ArticlePlant methods2023
Statistical analysis of organelle movement using state-space models.
Article in Plant methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 4 citations in OpenAlex.
- Circadian and environmental signal integration in a natural population ofProceedings of the National Academy of Sciences of the United States of America · 2024Article
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Authors and funding
3 authors at 2 institutions in 1 country.
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Abstract
backgroundOrganelle motility is essential for the correct cellular function of various eukaryotic cells. In plant cells, chloroplasts move towards the intracellular area irradiated by a weak light to maximise photosynthesis. To initiate this process, an unknown signal is transferred from the irradiated area to distant chloroplasts. Quantification of this chloroplast movement has been performed using visual estimations that are analyst-dependent and labour-intensive. Therefore, an objective and faster method is required.
resultsIn this study, we developed the cellssm package of R ( https://github.com/hnishio/cellssm.git ), which is a user-friendly tool for state-space modelling to statistically analyse the directional movement of cells or organelles. Our method showed a high accuracy in estimating the start time of chloroplast movement in the liverwort Marchantia polymorpha over a short period. The tool indicated that chloroplast movement accelerates during transport to the irradiated area and that signal transfer speed is uneven within a cell. We also developed a method to estimate the common dynamics among multiple chloroplasts in each cell, which clarified different characteristics among cells.
conclusionsWe demonstrated that state-space modelling is a powerful method to understand organelle movement in eukaryotic cells. The cellssm package can be applied to various directional movements (both accumulation and avoidance) at cellular and subcellular levels to estimate the true transition of states behind the time-series data.
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