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Image Processing for Bioluminescence Resonance Energy Transfer Measurement-. | LitMetric

Image Processing for Bioluminescence Resonance Energy Transfer Measurement-.

Front Comput Neurosci

Centre National de la Recherche Scientifique, UMR-5203, Institut de Génomique Fonctionnelle, Montpellier, France.

Published: January 2018

A growing number of tools now allow live recordings of various signaling pathways and protein-protein interaction dynamics in time and space by ratiometric measurements, such as Bioluminescence Resonance Energy Transfer (BRET) Imaging. Accurate and reproducible analysis of ratiometric measurements has thus become mandatory to interpret quantitative imaging. In order to fulfill this necessity, we have developed an open source toolset for Fiji--allowing a systematic analysis, from image processing to ratio quantification. We share this open source solution and a step-by-step tutorial at https://github.com/ychastagnier/BRET-Analyzer. This toolset proposes (1) image background subtraction, (2) image alignment over time, (3) a composite thresholding method of the image used as the denominator of the ratio to refine the precise limits of the sample, (4) pixel by pixel division of the images and efficient distribution of the ratio intensity on a pseudocolor scale, and (5) quantification of the ratio mean intensity and standard variation among pixels in chosen areas. In addition to systematize the analysis process, we show that the allows proper reconstitution and quantification of the ratiometric image in time and space, even from heterogeneous subcellular volumes. Indeed, analyzing twice the same images, we demonstrate that compared to standard analysis precisely define the luminescent specimen limits, enlightening proficient strengths from small and big ensembles over time. For example, we followed and quantified, in live, scaffold proteins interaction dynamics in neuronal sub-cellular compartments including dendritic spines, for half an hour. In conclusion, provides a complete, versatile and efficient toolset for automated reproducible and meaningful image ratio analysis.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5767221PMC
http://dx.doi.org/10.3389/fncom.2017.00118DOI Listing

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