Live Cell Fluorescence Microscopy – From Sample Preparation to Numbers and Plots
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Fluorescence microscopy images of biological samples contain a high amount of information. However, rigorous data analysis is required for a reliable assessment of the significance of changes in protein localization or fluorescence intensity changes. Most often, only the colocalization of two fluorescence signals is formally analyzed, where applicable. When additional analysis is made, both segmentation and measurements are performed manually. This is time-consuming, tedious and prone to experimenter error and bias. In our workflow, cells are immobilized on the microscope cover glass by a small agarose block, which enables the imaging of live cells in a state close to the cultivation conditions and enables the addition of chemicals during a time-lapse experiment. Cells in microscopy images are segmented using the Cellpose software and analyzed using custom-made Fiji (ImageJ) macros. Multiple parameters are measured automatically for each cell and the results table can be easily processed using GraphPad Prism or provided unix command line (bash) scripts. The use of the custom-written Fiji macros enables batch analysis across multiple large and independent data sets in a single run, in a non-biased manner.
Key features
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Simple sample preparation that enables immobilization of live cell-walled cells in a monolayer, imaging at almost natural conditions and direct treatment during a time-lapse experiment.
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Analysis developed for budding yeast with GFP-tagged proteins that allows batch-analysis of large microscopy data sets, regardless of imaging platform, cell/organelle type and labeling.
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Multiple parameters are reported for each object, including size, fluorescence intensity (both in the plasma membrane and cytosol) and the number of microdomains (i.e., areas of high local intensity).
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Largely automated analysis, requiring only verification of segmentation from the user, with clear-cut rules, blinded on demand.
This protocol is used in
Microbiol Spectr (2022), DOI: 10.1128/spectrum.01961-22 (Zahumenský et al., 2022); Microbiol Spectr (2022), DOI: 10.1128/spectrum.02489-22 (Balazova et al., 2022); J Cell Sci (2023), DOI: 10.1242/jcs.260554 (Vesela et al., 2023)
