PiQSARS: A pipeline for quantitative and statistical analyses of ratiometric fluorescent biosensors

Abstract

Genetically encoded ratiometric fluorescent probes are cutting-edge tools in biology. They allow precise and dynamic measurement of various physiological parameters within cell compartments. Because data extraction and analysis are time consuming and may lead to inconsistencies between results, we describe here a standardized pipeline for Semi-automated treatment of time-lapse fluorescence microscopy images. Quantification of individual cell signal. Statistical analysis of the data. First, a dedicated macro was developed using the FIJI software to reproducibly quantify the fluorescence ratio as a function of time. Raw data are then exported and analyzed using R and MATLAB softwares. Calculation and statistical analysis of selected graphic parameters are performed. In addition, a functional principal component analysis allows summarizing the dataset. Finally, a principal component analysis is performed to check consistency and final analysis is presented as a visual diagram. The method is adapted to any ratiometric fluorescent probe. As an example, the analysis of the cytoplasmic HyPer probe in response to an acute cell treatment with increasing amounts of hydrogen peroxide is shown. In conclusion, the pipeline allows to save time and analyze a larger amount of samples while reducing manual interventions and consequently increasing the robustness of the analysis.

Sharing data, sharing methods, sharing science.

“…Nevertheless, experiments may generate a considerable amount of data that need to be processed in different manners. A nice pipeline that offers a semiautomated treatment of time-lapse fluorescence microscopy images, quantification of individual cell signals, and a statistical analysis of the data was recently provided by Lévy et al [16] . The pipeline combines routines in the popular platforms FIJI, R, and MATLAB packages, automatizing the analysis of a large number of samples and increasing its statistical robustness.…”

A fast open-source Fiji-macro to quantify virus infection and transfection on single-cell level by fluorescence microscopy

“…, the inhibitor potency based on the IC 50 value) if one is able to extract this information from the image series. In recent years, several open-source software solutions have been developed that allow to extract image-based fluorescence information with single-cell resolution [6] , [7] , [8] , [9] , [10] , [11] , [12] . Each of these software solutions have different analysis strategies and aim for different fluorescence readouts.…”

“…For example, QuantIF [9] and FNMM [11] aim for the colocalization of the nucleus staining with another fluorescence signal. FluoQ [6] and PiQSARS [12] are tailored for time-lapse-based experiments, while Cytokit [ 8 ] is specialized in correlating single-cell parameters with spatial information (for details see Supplemental Materials Section S6: Image analysis software overview). However, as most software is highly specialized for specific tasks, the software solutions mentioned above may not necessarily meet the needs of all users.…”