Development of an object-based image analysis tool for mass spectrometry imaging ion classification

Mass spectrometry imaging (MSI) is an analytical technique that can detect and visualize thousands of m/z values resolved in two- and three-dimensional space. These m/z values lead to hundreds of molecular annotations, including on-tissue and background ions. Discrimination of sample-related analytes from ambient ions conventionally involves manual investigation of each ion heatmap, which requires significant researcher time and effort (for a single tissue image, it can take an hour to determine on-tissue and off-tissue species). Moreover, manual investigation lends itself to subjectivity. Herein, we present the utility of an ion classification tool (ICT) developed using object-based image analysis in MATLAB. The ICT functions by segmenting ion heatmap images into on-tissue and off-tissue objects through binary conversion. The binary images are analyzed and within seconds used to classify the ions as on-tissue or background using a binning approach based on the number of detected objects. In a representative dataset with 50 randomly selected annotations, the ICT was able to accurately classify 45/50 ions as on-tissue or background.

Automated summary

Mass spectrometry imaging (MSI) is an analytical technique that detects and visualizes thousands of m/z values in two- and three-dimensional space. Discriminating sample-related analytes from ambient ions conventionally requires manual investigation of each ion heatmap, which is time-consuming and subjective. In this study, an ion classification tool (ICT) was developed using object-based image analysis in MATLAB, which accurately classified ions as on-tissue or background based on binary conversion and the number of detected objects.