Automated Fluorescence Lifetime Imaging High-Content Analysis of Förster Resonance Energy Transfer between Endogenously Labeled Kinetochore Proteins in Live Budding Yeast Cells

Author:

Guo Wenjun12,Kumar Sunil12,Görlitz Frederik1,Garcia Edwin1,Alexandrov Yuriy12,Munro Ian1,Kelly Douglas J.13,Warren Sean4,Thorpe Peter25,Dunsby Christopher126,French Paul12ORCID

Affiliation:

1. Photonics Group, Department of Physics, Imperial College London, London, UK

2. Francis Crick Institute, London, UK

3. RIKEN Center for Biodynamic Systems Research, Kobe, Japan

4. Garvan Institute of Medical Research, University of New South Wales, Sydney, New South Wales, Australia

5. Queen Mary University of London, London, UK

6. Centre for Pathology, Imperial College London, London, UK

Abstract

We describe an open-source automated multiwell plate fluorescence lifetime imaging (FLIM) methodology to read out Förster resonance energy transfer (FRET) between fluorescent proteins (FPs) labeling endogenous kinetochore proteins (KPs) in live budding yeast cells. The low copy number of many KPs and their small spatial extent present significant challenges for the quantification of donor fluorescence lifetime in the presence of significant cellular autofluorescence and photobleaching. Automated FLIM data acquisition was controlled by µManager and incorporated wide-field time-gated imaging with optical sectioning to reduce background fluorescence. For data analysis, we used custom MATLAB-based software tools to perform kinetochore foci segmentation and local cellular background subtraction and fitted the fluorescence lifetime data using the open-source FLIMfit software. We validated the methodology using endogenous KPs labeled with mTurquoise2 FP and/or yellow FP and measured the donor fluorescence lifetimes for foci comprising 32 kinetochores with KP copy numbers as low as ~2 per kinetochore under an average labeling efficiency of 50%. We observed changes of median donor lifetime ≥250 ps for KPs known to form dimers. Thus, this FLIM high-content analysis platform enables the screening of relatively low-copy-number endogenous protein–protein interactions at spatially confined macromolecular complexes.

Funder

Wellcome Trust

Francis Crick Institute

Biotechnology and Biological Sciences Research Council

Medical Research Council

Publisher

Elsevier BV

Subject

Medical Laboratory Technology,Computer Science Applications

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