cited by 106 · see citing works

Hair follicles’ transit-amplifying cells govern concurrent dermal adipocyte production through Sonic HedgehogPreprint

Zhang, B., Tsai, P.-C., Gonzalez-Celeiro, M., Chung, O., Boumard, B., Perdigoto, C. N., Ezhkova, E., & Hsu, Y. (2016) · Genes & Development · doi:10.1101/gad.285429.116 · Cited by 97 · see citing works

Co-ChIP enables genome-wide mapping of histone mark co-occurrence at single-molecule resolution

Weiner, A., Lara‐Astiaso, D., Krupalnik, V., Gafni, O., David, E., Winter, D. R., Hanna, J. H., & Amit, I. (2016) · Nature Biotechnology · doi:10.1038/nbt.3652 · Cited by 88 · see citing works

Early enhancer establishment and regulatory locus complexity shape transcriptional programs in hematopoietic differentiation

González, A. J., Setty, M. N., & Leslie, C. S. (2015) · Nature Genetics · doi:10.1038/ng.3402 · Cited by 82 · see citing works

Single Cell and Open Chromatin Analysis Reveals Molecular Origin of Epidermal Cells of the Skin

Fan, X., Wang, D., Burgmaier, J. E., Teng, Y., Romano, R., Sinha, S., & Yi, R. (2018) · Developmental Cell · doi:10.1016/j.devcel.2018.08.010 · Cited by 68 · see citing works

Accurate loop calling for 3D genomic data with cLoops

Cao, Y., Chen, Z., Chen, X., Ai, D., Chen, G., McDermott, J., Huang, Y., Guo, X., & Han, J. J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz651 · Cited by 66 · see citing works

Transcription factor SOX‐5 enhances nasopharyngeal carcinoma progression by down‐regulating SPARC gene expression

Huang, D., Lin, Y., Jan, P. C., Hwang, Y., Liang, S., Peng, Y. G., Huang, C., Wu, H., & Lin, C. (2007) · The Journal of Pathology · doi:10.1002/path.2299 · Cited by 57 · see citing works

Emerging roles of transit-amplifying cells in tissue regeneration and cancer

Zhang, B., & Hsu, Y.-C. (2017) · Wiley Interdisciplinary Reviews: Developmental Biology · doi:10.1002/wdev.282 · Cited by 48 · see citing works

SnapATAC: A Comprehensive Analysis Package for Single Cell ATAC-seqPreprint

Fang, R.-X., Preissl, S., Li, Y., Hou, X.-M., Lucero, J., Wang, X.-X., Motamedi, A., Shiau, A., Zhou, X.-Z., Xie, F.-M., et al. (2019) · bioRxiv · doi:10.1101/615179 · Cited by 44 · see citing works

Flexible fate determination ensures robust differentiation in the hair follicle

Xin, T., Gonzalez, D. G., Rompolas, P., & Greco, V. (2018) · Nature Cell Biology · doi:10.1038/s41556-018-0232-y · Cited by 42 · see citing works

Single Cell and Open Chromatin Analysis Reveals Molecular Origin of Epidermal Cells of the Skin

Fan, X., Wang, D., Burgmaier, J. E., Teng, Y., Romano, R., Sinha, S., & Yi, R. (2018) · Developmental Cell · doi:10.1016/j.devcel.2018.09.019 · Cited by 17 · see citing works

Multi-omics single-cell analysis

Rusk, N. (2019) · Nature Methods · doi:10.1038/s41592-019-0519-3 · Cited by 7 · see citing works

Integrated analysis of multimodal single-cell data

Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508 · see citing works

Dictionary learning for integrative, multimodal and scalable single-cell analysis

Hao, Y., Stuart, T., Kowalski, M. H., Choudhary, S., Hoffman, P., Hartman, A., Srivastava, A., Molla, G., Madad, S., Fernandez-Granda, C., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01767-y · Cited by 5,631 · see citing works

Microglia states and nomenclature: A field at its crossroads

Paolicelli, R. C., Sierra, A., Stevens, B., Tremblay, M.-E., Aguzzi, A., Ajami, B., Amit, I., Audinat, E., Bechmann, I., Bennett, M., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.10.020 · Cited by 1,973 · see citing works

Single-cell chromatin state analysis with Signac

Stuart, T., Srivastava, A., Madad, S., Lareau, C. A., & Satija, R. (2021) · Nature Methods · doi:10.1038/s41592-021-01282-5 · Cited by 1,769 · 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

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

Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram

Biancalani, T., Scalia, G., Buffoni, L., Avasthi, R., Lu, Z., Sanger, A., Tokcan, N., Vanderburg, C. R., Segerstolpe, Å., Zhang, M., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01264-7 · Cited by 931 · see citing works

Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender

Fleming, S. J., Chaffin, M. D., Arduini, A., Akkad, A.-D., Banks, E., Marioni, J. C., Philippakis, A. A., Ellinor, P. T., & Babadi, M. (2023) · Nature Methods · doi:10.1038/s41592-023-01943-7 · Cited by 867 · see citing works

Differential abundance testing on single-cell data using k-nearest neighbor graphs

Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D., & Marioni, J. C. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01033-z · Cited by 938 · see citing works

SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks

Bravo González-Blas, C., De Winter, S., Hulselmans, G., Hecker, N., Matetovici, I., Christiaens, V., Poovathingal, S., Wouters, J., Aibar, S., & Aerts, S. (2023) · Nature Methods · doi:10.1038/s41592-023-01938-4 · Cited by 778 · see citing works

Large-scale foundation model on single-cell transcriptomics

Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Zhang, X., & Song, L. (2024) · Nature Methods · doi:10.1038/s41592-024-02305-7 · Cited by 628 · see citing works

Multi-omics single-cell data integration and regulatory inference with graph-linked embedding

Cao, Z.-J., & Gao, G. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01284-4 · Cited by 616 · see citing works

Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells

Mimitou, E. P., Lareau, C. A., Chen, K. Y., Zorzetto-Fernandes, A. L., Hao, Y., Takeshima, Y., Luo, W., Huang, T.-S., Yeung, B. Z., Papalexi, E., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00927-2 · Cited by 529 · see citing works

Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution

Trevino, A. E., Müller, F., Andersen, J., Sundaram, L., Kathiria, A., Shcherbina, A., Farh, K., Chang, H. Y., Pașca, A. M., Kundaje, A., et al. (2021) · Cell · doi:10.1016/j.cell.2021.07.039 · Cited by 478 · see citing works

Spatial epigenome–transcriptome co-profiling of mammalian tissues

Zhang, D., Deng, Y., Kukanja, P., Agirre, E., Bartosovic, M., Dong, M., Ma, C., Ma, S., Su, G., Bao, S., et al. (2023) · Nature · doi:10.1038/s41586-023-05795-1 · Cited by 483 · see citing works

Gene regulatory network inference in the era of single-cell multi-omics

Badia-i-Mompel, P., Wessels, L., Müller‐Dott, S., Trimbour, R., Flores, R. O. R., Argelaguet, R., & Sáez-Rodríguez, J. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00618-5 · Cited by 475 · see citing works

Computational 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

Molecular logic of cellular diversification in the mouse cerebral cortex

Di Bella, D. J., Habibi, E., Stickels, R. R., Scalia, G., Brown, J., Yadollahpour, P., Yang, S. M., Abbate, C., Biancalani, T., Macosko, E. Z., et al. (2021) · Nature · doi:10.1038/s41586-021-03670-5 · Cited by 464 · see citing works

Mapping transcriptomic vector fields of single cells

Qiu, X., Zhang, Y., Martin-Rufino, J. D., Weng, C., Hosseinzadeh, S., Yang, D., Pogson, A. N., Hein, M. Y., Min, K. H., Wang, L., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.045 · Cited by 436 · see citing works

Applications of single-cell RNA sequencing in drug discovery and development

de Sande, B. V., Lee, J. S., Mutasa-Gottgens, E., Naughton, B. J., Bacon, W., Manning, J. R., Wang, Y., Pollard, J. R., Mendez, M., Hill, J., et al. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00688-4 · Cited by 441 · see citing works

New opportunities and challenges of natural products research: When target identification meets single-cell multiomics

Zhu, Y., Ouyang, Z., Du, H., Wang, M., Wang, J., Sun, H., Kong, L., Xu, Q., Ma, H., & Sun, Y. (2022) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2022.08.022 · Cited by 422 · see citing works

Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function

Eraslan, G., Drokhlyansky, E., Anand, S. K., Fiškin, E., Subramanian, A., Slyper, M., Wang, J., Wittenberghe, N. V., Rouhana, J. M., Waldman, J., et al. (2022) · Science · doi:10.1126/science.abl4290 · Cited by 396 · see citing works

Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level

Deng, Y., Bartosovic, M., Kukanja, P., Zhang, D., Liu, Y., Su, G., Enninful, A., Bai, Z., Castelo-Branco, G., & Fan, R. (2022) · Science · doi:10.1126/science.abg7216 · Cited by 393 · see citing works

The Specious Art of Single-Cell Genomics

Chari, T., & Pachter, L. (2021) · doi:10.1101/2021.08.25.457696 · Cited by 379 · see citing works

Cis-regulatory sequences in plants: Their importance, discovery, and future challenges

Schmitz, R. J., Grotewold, E., & Stam, M. E. (2021) · The Plant Cell · doi:10.1093/plcell/koab281 · Cited by 358 · see citing works

Single-cell proteomics enabled by next-generation sequencing or mass spectrometry

Bennett, H. M., Stephenson, W., Rose, C. M., & Darmanis, S. (2023) · Nature Methods · doi:10.1038/s41592-023-01791-5 · Cited by 351 · see citing works

Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution

Yang, D., Jones, M. G., Naranjo, S., Rideout, W. M., Min, K. H., Ho, R., Wu, W., Replogle, J. M., Page, J. L., Quinn, J. J., et al. (2022) · Cell · doi:10.1016/j.cell.2022.04.015 · Cited by 333 · see citing works

Epigenetic regulation of T cell exhaustion

Belk, J. A., Dániel, B., & Satpathy, A. T. (2022) · Nature Immunology · doi:10.1038/s41590-022-01224-z · Cited by 334 · see citing works

Where Are the Disease-Associated eQTLs?

Umans, B. D., Battle, A., & Gilad, Y. (2021) · Trends in Genetics · doi:10.1016/j.tig.2020.08.009 · Cited by 296 · see citing works

Epigenetic memory of coronavirus infection in innate immune cells and their progenitors

Cheong, J.-G., Ravishankar, A., Sharma, S., Parkhurst, C. N., Grassmann, S. A., Wingert, C. K., Laurent, P., Ma, S., Paddock, L., Miranda, I. C., et al. (2023) · Cell · doi:10.1016/j.cell.2023.07.019 · Cited by 309 · see citing works

RNA velocity—current challenges and future perspectives

Bergen, V., Soldatov, R. A., Kharchenko, P. V., & Theis, F. J. (2021) · Molecular Systems Biology · doi:10.15252/msb.202110282 · Cited by 271 · see citing works

Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing

Datlinger, P., Rendeiro, A. F., Boenke, T., Senekowitsch, M., Krausgruber, T., Barreca, D., & Bock, C. (2021) · Nature Methods · doi:10.1038/s41592-021-01153-z · Cited by 261 · see citing works

Chromatin profiles classify castration-resistant prostate cancers suggesting therapeutic targets

Tang, F., Xu, D., Wang, S., Wong, C. K., Martinez‐Fundichely, A., Lee, C. J., Cohen, S. M., Park, J., Hill, C. E., Eng, K. W., et al. (2022) · Science · doi:10.1126/science.abe1505 · Cited by 261 · see citing works

Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq

Swanson, E., Lord, C., Reading, J., Heubeck, A. T., Genge, P. C., Thomson, Z., Weiss, M. D., Li, X.-J., Savage, A. K., Green, R. R., et al. (2021) · eLife · doi:10.7554/elife.63632 · Cited by 267 · see citing works

Deep learning-based approaches for multi-omics data integration and analysis

Ballard, J. L., Wang, Z., Li, W., Shen, L., & Long, Q. (2024) · BioData Mining · doi:10.1186/s13040-024-00391-z · Cited by 287 · see citing works

Epigenetic scars of CD8+ T cell exhaustion persist after cure of chronic infection in humans

Yates, K. B., Tonnerre, P., Martin, G. E., Gerdemann, U., Abosy, R. A., Comstock, D. E., Weiss, S. A., Wolski, D., Tully, D. C., Chung, R. T., et al. (2021) · Nature Immunology · doi:10.1038/s41590-021-00979-1 · Cited by 253 · see citing works

Characterizing cis-regulatory elements using single-cell epigenomics

Preissl, S., Gaulton, K. J., & Ren, B. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-022-00509-1 · Cited by 251 · see citing works

Functional inference of gene regulation using single-cell multi-omics

Kartha, V. K., Duarte, F. M., Hu, Y., Ma, S., Chew, J. G., Lareau, C. A., Earl, A., Burkett, Z. D., Kohlway, A. S., Lebofsky, R., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100166 · Cited by 224 · see citing works

Multi-omics integration in the age of million single-cell data

Miao, Z., Humphreys, B. D., McMahon, A. P., & Kim, J. (2021) · Nature Reviews Nephrology · doi:10.1038/s41581-021-00463-x · Cited by 249 · see citing works

Proper acquisition of cell class identity in organoids allows definition of fate specification programs of the human cerebral cortex

Uzquiano, A., Kedaigle, A. J., Pigoni, M., Paulsen, B. D. S., Adiconis, X., Kim, K., Faits, T., Nagaraja, S., Antón-Bolaños, N., Gerhardinger, C., et al. (2022) · Cell · doi:10.1016/j.cell.2022.09.010 · Cited by 250 · see citing works

A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell–Cell Communication

Cheng, C., Chen, W., Jin, H., & Chen, X. '. (2023) · Cells · doi:10.3390/cells12151970 · Cited by 237 · see citing works

Microfluidics-free single-cell genomics with templated emulsification

Clark, I. C., Fontanez, K. M., Meltzer, R. H., Xue, Y., Hayford, C. E., May-Zhang, A. A., D’Amato, C., Osman, A., Zhang, J. Q., Hettige, P., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01685-z · Cited by 225 · see citing works

A transcription factor atlas of directed differentiation

Joung, J., Ma, S., Tay, T., Geiger-Schuller, K., Kirchgatterer, P. C., Verdine, V. K., Guo, B., Arias-García, M. A., Allen, W. E., Singh, A., et al. (2023) · Cell · doi:10.1016/j.cell.2022.11.026 · Cited by 223 · see citing works

SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data

Persad, S., Choo, Z.-N., Dien, C., Sohail, N., Masilionis, I., Chaligné, R., Nawy, T., Brown, C. C., Sharma, R., Pe’er, I., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01716-9 · Cited by 222 · see citing works

Gene regulation by gonadal hormone receptors underlies brain sex differences

Gegenhuber, B., Wu, M. V., Bronstein, R., & Tollkuhn, J. (2022) · Nature · doi:10.1038/s41586-022-04686-1 · Cited by 221 · see citing works

Advances and Opportunities in Single-Cell Transcriptomics for Plant Research

Seyfferth, C., Renema, J., Wendrich, J. R., Eekhout, T., Seurinck, R., Vandamme, N., Blob, B., Saeys, Y., Helariutta, Y., Birnbaum, K. D., et al. (2021) · Annual Review of Plant Biology · doi:10.1146/annurev-arplant-081720-010120 · Cited by 216 · see citing works

The landscape of tumor cell states and spatial organization in H3-K27M mutant diffuse midline glioma across age and location

Liu, I., Jiang, L., Samuelsson, E. R., Salas, S. M., Beck, A., Hack, O. A., Jeong, D., Shaw, M. L., Englinger, B., LaBelle, J. J., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01236-3 · Cited by 213 · see citing works

Comprehensive single-cell transcriptional profiling defines shared and unique epithelial injury responses during kidney fibrosis

Li, H., Dixon, E. E., Wu, H., & Humphreys, B. D. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.09.026 · Cited by 211 · see citing works

Epigenetic regulation during cancer transitions across 11 tumour types

Terekhanova, N. V., Karpova, A. Y., Liang, W.-W., Strzalkowski, A. J., Chen, S., Li, Y., Southard-Smith, A. N., Iglesia, M. D., Wendl, M. C., Jayasinghe, R. G., et al. (2023) · Nature · doi:10.1038/s41586-023-06682-5 · Cited by 205 · see citing works

Chromatin accessibility profiling methods

Minnoye, L., Marinov, G. K., Krausgruber, T., Pan, L., Marand, A. P., Secchia, S., Greenleaf, W. J., Furlong, E. E. M., Zhao, K., Schmitz, R. J., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-020-00008-9 · Cited by 206 · see citing works

Dissecting the treatment-naive ecosystem of human melanoma brain metastasis

Biermann, J., Melms, J. C., Amin, A. D., Wang, Y., Caprio, L., Karz, A., Tagore, S., Barrera, I., Ibarra-Arellano, M. A., Andreatta, M., et al. (2022) · Cell · doi:10.1016/j.cell.2022.06.007 · Cited by 203 · see citing works

Multimodal single cell sequencing implicates chromatin accessibility and genetic background in diabetic kidney disease progression

Wilson, P. C., Muto, Y., Wu, H., Karihaloo, A. K., Waikar, S. S., & Humphreys, B. D. (2022) · Nature Communications · doi:10.1038/s41467-022-32972-z · Cited by 190 · see citing works

Human and mouse trigeminal ganglia cell atlas implicates multiple cell types in migraine

Yang, L., Xu, M., Bhuiyan, S. A., Li, J., Zhao, J., Cohrs, R. J., Susterich, J. T., Signorelli, S., Green, U., Stone, J. R., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.03.003 · Cited by 188 · see citing works

Advances 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

Statistical and machine learning methods for spatially resolved transcriptomics data analysis

Zeng, Z., Li, Y., Li, Y., & Luo, Y. (2022) · Genome Biology · doi:10.1186/s13059-022-02653-7 · Cited by 186 · see citing works

A fast, scalable and versatile tool for analysis of single-cell omics data

Zhang, K., Zemke, N. R., Armand, E. J., & Ren, B. (2024) · Nature Methods · doi:10.1038/s41592-023-02139-9 · Cited by 185 · see citing works

Enhancers in disease: molecular basis and emerging treatment strategies

Claringbould, A., & Zaugg, J. B. (2021) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2021.07.012 · Cited by 171 · see citing works

FOXA2 drives lineage plasticity and KIT pathway activation in neuroendocrine prostate cancer

Han, M., Li, F., Zhang, Y., Dai, P., He, J., Li, Y., Zhu, Y., Zheng, J., Huang, H., Bai, F., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.10.011 · Cited by 166 · see citing works

Linking genome structures to functions by simultaneous single-cell Hi-C and RNA-seq

Liu, Z., Chen, Y., Xia, Q., Liu, M., Xu, H., Chi, Y., Deng, Y., & Xing, D. (2023) · Science · doi:10.1126/science.adg3797 · Cited by 169 · see citing works

Single nucleus multi-omics identifies human cortical cell regulatory genome diversity

Luo, C., Liu, H., Xie, F., Armand, E. J., Siletti, K., Bakken, T. E., Fang, R., Doyle, W. I., Stuart, T., Hodge, R. D., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100107 · Cited by 167 · see citing works

Decoding gene regulation in the fly brain

Janssens, J., Aibar, S., Taskiran, I. I., Ismail, J. N., Gomez, A. E., Aughey, G., Spanier, K. I., De Rop, F. V., González-Blas, C. B., Dionne, M., et al. (2022) · Nature · doi:10.1038/s41586-021-04262-z · Cited by 170 · see citing works

Decoding gene regulation in the fly brain

Janssens, J., Aibar, S., Taskiran, I. I., Ismail, J. N., Spanier, K. I., Bravo González-Blas, C., Quan, X. J., Papasokrati, D., Hulselmans, G., Makhzami, S., et al. (2021) · doi:10.1101/2021.08.11.454937 · Cited by 170 · see citing works

Unsupervised removal of systematic background noise from droplet-based single-cell experiments using <tt>CellBender</tt>

Fleming, S. J., Chaffin, M. D., Arduini, A., Akkad, A.-D., Banks, E., Marioni, J. C., Philippakis, A. A., Ellinor, P. T., & Babadi, M. (2019) · doi:10.1101/791699 · Cited by 164 · see citing works

BABEL enables cross-modality translation between multiomic profiles at single-cell resolution

Wu, K., Yost, K. E., Chang, H. Y., & Zou, J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2023070118 · Cited by 164 · see citing works

The Human Cell Atlas from a cell census to a unified foundation model

Rood, J., Wynne, S., Robson, L., Hupalowska, A., Randell, J., Teichmann, S. A., & Regev, A. (2024) · Nature · doi:10.1038/s41586-024-08338-4 · Cited by 168 · see citing works

A mouse model with high clonal barcode diversity for joint lineage, transcriptomic, and epigenomic profiling in single cells

Li, L., Bowling, S., McGeary, S. E., Yu, Q., Lemke, B., Alcedo, K., Jia, Y., Liu, X., Ferreira, M., Klein, A. M., et al. (2023) · Cell · doi:10.1016/j.cell.2023.09.019 · Cited by 167 · see citing works

Single-nucleus chromatin accessibility profiling highlights regulatory mechanisms of coronary artery disease risk

Turner, A. W., Hu, S. S., Mosquera, J. V., Feng, W., Hodonsky, C. J., Wong, D., Auguste, G. E., Song, Y., Sol‐Church, K., Farber, E., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01069-0 · Cited by 151 · see citing works

Mapping cells through time and space with moscot

Klein, D., Palla, G., Lange, M., Klein, M., Piran, Z., Gander, M., Meng-Papaxanthos, L., Sterr, M., Saber, L., Jing, C., et al. (2025) · Nature · doi:10.1038/s41586-024-08453-2 · Cited by 202 · see citing works

Chromatin accessibility: methods, mechanisms, and biological insights

Mansisidor, A., & Risca, V. I. (2022) · Nucleus · doi:10.1080/19491034.2022.2143106 · Cited by 150 · see citing works

Multimodal single-cell chromatin analysis with SignacPreprint

Stuart, T., Srivastava, A., Lareau, C. A., & Satija, R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.09.373613 · Cited by 150 · see citing works

A mixture-of-experts deep generative model for integrated analysis of single-cell multiomics data

Minoura, K., Abe, K., Nam, H., Nishikawa, H., & Shimamura, T. (2021) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100071 · Cited by 147 · see citing works

Joint single-cell measurements of nuclear proteins and RNA in vivo

Chung, H., Parkhurst, C. N., Magee, E. M., Phillips, D., Habibi, E., Chen, F., Yeung, B. Z., Waldman, J., Artis, D., & Regev, A. (2021) · Nature Methods · doi:10.1038/s41592-021-01278-1 · Cited by 144 · see citing works

Recent advances in single-cell sequencing technologies

Wen, L., & Tang, F. (2022) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbac002 · Cited by 127 · see citing works

Multimodal chromatin profiling using nanobody-based single-cell CUT&amp;Tag

Bartosovic, M., & Castelo-Branco, G. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01535-4 · Cited by 146 · see citing works

A multi-omic single-cell landscape of human gynecologic malignancies

Regner, M. J., Wiśniewska, K., García‐Recio, S., Thennavan, A., Méndez-Giráldez, R., Malladi, V. S., Hawkins, G. M., Parker, J. S., Perou, C. M., Bae-Jump, V. L., et al. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.10.013 · Cited by 139 · see citing works

NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells

Chen, A. F., Parks, B., Kathiria, A. S., Ober-Reynolds, B., Goronzy, J. J., & Greenleaf, W. J. (2022) · Nature Methods · doi:10.1038/s41592-022-01461-y · Cited by 139 · see citing works

Gene regulatory networks controlling temporal patterning, neurogenesis, and cell-fate specification in mammalian retina

Lyu, P., Hoang, T. V., Santiago, C. P., Thomas, E. D., Timms, A. E., Appel, H., Gimmen, M. Y., Le, N., Jiang, L., Kim, D. W., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109994 · Cited by 137 · see citing works

Clustering of single-cell multi-omics data with a multimodal deep learning method

Lin, X., Tian, T., Wei, Z., & Hakonarson, H. (2022) · Nature Communications · doi:10.1038/s41467-022-35031-9 · Cited by 135 · see citing works

Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction

Li, C., Virgilio, M. C., Collins, K. L., & Welch, J. D. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01476-y · Cited by 130 · see citing works

Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse

Taavitsainen, S., Engedal, N., Cao, S., Handle, F., Erickson, A. M., Preković, S., Wetterskog, D., Tolonen, T. T., Vuorinen, E. M., Kiviaho, A., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25624-1 · Cited by 124 · see citing works

Towards multimodal foundation models in molecular cell biology

Cui, H., Tejada-Lapuerta, A., Brbić, M., Sáez-Rodríguez, J., Cristea, S., Goodarzi, H., Lotfollahi, M., Theis, F. J., & Wang, B. (2025) · Nature · doi:10.1038/s41586-025-08710-y · Cited by 131 · see citing works

Genetic and epigenetic coordination of cortical interneuron development

Allaway, K. C., Gabitto, M. I., Wapinski, O. L., Saldi, G.-A., Wang, C., Bandler, R. C., Wu, S. J., Bonneau, R., & Fishell, G. (2021) · Nature · doi:10.1038/s41586-021-03933-1 · Cited by 122 · see citing works

Simultaneous profiling of spatial gene expression and chromatin accessibility during mouse brain development

Jiang, F., Zhou, X., Qian, Y., Zhu, M., Wang, L., Li, Z., Shen, Q., Wang, M., Qu, F., Cui, G., et al. (2023) · Nature Methods · doi:10.1038/s41592-023-01884-1 · Cited by 127 · see citing works

Multi-omic profiling of the developing human cerebral cortex at the single-cell level

Zhu, K., Bendl, J. M., Rahman, S., Vicari, J. M., Coleman, C., Clarence, T., Latouche, O., Tsankova, N. M., Li, A., Brennand, K., et al. (2023) · Science Advances · doi:10.1126/sciadv.adg3754 · 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

ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells

Xu, W. Q., Yang, W., Zhang, Y., Chen, Y., Hong, N., Zhang, Q., Wang, X. J., Hu, Y., Song, K., Jin, W., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01601-4 · Cited by 115 · see citing works

Large Scale Foundation Model on Single-cell Transcriptomics

Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Song, L., & Zhang, X. (2023) · doi:10.1101/2023.05.29.542705 · Cited by 639 · see citing works

Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics

Wang, L., Trasanidis, N., Wu, T., Dong, G., Hu, M., Bauer, D. E., & Pinello, L. (2023) · Nature Methods · doi:10.1038/s41592-023-01971-3 · Cited by 117 · see citing works

Single cell cancer epigenetics

Casado-Peláez, M., Bueno-Costa, A., & Esteller, M. (2022) · Trends in cancer · doi:10.1016/j.trecan.2022.06.005 · Cited by 113 · 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

Integrative single-cell analysis of cardiogenesis identifies developmental trajectories and non-coding mutations in congenital heart disease

Ameen, M., Sundaram, L., Shen, M., Banerjee, A., Kundu, S., Nair, S., Shcherbina, A., Gu, M., Wilson, K. D., Varadarajan, A., et al. (2022) · Cell · doi:10.1016/j.cell.2022.11.028 · Cited by 109 · see citing works

Solid-phase capture and profiling of open chromatin by spatial ATAC

Llorens-Bobadilla, E., Zamboni, M., Marklund, M., Bhalla, N., Chen, X.-S., Hartman, J., Frisén, J., & Ståhl, P. L. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01603-9 · Cited by 94 · see citing works

SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks

González-Blas, C. B., De Winter, S., Hulselmans, G., Hecker, N., Matetovici, I., Christiaens, V., Poovathingal, S., Wouters, J., Aibar, S., & Aerts, S. (2022) · doi:10.1101/2022.08.19.504505 · Cited by 778 · see citing works

AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data

Thibodeau, A., Eroğlu, A., McGinnis, C. S., Lawlor, N. T., Nehar-Belaid, D., Kursawe, R., Marcheş, R., Conrad, D. N., Kuchel, G. A., Gartner, Z. J., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02469-x · Cited by 98 · see citing works

Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data

Kim, D. J., Tran, A., Kim, H. J., Lin, Y., Yang, J. Y. H., & Yang, P. (2023) · npj Systems Biology and Applications · doi:10.1038/s41540-023-00312-6 · Cited by 110 · see citing works

A deep generative model for multi-view profiling of single-cell RNA-seq and ATAC-seq data

Li, G., Fu, S., Wang, S., Zhu, C., Duan, B., Tang, C., Chen, X., Chuai, G., Wang, P. Q., & Liu, Q. (2022) · Genome biology · doi:10.1186/s13059-021-02595-6 · Cited by 100 · see citing works

Integrated analysis of multimodal single-cell data with structural similarity

Cao, Y., Fu, L., Wu, J., Peng, Q., Nie, Q. H., Zhang, J., & Xie, X. S. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac781 · Cited by 106 · see citing works

A generalization of t-SNE and UMAP to single-cell multimodal omics

Do, V. H., & Canzar, S. (2021) · Genome biology · doi:10.1186/s13059-021-02356-5 · Cited by 102 · see citing works

Single-cell multi-omics in cancer immunotherapy: from tumor heterogeneity to personalized precision treatment

Le, J., Dian, Y., Zhao, D., Guo, Z., Luo, Z., Chen, X., Zeng, F., & Deng, G. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02426-3 · Cited by 109 · see citing works

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer’s disease: review, recommendation, implementation and application

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

SHARE-seq reveals chromatin potential

Clyde, D. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-00308-6 · Cited by 11 · see citing works

Flexible and high-throughput simultaneous profiling of gene expression and chromatin accessibility in single cells

Soltys, V., Peters, M. A., Su, D., Kučka, M., & Chan, Y. F. (2026) · eLife · doi:10.7554/elife.110034.4

Simultaneous Single-Cell Profiling of the Transcriptome and Accessible Chromatin Using SHARE-seq

Kim, S. H., Marinov, G. K., Bagdatli, S. T., Higashino, S. I., Shipony, Z., Kundaje, A. B., & Greenleaf, W. J. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2899-7_11 · Cited by 9 · see citing works

ChromatinHD connects single-cell DNA accessibility and conformation to gene expression through scale-adaptive machine learning

Saelens, W., Pushkarev, O., & Deplancke, B. (2025) · Nature Communications · doi:10.1038/s41467-024-55447-9 · Cited by 9 · see citing works

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 47 · see citing works