Divide and Conquer: A Flexible Deep Learning Strategy for Exploring Metabolic Heterogeneity from Mass Spectrometry Imaging Data

Mass spectrometry imaging for spatially resolved multi-omics molecular mapping

The recent upswing in the integration of spatial multi-omics for conducting multidimensional information measurements is opening a new chapter in biological research. Mapping the landscape of various biomolecules including metabolites, proteins, nucleic acids, etc., and even deciphering their functional interactions and pathways is believed to provide a more holistic and nuanced exploration of the molecular intricacies within living systems. Mass spectrometry imaging (MSI) stands as a forefront technique for spatially mapping the metabolome, lipidome, and proteome within diverse tissue and cell samples. In this review, we offer a systematic survey delineating different MSI techniques for spatially resolved multi-omics analysis, elucidating their principles, capabilities, and limitations. Particularly, we focus on the advancements in methodologies aimed at augmenting the molecular sensitivity and specificity of MSI; and depict the burgeoning integration of MSI-based spatial metabolomics, lipidomics, and proteomics, encompassing the synergy with other imaging modalities. Furthermore, we offer speculative insights into the potential trajectory of MSI technology in the future.

[Research progress of deep learning applications in mass spectrometry imaging data analysis]

Mass spectrometry imaging (MSI) is a promising method for characterizing the spatial distribution of compounds. Given the diversified development of acquisition methods and continuous improvements in the sensitivity of this technology, both the total amount of generated data and complexity of analysis have exponentially increased, rendering increasing challenges of data postprocessing, such as large amounts of noise, background signal interferences, as well as image registration deviations caused by sample position changes and scan deviations, and etc. Deep learning (DL) is a powerful tool widely used in data analysis and image reconstruction. This tool enables the automatic feature extraction of data by building and training a neural network model, and achieves comprehensive and in-depth analysis of target data through transfer learning, which has great potential for MSI data analysis. This paper reviews the current research status, application progress and challenges of DL in MSI data analysis, focusing on four core stages: data preprocessing, image reconstruction, cluster analysis, and multimodal fusion. The application of a combination of DL and mass spectrometry imaging in the study of tumor diagnosis and subtype classification is also illustrated. This review also discusses trends of development in the future, aiming to promote a better combination of artificial intelligence and mass spectrometry technology.

Mass spectrometry imaging methods for visualizing tumor heterogeneity

Profiling spatial distributions of lipids, metabolites, and proteins in tumors can reveal unique cellular microenvironments and provide molecular evidence for cancer cell dysfunction and proliferation. Mass spectrometry imaging (MSI) is a label-free technique that can be used to map biomolecules in tumors in situ. Here, we discuss current progress in applying MSI to uncover molecular heterogeneity in tumors. First, the analytical strategies to profile small molecules and proteins are outlined, and current methods for multimodal imaging to maximize biological information are highlighted. Second, we present and summarize biological insights obtained by MSI of tumor tissue. Finally, we discuss important considerations for designing MSI experiments and several current analytical challenges.

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.

DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a high-throughput imaging technique capable of the qualitative and quantitative in situ detection of thousands of ions in biological samples. Ion image representation is a technique that produces a low-dimensional vector embedded with significant spectral and spatial information on an ion image, which further facilitates the distance-based similarity measurement for the identification of colocalized ions. However, given the low signal-to-noise ratios inherent in MSI data coupled with the scarcity of annotated data sets, achieving an effective ion image representation for each ion image remains a challenge. In this study, we propose DeepION, a novel deep learning-based method designed specifically for ion image representation, which is applied to the identification of colocalized ions and isotope ions. In DeepION, contrastive learning is introduced to ensure that the model can generate the ion image representation in a self-supervised manner without manual annotation. Since data augmentation is a crucial step in contrastive learning, a unique data augmentation strategy is designed by considering the characteristics of MSI data, such as the Poisson distribution of ion abundance and a random pattern of missing values, to generate plentiful ion image pairs for DeepION model training. Experimental results of rat brain tissue MSI show that DeepION outperforms other methods for both colocalized ion and isotope ion identification, demonstrating the effectiveness of ion image representation. The proposed model could serve as a crucial tool in the biomarker discovery and drug development of the MSI technique.

Mapping Spatiotemporal Heterogeneity in Multifocal Breast Tumor Progression by Noninvasive Ultrasound Elastography-Guided Mass Spectrometry Imaging Strategy

Spatiotemporal heterogeneity of tumors provides an escape mechanism for breast cancer cells, which can obstruct the investigation of tumor progression. While molecular profiling obtained from mass spectrometry imaging (MSI) is rich in biochemical information, it lacks the capacity for in vivo analysis. Ultrasound diagnosis has a high diagnostic accuracy but low chemical specificity. Here, we describe a noninvasive ultrasound elastography (UE)-guided MSI strategy (UEg-MSI) that integrates physical and biochemical characteristics of tumors acquired from both in vivo and in vitro imaging. Using UEg-MSI, both elasticity histopathology metabolism "fingerprints" and reciprocal crosstalk are revealed, indicating the intact, multifocal spatiotemporal heterogeneity of spontaneous tumorigenesis of the breast from early, middle, and late stages. Our results demonstrate a gradual increase in malignant degree of primary focus in cervical and thoracic mammary glands. This progression is characterized by increased stiffness according to elasticity scores, histopathological changes from hyperplasia to increased nests of neoplastic cells and necrotic areas, and regional metabolic heterogeneity and reprogramming at the spatiotemporal level. De novo fatty acid (FA) synthesis focused on independent (such as ω-9 FAs) and dependent (such as ω-6 FAs) dietary FA intake in the core cancerous nest areas in the middle and late stages of tumor or in the peripheral microareas in the early stage of the tumor. SM-Cer signaling pathway and GPs biosynthesis and degradation, as well as glycerophosphoinositol intensity, changed in multiple characteristic microareas. The UEg-MSI strategy holds the potential to expand MSI applications and enhance ultrasound-mediated cancer diagnosis. It offers new insight into early cancer discovery and the occurrence of metastasis.

Lipid metabolism analysis in esophageal cancer and associated drug discovery

Esophageal cancer is an upper gastrointestinal malignancy with a bleak prognosis. It is still being explored in depth due to its complex molecular mechanisms of occurrence and development. Lipids play a crucial role in cells by participating in energy supply, biofilm formation, and signal transduction processes, and lipid metabolic reprogramming also constitutes a significant characteristic of malignant tumors. More and more studies have found esophageal cancer has obvious lipid metabolism abnormalities throughout its beginning, progress, and treatment resistance. The inhibition of tumor growth and the enhancement of antitumor therapy efficacy can be achieved through the regulation of lipid metabolism. Therefore, we reviewed and analyzed the research results and latest findings for lipid metabolism and associated analysis techniques in esophageal cancer, and comprehensively proved the value of lipid metabolic reprogramming in the evolution and treatment resistance of esophageal cancer, as well as its significance in exploring potential therapeutic targets and biomarkers.

Charged chiral derivatization for enantioselective imaging of D-,L-2-hydroxyglutaric acid using ion mobility spectrometry/mass spectrometry

A newly synthesized charged chiral tag-enabled enantioselective imaging of D-,L-2-hydroxyglutaric acid, which are independently associated with the regulation of DNA methylation. The tag-conjugated diastereomers were ionized efficiently through MALDI, separated by ion mobility spectrometry, and further separated from other molecules in mass spectrometry. On-tissue chiral derivatization using the tag facilitated the visualization of different distributions of the two isomers in the mouse testis.