48-Hour and 24-Hour Time-lapse Single-nucleus Transcriptomics Reveal Cell-type specific Circadian Rhythms in Arabidopsis

Functional circadian clock is critical to the adaptation and survival of organisms. In land plants, the comprehensive profiling of circadian gene expression at the single-cell level is largely unknown partly due to the challenges in obtaining precisely-timed single cells embedded within cell walls. To bridge this gap, we employ time-lapse single-nucleus RNA sequencing (snRNA-seq) on Arabidopsis seedlings collected over a 48-hour window at 4-hour intervals, as well as over a 24-hour day at 2-hour intervals, yielding a total of over 77,142 and 130,000 nuclei. Here, we find that four cell clusters in the shoot share a coherent rhythm, while around 3000 genes display cell-type specific rhythmic expression. Our analysis indicates that genes encoding circadian regulators oscillate in multiple cell types, and the majority of them are well-documented core clock genes, suggesting the snRNA-seq circadian data could be used to identify more clock components oscillating in a cell-autonomous way. We identify ABF1 as a circadian regulator, whose overexpression and shortens the circadian period. Our data provides a comprehensive resource for plant circadian rhythmicity at the single-cell level (hosted at https://zhailab.bio.sustech.edu.cn/sc_circadian ).

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Publication Details

Journal
Nature Communications
Published
2025-05-05
DOI
https://doi.org/10.1038/s41467-025-59424-8
Citations
15
Primary Topic
Plant Molecular Biology Research
Type
article
Field-Weighted Citation Impact
26.25

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article

48-Hour and 24-Hour Time-lapse Single-nucleus Transcriptomics Reveal Cell-type specific Circadian Rhythms in Arabidopsis

Yanping Long, Jixian Zhai, María A. Nohales, Carlos Martínez-Vasallo et al.
15 citations
Nature Communications
Plant Molecular Biology Research
26.25
article

48-Hour and 24-Hour Time-lapse Single-nucleus Transcriptomics Reveal Cell-type specific Circadian Rhythms in Arabidopsis

Yanping Long, Jixian Zhai, María A. Nohales, Carlos Martínez-Vasallo, Z Liu, Xiaodong Xu, Qiguang Xie, Bin Liu, Yuwei Qin, Shiqi Gao, Ya Gao, Xinlong Zhu
article en
15 citations

Abstract

Functional circadian clock is critical to the adaptation and survival of organisms. In land plants, the comprehensive profiling of circadian gene expression at the single-cell level is largely unknown partly due to the challenges in obtaining precisely-timed single cells embedded within cell walls. To bridge this gap, we employ time-lapse single-nucleus RNA sequencing (snRNA-seq) on Arabidopsis seedlings collected over a 48-hour window at 4-hour intervals, as well as over a 24-hour day at 2-hour intervals, yielding a total of over 77,142 and 130,000 nuclei. Here, we find that four cell clusters in the shoot share a coherent rhythm, while around 3000 genes display cell-type specific rhythmic expression. Our analysis indicates that genes encoding circadian regulators oscillate in multiple cell types, and the majority of them are well-documented core clock genes, suggesting the snRNA-seq circadian data could be used to identify more clock components oscillating in a cell-autonomous way. We identify ABF1 as a circadian regulator, whose overexpression and shortens the circadian period. Our data provides a comprehensive resource for plant circadian rhythmicity at the single-cell level (hosted at https://zhailab.bio.sustech.edu.cn/sc_circadian ).

Nature CommunicationsVol. 16(1)
Consejo Superior de Investigaciones Científicas (ES), Henan University (CN), Northeast Normal University (CN), Southern University of Science and Technology (CN), Chinese Academy of Agricultural Sciences (CN), Institute of Crop Sciences (CN), Instituto de Biología Molecular y Celular de Plantas (ES), Universitat Politècnica de València (ES)
National Natural Science Foundation of China, Generalitat Valenciana, Natural Science Foundation of Guangdong Province, National Science and Technology Major Project, Agencia Estatal de Investigación
Zero hunger
Openalex Percentile: Top 0%
Plant Molecular Biology Research
26.25
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