High-mass-resolution MALDI mass spectrometry imaging of metabolites from formalin-fixed paraffin-embedded tissue

Spatial metabolomics and its application in the liver

Hepatocytes work in highly structured, repetitive hepatic lobules. Blood flow across the radial axis of the lobule generates oxygen, nutrient, and hormone gradients, which result in zoned spatial variability and functional diversity. This large heterogeneity suggests that hepatocytes in different lobule zones may have distinct gene expression profiles, metabolic features, regenerative capacity, and susceptibility to damage. Here, we describe the principles of liver zonation, introduce metabolomic approaches to study the spatial heterogeneity of the liver, and highlight the possibility of exploring the spatial metabolic profile, leading to a deeper understanding of the tissue metabolic organization. Spatial metabolomics can also reveal intercellular heterogeneity and its contribution to liver disease. These approaches facilitate the global characterization of liver metabolic function with high spatial resolution along physiological and pathological time scales. This review summarizes the state of the art for spatially resolved metabolomic analysis and the challenges that hinder the achievement of metabolome coverage at the single-cell level. We also discuss several major contributions to the understanding of liver spatial metabolism and conclude with our opinion on the future developments and applications of these exciting new technologies.

Metabolic heterogeneity affects trastuzumab response and survival in HER2-positive advanced gastric cancer

BACKGROUND

Trastuzumab is the only first-line treatment targeted against the human epidermal growth factor receptor 2 (HER2) approved for patients with HER2-positive advanced gastric cancer. The impact of metabolic heterogeneity on trastuzumab treatment efficacy remains unclear.

METHODS

Spatial metabolomics via high mass resolution imaging mass spectrometry was performed in pretherapeutic biopsies of patients with HER2-positive advanced gastric cancer in a prospective multicentre observational study. The mass spectra, representing the metabolic heterogeneity within tumour areas, were grouped by K-means clustering algorithm. Simpson's diversity index was applied to compare the metabolic heterogeneity level of individual patients.

RESULTS

Clustering analysis revealed metabolic heterogeneity in HER2-positive gastric cancer patients and uncovered nine tumour subpopulations. High metabolic heterogeneity was shown as a factor indicating sensitivity to trastuzumab (p = 0.008) and favourable prognosis at trend level. Two of the nine tumour subpopulations associated with favourable prognosis and trastuzumab sensitivity, and one subpopulation associated with poor prognosis and trastuzumab resistance.

CONCLUSIONS

This work revealed that tumour metabolic heterogeneity associated with prognosis and trastuzumab response based on tissue metabolomics of HER2-positive gastric cancer. Tumour metabolic subpopulations may provide an association with trastuzumab therapy efficacy.

CLINICAL TRIAL REGISTRATION

The patient cohort was conducted from a multicentre observational study (VARIANZ;NCT02305043).

Advancing rare cancer research by MALDI mass spectrometry imaging: Applications, challenges, and future perspectives in sarcoma

MALDI mass spectrometry imaging (MALDI imaging) uniquely advances cancer research, by measuring spatial distribution of endogenous and exogenous molecules directly from tissue sections. These molecular maps provide valuable insights into basic and translational cancer research, including tumor biology, tumor microenvironment, biomarker identification, drug treatment, and patient stratification. Despite its advantages, MALDI imaging is underutilized in studying rare cancers. Sarcomas, a group of malignant mesenchymal tumors, pose unique challenges in medical research due to their complex heterogeneity and low incidence, resulting in understudied subtypes with suboptimal management and outcomes. In this review, we explore the applicability of MALDI imaging in sarcoma research, showcasing its value in understanding this highly heterogeneous and challenging rare cancer. We summarize all MALDI imaging studies in sarcoma to date, highlight their impact on key research fields, including molecular signatures, cancer heterogeneity, and drug studies. We address specific challenges encountered when employing MALDI imaging for sarcomas, and propose solutions, such as using formalin-fixed paraffin-embedded tissues, and multiplexed experiments, and considerations for multi-site studies and digital data sharing practices. Through this review, we aim to spark collaboration between MALDI imaging researchers and clinical colleagues, to deploy the unique capabilities of MALDI imaging in the context of sarcoma.

Metabolomics unveils the exacerbating role of arachidonic acid metabolism in atherosclerosis

Atherosclerosis is a complex vascular disorder characterized by the deposition of lipids, inflammatory cascades, and plaque formation in arterial walls. A thorough understanding of its causes and progression is necessary to develop effective diagnostic and therapeutic strategies. Recent breakthroughs in metabolomics have provided valuable insights into the molecular mechanisms and genetic factors involved in atherosclerosis, leading to innovative approaches for preventing and treating the disease. In our study, we analyzed clinical serum samples from both atherosclerosis patients and animal models using laser desorption ionization mass spectrometry. By employing methods such as orthogonal partial least-squares discrimination analysis (OPLS-DA), heatmaps, and volcano plots, we can accurately classify atherosclerosis (AUC = 0.892) and identify key molecules associated with the disease. Specifically, we observed elevated levels of arachidonic acid and its metabolite, leukotriene B4, in atherosclerosis. By inhibiting arachidonic acid and monitoring its downstream metabolites, we discovered the crucial role of this metabolic pathway in regulating atherosclerosis. Metabolomic research provides detailed insights into the metabolic networks involved in atherosclerosis development and reveals the close connection between abnormal metabolism and the disease. These studies offer new possibilities for precise diagnosis, treatment, and monitoring of disease progression, as well as evaluating the effectiveness of therapeutic interventions.

A Guide to MALDI Imaging Mass Spectrometry for Tissues

Imaging mass spectrometry is a well-established technology that can easily and succinctly communicate the spatial localization of molecules within samples. This review communicates the recent advances in the field, with a specific focus on matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) applied on tissues. The general sample preparation strategies for different analyte classes are explored, including special considerations for sample types (fresh frozen or formalin-fixed,) strategies for various analytes (lipids, metabolites, proteins, peptides, and glycans) and how multimodal imaging strategies can leverage the strengths of each approach is mentioned. This work explores appropriate experimental design approaches and standardization of processes needed for successful studies, as well as the various data analysis platforms available to analyze data and their strengths. The review concludes with applications of imaging mass spectrometry in various fields, with a focus on medical research, and some examples from plant biology and microbe metabolism are mentioned, to illustrate the breadth and depth of MALDI IMS.

Spatial metabolomics identifies distinct tumor-specific and stroma-specific subtypes in patients with lung squamous cell carcinoma

Molecular subtyping of lung squamous cell carcinoma (LUSC) has been performed at the genomic, transcriptomic, and proteomic level. However, LUSC stratification based on tissue metabolomics is still lacking. Combining high-mass-resolution imaging mass spectrometry with consensus clustering, four tumor- and four stroma-specific subtypes with distinct metabolite patterns were identified in 330 LUSC patients. The first tumor subtype T1 negatively correlated with DNA damage and immunological features including CD3, CD8, and PD-L1. The same features positively correlated with the tumor subtype T2. Tumor subtype T4 was associated with high PD-L1 expression. Compared with the status of subtypes T1 and T4, patients with subtype T3 had improved prognosis, and T3 was an independent prognostic factor with regard to UICC stage. Similarly, stroma subtypes were linked to distinct immunological features and metabolic pathways. Stroma subtype S4 had a better prognosis than S2. Subsequently, analyses based on an independent LUSC cohort treated by neoadjuvant therapy revealed that the S2 stroma subtype was associated with chemotherapy resistance. Clinically relevant patient subtypes as determined by tissue-based spatial metabolomics are a valuable addition to existing molecular classification systems. Metabolic differences among the subtypes and their associations with immunological features may contribute to the improvement of personalized therapy.

Next-generation pathology practices with mass spectrometry imaging

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

Low-melting point agarose as embedding medium for MALDI mass spectrometry imaging and laser-capture microdissection-based proteomics

The combination of MALDI mass spectrometry imaging, laser-capture microdissection, and quantitative proteomics allows the identification and characterization of molecularly distinct tissue compartments. Such workflows are typically performed using consecutive tissue sections, and so reliable sectioning and mounting of high-quality tissue sections is a prerequisite of such investigations. Embedding media facilitate the sectioning process but can introduce contaminants which may adversely affect either the mass spectrometry imaging or proteomics analyses. Seven low-temperature embedding media were tested in terms of embedding temperature and cutting performance. The two media that provided the best results (5% gelatin and 2% low-melting point agarose) were compared with non-embedded tissue by both MALDI mass spectrometry imaging of lipids and laser-capture microdissection followed by bottom-up proteomics. Two out of the seven tested media (5% gelatin and 2% low-melting point agarose) provided the best performances on terms of mechanical properties. These media allowed for low-temperature embedding and for the collection of high-quality consecutive sections. Comparisons with non-embedded tissues revealed that both embedding media had no discernable effect on proteomics analysis; 5% gelatin showed a light ion suppression effect in the MALDI mass spectrometry imaging experiments, 2% agarose performed similarly to the non-embedded tissue. 2% low-melting point agarose is proposed for tissue embedding in experiments involving MALDI mass spectrometry imaging of lipids and laser-capture microdissection, proteomics of consecutive tissue sections.

Translating genomic tools to Raman spectroscopy analysis enables high-dimensional tissue characterization on molecular resolution

Spatial transcriptomics of histological sections have revolutionized research in life sciences and enabled unprecedented insights into genetic processes involved in tissue reorganization. However, in contrast to genomic analysis, the actual biomolecular composition of the sample has fallen behind, leaving a gap of potentially highly valuable information. Raman microspectroscopy provides untargeted spatiomolecular information at high resolution, capable of filling this gap. In this study we demonstrate spatially resolved Raman "spectromics" to reveal homogeneity, heterogeneity and dynamics of cell matrix on molecular levels by repurposing state-of-the-art bioinformatic analysis tools commonly used for transcriptomic analyses. By exploring sections of murine myocardial infarction and cardiac hypertrophy, we identify myocardial subclusters when spatially approaching the pathology, and define the surrounding metabolic and cellular (immune-) landscape. Our innovative, label-free, non-invasive "spectromics" approach could therefore open perspectives for a profound characterization of histological samples, while additionally allowing the combination with consecutive downstream analyses of the very same specimen.

Patterns of Carbon-Bound Exogenous Compounds Impact Disease Pathophysiology in Lung Cancer Subtypes in Different Ways

Carbon-bound exogenous compounds, such as polycyclic aromatic hydrocarbons (PAHs), tobacco-specific nitrosamines, aromatic amines, and organohalogens, are known to affect both tumor characteristics and patient outcomes in lung squamous cell carcinoma (LUSC); however, the roles of these compounds in lung adenocarcinoma (LUAD) remain unclear. We analyzed 11 carbon-bound exogenous compounds in LUAD and LUSC samples using in situ high mass-resolution matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry imaging and performed a cluster analysis to compare the patterns of carbon-bound exogenous compounds between these two lung cancer subtypes. Correlation analyses were conducted to investigate associations among exogenous compounds, endogenous metabolites, and clinical data, including patient survival outcomes and smoking behaviors. Additionally, we examined differences in exogenous compound patterns between normal and tumor tissues. Our analyses revealed that PAHs, aromatic amines, and organohalogens were more abundant in LUAD than in LUSC, whereas the tobacco-specific nitrosamine nicotine-derived nitrosamine ketone was more abundant in LUSC. Patients with LUAD and LUSC could be separated according to carbon-bound exogenous compound patterns detected in the tumor compartment. The same compounds had differential impacts on patient outcomes, depending on the cancer subtype. Correlation and network analyses indicated substantial differences between LUAD and LUSC metabolomes, associated with substantial differences in the patterns of the carbon-bound exogenous compounds. These data suggest that the contributions of these carcinogenic compounds to cancer biology may differ according to the cancer subtypes.

DropBlot: single-cell western blotting of chemically fixed cancer cells

To further realize proteomics of archived tissues for translational research, we introduce a hybrid microfluidic platform for high-specificity, high-sensitivity protein detection from individual chemically fixed cells. To streamline processing-to-analysis workflows and minimize signal loss, DropBlot serially integrates sample preparation using droplet-based antigen retrieval from single fixed cells with unified analysis-on-a-chip comprising microwell-based antigen extraction followed by chip-based single-cell western blotting. A water-in-oil droplet formulation proves robust to the harsh chemical (SDS, 6M urea) and thermal conditions (98°C, 1-2 hr.) required for sufficient antigen retrieval, and the electromechanical conditions required for electrotransfer of retrieved antigen from microwell-encapsulated droplets to single-cell electrophoresis. Protein-target retrieval was demonstrated for unfixed, paraformaldehyde-(PFA), and methanol-fixed cells. We observed higher protein electrophoresis separation resolution from PFA-fixed cells with sufficient immunoreactivity confirmed for key targets (HER2, GAPDH, EpCAM, Vimentin) from both fixation chemistries. Multiple forms of EpCAM and Vimentin were detected, a hallmark strength of western-blot analysis. DropBlot of PFA-fixed human-derived breast tumor specimens (n = 5) showed antigen retrieval from cells archived frozen for 6 yrs. DropBlot could provide a precision integrated workflow for single-cell resolution protein-biomarker mining of precious biospecimen repositories.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

A pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasia

Differentiating benign renal oncocytic tumors and malignant renal cell carcinoma (RCC) on imaging and histopathology is a critical problem that presents an everyday clinical challenge. This manuscript aims to demonstrate a novel methodology integrating metabolomics with radiomics features (RF) to differentiate between benign oncocytic neoplasia and malignant renal tumors. For this purpose, thirty-three renal tumors (14 renal oncocytic tumors and 19 RCC) were prospectively collected and histopathologically characterised. Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) was used to extract metabolomics data, while RF were extracted from CT scans of the same tumors. Statistical integration was used to generate multilevel network communities of -omics features. Metabolites and RF critical for the differentiation between the two groups (delta centrality > 0.1) were used for pathway enrichment analysis and machine learning classifier (XGboost) development. Receiver operating characteristics (ROC) curves and areas under the curve (AUC) were used to assess classifier performance. Radiometabolomics analysis demonstrated differential network node configuration between benign and malignant renal tumors. Fourteen nodes (6 RF and 8 metabolites) were crucial in distinguishing between the two groups. The combined radiometabolomics model achieved an AUC of 86.4%, whereas metabolomics-only and radiomics-only classifiers achieved AUC of 72.7% and 68.2%, respectively. Analysis of significant metabolite nodes identified three distinct tumour clusters (malignant, benign, and mixed) and differentially enriched metabolic pathways. In conclusion, radiometabolomics integration has been presented as an approach to evaluate disease entities. In our case study, the method identified RF and metabolites important in differentiating between benign oncocytic neoplasia and malignant renal tumors, highlighting pathways differentially expressed between the two groups. Key metabolites and RF identified by radiometabolomics can be used to improve the identification and differentiation between renal neoplasms.

Paving the path toward multi-omics approaches in the diagnostic challenges faced in thyroid pathology

INTRODUCTION

Despite advancements in diagnostic methods, the classification of indeterminate thyroid nodules still poses diagnostic challenges not only in pre-surgical evaluation but even after histological evaluation of surgical specimens. Proteomics, aided by mass spectrometry and integrated with artificial intelligence and machine learning algorithms, shows great promise in identifying diagnostic markers for thyroid lesions.

AREAS COVERED

This review provides in-depth exploration of how proteomics has contributed to the understanding of thyroid pathology. It discusses the technical advancements related to immunohistochemistry, genetic and proteomic techniques, such as mass spectrometry, which have greatly improved sensitivity and spatial resolution up to single-cell level. These improvements allowed the identification of specific protein signatures associated with different types of thyroid lesions.

EXPERT COMMENTARY

Among all the proteomics approaches, spatial proteomics stands out due to its unique ability to capture the spatial context of proteins in both cytological and tissue thyroid samples. The integration of multi-layers of molecular information combining spatial proteomics, genomics, immunohistochemistry or metabolomics and the implementation of artificial intelligence and machine learning approaches, represent hugely promising steps forward toward the possibility to uncover intricate relationships and interactions among various molecular components, providing a complete picture of the biological landscape whilst fostering thyroid nodule diagnosis.

A multimodal pipeline using NMR spectroscopy and MALDI-TOF mass spectrometry imaging from the same tissue sample

NMR spectroscopy and matrix assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) are both commonly used to detect large numbers of metabolites and lipids in metabolomic and lipidomic studies. We have demonstrated a new workflow, highlighting the benefits of both techniques to obtain metabolomic and lipidomic data, which has realized for the first time the combination of these two complementary and powerful technologies. NMR spectroscopy is frequently used to obtain quantitative metabolite information from cells and tissues. Lipid detection is also possible with NMR spectroscopy, with changes being visible across entire classes of molecules. Meanwhile, MALDI MSI provides relative measures of metabolite and lipid concentrations, mapping spatial information of many specific metabolite and lipid molecules across cells or tissues. We have used these two complementary techniques in combination to obtain metabolomic and lipidomic measurements from triple-negative human breast cancer cells and tumor xenograft models. We have emphasized critical experimental procedures that ensured the success of achieving NMR spectroscopy and MALDI MSI in a combined workflow from the same sample. Our data show that several phospholipid metabolite species were differentially distributed in viable and necrotic regions of breast tumor xenografts. This study emphasizes the power of combined NMR spectroscopy-MALDI imaging to advance metabolomic and lipidomic studies.

Visualization of Small Intact Proteins in Breast Cancer FFPE Tissue

Molecular visualization of metabolites, lipids, and proteins by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is becoming an in-demand analytical approach to aid the histopathological analysis of breast cancer. Particularly, proteins seem to play a role in cancer progression, and specific proteins are currently used in the clinic for staging. Formalin-fixed paraffin-embedded (FFPE) tissues are ideal for correlating the molecular markers with clinical outcomes due to their long-term storage. So far, to obtain proteomic information by MSI from this kind of tissue, antigen retrieval and tryptic digestion steps are required. In this chapter, we present a protocol to spatially detect small proteins in tumor and necrotic regions of patient-derived breast cancer xenograft FFPE tissues without employing any on-tissue digestion. This protocol can be used for other kinds of FFPE tissue following specific optimization of the sample preparation phases.

Simultaneous metabolite MALDI-MSI, whole exome and transcriptome analysis from formalin-fixed paraffin-embedded tissue sections

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) allows spatial analysis of proteins, metabolites, or small molecules from tissue sections. Here, we present the simultaneous generation and analysis of MALDI-MSI, whole-exome sequencing (WES), and RNA-sequencing data from the same formalin-fixed paraffin-embedded (FFPE) tissue sections. Genomic DNA and total RNA were extracted from (i) untreated, (ii) hematoxylin-eosin (HE) stained, and (iii) MALDI-MSI-analyzed FFPE tissue sections from three head and neck squamous cell carcinomas. MALDI-MSI data were generated by a time-of-flight analyzer prior to preprocessing and visualization. WES data were generated using a low-input protocol followed by detection of single-nucleotide variants (SNVs), tumor mutational burden, and mutational signatures. The transcriptome was determined using 3'-RNA sequencing and was examined for similarities and differences between processing stages. All data met the commonly accepted quality criteria. Besides SNVs commonly identified between differently processed tissues, FFPE-typical artifactual variants were detected. Tumor mutational burden was in the same range for tissues from the same patient and mutational signatures were highly overlapping. Transcriptome profiles showed high levels of correlation. Our data demonstrate that simultaneous molecular profiling of MALDI-MSI-processed FFPE tissue sections at the transcriptome and exome levels is feasible and reliable.

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.

The fatal trajectory of pulmonary COVID-19 is driven by lobular ischemia and fibrotic remodelling

BACKGROUND

COVID-19 is characterized by a heterogeneous clinical presentation, ranging from mild symptoms to severe courses of disease. 9-20% of hospitalized patients with severe lung disease die from COVID-19 and a substantial number of survivors develop long-COVID. Our objective was to provide comprehensive insights into the pathophysiology of severe COVID-19 and to identify liquid biomarkers for disease severity and therapy response.

METHODS

We studied a total of 85 lungs (n = 31 COVID autopsy samples; n = 7 influenza A autopsy samples; n = 18 interstitial lung disease explants; n = 24 healthy controls) using the highest resolution Synchrotron radiation-based hierarchical phase-contrast tomography, scanning electron microscopy of microvascular corrosion casts, immunohistochemistry, matrix-assisted laser desorption ionization mass spectrometry imaging, and analysis of mRNA expression and biological pathways. Plasma samples from all disease groups were used for liquid biomarker determination using ELISA. The anatomic/molecular data were analyzed as a function of patients' hospitalization time.

FINDINGS

The observed patchy/mosaic appearance of COVID-19 in conventional lung imaging resulted from microvascular occlusion and secondary lobular ischemia. The length of hospitalization was associated with increased intussusceptive angiogenesis. This was associated with enhanced angiogenic, and fibrotic gene expression demonstrated by molecular profiling and metabolomic analysis. Increased plasma fibrosis markers correlated with their pulmonary tissue transcript levels and predicted disease severity. Plasma analysis confirmed distinct fibrosis biomarkers (TSP2, GDF15, IGFBP7, Pro-C3) that predicted the fatal trajectory in COVID-19.

INTERPRETATION

Pulmonary severe COVID-19 is a consequence of secondary lobular microischemia and fibrotic remodelling, resulting in a distinctive form of fibrotic interstitial lung disease that contributes to long-COVID.

FUNDING

This project was made possible by a number of funders. The full list can be found within the Declaration of interests / Acknowledgements section at the end of the manuscript.

Evaluation of Formalin-Fixed and FFPE Tissues for Spatially Resolved Metabolomics and Drug Distribution Studies

Fixation of samples is broadly used prior to the histological evaluation of tissue samples. Though recent reports demonstrated the ability to use fixed tissues for mass spectrometry imaging (MSI) based proteomics, glycomics and tumor classification studies, to date comprehensive evaluation of fixation-related effects for spatially resolved metabolomics and drug disposition studies is still missing. In this study we used matrix assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI) MSI to investigate the effect of formalin-fixation and formalin-fixation combined with paraffin embedding on the detectable metabolome including xenobiotics. Formalin fixation was found to cause significant washout of polar molecular species, including inorganic salts, amino acids, organic acids and carnitine species, oxidation of endogenous lipids and formation of reaction products between lipids and fixative ingredients. The slow fixation kinetics under ambient conditions resulted in increased lipid hydrolysis in the tissue core, correlating with the time-dependent progression of the fixation. Paraffin embedding resulted in subsequent partial removal of structural lipids resulting in the distortion of the elucidated biodistributions.

Spatial Multiomics of Lipids, N-Glycans, and Tryptic Peptides on a Single FFPE Tissue Section

Mass spectrometry imaging (MSI) is an emerging technology that is capable of mapping various biomolecules within their native spatial context, and performing spatial multiomics on formalin-fixed paraffin-embedded (FFPE) tissues may further increase the molecular characterization of pathological states. Here we present a novel workflow which enables the sequential MSI of lipids, N-glycans, and tryptic peptides on a single FFPE tissue section and highlight the enhanced molecular characterization that is offered by combining the multiple spatial omics data sets. In murine brain and clear cell renal cell carcinoma (ccRCC) tissue, the three molecular levels provided complementary information and characterized different histological regions. Moreover, when the spatial omics data was integrated, the different histopathological regions of the ccRCC tissue could be better discriminated with respect to the imaging data set of any single omics class. Taken together, these promising findings demonstrate the capability to more comprehensively map the molecular complexity within pathological tissue.

What are we imaging? Software tools and experimental strategies for annotation and identification of small molecules in mass spectrometry imaging

Mass spectrometry imaging (MSI) has become a widespread analytical technique to perform nonlabeled spatial molecular identification. The Achilles' heel of MSI is the annotation and identification of molecular species due to intrinsic limitations of the technique (lack of chromatographic separation and the difficulty to apply tandem MS). Successful strategies to perform annotation and identification combine extra analytical steps, like using orthogonal analytical techniques to identify compounds; with algorithms that integrate the spectral and spatial information. In this review, we discuss different experimental strategies and bioinformatics tools to annotate and identify compounds in MSI experiments. We target strategies and tools for small molecule applications, such as lipidomics and metabolomics. First, we explain how sample preparation and the acquisition process influences annotation and identification, from sample preservation to the use of orthogonal techniques. Then, we review twelve software tools for annotation and identification in MSI. Finally, we offer perspectives on two current needs of the MSI community: the adaptation of guidelines for communicating confidence levels in identifications; and the creation of a standard format to store and exchange annotations and identifications in MSI.

Mass Spectrometry-Based Analysis of Lipid Involvement in Alzheimer’s Disease Pathology—A Review

Irregularities in lipid metabolism have been linked to numerous neurodegenerative diseases. The roles of abnormal brain, plasma, and cerebrospinal fluid (CSF) lipid levels in Alzheimer’s disease (AD) onset and progression specifically have been described to a great extent in the literature. Apparent hallmarks of AD include, but are not limited to, genetic predisposition involving the APOE Ɛ4 allele, oxidative stress, and inflammation. A common culprit tied to many of these hallmarks is disruption in brain lipid homeostasis. Therefore, it is important to understand the roles of lipids, under normal and abnormal conditions, in each process. Lipid influences in processes such as inflammation and blood–brain barrier (BBB) disturbance have been primarily studied via biochemical-based methods. There is a need, however, for studies focused on uncovering the relationship between lipid irregularities and AD by molecular-based quantitative analysis in transgenic animal models and human samples alike. In this review, mass spectrometry as it has been used as an analytical tool to address the convoluted relationships mentioned above is discussed. Additionally, molecular-based mass spectrometry strategies that should be used going forward to further relate structure and function relationships of lipid irregularities and hallmark AD pathology are outlined.

Spatial metabolomics for evaluating response to neoadjuvant therapy in non-small cell lung cancer patients

Background

The response to neoadjuvant chemotherapy (NAC) differs substantially among individual patients with non‐small cell lung cancer (NSCLC). Major pathological response (MPR) is a histomorphological read‐out used to assess treatment response and prognosis in patients NSCLC after NAC. Although spatial metabolomics is a promising tool for evaluating metabolic phenotypes, it has not yet been utilized to assess therapy responses in patients with NSCLC. We evaluated the potential application of spatial metabolomics in cancer tissues to assess the response to NAC, using a metabolic classifier that utilizes mass spectrometry imaging combined with machine learning.

Methods

Resected NSCLC tissue specimens obtained after NAC (n = 88) were subjected to high‐resolution mass spectrometry, and these data were used to develop an approach for assessing the response to NAC in patients with NSCLC. The specificities of the generated tumor cell and stroma classifiers were validated by applying this approach to a cohort of biologically matched chemotherapy‐naïve patients with NSCLC (n = 85).

Results

The developed tumor cell metabolic classifier stratified patients into different prognostic groups with 81.6% accuracy, whereas the stroma metabolic classifier displayed 78.4% accuracy. By contrast, the accuracies of MPR and TNM staging for stratification were 62.5% and 54.1%, respectively. The combination of metabolic and MPR classifiers showed slightly lower accuracy than either individual metabolic classifier. In multivariate analysis, metabolic classifiers were the only independent prognostic factors identified (tumor: P = 0.001, hazards ratio [HR] = 3.823, 95% confidence interval [CI] = 1.716–8.514; stroma: P = 0.049, HR = 2.180, 95% CI = 1.004–4.737), whereas MPR (P = 0.804; HR = 0.913; 95% CI = 0.445–1.874) and TNM staging (P = 0.078; HR = 1.223; 95% CI = 0.977–1.550) were not independent prognostic factors. Using Kaplan‐Meier survival analyses, both tumor and stroma metabolic classifiers were able to further stratify patients as NAC responders (P < 0.001) and non‐responders (P < 0.001).

Conclusions

Our findings indicate that the metabolic constitutions of both tumor cells and the stroma are valuable additions to the classical histomorphology‐based assessment of tumor response.

Automated Cancer Diagnostics via Analysis of Optical and Chemical Images by Deep and Shallow Learning

Optical microscopy has long been the gold standard to analyse tissue samples for the diagnostics of various diseases, such as cancer. The current diagnostic workflow is time-consuming and labour-intensive, and manual annotation by a qualified pathologist is needed. With the ever-increasing number of tissue blocks and the complexity of molecular diagnostics, new approaches have been developed as complimentary or alternative solutions for the current workflow, such as digital pathology and mass spectrometry imaging (MSI). This study compares the performance of a digital pathology workflow using deep learning for tissue recognition and an MSI approach utilising shallow learning to annotate formalin-fixed and paraffin-embedded (FFPE) breast cancer tissue microarrays (TMAs). Results show that both deep learning algorithms based on conventional optical images and MSI-based shallow learning can provide automated diagnostics with F1-scores higher than 90%, with the latter intrinsically built on biochemical information that can be used for further analysis.

Surface-assisted laser desorption/ionization mass spectrometry imaging: A review

In the last decades, surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) has attracted increasing interest due to its unique capabilities, achievable through the nanostructured substrates used to promote the analyte desorption/ionization. While the most widely recognized asset of SALDI-MS is the untargeted analysis of small molecules, this technique also offers the possibility of targeted approaches. In particular, the implementation of SALDI-MS imaging (SALDI-MSI), which is the focus of this review, opens up new opportunities. After a brief discussion of the nomenclature and the fundamental mechanisms associated with this technique, which are still highly controversial, the analytical strategies to perform SALDI-MSI are extensively discussed. Emphasis is placed on the sample preparation but also on the selection of the nanosubstrate (in terms of chemical composition and morphology) as well as its functionalization possibilities for the selective analysis of specific compounds in targeted approaches. Subsequently, some selected applications of SALDI-MSI in various fields (i.e., biomedical, biological, environmental, and forensic) are presented. The strengths and the remaining limitations of SALDI-MSI are finally summarized in the conclusion and some perspectives of this technique, which has a bright future, are proposed in this section.

Development of an Automatic Ultrasonic Matrix Sprayer for Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) has emerged as a powerful tool for studying the spatial distribution of various types of molecules. Matrix deposition is a critical step for obtaining high-quality imaging data. An automatic device named SoniCoat was fabricated based on an ultrasonic nozzle driven by a pizeoelectric ceramic for matrix coating in the present study. Compared with the minihumidifier developed previously by our group for matrix deposition, SoniCoat realized automation, and the ultrasonic nozzle of this device is easy to clean and less likely to get clogged, indicating a longer life. Compared with commercial and homemade instruments such as the TM-Sprayer and electrospray matrix coating device, our newly developed device achieved a good nebulization effect without the need for high-pressure gas flow and high voltage. Matrix deposition by SoniCoat generated more homogeneous matrix crystals with diameters of about 10 μm and reduced the occurrence of molecular diffusion to a greater extent, compared with the results by TM-Sprayer. Repeatable high-spatial-resolution MS imaging data were obtained with the help of SoniCoat. Furthermore, the newly developed device can also be successfully applied for in situ derivatization.

Spatial metabolomics identifies distinct tumor-specific subtypes in gastric cancer patients

PURPOSE

Current systems of gastric cancer (GC) molecular classification include genomic, molecular, and morphological features. GC classification based on tissue metabolomics remains lacking. This study aimed to define metabolically distinct GC subtypes and identify their clinicopathological and molecular characteristics.

EXPERIMENTAL DESIGN

Spatial metabolomics by high mass resolution imaging mass spectrometry was performed in 362 GC patients. K-means clustering was used to define tumor and stroma-related subtypes based on tissue metabolites. The identified subtypes were linked with clinicopathological characteristics, molecular features, and metabolic signatures. Responses to trastuzumab treatment were investigated across the subtypes by introducing an independent patient cohort with HER2-positive GC from a multicenter observational study.

RESULTS

Three tumor- and three stroma-specific subtypes with distinct tissue metabolite patterns were identified. Tumor-specific subtype T1(HER2+MIB+CD3+) positively correlated with HER2, MIB1, DEFA-1, CD3, CD8, FOXP3, but negatively correlated with MMR. Tumor-specific subtype T2(HER2-MIB-CD3-) negatively correlated with HER2, MIB1, CD3, FOXP3, but positively correlated with MMR. Tumor-specific subtype T3(pEGFR+) positively correlated with pEGFR. Patients with tumor subtype T1(HER2+MIB+CD3+) had elevated nucleotide levels, enhanced DNA metabolism, and a better prognosis than T2(HER2-MIB-CD3-) and T3(pEGFR+). An independent validation cohort confirmed that the T1 subtype benefited from trastuzumab therapy. Stroma-specific subtypes had no association with clinicopathological characteristics, however linked to distinct metabolic pathways and molecular features.

CONCLUSIONS

Patient subtypes derived by tissue-based spatial metabolomics are a valuable addition to existing GC molecular classification systems. Metabolic differences between the subtypes and their associations with molecular features could provide a valuable tool to aid in selecting specific treatment approaches.

MALDI Mass Spectrometry Imaging-Prognostic Pathways and Metabolites for Renal Cell Carcinomas

Simple Summary

Renal cell carcinoma (RCC) is the seventh most common cancer type and accounts for more than 80% of all renal tumors. Nevertheless, prognostic biomarkers for RCC are still missing. Therefore, we analyzed a large, multicenter cohort including the three most common RCC subtypes (clear cell RCC (ccRCC), papillary RCC (pRCC) and chromophobe RCC (chRCC)) by high mass resolution matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) for prognostic biomarker detection. This is a suitable method for biomarker detection for several tumor entities. We detected several pathways and metabolites with prognostic power for RCC in general and also for different RCC subtypes.

Abstract

High mass resolution matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is a suitable method for biomarker detection for several tumor entities. Renal cell carcinoma (RCC) is the seventh most common cancer type and accounts for more than 80% of all renal tumors. Prognostic biomarkers for RCC are still missing. Therefore, we analyzed a large, multicenter cohort including the three most common RCC subtypes (clear cell RCC (ccRCC), papillary RCC (pRCC) and chromophobe RCC (chRCC)) by MALDI for prognostic biomarker detection. MALDI-Fourier-transform ion cyclotron resonance (FT-ICR)-MSI analysis was performed for renal carcinoma tissue sections from 782 patients. SPACiAL pipeline was integrated for automated co-registration of histological and molecular features. Kaplan–Meier analyses with overall survival as endpoint were executed to determine the metabolic features associated with clinical outcome. We detected several pathways and metabolites with prognostic power for RCC in general and also for different RCC subtypes.

Adrenal tropism of SARS-CoV-2 and adrenal findings in a post-mortem case series of patients with severe fatal COVID-19

Progressive respiratory failure and hyperinflammatory response is the primary cause of death in the coronavirus disease 2019 (COVID-19) pandemic. Despite mounting evidence of disruption of the hypothalamus-pituitary-adrenal axis in COVID-19, relatively little is known about the tropism of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to adrenal glands and associated changes. Here we demonstrate adrenal viral tropism and replication in COVID-19 patients. Adrenal glands showed inflammation accompanied by inflammatory cell death. Histopathologic analysis revealed widespread microthrombosis and severe adrenal injury. In addition, activation of the glycerophospholipid metabolism and reduction of cortisone intensities were characteristic for COVID-19 specimens. In conclusion, our autopsy series suggests that SARS-CoV-2 facilitates the induction of adrenalitis. Given the central role of adrenal glands in immunoregulation and taking into account the significant adrenal injury observed, monitoring of developing adrenal insufficiency might be essential in acute SARS-CoV-2 infection and during recovery.

Advances in MALDI Mass Spectrometry Imaging Single Cell and Tissues

Compared with conventional optical microscopy techniques, mass spectrometry imaging (MSI) or imaging mass spectrometry (IMS) is a powerful, label-free analytical technique, which can sensitively and simultaneously detect, quantify, and map hundreds of biomolecules, such as peptides, proteins, lipid, and other organic compounds in cells and tissues. So far, although several soft ionization techniques, such as desorption electrospray ionization (DESI) and secondary ion mass spectrometry (SIMS) have been used for imaging biomolecules, matrix-assisted laser desorption/ionization (MALDI) is still the most widespread MSI scanning method. Here, we aim to provide a comprehensive review of MALDI-MSI with an emphasis on its advances of the instrumentation, methods, application, and future directions in single cell and biological tissues.

Imaging Matrix-Assisted Laser Desorption/Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry of oxaliplatin derivatives in human tissue sections

Mass Spectrometry Imaging is an effective technology that allows to determine the in-situ distribution of endogen and/or exogen small molecules. It is a rapidly emerging approach for visualizing drugs and their metabolites within biological tissues. Matrix-Assisted Laser Desorption Ionization (MALDI) Mass Spectrometry Imaging (MSI) coupled to high resolving power analyzer (e.g. TOF) was already investigated for metallodrug localization and metabolization studies, but was proved to suffer from a lack of sensitivity and resolution, leading to poor coverage and assignment. To counter these technological limitations, the use of ultra-high resolving power analyzer such as Fourier Transform Ion Cyclotron Resonance (FTICR) could be revealed as a technique of choice. The high field FTICR MS provides ultra-high resolving power and mass accuracy that allows exhaustive molecule coverage and non-ambiguous molecular formula assignments. Platinum derivatives, such as oxaliplatin, are widely used as therapeutic agents for cancer treatment. The assessment of their intake, distribution and metabolism within the organs is important to know the risks associated with their use. In this study, MALDI FTICR MSI analyses were performed to better understand the penetration and metabolization of platinum derivatives in ovaries of women treated by Hyperthermic Intraperitoneal Chemotherapy (HIPEC) for peritoneal metastasis of colorectal or appendicular origin. Twelve ovary sections, from six ovary samples in six women donors, before and after treatment, were analyzed with 120 μm spatial resolution. For the first time, the high resolving power (220,000 at m/z 457) and sub-ppm accuracy (<1 ppm) of the FTICR combined with an Isotopic Fine Structure study enabled to distinguish two Pt-isobaric species derived from oxaliplatin in biological tissues. One of these, which is unknown, was specifically localized at the contour of the ovary.

Molecular histoproteomy by MALDI mass spectrometry imaging to uncover markers of the impact of Nosema on Apis mellifera

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a powerful technology used to investigate the spatio-temporal distribution of a huge number of molecules throughout a body/tissue section. In this paper, we report the use of MALDI IMS to follow the molecular impact of an experimental infection of Apis mellifera with the microsporidia Nosema ceranae. We performed representative molecular mass fingerprints of selected tissues obtained by dissection. This was followed by MALDI IMS workflows optimization including specimen embedding and positioning as well as washing and matrix application. We recorded the local distribution of peptides/proteins within different tissues from experimentally infected versus non infected honeybees. As expected, a distinction in these molecular profiles between the two conditions was recorded from different anatomical sections of the gut tissue. More importantly, we observed differences in the molecular profiles in the brain, thoracic ganglia, hypopharyngeal glands, and hemolymph. We introduced MALDI IMS as an effective approach to monitor the impact of N. ceranae infection on A. mellifera. This opens perspectives for the discovery of molecular changes in peptides/proteins markers that could contribute to a better understanding of the impact of stressors and toxicity on different tissues of a bee in a single experiment.

Capturing the third dimension in drug discovery: Spatially-resolved tools for interrogation of complex 3D cell models

Advanced three-dimensional (3D) cell models have proven to be capable of depicting architectural and microenvironmental features of several tissues. By providing data of higher physiological and pathophysiological relevance, 3D cell models have been contributing to a better understanding of human development, pathology onset and progression mechanisms, as well as for 3D cell-based assays for drug discovery. Nonetheless, the characterization and interrogation of these tissue-like structures pose major challenges on the conventional analytical methods, pushing the development of spatially-resolved technologies. Herein, we review recent advances and pioneering technologies suitable for the interrogation of multicellular 3D models, while capable of retaining biological spatial information. We focused on imaging technologies and omics tools, namely transcriptomics, proteomics and metabolomics. The advantages and shortcomings of these novel methodologies are discussed, alongside the opportunities to intertwine data from the different tools.

Tissue fixation effects on human retinal lipid analysis by MALDI imaging and LC-MS/MS technologies

Imaging mass spectrometry (IMS) allows the location and abundance of lipids to be mapped across tissue sections of human retina. For reproducible and accurate information, sample preparation methods need to be optimized. Paraformaldehyde fixation of a delicate multilayer structure like human retina facilitates the preservation of tissue morphology by forming methylene bridge crosslinks between formaldehyde and amine/thiols in biomolecules; however, retina sections analyzed by IMS are typically fresh-frozen. To determine if clinically significant inferences could be reliably based on fixed tissue, we evaluated the effect of fixation on analyte detection, spatial localization, and introduction of artifactual signals. Hence, we assessed the molecular identity of lipids generated by matrix-assisted laser desorption ionization (MALDI-IMS) and liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) for fixed and fresh-frozen retina tissues in positive and negative ion modes. Based on MALDI-IMS analysis, more lipid signals were observed in fixed compared with fresh-frozen retina. More potassium adducts were observed in fresh-frozen tissues than fixed as the fixation process caused displacement of potassium adducts to protonated and sodiated species in ion positive ion mode. LC-MS/MS analysis revealed an overall decrease in lipid signals due to fixation that reduced glycerophospholipids and glycerolipids and conserved most sphingolipids and cholesteryl esters. The high quality and reproducible information from untargeted lipidomics analysis of fixed retina informs on all major lipid classes, similar to fresh-frozen retina, and serves as a steppingstone towards understanding of lipid alterations in retinal diseases.

Label-free cell assays to determine compound uptake or drug action using MALDI-TOF mass spectrometry

Cell-based assays for compound screening and profiling are fundamentally important in life sciences, chemical biology and pharmaceutical research. Most cell assays measure the amount of a single reporter molecule or cellular endpoint, and require the use of fluorescence or other labeled materials. Consequently, there is high demand for label-free technologies that enable multiple biomolecules or endpoints to be measured simultaneously. Here, we describe how to develop, optimize and validate MALDI-TOF mass spectrometry (MS) cell assays that can be used to measure cellular uptake of transporter substrates, to monitor cellular drug target engagement or to discover cellular drug-response markers. In uptake assays, intracellular accumulation of a transporter substrate and its inhibition by test compounds is measured. In drug response assays, changes to multiple cellular metabolites or to abundant posttranslational protein modifications are monitored as reporters of drug activity. We detail a ten-part optimization protocol with every part taking 1-2 d that leads to a final 2 d optimized procedure, which includes cell treatment, transfer, MALDI MS-specific sample preparation, quantification using stable-isotope-labeled standards, MALDI-TOF MS data acquisition, data processing and analysis. Key considerations for validation and automation of MALDI-TOF MS cell assays are outlined. Overall, label-free MS cell-based assays offer speed, sensitivity, accuracy and versatility in drug research.

Native glycan fragments detected by MALDI mass spectrometry imaging are independent prognostic factors in pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies to date. The impressively developed stroma that surrounds and modulates the behavior of cancer cells is one of the main factors regulating the PDAC growth, metastasis and therapy resistance. Here, we postulate that stromal and cancer cell compartments differentiate in protein/lipid glycosylation patterns and analyze differences in glycan fragments in those compartments with clinicopathologic correlates.

Results

We analyzed native glycan fragments in 109 human FFPE PDAC samples using high mass resolution matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometric imaging (MALDI-FT-ICR-MSI). Our method allows detection of native glycan fragments without previous digestion with PNGase or any other biochemical reaction. With this method, 8 and 18 native glycans were identified as uniquely expressed in only stromal or only cancer cell compartment, respectively. Kaplan–Meier survival model identified glycan fragments that are expressed in cancer cell or stromal compartment and significantly associated with patient outcome. Among cancer cell region-specific glycans, 10 predicted better and 6 worse patient survival. In the stroma, 1 glycan predicted good and 4 poor patient survival. Using factor analysis as a dimension reduction method, we were able to group the identified glycans in 2 factors. Multivariate analysis revealed that these factors can be used as independent survival prognostic elements with regard to the established Union for International Cancer Control (UICC) classification both in tumor and stroma regions.

Conclusion

Our method allows in situ detection of naturally occurring glycans in FFPE samples of human PDAC tissue and highlights the differences among glycans found in stromal and cancer cell compartment offering a basis for further exploration on the role of specific glycans in cancer–stroma communication.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13550-021-00862-y.

Spatial mapping of cancer tissues by OMICS technologies

Spatial mapping of heterogeneity in gene expression in cancer tissues can improve our understanding of cancers and help in the rapid detection of cancers with high accuracy and reliability. Significant advancements have been made in recent years in OMICS technologies, which possess the strong potential to be applied in the spatial mapping of biopsy tissue samples and their molecular profiling to a single-cell level. The clinical application of OMICS technologies in spatial profiling of cancer tissues is also advancing. The current review presents recent advancements and prospects of applying OMICS technologies to the spatial mapping of various analytes in cancer tissues. We benchmark the current state of the art in the field to advance existing OMICS technologies for high throughput spatial profiling. The factors taken into consideration include spatial resolution, types of biomolecules, number of different biomolecules that can be detected from the same assay, labeled versus label-free approaches, and approximate time required for each assay. Further advancements are still needed for the widespread application of OMICs technologies in performing fast and high throughput spatial mapping of cancer tissues as well as their effective use in research and clinical applications.

The effect of sample age on the metabolic information extracted from formalin-fixed and paraffin embedded tissue samples using desorption electrospray ionization mass spectrometry imaging

Background: Metabolites, especially lipids, have been shown to be promising therapeutic targets. In conjugation with genes and proteins they can be used to identify phenotypes of disease and support the development of targeted treatments. The majority of clinically collected tissue samples are stored in formalin-fixed and paraffin embedded (FFPE) blocks due to their tissue conservation ability and indefinite storage capacity. For metabolic analysis, however, fresh frozen (FF) samples are currently preferred over FFPE samples due to concerns of metabolic information being lost when preparing the samples. With little or no sample preparation, desorption electrospray ionisation mass spectrometry imaging (DESI-MSI) allows for the study of spatial as well as spectral information. Methods: DESI-MSI analysis was performed on FFPE breast cancer tissue microarray samples from 213 patients collected between the years 1935-2013. Logistic regression (LR) models were built to classify samples based on age and FF samples were used for feature validation. Results: LR models developed on the FFPE samples achieved an average classification accuracy of 96% when predicting their age with a 10-year grouping. Closer examination of the metabolic change over time revealed that the mean signal intensities for the lower mass range (100 - 500 m/z) linearly decrease over time, while the mean intensities for the higher mass range (500 - 900 m/z), remained relatively constant. Conclusions: In our samples, which span over 70 years, sample age has a weak yet quantifiable impact on metabolite content in FFPE samples, while the higher mass range is seemingly unaffected. FFPE samples thus provide an alternative avenue for metabolic analysis of lipids.

Direct Water-Assisted Laser Desorption/Ionization Mass Spectrometry Lipidomic Analysis and Classification of Formalin-Fixed Paraffin-Embedded Sarcoma Tissues without Dewaxing

BACKGROUND

Formalin-fixed paraffin-embedded (FFPE) tissue has been the gold standard for routine pathology for general and cancer postoperative diagnostics. Despite robust histopathology, immunohistochemistry, and molecular methods, accurate diagnosis remains difficult for certain cases. Overall, the entire process can be time consuming, labor intensive, and does not reach over 90% diagnostic sensitivity and specificity. There is a growing need in onco-pathology for adjunct novel rapid, accurate, reliable, diagnostically sensitive, and specific methods for high-throughput biomolecular identification. Lipids have long been considered only as building blocks of cell membranes or signaling molecules, but have recently been introduced as central players in cancer. Due to sample processing, which limits their detection, lipid analysis directly from unprocessed FFPE tissues has never been reported.

METHODS

We present a proof-of-concept with direct analysis of tissue-lipidomic signatures from FFPE tissues without dewaxing and minimal sample preparation using water-assisted laser desorption ionization mass spectrometry and deep-learning.

RESULTS

On a cohort of difficult canine and human sarcoma cases, classification for canine sarcoma subtyping was possible with 99.1% accuracy using "5-fold" and 98.5% using "leave-one-patient out," and 91.2% accuracy for human sarcoma using 5-fold and 73.8% using leave-one-patient out. The developed classification model enabled stratification of blind samples in <5 min and showed >95% probability for discriminating 2 human sarcoma blind samples.

CONCLUSION

It is possible to create a rapid diagnostic platform to screen clinical FFPE tissues with minimal sample preparation for molecular pathology.

Metabolic tumor constitution is superior to tumor regression grading for evaluating response to neoadjuvant therapy of esophageal adenocarcinoma patients

The response to neoadjuvant therapy can vary widely between individual patients. Histopathological tumor regression grading (TRG) is a strong factor for treatment response and survival prognosis of esophageal adenocarcinoma (EAC) patients following neoadjuvant treatment and surgery. However, TRG systems are usually based on the estimation of residual tumor but do not consider stromal or metabolic changes after treatment. Spatial metabolomics analysis is a powerful tool for molecular tissue phenotyping but has not been used so far in the context of neoadjuvant treatment of esophageal cancer. We used imaging mass spectrometry to assess the potential of spatial metabolomics on tumor and stroma tissue for evaluating therapy response of neoadjuvant-treated EAC patients. With an accuracy of 89.7%, the binary classifier trained on spatial tumor metabolite data proved to be superior for stratifying patients when compared to histopathological response assessment which had an accuracy of 70.5%. Sensitivities and specificities for the poor and favorable survival patient groups ranged from 84.9 to 93.3% using the metabolic classifier and from 62.2 to 78.1% using TRG. The tumor classifier was the only significant prognostic factor (HR 3.38, 95% CI = 1.40-8.12, P = 0.007) when adjusted for clinicopathological parameters such as TRG (HR 1.01, 95% CI = 0.67-1.53, P = 0.968) or stromal classifier (HR 1.856, 95% CI = 0.81-4.25, P = 0.143). The classifier even allowed to further stratify patients within the TRG1-3 categories. The underlying mechanisms of response to treatment has been figured out through network analysis. In summary, metabolic response evaluation outperformed histopathological response evaluation in our study with regard to prognostic stratification. This finding indicates that the metabolic constitution of tumor may have a greater impact on patient survival than the quantity of residual tumor cells or the stroma. This article is protected by copyright. All rights reserved.

The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging

Knowing the precise location of analytes in the tissue has the potential to provide information about the organs’ function and predict its behavior. It is especially powerful when used in diagnosis and prognosis prediction of pathologies, such as cancer. Spatial proteomics, in particular mass spectrometry imaging, together with machine learning approaches, has been proven to be a very helpful tool in answering some histopathology conundrums. To gain accurate information about the tissue, there is a need to build robust classification models. We have investigated the impact of histological annotation on the classification accuracy of different tumor tissues. Intrinsic tissue heterogeneity directly impacts the efficacy of the annotations, having a more pronounced effect on more heterogeneous tissues, as pancreatic ductal adenocarcinoma, where the impact is over 20% in accuracy. On the other hand, in more homogeneous samples, such as kidney tumors, histological annotations have a slenderer impact on the classification accuracy.

Patterns of carbon-bound exogenous compounds in lung cancer patients and association with disease pathophysiology

Asymptomatic anthracosis is the accumulation of black carbon particles in adult human lungs. It is a common occurrence, but the pathophysiological significance of anthracosis is debatable. Using in situ high mass resolution matrix-assisted laser desorption/ionization (MALDI) fourier-transform ion cyclotron resonance (FT-ICR) mass spectrometry imaging analysis, we discovered noxious carbon-bound exogenous compounds, such as polycyclic aromatic hydrocarbons (PAHs), tobacco-specific nitrosamines, or aromatic amines, in a series of 330 lung cancer patients in highly variable and unique patterns. The characteristic nature of carbon-bound exogenous compound had a strong association with patient outcome, tumor progression, the tumor immune microenvironment, PD-L1 expression, and DNA damage. Spatial correlation network analyses revealed substantial differences in the metabolome of tumor cells compared to tumor stroma depending on carbon-bound exogenous compounds. Overall, the bioactive pool of exogenous compounds is associated with several changes in lung cancer pathophysiology and correlates with patient outcome. Given the high prevalence of anthracosis in the lungs of adult humans, future work should investigate the role of carbon-bound exogenous compounds in lung carcinogenesis and lung cancer therapy.

Copper adhesive tape attached to the reverse side of a non-conductive glass slide to achieve protein MALDI-imaging in FFPE-tissue sections

Herein, copper adhesive tape attached to the reverse side of a glass slide was developed as a new method to achieve protein in situ detection and imaging in a formalin fixed paraffin-embedded (FFPE) tissue section on a non-conductive glass slide by MALDI-MSI. The use of this new method led to 223 protein ions being imaged from a rat brain FFPE-tissue section on a non-conductive glass slide by MALDI-MS, compared to only 145 and 163 protein ions detected on an ITO glass slide and an AnchorChip target plate, respectively. This new method has great potential to become standard practice for protein MALDI-imaging in FFPE-tissue sections on non-conductive glass slides.

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.

MASS SPECTROMETRY TECHNOLOGIES TO ADVANCE CARE FOR CANCER PATIENTS IN CLINICAL AND INTRAOPERATIVE USE

Developments in mass spectrometry technologies have driven a widespread interest and expanded their use in cancer-related research and clinical applications. In this review, we highlight the developments in mass spectrometry methods and instrumentation applied to direct tissue analysis that have been tailored at enhancing performance in clinical research as well as facilitating translation and implementation of mass spectrometry in clinical settings, with a focus on cancer-related studies. Notable studies demonstrating the capabilities of direct mass spectrometry analysis in biomarker discovery, cancer diagnosis and prognosis, tissue analysis during oncologic surgeries, and other clinically relevant problems that have the potential to substantially advance cancer patient care are discussed. Key challenges that need to be addressed before routine clinical implementation including regulatory efforts are also discussed. Overall, the studies highlighted in this review demonstrate the transformative potential of mass spectrometry technologies to advance clinical research and care for cancer patients. © 2020 Wiley Periodicals, Inc. Mass Spec Rev.