TockyLocus: Quantitative Analysis Methods for Flow Cytometric Fluorescent Timer Data

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1. Verfasser: Ono, Masahiro
Format: Preprint
Veröffentlicht: 2024
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author Ono, Masahiro
author_facet Ono, Masahiro
contents Fluorescent Timer proteins, which spontaneously change their emission spectra over time, are valuable tools for analyzing temporal changes in cellular activities at the single-cell level. Traditional analysis of Fluorescent Timer data has mostly relied on conventional flow cytometric methods, which lacks the sophistication needed for detailed quantitative analysis. Recently, we developed the Timer-of-Cell-Kinetics-and-Activity (Tocky) tools, employing transgenic reporter systems using an mCherry mutant Timer protein, Fast-FT, to analyze these changes, implementing data preprocessing methods for Timer fluorescence. Despite this advancement, the computational implementation of effective quantitative analysis methods for Fluorescent Timer data has been lacking. In this study, we introduce rigorous algorithms for a data categorization method, designated as the Tocky locus approach, which uses normalized and trigonometric-transformed Fluorescent Timer data, suitable for quantitative analysis. We found that the five-locus approach, which categorizes Timer fluorescence into five categories, optimally captures the dynamics of Timer profiles, thus enabling effective quantitative statistical analysis and biological interpretation. The current study has developed comprehensive tools for data categorization, visualization, and statistical analysis optimized to Tocky Locus data. These algorithms have been implemented in the TockyLocus package, a new R package designed to facilitate the quantitative analysis of Timer fluorescence data. This toolkit is expected to significantly enhance the utility of experimental systems using Fluorescent Timer proteins, particularly the Tocky tools, by standardizing methods for analyzing dynamic cellular events and responses.
format Preprint
id arxiv_https___arxiv_org_abs_2411_09386
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle TockyLocus: Quantitative Analysis Methods for Flow Cytometric Fluorescent Timer Data
Ono, Masahiro
Quantitative Methods
Fluorescent Timer proteins, which spontaneously change their emission spectra over time, are valuable tools for analyzing temporal changes in cellular activities at the single-cell level. Traditional analysis of Fluorescent Timer data has mostly relied on conventional flow cytometric methods, which lacks the sophistication needed for detailed quantitative analysis. Recently, we developed the Timer-of-Cell-Kinetics-and-Activity (Tocky) tools, employing transgenic reporter systems using an mCherry mutant Timer protein, Fast-FT, to analyze these changes, implementing data preprocessing methods for Timer fluorescence. Despite this advancement, the computational implementation of effective quantitative analysis methods for Fluorescent Timer data has been lacking. In this study, we introduce rigorous algorithms for a data categorization method, designated as the Tocky locus approach, which uses normalized and trigonometric-transformed Fluorescent Timer data, suitable for quantitative analysis. We found that the five-locus approach, which categorizes Timer fluorescence into five categories, optimally captures the dynamics of Timer profiles, thus enabling effective quantitative statistical analysis and biological interpretation. The current study has developed comprehensive tools for data categorization, visualization, and statistical analysis optimized to Tocky Locus data. These algorithms have been implemented in the TockyLocus package, a new R package designed to facilitate the quantitative analysis of Timer fluorescence data. This toolkit is expected to significantly enhance the utility of experimental systems using Fluorescent Timer proteins, particularly the Tocky tools, by standardizing methods for analyzing dynamic cellular events and responses.
title TockyLocus: Quantitative Analysis Methods for Flow Cytometric Fluorescent Timer Data
topic Quantitative Methods
url https://arxiv.org/abs/2411.09386