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Single seeds exhibit transcriptional heterogeneity during secondary dormancy induction
Author(s) -
Michał Krzysztoń,
Ruslan Yatusevich,
Magdalena Wrona,
Sebastian Sacharowski,
Dorota Adamska,
Szymon Świeżewski
Publication year - 2022
Publication title -
plant physiology
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 3.554
H-Index - 312
eISSN - 1532-2548
pISSN - 0032-0889
DOI - 10.1093/plphys/kiac265
Subject(s) - germination , transcriptome , biology , dormancy , seed dormancy , arabidopsis , population , gene expression , gene , botany , horticulture , mutant , genetics , demography , sociology
Seeds are highly resilient to the external environment, which allows plants to persist in unpredictable and unfavorable conditions. Some plant species have adopted a bet-hedging strategy to germinate a variable fraction of seeds in any given condition, and this could be explained by population-based threshold models. Here, in the model plant Arabidopsis (Arabidopsis thaliana), we induced secondary dormancy (SD) to address the transcriptional heterogeneity among seeds that leads to binary germination/nongermination outcomes. We developed a single-seed RNA-seq strategy that allowed us to observe a reduction in seed transcriptional heterogeneity as seeds enter stress conditions, followed by an increase during recovery. We identified groups of genes whose expression showed a specific pattern through a time course and used these groups to position the individual seeds along the transcriptional gradient of germination competence. In agreement, transcriptomes of dormancy-deficient seeds (mutant of DELAY OF GERMINATION 1) showed a shift toward higher values of the germination competence index. Interestingly, a significant fraction of genes with variable expression encoded translation-related factors. In summary, interrogating hundreds of single-seed transcriptomes during SD-inducing treatment revealed variability among the transcriptomes that could result from the distribution of population-based sensitivity thresholds. Our results also showed that single-seed RNA-seq is the method of choice for analyzing seed bet-hedging-related phenomena.

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