Virtual Calibration Quantitative Mass Spectrometry Imaging for Accurately Mapping Analytes across Heterogenous Biotissue

Mass Spectrometry Imaging for Spatial Toxicology Research

The spatial information of xenobiotics distribution, metabolism, and toxicity mechanisms in situ has drawn increasing attention in both pharmaceutical and environmental toxicology research to aid drug development and environmental risk assessments. Mass spectrometry imaging (MSI) provides a label-free, multiplexed, and high-throughput tool to characterize xenobiotics, their metabolites, and endogenous molecules in situ with spatial resolution, providing knowledge on spatially resolved absorption, distribution, metabolism, excretion, and toxicity on the molecular level. In this perspective, we briefly summarize applications of MSI in toxicology on xenobiotic distribution and metabolism, quantification, toxicity mechanisms, and biomarker discovery. We identified several challenges regarding how we can fully harness the power of MSI in both fundamental toxicology research and regulatory practices. First, how can we increase the coverage, sensitivity, and specificity in detecting xenobiotics and their metabolites in complex biological matrices? Second, how can we link the spatial molecular information of xenobiotics to toxicity consequences to understand toxicity mechanisms, predict exposure outcomes, and aid biomarker discovery? Finally, how can we standardize the MSI experiment and data analysis workflow to provide robust conclusions for regulation and drug development? With these questions in mind, we provide our perspectives on the future directions of MSI as a promising tool in spatial toxicology research.

Ensemble of machine learning techniques to predict survival in kidney transplant recipients

Kidney transplant recipients face a high cardiovascular risk, which is a leading cause of death in this patient group. This article proposes the application of clustering techniques and feature selection to predict the survival outcomes of kidney transplant recipients based on machine learning techniques and mainstream statistical methods. First, feature selection techniques (Boruta, Random Survival Forest and Elastic Net) are used to detect the most relevant variables. Subsequently, each set of variables obtained by each feature selection technique is used as input for the clustering algorithms used (Consensus Clustering, Self-Organizing Map and Agglomerative Clustering) to determine which combination of feature selection, clustering algorithm and number of clusters maximizes intercluster variability. Next, the mechanism called False Clustering Discovery Reduction is applied to obtain the minimum number of statistically differentiable populations after applying a control metric. This metric is based on a variance test to confirm that reducing the number of clusters does not generate significant losses in the heterogeneity obtained. This approach was applied to the Organ Procurement and Transplantation Network medical dataset (n = 11,332). The combination of Random Survival Forest and consensus clustering yielded the optimal result of 4 clusters starting from 8 initial ones. Finally, for each population, Kaplan-Meier survival curves are generated to predict the survival of new patients based on the predictions of the XGBoost classifier, with an overall multi-class AUC of 98.11%.

Illustrate the distribution and metabolic regulatory effects of pterostilbene in cerebral ischemia-reperfusion rat brain by mass spectrometry imaging and spatial metabolomics

Pterostilbene is a promising molecule with superior pharmacological activities and pharmacokinetic characteristics compared to its structural analogue resveratrol, which could be used to treat ischemic stroke. However, its mechanism is still unclear. The cutting-edge air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) and spatial metabolomics analysis were applied to investigate the distribution of pterostilbene in ischemic rat brain and the changes of related small molecule metabolic pathways to further explore the potential mechanisms of pterostilbene against cerebral ischemia-reperfusion injury. This research found that pterostilbene could significantly restore cerebral microcirculation blood flow, reduce infarct volume, improve neurological function and ameliorate neuronal damage in ischemic rats. Moreover, pterostilbene was widely and abundantly distributed in ischemic brain tissue, laying a solid foundation for the rescue of ischemic penumbra. Further study revealed that pterostilbene played a therapeutic role in restoring energy supply, rebalancing neurotransmitters, reducing abnormal polyamine accumulation and phospholipid metabolism. These findings offer an opportunity to illustrate novel mechanisms of pterostilbene in the treatment of cerebral ischemia/reperfusion injury resulting from ischemic stroke.

A cyclodextrin-based reagent for cis/trans-geometrical isomers separation by mobility measurements and chemical calculations

Identification of cis/trans-carbon-carbon double-bond (CC) isomers remain challenging. Herein, a simple and rapid method for the separation and analysis of cis/trans-maleic acid (MA) and aconitic acid (AA) using Trapped Ion Mobility Spectrometry (TIMS) was developed. α-, β-, γ-cyclodextrin (CD) were served as the separation reagent, slight difference in mobility separation was obtained by [CD-MA/AA-H]^-. Specially, with the addition of divalent metal ion (G²⁺) as coordination metal ion, the separation effect was much increased by [CD-MA/AA + G-H]⁺, and α-CD has better mobility separation effect than β-/γ-CD. Moreover, chemical calculations revealed the binary and ternary complexes are in the inclusion forms, and microscopic interactions between cis/trans-MA/AA, CDs, and G²⁺ are somewhat different that making their mobility separation. Finally, quantifications of cis/trans-isomers were analyzed in food samples, with good linearity (R² > 0.99) and recoveries obtained from 87.25 % to 100.73 %.

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.

Delineating regions of interest for mass spectrometry imaging by multimodally corroborated spatial segmentation

Mass spectrometry imaging (MSI), which localizes molecules in a tag-free, spatially resolved manner, is a powerful tool for the understanding of underlying biochemical mechanisms of biological phenomena. When analyzing MSI data, it is essential to delineate regions of interest (ROIs) that correspond to tissue areas of different anatomical or pathological labels. Spatial segmentation, obtained by clustering MSI pixels according to their mass spectral similarities, is a popular approach to automate ROI definition. However, how to select the number of clusters (#Clusters), which determines the granularity of segmentation, remains to be resolved, and an inappropriate #Clusters may lead to ROIs not biologically real. Here we report a multimodal fusion strategy to enable an objective and trustworthy selection of #Clusters by utilizing additional information from corresponding histology images. A deep learning-based algorithm is proposed to extract "histomorphological feature spectra" across an entire hematoxylin and eosin image. Clustering is then similarly performed to produce histology segmentation. Since ROIs originating from instrumental noise or artifacts would not be reproduced cross-modally, the consistency between histology and MSI segmentation becomes an effective measure of the biological validity of the results. So, #Clusters that maximize the consistency is deemed as most probable. We validated our strategy on mouse kidney and renal tumor specimens by producing multimodally corroborated ROIs that agreed excellently with ground truths. Downstream analysis based on the said ROIs revealed lipid molecules highly specific to tissue anatomy or pathology. Our work will greatly facilitate MSI-mediated spatial lipidomics, metabolomics, and proteomics research by providing intelligent software to automatically and reliably generate ROIs.

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.

Mass spectrometry imaging: new eyes on natural products for drug research and development

Natural products (NPs) and their structural analogs represent a major source of novel drug development for disease prevention and treatment. The development of new drugs from NPs includes two crucial aspects. One is the discovery of NPs from medicinal plants/microorganisms, and the other is the evaluation of the NPs in vivo at various physiological and pathological states. The heterogeneous spatial distribution of NPs in medicinal plants/microorganisms or in vivo can provide valuable information for drug development. However, few molecular imaging technologies can detect thousands of compounds simultaneously on a label-free basis. Over the last two decades, mass spectrometry imaging (MSI) methods have progressively improved and diversified, thereby allowing for the development of various applications of NPs in plants/microorganisms and in vivo NP research. Because MSI allows for the spatial mapping of the production and distribution of numerous molecules in situ without labeling, it provides a visualization tool for NP research. Therefore, we have focused this mini-review on summarizing the applications of MSI technology in discovering NPs from medicinal plants and evaluating NPs in preclinical studies from the perspective of new drug research and development (R&D). Additionally, we briefly reviewed the factors that should be carefully considered to obtain the desired MSI results. Finally, the future development of MSI in new drug R&D is proposed.

A review on quantitation-related factors and quantitation strategies in mass spectrometry imaging of small biomolecules

Accurate quantitative information of the analytes in mass spectrometry imaging (MSI) is fundamental for determining the accurate spatial distribution, which can provide additional insight into the living processes, disease progression or the pharmacokinetic-pharmacodynamic mechanisms. However, performing a quantitative analysis in MSI is still challenging. This review focuses on the quantitation-related factors and recent advances in the strategies of quantitative MSI (q-MSI) of small molecules. The main quantitation-related factors are discussed according to the new investigations in recent years, including the regionally varied extraction efficiencies and ionization efficiencies, signal-concentration regression functions, and the repeatability of surface sampling/ionization methods. Newly developed quantitation strategies in MSI based on aforementioned factors are introduced, including new techniques in standard curve calibration with normalization to an internal standard, kinetic calibration, and chemometric methods. Different strategies for validating q-MSI methods are discussed. Finally, the future perspectives to q-MSI are proposed.

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.

Cholesterol sulfate alleviates ulcerative colitis by promoting cholesterol biosynthesis in colonic epithelial cells

Cholesterol sulfate, produced by hydroxysteroid sulfotransferase 2B1 (SULT2B1), is highly abundant in the intestine. Herein, we study the functional role and underlying intestinal epithelial repair mechanisms of cholesterol sulfate in ulcerative colitis. The levels of cholesterol and cholesterol sulfate, as well as the expression of Sult2b1 and genes involved in cholesterol biosynthesis, are significantly higher in inflamed tissues from patients with ulcerative colitis than in intestinal mucosa from healthy controls. Cholesterol sulfate in the gut and circulation is mainly catalyzed by intestinal epithelial SULT2B1. Specific deletion of the Sult2b1 gene in the intestinal epithelial cells aggravates dextran sulfate sodium-induced colitis; however, dietary supplementation with cholesterol sulfate ameliorates this effect in acute and chronic ulcerative colitis in mice. Cholesterol sulfate promotes cholesterol biosynthesis by binding to Niemann-Pick type C2 protein and activating sterol regulatory element binding protein 2 in colonic epithelial cells, thereby alleviates ulcerative colitis. In conclusion, cholesterol sulfate contributes to the healing of the mucosal barrier and exhibits therapeutic efficacy against ulcerative colitis in mice.

New treatment strategies are required for ulcerative colitis. Here the authors show in mouse models that cholesterol sulfate, an endogenous active cholesterol derivative, contributes to the healing of the mucosal barrier by promoting cholesterol biosynthesis in colonic epithelial cells and exhibits therapeutic efficacy against ulcerative colitis.

MALDI-MSI Towards Multimodal Imaging: Challenges and Perspectives

Multimodal imaging is a powerful strategy for combining information from multiple images. It involves several fields in the acquisition, processing and interpretation of images. As multimodal imaging is a vast subject area with various combinations of imaging techniques, it has been extensively reviewed. Here we focus on Matrix-assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) coupling other imaging modalities in multimodal approaches. While MALDI-MS images convey a substantial amount of chemical information, they are not readily informative about the morphological nature of the tissue. By providing a supplementary modality, MALDI-MS images can be more informative and better reflect the nature of the tissue. In this mini review, we emphasize the analytical and computational strategies to address multimodal MALDI-MSI.

Three-dimensional quantitative mass spectrometry imaging in complex system: From subcellular to whole organism

Mass spectrometry imaging (MSI) has been applied for label-free three-dimensional (3D) imaging from position array across the whole organism, which provides high-dimensional quantitative data of inorganic or organic compounds that may play an important role in the regulation of cellular signaling, including metals, metabolites, lipids, drugs, peptides, and proteins. While MSI is suitable for investigation of the spatial distribution of molecules, it has a limitation with visualization and quantification of multiple molecules. 3D-MSI, however, can be applied toward exploring metabolic pathway as well as the interactions of lipid-protein, protein-protein, and metal-protein in complex systems from subcellular to the whole organism through an untargeted methodology. In this review, we highlight the methods and applications of MS-based 3D imaging to address the complexity of molecular interaction from nano- to micrometer lateral resolution, with particular focus on: (a) common and hybrid 3D-MSI techniques; (b) quantitative MSI methodology, including the methods using a stable isotope labeling internal standard (SILIS) and SILIS-free approaches with tissue extinction coefficient or virtual calibration; (c) reconstruction of the 3D organ; (d) application of 3D-MSI for biomarker screening and environmental toxicological research. 3D-MSI quantitative analysis provides accurate spatial information and quantitative variation of biomolecules, which may be valuable for the exploration of the molecular mechanism of the disease progresses and toxicological assessment of environmental pollutants in the whole organism. Additionally, we also discuss the challenges and perspectives on the future of 3D quantitative MSI.

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.

Recent Advances of Ambient Mass Spectrometry Imaging and Its Applications in Lipid and Metabolite Analysis

Ambient mass spectrometry imaging (AMSI) has attracted much attention in recent years. As a kind of unlabeled molecular imaging technique, AMSI can enable in situ visualization of a large number of compounds in biological tissue sections in ambient conditions. In this review, the developments of various AMSI techniques are discussed according to one-step and two-step ionization strategies. In addition, recent applications of AMSI for lipid and metabolite analysis (from 2016 to 2021) in disease diagnosis, animal model research, plant science, drug metabolism and toxicology research, etc., are summarized. Finally, further perspectives of AMSI in spatial resolution, sensitivity, quantitative ability, convenience and software development are proposed.

Per-pixel absolute quantitation for mass spectrometry imaging of endogenous lipidomes by model prediction of mass transfer kinetics in single-probe-based ambient liquid extraction

With the development of mass spectrometry imaging (MSI), techniques providing quantitative information on the spatial distribution have attracted more attentions recent years. However, for MSI of endogenous compounds in bio-samples, the uncertainty of locally varied sampling efficiencies always hinders accurate absolute quantitation. Here single-probe was used for ambient liquid extraction MSI in rat cerebellum, and standards of phosphatidylcholines (PCs) and cerebrosides (CBs) were doped in extraction solvent. The extraction kinetic curves of endogenous lipids in the ambient liquid extraction during probe parking in single pixel of tissue were investigated. From the results, the extraction kinetic curves were varied between different lipid species in different brain regions, resulting in variations of extraction efficiencies between imaging pixels, and calibration with standards deposited in tissue could not compensate for the variations. In our approach, the theoretical kinetic model of ambient liquid extraction was established, and original concentrations of endogenous lipids in each pixel of tissue were predicted by fitting the experimental extraction kinetic curve in each imaging pixel to the model. The experimental data was demonstrated to be well fitted to the kinetic model with R² > 0.86, and only with 18-s extraction in each pixel, the original lipid concentrations were predicted accurately with relative errors <23%. With the new method, totally 157 lipids and small metabolites were imaged, and per-pixel quantitation was achieved for 19 PCs and 4 CBs. Compared with conventional quantitative MSI (q-MSI) method, the new q-MSI method had better reproducibility and wider linear range, and produced better contrast in the quantitative images of lipids in brain tissue with less hot spots and noises. The absolute quantitation results by the new method were verified by quantitative LC-MS method with Pearson'r > 0.9 and the slope of the linear fitting line of the correlation plot near 1.

Desorption electrospray ionization (DESI) source coupling ion mobility mass spectrometry for imaging fluoropezil (DC20) distribution in rat brain

Fluoropezil (DC20) is a new selective acetylcholinesterase inhibitor, and it was developed for the treatment of Alzheimer's disease patients. In this study, a desorption electrospray ionization source coupling ion mobility mass spectrometry imaging (DESI/IMS-MSI) method was developed to explore the distribution of DC20 in brain tissue following oral administration. Rat brain coronal slices obtained 1 h and 3 h following drug dosing were used in the study. D6-DC20 was used as internal standard and sprayed by matrix sprayer on the brain slices to calibrate the matrix effect. Ion mobility separation was used to reduce the interference from background noise and the biological matrix. By optimizing DESI-MSI parameters for improved sensitivity, the limit of quantitation of the method was 1.45 pg/mm² with a linear range from 1.45 to 72.7 pg/mm². DESI-MSI data showed that DC20 could quickly enter and diffuse across whole brain and tended to be much more enriched in striatum than cerebral cortex and hippocampus, which was consistent with quantitative analysis using high-performance liquid chromatography-electrospray tandem mass spectrometry to measure DC20 concentration in each homogenized brain sub-region. The workflow of tissue imaging method optimization and strategy were established, and for the first time, the DESI-MSI technique and optimized method were used to explore the distribution characteristics of DC20 in rat brain, which could help elucidate pharmacological effect mechanisms and improve clinical outcomes.

Spatially resolved absolute quantitation in thin tissue by mass spectrometry

Mass spectrometry (MS) has become the de facto tool for routine quantitative analysis of biomolecules. MS is increasingly being used to reveal the spatial distribution of proteins, metabolites, and pharmaceuticals in tissue and interest in this area has led to a number of novel spatially resolved MS technologies. Most spatially resolved MS measurements are qualitative in nature due to a myriad of potential biases, such as sample heterogeneity, sampling artifacts, and ionization effects. As applications of spatially resolved MS in the pharmacological and clinical fields increase, demand has become high for quantitative MS imaging and profiling data. As a result, several varied technologies now exist that provide differing levels of spatial and quantitative information. This review provides an overview of MS profiling and imaging technologies that have demonstrated quantitative analysis from tissue. Focus is given on the fundamental processes affecting quantitative analysis in an array of MS imaging and profiling technologies and methods to address these biases.Graphical abstract.

Quantitative Mass Spectrometry Imaging of Biological Systems

Mass spectrometry imaging (MSI) is a powerful, label-free technique that provides detailed maps of hundreds of molecules in complex samples with high sensitivity and subcellular spatial resolution. Accurate quantification in MSI relies on a detailed understanding of matrix effects associated with the ionization process along with evaluation of the extraction efficiency and mass-dependent ion losses occurring in the analysis step. We present a critical summary of approaches developed for quantitative MSI of metabolites, lipids, and proteins in biological tissues and discuss their current and future applications.

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

Mass spectrometry imaging (MSI) could provide vast amounts of data at the temporal-spatial scale in heterogeneous biological specimens, which challenges us to segment accurately suborgans/microregions from complex MSI data. Several pipelines had been proposed for MSI spatial segmentation in the past decade. More importantly, data filtering was found to be an efficient procedure to improve the outcomes of MSI segmentation pipelines. It is not clear, however, how the filtering procedure affects the MSI segmentation. An improved pipeline was established by elaborating the filtering prioritization and filtering algorithm. Lipidomic-characteristic-based MSI data of a whole-body mouse fetus was used to evaluate the established pipeline on localization of the physiological position of suborgans by comparing with three commonly used pipelines and commercial SCiLS Lab software. Two structural measurements were used to quantify the performances of the pipelines including the percentage of abnormal edge pixel (PAEP) and CHAOS. Our results demonstrated that the established pipeline outperformed the other pipelines in visual inspection, spatial consistence, time-cost, and robustness analysis. For example, the dorsal pallium (isocortex) and hippocampal formation (Hpf) regions, midbrain, cerebellum, and brainstem on the mouse brain were annotated and located by the established pipeline. As a generic pipeline, the established pipeline could help with the accurate assessment and screening of drug/chemical-induced targeted organs and exploration of the progression and molecular mechanisms of diseases. The filter-based strategy is expected to become a critical component in the standard operating procedure of MSI data sets.

Natural Products, the Fourth Industrial Revolution, and the Quintuple Helix

The profound interconnectedness of the sciences and technologies embodied in the Fourth Industrial Revolution is discussed in terms of the global role of natural products, and how that interplays with the development of sustainable and climate-conscious practices of cyberecoethnopharmacolomics within the Quintuple Helix for the promotion of a healthier planet and society.

The local-balanced model for improved machine learning outcomes on mass spectrometry data sets and other instrumental data

One unifying challenge when classifying biological samples with mass spectrometry data is overcoming the obstacle of sample-to-sample variability so that differences between groups, such as between a healthy set and a disease set, can be identified. Similarly, when the same sample is re-analyzed under identical conditions, instrument signals can fluctuate by more than 10%. This signal inconsistency imposes difficulties in identifying subtle differences across a set of samples, and it weakens the mass spectrometrist’s ability to effectively leverage data in domains as diverse as proteomics, metabolomics, glycomics, and imaging. We selected challenging data sets in the fields of glycomics, mass spectrometry imaging, and bacterial typing to study the problem of within-group signal variability and adapted a 30 year old statistical approach to address the problem. The solution, “local-balanced model,” relies on using balanced subsets of training data to classify test samples. This analysis strategy was assessed on ESI-MS data of IgG-based glycopeptides and MALDI-MS imaging data of endogenous lipids, and MALDI-MS data of bacterial proteins. Two preliminary examples on non-mass spectrometry data sets are also included to show the potential generality of the method outside the field of MS analysis. We demonstrate that this approach is superior to simple normalization methods, generalizable to multiple mass spectrometry domains, and potentially appropriate in fields as diverse as physics and satellite imaging. In some cases, improvements in classification can be dramatic, with accuracy escalating from 60% with normalization alone to over 90% with the additional development described herein.

Lipids and cancer: Emerging roles in pathogenesis, diagnosis and therapeutic intervention

With the advent of effective tools to study lipids, including mass spectrometry-based lipidomics, lipids are emerging as central players in cancer biology. Lipids function as essential building blocks for membranes, serve as fuel to drive energy-demanding processes and play a key role as signaling molecules and as regulators of numerous cellular functions. Not unexpectedly, cancer cells, as well as other cell types in the tumor microenvironment, exploit various ways to acquire lipids and extensively rewire their metabolism as part of a plastic and context-dependent metabolic reprogramming that is driven by both oncogenic and environmental cues. The resulting changes in the fate and composition of lipids help cancer cells to thrive in a changing microenvironment by supporting key oncogenic functions and cancer hallmarks, including cellular energetics, promoting feedforward oncogenic signaling, resisting oxidative and other stresses, regulating intercellular communication and immune responses. Supported by the close connection between altered lipid metabolism and the pathogenic process, specific lipid profiles are emerging as unique disease biomarkers, with diagnostic, prognostic and predictive potential. Multiple preclinical studies illustrate the translational promise of exploiting lipid metabolism in cancer, and critically, have shown context dependent actionable vulnerabilities that can be rationally targeted, particularly in combinatorial approaches. Moreover, lipids themselves can be used as membrane disrupting agents or as key components of nanocarriers of various therapeutics. With a number of preclinical compounds and strategies that are approaching clinical trials, we are at the doorstep of exploiting a hitherto underappreciated hallmark of cancer and promising target in the oncologist's strategy to combat cancer.

Quantitative Imprint Mass Spectrometry Imaging of Endogenous Ceramides in Rat Brain Tissue with Kinetic Calibration

Quantitative mass spectrometry imaging (MSI) is an effective technique for determining the spatial distribution of molecules in a variety of sample types; however, the quality of the ion signals is related to the chemical and morphological properties of the tissue and the targeted analyte(s). Issues may arise with the incorporation of standards into the tissue at repeatable, well-defined concentrations, as well as with the extraction and incorporation of endogenous analytes versus standards from tissue into the matrix. To address these concerns, we combine imprint MSI (iMSI) with kinetic calibration and use it to quantify lipids in rat brain tissue samples. Briefly, tissues were imprinted on slides coated with a dopamine-modified TiO₂ monolith pretreated with analyte standards, resulting in the adsorption of endogenous analytes onto the coating and desorption of standards into the tissue. The incorporation of standards into the tissue enabled quantification of the measured analytes using kinetic calibration. Moreover, matrix effects were reduced, and the intensities of analyte standard signals became more uniform. The symmetry of the adsorption of endogenous ceramides and the desorption of ceramide standards suggest that the content of adsorbed endogenous ceramide can be determined by measuring the content of desorbed ceramide standard. Using kinetic calibration, endogenous ceramide concentrations were calculated for a range of dry and wet tissue imprinting conditions and compared to quantitative MSI using a standard spiking approach. We validated the relative quantitative values from iMSI using liquid chromatography tandem mass spectrometry (LC-MS/MS) and found that the ratios from iMSI as compared to LC-MS/MS were in the range of 70-200% over the concentration range of endogenous ceramides; the correlation coefficient between iMSI and LC-MS/MS was over 0.9 (Pearson's r), while the relative recoveries via traditional standard spiking were between 200% and 5000% depending on the brain region and sample preparation conditions.

Evaluation of the tumor-targeting efficiency and intratumor heterogeneity of anticancer drugs using quantitative mass spectrometry imaging

The development of improved or targeted drugs that discriminate between normal and tumor tissues is the key therapeutic issue in cancer research. However, the development of an analytical method with a high accuracy and sensitivity to achieve quantitative assessment of the tumor targeting of anticancer drugs and even intratumor heterogeneous distribution of these drugs at the early stages of drug research and development is a major challenge. Mass spectrometry imaging is a label-free molecular imaging technique that provides spatial-temporal information on the distribution of drugs and metabolites in organisms, and its application in the field of pharmaceutical development is rapidly increasing. Methods: The study presented here accurately quantified the distribution of paclitaxel (PTX) and its prodrug (PTX-R) in whole-body animal sections based on the virtual calibration quantitative mass spectrometry imaging (VC-QMSI) method, which is label-free and does not require internal standards, and then applied this technique to evaluate the tumor targeting efficiency in three treatment groups-the PTX-injection treatment group, PTX-liposome treatment group and PTX-R treatment group-in nude mice bearing subcutaneous A549 xenograft tumors. Results: These results indicated that PTX was widely distributed in multiple organs throughout the dosed body in the PTX-injection group and the PTX-liposome group. Notably, in the PTX-R group, both the prodrug and metabolized PTX were mainly distributed in the tumor tissue, and this group showed a significant difference compared with the PTX-liposome group, the relative targeting efficiency of PTX-R group was increased approximately 50-fold, leading to substantially decreased systemic toxicities. In addition, PTX-R showed a significant and specific accumulation in the poorly differentiated intratumor area and necrotic area. Conclusion: This method was demonstrated to be a reliable, feasible and easy-to-implement strategy to quantitatively map the absorption, distribution, metabolism and excretion (ADME) of a drug in the whole-body and tissue microregions and could therefore evaluate the tumor-targeting efficiency of anticancer drugs to predict drug efficacy and safety and provide key insights into drug disposition and mechanisms of action and resistance. Thus, this strategy could significantly facilitate the design and optimization of drugs at the early stage of drug research and development.