HUSCAP logo Hokkaido Univ. logo

Hokkaido University Collection of Scholarly and Academic Papers >
Theses >
博士 (生命科学) >

Concentration and Brightness Imaging for Fluorescent Molecules in Cells: Statistical Image Analysis by Empirical Bayes Method

Files in This Item:
Ryosuke_Fukushima.pdf7.65 MBPDFView/Open
Please use this identifier to cite or link to this item:https://doi.org/10.14943/doctoral.k14607
Related Items in HUSCAP:

Title: Concentration and Brightness Imaging for Fluorescent Molecules in Cells: Statistical Image Analysis by Empirical Bayes Method
Other Titles: 細胞内蛍光分子の濃度と輝度分布定量:経験ベイズ法による統計的画像解析
Authors: 福島, 綾介1 Browse this author
Authors(alt): Fukushima, Ryosuke1
Issue Date: 30-Jun-2021
Abstract: Background: Fluorescence live cell imaging is useful for monitoring the localization and distribution of fluorescently labeled molecules in cells. However, monitoring the concentration and oligomeric state of these molecules is difficult. The concentration of molecules is strongly associated with the advancement of chemical reactions in cells, and these reactions regulate cellular functions. Furthermore, some proteins form oligomers during cell signaling, changing their oligomeric state. Thus, quantifying the concentration and oligomeric state would yield valuable information about the regulations and functions of cells. In this study, we have developed statistical methods for quantifying the concentration and oligomeric state of fluorescently labeled molecules in cells. Problem and Solutions: Number and Brightness (N&B) analysis statistically determines the number and brightness of particles, which reflect the concentration and oligomeric state, respectively. N&B analysis is used to analyze the temporal fluctuation of fluorescence images obtained using confocal laser scanning microscopy (CLSM). However, because of low excitation and a limited number of images, the statistical accuracy and precision of this analysis are limited in actual experiments with fluorescent proteins. In one of our methods, we applied maximum a posteriori (MAP) estimation, along with the empirical Bayes (EB) method (referred to as EB−MAP). In EB−MAP, we constructed a statistical model for effectively using spatial information. We assumed that the number of particles at a pixel and that at the surrounding pixels are similar. The assumption of the similarity would be realistic because of the diffraction limit and overlap of confocal volume during sampling. Results: We conducted simulations and experiments and compared results to evaluate the accuracy and precision of EB−MAP. The results showed that the precision of EB−MAP was greater by an order of magnitude in terms of the number of particles and 1.5 times higher in terms of the brightness of particles than conventional N&B analysis. Conclusion: We have developed methods for monitoring the concentration and oligomeric state of fluorescently labeled molecules in cells. We have demonstrated that the developed methods are feasible and achieve high accuracy and precision. Our methods have a wide range of applications in the field of fluorescence live cell imaging. Furthermore, these methods would contribute to the understanding of the dynamic processes in protein oligomerization in cells.
Conffering University: 北海道大学
Degree Report Number: 甲第14607号
Degree Level: 博士
Degree Discipline: 生命科学
Examination Committee Members: (主査) 教授 金城 政孝, 教授 芳賀 永, 准教授 中岡 愼治, 講師 北村 朗
Degree Affiliation: 生命科学院(生命科学専攻)
Type: theses (doctoral)
URI: http://hdl.handle.net/2115/86421
Appears in Collections:課程博士 (Doctorate by way of Advanced Course) > 生命科学院(Graduate School of Life Science)
学位論文 (Theses) > 博士 (生命科学)

Export metadata:

OAI-PMH ( junii2 , jpcoar_1.0 )

MathJax is now OFF:


 

 - Hokkaido University