Data Filtering and Its Prioritization in Pipelines for Spatial Segmentation of Mass Spectrometry Imaging

Untargeted Pixel-by-Pixel Imaging of Metabolite Ratio Pairs as a Novel Tool for Biomedical Discovery in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of structurally identified and yet-undefined metabolites across tissue cryosections. While numerous software packages enable pixel-by-pixel imaging of individual metabolites, the research community lacks a discovery tool that images all metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs informs discovery of unanticipated molecules contributing to shared metabolic pathways, uncovers hidden metabolic heterogeneity across cells and tissue subregions, and indicates single-timepoint flux through pathways of interest. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling and instrument drift, markedly enhances spatial image resolution, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent tool to enhance knowledge obtained from current spatial metabolite profiling technologies.

Modern machine-learning applications in ambient ionization mass spectrometry

This article provides a comprehensive overview of the applications of methods of machine learning (ML) and artificial intelligence (AI) in ambient ionization mass spectrometry (AIMS). AIMS has emerged as a powerful analytical tool in recent years, allowing for rapid and sensitive analysis of various samples without the need for extensive sample preparation. The integration of ML/AI algorithms with AIMS has further expanded its capabilities, enabling enhanced data analysis. This review discusses ML/AI algorithms applicable to the AIMS data and highlights the key advancements and potential benefits of utilizing ML/AI in the field of mass spectrometry, with a focus on the AIMS community.

Benchmarking spatial clustering methods with spatially resolved transcriptomics data

Spatial clustering, which shares an analogy with single-cell clustering, has expanded the scope of tissue physiology studies from cell-centroid to structure-centroid with spatially resolved transcriptomics (SRT) data. Computational methods have undergone remarkable development in recent years, but a comprehensive benchmark study is still lacking. Here we present a benchmark study of 13 computational methods on 34 SRT data (7 datasets). The performance was evaluated on the basis of accuracy, spatial continuity, marker genes detection, scalability, and robustness. We found existing methods were complementary in terms of their performance and functionality, and we provide guidance for selecting appropriate methods for given scenarios. On testing additional 22 challenging datasets, we identified challenges in identifying noncontinuous spatial domains and limitations of existing methods, highlighting their inadequacies in handling recent large-scale tasks. Furthermore, with 145 simulated data, we examined the robustness of these methods against four different factors, and assessed the impact of pre- and postprocessing approaches. Our study offers a comprehensive evaluation of existing spatial clustering methods with SRT data, paving the way for future advancements in this rapidly evolving field.

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.

Spatial segmentation of mass spectrometry imaging data featuring selected principal components

Spatial segmentation aims to find homogeneous/heterogeneous subgroups of spectra or ion images in mass spectrometry imaging (MSI) data. The maps it generated inform researchers of vital characteristics of the data and thus provide the basis for strategizing further biological analysis. Dimensional reduction and clustering are two basic steps of segmentation. Due to the variations in the quality, resolution, density of spectral information, and sizes, not all datasets could be segmented ideally with combinations of different dimensional reduction and clustering algorithms. Here, we proposed a segmentation pipeline that utilized pattern compression by principal component analysis (PCA) and represented by principal components. Instead of preprocessed or raw MSI data, normalized principal components were used for the segmentation process. Multiple datasets of rat brains and mouse kidneys were tested, and the proposed segmentation pipeline presented the obvious advantage of easy-to-use and can be readily intergraded with other existing innovative pipelines.

Emerging Computational Methods in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.

Spatially aware dimension reduction for spatial transcriptomics

Spatial transcriptomics are a collection of genomic technologies that have enabled transcriptomic profiling on tissues with spatial localization information. Analyzing spatial transcriptomic data is computationally challenging, as the data collected from various spatial transcriptomic technologies are often noisy and display substantial spatial correlation across tissue locations. Here, we develop a spatially-aware dimension reduction method, SpatialPCA, that can extract a low dimensional representation of the spatial transcriptomics data with biological signal and preserved spatial correlation structure, thus unlocking many existing computational tools previously developed in single-cell RNAseq studies for tailored analysis of spatial transcriptomics. We illustrate the benefits of SpatialPCA for spatial domain detection and explores its utility for trajectory inference on the tissue and for high-resolution spatial map construction. In the real data applications, SpatialPCA identifies key molecular and immunological signatures in a detected tumor surrounding microenvironment, including a tertiary lymphoid structure that shapes the gradual transcriptomic transition during tumorigenesis and metastasis. In addition, SpatialPCA detects the past neuronal developmental history that underlies the current transcriptomic landscape across tissue locations in the cortex.

iSegMSI: An Interactive Strategy to Improve Spatial Segmentation of Mass Spectrometry Imaging Data

Spatial segmentation is a critical procedure in mass spectrometry imaging (MSI)-based biochemical analysis. However, the commonly used unsupervised MSI segmentation methods may lead to inappropriate segmentation results as the MSI data is characterized by high dimensionality and low signal-to-noise ratio. This process can be improved by the incorporation of precise prior knowledge, which is hard to obtain in most cases. In this study, we show that the incorporation of partial or coarse prior knowledge from different sources such as reference images or biological knowledge may also help to improve MSI segmentation results. Here, we propose a novel interactive segmentation strategy for MSI data called iSegMSI, which incorporates prior information in the form of scribble-regularization of the unsupervised model to fine-tune the segmentation results. By using two typical MSI data sets (including a whole-body mouse fetus and human thyroid cancer), the present results demonstrate the effectiveness of the iSegMSI strategy in improving the MSI segmentations. Specifically, the method can be used to subdivide a region into several subregions specified by the user-defined scribbles or to merge several subregions into a single region. Additionally, these fine-tuned results are highly tolerant to the imprecision of the scribbles. Our results suggest that the proposed iSegMSI method may be an effective preprocessing strategy to facilitate the analysis of MSI data.

Data-Driven Deciphering of Latent Lesions in Heterogeneous Tissue Using Function-Directed t-SNE of Mass Spectrometry Imaging Data

Mass spectrometry imaging (MSI), which quantifies the underlying chemistry with molecular spatial information in tissue, represents an emerging tool for the functional exploration of pathological progression. Unsupervised machine learning of MSI datasets usually gives an overall interpretation of the metabolic features derived from the abundant ions. However, the features related to the latent lesions are always concealed by the abundant ion features, which hinders precise delineation of the lesions. Herein, we report a data-driven MSI data segmentation approach for recognizing the hidden lesions in the heterogeneous tissue without prior knowledge, which utilizes one-step prediction for feature selection to generate function-specific segmentation maps of the tissue. The performance and robustness of this approach are demonstrated on the MSI datasets of the ischemic rat brain tissues and the human glioma tissue, both possessing different structural complexity and metabolic heterogeneity. Application of the approach to the MSI datasets of the ischemic rat brain tissues reveals the location of the ischemic penumbra, a hidden zone between the ischemic core and the healthy tissue, and instantly discovers the metabolic signatures related to the penumbra. In view of the precise demarcation of latent lesions and the screening of lesion-specific metabolic signatures in tissues, this approach has great potential for in-depth exploration of the metabolic organization of complex tissue.

Advances in mass spectrometry imaging for toxicological analysis and safety evaluation of pharmaceuticals

Safety issues caused by pharmaceuticals have frequently occurred worldwide, posing a tremendous threat to human health. As an essential part of drug development, the toxicological analysis and safety evaluation is of great significance. In addition, the risk of pharmaceuticals accumulation in the environment and the monitoring of the toxicity from natural medicines have also received ongoing concerns. Due to a lack of spatial distribution information provided by common analytical methods, analyses that provide spatial dimensions could serve as complementary safety evaluation methods for better prediction and evaluation of drug toxicity. With advances in technical solutions and software algorithms, mass spectrometry imaging (MSI) has received increasing attention as a popular analytical tool that enables the simultaneous implementation of qualitative, quantitative, and localization without complex sample pretreatment and labeling steps. In recent years, MSI has become more attractive, powerful, and sensitive and has been applied in several scientific fields that can meet the safety assessment requirements. This review aims to cover a detailed summary of the various MSI technologies utilized in the biomedical and pharmaceutical area, including technical principles, advantages, current status, and future trends. Representative applications and developments in the safety-related issues of different pharmaceuticals and natural medicines are also described to provide a reference for pharmaceutical research, improve rational clinical medicine use, and ensure public safety.

Denoising of Mass Spectrometry Images via Inverse Maximum Signal Factors Analysis

Improving signal-to-noise and, thereby, image contrast is one of the key challenges needed to expand the useful applications of mass spectrometry imaging (MSI). Both instrumental and data analysis approaches are of importance. Univariate denoising techniques have been used to improve contrast in MSI images with varying levels of success. Additionally, various multivariate analysis (MVA) methods have proven to be effective for improving image contrast. However, the distribution of important but low intensity ions can be obscured in the MVA analysis, leading to a loss of chemically specific information. In this work we propose inverse maximum signal factors (MSF) denoising as an alternative approach to both denoising and multivariate analysis for MSI imaging. This approach differs from the standard MVA techniques in that the output is denoised images for each original mass peak rather than the frequently difficult to interpret scores and loadings. Five tests have been developed to optimize and validate the resulting denoised images. The algorithm has been tested on a range of simulated data with different levels of noise, correlated noise, varying numbers of underlying components, and nonlinear effects. In the simulations, an excellent correlation between the true images and the denoised images was observed for peaks with an original signal-to-noise ratio as low as 0.1, as long as there was sufficient intensity in the sum of the selected peaks. The power of the approach was then demonstrated on two time-of-flight secondary ion mass spectrometry (ToF-SIMS) images that contained largely uncorrelated noise and a laser post-ionization matrix-assisted laser desorption/ionization mass spectrometry (MALDI-2-MS) image that contained strongly correlated noise. The improvements in signal-to-noise increased with decreasing intensity of the original peaks. A signal-to-noise improvement of as much as two orders of magnitude was achieved for very low intensity peaks. MSF denoising is a powerful addition to the suite of image processing techniques available for studying mass spectrometry images.

Ratiometric Mass Spectrometry Imaging for Stain-Free Delineation of Ischemic Tissue and Spatial Profiling of Ischemia-Related Molecular Signatures

Mass spectrometry imaging (MSI) serves as an emerging tool for spatial profiling of metabolic dysfunction in ischemic tissue. Prior to MSI data analysis, commonly used staining methods, e.g., triphenyltetrazole chloride (TTC) staining, need to be implemented on the adjacent tissue for delineating lesion area and evaluating infarction, resulting in extra consumption of the tissue sample as well as morphological mismatch. Here, we propose an in situ ratiometric MSI method for simultaneous demarcation of lesion border and spatial annotation of metabolic and enzymatic signatures in ischemic tissue on identical tissue sections. In this method, the ion abundance ratio of a reactant pair in the TCA cycle, e.g., fumarate to malate, is extracted pixel-by-pixel from an ambient MSI dataset of ischemic tissue and used as a surrogate indicator for metabolic activity of mitochondria to delineate lesion area as if the tissue has been chemically stained. This method is shown to be precise and robust in identifying lesions in brain tissues and tissue samples from different ischemic models including heart, liver, and kidney. Furthermore, the proposed method allows screening and predicting metabolic and enzymatic alterations which are related to mitochondrial dysfunction. Being capable of concurrent lesion identification, in situ metabolomics analysis, and screening of enzymatic alterations, the ratiometric MSI method bears great potential to explore ischemic damages at both metabolic and enzymatic levels in biological research.

Spatially Resolved Metabolomics and Lipidomics Reveal Salinity and Drought-Tolerant Mechanisms of Cottonseeds

In the current era of global climate change, environmental stresses, especially drought and salt, have impaired the growth and productivity of crops, e.g., cotton. Understanding the mechanisms of plants' adaptation to these abiotic stresses is crucial to breed stress-tolerant crop species. In the present study, integrated metabolomics, lipidomics, and mass spectrometry imaging (MSI) were used to discover the spatial distribution of differential metabolites and lipids in two cottonseed cultivars with contrasting drought and salt tolerance properties. Seventeen differential metabolites and 125 differential lipids were identified. Their possible roles in augmenting stress tolerance were illustrated, which were involved in reactive oxygen species scavenging, osmotic adjustment, and cell membrane structure reconstruction. MSI analysis provided a visualization of nine differential lipids and four differential metabolites in cottonseeds with varied abundances and distributions. The results may help understand cottonseeds' convictive metabolic and lipidomic regulatory networks in coping with salinity and drought stresses and give new insights into the stress-tolerance traits relevant to other crops.