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Cited by 154Epigenetics and DNA MethylationSingle-cell and spatial transcriptomicsGenomics and Chromatin Dynamics

Single-cell DNA methylation and 3D genome architecture in the human brain

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Tian, Wei, Zhou, Jingtian, Bartlett, Anna, Zeng, Qiurui, Liu, Hanqing, Castanon, Rosa G., Kenworthy, Mia, Altshul, Jordan +31 more·2023·Science
DOI 10.1126/science.adf5357PMC PMC10572106
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Abstract

Delineating the gene-regulatory programs underlying complex cell types is fundamental for understanding brain function in health and disease. Here, we comprehensively examined human brain cell epigenomes by probing DNA methylation and chromatin conformation at single-cell resolution in 517 thousand cells (399 thousand neurons and 118 thousand non-neurons) from 46 regions of three adult male brains. We identified 188 cell types and characterized their molecular signatures. Integrative analyses revealed concordant changes in DNA methylation, chromatin accessibility, chromatin organization, and gene expression across cell types, cortical areas, and basal ganglia structures. We further developed single-cell methylation barcodes that reliably predict brain cell types using the methylation status of select genomic sites. This multimodal epigenomic brain cell atlas provides new insights into the complexity of cell-type–specific gene regulation in adult human brains.

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Crossref · 10.1126/science.adf5357

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  • Brains were collected using highly optimized brain preparation methods with a mean postmortem interval of 7.0 h that enable exceptionally high-quality snRNA-seq, snATAC-seq and MERFISH profiling.

    “Brains were collected using highly optimized brain preparation methods (mean postmortem interval = 7.0 h; Extended Data Fig. 1b) that enable exceptionally high-quality snRNA-seq, snATAC–seq and MERFISH profiling17–19,23,36,37.”

    From Integrated multimodal cell atlas of Alzheimer’s disease (2024) · cites this paper as [37]

    The Epigenome-based brain cell type taxonomies section says the study used tissue from three adult donors and compared its data with snRNA-seq and snATAC-seq data from the same brains. The Discussion also refers to the availability of high-quality tissue. Our copy does not substantiate the claimed optimized preparation methods, 7.0-hour mean postmortem interval, or MERFISH profiling (searched for those terms); page numbers were not available. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Each cell category within the body and the brain possesses a distinct epigenome.

    “given that each cell category within the body and the brain possesses a distinct epigenome [475,476]”

    From Revisiting the Pathogenesis of X-Linked Adrenoleukodystrophy (2025) · cites this paper as [476]

    The Discussion reports 188 epigenetically distinct cell types across human brain regions, and the cell-type-specific DNA methylation section describes distinct epigenetic signatures for brain cell identities. The study examines brain cells, not every cell category throughout the body, so that broader part of the claim is unsupported. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Methylation barcodes and epigenetic clocks represent the latest advancements in the field.

    “Methylation barcodes and epigenetic clocks represent the latest advancements in the field [58,59,60].”

    From Genetic Insights into the Economic Toll of Cell Line Misidentification: A Comprehensive Review (2026) · cites this paper as [58]

    The section on single-cell methylation barcodes describes scMCodes developed to identify human brain cell types, supporting the barcode part of the claim. The Discussion mentions predicting epigenetic age, but does not identify epigenetic clocks as an advancement made or assessed by this paper, nor establish that both are the latest advancements. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Cites

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Research works printed but not linked: 45

  1. He Y, Hariharan M, Gorkin DU, Dickel DE, Luo C, Castanon RG, Nery JR, Lee AY, Zhao Y, Huang H, Williams BA, Trout D, Amrhein H, Fang R, Chen H, Li B, Visel A, Pennacchio LA, Ren B, Ecker JR, Spatiotemporal DNA methylome dynamics of the developing mouse fetus. Nature. 583, 752-759 (2020). [PubMed: 32728242]
  2. de Mendoza A, Poppe D, Buckberry S, Pflueger J, Albertin CB, Daish T, Bertrand S, de la Calle-Mustienes E, Gómez-Skarmeta JL, Nery JR, Ecker JR, Baer B, Ragsdale CW, Grützner F, Escriva H, Venkatesh B, Bogdanovic O, Lister R, The emergence of the brain non-CpG methylation system in vertebrates. Nat Ecol Evol. 5, 369-378 (2021). [PubMed: 33462491]
  3. Lister R, Mukamel EA, Nery JR, Urich M, Puddifoot CA, Johnson ND, Lucero J, Huang Y, Dwork AJ, Schultz MD, Yu M, Tonti-Filippini J, Heyn H, Hu S, Wu JC, Rao A, Esteller M, He C, Haghighi FG, Sejnowski TJ, Behrens MM, Ecker JR, Global epigenomic reconfiguration during mammalian brain development. Science. 341, 1237905 (2013). [PubMed: 23828890]
  4. Tillotson R, Cholewa-Waclaw J, Chhatbar K, Connelly JC, Kirschner SA, Webb S, Koerner MV, Selfridge J, Kelly DA, De Sousa D, Brown K, Lyst MJ, Kriaucionis S, Bird A, Neuronal non-CG methylation is an essential target for MeCP2 function. Mol. Cell. 81, 1260-1275.e12 (2021). [PubMed: 33561390]
  5. Dekker J, Mirny L, The 3D Genome as Moderator of Chromosomal Communication. Cell. 164, 1110-1121 (2016). [PubMed: 26967279]
  6. Luo C, Rivkin A, Zhou J, Sandoval JP, Kurihara L, Lucero J, Castanon R, Nery JR, Pinto-Duarte A, Bui B, Fitzpatrick C, O'Connor C, Ruga S, Van Eden ME, Davis DA, Mash DC, Behrens MM, Ecker JR, Robust single-cell DNA methylome profiling with snmC-seq2. Nat. Commun. 9, 3824 (2018). [PubMed: 30237449]
  7. Siletti K, Hodge R, Albiach AM, Hu L, Lee KW, Lönnerberg P, Bakken T, Ding S-L, Clark M, Casper T, Dee N, Gloe J, Dirk Keene C, Nyhus J, Tung H, Yanny AM, Arenas E, Lein ES, Linnarsson S, Transcriptomic diversity of cell types across the adult human brain. bioRxiv (2022), p. 2022.10.12.511898.
  8. Feng J, Zhou Y, Campbell SL, Le T, Li E, Sweatt JD, Silva AJ, Fan G, Dnmt1 and Dnmt3a maintain DNA methylation and regulate synaptic function in adult forebrain neurons. Nat. Neurosci. 13, 423-430 (2010). [PubMed: 20228804]
  9. He Y, Ecker JR, Non-CG Methylation in the Human Genome. Annu. Rev. Genomics Hum. Genet. 16, 55-77 (2015). [PubMed: 26077819]
  10. Tan L, Ma W, Wu H, Zheng Y, Xing D, Chen R, Li X, Daley N, Deisseroth K, Xie XS, Changes in genome architecture and transcriptional dynamics progress independently of sensory experience during post-natal brain development. Cell. 184, 741-758.e17 (2021). [PubMed: 33484631]
  11. Nagano T, Lubling Y, Várnai C, Dudley C, Leung W, Baran Y, Mendelson Cohen N, Wingett S, Fraser P, Tanay A, Cell-cycle dynamics of chromosomal organization at single-cell resolution. Nature. 547, 61-67 (2017). [PubMed: 28682332]
  12. Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP, Chang HY, Greenleaf WJ, Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. 523, 486-490 (2015). [PubMed: 26083756]
  13. Zhang R, Zhou T, Ma J, Multiscale and integrative single-cell Hi-C analysis with Higashi. Nat. Biotechnol. 40, 254-261 (2022). [PubMed: 34635838]
  14. Zhang R, Zhou T, Ma J, Ultrafast and interpretable single-cell 3D genome analysis with Fast- Higashi. Cell Syst. 13, 798-807.e6 (2022). [PubMed: 36265466]
  15. Rao SSP, Huntley MH, Durand NC, Stamenova EK, Bochkov ID, Robinson JT, Sanborn AL, Machol I, Omer AD, Lander ES, Aiden EL, A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell. 159, 1665-1680 (2014). [PubMed: 25497547]
  16. Zhang Y, Li T, Preissl S, Amaral ML, Grinstein JD, Farah EN, Destici E, Qiu Y, Hu R, Lee AY, Chee S, Ma K, Ye Z, Zhu Q, Huang H, Fang R, Yu L, Izpisua Belmonte JC, Wu J, Evans SM, Chi NC, Ren B, Transcriptionally active HERV-H retrotransposons demarcate topologically associating domains in human pluripotent stem cells. Nat. Genet. 51, 1380-1388 (2019). [PubMed: 31427791]
  17. Winick-Ng W, Kukalev A, Harabula I, Zea-Redondo L, Szabó D, Meijer M, Serebreni L, Zhang Y, Bianco S, Chiariello AM, Irastorza-Azcarate I, Thieme CJ, Sparks TM, Carvalho S, Fiorillo L, Musella F, Irani E, Triglia ET, Kolodziejczyk AA, Abentung A, Apostolova G, Paul EJ, Franke V, Kempfer R, Akalin A, Teichmann SA, Dechant G, Ungless MA, Nicodemi M, Welch L, Castelo-Branco G, Pombo A, Cell-type specialization is encoded by specific chromatin topologies. Nature, 1-8 (2021).
  18. Wiehle L, Thorn GJ, Raddatz G, Clarkson CT, Rippe K, Lyko F, Breiling A, Teif VB, DNA (de)methylation in embryonic stem cells controls CTCF-dependent chromatin boundaries. Genome Res. 29, 750-761 (2019). [PubMed: 30948436]
  19. Noack F, Vangelisti S, Raffl G, Carido M, Diwakar J, Chong F, Bonev B, Multimodal profiling of the transcriptional regulatory landscape of the developing mouse cortex identifies Neurog2 as a key epigenome remodeler. Nat. Neurosci. 25, 154-167 (2022). [PubMed: 35132236]
  20. Scelfo A, Barra V, Abdennur N, Spracklin G, Busato F, Salinas-Luypaert C, Bonaiti E, Velasco G, Chipont A, Guérin C, Tijhuis AE, Spierings DCJ, Francastel C, Foijer F, Tost J, Mirny L, Fachinetti D, Tunable DNMT1 degradation reveals cooperation of DNMT1 and DNMT3B in regulating DNA methylation dynamics and genome organization. bioRxiv (2023), p. 2023.05.04.539406.
  21. Mo A, Mukamel EA, Davis FP, Luo C, Henry GL, Picard S, Urich MA, Nery JR, Sejnowski TJ, Lister R, Eddy SR, Ecker JR, Nathans J, Epigenomic Signatures of Neuronal Diversity in the Mammalian Brain. Neuron. 86, 1369-1384 (2015). [PubMed: 26087164]
  22. Heavner WE, Ji S, Notwell JH, Dyer ES, Tseng AM, Birgmeier J, Yoo B, Bejerano G, McConnell SK, Transcription factor expression defines subclasses of developing projection neurons highly similar to single-cell RNA-seq subtypes. Proc. Natl. Acad. Sci. U. S. A. 117, 25074-25084 (2020). [PubMed: 32948690]
  23. Warming S, Rachel RA, Jenkins NA, Copeland NG, Zfp423 is required for normal cerebellar development. Mol. Cell. Biol. 26, 6913-6922 (2006). [PubMed: 16943432]
  24. Casoni F, Croci L, Bosone C, D'Ambrosio R, Badaloni A, Gaudesi D, Barili V, Sarna JR, Tessarollo L, Cremona O, Hawkes R, Warming S, Consalez GG, Zfp423/ZNF423 regulates cell cycle progression, the mode of cell division and the DNA-damage response in Purkinje neuron progenitors. Development. 144, 3686-3697 (2017). [PubMed: 28893945]
  25. Croci L, Chung S-H, Masserdotti G, Gianola S, Bizzoca A, Gennarini G, Corradi A, Rossi F, Hawkes R, Consalez GG, A key role for the HLH transcription factor EBF2COE2,O/E-3 in Purkinje neuron migration and cerebellar cortical topography. Development. 133, 2719-2729 (2006). [PubMed: 16774995]
  26. Takahashi K, Liu F-C, Oishi T, Mori T, Higo N, Hayashi M, Hirokawa K, Takahashi H, Expression of FOXP2 in the developing monkey forebrain: comparison with the expression of the genes FOXP1, PBX3, and MEIS2. J. Comp. Neurol. 509, 180-189 (2008). [PubMed: 18461604]
  27. Kaoru T, Liu F-C, Ishida M, Oishi T, Hayashi M, Kitagawa M, Shimoda K, Takahashi H, Molecular characterization of the intercalated cell masses of the amygdala: implications for the relationship with the striatum. Neuroscience. 166, 220-230 (2010). [PubMed: 20004711]
  28. Nasser J, Bergman DT, Fulco CP, Guckelberger P, Doughty BR, Patwardhan TA, Jones TR, Nguyen TH, Ulirsch JC, Lekschas F, Mualim K, Natri HM, Weeks EM, Munson G, Kane M, Kang HY, Cui A, Ray JP, Eisenhaure TM, Collins RL, Dey K, Pfister H, Price AL, Epstein CB, Kundaje A, Xavier RJ, Daly MJ, Huang H, Finucane HK, Hacohen N, Lander ES, Engreitz JM, Genome-wide enhancer maps link risk variants to disease genes. Nature. 593, 238-243 (2021). [PubMed: 33828297]
  29. Liu M, Jiang Y, Wedow R, Li Y, Brazel DM, Chen F, Datta G, Davila-Velderrain J, McGuire D, Tian C, Zhan X, 23andMe Research Team, HUNT All-In Psychiatry, Choquet H, Docherty AR, Faul JD, Foerster JR, Fritsche LG, Gabrielsen ME, Gordon SD, Haessler J, Hottenga J-J, Huang H, Jang S-K, Jansen PR, Ling Y, Mägi R, Matoba N, McMahon G, Mulas A, Orrù V, Palviainen T, Pandit A, Reginsson GW, Skogholt AH, Smith JA, Taylor AE, Turman C, Willemsen G, Young H, Young KA, Zajac GJM, Zhao W, Zhou W, Bjornsdottir G, Boardman JD, Boehnke M, Boomsma DI, Chen C, Cucca F, Davies GE, Eaton CB, Ehringer MA, Esko T, Fiorillo E, Gillespie NA, Gudbjartsson DF, Haller T, Harris KM, Heath AC, Hewitt JK, Hickie IB, Hokanson JE, Hopfer CJ, Hunter DJ, Iacono WG, Johnson EO, Kamatani Y, Kardia SLR, Keller MC, Kellis M, Kooperberg C, Kraft P, Krauter KS, Laakso M, Lind PA, Loukola A, Lutz SM, Madden PAF, Martin NG, McGue M, McQueen MB, Medland SE, Metspalu A, Mohlke KL, Nielsen JB, Okada Y, Peters U, Polderman TJC, Posthuma D, Reiner AP, Rice JP, Rimm E, Rose RJ, Runarsdottir V, Stallings MC, Stančáková A, Stefansson H, Thai KK, Tindle HA, Tyrfingsson T, Wall TL, Weir DR, Weisner C, Whitfield JB, Winsvold BS, Yin J, Zuccolo L, Bierut LJ, Hveem K, Lee JJ, Munafò MR, Saccone NL, Willer CJ, Cornelis MC, David SP, Hinds DA, Jorgenson E, Kaprio J, Stitzel JA, Stefansson K, Thorgeirsson TE, Abecasis G, Liu DJ, Vrieze S, Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat. Genet. 51, 237-244 (2019). [PubMed: 30643251]
  30. Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, van de Lagemaat LN, Smith KA, Ebbert A, Riley ZL, Abajian C, Beckmann CF, Bernard A, Bertagnolli D, Boe AF, Cartagena PM, Chakravarty MM, Chapin M, Chong J, Dalley RA, David Daly B, Dang C, Datta S, Dee N, Dolbeare TA, Faber V, Feng D, Fowler DR, Goldy J, Gregor BW, Haradon Z, Haynor DR, Hohmann JG, Horvath S, Howard RE, Jeromin A, Jochim JM, Kinnunen M, Lau C, Lazarz ET, Lee C, Lemon TA, Li L, Li Y, Morris JA, Overly CC, Parker PD, Parry SE, Reding M, Royall JJ, Schulkin J, Sequeira PA, Slaughterbeck CR, Smith SC, Sodt AJ, Sunkin SM, Swanson BE, Vawter MP, Williams D, Wohnoutka P, Zielke HR, Geschwind DH, Hof PR, Smith SM, Koch C, Grant SGN, Jones AR, An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 489, 391-399 (2012). [PubMed: 22996553]
  31. Yao Z, Liu H, Xie F, Fischer S, Adkins RS, Aldridge AI, Ament SA, Bartlett A, Behrens MM, Van den Berge K, Bertagnolli D, de Bézieux HR, Biancalani T, Booeshaghi AS, Bravo HC, Casper T, Colantuoni C, Crabtree J, Creasy H, Crichton K, Crow M, Dee N, Dougherty EL, Doyle WI, Dudoit S, Fang R, Felix V, Fong O, Giglio M, Goldy J, Hawrylycz M, Herb BR, Hertzano R, Hou X, Hu Q, Kancherla J, Kroll M, Lathia K, Li YE, Lucero JD, Luo C, Mahurkar A, McMillen D, Nadaf NM, Nery JR, Nguyen TN, Niu S-Y, Ntranos V, Orvis J, Osteen JK, Pham T, Pinto-Duarte A, Poirion O, Preissl S, Purdom E, Rimorin C, Risso D, Rivkin AC, Smith K, Street K, Sulc J, Svensson V, Tieu M, Torkelson A, Tung H, Vaishnav ED, Vanderburg CR, van Velthoven C, Wang X, White OR, Huang ZJ, Kharchenko PV, Pachter L, Ngai J, Regev A, Tasic B, Welch JD, Gillis J, Macosko EZ, Ren B, Ecker JR, Zeng H, Mukamel EA, A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex. Nature. 598, 103-110 (2021). [PubMed: 34616066]
  32. Chen H, Albergante L, Hsu JY, Lareau CA, Lo Bosco G, Guan J, Zhou S, Gorban AN, Bauer DE, Aryee MJ, Langenau DM, Zinovyev A, Buenrostro JD, Yuan G-C, Pinello L, Single- cell trajectories reconstruction, exploration and mapping of omics data with STREAM. Nature Communications. 10 (2019), , doi:10.1038/s41467-019-09670-4. · DOI
  33. Feng J, Hsu W-H, Patterson D, Tseng C-S, Hsing H-W, Zhuang Z-H, Huang Y-T, Faedo A, Rubenstein JL, Touboul J, Chou S-J, COUP-TFI specifies the medial entorhinal cortex identity and induces differential cell adhesion to determine the integrity of its boundary with neocortex. Sci Adv. 7 (2021), doi:10.1126/sciadv.abf6808. · DOI
  34. Alexander GE, DeLong MR, Strick PL, Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu. Rev. Neurosci. 9, 357-381 (1986). [PubMed: 3085570]
  35. Middleton FA, Strick PL, Basal ganglia and cerebellar loops: motor and cognitive circuits. Brain Res. Brain Res. Rev. 31, 236-250 (2000). [PubMed: 10719151]
  36. Bakken TE, Jorstad NL, Hu Q, Lake BB, Tian W, Kalmbach BE, Crow M, Hodge RD, Krienen FM, Sorensen SA, Eggermont J, Yao Z, Aevermann BD, Aldridge AI, Bartlett A, Bertagnolli D, Casper T, Castanon RG, Crichton K, Daigle TL, Dalley R, Dee N, Dembrow N, Diep D, Ding S-L, Dong W, Fang R, Fischer S, Goldman M, Goldy J, Graybuck LT, Herb BR, Hou X, Kancherla J, Kroll M, Lathia K, van Lew B, Li YE, Liu CS, Liu H, Lucero JD, Mahurkar A, McMillen D, Miller JA, Moussa M, Nery JR, Nicovich PR, Niu S-Y, Orvis J, Osteen JK, Owen S, Palmer CR, Pham T, Plongthongkum N, Poirion O, Reed NM, Rimorin C, Rivkin A, Romanow WJ, Sedeño-Cortés AE, Siletti K, Somasundaram S, Sulc J, Tieu M, Torkelson A, Tung H, Wang X, Xie F, Yanny AM, Zhang R, Ament SA, Behrens MM, Bravo HC, Chun J, Dobin A, Gillis J, Hertzano R, Hof PR, Höllt T, Horwitz GD, Keene CD, Kharchenko PV, Ko AL, Lelieveldt BP, Luo C, Mukamel EA, Pinto-Duarte A, Preissl S, Regev A, Ren B, Scheuermann RH, Smith K, Spain WJ, White OR, Koch C, Hawrylycz M, Tasic B, Macosko EZ, McCarroll SA, Ting JT, Zeng H, Zhang K, Feng G, Ecker JR, Linnarsson S, Lein ES, Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature. 598, 111-119 (2021). [PubMed: 34616062]
  37. Vormstein-Schneider D, Lin JD, Pelkey KA, Chittajallu R, Guo B, Arias-Garcia MA, Allaway K, Sakopoulos S, Schneider G, Stevenson O, Vergara J, Sharma J, Zhang Q, Franken TP, Smith J, Ibrahim LA, Mastro KJ, Sabri E, Huang S, Favuzzi E, Burbridge T, Xu Q, Guo L, Vogel I, Sanchez V, Saldi GA, Gorissen BL, Yuan X, Zaghloul KA, Devinsky O, Sabatini BL, Batista-Brito R, Reynolds J, Feng G, Fu Z, McBain CJ, Fishell G, Dimidschstein J, Viral manipulation of functionally distinct interneurons in mice, non-human primates and humans. Nat. Neurosci. 23, 1629-1636 (2020). [PubMed: 32807948]
  38. Hon GC, Rajagopal N, Shen Y, McCleary DF, Yue F, Dang MD, Ren B, Epigenetic memory at embryonic enhancers identified in DNA methylation maps from adult mouse tissues. Nat. Genet. 45, 1198-1206 (2013). [PubMed: 23995138]
  39. Luo C, Liu H, Xie F, Armand EJ, Siletti K, Bakken TE, Fang R, Doyle WI, Stuart T, Hodge RD, Hu L, Wang B-A, Zhang Z, Preissl S, Lee D-S, Zhou J, Niu S-Y, Castanon R, Bartlett A, Rivkin A, Wang X, Lucero J, Nery JR, Davis DA, Mash DC, Satija R, Dixon JR, Linnarsson S, Lein E, Margarita Behrens M, Ren B, Mukamel EA, Ecker JR, Single nucleus multi-omics identifies human cortical cell regulatory genome diversity. Cell Genomics. 2 (2022), p. 100107. [PubMed: 35419551]
  40. Song M, Pebworth M-P, Yang X, Abnousi A, Fan C, Wen J, Rosen JD, Choudhary MNK, Cui X, Jones IR, Bergenholtz S, Eze UC, Juric I, Li B, Maliskova L, Lee J, Liu W, Pollen AA, Li Y, Wang T, Hu M, Kriegstein AR, Shen Y, Cell-type-specific 3D epigenomes in the developing human cortex. Nature. 587, 644-649 (2020). [PubMed: 33057195]
  41. Horvath S, Zhang Y, Langfelder P, Kahn RS, Boks MPM, van Eijk K, van den Berg LH, Ophoff RA, Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol. 13, R97 (2012). [PubMed: 23034122]
  42. Gabbutt C, Schenck RO, Weisenberger DJ, Kimberley C, Berner A, Househam J, Lakatos E, Robertson-Tessi M, Martin I, Patel R, Clark SK, Latchford A, Barnes CP, Leedham SJ, Anderson ARA, Graham TA, Shibata D, Fluctuating methylation clocks for cell lineage tracing at high temporal resolution in human tissues. Nat. Biotechnol. 40, 720-730 (2022). [PubMed: 34980912]
  43. Salas LA, Wiencke JK, Koestler DC, Zhang Z, Christensen BC, Kelsey KT, Tracing human stem cell lineage during development using DNA methylation. Genome Res. 28, 1285-1295 (2018). [PubMed: 30072366]
  44. Gormally E, Caboux E, Vineis P, Hainaut P, Circulating free DNA in plasma or serum as biomarker of carcinogenesis: practical aspects and biological significance. Mutat. Res. 635, 105-117 (2007). [PubMed: 17257890]
  45. Cisneros-Villanueva M, Hidalgo-Pérez L, Rios-Romero M, Cedro-Tanda A, Ruiz-Villavicencio CA, Page K, Hastings R, Fernandez-Garcia D, Allsopp R, Fonseca-Montaño MA, Jimenez- Morales S, Padilla-Palma V, Shaw JA, Hidalgo-Miranda A, Cell-free DNA analysis in current cancer clinical trials: a review. Br. J. Cancer. 126, 391-400 (2022). [PubMed: 35027672]

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Authors
Tian, W., Zhou, J., Bartlett, A., Zeng, Q., Liu, H., Castanon, R. G., Kenworthy, M., Altshul, J., Valadon, C., Aldridge, A., Nery, J. R., Chen, H., Xu, J., Johnson, N. D., Lucero, J., Osteen, J. K., Emerson, N., Rink, J., Lee, J., Li, Y. E., Siletti, K., Liem, M., Claffey, N., O’Connor, C., Yanny, A. M., Nyhus, J., Dee, N., Casper, T., Shapovalova, N., Hirschstein, D., Ding, S.-L., Hodge, R., Levi, B. P., Keene, C. D., Linnarsson, S., Lein, E., Ren, B., Behrens, M. M., & Ecker, J. R.
Affiliations
  • Salk Institute for Biological Studies (US)
  • Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
  • Ludwig Cancer Research (US)
  • Karolinska Institutet (SE)
  • Allen Institute for Brain Science (US)
  • University of Washington (US)
  • University of California San Diego (US)
Corresponding author
  • Tian, Wei
  • Zhou, Jingtian
  • Ecker, Joseph R.
ORCID
Venue
Science
Publisher
American Association for the Advancement of Science (AAAS)
Published
2023-10-13
Volume
382
Issue
6667
Pages
eadf5357-eadf5357
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
DOI
10.1126/science.adf5357
PMC
PMC10572106
OpenAlex
W4387568772
Cited by
154
References
28
Citations by year
2026: 44 · 2025: 64 · 2024: 40 · 2023: 6
FWCI
13.81
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Science (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion, cc-by)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
Funders
  • Howard Hughes Medical Institute
  • Nancy and Buster Alvord Endowment
  • National Institutes of Health (5u01mh114812-03, P30 014195, 1s10od023689-01a1, U01MH114812)
  • National Institute of Mental Health (U01MH114812, U01MH121282, UM1MH130994)
  • National Cancer Institute (S10-OD023689, P30014195, P30CA014195)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Keywords
DNA methylation, 3D genome organization, chromatin conformation, human brain, single-cell epigenomics, brain cell types, cell type-specific gene regulation, chromatin accessibility, cortical regions, basal ganglia, adult human brain
MeSH terms
Adult, Atlases as Topic, Brain, Chromatin, Humans, Male, Genome, Human, DNA Methylation, Imaging, Three-Dimensional, Epigenesis, Genetic, Single-Cell Analysis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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