Abstract
We introduce here MATtrack, an open source MATLAB-based computational platform developed to process multi-Tiff files produced by a photo-conversion time lapse protocol for live cell fluorescent microscopy. MATtrack automatically performs a series of steps required for image processing, including extraction and import of numerical values from Multi-Tiff files, red/green image classification using gating parameters, noise filtering, background extraction, contrast stretching and temporal smoothing. MATtrack also integrates a series of algorithms for quantitative image analysis enabling the construction of mean and standard deviation images, clustering and classification of subcellular regions and injection point approximation. In addition, MATtrack features a simple user interface, which enables monitoring of Fluorescent Signal Intensity in multiple Regions of Interest, over time. The latter encapsulates a region growing method to automatically delineate the contours of Regions of Interest selected by the user, and performs background and regional Average Fluorescence Tracking, and automatic plotting. Finally, MATtrack computes convenient visualization and exploration tools including a migration map, which provides an overview of the protein intracellular trajectories and accumulation areas. In conclusion, MATtrack is an open source MATLAB-based software package tailored to facilitate the analysis and visualization of large data files derived from real-time live cell fluorescent microscopy using photoconvertible proteins. It is flexible, user friendly, compatible with Windows, Mac, and Linux, and a wide range of data acquisition software. MATtrack is freely available for download at eleceng.dit.ie/courtney/MATtrack.zip.
Highlights
Optical highlighters comprise a class of fluorescent proteins which either turn on (Photo-Activation, PA) or change (Photo-Conversion, PC) their emission wave length in response to photo-stimulation with Ultra-Violet light [1,2]
A typical PC experiment consists of defining a Region of Interest (ROI) in the green channel and photo-converting the ROI to red using a short laser pulse
While software packages are readily available for Fluorescence Recovery After Photobleaching (FRAP) datasets (e.g. Virtual FRAP, easyFRAP, FRAPCalc [19]), key experimental differences between FRAP and PC protocols make these packages ill-suited for analysis of PC datasets [20]
Summary
Optical highlighters comprise a class of fluorescent proteins which either turn on (Photo-Activation, PA) or change (Photo-Conversion, PC) their emission wave length in response to photo-stimulation with Ultra-Violet light [1,2]. Typical analysis of PC data requires the extraction of fluorescence intensity values within the ROIs, widely handled by commercial microscope software control packages in conjunction with the open source project, ImageJ [14], and its associated plugins before using spreadsheet software to manually normalize and plot intensity values from different ROIs [15,16,17]. This process can be very time consuming and prone to error, prompting a demand for a new software enabling the automated analysis of PC datasets [18]. A more dedicated analysis package with tailored noise filtering and segmentation algorithms is required in order to successfully quantify and retain the low intensity, high frequency fluorescent signals obtained from PC experiments
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