Combined Immunohistochemistry after Mass Spectrometry Imaging for Superior Spatial Information

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.

A Comparison of Different Sample Processing Protocols for MALDI Imaging Mass Spectrometry Analysis of Formalin-Fixed Multiple Myeloma Cells

Sample processing of formalin-fixed specimens constitutes a major challenge in molecular profiling efforts. Pre-analytical factors such as fixative temperature, dehydration, and embedding media affect downstream analysis, generating data dependent on technical processing rather than disease state. In this study, we investigated two different sample processing methods, including the use of the cytospin sample preparation and automated sample processing apparatuses for proteomic analysis of multiple myeloma (MM) cell lines using imaging mass spectrometry (IMS). In addition, two sample-embedding instruments using different reagents and processing times were considered. Three MM cell lines fixed in 4% paraformaldehyde were either directly centrifuged onto glass slides using cytospin preparation techniques or processed to create paraffin-embedded specimens with an automatic tissue processor, and further cut onto glass slides for IMS analysis. The number of peaks obtained from paraffin-embedded samples was comparable between the two different sample processing instruments. Interestingly, spectra profiles showed enhanced ion yield in cytospin compared to paraffin-embedded samples along with high reproducibility compared to the sample replicate.

Optimal Combination of Neuroendocrine Markers for the Detection of High-Grade Neuroendocrine Tumors of the Sinonasal Tract and Lung

PURPOSE OF REVIEW

Identification of neuroendocrine (NE) differentiation is critical to the classification of head and neck (HN) and lung tumors. In combination with tumor morphology, immunohistochemical (IHC) documentation of NE differentiation is necessary for the diagnosis of NE tumors. The purpose of this study is to determine the sensitivity and concordance of two novel NE markers (mASH1, INSM1) across a group of high-grade NE tumors of the sinonasal tract and lung, and to compare their expression with the current widespread use of conventional NE markers, synaptophysin (SYN) and chromogranin A (CGA). In addition, expression of PARP1 is examined as a potential novel therapeutic target.

RECENT FINDINGS

Thirty-nine high-grade NE tumors, 23 of the HN and 16 of the lung, were reevaluated by two subspecialized HN and thoracic pathologists, and subsequently stained with mASH1, INSM1, and PARP1. Sensitivity and degree of concordance of all possible combinations of markers were assessed. Sensitivities (standard error) were as follows: mASH1 41% (0.08), INSM1 44% (0.08), SYN 56% (0.08), and CGA 42% (0.09); combination of all four NE markers: 73% (0.08). Sensitivity and standard error for PARP1 was 90% and 0.05, respectively. Highest sensitivity to detect NE differentiation in high-grade NE tumors of the HN and thoracic region was achieved with a combination of four NE markers. Moderate concordance was found with combinations of mASH1 and INSM1 and traditional NE markers, respectively. Consistent overexpression of PARP1 in high-grade tumors with NE differentiation in the HN and lung opens eligibility for PARP1 inhibitor trials.

Mass Spectrometry Imaging Spatial Tissue Analysis toward Personalized Medicine

Novel profiling methodologies are redefining the diagnostic capabilities and therapeutic approaches towards more precise and personalized healthcare. Complementary information can be obtained from different omic approaches in combination with the traditional macro- and microscopic analysis of the tissue, providing a more complete assessment of the disease. Mass spectrometry imaging, as a tissue typing approach, provides information on the molecular level directly measured from the tissue. Lipids, metabolites, glycans, and proteins can be used for better understanding imbalances in the DNA to RNA to protein translation, which leads to aberrant cellular behavior. Several studies have explored the capabilities of this technology to be applied to tumor subtyping, patient prognosis, and tissue profiling for intraoperative tissue evaluation. In the future, intercenter studies may provide the needed confirmation on the reproducibility, robustness, and applicability of the developed classification models for tissue characterization to assist in disease management.

Multicenter Evaluation of Tissue Classification by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

Many studies have demonstrated that tissue phenotyping (tissue typing) based on mass spectrometric imaging data is possible; however, comprehensive studies assessing variation and classifier transferability are largely lacking. This study evaluated the generalization of tissue classification based on Matrix Assisted Laser Desorption/Ionization (MALDI) mass spectrometric imaging (MSI) across measurements performed at different sites. Sections of a tissue microarray (TMA) consisting of different formalin-fixed and paraffin-embedded (FFPE) human tissue samples from different tumor entities (leiomyoma, seminoma, mantle cell lymphoma, melanoma, breast cancer, and squamous cell carcinoma of the lung) were prepared and measured by MALDI-MSI at different sites using a standard protocol (SOP). Technical variation was deliberately introduced on two separate measurements via a different sample preparation protocol and a MALDI Time of Flight mass spectrometer that was not tuned to optimal performance. Using standard data preprocessing, a classification accuracy of 91.4% per pixel was achieved for intrasite classifications. When applying a leave-one-site-out cross-validation strategy, accuracy per pixel over sites was 78.6% for the SOP-compliant data sets and as low as 36.1% for the mistuned instrument data set. Data preprocessing designed to remove technical variation while retaining biological information substantially increased classification accuracy for all data sets with SOP-compliant data sets improved to 94.3%. In particular, classification accuracy of the mistuned instrument data set improved to 81.3% and from 67.0% to 87.8% per pixel for the non-SOP-compliant data set. We demonstrate that MALDI-MSI-based tissue classification is possible across sites when applying histological annotation and an optimized data preprocessing pipeline to improve generalization of classifications over technical variation and increasing overall robustness.

MALDI Mass Spectrometry Imaging for the Distinction of Adenocarcinomas of the Pancreas and Biliary Tree

Pancreatic ductal adenocarcinoma and cholangiocarcinoma constitute two aggressive tumor types that originate from the epithelial lining of the excretory ducts of the pancreatobiliary tract. Given their close histomorphological resemblance, a correct diagnosis can be challenging and almost impossible without clinical information. In this study, we investigated whether mass spectrometric peptide features could be employed to distinguish pancreatic ductal adenocarcinoma from cholangiocarcinoma. Three tissue microarrays of formalin-fixed and paraffin-embedded material (FFPE) comprising 41 cases of pancreatic ductal adenocarcinoma and 41 cases of cholangiocarcinoma were analyzed by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). The derived peptide features and respective intensities were used to build different supervised classification algorithms: gradient boosting (GB), support vector machine (SVM), and k-nearest neighbors (KNN). On a pixel-by-pixel level, a classification accuracy of up to 95% could be achieved. The tentative identification of discriminative tryptic peptide signatures revealed proteins that are involved in the epigenetic regulation of the genome and tumor microenvironment. Despite their histomorphological similarities, mass spectrometry imaging represents an efficient and reliable approach for the distinction of PDAC from CC, offering a promising complementary or alternative approach to the existing tools used in diagnostics such as immunohistochemistry.

A Mass Spectrometry Imaging Based Approach for Prognosis Prediction in UICC Stage I/II Colon Cancer

Simple Summary

Tumor treatment is heavily dictated by the tumor progression status. However, in colon cancer, it is difficult to predict disease progression in the early stages. In this study, we have employed a proteomic analysis using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). MALDI-MSI is a technique that measures the molecular content of (tumor) tissue. We analyzed tumor samples of 276 patients. If the patients developed distant metastasis, they were considered to have a more aggressive tumor type than the patients that did not. In this comparative study, we have developed bioinformatics methods that can predict the tendency of tumor progression and advance a couple of molecules that could be used as prognostic markers of colon cancer. The prediction of tumor progression can help to choose a more adequate treatment for each individual patient.

Abstract

Currently, pathological evaluation of stage I/II colon cancer, following the Union Internationale Contre Le Cancer (UICC) guidelines, is insufficient to identify patients that would benefit from adjuvant treatment. In our study, we analyzed tissue samples from 276 patients with colon cancer utilizing mass spectrometry imaging. Two distinct approaches are herein presented for data processing and analysis. In one approach, four different machine learning algorithms were applied to predict the tendency to develop metastasis, which yielded accuracies over 90% for three of the models. In the other approach, 1007 m/z features were evaluated with regards to their prognostic capabilities, yielding two m/z features as promising prognostic markers. One feature was identified as a fragment from collagen (collagen 3A1), hinting that a higher collagen content within the tumor is associated with poorer outcomes. Identification of proteins that reflect changes in the tumor and its microenvironment could give a very much-needed prediction of a patient’s prognosis, and subsequently assist in the choice of a more adequate treatment.

Applying Probe Electrospray Ionization Mass Spectrometry to Cytological Diagnosis: A Preliminary Study by Using Cultured Lung Cancer Cells

OBJECTIVES

Cytology and histology are 2 indispensable diagnostic tools for cancer diagnosis, which are rapidly increasing in importance with aging populations. We applied mass spectrometry (MS) as a rapid approach for swiftly acquiring nonmorphological information of interested cells. Conventional MS, which primarily rely on promoting ionization by pre-applying a matrix to cells, has the drawback of time-consuming both on data acquisition and analysis. As an emerging method, probe electrospray ionization-MS (PESI-MS) with a dedicated probe is capable to pierce sample and measure specimen in small amounts, either liquid or solid, without the requirement for sample pretreatment. Furthermore, PESI-MS is timesaving compared to the conventional MS. Herein, we investigated the capability of PESI-MS to characterize the cell types derived from the respiratory tract of human tissues.

STUDY DESIGN

PESI-MS analyses with DPiMS-2020 were performed on various type of cultured cells including 5 lung squamous cell carcinomas, 5 lung adenocarcinomas, 5 small-cell carcinomas, 4 malignant mesotheliomas, and 2 normal controls.

RESULTS

Several characteristic peaks were detected at around m/z 200 and 800 that were common in all samples. As expected, partial least squares-discriminant analysis of PESI-MS data distinguished the cancer cell types from normal control cells. Moreover, distinct clusters divided squamous cell carcinoma from adenocarcinoma.

CONCLUSION

PESI-MS presented a promising potential as a novel diagnostic modality for swiftly acquiring specific cytological information.

Predictive Markers of Immunogenicity and Efficacy for Human Vaccines

Vaccines represent one of the major advances of modern medicine. Despite the many successes of vaccination, continuous efforts to design new vaccines are needed to fight "old" pandemics, such as tuberculosis and malaria, as well as emerging pathogens, such as Zika virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Vaccination aims at reaching sterilizing immunity, however assessing vaccine efficacy is still challenging and underscores the need for a better understanding of immune protective responses. Identifying reliable predictive markers of immunogenicity can help to select and develop promising vaccine candidates during early preclinical studies and can lead to improved, personalized, vaccination strategies. A systems biology approach is increasingly being adopted to address these major challenges using multiple high-dimensional technologies combined with in silico models. Although the goal is to develop predictive models of vaccine efficacy in humans, applying this approach to animal models empowers basic and translational vaccine research. In this review, we provide an overview of vaccine immune signatures in preclinical models, as well as in target human populations. We also discuss high-throughput technologies used to probe vaccine-induced responses, along with data analysis and computational methodologies applied to the predictive modeling of vaccine efficacy.

Streamlined Multimodal DESI and MALDI Mass Spectrometry Imaging on a Singular Dual-Source FT-ICR Mass Spectrometer

The study of biological specimens by mass spectrometry imaging (MSI) has had a profound influence in the various forms of spatial-omics over the past two decades including applications for the identification of clinical biomarker analysis; the metabolic fingerprinting of disease states; treatment with therapeutics; and the profiling of lipids, peptides and proteins. No singular approach is able to globally map all biomolecular classes simultaneously. This led to the development of many complementary multimodal imaging approaches to solve analytical problems: fusing multiple ionization techniques, imaging microscopy or spectroscopy, or local extractions into robust multimodal imaging methods. However, each fusion typically requires the melding of analytical information from multiple commercial platforms, and the tandem utilization of multiple commercial or third-party software platforms—even in some cases requiring computer coding. Herein, we report the use of matrix-assisted laser desorption/ionization (MALDI) in tandem with desorption electrospray ionization (DESI) imaging in the positive ion mode on a singular commercial orthogonal dual-source Fourier transform ion cyclotron resonance (FT-ICR) instrument for the complementary detection of multiple analyte classes by MSI from tissue. The DESI source was 3D printed and the commercial Bruker Daltonics software suite was used to generate mass spectrometry images in tandem with the commercial MALDI source. This approach allows for the generation of multiple modes of mass spectrometry images without the need for third-party software and a customizable platform for ambient ionization imaging. Highlighted is the streamlined workflow needed to obtain phospholipid profiles, as well as increased depth of coverage of both annotated phospholipid, cardiolipin, and ganglioside species from rat brain with both high spatial and mass resolution.

Multimodal Imaging Mass Spectrometry: Next Generation Molecular Mapping in Biology and Medicine

Imaging mass spectrometry has become a mature molecular mapping technology that is used for molecular discovery in many medical and biological systems. While powerful by itself, imaging mass spectrometry can be complemented by the addition of other orthogonal, chemically informative imaging technologies to maximize the information gained from a single experiment and enable deeper understanding of biological processes. Within this review, we describe MALDI, SIMS, and DESI imaging mass spectrometric technologies and how these have been integrated with other analytical modalities such as microscopy, transcriptomics, spectroscopy, and electrochemistry in a field termed multimodal imaging. We explore the future of this field and discuss forthcoming developments that will bring new insights to help unravel the molecular complexities of biological systems, from single cells to functional tissue structures and organs.

Mass Spectrometry Imaging for Reliable and Fast Classification of Non-Small Cell Lung Cancer Subtypes

Simple Summary

Diagnostic subtyping of non-small cell lung cancer is paramount for therapy stratification. Our study shows that the subtyping into pulmonary adenocarcinoma and pulmonary squamous cell carcinoma by mass spectrometry imaging is rapid and accurate using limited tissue material.

Abstract

Subtyping of non-small cell lung cancer (NSCLC) is paramount for therapy stratification. In this study, we analyzed the largest NSCLC cohort by mass spectrometry imaging (MSI) to date. We sought to test different classification algorithms and to validate results obtained in smaller patient cohorts. Tissue microarrays (TMAs) from including adenocarcinoma (ADC, n = 499) and squamous cell carcinoma (SqCC, n = 440), were analyzed. Linear discriminant analysis, support vector machine, and random forest (RF) were applied using samples randomly assigned for training (66%) and validation (33%). The m/z species most relevant for the classification were identified by on-tissue tandem mass spectrometry and validated by immunohistochemistry (IHC). Measurements from multiple TMAs were comparable using standardized protocols. RF yielded the best classification results. The classification accuracy decreased after including less than six of the most relevant m/z species. The sensitivity and specificity of MSI in the validation cohort were 92.9% and 89.3%, comparable to IHC. The most important protein for the discrimination of both tumors was cytokeratin 5. We investigated the largest NSCLC cohort by MSI to date and found that the classification of NSCLC into ADC and SqCC is possible with high accuracy using a limited set of m/z species.

Mass Spectrometry Imaging Differentiates Chromophobe Renal Cell Carcinoma and Renal Oncocytoma with High Accuracy

Background: While subtyping of the majority of malignant chromophobe renal cell carcinoma (cRCC) and benign renal oncocytoma (rO) is possible on morphology alone, additional histochemical, immunohistochemical or molecular investigations are required in a subset of cases. As currently used histochemical and immunohistological stains as well as genetic aberrations show considerable overlap in both tumors, additional techniques are required for differential diagnostics. Mass spectrometry imaging (MSI) combining the detection of multiple peptides with information about their localization in tissue may be a suitable technology to overcome this diagnostic challenge.

Patients and Methods: Formalin-fixed paraffin embedded (FFPE) tissue specimens from cRCC (n=71) and rO (n=64) were analyzed by MSI. Data were classified by linear discriminant analysis (LDA), classification and regression trees (CART), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) algorithm with internal cross validation and visualized by t-distributed stochastic neighbor embedding (t-SNE). Most important variables for classification were identified and the classification algorithm was optimized.

Results: Applying different machine learning algorithms on all m/z peaks, classification accuracy between cRCC and rO was 85%, 82%, 84%, 77% and 64% for RF, SVM, KNN, CART and LDA. Under the assumption that a reduction of m/z peaks would lead to improved classification accuracy, m/z peaks were ranked based on their variable importance. Reduction to six most important m/z peaks resulted in improved accuracy of 89%, 85%, 85% and 85% for RF, SVM, KNN, and LDA and remained at the level of 77% for CART. t-SNE showed clear separation of cRCC and rO after algorithm improvement.

Conclusion: In summary, we acquired MSI data on FFPE tissue specimens of cRCC and rO, performed classification and detected most relevant biomarkers for the differential diagnosis of both diseases. MSI data might be a useful adjunct method in the differential diagnosis of cRCC and rO.

Mass Spectrometry Imaging Reveals Neutrophil Defensins as Additional Biomarkers for Anti-PD-(L)1 Immunotherapy Response in NSCLC Patients

(1) Background: Therapeutic blocking of the interaction between programmed death-1 (PD-1) with its ligand PD-L1, an immune checkpoint, is a promising approach to restore the antitumor immune response. Improved clinical outcomes have been shown in different human cancers, including non-small cell lung cancer (NSCLC). Unfortunately, still a high number of NSCLC patients are treated with immunotherapy without obtaining any clinical benefit, due to the limitations of PD-L1 protein expression as the currently sole predictive biomarker for clinical use; (2) Methods: In this study, we applied mass spectrometry imaging (MSI) to discover new protein biomarkers, and to assess the possible correlation between candidate biomarkers and a positive immunotherapy response by matrix-assisted laser desorption/ionization (MALDI) MSI in 25 formalin-fixed paraffin-embedded (FFPE) pretreatment tumor biopsies (Biobank@UZA); (3) Results: Using MALDI MSI, we revealed that the addition of neutrophil defensin 1, 2 and 3 as pretreatment biomarkers may more accurately predict the outcome of immunotherapy treatment in NSCLC. These results were verified and confirmed with immunohistochemical analyses. In addition, we provide in-vitro evidence of the immune stimulatory effect of neutrophil defensins towards cancer cells; and (4) Conclusions: With proteomic approaches, we have discovered neutrophil defensins as additional prospective biomarkers for an anti-PD-(L)1 immunotherapy response. Thereby, we also demonstrated that the neutrophil defensins contribute in the activation of the immune response towards cancer cells, which could provide a new lead towards an anticancer therapy.

The choice of tissue fixative is a key determinant for mass spectrometry imaging based tumor metabolic reprogramming characterization

The application of mass spectrometry imaging (MSI) for the study of spatiotemporal alterations of the metabolites in tumors has brought a number of significant biological results. At present, metabolite profiling based on MSI is typically performed on frozen tissue sections; however, the majority of clinical specimens need to be fixed in tissue fixative to avoid autolysis and to preserve antigenicity. In this study, we present the global impacts of different fixatives on the MS imaging of gastric cancer tissue metabolites. The MSI performances of 17 kinds of metabolites, such as amino acids, polyamines, cholines, organic acids, polypeptides, nucleotides, nucleosides, nitrogen bases, cholesterols, fatty acids, and phospholipids, in untreated, 10% formalin-, 4% paraformaldehyde-, acetone-, and 95% ethanol-fixed gastric cancer tissues were thoroughly explored for the first time. Furthermore, we also investigated the spatial expressions of 6 metabolic enzymes, namely, GLS, FASN, CHKA, PLD2, cPLA2, and EGFR, closely related to tumor-associated metabolites. Immunohistochemical staining carried out on the same tissue sections' which have undergone MSI analysis' suggests that enzymatic characterization is feasible after metabolite imaging. Combining the spatial signatures of metabolites and pathway-related metabolic enzymes in heterogeneous tumor tissues offers an insight to understand the complex tumor metabolism. Graphical abstract.

De novo discovery of metabolic heterogeneity with immunophenotype-guided imaging mass spectrometry

Imaging mass spectrometry enables in situ label-free detection of thousands of metabolites from intact tissue samples. However, automated steps for multi-omics analyses and interpretation of histological images have not yet been implemented in mass spectrometry data analysis workflows. The characterization of molecular properties within cellular and histological features is done via time-consuming, non-objective, and irreproducible definitions of regions of interest, which are often accompanied by a loss of spatial resolution due to mass spectra averaging.

Methods: We developed a new imaging pipeline called Spatial Correlation Image Analysis (SPACiAL), which is a computational multimodal workflow designed to combine molecular imaging data with multiplex immunohistochemistry (IHC). SPACiAL allows comprehensive and spatially resolved in situ correlation analyses on a cellular resolution. To demonstrate the method, matrix-assisted laser desorption-ionization (MALDI) Fourier-transform ion cyclotron resonance (FTICR) imaging mass spectrometry of metabolites and multiplex IHC staining were performed on the very same tissue section of mouse pancreatic islets and on human gastric cancer tissue specimens. The SPACiAL pipeline was used to perform an automatic, semantic-based, functional tissue annotation of histological and cellular features to identify metabolic profiles. Spatial correlation networks were generated to analyze metabolic heterogeneity associated with cellular features.

Results: To demonstrate the new method, the SPACiAL pipeline was used to identify metabolic signatures of alpha and beta cells within islets of Langerhans, which are cell types that are not distinguishable via morphology alone. The semantic-based, functional tissue annotation allows an unprecedented analysis of metabolic heterogeneity via the generation of spatial correlation networks. Additionally, we demonstrated intra- and intertumoral metabolic heterogeneity within HER2/neu-positive and -negative gastric tumor cells.

Conclusions: We developed the SPACiAL workflow to provide IHC-guided in situ metabolomics on intact tissue sections. Diminishing the workload by automated recognition of histological and functional features, the pipeline allows comprehensive analyses of metabolic heterogeneity. The multimodality of immunohistochemical staining and extensive molecular information from imaging mass spectrometry has the advantage of increasing both the efficiency and precision for spatially resolved analyses of specific cell types. The SPACiAL method is a stepping stone for the objective analysis of high-throughput, multi-omics data from clinical research and practice that is required for diagnostics, biomarker discovery, or therapy response prediction.

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Novel method enables phenotype-guided in situ metabolomics on intact tissue sections.

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Metabolic heterogeneity can be objectified under (patho-)physiological conditions.

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Innovative approach for tissue-based, preclinical, and clinical research.

Spatially resolved proteomics in osteoarthritis: State of the art and new perspectives

Osteoarthritis (OA) is one of the most common diseases worldwide caused by chronic degeneration of the joints. Its high prevalence and the involvement of several tissues define OA as a highly heterogeneous disease. New biological markers to evaluate the progression of the pathology and improve its prognosis are needed. Among all the different -omic strategies applied to OA, solution phase bottom-up proteomics has made an extensive contribution to the field of biomarker research. However, new technologies for protein analysis should be considered for a better understanding of the disease. This review focuses on complementary proteomic methodologies and new technologies for translational research of OA and other rheumatic pathologies, especially mass spectrometry imaging and protein imaging methods not applied by the OA community yet.

Multimodal analysis of formalin-fixed and paraffin-embedded tissue by MALDI imaging and fluorescence in situ hybridization for combined genetic and metabolic analysis

Multimodal tissue analyses that combine two or more detection technologies provide synergistic value compared to single methods and are employed increasingly in the field of tissue-based diagnostics and research. Here, we report a technical pipeline that describes a combined approach of HER2/CEP17 fluorescence in situ hybridization (FISH) analysis with MALDI imaging on the very same section of formalin-fixed and paraffin-embedded (FFPE) tissue. FFPE biopsies and a tissue microarray of human gastroesophageal adenocarcinoma were analyzed by MALDI imaging. Subsequently, the very same section was hybridized by HER2/CEP17 FISH. We found that tissue morphology of both, the biopsies and the tissue microarray, was unaffected by MALDI imaging and the HER2 and CEP17 FISH signals were analyzable. In comparison with FISH analysis of samples without MALDI imaging, we observed no difference in terms of fluorescence signal intensity and gene copy number. Our combined approach revealed adenosine monophosphate, measured by MALDI imaging, as a prognostic marker. HER2 amplification, which was detected by FISH, is a stratifier between good and poor patient prognosis. By integrating both stratification parameters on the basis of our combined approach, we were able to strikingly improve the prognostic effect. Combining molecules detected by MALDI imaging with the gene copy number detected by HER2/CEP17 FISH, we found a synergistic effect, which enhances patient prognosis. This study shows that our combined approach allows the detection of genetic and metabolic properties from one very same FFPE tissue section, which are specific for HER2 and hence suitable for prognosis. Furthermore, this synergism might be useful for response prediction in tumors.

"Redox lipidomics technology: Looking for a needle in a haystack"

Aerobic life is based on numerous metabolic oxidation reactions as well as biosynthesis of oxygenated signaling compounds. Among the latter are the myriads of oxygenated lipids including a well-studied group of polyunsaturated fatty acids (PUFA) - octadecanoids, eicosanoids, and docosanoids. During the last two decades, remarkable progress in liquid-chromatography-mass spectrometry has led to significant progress in the characterization of oxygenated PUFA-containing phospholipids, thus designating the emergence of a new field of lipidomics, redox lipidomics. Although non-enzymatic free radical reactions of lipid peroxidation have been mostly associated with the aberrant metabolism typical of acute injury or chronic degenerative processes, newly accumulated evidence suggests that enzymatically catalyzed (phospho)lipid oxygenation reactions are essential mechanisms of many physiological pathways. In this review, we discuss a variety of contemporary protocols applicable for identification and quantitative characterization of different classes of peroxidized (phospho)lipids. We describe applications of different types of LCMS for analysis of peroxidized (phospho)lipids, particularly cardiolipins and phosphatidylethanolalmines, in two important types of programmed cell death - apoptosis and ferroptosis. We discuss the role of peroxidized phosphatidylserines in phagocytotic signaling. We exemplify the participation of peroxidized neutral lipids, particularly tri-acylglycerides, in immuno-suppressive signaling in cancer. We also consider new approaches to exploring the spatial distribution of phospholipids in the context of their oxidizability by MS imaging, including the latest achievements in high resolution imaging techniques. We present innovative approaches to the interpretation of LC-MS data, including audio-representation analysis. Overall, we emphasize the role of redox lipidomics as a communication language, unprecedented in diversity and richness, through the analysis of peroxidized (phospho)lipids.

MALDI Imaging for Proteomic Painting of Heterogeneous Tissue Structures

PURPOSE

To present matrix-assisted laser desorption/ionization (MALDI) imaging as a powerful method to highlight various tissue compartments.

EXPERIMENTAL DESIGN

Formalin-fixed paraffin-embedded (FFPE) tissue of a uterine cervix, a pancreas, a duodenum, a teratoma, and a breast cancer tissue microarray (TMA) are analyzed by MALDI imaging and by immunohistochemistry (IHC). Peptide images are visualized and analyzed using FlexImaging and SCiLS Lab software. Different histological compartments are compared by hierarchical cluster analysis.

RESULTS

MALDI imaging highlights tissue compartments comparable to IHC. In cervical tissue, normal epithelium can be discerned from intraepithelial neoplasia. In pancreatic and duodenal tissues, m/z signals from lymph follicles, vessels, duodenal mucosa, normal pancreas, and smooth muscle structures can be visualized. In teratoma, specific m/z signals to discriminate squamous epithelium, sebaceous glands, and soft tissue are detected. Additionally, tumor tissue can be discerned from the surrounding stroma in small tissue cores of TMAs. Proteomic data acquisition of complex tissue compartments in FFPE tissue requires less than 1 h with recent mass spectrometers.

CONCLUSION AND CLINICAL RELEVANCE

The simultaneous characterization of morphological and proteomic features in the same tissue section adds proteomic information for histopathological diagnostics, which relies at present on conventional hematoxylin and eosin staining, histochemical, IHC and molecular methods.

In MALDI-Mass Spectrometry Imaging on Formalin-Fixed Paraffin-Embedded Tissue Specimen Section Thickness Significantly Influences m/z Peak Intensity

BACKGROUND

In matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) standardized sample preparation is important to obtain reliable results. Herein, the impact of section thickness in formalin-fixed paraffin embedded (FFPE) tissue microarrays (TMA) on spectral intensities is investigated.

PATIENTS AND METHODS

TMAs consisting of ten different tissues represented by duplicates of ten patients (n = 200 cores) are cut at 1, 3, and 5 μm. MSI analysis is performed and mean intensities of all evaluable cores are extracted. Measurements are merged and mean m/z intensities are compared.

RESULTS

Visual inspection of spectral intensities between 1, 3, and 5 μm reveals generally higher intensities in thinner tissue sections. Specifically, higher intensities are observed in the vast majority of peaks (98.6%, p < 0.01) in 1 μm compared with 5 μm sections. Note that 28.4% and 2.1% of m/z values exhibit a at least two- and threefold intensity difference (p < 0.01) in 1 μm compared to 5 μm sections, respectively.

CONCLUSION

A section thickness of 1 μm results in higher spectral intensities compared with 5 μm. The results highlight the importance of standardized protocols in light of recent efforts to identify clinically relevant biomarkers using MSI. The use of TMAs for comparative analysis seems advantageous, as section thickness displays less variability.