Molecular Imaging of Biological Samples on Nanophotonic Laser Desorption Ionization Platforms

From sampling to simulating: Single-cell multiomics in systems pathophysiological modeling

As single-cell omics data sampling and acquisition methods have accumulated at an unprecedented rate, various data analysis pipelines have been developed for the inference of cell types, cell states and their distribution, state transitions, state trajectories, and state interactions. This presents a new opportunity in which single-cell omics data can be utilized to generate high-resolution, high-fidelity computational models. In this review, we discuss how single-cell omics data can be used to build computational models to simulate biological systems at various scales. We propose that single-cell data can be integrated with physiological information to generate organ-specific models, which can then be assembled to generate multi-organ systems pathophysiological models. Finally, we discuss how generic multi-organ models can be brought to the patient-specific level thus permitting their use in the clinical setting.

Unraveling the drug distribution in brain enabled by MALDI MS imaging with laser-assisted chemical transfer

Accurate localization of central nervous system (CNS) drug distribution in the brain is quite challenging to matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI), owing to the ionization competition/suppression of highly abundant endogenous biomolecules and MALDI matrix. Herein, we developed a highly efficient sample preparation technique, laser-assisted chemical transfer (LACT), to enhance the detection sensitivity of CNS drugs in brain tissues. A focused diode laser source transilluminated the tissue slide coated with α-cyano-4-hydroxycinnamic acid, an optimal matrix to highly absorb the laser radiation at 405 nm, and a very thin-layer chemical film mainly containing drug molecule was transferred to the acceptor glass slide. Subsequently, MALDI MSI was performed on the chemical film without additional sample treatment. One major advantage of LACT is to minimize ionization competition/suppression from the tissue itself by removing abundant endogenous lipid and protein components. The superior performance of LACT led to the successful visualization of regional distribution patterns of 16 CNS drugs in the mouse brain. Furthermore, the dynamic spatial changes of risperidone and its metabolite were visualized over a 24-h period. Also, the brain-to-plasma (B/P) ratio could be obtained according to MALDI MSI results, providing an alternative means to assess brain penetration in drug discovery.

Nanocomposite-Based Matrices in Laser Desorption/Ionization Mass Spectrometry for Small-Molecule Analysis

The efficient detection of small molecules is of significance for environmental monitoring, pharmacology, metabolomics, and lipidomics. The laser desorption/ionization mass spectrometry (LDI MS) platform enables high sensitivity, accuracy, resolution, and throughput in molecular analysis, but its analytical capability with respect to small molecules is limited due to inherent drawbacks arising from conventional organic matrices. The selection of an appropriate matrix is thus a precondition for small molecule detection by LDI MS. To date, various inorganic matrices have been developed, with a growing interest in composite materials displaying synergetic effects. This Minireview highlights the development of nanocomposites as LDI matrices driven by numerous innovations in material science, and their emerging use in small-molecule analysis.

Mass Spectrometry Imaging of Bio-oligomer Polydispersity in Plant Tissues by Laser Desorption Ionization from Silicon Nanopost Arrays

Mass spectrometry imaging (MSI) enables simultaneous spatial mapping for diverse molecules in biological tissues. Matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS) has been a mainstream MSI method for a wide range of biomolecules. However, MALDI-MSI of biological homopolymers used for energy storage and molecular feedstock is limited by, e.g., preferential ionization for certain molecular classes. Matrix-free nanophotonic ionization from silicon nanopost arrays (NAPAs) is an emerging laser desorption ionization (LDI) platform with ultra-trace sensitivity and molecular imaging capabilities. Here, we show complementary analysis and MSI of polyhydroxybutyric acid (PHB), polyglutamic acid (PGA), and polysaccharide oligomers in soybean root nodule sections by NAPA-LDI and MALDI. For PHB, number and weight average molar mass, polydispersity, and oligomer size distributions across the tissue section and in regions of interest were characterized by NAPA-LDI-MSI.

Structure-Preserving and Perceptually Consistent Approach for Visualization of Mass Spectrometry Imaging Datasets

Mass spectrometry imaging (MSI) has become an important tool for 2D profiling of biological tissues, allowing for the visualization of individual compound distributions in the sample. Based on this information, it is possible to investigate the molecular organization within any particular tissue and detect abnormal regions (such as tumor regions) and many other biologically relevant phenomena. However, the large number of compounds present in the spectra hinders the productive analysis of large MSI datasets when utilizing standard tools. The heterogeneity of samples makes exploratory visualization (a presentation of the general idea of the molecular and structural organization of the inspected tissues) challenging. Here, we explore the application of various dimensionality reduction techniques that have been used extensively in the visualization of hyperspectral images and the MSI data specifically, such as principal component analysis, independent component analysis, non-negative matrix factorization, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection. Further, we propose a new approach based on a combination of structure preserving visualization with nonlinear manifold embedding of normalized spectral data. This way, we aim to preserve as much spatially overlapping signals as possible while augmenting them with information on compositional (spectral) variation. The proposed approach can be used for exploratory visualization of MSI datasets without prior deep chemical or histological knowledge of the sample. Thus, different datasets can be visually compared employing the proposed method. The proposed approach allowed for the clear visualization of the molecular layer, granular layer, and white matter in chimpanzee and macaque cerebellum slices.

A Sensitive and Wide Coverage Ambient Mass Spectrometry Imaging Method for Functional Metabolites Based Molecular Histology

Histological examination with a deep link between functional metabolites and tissue structure and biofunctions will provide important in situ biochemical information, and then essentially reveal what has happened in tissue at the molecular level. However, due to the complexity and heterogeneity of tissue samples and the large number of metabolites, it is still a challenge to globally map the diverse metabolites, especially for those low-abundance functional ones. Here, a sensitive air flow-assisted desorption electrospray ionization mass spectrometry imaging method for the mapping of a broad range of metabolites is presented. It exhibits properties characteristic of wide coverage, high sensitivity, wide dynamic range, rapid analysis procedure, and high specificity for tissue metabolites imaging. More than 1500 metabolites, including cholines, polyamines, amino acids, carnitines, nucleosides, nucleotides, nitrogen bases, organic acids, carbohydrates, cholesterol sulfate, cholic acid, lipids, etc., can be visualized in an untargeted analysis. The distribution of metabolites shows good spatial match with tissue histological structure and biofunctions in heterogeneous rat kidney, rat brain, and human esophageal cancer tissue. This method possesses the ability to globally showcase the molecular processes in tissue, and provide an insightful way for structural and functional molecular recognition in histological examination, even for intraoperative decision-making.