Abstract
Mass Spectrometry Imaging (MSI) is an established and still evolving technique for the spatial analysis of molecular co-location in biological samples. Nowadays, MSI is expanding into new domains such as clinical pathology. In order to increase the value of MSI data, software for visual analysis is required that is intuitive and technique independent. Here, we present QUIMBI (QUIck exploration tool for Multivariate BioImages) a new tool for the visual analysis of MSI data. QUIMBI is an interactive visual exploration tool that provides the user with a convenient and straightforward visual exploration of morphological and spectral features of MSI data. To improve the overall quality of MSI data by reducing non-tissue specific signals and to ensure optimal compatibility with QUIMBI, the tool is combined with the new pre-processing tool ProViM (Processing for Visualization and multivariate analysis of MSI Data), presented in this work. The features of the proposed visual analysis approach for MSI data analysis are demonstrated with two use cases. The results show that the use of ProViM and QUIMBI not only provides a new fast and intuitive visual analysis, but also allows the detection of new co-location patterns in MSI data that are difficult to find with other methods.
Highlights
Mass Spectrometry Imaging (MSI) is an established and still evolving technique for the spatial analysis of molecular co-location in biological samples
The presented method for visual MSI analysis consists of two building blocks: (1) the processing tool to prepare the MSI data (ProViM) and (2) the interactive visualization tool (QUIMBI)
This modular approach allows users, to apply both tools together consecutively, or to combine one of the tools with another one from the realm of MSI software
Summary
Mass Spectrometry Imaging (MSI) is an established and still evolving technique for the spatial analysis of molecular co-location in biological samples. Interactive visual exploration of digital MSI data has the potential to become an efficient and effective method for tissue section interpretation with only little training as it can provide the additional level of molecular information in contrast to the classical histological staining. The open source tool pyBASIS8 offers an extensive pre-processing pipeline with mass correction, normalization and variance stabilization transformation for large-scale sample sets and provides an automatic identification procedure of matrix peaks via k-means clustering Commercial tools such as SCiLS (Bruker Daltonics), Xcalibur/ImageQuest (Thermo Fisher Scientific) and High Definition Imaging (HDI, Waters Corporation) provide straightforward pre-processing, a limited interactive visualization of mass channels and statistical methods such as PCA and k-means clustering. Since this detection is fully automated, it does not provide the possibility to manually investigate and correct the result
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