Clusterwise Peak Detection and Filtering Based on Spatial Distribution To Efficiently Mine Mass Spectrometry Imaging Data

Next-generation pathology practices with mass spectrometry imaging

Mass spectrometry imaging (MSI) is a powerful technique that reveals the spatial distribution of various molecules in biological samples, and it is widely used in pathology-related research. In this review, we summarize common MSI techniques, including matrix-assisted laser desorption/ionization and desorption electrospray ionization MSI, and their applications in pathological research, including disease diagnosis, microbiology, and drug discovery. We also describe the improvements of MSI, focusing on the accumulation of imaging data sets, expansion of chemical coverage, and identification of biological significant molecules, that have prompted the evolution of MSI to meet the requirements of pathology practices. Overall, this review details the applications and improvements of MSI techniques, demonstrating the potential of integrating MSI techniques into next-generation pathology practices.

Open Search-Based Proteomics Reveals Widespread Tryptophan Modifications Associated with Hypoxia in Lung Cancer

The tryptophan residue has a large hydrophobic surface that plays a unique role in the folded protein conformation and functions. Tryptophan modifications are presumably to be readily detected in proteins due to the vulnerability of the indole structure to electrophilic attacks. In this study, we report a systematic identification of sequence variations at tryptophan, termed tryptophan variants, from the proteome of patients with nonsmall cell lung cancer (NSCLC). Using shotgun proteomics and a modified open search algorithm, 25 tryptophan variants on 2481 sites in over 858 proteins were identified. Among these, 6 tryptophan variants are previously identified, 15 are newly annotated, and 4 are still unknown, most of which are involved in the cascade of oxidation in the blood microparticle. Remarkably, Trp313 of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was up-oxidized whereas Trp16 and Trp38 of hemoglobin (HBB) were down-oxidized in NCSLC tissues. The results were further supported by an independent cohort of 103 lung adenocarcinoma samples, reflecting a negative feedback and potential detoxification mechanism against tumor glycolysis and hypoxia. Overall, the study reports a quick approach to explore tryptophan variants at the proteomic scale. Our findings highlight the predominant role of tryptophan oxidation in regulating the redox balance of cancer cells and its potential role as prognostic biomarker for patients with NSCLC.

A versatile deep learning architecture for classification and label-free prediction of hyperspectral images

Hyperspectral imaging is a technique that provides rich chemical or compositional information not regularly available to traditional imaging modalities such as intensity imaging or color imaging based on the reflection, transmission, or emission of light. Analysis of hyperspectral imaging often relies on machine learning methods to extract information. Here, we present a new flexible architecture, the U-within-U-Net, that can perform classification, segmentation, and prediction of orthogonal imaging modalities on a variety of hyperspectral imaging techniques. Specifically, we demonstrate feature segmentation and classification on the Indian Pines hyperspectral dataset and simultaneous location prediction of multiple drugs in mass spectrometry imaging of rat liver tissue. We further demonstrate label-free fluorescence image prediction from hyperspectral stimulated Raman scattering microscopy images. The applicability of the U-within-U-Net architecture on diverse datasets with widely varying input and output dimensions and data sources suggest that it has great potential in advancing the use of hyperspectral imaging across many different application areas ranging from remote sensing, to medical imaging, to microscopy.

MSIWarp: A General Approach to Mass Alignment in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a technique that provides comprehensive molecular information with high spatial resolution from tissue. Today, there is a strong push toward sharing data sets through public repositories in many research fields where MSI is commonly applied; yet, there is no standardized protocol for analyzing these data sets in a reproducible manner. Shifts in the mass-to-charge ratio (m/z) of molecular peaks present a major obstacle that can make it impossible to distinguish one compound from another. Here, we present a label-free m/z alignment approach that is compatible with multiple instrument types and makes no assumptions on the sample's molecular composition. Our approach, MSIWarp (https://github.com/horvatovichlab/MSIWarp), finds an m/z recalibration function by maximizing a similarity score that considers both the intensity and m/z position of peaks matched between two spectra. MSIWarp requires only centroid spectra to find the recalibration function and is thereby readily applicable to almost any MSI data set. To deal with particularly misaligned or peak-sparse spectra, we provide an option to detect and exclude spurious peak matches with a tailored random sample consensus (RANSAC) procedure. We evaluate our approach with four publicly available data sets from both time-of-flight (TOF) and Orbitrap instruments and demonstrate up to 88% improvement in m/z alignment.