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Statistical analysis of organelle movement using state-space models.
Haruki Nishio1,2, Satoyuki Hirano3,4, Yutaka Kodama5,6
1Data Science and AI Innovation Research Promotion Center, Shiga University, Shiga, 522‑8522, Japan. harukin218@gmail.com.
Plant Methods
|July 5, 2023
Summary
Researchers developed a new R package, cellssm, for objective analysis of organelle movement. This tool accurately quantifies chloroplasts
Area of Science:
- Cell Biology
- Plant Science
- Biophysics
Background:
- Organelle motility is crucial for eukaryotic cellular function.
- In plants, chloroplasts move towards light to optimize photosynthesis, a process initiated by an unknown signal.
- Current quantification methods for chloroplast movement are subjective and time-consuming.
Purpose of the Study:
- To develop an objective and efficient method for analyzing organelle directional movement.
- To statistically model and quantify chloroplast movement in response to light stimuli.
- To understand the dynamics of signal transfer and chloroplast transport.
Main Methods:
- Developed the 'cellssm' R package utilizing state-space modeling.
- Applied the package to analyze chloroplast movement in the liverwort *Marchantia polymorpha*.
- Implemented methods to estimate common dynamics among multiple chloroplasts.
Main Results:
- The 'cellssm' package accurately estimated the onset of chloroplast movement.
- Chloroplasts accelerate movement towards the light source, with uneven signal transfer speeds observed.
- Analysis revealed distinct cellular characteristics in chloroplast movement dynamics.
Conclusions:
- State-space modeling provides a powerful approach for studying organelle motility.
- The 'cellssm' package offers a versatile tool for analyzing directional cellular and subcellular movements.
- This method enables accurate estimation of underlying state transitions in time-series data.
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