MALDI Imaging mass spectrometry: current frontiers and perspectives in pathology research and practice

Subtyping non-small cell lung cancer by histology-guided spatial metabolomics

Purpose

Most cancer-related deaths worldwide are associated with lung cancer. Subtyping of non-small cell lung cancer (NSCLC) into adenocarcinoma (AC) and squamous cell carcinoma (SqCC) is of importance, as therapy regimes differ. However, conventional staining and immunohistochemistry have their limitations. Therefore, a spatial metabolomics approach was aimed to detect differences between subtypes and to discriminate tumor and stroma regions in tissues.

Methods

Fresh-frozen NSCLC tissues (n = 35) were analyzed by matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) of small molecules (< m/z 1000). Measured samples were subsequently stained and histopathologically examined. A differentiation of subtypes and a discrimination of tumor and stroma regions was performed by receiver operating characteristic analysis and machine learning algorithms.

Results

Histology-guided spatial metabolomics revealed differences between AC and SqCC and between NSCLC tumor and tumor microenvironment. A diagnostic ability of 0.95 was achieved for the discrimination of AC and SqCC. Metabolomic contrast to the tumor microenvironment was revealed with an area under the curve of 0.96 due to differences in phospholipid profile. Furthermore, the detection of NSCLC with rarely arising mutations of the isocitrate dehydrogenase (IDH) gene was demonstrated through 45 times enhanced oncometabolite levels.

Conclusion

MALDI-MSI of small molecules can contribute to NSCLC subtyping. Measurements can be performed intraoperatively on a single tissue section to support currently available approaches. Moreover, the technique can be beneficial in screening of IDH-mutants for the characterization of these seldom cases promoting the development of treatment strategies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-021-03834-w.

The Role of Machine Learning in Cardiovascular Pathology

Machine learning has seen slow but steady uptake in diagnostic pathology over the past decade to assess digital whole-slide images. Machine learning tools have incredible potential to standardise, and likely even improve, histopathologic diagnoses, but they are not yet widely used in clinical practice. We describe the principles of these tools and technologies and some successful preclinical and pretranslational efforts in cardiovascular pathology, as well as a roadmap for moving forward. In nonhuman animal models, one proof-of-principle application is in rodent progressive cardiomyopathy, which is of particular significance to drug toxicity studies. Basic science successes include screening the quality of differentiated stem cells and characterising cardiomyocyte developmental stages, with potential applications for research and toxicology/drug safety screening using derived or native human pluripotent stem cells differentiated into cardiomyocytes. Translational studies of particular note include those with success in diagnosing the various forms of heart allograft rejection. For fully realising the value of these tools in clinical cardiovascular pathology, we identify 3 essential challenges. First is image quality standardisation to ensure that algorithms can be developed and implemented on robust, consistent data. The second is consensus diagnosis; experts don't always agree, and thus "truth" may be difficult to establish, but the algorithms themselves may provide a solution. The third is the need for large-enough data sets to facilitate robust algorithm development, necessitating large cross-institutional shared image databases. The power of histopathology-based machine learning technologies is tremendous, and we outline the next steps needed to capitalise on this power.

Peptide Signatures for Prognostic Markers of Pancreatic Cancer by MALDI Mass Spectrometry Imaging

Despite the overall poor prognosis of pancreatic cancer there is heterogeneity in clinical courses of tumors not assessed by conventional risk stratification. This yields the need of additional markers for proper assessment of prognosis and multimodal clinical management. We provide a proof of concept study evaluating the feasibility of Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to identify specific peptide signatures linked to prognostic parameters of pancreatic cancer. On 18 patients with exocrine pancreatic cancer after tumor resection, MALDI imaging analysis was performed additional to histopathological assessment. Principal component analysis (PCA) was used to explore discrimination of peptide signatures of prognostic histopathological features and receiver operator characteristic (ROC) to identify which specific m/z values are the most discriminative between the prognostic subgroups of patients. Out of 557 aligned m/z values discriminate peptide signatures for the prognostic histopathological features lymphatic vessel invasion (pL, 16 m/z values, eight proteins), nodal metastasis (pN, two m/z values, one protein) and angioinvasion (pV, 4 m/z values, two proteins) were identified. These results yield proof of concept that MALDI-MSI of pancreatic cancer tissue is feasible to identify peptide signatures of prognostic relevance and can augment risk assessment.

Molecular tissue profiling by MALDI imaging: recent progress and applications in cancer research

Matrix-assisted laser desorption/ionization (MALDI) imaging is an emergent technology that has been increasingly adopted in cancer research. MALDI imaging is capable of providing global molecular mapping of the abundance and spatial information of biomolecules directly in the tissues without labeling. It enables the characterization of a wide spectrum of analytes, including proteins, peptides, glycans, lipids, drugs, and metabolites and is well suited for both discovery and targeted analysis. An advantage of MALDI imaging is that it maintains tissue integrity, which allows correlation with histological features. It has proven to be a valuable tool for probing tumor heterogeneity and has been increasingly applied to interrogate molecular events associated with cancer. It provides unique insights into both the molecular content and spatial details that are not accessible by other techniques, and it has allowed considerable progress in the field of cancer research. In this review, we first provide an overview of the MALDI imaging workflow and approach. We then highlight some useful applications in various niches of cancer research, followed by a discussion of the challenges, recent developments and future prospect of this technique in the field.

Molecular Mapping of Neutral Lipids Using Silicon Nanopost Arrays and TIMS Imaging Mass Spectrometry

We demonstrate the utility of combining silicon nanopost arrays (NAPA) and trapped ion mobility imaging mass spectrometry (TIMS IMS) for high spatial resolution and specificity mapping of neutral lipid classes in tissue. Ionization of neutral lipid species such as triglycerides (TGs), cholestryl esters (CEs), and hexosylceramides (HexCers) from biological tissues has remained a challenge for imaging applications. NAPA, a matrix-free laser desorption ionization substrate, provides enhanced ionization efficiency for the above-mentioned neutral lipid species, providing complementary lipid coverage to matrix-assisted laser desorption ionization (MALDI). The combination of NAPA and TIMS IMS enables imaging of neutral lipid species at 20 μm spatial resolution while also increasing molecular coverage greater than 2-fold using gas-phase ion mobility separations. This is a significant improvement with respect to sensitivity, specificity, and spatial resolution compared to previously reported imaging studies using NAPA alone. Improved specificity for neutral lipid analysis using TIMS IMS was shown using rat kidney tissue to separate TGs, CEs, HexCers, and phospholipids into distinct ion mobility trendlines. Further, this technology allowed for the separation of isomeric species, including mobility resolved isomers of Cer(d42:2) (m/z 686.585) with distinct spatial localizations measured in rat kidney tissue section.

Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization

Classical approaches in cluster analysis are typically based on a feature space analysis. However, many applications lead to datasets with additional spatial information and a ground truth with spatially coherent classes, which will not necessarily be reconstructed well by standard clustering methods. Motivated by applications in hyperspectral imaging, we introduce in this work clustering models based on Orthogonal Nonnegative Matrix Factorization (ONMF), which include an additional Total Variation (TV) regularization procedure on the cluster membership matrix to enforce the needed spatial coherence in the clusters. We propose several approaches with different optimization techniques, where the TV regularization is either performed as a subsequent post-processing step or included into the clustering algorithm. Finally, we provide a numerical evaluation of 12 different TV regularized ONMF methods on a hyperspectral dataset obtained from a matrix-assisted laser desorption/ionization imaging measurement, which leads to significantly better clustering results compared to classical clustering models.

Peak learning of mass spectrometry imaging data using artificial neural networks

Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving, biomarker discovery, metabolomics research, pharmaceutical applications and clinical diagnosis. Despite many solutions being developed, the large data size and high dimensional nature of MSI, especially 3D datasets, still pose computational and memory complexities that hinder accurate identification of biologically relevant molecular patterns. Moreover, the subjectivity in the selection of parameters for conventional pre-processing approaches can lead to bias. Therefore, we assess if a probabilistic generative model based on a fully connected variational autoencoder can be used for unsupervised analysis and peak learning of MSI data to uncover hidden structures. The resulting msiPL method learns and visualizes the underlying non-linear spectral manifold, revealing biologically relevant clusters of tissue anatomy in a mouse kidney and tumor heterogeneity in human prostatectomy tissue, colorectal carcinoma, and glioblastoma mouse model, with identification of underlying m/z peaks. The method is applied for the analysis of MSI datasets ranging from 3.3 to 78.9 GB, without prior pre-processing and peak picking, and acquired using different mass spectrometers at different centers.

α-Cyano-4-hydroxycinnamic Acid and Tri-Potassium Citrate Salt Pre-Coated Silicon Nanopost Array Provides Enhanced Lipid Detection for High Spatial Resolution MALDI Imaging Mass Spectrometry

We have developed a pre-coated substrate for matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) that enables high spatial resolution mapping of both phospholipids and neutral lipid classes in positive ion mode as metal cation adducts. The MALDI substrates are constructed by depositing a layer of α-cyano-4-hydroxycinnamic acid (CHCA) and potassium salts onto silicon nanopost arrays (NAPA) prior to tissue mounting. The matrix/salt pre-coated NAPA substrate significantly enhances all detected lipid signals allowing lipids to be detected at lower laser energies than bare NAPA. The improved sensitivity at lower laser energy enabled ion images to be generated at 10 μm spatial resolution from rat retinal tissue. Optimization of matrix pre-coated NAPA consisted of testing lithium, sodium, and potassium salts along with various matrices to investigate the increased sensitivity toward lipids for MALDI IMS experiments. It was determined that pre-coating NAPA with CHCA and potassium salts before thaw-mounting of tissue resulted in a signal intensity increase of at least 5.8 ± 0.1-fold for phospholipids and 2.0 ± 0.1-fold for neutral lipids compared to bare NAPA. Pre-coating NAPA with matrix and salt also reduced the necessary laser power to achieve desorption/ionization by ∼35%. This reduced the effective diameter of the ablation area from 13 ± 2 μm down to 8 ± 1 μm, enabling high spatial resolution MALDI IMS. Using pre-coated NAPA with CHCA and potassium salts offers a MALDI IMS substrate with broad molecular coverage of lipids in a single polarity that eliminates the need for extensive sample preparation after sectioning.

Automated biomarker candidate discovery in imaging mass spectrometry data through spatially localized Shapley additive explanations

The search for molecular species that are differentially expressed between biological states is an important step towards discovering promising biomarker candidates. In imaging mass spectrometry (IMS), performing this search manually is often impractical due to the large size and high-dimensionality of IMS datasets. Instead, we propose an interpretable machine learning workflow that automatically identifies biomarker candidates by their mass-to-charge ratios, and that quantitatively estimates their relevance to recognizing a given biological class using Shapley additive explanations (SHAP). The task of biomarker candidate discovery is translated into a feature ranking problem: given a classification model that assigns pixels to different biological classes on the basis of their mass spectra, the molecular species that the model uses as features are ranked in descending order of relative predictive importance such that the top-ranking features have a higher likelihood of being useful biomarkers. Besides providing the user with an experiment-wide measure of a molecular species' biomarker potential, our workflow delivers spatially localized explanations of the classification model's decision-making process in the form of a novel representation called SHAP maps. SHAP maps deliver insight into the spatial specificity of biomarker candidates by highlighting in which regions of the tissue sample each feature provides discriminative information and in which regions it does not. SHAP maps also enable one to determine whether the relationship between a biomarker candidate and a biological state of interest is correlative or anticorrelative. Our automated approach to estimating a molecular species' potential for characterizing a user-provided biological class, combined with the untargeted and multiplexed nature of IMS, allows for the rapid screening of thousands of molecular species and the obtention of a broader biomarker candidate shortlist than would be possible through targeted manual assessment. Our biomarker candidate discovery workflow is demonstrated on mouse-pup and rat kidney case studies.

Use of MALDI Mass Spectrometry Imaging to Identify Proteomic Signatures in Aortic Aneurysms after Endovascular Repair

Endovascular repair (EVAR) has become the standard procedure in treating thoracic (TAA) or abdominal aortic aneurysms (AAA). Not entirely free of complications, a persisting perfusion of the aneurysm after EVAR, called Endoleak (EL), leads to reintervention and risk of secondary rupture. How the aortic wall responds to the implantation of a stentgraft and EL is mostly uncertain. We present a pilot study to identify peptide signatures and gain new insights in pathophysiological alterations of the aortic wall after EVAR using matrix-assisted laser desorption or ionization mass spectrometry imaging (MALDI-MSI). In course of or accompanying an open aortic repair, tissue sections from 15 patients (TAA = 5, AAA = 5, EVAR = 5) were collected. Regions of interest (tunica media and tunica adventitia) were defined and univariate (receiver operating characteristic analysis) statistical analysis for subgroup comparison was used. This proof-of-concept study demonstrates that MALDI-MSI is feasible to identify discriminatory peptide signatures separating TAA, AAA and EVAR. Decreased intensity distributions for actin, tropomyosin, and troponin after EVAR suggest impaired contractility in vascular smooth muscle cells. Furthermore, inability to provide energy caused by impaired respiratory chain function and continuous degradation of extracellular matrix components (collagen) might support aortic wall destabilization. In case of EL after EVAR, this mechanism may result in a weakened aortic wall with lacking ability to react on reinstating pulsatile blood flow.

Matrix-assisted laser desorption ionisation (MALDI) mass spectrometry (MS): basics and clinical applications

Background Matrix-assisted laser desorption ionisation (MALDI) mass spectrometry (MS) has been used for more than 30 years. Compared with other analytical techniques, it offers ease of use, high throughput, robustness, cost-effectiveness, rapid analysis and sensitivity. As advantages, current clinical techniques (e.g. immunoassays) are unable to directly measure the biomarker; rather, they measure secondary signals. MALDI-MS has been extensively researched for clinical applications, and it is set for a breakthrough as a routine tool for clinical diagnostics. Content This review reports on the principles of MALDI-MS and discusses current clinical applications and the future clinical prospects for MALDI-MS. Furthermore, the review assesses the limitations currently experienced in clinical assays, the advantages and the impact of MALDI-MS to transform clinical laboratories. Summary MALDI-MS is widely used in clinical microbiology for the screening of microbial isolates; however, there is scope to apply MALDI-MS in the diagnosis, prognosis, therapeutic drug monitoring and biopsy imaging in many diseases. Outlook There is considerable potential for MALDI-MS in clinic as a tool for screening, profiling and imaging because of its high sensitivity and specificity over alternative techniques.

Spatially Resolved Immunometabolism to Understand Infectious Disease Progression

Infectious diseases, including those of viral, bacterial, fungal, and parasitic origin are often characterized by focal inflammation occurring in one or more distinct tissues. Tissue-specific outcomes of infection are also evident in many infectious diseases, suggesting that the local microenvironment may instruct complex and diverse innate and adaptive cellular responses resulting in locally distinct molecular signatures. In turn, these molecular signatures may both drive and be responsive to local metabolic changes in immune as well as non-immune cells, ultimately shaping the outcome of infection. Given the spatial complexity of immune and inflammatory responses during infection, it is evident that understanding the spatial organization of transcripts, proteins, lipids, and metabolites is pivotal to delineating the underlying regulation of local immunity. Molecular imaging techniques like mass spectrometry imaging and spatially resolved, highly multiplexed immunohistochemistry and transcriptomics can define detailed metabolic signatures at the microenvironmental level. Moreover, a successful complementation of these two imaging techniques would allow multi-omics analyses of inflammatory microenvironments to facilitate understanding of disease pathogenesis and identify novel targets for therapeutic intervention. Here, we describe strategies for downstream data analysis of spatially resolved multi-omics data and, using leishmaniasis as an exemplar, describe how such analysis can be applied in a disease-specific context.

Lipidomics in Biomarker Research

Lipids are natural substances found in all living organisms and involved in many biological functions. Imbalances in the lipid metabolism are linked to various diseases such as obesity, diabetes, or cardiovascular disease. Lipids comprise thousands of chemically distinct species making them a challenge to analyze because of their great structural diversity.Thanks to the technological improvements in the fields of chromatography, high-resolution mass spectrometry, and bioinformatics over the last years, it is now possible to perform global lipidomics analyses, allowing the concomitant detection, identification, and relative quantification of hundreds of lipid species. This review shall provide an insight into a general lipidomics workflow and its application in metabolic biomarker research.

Unequivocal Identification of Aspartic Acid and isoAspartic Acid by MALDI-TOF/TOF: From Peptide Standards to a Therapeutic Antibody

Aspartic acid (Asp) to isoaspartic acid (isoAsp) isomerization in therapeutic monoclonal antibodies (mAbs) and other biotherapeutics is a critical quality attribute (CQA) that requires careful control and monitoring during the drug discovery and production processes. The unwanted formation of isoAsp within biotherapeutics and resultant structural changes in the peptide backbone may negatively impact the efficacy, potency, and safety of the molecule or become immunogenic, especially if the isomerization occurs within the mAb complementarity determining region (CDR). Herein we describe a MALDI-TOF/TOF mass spectrometry method that affords unequivocal identification of the presence and the exact position of the isoAsp residue(s) in peptide standards ranging in size from a tripeptide to a docosapeptide (22 residues). In general, the peptide bond immediately N-terminal to the isoAsp residue is more susceptible to MALDI-TOF/TOF fragmentation than its unmodified counterpart. In some of the peptides evaluated in this study, fragmentation of the peptide bond C-terminal to the isoAsp residue (the aspartate effect) is also enhanced when compared to the control. Relative quantification by MALDI-TOF/TOF of this chemical modification is dependent upon a successful reversed-phase HPLC (rpHPLC) separation of the control and modified peptides. This method has also been validated on a therapeutic mAb that contains a well-documented isoAsp residue in the heavy chain CDR3 after forced degradation. Moreover, we also demonstrate that higher energy C-trap dissociation of only the singly charged species, and not the multiply charged form, of the isoAsp containing peptide, separated by rpHPLC, results in LC-MS/MS fragmentation that is highly consistent to that of MALDI-TOF/TOF.

Mass spectrometry imaging in zebrafish larvae for assessing drug safety and metabolism

Drug safety assessment in the early phases of drug discovery is critical to facilitate the rapid development of novel therapeutics. Recently, teleost zebrafish (Danio rerio) has emerged as a promising vertebrate model for the assessment of drug safety. Zebrafish is a convenient model because of its small size, high fecundity, embryo transparency, and ex utero development. In this study, we developed a matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) method applied to zebrafish larvae to investigate safety and metabolism of sahaquine (Sq), an anticancer agent inhibiting histone deacetylase 6. This technique improves on prior studies using liquid chromatography-mass spectrometry (LC-MS) by adding analysis of the drug spatial distribution. Using this method, it was determined that Sq dissolved in fish water (1-2000 μM) did not reach the larval body and was mainly distributed throughout the yolk. High Sq concentration (800 μM) administered intravenously allowed the compound to reach the larval body but did not induce phenotypic abnormalities. Sq was metabolized into its glucuronidated form within 24 h and was excreted within 72 h. MALDI MSI was instrumental in showing that Sq-glucuronide was mainly formed in the gut and slightly in yolk syncytial layer, and provided valuable insights into xenobiotics elimination in zebrafish larvae. This study indicates that Sq has a good safety profile and merits further investigations in other disease models. In addition, the optimized MALDI MSI protocol provided here can be widely applied to study distribution and metabolic fate of other structurally related molecules.

Discovery of Spatial Peptide Signatures for Neuroblastoma Risk Assessment by MALDI Mass Spectrometry Imaging

Simple Summary

The childhood tumor, neuroblastoma, has a broad clinical presentation. Risk assessment at diagnosis is particularly difficult in molecularly heterogeneous high-risk cases. Here we investigate the potential of imaging mass spectrometry to directly detect intratumor heterogeneity on the protein level in tissue sections. We show that this approach can produce discriminatory peptide signatures separating high- from low- and intermediate-risk tumors, identify 8 proteins aassociated with these signatures and validate two marker proteins using tissue immunostaining that have promise for further basic and translational research in neuroblastoma. We provide proof-of-concept that mass spectrometry-based technology could assist early risk assessment in neuroblastoma and provide insights into peptide signature-based detection of intratumor heterogeneity.

Abstract

Risk classification plays a crucial role in clinical management and therapy decisions in children with neuroblastoma. Risk assessment is currently based on patient criteria and molecular factors in single tumor biopsies at diagnosis. Growing evidence of extensive neuroblastoma intratumor heterogeneity drives the need for novel diagnostics to assess molecular profiles more comprehensively in spatial resolution to better predict risk for tumor progression and therapy resistance. We present a pilot study investigating the feasibility and potential of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to identify spatial peptide heterogeneity in neuroblastoma tissues of divergent current risk classification: high versus low/intermediate risk. Univariate (receiver operating characteristic analysis) and multivariate (segmentation, principal component analysis) statistical strategies identified spatially discriminative risk-associated MALDI-based peptide signatures. The AHNAK nucleoprotein and collapsin response mediator protein 1 (CRMP1) were identified as proteins associated with these peptide signatures, and their differential expression in the neuroblastomas of divergent risk was immunohistochemically validated. This proof-of-concept study demonstrates that MALDI-MSI combined with univariate and multivariate analysis strategies can identify spatially discriminative risk-associated peptide signatures in neuroblastoma tissues. These results suggest a promising new analytical strategy improving risk classification and providing new biological insights into neuroblastoma intratumor heterogeneity.

Lasers for matrix-assisted laser desorption ionization

Matrix-assisted laser desorption ionization (MALDI) was introduced 35 years ago and has advanced from a general method for producing intact ions from large biomolecules to wide use in applications ranging from bacteria identification to tissue imaging. MALDI was enabled by the development of high energy pulsed lasers that create ions from solid samples for analysis by mass spectrometry. The original lasers used for MALDI were ultraviolet fixed-wavelength nitrogen and Nd:YAG lasers, and a number of additional laser sources have been subsequently introduced with wavelengths ranging from the infrared to the ultraviolet and pulse widths from nanosecond to femtosecond. Wavelength tunable sources have been employed both in the IR and UV, and repetition rates have increased from tens of Hz to tens of kHz as MALDI has moved into mass spectrometry imaging. Dual-pulse configurations have been implemented with two lasers directed at the target or with a second laser creating ions in the plume of desorbed material. This review provides a brief history of the use of lasers for ionization in mass spectrometry and describes the various types of lasers and configurations used for MALDI.

Subcellular proteomics

The eukaryotic cell is compartmentalized into subcellular niches, including membrane-bound and membrane-less organelles. Proteins localize to these niches to fulfil their function, enabling discreet biological processes to occur in synchrony. Dynamic movement of proteins between niches is essential for cellular processes such as signalling, growth, proliferation, motility and programmed cell death, and mutations causing aberrant protein localization are associated with a wide range of diseases. Determining the location of proteins in different cell states and cell types and how proteins relocalize following perturbation is important for understanding their functions, related cellular processes and pathologies associated with their mislocalization. In this Primer, we cover the major spatial proteomics methods for determining the location, distribution and abundance of proteins within subcellular structures. These technologies include fluorescent imaging, protein proximity labelling, organelle purification and cell-wide biochemical fractionation. We describe their workflows, data outputs and applications in exploring different cell biological scenarios, and discuss their main limitations. Finally, we describe emerging technologies and identify areas that require technological innovation to allow better characterization of the spatial proteome.

Targeting immune cell metabolism in kidney diseases

Insights into the relationship between immunometabolism and inflammation have enabled the targeting of several immunity-mediated inflammatory processes that underlie infectious diseases and cancer or drive transplant rejection, but this field remains largely unexplored in kidney diseases. The kidneys comprise heterogeneous cell populations, contain distinct microenvironments such as areas of hypoxia and hypersalinity, and are responsible for a functional triad of filtration, reabsorption and secretion. These distinctive features create myriad potential metabolic therapeutic targets in the kidney. Immune cells have crucial roles in the maintenance of kidney homeostasis and in the response to kidney injury, and their function is intricately connected to their metabolic properties. Changes in nutrient availability and biomolecules, such as cytokines, growth factors and hormones, initiate cellular signalling events that involve energy-sensing molecules and other metabolism-related proteins to coordinate immune cell differentiation, activation and function. Disruption of homeostasis promptly triggers the metabolic reorganization of kidney immune and non-immune cells, which can promote inflammation and tissue damage. The metabolic differences between kidney and immune cells offer an opportunity to specifically target immunometabolism in the kidney.

Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging

Simple Summary

High-grade serous ovarian cancer (HGSOC) accounts for 70% of ovarian carcinomas with sobering survival rates. The mechanisms mediating treatment efficacy are still poorly understood with no adequate biomarkers of response to treatment and risk assessment. This variability of treatment response might be due to its molecular heterogeneity. Therefore, identification of biomarkers or molecular signatures to stratify patients and offer personalized treatment is of utmost priority. Currently, comprehensive gene expression profiling is time- and cost-extensive and limited by tissue heterogeneity. Thus, it has not been implemented into clinical practice. This study demonstrates for the first time a spatially resolved, time- and cost-effective approach to stratifying HGSOC patients by combining novel matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) technology with machine-learning algorithms. Eventually, MALDI-derived predictive signatures for treatment efficacy, recurrent risk, or, as demonstrated here, molecular subtypes might be utilized for emerging clinical challenges to ultimately improve patient outcomes.

Abstract

Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma-associated spectra provides tangible improvements to classification quality (AUC = 0.988). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). Here, we present a concept integrating MALDI-IMS with machine-learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for personalized treatment.

High-resolution AP-SMALDI MSI as a tool for drug imaging in Schistosoma mansoni

Schistosoma mansoni is a parasitic flatworm causing schistosomiasis, an infectious disease affecting several hundred million people worldwide. Schistosomes live dioeciously, and upon pairing with the male, the female starts massive egg production, which causes pathology. Praziquantel (PZQ) is the only drug used, but it has an inherent risk of resistance development. Therefore, alternatives are needed. In the context of drug repurposing, the cancer drug imatinib was tested, showing high efficacy against S. mansoni in vitro. Besides the gonads, imatinib mainly affected the integrity of the intestine in males and females. In this study, we investigated the potential uptake and distribution of imatinib in adult schistosomes including its distribution kinetics. To this end, we applied for the first time atmospheric-pressure scanning microprobe matrix-assisted laser desorption/ionization mass spectrometry imaging (AP-SMALDI MSI) for drug imaging in paired S. mansoni. Our results indicate that imatinib was present in the esophagus and intestine of the male as early as 20 min after in vitro exposure, suggesting an oral uptake route. After one hour, the drug was also found inside the paired female. The detection of the main metabolite, N-desmethyl imatinib, indicated metabolization of the drug. Additionally, a marker signal for the female ovary was successfully applied to facilitate further conclusions regarding organ tropism of imatinib. Our results demonstrate that AP-SMALDI MSI is a useful method to study the uptake, tissue distribution, and metabolization of imatinib in S. mansoni. The results suggest using AP-SMALDI MSI also for investigating other antiparasitic compounds and their metabolites in schistosomes and other parasites.

Deep multiple instance learning classifies subtissue locations in mass spectrometry images from tissue-level annotations

MOTIVATION

Mass spectrometry imaging (MSI) characterizes the molecular composition of tissues at spatial resolution, and has a strong potential for distinguishing tissue types, or disease states. This can be achieved by supervised classification, which takes as input MSI spectra, and assigns class labels to subtissue locations. Unfortunately, developing such classifiers is hindered by the limited availability of training sets with subtissue labels as the ground truth. Subtissue labeling is prohibitively expensive, and only rough annotations of the entire tissues are typically available. Classifiers trained on data with approximate labels have sub-optimal performance.

RESULTS

To alleviate this challenge, we contribute a semi-supervised approach mi-CNN. mi-CNN implements multiple instance learning with a convolutional neural network (CNN). The multiple instance aspect enables weak supervision from tissue-level annotations when classifying subtissue locations. The convolutional architecture of the CNN captures contextual dependencies between the spectral features. Evaluations on simulated and experimental datasets demonstrated that mi-CNN improved the subtissue classification as compared to traditional classifiers. We propose mi-CNN as an important step toward accurate subtissue classification in MSI, enabling rapid distinction between tissue types and disease states.

AVAILABILITY AND IMPLEMENTATION

The data and code are available at https://github.com/Vitek-Lab/mi-CNN_MSI.

Detecting early myocardial ischemia in rat heart by MALDI imaging mass spectrometry

Diagnostics of myocardial infarction in human post-mortem hearts can be achieved only if ischemia persisted for at least 6–12 h when certain morphological changes appear in myocardium. The initial 4 h of ischemia is difficult to diagnose due to lack of a standardized method. Developing a panel of molecular tissue markers is a promising approach and can be accelerated by characterization of molecular changes. This study is the first untargeted metabolomic profiling of ischemic myocardium during the initial 4 h directly from tissue section. Ischemic hearts from an ex-vivo Langendorff model were analysed using matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) at 15 min, 30 min, 1 h, 2 h, and 4 h. Region-specific molecular changes were identified even in absence of evident histological lesions and were segregated by unsupervised cluster analysis. Significantly differentially expressed features were detected by multivariate analysis starting at 15 min while their number increased with prolonged ischemia. The biggest significant increase at 15 min was observed for m/z 682.1294 (likely corresponding to S-NADHX—a damage product of nicotinamide adenine dinucleotide (NADH)). Based on the previously reported role of NAD⁺/NADH ratio in regulating localization of the sodium channel (Na_v1.5) at the plasma membrane, Na_v1.5 was evaluated by immunofluorescence. As expected, a fainter signal was observed at the plasma membrane in the predicted ischemic region starting 30 min of ischemia and the change became the most pronounced by 4 h. Metabolomic changes occur early during ischemia, can assist in identifying markers for post-mortem diagnostics and improve understanding of molecular mechanisms.

Spatially-resolved proteomics and transcriptomics: An emerging digital spatial profiling approach for tumor microenvironment

Digital spatial profiling (DSP) is an emerging powerful technology for proteomics and transcriptomics analyses in a spatially resolved manner for formalin-fixed paraffin-embedded (FFPE) samples developed by nanoString Technologies. DSP applies several advanced technologies, including high-throughput readout technologies (digital optical barcodes by nCounter instruments or next generation sequencing (NGS)), programmable digital micromirror device (DMD) technology, and microfluidic sampling technologies into traditional immunohistochemistry (IHC) and RNA in situ hybridization (ISH) approaches, creating an innovative tool for discovery, translational research, and clinical uses. Since its launch in 2019, DSP has been rapidly adopted, especially in immuno-oncology and tumor microenvironment research areas, and has revealed valuable information that was inaccessible before. In this article, we report the successful setup and validation of the first DSP technology platform in China. Both DSP spatial protein and RNA profiling approaches were validated using FFPE colorectal cancer tissues. Regions of interest (ROIs) were selected in the areas enriched with tumor cells, stroma/immune cells, or normal epithelial cells, and multiplex spatial profiling of both proteins and RNAs were performed. DSP spatial profiling data were processed and normalized accordingly, validating the high quality and consistency of the data. Unsupervised hierarchical clustering as well as principal component analysis (PCA) grouped tumor, stroma/immune cells, and normal epithelial cells into distinct clusters, indicating that the DSP approach effectively captured the spatially resolved proteomics and transcriptomics profiles of different compartments within the tumor microenvironment. In summary, the results confirmed the expected sensitivity and robustness of the DSP approach in profiling both proteins and RNAs in a spatially resolved manner. As a novel technology in highly complex spatial analyses, DSP endows refined analytical power from the tumor microenvironment perspective with the potential of scaling up to more analyzable targets at relatively low cell input levels. We expect that the DSP technology will greatly advance a wide range of biomedical research, especially in immuno-oncology and tumor microenvironment research areas.

Omics-Driven Biotechnology for Industrial Applications

Biomanufacturing is a key component of biotechnology that uses biological systems to produce bioproducts of commercial relevance, which are of great interest to the energy, material, pharmaceutical, food, and agriculture industries. Biotechnology-based approaches, such as synthetic biology and metabolic engineering are heavily reliant on "omics" driven systems biology to characterize and understand metabolic networks. Knowledge gained from systems biology experiments aid the development of synthetic biology tools and the advancement of metabolic engineering studies toward establishing robust industrial biomanufacturing platforms. In this review, we discuss recent advances in "omics" technologies, compare the pros and cons of the different "omics" technologies, and discuss the necessary requirements for carrying out multi-omics experiments. We highlight the influence of "omics" technologies on the production of biofuels and bioproducts by metabolic engineering. Finally, we discuss the application of "omics" technologies to agricultural and food biotechnology, and review the impact of "omics" on current COVID-19 research.

Metabolomic analysis and mass spectrometry imaging after neonatal stroke and cell therapies in mouse brains

Ischemic brain injury provokes complex, time-dependent downstream pathways that ultimately lead to cell death. We aimed to demonstrate the levels of a wide range of metabolites in brain lysates and their on-tissue distribution following neonatal stroke and cell therapies. Postnatal day 12 mice underwent middle cerebral artery occlusion (MCAO) and were administered 1 × 10⁵ cells after 48 h. Metabolomic analysis of the injured hemisphere demonstrated that a variety of amino acids were significantly increased and that tricarboxylic acid cycle intermediates and some related amino acids, such as glutamate, were decreased. With the exception of the changes in citric acid, neither mesenchymal stem/stromal cells nor CD34⁺ cells ameliorated these changes. On-tissue visualization with matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS) imaging revealed that the signal intensity of glutamate was significantly decreased in the infarct area, consistent with the metabolomic analysis, while its intensity was significantly increased in the peri-infarct area after MCAO. Although cell therapies did not ameliorate the changes in metabolites in the infarct area, mesenchymal stem cells ameliorated the increased levels of glutamate and carnitine in the peri-infarct area. MALDI-MS imaging showed the location-specific effect of cell therapies even in this subacute setting after MCAO. These methodologies may be useful for further investigation of possible treatments for ischemic brain injury.

Interactive Visual Analysis of Mass Spectrometry Imaging Data Using Linear and Non-Linear Embeddings

Mass spectrometry imaging (MSI) is an imaging technique used in analytical chemistry to study the molecular distribution of various compounds at a micro-scale level. For each pixel, MSI stores a mass spectrum obtained by measuring signal intensities of thousands of mass-to-charge ratios (m/z-ratios), each linked to an individual molecular ion species. Traditional analysis tools focus on few individual m/z-ratios, which neglects most of the data. Recently, clustering methods of the spectral information have emerged, but faithful detection of all relevant image regions is not always possible. We propose an interactive visual analysis approach that considers all available information in coordinated views of image and spectral space visualizations, where the spectral space is treated as a multi-dimensional space. We use non-linear embeddings of the spectral information to interactively define clusters and respective image regions. Of particular interest is, then, which of the molecular ion species cause the formation of the clusters. We propose to use linear embeddings of the clustered data, as they allow for relating the projected views to the given dimensions. We document the effectiveness of our approach in analyzing matrix-assisted laser desorption/ionization (MALDI-2) imaging data with ground truth obtained from histological images.

3D projection electrophoresis for single-cell immunoblotting

Immunoassays and mass spectrometry are powerful single-cell protein analysis tools; however, interfacing and throughput bottlenecks remain. Here, we introduce three-dimensional single-cell immunoblots to detect both cytosolic and nuclear proteins. The 3D microfluidic device is a photoactive polyacrylamide gel with a microwell array-patterned face (xy) for cell isolation and lysis. Single-cell lysate in each microwell is "electrophoretically projected" into the 3rd dimension (z-axis), separated by size, and photo-captured in the gel for immunoprobing and confocal/light-sheet imaging. Design and analysis are informed by the physics of 3D diffusion. Electrophoresis throughput is > 2.5 cells/s (70× faster than published serial sampling), with 25 immunoblots/mm2 device area (>10× increase over previous immunoblots). The 3D microdevice design synchronizes analyses of hundreds of cells, compared to status quo serial analyses that impart hours-long delay between the first and last cells. Here, we introduce projection electrophoresis to augment the heavily genomic and transcriptomic single-cell atlases with protein-level profiling.

MALDI MSI Reveals the Spatial Distribution of Protein Markers in Tracheobronchial Lymph Nodes and Lung of Pigs after Respiratory Infection

Respiratory infections are a real threat for humans, and therefore the pig model is of interest for studies. As one of a case for studies, Actinobacillus pleuropneumoniae (APP) caused infections and still worries many pig breeders around the world. To better understand the influence of pathogenic effect of APP on a respiratory system-lungs and tracheobronchial lymph nodes (TBLN), we aimed to employ matrix-assisted laser desorption/ionization time-of-flight mass spectrometry imaging (MALDI-TOF MSI). In this study, six pigs were intranasally infected by APP and two were used as non-infected control, and 48 cryosections have been obtained. MALDI-TOF MSI and immunohistochemistry (IHC) were used to study spatial distribution of infectious markers, especially interleukins, in cryosections of porcine tissues of lungs (necrotic area, marginal zone) and tracheobronchial lymph nodes (TBLN) from pigs infected by APP. CD163, interleukin 1β (IL‑1β) and a protegrin-4 precursor were successfully detected based on their tryptic fragments. CD163 and IL‑1β were confirmed also by IHC. The protegrin-4 precursor was identified by MALDI-TOF/TOF directly on the tissue cryosections. CD163, IL‑1β and protegrin‑4 precursor were all significantly (p < 0.001) more expressed in necrotic areas of lungs infected by APP than in marginal zone, TBLN and in control lungs.

Glutamine metabolism modulates azole susceptibility in Trypanosoma cruzi amastigotes

The mechanisms underlying resistance of the Chagas disease parasite, Trypanosoma cruzi, to current therapies are not well understood, including the role of metabolic heterogeneity. We found that limiting exogenous glutamine protects actively dividing amastigotes from ergosterol biosynthesis inhibitors (azoles), independent of parasite growth rate. The antiparasitic properties of azoles are derived from inhibition of lanosterol 14α-demethylase (CYP51) in the endogenous sterol synthesis pathway. We find that carbons from ¹³C-glutamine feed into amastigote sterols and into metabolic intermediates that accumulate upon CYP51 inhibition. Incorporation of ¹³C-glutamine into endogenously synthesized sterols is increased with BPTES treatment, an inhibitor of host glutamine metabolism that sensitizes amastigotes to azoles. Similarly, amastigotes are re-sensitized to azoles following addition of metabolites upstream of CYP51, raising the possibility that flux through the sterol synthesis pathway is a determinant of sensitivity to azoles and highlighting the potential role for metabolic heterogeneity in recalcitrant T. cruzi infection.

Applications of mass spectrometry imaging in virus research

Mass spectrometry imaging (MSI) is a label-free molecular imaging technique allowing an untargeted detection of a broad range of biomolecules and xenobiotics. MSI enables imaging of the spatial distribution of proteins, peptides, lipids and metabolites from a wide range of samples. To date, this technique is commonly applied to tissue sections in cancer diagnostics and biomarker development, but also molecular histology in general. Advances in the methodology and bioinformatics improved the resolution of MS images below the single cell level and increased the flexibility of the workflow. However, MSI-based research in virology is just starting to gain momentum and its full potential has not been exploited yet. In this review, we discuss the main applications of MSI in virology. We review important aspects of matrix-assisted laser desorption/ionization (MALDI) MSI, the most widely used MSI technique in virology. In addition, we summarize relevant literature on MSI studies that aim to unravel virus-host interactions and virus pathogenesis, to elucidate antiviral drug kinetics and to improve current viral disease diagnostics. Collectively, these studies strongly improve our general understanding of virus-induced changes in the proteome, metabolome and metabolite distribution in host tissues of humans, animals and plants upon infection. Furthermore, latest MSI research provided important insights into the drug distribution and distribution kinetics, especially in antiretroviral research. Finally, MSI-based investigations of oncogenic viruses greatly increased our knowledge on tumor mass signatures and facilitated the identification of cancer biomarkers.

Accelerating Fourier Transform-Ion Cyclotron Resonance Mass Spectrometry Imaging Using a Subspace Approach

We present a subspace method that accelerates data acquisition using Fourier transform-ion cyclotron resonance (FT-ICR) mass spectrometry imaging (MSI). For MSI of biological tissue samples, there is a finite number of heterogeneous tissue types with distinct chemical profiles that introduce redundancy in the high-dimensional measurements. Our subspace model exploits the redundancy in data measured from whole-slice tissue samples by decomposing the transient signals into linear combinations of a set of basis transients with the desired spectral resolution. This decomposition allowed us to design a strategy that acquires a subset of long transients for basis determination and short transients for the remaining pixels, drastically reducing the acquisition time. The computational reconstruction strategy can maintain high-mass-resolution and spatial-resolution MSI while providing a 10-fold improvement in throughput. We validated the capability of the subspace model using a rat sagittal brain slice imaging data set. Comprehensive evaluation of the quality of the mass spectral and ion images demonstrated that the reconstructed data produced by the reported method required only 15% of the typical acquisition time and exhibited both qualitative and quantitative consistency when compared to the original data. Our method enables either higher sample throughput or higher-resolution images at similar acquisition lengths, providing greater flexibility in obtaining FT-ICR MSI measurements.

Integrating High-Resolution MALDI Imaging into the Development Pipeline of Anti-Tuberculosis Drugs

Successful treatment of tuberculosis (TB) requires antibiotics to reach their intended point of action, i.e., necrotizing granulomas in the lung. MALDI mass spectrometry imaging (MSI) is able to visualize the distribution of antibiotics in tissue, but resolving the small histological structures in mice, which are most commonly used in preclinical trials, requires high spatial resolution. We developed a MALDI MSI method to image antibiotics in the mouse lung with high mass resolution (240k @ m/z 200 fwhm) and high spatial resolution (10 μm pixel size). A crucial step was to develop a cryosectioning protocol that retains the distribution of water-soluble drugs in small and fragile murine lung lobes without inflation or embedding. Choice and application of matrices were optimized to detect human-equivalent drug concentrations in tissue, and measurement parameters were optimized to detect multiple drugs in a single tissue section. We succeeded in visualizing the distribution of all current first-line anti-TB drugs (pyrazinamide, rifampicin, ethambutol, isoniazid) and the second-line drugs moxifloxacin and clofazimine. Four of these compounds were imaged for the first time in the mouse lung. Accurate mass identification was confirmed by on-tissue MS/MS. Evaluation of fragmentation pathways revealed the structure of the double-protonated molecular ion of pyrazinamide. Clofazimine was imaged for the first time with 10 μm pixel size revealing clofazimine accumulation in lipid deposits around airways. In summary, we developed a platform to resolve the detailed histology in the murine lung and to reliably detect a range of anti-TB drugs at human-equivalent doses. Our workflow is currently being employed in preclinical mouse studies to evaluate the efficacy of novel anti-TB drugs.

Role of MALDI-MSI in combination with 3D tissue models for early stage efficacy and safety testing of drugs and toxicants

Introduction: Three-dimensional (3D) cell cultures have become increasingly important materials to investigate biological processes and drug efficacy and toxicity. The ability of 3D cultures to mimic the physiology of primary tissues and organs in the human body enables further insight into cellular behavior and is hence highly desirable in early-stage drug development. Analyzing the spatial distribution of drug compounds and endogenous molecules provides an insight into the efficacy of a drug whilst simultaneously giving information on biological responses. Areas Covered: In this review we will examine the main 3D cell culture systems employed and applications, which describe their integration with mass spectrometry imaging (MSI). Expert Opinion: MSI is a powerful technique that can map a vast range of molecules simultaneously in tissues without the addition of labels that can provide insights into the efficacy and safety of a new drug. The combination of MSI and 3D cell cultures has emerged as a promising tool in early-stage drug analysis. However, the most common administration route for pharmaceutical drugs is via oral delivery. The use of MSI in combination with models of the GI tract is an area that has been little explored to date, the reasons for this are discussed.

Emerging mass spectrometry-based proteomics methodologies for novel biomedical applications

Research into the basic biology of human health and disease, as well as translational human research and clinical applications, all benefit from the growing accessibility and versatility of mass spectrometry (MS)-based proteomics. Although once limited in throughput and sensitivity, proteomic studies have quickly grown in scope and scale over the last decade due to significant advances in instrumentation, computational approaches, and bio-sample preparation. Here, we review these latest developments in MS and highlight how these techniques are used to study the mechanisms, diagnosis, and treatment of human diseases. We first describe recent groundbreaking technological advancements for MS-based proteomics, including novel data acquisition techniques and protein quantification approaches. Next, we describe innovations that enable the unprecedented depth of coverage in protein signaling and spatiotemporal protein distributions, including studies of post-translational modifications, protein turnover, and single-cell proteomics. Finally, we explore new workflows to investigate protein complexes and structures, and we present new approaches for protein-protein interaction studies and intact protein or top-down MS. While these approaches are only recently incipient, we anticipate that their use in biomedical MS proteomics research will offer actionable discoveries for the improvement of human health.

MALDI-MSI-A Step Forward in Overcoming the Diagnostic Challenges in Ovarian Tumors

This study presents the use of matrix-assisted laser desorption and ionization mass spectrometry imaging (MALDI-MSI) directly on the tissue of two ovarian tumors that often present a diagnostic challenge, a low-grade serous borderline ovarian tumor and ovarian fibrothecoma. Different spatial distribution of m/z values within the tissue samples was observed, and regiospecific peaks were identified. Among the 106 peaks in the borderline ovarian tumor five, regiospecific peaks (m/z: 2861.35; 2775.79; 3368.34; 3438.43; 4936.37) were selected using FlexImaging software. Subsequently, the distribution of those selected peaks was visualized on the fibrothecoma tissue section, which demonstrated the differences in the tissue homo-/heterogeneous structure of both tumors. The comparison with the histopathological staining of the ovarian borderline tumor tissue section, obtained during serial sectioning, showed a close correlation of the molecular map with the morphological and histopathological features of the tissue and allowed the identification of different tissue types within the sample. This study highlights the potential significance of MSI in enabling morphological characterization of ovarian tumors as well as correct diagnosis and further prognosis than thus far seen in the literature. Osteopontin, tropomyosin and orosomucoid are only a couple of the molecules investigated using MALDI-MSI in ovarian cancer research. This study, in line with the available literature, proves the potential of MALDI-MSI to overcome the current limitations of classic histopathological examination giving a more in-depth insight into the tissue structure and thus lead to the more accurate differential diagnosis of ovarian tumors, especially in the most challenging cases.

137Cs γ Ray and 28Si Irradiation Induced Murine Hepatocellular Carcinoma Lipid Changes in Liver Assessed by MALDI-MSI Combined with Spatial Shrunken Centroid Clustering Algorithm: A Pilot Study

Characterization of lipids by matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) is of great interest because not only are lipids important structural molecules in both the cell and internal organelle membranes, but they are also important signaling molecules. MALDI-MSI combined with spatial image segmentation has been previously used to identify tumor heterogeneities within tissues with distinct anatomical regions such as the brain. However, there has been no systematic study utilizing MALDI-MSI combined with spatial image segmentation to assess the tumor microenvironment in the liver. Here, we present that image segmentation can be used to evaluate the tumor microenvironment in the liver. In particular, to better understand the molecular mechanisms of irradiation-induced hepatic carcinogenesis, we used MALDI-MSI in the negative ion mode to identify lipid changes 12 months post exposure to low dose ²⁸Si and ¹³⁷Cs γ ray irradiation. We report here the changes in the lipid profiles of male C3H/HeNCrl mice liver tissues after exposure to irradiation and analyzed using the spatial shrunken centroid clustering algorithm. These findings provide valuable information as astronauts will be exposed to high-charge high-energy (HZE) particles and low-energy γ-ray irradiation during deep space travel. Even at low doses, exposure to these irradiations can lead to cancer. Previous studies infer that irradiation of mice with low-dose HZE particles induces oxidative damage and microenvironmental changes that are thought to play roles in the pathophysiology of hepatocellular carcinoma.

Mass Spectrometry Analysis of Intact Proteins from Crude Samples

Analysis of intact proteins by native mass spectrometry has emerged as a powerful tool for obtaining insight into subunit diversity, post-translational modifications, stoichiometry, structural arrangement, stability, and overall architecture. Typically, such an analysis is performed following protein purification procedures, which are time consuming, costly, and labor intensive. As this technology continues to move forward, advances in sample handling and instrumentation have enabled the investigation of intact proteins in situ and in crude samples, offering rapid analysis and improved conservation of the biological context. This emerging field, which involves various ion source platforms such as matrix-assisted laser desorption ionization (MALDI) and electrospray ionization (ESI) for both spatial imaging and solution-based analysis, is expected to impact many scientific fields, including biotechnology, pharmaceuticals, and clinical sciences. In this Perspective, we discuss the information that can be retrieved by such experiments as well as the current advantages and technical challenges associated with the different sampling strategies. Furthermore, we present future directions of these MS-based methods, including current limitations and efforts that should be made to make these approaches more accessible. Considering the vast progress we have witnessed in recent years, we anticipate that the advent of further innovations enabling minimal handling of MS samples will make this field more robust, user friendly, and widespread.

Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis

Technological advances enable assaying multiplexed spatially resolved RNA and protein expression profiling of individual cells, thereby capturing molecular variations in physiological contexts. While these methods are increasingly accessible, computational approaches for studying the interplay of the spatial structure of tissues and cell-cell heterogeneity are only beginning to emerge. Here, we present spatial variance component analysis (SVCA), a computational framework for the analysis of spatial molecular data. SVCA enables quantifying different dimensions of spatial variation and in particular quantifies the effect of cell-cell interactions on gene expression. In a breast cancer Imaging Mass Cytometry dataset, our model yields interpretable spatial variance signatures, which reveal cell-cell interactions as a major driver of protein expression heterogeneity. Applied to high-dimensional imaging-derived RNA data, SVCA identifies plausible gene families that are linked to cell-cell interactions. SVCA is available as a free software tool that can be widely applied to spatial data from different technologies.

Detailed Characterization of the Postionization Efficiencies in MALDI-2 as a Function of Relevant Input Parameters

A recently introduced technique based on MALDI with laser-induced postionization (PI), also named MALDI-2, increases the ion yields for numerous classes of lipids, metabolites, and carbohydrates in MALDI-MS imaging experiments under certain experimental conditions. Here, we used a semiautomatic LabVIEW-based protocol to investigate and optimize the efficiency of the PI process dependent on four relevant input parameters and a dense parameter grid: pulse energies of the two lasers, delay between the laser pulses, and buffer gas pressure in the ion source. All experiments were conducted with a modified MALDI-2 Synapt G2-S mass spectrometer (Waters) and use of a focal spot size on the sample of 15-17 μm. A wavelength-tunable optical parametric oscillator (OPO) laser served for PI at 260 or 280 nm. The investigated MALDI matrices were: 2,5-dihydroxybenzoic acid (positive ion mode, +), 2,5-dihydroxyacetophenone (+), α-cyano-4-hydroxycinnamic acid (+), norharmane (negative-ion mode, -), and 1,5-diaminonapthalene (-). A porcine brain extract served as lipid standard. In the positive-ion mode, a maximum boost for the generated [M + H]⁺ species was found with a N₂ buffer gas pressure of ∼2 mbar and a delay between the laser emissions of ∼10 μs. Higher optimal delay settings of several 10 μs were registered for the two studied matrices in negative-ion mode. With regard to the laser fluences, best PI efficiencies were reached using maximum available ablation and PI laser pulse energies of up to 25 and 160 μJ, respectively. For analytes not profiting from MALDI-2, best ion signal yields were recorded for ablation laser pulse energies of around 7 μJ, depending on the MALDI matrix. At higher laser pulse energies, sizable fragmentation is observed for these ions. The PI laser pulse energy did not have any influence on the ion signals of these species. For optimal ion yield of all analyte species, best results were obtained with an ablation laser pulse energy of ∼7 μJ and a PI laser pulse energy of ∼160 μJ. Our comprehensive data set provides valuable insight into the mechanisms underlying the MALDI-2 processes and could help to further optimize this emerging technique.

Light sheet fluorescence microscopy guided MALDI-imaging mass spectrometry of cleared tissue samples

Light sheet fluorescence microscopy (LSFM) of optically cleared biological samples represents a powerful tool to analyze the 3-dimensional morphology of tissues and organs. Multimodal combinations of LSFM with additional analyses of the identical sample help to limit the consumption of restricted specimen and reduce inter-sample variation. Here, we demonstrate the proof-of-concept that LSFM of cleared brain tissue samples can be combined with Matrix Assisted Laser Desorption/Ionization-Mass Spectrometry Imaging (MALDI-MSI) for detection and quantification of proteins. Samples of freshly dissected murine brain and of archived formalin-fixed paraffin-embedded (FFPE) human brain tissue were cleared (3DISCO). Tissue regions of interest were defined by LSFM and excised, (re)-embedded in paraffin, and sectioned. Mouse sections were coated with sinapinic acid matrix. Human brain sections were pre-digested with trypsin and coated with α-cyano-4-hydroxycinnamic acid matrix. Subsequently, sections were subjected to MALDI-time-of-flight (TOF)-MSI in mass ranges between 0.8 to 4 kDa (human tissue sections), or 2.5–25 kDa (mouse tissue sections) with a lateral resolution of 50 µm. Protein- and peptide-identities corresponding to acquired MALDI-MSI spectra were confirmed by parallel liquid chromatography tandem mass spectrometry (LC–MS/MS) analysis. The spatial abundance- and intensity-patterns of established marker proteins detected by MALDI-MSI were also confirmed by immunohistochemistry.

Classification of Thyroid Tumors Based on Mass Spectrometry Imaging of Tissue Microarrays; a Single-Pixel Approach

The primary diagnosis of thyroid tumors based on histopathological patterns can be ambiguous in some cases, so proper classification of thyroid diseases might be improved if molecular biomarkers support cytological and histological assessment. In this work, tissue microarrays representative for major types of thyroid malignancies—papillary thyroid cancer (classical and follicular variant), follicular thyroid cancer, anaplastic thyroid cancer, and medullary thyroid cancer—and benign thyroid follicular adenoma and normal thyroid were analyzed by mass spectrometry imaging (MSI), and then different computation approaches were implemented to test the suitability of the registered profiles of tryptic peptides for tumor classification. Molecular similarity among all seven types of thyroid specimens was estimated, and multicomponent classifiers were built for sample classification using individual MSI spectra that corresponded to small clusters of cells. Moreover, MSI components showing the most significant differences in abundance between the compared types of tissues detected and their putative identity were established by annotation with fragments of proteins identified by liquid chromatography-tandem mass spectrometry in corresponding tissue lysates. In general, high accuracy of sample classification was associated with low inter-tissue similarity index and a high number of components with significant differences in abundance between the tissues. Particularly, high molecular similarity was noted between three types of tumors with follicular morphology (adenoma, follicular cancer, and follicular variant of papillary cancer), whose differentiation represented the major classification problem in our dataset. However, low level of the intra-tissue heterogeneity increased the accuracy of classification despite high inter-tissue similarity (which was exemplified by normal thyroid and benign adenoma). We compared classifiers based on all detected MSI components (n = 1536) and the subset of the most abundant components (n = 147). Despite relatively higher contribution of components with significantly different abundance and lower overall inter-tissue similarity in the latter case, the precision of classification was generally higher using all MSI components. Moreover, the classification model based on individual spectra (a single-pixel approach) outperformed the model based on mean spectra of tissue cores. Our result confirmed the high feasibility of MSI-based approaches to multi-class detection of cancer types and proved the good performance of sample classification based on individual spectra (molecular image pixels) that overcame problems related to small amounts of heterogeneous material, which limit the applicability of classical proteomics.

Addressing Cellular Heterogeneity in Cancer through Precision Proteomics

Cells exhibit a broad spectrum of functions driven by differences in molecular phenotype. Understanding the heterogeneity between and within cell types has led to advances in our ability to diagnose and manipulate biological systems. Heterogeneity within and between tumors still poses a challenge to the development and efficacy of therapeutics. In this Perspective we review the toolkit of protein-level experimental approaches for investigating cellular heterogeneity. We describe how innovative approaches and technical developments have supported the advent of bottom-up single-cell proteomic analysis and present opportunities and challenges within cancer research. Finally, we introduce the concept of "precision proteomics" and discuss how the advantages and limitations of various experimental approaches render them suitable for different biological systems and questions.

Discovery of Prognostic Markers for Early-Stage High-Grade Serous Ovarian Cancer by Maldi-Imaging

With regard to relapse and survival, early-stage high-grade serous ovarian (HGSOC) patients comprise a heterogeneous group and there is no clear consensus on first-line treatment. Currently, no prognostic markers are available for risk assessment by standard targeted immunohistochemistry and novel approaches are urgently required. Here, we applied MALDI-imaging mass spectrometry (MALDI-IMS), a new method to identify distinct mass profiles including protein signatures on paraffin-embedded tissue sections. In search of prognostic biomarker candidates, we compared proteomic profiles of primary tumor sections from early-stage HGSOC patients with either recurrent (RD) or non-recurrent disease (N = 4; each group) as a proof of concept study. In total, MALDI-IMS analysis resulted in 7537 spectra from the malignant tumor areas. Using receiver operating characteristic (ROC) analysis, 151 peptides were able to discriminate between patients with RD and non-RD (AUC > 0.6 or < 0.4; p < 0.01), and 13 of them could be annotated to proteins. Strongest expression levels of specific peptides linked to Keratin type1 and Collagen alpha-2(I) were observed and associated with poor prognosis (AUC > 0.7). These results confirm that in using IMS, we could identify new candidates to predict clinical outcome and treatment extent for patients with early-stage HGSOC.

Analysis of Hashimoto's thyroiditis on fine needle aspiration samples by MALDI-Imaging

Matrix-Assisted Laser Desorption/Ionization (MALDI)-Mass Spectrometry imaging (MSI) has been applied in various diseases aimed to biomarkers discovery. In this study diagnosis and prognosis of Hashimoto Thyroiditis (HT) in cytopathology by MALDI-MSI has been investigated. Specimens from a routine series of subjects who underwent UltraSound-guided thyroid Fine Needle Aspirations (FNAs) were used. The molecular classifier trained in a previous study was modified to include HT as a separate entity in the group of benign lesions, in the diagnostic proteomic triage of thyroid nodules. The statistical analysis confirmed the existence of signals that HT shares with hyperplastic lesions and others that are specific and characterize this subgroup. Statistically relevant HT-related peaks were included in the model. Then, the discriminatory capability of the classifier was tested in a second validation phase, showing a good agreement with cytological diagnoses. The possibility to overlap the molecular signatures of both the lymphocytes and epithelial cells components (ROIs or pixel-by-pixel analysis) confirmed the composite proteomic background of HT. These results open the way to their possible translation as alternative serum biomarkers of this autoimmune condition.