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Scaling single-cell genomics from phenomenology to mechanism

Author

Listed:
  • Amos Tanay

    (Weizmann Institute of Science
    Weizmann Institute of Science)

  • Aviv Regev

    (Broad Institute of MIT and Harvard
    Howard Hughes Medical Institute, Massachusetts Institute of Technology
    Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology)

Abstract

Three of the most fundamental questions in biology are how individual cells differentiate to form tissues, how tissues function in a coordinated and flexible fashion and which gene regulatory mechanisms support these processes. Single-cell genomics is opening up new ways to tackle these questions by combining the comprehensive nature of genomics with the microscopic resolution that is required to describe complex multicellular systems. Initial single-cell genomic studies provided a remarkably rich phenomenology of heterogeneous cellular states, but transforming observational studies into models of dynamics and causal mechanisms in tissues poses fresh challenges and requires stronger integration of theoretical, computational and experimental frameworks.

Suggested Citation

  • Amos Tanay & Aviv Regev, 2017. "Scaling single-cell genomics from phenomenology to mechanism," Nature, Nature, vol. 541(7637), pages 331-338, January.
    • Handle: RePEc:nat:nature:v:541:y:2017:i:7637:d:10.1038_nature21350
    DOI: 10.1038/nature21350
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    Cited by:

    1. Duy Pham & Xiao Tan & Brad Balderson & Jun Xu & Laura F. Grice & Sohye Yoon & Emily F. Willis & Minh Tran & Pui Yeng Lam & Arti Raghubar & Priyakshi Kalita-de Croft & Sunil Lakhani & Jana Vukovic & Ma, 2023. "Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues," Nature Communications, Nature, vol. 14(1), pages 1-25, December.
    2. Qi Jiang & Shuo Zhang & Lin Wan, 2022. "Dynamic inference of cell developmental complex energy landscape from time series single-cell transcriptomic data," PLOS Computational Biology, Public Library of Science, vol. 18(1), pages 1-22, January.
    3. Peizhuo Wang & Xiao Wen & Han Li & Peng Lang & Shuya Li & Yipin Lei & Hantao Shu & Lin Gao & Dan Zhao & Jianyang Zeng, 2023. "Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    4. Hanae Sato & Robert H. Singer, 2021. "Cellular variability of nonsense-mediated mRNA decay," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    5. Axel Theorell & Yenan Troi Bryceson & Jakob Theorell, 2019. "Determination of essential phenotypic elements of clusters in high-dimensional entities—DEPECHE," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-15, March.
    6. Samuel S. Kim & Buu Truong & Karthik Jagadeesh & Kushal K. Dey & Amber Z. Shen & Soumya Raychaudhuri & Manolis Kellis & Alkes L. Price, 2024. "Leveraging single-cell ATAC-seq and RNA-seq to identify disease-critical fetal and adult brain cell types," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    7. Shilu Zhang & Saptarshi Pyne & Stefan Pietrzak & Spencer Halberg & Sunnie Grace McCalla & Alireza Fotuhi Siahpirani & Rupa Sridharan & Sushmita Roy, 2023. "Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets," Nature Communications, Nature, vol. 14(1), pages 1-25, December.
    8. William J. Young & Najim Lahrouchi & Aaron Isaacs & ThuyVy Duong & Luisa Foco & Farah Ahmed & Jennifer A. Brody & Reem Salman & Raymond Noordam & Jan-Walter Benjamins & Jeffrey Haessler & Leo-Pekka Ly, 2022. "Genetic analyses of the electrocardiographic QT interval and its components identify additional loci and pathways," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    9. Jifan Shi & Tiejun Li & Luonan Chen & Kazuyuki Aihara, 2019. "Quantifying pluripotency landscape of cell differentiation from scRNA-seq data by continuous birth-death process," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-17, November.
    10. 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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