Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level
Open accessPublished version of a preprint
Abstract
Spatial omics emerged as a new frontier of biological and biomedical research. Here, we present spatial-CUT&Tag for spatially resolved genome-wide profiling of histone modifications by combining in situ CUT&Tag chemistry, microfluidic deterministic barcoding, and next-generation sequencing. Spatially resolved chromatin states in mouse embryos revealed tissue-type-specific epigenetic regulations in concordance with ENCODE references and provide spatial information at tissue scale. Spatial-CUT&Tag revealed epigenetic control of the cortical layer development and spatial patterning of cell types determined by histone modification in mouse brain. Single-cell epigenomes can be derived in situ by identifying 20-micrometer pixels containing only one nucleus using immunofluorescence imaging. Spatial chromatin modification profiling in tissue may offer new opportunities to study epigenetic regulation, cell function, and fate decision in normal physiology and pathogenesis.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
DBiT-seq was the first reported spatial multi-omics technology to co-map tens of proteins and whole transcriptome.
“DBiT-seq was the first reported spatial multi-omics technology that demonstrated co-mapping of tens of proteins and with a whole transcriptome, and this method has further expanded to spatial epigenomics profiling of chromatin accessibility124 and histone modification125.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [125]
High chromatin silencing score indicates repressed gene expression because of the transcriptional repression function of H3K27me3.
“High CSS indicates repressed gene expression because of the transcriptional repression function of H3K27me3.”
From Spatial epigenome–transcriptome co-profiling of mammalian tissues (2023) · cites this paper as [2]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012) · OMICS: A Journal of Integrative Biology · doi:10.1089/omi.2011.0118 · Cited by 36,259 · 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
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174 · see citing works
Shrestha, S., & Offer, S. M. (2016) · Medical Epigenetics · doi:10.1159/000444713 · Cited by 8,949 · see citing works
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · see citing works
Genome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572 · see citing works
Self-citationThe GeneCards suite: from gene data mining to disease genome sequence analyses
Stelzer, G., Rosen, N., Plaschkes, I., Zimmerman, S., Twik, M., Fishilevich, S., Stein, T. I., Nudel, R., Lieder, I., Mazor, Y., et al. (2016) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.5 · Cited by 5,550 · see citing works
The single-cell transcriptional landscape of mammalian organogenesis
Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., et al. (2019) · Nature · doi:10.1038/s41586-019-0969-x · Cited by 4,490 · 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
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356 · see citing works
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
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
CUT&Tag for efficient epigenomic profiling of small samples and single cells
Kaya-Okur, H. S., Wu, S. J., Codomo, C. A., Pledger, E. S., Bryson, T. D., Henikoff, J. G., Ahmad, K., & Henikoff, S. (2019) · Nature Communications · doi:10.1038/s41467-019-09982-5 · Cited by 2,187 · see citing works
Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity
Weirauch, M. T., Yang, A. W. H., Albu, M., Coté, A. G., Montenegro‐Montero, A., Drewe, P., Najafabadi, H. S., Lambert, S. A., Mann, I. K., Cook, K. B., et al. (2014) · Cell · doi:10.1016/j.cell.2014.08.009 · Cited by 1,705 · 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
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
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
VISTA Enhancer Browser—a database of tissue-specific human enhancers
Visel, A., Minovitsky, S., Dubchak, I., & Pennacchio, L. A. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkl822 · Cited by 1,080 · see citing works
Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain
Lake, B. B., Chen, S., Sos, B. C., Fan, J., Kaeser, G. E., Yung, Y. C., Duong, T. E., Gao, D., Chun, J., Kharchenko, P. V., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4038 · Cited by 983 · 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
Self-citationSingle-cell ChIP-seq reveals cell subpopulations defined by chromatin state
Rotem, A., Ram, O., Shoresh, N., Sperling, R. A., Goren, A., Weitz, D. A., & Bernstein, B. E. (2015) · Nature Biotechnology · doi:10.1038/nbt.3383 · Cited by 854 · see citing works
Denisenko, E., Guo, B. B., Jones, M., Hou, R., de Kock, L., Lassmann, T., Poppe, D., Clément, O., Simmons, R. K., Lister, R., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-02048-6 · Cited by 686 · see citing works
Neural crest origins of the neck and shoulder
Matsuoka, T., Ahlberg, P., Kessaris, N., Iannarelli, P., Dennehy, U., Richardson, W. D., McMahon, A. P., & Koentges, G. (2005) · Nature · doi:10.1038/nature03837 · Cited by 525 · see citing works
Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues
Bartosovic, M., Kabbe, M., & Castelo-Branco, G. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00869-9 · Cited by 483 · see citing works
Self-citationHigh-throughput single-cell ChIP-seq identifies heterogeneity of chromatin states in breast cancer
Grosselin, K., Durand, A., Marsolier, J., Poitou, A., Marangoni, E., Nemati, F., Dahmani, A., Lameiras, S., Reyal, F., Frenoy, O., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0424-9 · Cited by 466 · see citing works
Combinatorial labeling of single cells for gene expression cytometry
Fan, H. C., Fu, G. K., & Fodor, S. P. A. (2015) · Science · doi:10.1126/science.1258367 · Cited by 455 · see citing works
Hirabayashi, Y., Suzki, N., Tsuboi, M., Endo, T. A., Toyoda, T., Shinga, J., Koseki, H., Vidal, M., & Gotoh, Y. (2009) · Neuron · doi:10.1016/j.neuron.2009.08.021 · Cited by 447 · see citing works
Pereira, J. D., Sansom, S. N., Smith, J. C., Dobenecker, M., Tarakhovsky, A., & Livesey, F. J. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1002530107 · Cited by 445 · 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
An atlas of dynamic chromatin landscapes in mouse fetal development
Gorkin, D. U., Barozzi, I., Zhao, Y., Zhang, Y., Huang, H., Lee, A. Y., Li, B., Chiou, J., Wildberg, A., Ding, B., et al. (2020) · Nature · doi:10.1038/s41586-020-2093-3 · Cited by 364 · see citing works
Characterization of Nkx6-2-Derived Neocortical Interneuron Lineages
Sousa, V. H., Miyoshi, G., Hjerling-Leffler, J., Karayannis, T., & Fishell, G. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhp038 · Cited by 328 · see citing works
Spatial transcriptomics coming of age
Burgess, D. J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0129-z · Cited by 323 · see citing works
The epigenetic basis of cellular heterogeneity
Carter, B. C., & Zhao, K. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-00300-0 · Cited by 314 · see citing works
Spatially resolved transcriptomics adds a new dimension to genomics
Larsson, L., Frisén, J., & Lundeberg, J. (2021) · Nature Methods · doi:10.1038/s41592-020-01038-7 · Cited by 313 · see citing works
Simultaneous profiling of transcriptome and DNA methylome from a single cell
Hu, Y., Huang, K., An, Q., Du, G., Hu, G., Xue, J., Zhu, X., Wang, C.-Y., Xue, Z., & Fan, G. (2016) · Genome Biology · doi:10.1186/s13059-016-0950-z · Cited by 300 · see citing works
Dura, B., Choi, J.-Y., Zhang, K., Damsky, W., Thakral, D., Bosenberg, M., Craft, J. E., & Fan, R. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1173 · Cited by 285 · see citing works
Self-citationSingle-cell epigenomics: techniques and emerging applications
Schwartzman, O., & Tanay, A. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3980 · Cited by 282 · see citing works
Embryo-scale, single-cell spatial transcriptomics
Srivatsan, S. R., Regier, M. C., Barkan, E. R., Franks, J. M., Packer, J. S., Grosjean, P., Duran, M., Saxton, S. H., Ladd, J. J., Spielmann, M., et al. (2021) · Science · doi:10.1126/science.abb9536 · Cited by 265 · see citing works
Lu, Y., Xue, Q., Eisele, M. R., Sulistijo, E., Brower, K., Han, L., Amir, E., Pe'er, D., Miller-Jensen, K., & Fan, R. (2015) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1416756112 · Cited by 246 · see citing works
Self-citationSingle-cell CUT&Tag analysis of chromatin modifications in differentiation and tumor progression
Wu, S. J., Furlan, S. N., Mihalas, A. B., Kaya-Okur, H. S., Feroze, A. H., Emerson, S. N., Zheng, Y., Carson, K., Cimino, P. J., Keene, C. D., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00865-z · Cited by 201 · see citing works
Experimental Considerations for Single-Cell RNA Sequencing Approaches
Nguyen, Q. H., Pervolarakis, N., Nee, K., & Kessenbrock, K. (2018) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2018.00108 · Cited by 170 · see citing works
Efficient chromatin accessibility mapping in situ by nucleosome-tethered tagmentation
Henikoff, S., Henikoff, J. G., Kaya-Okur, H. S., & Ahmad, K. (2020) · eLife · doi:10.7554/elife.63274 · Cited by 153 · 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
Scalable microfluidics for single-cell RNA printing and sequencing
Bose, S., Wan, Z., Carr, A., Rizvi, A. H., Vieira, G., Pe’er, D., & Sims, P. A. (2015) · Genome biology · doi:10.1186/s13059-015-0684-3 · Cited by 131 · see citing works
Shelley, B., Mandefro, B., Sareen, D., Svendsen, C. N., Milani, P., Escalante-Chong, R., Patel-Murray, N. L., Xin, X., Adam, M., & Fraenkel, E. (2016) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 69 · see citing works
Single-Cell Omics Analyses Enabled by Microchip Technologies
Deng, Y., Finck, A., & Fan, R. (2019) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-060418-052538 · Cited by 65 · see citing works
Self-citationArchR: An integrative and scalable software package for single-cell chromatin accessibility analysisPreprint
Granja, J. M., Corces, M. R., Pierce, S. E., et al. (2020) · bioRxiv · doi:10.1101/2020.04.28.066498 · Cited by 61 · see citing works
Han, L. G., Wu, H., Zhu, H., Kim, K., Marjani, S. L., Riester, M., Euskirchen, G. M., Zi, X., Yang, J., Han, J., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx026 · Cited by 53 · see citing works
Self-citationLuo, W., Lin, G. N., Song, W., Zhang, Y., Lai, H., Zhang, M., Miao, J., Cheng, X., Wang, Y., Li, W., et al. (2021) · BMC Biology · doi:10.1186/s12915-021-01071-8 · Cited by 44 · see citing works
Spatial transcriptome sequencing of FFPE tissues at cellular level
Liu, Y., Enninful, A., Deng, Y., & Fan, R. (2020) · doi:10.1101/2020.10.13.338475 · Cited by 26 · see citing works
Self-citationHigh-density spatial transcriptomics arrays for in situ tissue profilingPreprint
Vicković, S., Eraslan, G., Klughammer, J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/563338 · Cited by 25 · see citing works
Tn5 transposase-based epigenomic profiling methods are prone to open chromatin biasPreprint
Wang, M., & Zhang, Y. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.09.451758 · Cited by 25 · see citing works
Single-cell profiling of histone modifications in the mouse brainPreprint
Bartošovič, M., Kabbe, M., & Castelo‐Branco, G. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.02.279703 · Cited by 15 · see citing works
Self-citationMilani, P., Escalante-Chong, R., Shelley, B., Patel-Murray, N. L., Xin, X., Adam, M., Mandefro, B., Sareen, D., Svendsen, C. N., & Fraenkel, E. (2016) · Scientific Reports · doi:10.1038/srep25474 · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Exploring tissue architecture using spatial transcriptomics
Rao, A., Barkley, D., França, G. S., & Yanai, I. (2021) · Nature · doi:10.1038/s41586-021-03634-9 · Cited by 1,700 · 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
Histone post-translational modifications - cause and consequence of genome function
Millán-Zambrano, G., Burton, A., Bannister, A. J., & Schneider, R. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00468-7 · Cited by 1,116 · 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
Self-citationIntegrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics
Longo, S. K., Guo, M. G., Ji, A. L., & Khavari, P. A. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00370-8 · Cited by 1,059 · see citing works
Macrophage diversity in cancer revisited in the era of single-cell omics
Ma, R., Black, A., & Qian, B. (2022) · Trends in Immunology · doi:10.1016/j.it.2022.04.008 · Cited by 706 · see citing works
Bressan, D., Battistoni, G., & Hannon, G. J. (2023) · Science · doi:10.1126/science.abq4964 · Cited by 606 · see citing works
From bulk, single-cell to spatial RNA sequencing
Li, X., & Wang, C.-Y. (2021) · International Journal of Oral Science · doi:10.1038/s41368-021-00146-0 · Cited by 605 · see citing works
Spatial profiling technologies illuminate the tumor microenvironment
Elhanani, O., Ben-Uri, R., & Keren, L. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.01.010 · Cited by 526 · see citing works
Microscopic examination of spatial transcriptome using Seq-Scope
Cho, C.-S., Xi, J., Si, Y., Park, S.-R., Hsu, J.-E., Kim, M., Jun, G., Kang, H. M., & Lee, J. H. (2021) · Cell · doi:10.1016/j.cell.2021.05.010 · Cited by 520 · see citing works
Rational combinations of targeted cancer therapies: background, advances and challenges
Jin, H., Wang, L., & Bernards, R. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-022-00615-z · Cited by 503 · 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
Self-citationThe 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
Self-citationUnbiased spatial proteomics with single-cell resolution in tissues
Mund, A., Brunner, A.-D., & Mann, M. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.05.022 · Cited by 259 · 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
Identifying multicellular spatiotemporal organization of cells with SpaceFlow
Ren, H., Walker, B. L., Cang, Z., & Nie, Q. H. (2022) · Nature Communications · doi:10.1038/s41467-022-31739-w · Cited by 249 · see citing works
Spatiotemporal omics for biology and medicine
Liu, L., Chen, A., Li, Y., Mulder, J., Heyn, H., & Xu, X. (2024) · Cell · doi:10.1016/j.cell.2024.07.040 · Cited by 239 · see citing works
CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity
Varrone, M., Tavernari, D., Santamaria‐Martínez, A., Walsh, L. A., & Ciriello, G. (2023) · Nature Genetics · doi:10.1038/s41588-023-01588-4 · Cited by 210 · see citing works
Liu, X., Peng, T., Xu, M., Lin, S., Hu, B., Chu, T., Liu, B., Xu, Y., Ding, W., Li, L., et al. (2024) · Journal of Hematology & Oncology · doi:10.1186/s13045-024-01596-9 · Cited by 229 · see citing works
Spatial biology of cancer evolution
Seferbekova, Z., Lomakin, A., Yates, L. R., & Gerstung, M. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00553-x · Cited by 210 · see citing works
Spatially resolved epigenomic profiling of single cells in complex tissues
Lu, T., Ang, C. E., & Zhuang, X. (2022) · Cell · doi:10.1016/j.cell.2022.09.035 · Cited by 197 · see citing works
Cheng, M., Jiang, Y., Xu, J., Mentis, A. A., Wang, S., Zheng, H., Sahu, S. K., Liu, L., & Xu, X. (2023) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2023.03.011 · Cited by 190 · 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
Akhoundova, D., & Rubin, M. A. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.08.011 · Cited by 188 · see citing works
Yu, X., Liu, Z., & Sun, X. (2022) · Plant Communications · doi:10.1016/j.xplc.2022.100508 · Cited by 173 · see citing works
Epigenetic regulation of early human embryo development
Wilkinson, A. L., Zorzan, I., & Rugg‐Gunn, P. J. (2023) · Cell stem cell · doi:10.1016/j.stem.2023.09.010 · Cited by 160 · see citing works
High-throughput single-сell sequencing in cancer research
Jia, Q., Chu, H., Jin, Z., Long, H., & Zhu, B. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-00990-4 · Cited by 152 · see citing works
Spatial Transcriptomic Technologies
Chen, T.-Y., You, L., Hardillo, J. A., & Chien, M. (2023) · Cells · doi:10.3390/cells12162042 · Cited by 153 · see citing works
Spatial metatranscriptomics resolves host–bacteria–fungi interactomes
Saarenpää, S., Shalev, O., Ashkenazy, H., Carlos, V., Lundberg, D. S., Weigel, D., & Giacomello, S. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01979-2 · Cited by 167 · see citing works
Qu, Y., Wang, Z., Shen, Y., Lan, Y., Zhong, X., Luo, X., Yang, T., Zhang, M., Zuo, B., Zeng, T., et al. (2024) · Advanced Science · doi:10.1002/advs.202309290 · Cited by 144 · see citing works
Spatial mapping of cellular senescence: emerging challenges and opportunities
Gurkar, A. U., Gerencser, A. A., Mora, A. L., Nelson, A. C., Zhang, A. R., Lagnado, A. B., Enninful, A., Benz, C., Furman, D., Beaulieu, D., et al. (2023) · Nature Aging · doi:10.1038/s43587-023-00446-6 · Cited by 139 · see citing works
Self-citationSpatial omics: Navigating to the golden era of cancer research
Wu, Y. C., Cheng, Y., Wang, X., Fan, J., & Gao, Q. (2022) · Clinical and Translational Medicine · doi:10.1002/ctm2.696 · Cited by 133 · see citing works
Advancing CAR T cell therapy through the use of multidimensional omics data
Yang, J., Chen, Y., Jing, Y., Green, M. R., & Han, L. (2023) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-023-00729-2 · Cited by 128 · see citing works
Spatial omics technologies at multimodal and single cell/subcellular level
Park, J., Kim, J., Lewy, T., Rice, C. M., Elemento, O., Rendeiro, A. F., & Mason, C. E. (2022) · Genome biology · doi:10.1186/s13059-022-02824-6 · Cited by 125 · 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
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
Xia, P., & Du, J. (2025) · Molecular Biomedicine · doi:10.1186/s43556-025-00275-6 · Cited by 126 · see citing works
A Comparative Overview of Epigenomic Profiling Methods
Mehrmohamadi, M., Sepehri, M. H., Nazer, N., & Norouzi, M. R. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.714687 · Cited by 112 · 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
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
Multifactorial profiling of epigenetic landscapes at single-cell resolution using MulTI-Tag
Meers, M. P., Llagas, G., Janssens, D. H., Codomo, C. A., & Henikoff, S. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01522-9 · Cited by 103 · see citing works
MacDonald, W., Purcell, C., Pinho-Schwermann, M. P., Stubbs, N. M., Srinivasan, P. R., & El‐Deiry, W. S. (2025) · Cancers · doi:10.3390/cancers17030441 · Cited by 110 · see citing works
O’Connor, L. M., O’Connor, B. A., Lim, S. B., Zeng, J., & Lo, C. H. (2023) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2023.06.011 · Cited by 107 · see citing works
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
Park, H., Jo, S. H., Lee, R. H., Macks, C. P., Ku, T., Park, J., Lee, C. W., Hur, J. K., & Sohn, C. H. (2023) · Advanced Science · doi:10.1002/advs.202206939 · Cited by 98 · see citing works
Epigenomic heterogeneity as a source of tumour evolution
Laisné, M., Lupien, M., & Vallot, C. (2024) · Nature reviews. Cancer · doi:10.1038/s41568-024-00757-9 · Cited by 89 · see citing works
Histone variants and modifications during abiotic stress response
Nunez-Vazquez, R., Desvoyes, B., & Gutiérrez, C. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.984702 · Cited by 79 · see citing works
Single-cell omics: experimental workflow, data analyses and applications
Sun, F., Li, H., Sun, D., Fu, S., Gu, L., Shao, X., Wang, Q., Dong, X., Duan, B., Xing, F., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-023-2561-0 · Cited by 81 · see citing works
Spatial dynamics of brain development and neuroinflammation
Zhang, D., Kirby, L. A., Lin, Y., Wang, W., Song, M., Wang, L., Wang, L., Kanatani, S., Jimenez-Beristain, T., Dang, Y., et al. (2025) · Nature · doi:10.1038/s41586-025-09663-y · Cited by 123 · see citing works
Self-citationMicrofluidic Biochips for Single‐Cell Isolation and Single‐Cell Analysis of Multiomics and Exosomes
Wang, C., Qiu, J., Liu, M., Wang, Y., Yu, Y., Liu, H., Zhang, Y., & Han, L. G. (2024) · Advanced Science · doi:10.1002/advs.202401263 · Cited by 81 · see citing works
Interpreting non-coding disease-associated human variants using single-cell epigenomics
Gaulton, K. J., Preißl, S., & Ren, B. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00598-6 · Cited by 79 · see citing works
Multiplexed spatial mapping of chromatin features, transcriptome and proteins in tissues
Guo, P., Mao, L., Chen, Y., Lee, C. N., Cardilla, A., Li, M., Bartošovič, M., & Deng, Y. (2025) · Nature Methods · doi:10.1038/s41592-024-02576-0 · Cited by 71 · see citing works
Self-citationLee, Y. K., Lee, M., Shin, Y., Kim, K., & Kim, T. J. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26093949 · Cited by 75 · see citing works
Integrated Spatial Transcriptomic and Proteomic Analysis of Fresh Frozen Tissue Based on Stereo-seqPreprint
Liao, S., Heng, Y., Liu, W., Xiang, J., Ma, Y., Chen, L., Feng, X., Jia, D., Liang, D., Huang, C., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.28.538364 · Cited by 68 · see citing works
Recent advances in high-throughput single-cell transcriptomics and spatial transcriptomics
Shen, X., Zhao, Y., Wang, Z., & Shi, Q. (2022) · Lab on a Chip · doi:10.1039/d2lc00633b · Cited by 65 · see citing works
Advances and applications in single-cell and spatial genomics
Wang, J., Ye, F., Chai, H., Jiang, Y., Wang, T., Ran, X., Xia, Q., Xu, Z., Fu, Y., Zhang, G., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-024-2770-x · Cited by 69 · see citing works
A framework for ultra-low-input spatial tissue proteomics
Makhmut, A., Qin, D., Fritzsche, S., Nimo, J., König, J., & Coscia, F. (2023) · Cell Systems · doi:10.1016/j.cels.2023.10.003 · Cited by 67 · see citing works
Scalable and model-free detection of spatial patterns and colocalizationPreprint
Liu, Q., Hsu, C., & Shyr, Y. (2022) · Genome Research · doi:10.1101/gr.276851.122 · Cited by 64 · see citing works
Microfluidic Platforms for Real‐Time In Situ Monitoring of Biomarkers for Cellular Processes
Lou, C., Yang, H., Hou, Y., Huang, H., Qiu, J., Wang, C., Sang, Y., Liu, H., & Han, L. G. (2023) · Advanced Materials · doi:10.1002/adma.202307051 · Cited by 61 · see citing works
Cooperative integration of spatially resolved multi-omics data with COSMOS
Zhou, Y., Xiao, X., Dong, L., Tang, C., Xiao, G., & Xu, L. (2025) · Nature Communications · doi:10.1038/s41467-024-55204-y · Cited by 63 · see citing works
Jing, S., Wang, H.-Q., Lin, P., Yuan, J., Tang, Z., & Li, H. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00857-1 · Cited by 63 · see citing works
Integrated multi-omics with machine learning to uncover the intricacies of kidney disease
Liu, X., Shi, J., Jiao, Y., An, J., Tian, J., Yang, Y., & Zhuo, L. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae364 · Cited by 58 · see citing works
Mapping cancer biology in space: applications and perspectives on spatial omics for oncology
Lee, S., Kim, G., Lee, J., Lee, A. C., & Kwon, S. (2024) · Molecular Cancer · doi:10.1186/s12943-024-01941-z · Cited by 56 · see citing works
Cell-type deconvolution methods for spatial transcriptomics
Gaspard-Boulinc, L., Gortana, L., Walter, T., Barillot, E., & Cavalli, F. M. G. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00845-y · Cited by 54 · see citing works
Spatial transcriptomics using multiplexed deterministic barcoding in tissue
Wirth, J., Huber, N., Yin, K., Brood, S., Chang, S., Martinez‐Jimenez, C. P., & Meier, M. (2023) · Nature Communications · doi:10.1038/s41467-023-37111-w · Cited by 48 · see citing works
Cut&tag: a powerful epigenetic tool for chromatin profiling
Fu, Z., Jiang, S., Sun, Y., zheng, S., Zong, L., & Li, P. (2023) · Epigenetics · doi:10.1080/15592294.2023.2293411 · Cited by 51 · see citing works
Spatial integration of multi-omics single-cell data with SIMO
Yang, P., Jin, K., Yao, Y., Jin, L., Shao, X., Li, C., Lu, X., & Fan, X. (2025) · Nature Communications · doi:10.1038/s41467-025-56523-4 · Cited by 49 · see citing works
Microtechnologies for single-cell and spatial multi-omics
Deng, Y., Bai, Z., & Fan, R. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00084-y · Cited by 45 · see citing works
Self-citationZhang, D., Kirby, L. A., Lin, Y., Song, M., Wang, L., Wang, L., Kanatani, S., Jimenez-Beristain, T., Dang, Y., Zhong, M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.28.605493 · Cited by 45 · see citing works
Self-citationLi, H., Bao, S., Farzad, N., Qin, X., Fung, A. A., Zhang, D., Bai, Z., Tao, B., & Fan, R. (2025) · Nature Protocols · doi:10.1038/s41596-025-01145-9 · Cited by 43 · see citing works
Self-citationClinical challenges of tissue preparation for spatial transcriptome
Liu, X., Jiang, Y., Song, D., Zhang, L., Xu, G. S., Hou, R., Zhang, Y., Chen, J., Cheng, Y., Liu, L., et al. (2022) · Clinical and Translational Medicine · doi:10.1002/ctm2.669 · Cited by 41 · see citing works
Spotiphy enables single-cell spatial whole transcriptomics across an entire section
Yang, J., Zheng, Z., Jiao, Y., Yu, K., Bhatara, S., Yang, X., Natarajan, S., Zhang, J., Pan, Q., Easton, J. B., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02622-5 · Cited by 39 · see citing works
Genomic enhancers in cardiac development and disease
Anene-Nzelu, C. G., Lee, C. J. M., Tan, W. L. W., Dashi, A., & Foo, R. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-021-00597-2 · Cited by 39 · see citing works
Spatially Resolved Transcriptomics Technology Facilitates Cancer Research
Wang, Q., Zhi, Y., Zi, M., Mo, Y., Wang, Y., Liao, Q., Zhang, S., Gong, Z., Wang, F., Zeng, Z., et al. (2023) · Advanced Science · doi:10.1002/advs.202302558 · Cited by 36 · see citing works
From morphology to single-cell molecules: high-resolution 3D histology in biomedicine
Xu, X., Su, J., Zhu, R., Li, K., Zhao, X., Fan, J., & Mao, F. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02240-x · Cited by 41 · see citing works
Epigenomic analysis of formalin-fixed paraffin-embedded samples by CUT&Tag
Henikoff, S., Henikoff, J. G., Ahmad, K., Paranal, R. M., Janssens, D. H., Russell, Z. R., Szulzewsky, F., Kugel, S., & Holland, E. C. (2023) · Nature Communications · doi:10.1038/s41467-023-41666-z · Cited by 37 · see citing works
Bonev, B., Castelo‐Branco, G., Chen, F., Codeluppi, S., Corces, M. R., Fan, J., Heiman, M., Harris, K., Inoue, F., Kellis, M., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01806-0 · Cited by 39 · see citing works
Self-citationSANTO: a coarse-to-fine alignment and stitching method for spatial omics
Li, H., Lin, Y., He, W., Han, W., Xu, X., Xu, C., Gao, E., Zhao, H., & Gao, X. (2024) · Nature Communications · doi:10.1038/s41467-024-50308-x · Cited by 36 · see citing works
Spatially resolved transcriptomics: advances and applications
Duan, H., Cheng, T., & Cheng, H. (2022) · Blood Science · doi:10.1097/bs9.0000000000000141 · Cited by 34 · see citing works
Targeting the epigenome to reinvigorate T cells for cancer immunotherapy
Xiong, D., Zhang, L., & Sun, Z. (2023) · Military Medical Research · doi:10.1186/s40779-023-00496-2 · Cited by 35 · see citing works
Exploring the complexity of MECP2 function in Rett syndrome
Liu, Y., Whitfield, T. W., Bell, G. W., Guo, R., Flamier, A., Young, R. A., & Jaenisch, R. (2025) · Nature reviews. Neuroscience · doi:10.1038/s41583-025-00926-1 · Cited by 37 · see citing works
Self-citationEnninful, A., Zhang, Z., Klymyshyn, D., Ingalls, M. H., Yang, M., Zong, H., Bai, Z., Farzad, N., Su, G., Baysoy, A., et al. (2026) · Nature Methods · doi:10.1038/s41592-025-02948-0 · Cited by 39 · see citing works
Self-citationMulti-slice spatial transcriptome domain analysis with SpaDo
Duan, B., Chen, S., Cheng, X., & Liu, Q. (2024) · Genome biology · doi:10.1186/s13059-024-03213-x · Cited by 33 · see citing works
A practical guide to spatial transcriptomics
Valihrach, L., Žucha, D., Abaffy, P., & Kubista, M. (2024) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2024.101276 · Cited by 33 · see citing works
Spatial mapping of cancer tissues by OMICS technologies
Ahmed, R., Augustine, R., Valera, E., Ganguli, A., Mesaeli, N., Ahmad, I. S., Bashir, R., & Hasan, A. (2021) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2021.188663 · Cited by 32 · see citing works
Li, B., Bao, F., Hou, Y., Li, F., Li, H., Deng, Y. C., & Dai, Q. (2024) · Nature Communications · doi:10.1038/s41467-024-50837-5 · Cited by 32 · see citing works
Self-citationSpatial architecture of development and disease
Lázár, E., & Lundeberg, J. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00892-5 · Cited by 37 · see citing works
Unlocking T cell exhaustion: Insights and implications for CAR-T cell therapy
Xiong, D., Yu, H., & Sun, Z. (2024) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2024.04.022 · Cited by 31 · see citing works
Harnessing transposable elements for plant functional genomics and genome engineering.
Tao, X.-Y., Feng, S.-L., Yuan, L., Li, Y.-J., Li, X.-J., Guan, X.-Y., Chen, Z.-H., & Xu, S. (2025) · Trends in Plant Science · doi:10.1016/j.tplants.2025.03.007 · Cited by 28 · see citing works
Spatial omics enters the microscopic realm: opportunities and challenges
Si, Y., Lee, J. S., Jun, G., Kang, H. M., & Lee, J. H. (2025) · Trends in Genetics · doi:10.1016/j.tig.2025.05.002 · Cited by 31 · see citing works
Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine
Chu, L.-X., Wang, W.-J., Gu, X.-P., Wu, P., Gao, C., Zhang, Q., Wu, J., Jiang, D.-W., Huang, J.-Q., Ying, X.-W., et al. (2024) · Military Medical Research · doi:10.1186/s40779-024-00537-4 · Cited by 29 · see citing works
From tissue architecture to clinical insights: Spatial transcriptomics in solid tumor studies
Sharma, A., Raut, S. S., Shukla, A., Gupta, S., Mishra, A., & Singh, A. (2025) · Seminars in Oncology · doi:10.1016/j.seminoncol.2025.152389 · Cited by 29 · see citing works
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 · see citing works
Microfluidics-based single cell analysis: from transcriptomics to spatiotemporal multi-omics
Cao, J., Chen, X., Huang, S., Shi, W., Fan, Q., Gong, Y., Peng, Y., Wu, L., & Yang, C. J. (2022) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2022.116868 · Cited by 29 · see citing works
Epigenetics in obesity: Mechanisms and advances in therapies based on natural products
Chen, P., Wang, Y., Chen, F., & Zhou, B. (2024) · Pharmacology Research & Perspectives · doi:10.1002/prp2.1171 · Cited by 27 · see citing works
The sex-specific factor SOA controls dosage compensation in Anopheles mosquitoes
Kalita, A. I., Marois, É., Kozielska, M., Weissing, F. J., Jaouen, E., Möckel, M. M., Rühle, F., Butter, F., Basilicata, M. F., & Valsecchi, C. I. K. (2023) · Nature · doi:10.1038/s41586-023-06641-0 · Cited by 27 · see citing works
Wang, L., Xu, W., Zhang, S., Gundberg, G. C., Zheng, C. R., Wan, Z., Mustafina, K., Caliendo, F., Sandt, H., Kamm, R. D., et al. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01229-z · Cited by 26 · see citing works
Spatiotemporal Omics-Refining the landscape of precision medicine
Zhang, J., Yin, J., Heng, Y., Xie, K., Chen, A., Amit, I., Bian, X.-W., & Xu, X. (2022) · Life Medicine · doi:10.1093/lifemedi/lnac053 · Cited by 27 · see citing works
Are we there yet? Exploring astrocyte heterogeneity one cell at a time
O’Dea, M. R., & Hasel, P. (2024) · Glia · doi:10.1002/glia.24621 · Cited by 27 · see citing works
- Authors
- Deng, Y., Bartosovic, M., Kukanja, P., Zhang, D., Liu, Y., Su, G., Enninful, A., Bai, Z., Castelo-Branco, G., & Fan, R.
- Affiliations
- Yale Cancer Center (US)
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, 17177 Stockholm, Sweden.
- Karolinska Institutet (SE)
- Yale University (US)
- Corresponding author
- Fan, Rong
- ORCID
- Venue
- Science
- Published
- 2022-02-11
- Volume
- 375
- Issue
- 6581
- Pages
- 681-686
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC7612972
- OpenAlex
- W4211162540
- Cited by
- 393
- References
- 53
- Citations by year
- 2026: 91 · 2025: 92 · 2024: 71 · 2023: 80 · 2022: 48 · 2021: 10 · 2019: 1
- FWCI
- 23.76
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nd
- Locations
- Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Yale University
- Li Ka Shing Foundation
- Society for Immunotherapy of Cancer
- National Institutes of Health (1rf1mh128876-01, 2012-38215, UL1TR001863)
- Stand Up To Cancer
- National Institute of Mental Health (RF1 MH128876)
- National Cancer Institute (UH3CA257393, UG3CA257393, U54CA209992, R01CA245313)
- National Institute of Diabetes and Digestive and Kidney Diseases (U54DK106857)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- histone modifications, spatial omics, single-cell epigenomics, mouse embryo, immunofluorescence microscopy
- MeSH terms
- Epigenome, Animals, Brain, Cell Nucleus, Chromatin, Histones, Neurons, Histone Code, Microfluidics, Epigenesis, Genetic, Mice, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review