Quantitative Mass Spectrometry Imaging Using Multivariate Curve Resolution and Deep Learning: A Case Study

Interpolation of Imaging Mass Spectrometry Data by a Window-Based Adversarial Autoencoder Method

Imaging mass spectrometry (IMS) is a technique for simultaneously acquiring the expression and distribution of molecules on the surface of a sample, and it plays a crucial role in spatial omics research. In IMS, the time cost and instrument load required for large data sets must be considered, as IMS typically involves tens of thousands of pixels or more. In this study, we developed a high-resolution method for IMS data reconstruction using a window-based Adversarial Autoencoder (AAE) method. We acquired IMS data from partial cerebellum regions of mice with a pitch size of 75 μm and then down-sampled the data to a pitch size of 150 μm, selecting 22 m/z peak intensity values per pixel. We established an AAE model to generate three pixels from the surrounding nine pixels within a window to reconstruct the image data at a pitch size of 75 μm. Compared with two alternative interpolation methods, Bilinear and Bicubic interpolation, our window-based AAE model demonstrated superior performance on image evaluation metrics for the validation data sets. A similar model was constructed for larger mouse kidney tissues, where the AAE model achieved high image evaluation metrics. Our method is expected to be valuable for IMS measurements of large animal organs across extensive areas.

A Machine Learning-Driven Comparison of Ion Images Obtained by MALDI and MALDI-2 Mass Spectrometry Imaging

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) enables label-free imaging of biomolecules in biological tissues. However, many species remain undetected due to their poor ionization efficiencies. MALDI-2 (laser-induced post-ionization) is the most widely used post-ionization method for improving analyte ionization efficiencies. Mass spectra acquired using MALDI-2 constitute a combination of ions generated by both MALDI and MALDI-2 processes. Until now, no studies have focused on a detailed comparison between the ion images (as opposed to the generated m/z values) produced by MALDI and MALDI-2 for mass spectrometry imaging (MSI) experiments. Herein, we investigated the ion images produced by both MALDI and MALDI-2 on the same tissue section using correlation analysis (to explore similarities in ion images for ions common to both MALDI and MALDI-2) and a deep learning approach. For the latter, we used an analytical workflow based on the Xception convolutional neural network, which was originally trained for human-like natural image classification but which we adapted to elucidate similarities and differences in ion images obtained using the two MSI techniques. Correlation analysis demonstrated that common ions yielded similar spatial distributions with low-correlation species explained by either poor signal intensity in MALDI or the generation of additional unresolved signals using MALDI-2. Using the Xception-based method, we identified many regions in the t-SNE space of spatially similar ion images containing MALDI and MALDI-2-related signals. More notably, the method revealed distinct regions containing only MALDI-2 ion images with unique spatial distributions that were not observed using MALDI. These data explicitly demonstrate the ability of MALDI-2 to reveal molecular features and patterns as well as histological regions of interest that are not visible when using conventional MALDI.

An Alternative Method for Quantitative Mass Spectrometry Imaging (q-MSI) of Dopamine Utilizing Fragments of Animal Tissue

Mass spectrometry imaging (MSI) is a well-known method for the ionization of molecules on tissue sections and the visualization of their localization. Recently, different sample preparation methods and new instruments have been used for MSI, and different molecules are becoming visible. On the other hand, although several quantification methods (q-MSI) have been proposed, there is still room for the development of a simplified procedure. Here, we have attempted to develop a reproducible and reliable quantification method using a calibration curve prepared from tissue debris of a frozen section of a sample when we trim the frozen blocks. We discuss the reproducibility of this method across different sample lots and the effect of the biological matrix (ion suppression) on our results. The quantitative performance was evaluated in terms of accuracy and relative standard deviation, and the reliability of the quantitative values obtained by matrix-assisted laser desorption/ionization-MSI was further evaluated by enzyme-linked immunosorbent assay (ELISA). Our q-MSI method for the quantification of dopamine in mouse brain tissue was found to be highly linear, accurate, and precise. The quantitative values obtained by MSI were found to be highly comparable (>85% similarity) to the results obtained by ELISA from the same tissue extracts.