Imaging mass spectrometry (IMS) is a powerful tool for detecting and visualizing biomolecules in tissue sections. The technology has been applied to several fields, and many researchers have started to apply it to pathological samples. However, it is very difficult for inexperienced users to extract meaningful signals from enormous IMS datasets, and the procedure is time-consuming. We have developed software, called IMS Convolution with regions of interest (ROI), to automatically extract meaningful signals from IMS datasets. The processing is based on the detection of common peaks within the ordered area in the IMS dataset. In this study, the IMS dataset from a mouse eyeball section was acquired by a mass microscope that we recently developed, and the peaks extracted by manual and automatic procedures were compared. The manual procedure extracted 16 peaks with higher intensity in mass spectra averaged in whole measurement points. On the other hand, the automatic procedure using IMS Convolution easily and equally extracted peaks without any effort. Moreover, the use of ROIs with IMS Convolution enabled us to extract the peak on each ROI area, and all of the 16 ion images on mouse eyeball tissue were from phosphatidylcholine species. Therefore, we believe that IMS Convolution with ROIs could automatically extract the meaningful peaks from large-volume IMS datasets for inexperienced users as well as for researchers who have performed the analysis.
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