Abstract

Understanding cellular signaling flow is required to comprehend living organisms. Various live cell imaging tools have been developed but challenges remain due to complex cross-talk between pathways and response heterogeneities among cells. We have focused on multiplex live cell imaging for statistical analysis to address the difficulties and developed simple multiple fluorescence imaging system to quantify cell signaling at single-cell resolution using Förster Resonance Energy Transfer (FRET)-based chimeric molecular sensors comprised of fluorescent proteins and dyes. The dye-fluorescent protein conjugate is robust for a wide selection of combinations, facilitating rearrangement for coordinating emission profile of molecular sensors to adjust for visualization conditions, target phenomena, and simultaneous use. As the molecular sensor could exhibit highly sensitive in detection for protease activity, we customized molecular sensor of caspase-9 and combine the established sensor for caspase-3 to validate the system by observation of caspase-9 and -3 dynamics simultaneously, key signaling flow of apoptosis. We found cumulative caspase-9 activity rather than reaction rate inversely regulated caspase-3 execution times for apoptotic cell death. Imaging-derived statistics were thus applied to discern the dominating aspects of apoptotic signaling unavailable by common live cell imaging and proteomics protein analysis. Adopted to various visualization targets, the technique can discriminate between rivalling explanations and should help unravel other protease involved signaling pathways.

Details

Title
Live imaging of apoptotic signaling flow using tunable combinatorial FRET-based bioprobes for cell population analysis of caspase cascades
Author
Suzuki, Miho 1 ; Shindo, Yutaka 2 ; Yamanaka, Ryu 3 ; Oka, Kotaro 4 

 Saitama University, Department of Applied Chemistry, Graduate School of Science and Engineering, Saitama, Japan (GRID:grid.263023.6) (ISNI:0000 0001 0703 3735) 
 Keio University, Department of Bioscience and Informatics, Faculty of Science and Technology, Kanagawa, Japan (GRID:grid.26091.3c) (ISNI:0000 0004 1936 9959) 
 Sanyo-Onoda City University, Faculty of Pharmaceutical Sciences, Yamaguchi, Japan (GRID:grid.469470.8) (ISNI:0000 0004 0617 5071) 
 Keio University, Department of Bioscience and Informatics, Faculty of Science and Technology, Kanagawa, Japan (GRID:grid.26091.3c) (ISNI:0000 0004 1936 9959); Kaohsiung Medical University, Graduate Institute of Medicine, Kaohsiung, Taiwan (GRID:grid.412019.f) (ISNI:0000 0000 9476 5696); Waseda University, Waseda Research Institute for Science and Engineering, Tokyo, Japan (GRID:grid.5290.e) (ISNI:0000 0004 1936 9975) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2747549479
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.