Methods and applications for single-cell and spatial multi-omics

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

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

Scenic: single-cell regulatory network inference and clustering.

Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520 · see citing works

Full-length RNA-seq from single cells using Smart-seq2

Picelli, S., Faridani, O. R., Björklund, Å. K., Winberg, G., Sagasser, S., & Sandberg, R. (2014) · Nature Protocols · doi:10.1038/nprot.2014.006 · Cited by 4,137 · see citing works

Visualization and analysis of gene expression in tissue sections by spatial transcriptomics

Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834 · 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

Spatially resolved, highly multiplexed RNA profiling in single cells

Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S., & Zhuang, X. (2015) · Science · doi:10.1126/science.aaa6090 · Cited by 2,969 · see citing works

Untitled work

(2012) · doi:10.1016/j.jmoldx.2011.08.002 · Cited by 2,567 · see citing works

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

Rodriques, S. G., Stickels, R. R., Goeva, A., Martin, C. A., Murray, E., Vanderburg, C. R., Welch, J., Chen, L. M., Chen, F., & Macosko, E. Z. (2019) · Science · doi:10.1126/science.aaw1219 · Cited by 2,424 · see citing works

The human cell atlas

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

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

Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays

Chen, A., Liao, S., Cheng, M., Ma, K., Wu, L., Lai, Y., Qiu, X., Yang, J., Xu, J., Hao, S., et al. (2022) · Cell · doi:10.1016/j.cell.2022.04.003 · Cited by 1,784 · see citing works

Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH

Eng, C.-H. L., Lawson, M., Zhu, Q., Dries, R., Koulena, N., Takei, Y., Yun, J., Cronin, C., Karp, C., Yuan, G.-C., et al. (2019) · Nature · doi:10.1038/s41586-019-1049-y · Cited by 1,754 · see citing works

The Technology and Biology of Single-Cell RNA Sequencing

Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C., & Teichmann, S. A. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.04.005 · Cited by 1,487 · see citing works

The Human Cell Atlas

Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., & al, E. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-162597 · Cited by 1,457 · see citing works

Benchmarking atlas-level data integration in single-cell genomics

Luecken, M. D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01336-8 · Cited by 1,438 · see citing works

ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis

Granja, J. M., Corces, M. R., Pierce, S. E., Bagdatli, S. T., Choudhry, H., Chang, H. Y., & Greenleaf, W. J. (2021) · Nature Genetics · doi:10.1038/s41588-021-00790-6 · Cited by 1,392 · see citing works

Single-cell genome sequencing: current state of the science

Gawad, C., Koh, W., & Quake, S. R. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2015.16 · Cited by 1,334 · see citing works

Cell2location maps fine-grained cell types in spatial transcriptomics

Kleshchevnikov, V., Shmatko, A., Dann, E., Aivazidis, A., King, H. W., Li, T., Elmentaite, R., Lomakin, A., Kedlian, V., Gayoso, A., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01139-4 · Cited by 1,279 · see citing works

Museum of spatial transcriptomics

Moses, L., & Pachter, L. (2022) · Nature Methods · doi:10.1038/s41592-022-01409-2 · Cited by 1,255 · 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

High-definition spatial transcriptomics for in situ tissue profiling

Vickovic, S., Eraslan, G., Salmén, F., Klughammer, J., Stenbeck, L., Schapiro, D., Äijö, T., Bonneau, R., Bergenstråhle, L., Navarro, J. F., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0548-y · Cited by 1,150 · see citing works

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

Ma, S., Zhang, B., LaFave, L. M., Earl, A. S., Chiang, Z., Hu, Y., Ding, J., Brack, A., Kartha, V. K., Tay, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.056 · Cited by 1,146 · see citing works

Squidpy: a scalable framework for spatial omics analysis

Palla, G., Spitzer, H., Klein, M., Fischer, D. S., Schaar, A. C., Kuemmerle, L. B., Rybakov, S., Ibarra, I. L., Holmberg, O., Virshup, I., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01358-2 · Cited by 1,125 · see citing works

Pooled CRISPR screening with single-cell transcriptome readout.

Datlinger, P., Rendeiro, A. F., Schmidl, C., Krausgruber, T., Traxler, P., Klughammer, J., Schuster, L. C., Kuchler, A., Alpar, D., & Bock, C. (2017) · Nature Methods · doi:10.1038/nmeth.4177 · Cited by 1,096 · see citing works

MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data

Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J. C., & Stegle, O. (2020) · Genome Biology · doi:10.1186/s13059-020-02015-1 · Cited by 1,091 · see citing works

Multiplex digital spatial profiling of proteins and RNA in fixed tissue

Merritt, C. R., Ong, G. T., Church, S. E., Barker, K., Danaher, P., Geiss, G., Hoang, M., Jung, J., Liang, Y., McKay-Fleisch, J., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0472-9 · Cited by 1,072 · see citing works

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches

Guilliams, M., Bonnardel, J., Haest, B., Vanderborght, B., Wagner, C., Remmerie, A., Bujko, A., Martens, L., Thoné, T., Browaeys, R., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.018 · Cited by 985 · see citing works

Highly Multiplexed Subcellular RNA Sequencing in Situ

Lee, J. H., Daugharthy, E. R., Scheiman, J., Kalhor, R., Yang, J. L., Ferrante, T. C., Terry, R., Jeanty, S. S. F., Li, C., Amamoto, R., et al. (2014) · Science · doi:10.1126/science.1250212 · Cited by 973 · see citing works

High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue

Liu, Y., Yang, M., Deng, Y., Su, G., Enninful, A., Guo, C. C., Tebaldi, T., Zhang, D., Kim, D., Bai, Z., et al. (2020) · Cell · doi:10.1016/j.cell.2020.10.026 · Cited by 966 · 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

Exponential scaling of single-cell RNA-seq in the past decade

Svensson, V., Vento‐Tormo, R., & Teichmann, S. A. (2018) · Nature Protocols · doi:10.1038/nprot.2017.149 · Cited by 936 · see citing works

In situ sequencing for RNA analysis in preserved tissue and cells

Ke, R., Mignardi, M., Pacureanu, A., Svedlund, J., Botling, J., Wählby, C., & Nilsson, M. (2013) · Nature Methods · doi:10.1038/nmeth.2563 · Cited by 934 · 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

High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis

Janesick, A., Shelansky, R. I., Gottscho, A. D., Wagner, F., Williams, S. R., Rouault, M., Beliakoff, G., Morrison, C. A., Oliveira, M. F., Sicherman, J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43458-x · Cited by 909 · see citing works

Multiplexed quantification of proteins and transcripts in single cells

Peterson, V. M., Zhang, K. X., Kumar, N., Wong, J., Li, L., Wilson, D. C., Moore, R., McClanahan, T. K., Sadekova, S., & Klappenbach, J. A. (2017) · Nature Biotechnology · doi:10.1038/nbt.3973 · Cited by 832 · see citing works

High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging

He, S., Bhatt, R., Brown, C., Brown, E. A., Buhr, D. L., Chantranuvatana, K., Danaher, P., Dunaway, D., Garrison, R. G., Geiss, G., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01483-z · Cited by 814 · 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

Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq

Jaitin, D. A., Weiner, A., Yofe, I., Lara-Astiaso, D., Keren-Shaul, H., David, E., Salame, T. M., Tanay, A., van Oudenaarden, A., & Amit, I. (2016) · Cell · doi:10.1016/j.cell.2016.11.039 · Cited by 777 · see citing works

Toward Minimal Residual Disease-Directed Therapy in Melanoma

Rambow, F., Rogiers, A., Marin-Bejar, O., Aibar, S., Femel, J., Dewaele, M., Karras, P., Brown, D., Chang, Y. H., Debiec-Rychter, M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.025 · Cited by 771 · see citing works

Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression

Xia, C., Fan, J., Emanuel, G., Hao, J., & Zhuang, X. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912459116 · Cited by 755 · see citing works

Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq

Replogle, J. M., Saunders, R. A., Pogson, A. N., Hussmann, J. A., Lenail, A., Guna, A., Mascibroda, L., Wagner, E. J., Adelman, K., Lithwick-Yanai, G., et al. (2022) · Cell · doi:10.1016/j.cell.2022.05.013 · Cited by 751 · cited 1× · see citing works

G&T-seq: parallel sequencing of single-cell genomes and transcriptomes

Macaulay, I. C., Haerty, W., Kumar, P., Li, Y. I., Hu, T. X., Teng, M. J., Goolam, M., Saurat, N., Coupland, P., Shirley, L. M., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3370 · Cited by 750 · see citing works

SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes

Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I., & Heyn, H. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab043 · Cited by 732 · see citing works

Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity

Angermueller, C., Clark, S. J., Lee, H. J., Macaulay, I. C., Teng, M. J., Hu, T. X., Krueger, F., Smallwood, S. A., Ponting, C. P., Voet, T., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3728 · Cited by 717 · see citing works

The epitranscriptome beyond m6A

Wiener, D., & Schwartz, S. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-00295-8 · Cited by 717 · see citing works

Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma

Ravi, V. M., Will, P., Kueckelhaus, J., Sun, N., Joseph, K., Salié, H., Vollmer, L., Kuliešiūtė, U., von Ehr, J., Benotmane, J. K., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.05.009 · Cited by 678 · see citing works

CODEX multiplexed tissue imaging with DNA-conjugated antibodies

Black, S. A., Phillips, D. J., Hickey, J. W., Kennedy‐Darling, J., Venkataraaman, V. G., Samusik, N., Goltsev, Y., Schürch, C. M., & Nolan, G. P. (2021) · Nature Protocols · doi:10.1038/s41596-021-00556-8 · Cited by 668 · see citing works

Spatially resolved transcriptomes—next generation tools for tissue exploration

Asp, M., Bergenstråhle, J., & Lundeberg, J. (2020) · BioEssays · doi:10.1002/bies.201900221 · Cited by 666 · see citing works

scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells

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

Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas

Hou, Y., Guo, H., Cao, C., Li, X., Hu, B., Zhu, P., Wu, X., Wen, L., Tang, F., Huang, Y., et al. (2016) · Cell Research · doi:10.1038/cr.2016.23 · Cited by 647 · see citing works

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

Spatial proteomics: a powerful discovery tool for cell biology

Lundberg, E., & Borner, G. H. H. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-018-0094-y · Cited by 591 · see citing works

Genome-Scale Imaging of the 3D Organization and Transcriptional Activity of Chromatin

Su, J.-H., Zheng, P., Kinrot, S. S., Bintu, B., & Zhuang, X. (2020) · Cell · doi:10.1016/j.cell.2020.07.032 · Cited by 562 · see citing works

Design and Analysis of Single-Cell Sequencing Experiments

Grün, D., & van Oudenaarden, A. (2015) · Cell · doi:10.1016/j.cell.2015.10.039 · Cited by 536 · see citing works

Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues

Lee, J. H., Daugharthy, E. R., Scheiman, J., Kalhor, R., Ferrante, T. C., Terry, R., Turczyk, B. M., Yang, J. L., Lee, H. S., Aach, J., et al. (2015) · Nature Protocols · doi:10.1038/nprot.2014.191 · Cited by 529 · 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

Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells

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

Enzymatic methyl sequencing detects DNA methylation at single-base resolution from picograms of DNAPreprint

Vaisvila, R., Ponnaluri, V. K. C., Sun, Z., Langhorst, B. W., Saleh, L., Guan, S., Dai, N., Campbell, M. A., Sexton, B. S., Marks, K., et al. (2021) · Genome Research · doi:10.1101/gr.266551.120 · Cited by 520 · see citing works

A test metric for assessing single-cell RNA-seq batch correction

Büttner, M., Miao, Z., Wolf, F. A., Teichmann, S. A., & Theis, F. J. (2019) · Nature Methods · doi:10.1038/s41592-018-0254-1 · Cited by 511 · 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

Immuno-SABER enables highly multiplexed and amplified protein imaging in tissues

Saka, S. K., Wang, Y., Kishi, J. Y., Zhu, A., Zeng, Y., Xie, W., Kırlı, K., Yapp, C., Cicconet, M., Beliveau, B. J., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0207-y · Cited by 485 · see citing works

Highly multiplexed <i>in situ</i> protein imaging with signal amplification by Immuno-SABER

Saka, S. K., Wang, Y., Kishi, J. Y., Zhu, A., Zeng, Y., Xie, W., Kirli, K., Yapp, C., Cicconet, M., Beliveau, B. J., et al. (2018) · doi:10.1101/507566 · Cited by 485 · 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

Bisulfite-free direct detection of 5-methylcytosine and 5-hydroxymethylcytosine at base resolution

Liu, Y., Siejka-Zielińska, P., Velikova, G., Bi, Y., Yuan, F., Tomková, M., Bai, C., Chen, L., Schuster‐Böckler, B., & Song, C. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0041-2 · Cited by 474 · 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

Multi-omics profiling of mouse gastrulation at single-cell resolution

Argelaguet, R., Clark, S. J., Mohammed, H., Stapel, L. C., Krueger, C., Kapourani, C.-A., Imaz-Rosshandler, I., Lohoff, T., Xiang, Y., Hanna, C. W., et al. (2019) · Nature · doi:10.1038/s41586-019-1825-8 · Cited by 450 · see citing works

Visualizing DNA folding and RNA in embryos at single-cell resolution

Mateo, L. J., Murphy, S. E., Hafner, A., Cinquini, I. S., Walker, C. A., & Boettiger, A. N. (2019) · Nature · doi:10.1038/s41586-019-1035-4 · Cited by 446 · see citing works

The emerging landscape of spatial profiling technologies

Moffitt, J. R., Lundberg, E., & Heyn, H. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00515-3 · Cited by 419 · see citing works

Spatial profiling of chromatin accessibility in mouse and human tissues

Deng, Y., Bartosovic, M., Ma, S., Zhang, D., Kukanja, P., Xiao, Y., Su, G., Liu, Y., Qin, X., Rosoklija, G. B., et al. (2022) · Nature · doi:10.1038/s41586-022-05094-1 · Cited by 417 · see citing works

Expansion sequencing: Spatially precise in situ transcriptomics in intact biological systems

Alon, S., Goodwin, D., Sinha, A., Wassie, A. T., Chen, F., Daugharthy, E. R., Bando, Y., Kajita, A., Xue, A. G., Marrett, K., et al. (2021) · Science · doi:10.1126/science.aax2656 · Cited by 413 · see citing works

Integrated spatial genomics reveals global architecture of single nuclei

Takei, Y., Yun, J., Zheng, S., Ollikainen, N., Pierson, N. G., White, J. A., Shah, S., Thomassie, J., Suo, S., Eng, C.-H. L., et al. (2021) · Nature · doi:10.1038/s41586-020-03126-2 · Cited by 399 · see citing works

Spatial-CUT&amp;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

Genome editing reveals a role for OCT4 in human embryogenesis

Fogarty, N. M. E., McCarthy, A., Snijders, K. E., Powell, B. E., Kubikova, N., Blakeley, P., Lea, R., Elder, K., Wamaitha, S. E., Kim, D., et al. (2017) · Nature · doi:10.1038/nature24033 · Cited by 392 · see citing works

Exploring genetic interaction manifolds constructed from rich single-cell phenotypes

Norman, T. M., Horlbeck, M. A., Replogle, J. M., Ge, A. Y., Xu, A., Jost, M., Gilbert, L. A., & Weissman, J. S. (2019) · Science · doi:10.1126/science.aax4438 · Cited by 392 · see citing works

Single Cell Genomics: Advances and Future Perspectives

Macaulay, I. C., & Voet, T. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004126 · Cited by 384 · 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 &amp; Molecular Biology · doi:10.1038/s41594-019-0323-x · Cited by 344 · see citing works

Simultaneous profiling of 3D genome structure and DNA methylation in single human cells

Lee, D.-S., Luo, C., Zhou, J., Chandran, S., Rivkin, A., Bartlett, A., Nery, J. R., Fitzpatrick, C., O’Connor, C., Dixon, J. R., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0547-z · Cited by 340 · see citing works

Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression

Cuomo, A. S. E., Seaton, D. D., McCarthy, D. J., Martinez, I., Bonder, M. J., Garcia-Bernardo, J., Amatya, S., Madrigal, P., Isaacson, A., Buettner, F., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14457-z · Cited by 331 · see citing works

Single-cell multiomics sequencing and analyses of human colorectal cancer

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Dynamics and Spatial Genomics of the Nascent Transcriptome by Intron seqFISH

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