4.8 Article

A guiding role of the Arabidopsis circadian clock in cell differentiation revealed by time-series single-cell RNA sequencing

Journal

CELL REPORTS
Volume 40, Issue 2, Pages -

Publisher

CELL PRESS
DOI: 10.1016/j.celrep.2022.111059

Keywords

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Funding

  1. JST PRESTO Grant [888067]
  2. JSPS KAKENHI [20K21428, 18H02461, 19H05674, 19H05670, 19H04866, 19K16170, 18K14732, 20K15818, 17J08107]
  3. Yamada Science Foundation
  4. Senri Life Science Foundation
  5. Lotte Foundation
  6. Daiichi Sankyo Foundation of Life Science
  7. Takeda Science Foundation
  8. Nakajima Foundation
  9. Sekisui Chemical Grant Program
  10. ISHIZUE 2017 of the Kyoto University Research Development Program
  11. SEI Group CSR Foundation
  12. Secom Science and Technology Foundation
  13. Tokyo Kasei Chemical Promotion foundation
  14. MitsubishiFoundation
  15. [25113005]

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This study reveals the guiding role of the plant circadian clock in cell differentiation and provides an approach for time-series analysis at single-cell resolution.
Circadian rhythms and progression of cell differentiation are closely coupled in multicellular organisms. How-ever, whether establishment of circadian rhythms regulates cell differentiation or vice versa has not been elucidated due to technical limitations. Here, we exploit high cell fate plasticity of plant cells to perform sin-gle-cell RNA sequencing during the entire process of cell differentiation. By analyzing reconstructed actual time series of the differentiation processes at single-cell resolution using a method we developed (PeakMatch), we find that the expression profile of clock genes is changed prior to cell differentiation, including induction of the clock gene LUX ARRYTHMO (LUX). ChIP sequencing analysis reveals that LUX in-duction in early differentiating cells directly targets genes involved in cell-cycle progression to regulate cell differentiation. Taken together, these results not only reveal a guiding role of the plant circadian clock in cell differentiation but also provide an approach for time-series analysis at single-cell resolution.

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