4.3 Article

An easy-to-use graphical user interface for mass spectrometry imaging analysis

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ELSEVIER
DOI: 10.1016/j.ijms.2023.117105

Keywords

Mass spectrometry imaging; MCR-ALS; ROI; MATLAB; Clustering; Image registration

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The authors developed a user-friendly graphical interface, MSItoolbox, which provides default parameters for beginners to visualize and compress large MSI datasets without programming knowledge. This toolbox supports 2D and 3D visualization, ROI method for data compression, and common functions such as single-ion imaging display and image registration. It also offers PCA and t-SNE for data interpretation and mining, and the ability to search for small molecule compounds based on a homemade proteomics mass spectrometry library.
The authors developed a user-friendly graphical interface, MSItoolbox, for beginners without programming knowledge by providing most of the default parameters for guidance. The toolbox can provide 2D and 3D visualization based on mass spectra preprocessing and modeling of MSI data from one or more experiments. The ROI method is applied to significantly compress the original MS data while preserving most of the relevant information of the original data without losing instrument quality accuracy. The toolbox can process the MSI dataset with a large data file size. In the testing example, with appropriate parameters, it was able to compress 244 GB of data files to 14.7 MB in a commonly used laptop without suffering the out-of-memory problem in less than 2.5 h. This toolbox supports importing all pixels or pixels in a specified area in a customized shape. The software provides commonly used functions: single-ion imaging display, MCR-ALS, spatial segmentation, ROI mass spectra average, and image registration. PCA and t-SNE were also provided to help with data interpretation, evaluation of data reliability, and data mining to explore important spatiotemporal relationships in the analyzed samples. Users can import a homemade proteomics mass spectrometry library based on LC-MS to search for small molecule candidate compounds. Image registration was included to combine the information of stained images with MSI and give the potential to use supervised methods for the analysis of MSI data.

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