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Single-cell chromatin accessibility reveals principles of regulatory variation

Author

Listed:
  • Jason D. Buenrostro

    (Stanford University School of Medicine
    Program in Epithelial Biology and the Howard Hughes Medical Institute, Stanford University School of Medicine)

  • Beijing Wu

    (Stanford University School of Medicine)

  • Ulrike M. Litzenburger

    (Program in Epithelial Biology and the Howard Hughes Medical Institute, Stanford University School of Medicine)

  • Dave Ruff

    (Fluidigm Corporation)

  • Michael L. Gonzales

    (Fluidigm Corporation)

  • Michael P. Snyder

    (Stanford University School of Medicine)

  • Howard Y. Chang

    (Program in Epithelial Biology and the Howard Hughes Medical Institute, Stanford University School of Medicine)

  • William J. Greenleaf

    (Stanford University School of Medicine
    Stanford University)

Abstract

A single-cell method for probing genome-wide chromatin accessibility has been developed; the results provide insight into the relationship between cell-to-cell variation associated with specific trans-factors and cis-elements, as well insights into the relationship between chromatin accessibility and three-dimensional genome organization.

Suggested Citation

  • Jason D. Buenrostro & Beijing Wu & Ulrike M. Litzenburger & Dave Ruff & Michael L. Gonzales & Michael P. Snyder & Howard Y. Chang & William J. Greenleaf, 2015. "Single-cell chromatin accessibility reveals principles of regulatory variation," Nature, Nature, vol. 523(7561), pages 486-490, July.
    • Handle: RePEc:nat:nature:v:523:y:2015:i:7561:d:10.1038_nature14590
    DOI: 10.1038/nature14590
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    Citations

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    Cited by:

    1. Songming Tang & Xuejian Cui & Rongxiang Wang & Sijie Li & Siyu Li & Xin Huang & Shengquan Chen, 2024. "scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    2. Shengen Shawn Hu & Lin Liu & Qi Li & Wenjing Ma & Michael J. Guertin & Clifford A. Meyer & Ke Deng & Tingting Zhang & Chongzhi Zang, 2022. "Intrinsic bias estimation for improved analysis of bulk and single-cell chromatin accessibility profiles using SELMA," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    3. Youtao Lu & Jaehee Lee & Jifen Li & Srinivasa Rao Allu & Jinhui Wang & HyunBum Kim & Kevin L. Bullaughey & Stephen A. Fisher & C. Erik Nordgren & Jean G. Rosario & Stewart A. Anderson & Alexandra V. U, 2023. "CHEX-seq detects single-cell genomic single-stranded DNA with catalytical potential," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    4. Aaron T L Lun & Hervé Pagès & Mike L Smith, 2018. "beachmat: A Bioconductor C++ API for accessing high-throughput biological data from a variety of R matrix types," PLOS Computational Biology, Public Library of Science, vol. 14(5), pages 1-15, May.
    5. Jingyang Qian & Jie Liao & Ziqi Liu & Ying Chi & Yin Fang & Yanrong Zheng & Xin Shao & Bingqi Liu & Yongjin Cui & Wenbo Guo & Yining Hu & Hudong Bao & Penghui Yang & Qian Chen & Mingxiao Li & Bing Zha, 2023. "Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    6. Haowen Zhang & Li Song & Xiaotao Wang & Haoyu Cheng & Chenfei Wang & Clifford A. Meyer & Tao Liu & Ming Tang & Srinivas Aluru & Feng Yue & X. Shirley Liu & Heng Li, 2021. "Fast alignment and preprocessing of chromatin profiles with Chromap," Nature Communications, Nature, vol. 12(1), pages 1-6, December.
    7. B. Edginton-White & A. Maytum & S. G. Kellaway & D. K. Goode & P. Keane & I. Pagnuco & S. A. Assi & L. Ames & M. Clarke & P. N. Cockerill & B. Göttgens & J. B. Cazier & C. Bonifer, 2023. "A genome-wide relay of signalling-responsive enhancers drives hematopoietic specification," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    8. Parashar Dhapola & Johan Rodhe & Rasmus Olofzon & Thomas Bonald & Eva Erlandsson & Shamit Soneji & Göran Karlsson, 2022. "Scarf enables a highly memory-efficient analysis of large-scale single-cell genomics data," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    9. Suhas V. Vasaikar & Adam K. Savage & Qiuyu Gong & Elliott Swanson & Aarthi Talla & Cara Lord & Alexander T. Heubeck & Julian Reading & Lucas T. Graybuck & Paul Meijer & Troy R. Torgerson & Peter J. Sk, 2023. "A comprehensive platform for analyzing longitudinal multi-omics data," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    10. Wenjing Ma & Jiaying Lu & Hao Wu, 2023. "Cellcano: supervised cell type identification for single cell ATAC-seq data," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    11. Zhijian Li & Christoph Kuppe & Susanne Ziegler & Mingbo Cheng & Nazanin Kabgani & Sylvia Menzel & Martin Zenke & Rafael Kramann & Ivan G. Costa, 2021. "Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    12. Anna Laddach & Song Hui Chng & Reena Lasrado & Fränze Progatzky & Michael Shapiro & Alek Erickson & Marisol Sampedro Castaneda & Artem V. Artemov & Ana Carina Bon-Frauches & Eleni-Maria Amaniti & Jens, 2023. "A branching model of lineage differentiation underpinning the neurogenic potential of enteric glia," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    13. Xiang Lin & Tian Tian & Zhi Wei & Hakon Hakonarson, 2022. "Clustering of single-cell multi-omics data with a multimodal deep learning method," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    14. Samir Rachid Zaim & Mark-Phillip Pebworth & Imran McGrath & Lauren Okada & Morgan Weiss & Julian Reading & Julie L. Czartoski & Troy R. Torgerson & M. Juliana McElrath & Thomas F. Bumol & Peter J. Ske, 2024. "MOCHA’s advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts," Nature Communications, Nature, vol. 15(1), pages 1-24, December.
    15. Yoshiharu Muto & Eryn E. Dixon & Yasuhiro Yoshimura & Haojia Wu & Kohei Omachi & Nicolas Ledru & Parker C. Wilson & Andrew J. King & N. Eric Olson & Marvin G. Gunawan & Jay J. Kuo & Jennifer H. Cox & , 2022. "Defining cellular complexity in human autosomal dominant polycystic kidney disease by multimodal single cell analysis," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    16. Alan Selewa & Kaixuan Luo & Michael Wasney & Linsin Smith & Xiaotong Sun & Chenwei Tang & Heather Eckart & Ivan P. Moskowitz & Anindita Basu & Xin He & Sebastian Pott, 2023. "Single-cell genomics improves the discovery of risk variants and genes of atrial fibrillation," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    17. Fangfang Yan & Akiko Suzuki & Chihiro Iwaya & Guangsheng Pei & Xian Chen & Hiroki Yoshioka & Meifang Yu & Lukas M. Simon & Junichi Iwata & Zhongming Zhao, 2024. "Single-cell multiomics decodes regulatory programs for mouse secondary palate development," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    18. Kristina Handler & Karsten Bach & Costanza Borrelli & Salvatore Piscuoglio & Xenia Ficht & Ilhan E. Acar & Andreas E. Moor, 2023. "Fragment-sequencing unveils local tissue microenvironments at single-cell resolution," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    19. Lei Xiong & Kang Tian & Yuzhe Li & Weixi Ning & Xin Gao & Qiangfeng Cliff Zhang, 2022. "Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space," Nature Communications, Nature, vol. 13(1), pages 1-17, December.

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