High-throughput proteomic analysis of formalin-fixed paraffin-embedded tissue microarrays using MALDI imaging mass spectrometry

6-Aza-2-Thiothymine as an Alternative Matrix for Spatial Proteomics with MALDI-MSI

Matrix Assisted Laser Desorption/Ionisation-Mass Spectrometry Imaging (MALDI-MSI) is a well-established spatial omic technique which enables the untargeted mapping of various classes of biomolecules, including tryptic peptides, directly on tissue. This method relies on the use of matrices for the ionisation and volatilisation of analytes, and α-Cyano-4-hydroxycinnamic acid (CHCA) represents the most widespread matrix for tryptic peptides analysis. However, CHCA also presents certain limitations that foster the quest for novel matrix compounds. 6-aza-2-thiothymine (ATT), traditionally used in MALDI mass spectrometry (MS) for oligonucleotides, small molecules and oxidised phospholipids, has not been thoroughly investigated as a potential matrix for tryptic peptide analysis in MALDI-MS or MALDI-MSI. Therefore, this study addresses this gap by evaluating the capability of ATT to ionise tryptic peptides from Bovine Serum Albumin (BSA) and map in situ-digested peptides from formalin-fixed paraffin-embedded (FFPE) tissue sections in these respective applications. Comparative analysis with CHCA demonstrated the complementary strengths of these matrices for detecting tryptic peptides, establishing ATT as a feasible alternative to CHCA in the MALDI-MSI field and paving the way for future advancements in spatial proteomics.

Mass Spectrometry Imaging Techniques: Non-Ambient and Ambient Ionization Approaches

Molecular information can be acquired from sample surfaces in real time using a revolutionary molecular imaging technique called mass spectrometry imaging (MSI). The technique can concurrently provide high spatial resolution information on the spatial distribution and relative proportion of many different compounds. Thus, many scientists have been drawn to the innovative capabilities of the MSI approach, leading to significant focus in various fields during the past few decades. This review describes the sampling protocol, working principle and applications of a few non-ambient and ambient ionization mass spectrometry imaging techniques. The non-ambient techniques include secondary ionization mass spectrometry and matrix-assisted laser desorption ionization, while the ambient techniques include desorption electrospray ionization, laser ablation electrospray ionization, probe electro-spray ionization, desorption atmospheric pressure photo-ionization and femtosecond laser desorption ionization. The review additionally addresses the advantages and disadvantages of ambient and non-ambient MSI techniques in relation to their suitability, particularly for biological samples used in tissue diagnostics. Last but not least, suggestions and conclusions are made regarding the challenges and future prospects of MSI.

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.

Mass spectrometry methods for analysis of extracellular matrix components in neurological diseases

The brain extracellular matrix (ECM) is a highly glycosylated environment and plays important roles in many processes including cell communication, growth factor binding, and scaffolding. The formation of structures such as perineuronal nets (PNNs) is critical in neuroprotection and neural plasticity, and the formation of molecular networks is dependent in part on glycans. The ECM is also implicated in the neuropathophysiology of disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), and Schizophrenia (SZ). As such, it is of interest to understand both the proteomic and glycomic makeup of healthy and diseased brain ECM. Further, there is a growing need for site-specific glycoproteomic information. Over the past decade, sample preparation, mass spectrometry, and bioinformatic methods have been developed and refined to provide comprehensive information about the glycoproteome. Core ECM molecules including versican, hyaluronan and proteoglycan link proteins, and tenascin are dysregulated in AD, PD, and SZ. Glycomic changes such as differential sialylation, sulfation, and branching are also associated with neurodegeneration. A more thorough understanding of the ECM and its proteomic, glycomic, and glycoproteomic changes in brain diseases may provide pathways to new therapeutic options.

Imaging Mass Spectrometry for the Classification of Melanoma Based on BRAF/NRAS Mutational Status

Mutations of the oncogenes v-raf murine sarcoma viral oncogene homolog B1 (BRAF) and neuroblastoma RAS viral oncogene homolog (NRAS) are the most frequent genetic alterations in melanoma and are mutually exclusive. BRAF V600 mutations are predictive for response to the two BRAF inhibitors vemurafenib and dabrafenib and the mitogen-activated protein kinase kinase (MEK) inhibitor trametinib. However, inter- and intra-tumoral heterogeneity and the development of acquired resistance to BRAF inhibitors have important clinical implications. Here, we investigated and compared the molecular profile of BRAF and NRAS mutated and wildtype melanoma patients' tissue samples using imaging mass spectrometry-based proteomic technology, to identify specific molecular signatures associated with the respective tumors. SCiLSLab and R-statistical software were used to classify peptide profiles using linear discriminant analysis and support vector machine models optimized with two internal cross-validation methods (leave-one-out, k-fold). Classification models showed molecular differences between BRAF and NRAS mutated melanoma, and identification of both was possible with an accuracy of 87-89% and 76-79%, depending on the respective classification method applied. In addition, differential expression of some predictive proteins, such as histones or glyceraldehyde-3-phosphate-dehydrogenase, correlated with BRAF or NRAS mutation status. Overall, these findings provide a new molecular method to classify melanoma patients carrying BRAF and NRAS mutations and help provide a broader view of the molecular characteristics of these patients that may help understand the signaling pathways and interactions involving the altered genes.

Advances in high-throughput mass spectrometry in drug discovery

High-throughput (HT) screening drug discovery, during which thousands or millions of compounds are screened, remains the key methodology for identifying active chemical matter in early drug discovery pipelines. Recent technological developments in mass spectrometry (MS) and automation have revolutionized the application of MS for use in HT screens. These methods allow the targeting of unlabelled biomolecules in HT assays, thereby expanding the breadth of targets for which HT assays can be developed compared to traditional approaches. Moreover, these label-free MS assays are often cheaper, faster, and more physiologically relevant than competing assay technologies. In this review, we will describe current MS techniques used in drug discovery and explain their advantages and disadvantages. We will highlight the power of mass spectrometry in label-free in vitro assays, and its application for setting up multiplexed cellular phenotypic assays, providing an exciting new tool for screening compounds in cell lines, and even primary cells. Finally, we will give an outlook on how technological advances will increase the future use and the capabilities of mass spectrometry in drug discovery.

HistoSnap: A Novel Software Tool to Extract m/z-Specific Images from Large MSHC Datasets

We describe an informatics tool for comfortable browsing through highly complex, multi-gigabyte mass spectrometry histochemistry (MSHC) datasets, via clever ion-specific image extraction.The package is developed particularly for the untargeted localization/discovery of biomolecules such as endogenous (neuro)secretory peptides on histological sections of biobanked formaldehyde-fixed paraffin-embedded (FFPE) samples straight from tissue banks.Atmospheric pressure-MALDI-Orbitrap MSHC data of sections through human pituitary adenomas in which two well-known human neuropeptides are detected are used as an example to demonstrate the key features of the novel software, named HistoSnap.

From protein biomarkers to proteomics in dementia with Lewy Bodies

Dementia with Lewy Bodies (DLB) is the second most common neurodegenerative dementia. Despite considerable research progress, there remain gaps in our understanding of the pathophysiology and there is no disease-modifying treatment. Proteomics is a powerful tool to elucidate complex biological pathways across heterogenous conditions. This review summarizes the widely used proteomic methods and presents evidence for protein dysregulation in the brain and peripheral tissues in DLB. Proteomics of post-mortem brain tissue shows that DLB shares common features with other dementias, such as synaptic dysfunction, but retains a unique protein signature. Promising diagnostic biomarkers are being identified in cerebrospinal fluid (CSF), blood, and peripheral tissues, such as serum Heart-type fatty acid binding protein. Research is needed to track these changes from the prodromal stage to established dementia, with standardized workflows to ensure replicability. Identifying novel protein targets in causative biological pathways could lead to the development of new targeted therapeutics or the stratification of participants for clinical trials.

DiviK: divisive intelligent K-means for hands-free unsupervised clustering in big biological data

BACKGROUND

Investigating molecular heterogeneity provides insights into tumour origin and metabolomics. The increasing amount of data gathered makes manual analyses infeasible-therefore, automated unsupervised learning approaches are utilised for discovering tissue heterogeneity. However, automated analyses require experience setting the algorithms' hyperparameters and expert knowledge about the analysed biological processes. Moreover, feature engineering is needed to obtain valuable results because of the numerous features measured.

RESULTS

We propose DiviK: a scalable stepwise algorithm with local data-driven feature space adaptation for segmenting high-dimensional datasets. The algorithm is compared to the optional solutions (regular k-means, spatial and spectral approaches) combined with different feature engineering techniques (None, PCA, EXIMS, UMAP, Neural Ions). Three quality indices: Dice Index, Rand Index and EXIMS score, focusing on the overall composition of the clustering, coverage of the tumour region and spatial cluster consistency, are used to assess the quality of unsupervised analyses. Algorithms were validated on mass spectrometry imaging (MSI) datasets-2D human cancer tissue samples and 3D mouse kidney images. DiviK algorithm performed the best among the four clustering algorithms compared (overall quality score 1.24, 0.58 and 162 for d(0, 0, 0), d(1, 1, 1) and the sum of ranks, respectively), with spectral clustering being mostly second. Feature engineering techniques impact the overall clustering results less than the algorithms themselves (partial [Formula: see text] effect size: 0.141 versus 0.345, Kendall's concordance index: 0.424 versus 0.138 for d(0, 0, 0)).

CONCLUSIONS

DiviK could be the default choice in the exploration of MSI data. Thanks to its unique, GMM-based local optimisation of the feature space and deglomerative schema, DiviK results do not strongly depend on the feature engineering technique applied and can reveal the hidden structure in a tissue sample. Additionally, DiviK shows high scalability, and it can process at once the big omics data with more than 1.5 mln instances and a few thousand features. Finally, due to its simplicity, DiviK is easily generalisable to an even more flexible framework. Therefore, it is helpful for other -omics data (as single cell spatial transcriptomic) or tabular data in general (including medical images after appropriate embedding). A generic implementation is freely available under Apache 2.0 license at https://github.com/gmrukwa/divik .

High-throughput analysis of tissue microarrays using automated desorption electrospray ionization mass spectrometry

Tissue microarrays (TMAs) are commonly used for the rapid analysis of large numbers of tissue samples, often in morphological assessments but increasingly in spectroscopic analysis, where specific molecular markers are targeted via immunostaining. Here we report the use of an automated high-throughput system based on desorption electrospray ionization (DESI) mass spectrometry (MS) for the rapid generation and online analysis of high-density (6144 samples/array) TMAs, at rates better than 1 sample/second. Direct open-air analysis of tissue samples (hundreds of nanograms) not subjected to prior preparation, plus the ability to provide molecular characterization by tandem mass spectrometry (MS/MS), make this experiment versatile and applicable to both targeted and untargeted analysis in a label-free manner. These capabilities are demonstrated in a proof-of-concept study of frozen brain tissue biopsies where we showcase (i) a targeted MS/MS application aimed at identification of isocitrate dehydrogenase mutation in glioma samples and (ii) an untargeted MS tissue type classification using lipid profiles and correlation with tumor cell percentage estimates from histopathology. The small sample sizes and large sample numbers accessible with this methodology make for a powerful analytical system that facilitates the identification of molecular markers for later use in intraoperative applications to guide precision surgeries and ultimately improve patient outcomes.

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 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.

Robust subtyping of non-small cell lung cancer whole sections through MALDI mass spectrometry imaging

Subtyping of the most common non-small cell lung cancer (NSCLC) tumor types adenocarcinoma (ADC) and squamous cell carcinoma (SqCC) is still a challenge in the clinical routine and a correct diagnosis is crucial for an adequate therapy selection. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) has shown potential for NSCLC subtyping but is subject to strong technical variability and has only been applied to tissue samples assembled in tissue microarrays (TMAs). To our knowledge, a successful transfer of a classifier from TMAs to whole sections, which are generated in the standard clinical routine, has not been presented in the literature as of yet. We introduce a classification algorithm using extensive preprocessing and a classifier (either a neural network or a linear discriminant analysis (LDA)) to robustly classify whole sections of ADC and SqCC lung tissue. The classifiers were trained on TMAs and validated and tested on whole sections. Vital for a successful application on whole sections is the extensive preprocessing and the use of whole sections for hyperparameter selection. The classification system with the neural network/LDA results in 99.0%/98.3% test accuracy on spectra level and 100.0%/100.0% test accuracy on whole section level, respectively, and, therefore, provides a powerful tool to support the pathologist's decision making process. The presented method is a step further towards a clinical application of MALDI MSI and artificial intelligence for subtyping of NSCLC tissue sections.

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.

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.

MALDI Mass Spectrometry Imaging of Lipids on Free-Floating Brain Sections and Immunohistochemically Colocalized Markers of Neurodegeneration

In mass spectrometry imaging (MSI), the essential steps in sample preparation include collection and storage. The most widely used preservation procedure for MSI consists in freezing samples and storing them at temperatures below -80 °C. On the other hand, the most common method for preserving biological samples in clinical practice is their fixation in paraformaldehyde. The storage of free-floating sections is a particular type of the preservation of paraformaldehyde-fixed tissues that is used in immunohistochemistry. This chapter describes the approach of the multimodal imaging of free-floating brain sections using the MSI of lipids and the immunohistochemistry of neurodegeneration markers.

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.

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.

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.

Evolution of the liver biopsy and its future

Liver biopsies are commonly used to evaluate a wide variety of medical disorders, including neoplasms and post-transplant complications. However, its use is being impacted by improved clinical diagnosis of disorders, and non-invasive methods for evaluating liver tissue and as a result the indications of a liver biopsy have undergone major changes in the last decade. The evolution of highly effective treatments for some of the common indications for liver biopsy in the last decade (e.g., viral hepatitis B and C) has led to a decline in the number of liver biopsies in recent years. At the same time, the emergence of better technologies for histologic evaluation, tissue content analysis and genomics are among the many new and exciting developments in the field that hold great promise for the future and are going to shape the indications for a liver biopsy in the future. Recent advances in slide scanners now allow creation of "digital/virtual" slides that have image of the entire tissue section present in a slide [whole slide imaging (WSI)]. WSI can now be done very rapidly and at very high resolution, allowing its use in routine clinical practice. In addition, a variety of technologies have been developed in recent years that use different light sources and/or microscopes allowing visualization of tissues in a completely different way. One such technique that is applicable to liver specimens combines multiphoton microscopy (MPM) with advanced clearing and fluorescent stains known as Clearing Histology with MultiPhoton Microscopy (CHiMP). Although it has not yet been extensively validated, the technique has the potential to decrease inefficiency, reduce artifacts, and increase data while being readily integrable into clinical workflows. Another technology that can provide rapid and in-depth characterization of thousands of molecules in a tissue sample, including liver tissues, is matrix assisted laser desorption/ionization (MALDI) mass spectrometry. MALDI has already been applied in a clinical research setting with promising diagnostic and prognostic capabilities, as well as being able to elucidate mechanisms of liver diseases that may be targeted for the development of new therapies. The logical next step in huge data sets obtained from such advanced analysis of liver tissues is the application of machine learning (ML) algorithms and application of artificial intelligence (AI), for automated generation of diagnoses and prognoses. This review discusses the evolving role of liver biopsies in clinical practice over the decades, and describes newer technologies that are likely to have a significant impact on how they will be used in the future.

Native Mass Spectrometry Imaging of Proteins and Protein Complexes by Nano-DESI

Previously, we have demonstrated native mass spectrometry imaging (native MSI) in which the spatial distribution of proteins maintained in their native-like, folded conformations was determined using liquid extraction surface analysis (LESA). While providing an excellent testbed for proof of principle, the spatial resolution of LESA is currently limited for imaging primarily by the physical size of the sampling pipette tip. Here, we report the adoption of nanospray-desorption electrospray ionization (nano-DESI) for native MSI, delivering substantial improvements in resolution versus native LESA MSI. In addition, native nano-DESI may be used for location-targeted top–down proteomics analysis directly from tissue. Proteins, including a homodimeric complex not previously detected by native MSI, were identified through a combination of collisional activation, high-resolution MS and proton transfer charge reduction.

Multilabel Per-Pixel Quantitation in Mass Spectrometry Imaging

In quantitative mass spectrometry imaging (MSI), the gold standard adds a single structural homologue of the target compound at a known concentration to the sample. This internal standard enables to map the detected intensity of the target molecule against an external calibration curve. This approach, however, ignores local noise levels and disproportional ion suppression effects, which might depend on the concentration of the target compound. To overcome these issues, we propose a novel approach that applies several isotopically labeled versions, each at a different concentration, to the sample. This allows creating individual internal calibration curves for every MSI pixel. As proof of principle, we have quantified an endogenous peptide of histone H4 by matrix-assisted laser desorption/ionization-Q-MSI (MALDI-Q-MSI), using a mixture of three isotopically labeled versions. The usage of a fourth label allowed us to compare the gold standard to our multilabel approach. We observed substantial heterogeneity in ion suppression across the tissue, which disclosed itself as varying slopes in the per-pixel regression analyses. These slopes were histology-dependent and differed from each other by up to a factor of 4. The results were validated by liquid chromatography-mass spectrometry (LC-MS), exhibiting a high agreement between LC-MS and MALDI-Q-MSI (Pearson correlation r = 0.87). A comparison between the multilabel and single-label approaches revealed a higher accuracy for the multilabel method when the local target compound concentration differed too much from the concentration of the single label. In conclusion, we show that the multilabel approach provides superior quantitation compared to a single-label approach, in case the target compound is inhomogeneously distributed at a wide concentration range in the tissue.

Omics in Systems Biology: Current Progress and Future Outlook

Biological research has undergone tremendous changes over the past three decades. Research used to almost exclusively focus on a single aspect of a single molecule per experiment. Modern technologies have enabled thousands of molecules to be simultaneously analyzed and the way that these molecules influence each other to be discerned. The change is so dramatic that it has given rise to a whole new descriptive suffix (i.e., omics) to describe these fields of study. While genomics was arguably the initial driver of this new trend, it quickly spread to other biological entities resulting in the creation of transcriptomics, proteomics, metabolomics, etc. The development of these "big four omics" created a wave of other omic fields, such as epigenomics, glycomics, lipidomics, microbiomics, and even foodomics; all with the purpose of comprehensively studying all the molecular entities or processes within their respective domain. The large number of omic fields that are invented even led to the term "panomics" as a way to classify them all under one category. Ultimately, all of these omic fields are setting the foundation for developing systems biology; in which the focus will be on determining the complex interactions that occur within biological systems.

A matrix-assisted laser desorption/ionization imaging mass spectrometric approach to study weight-related changes within thyroid tissue

Obesity is associated with numerous comorbidities along with abnormalities of the endocrine system, more commonly manifesting as dysfunctions of the thyroid gland such as goiter. Changes in weight, especially an increase, could lead to an increase in the incidence of thyroid dysfunction; however, its pathophysiology remains to be elucidated. In the present study, we aimed to interrogate the changes in the protein distribution and abundance between the lean patients and patients with obesity thyroid tissue sections through utilizing this technique. The FFPE-fixed thyroid tissue blocks from the selected cases and controls were identified and targeted for matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) analysis. Patients in the 30 to 75 years age group and undergoing total thyroidectomy for benign thyroid disease were recruited. Patients with thyroid cancers, autoimmune disorders, and other inflammatory conditions were excluded from the study. The selected patients were divided into two groups according to their BMIs: lean (BMI < 25) and obese (BMI > 35). An initial trial set was used as a pilot study for the optimization of the MALDI IMS protocol that was next applied to the selected thyroid tissues. MALDI IMS data from all the samples were aligned and statistical analysis carried out by k-means and linear discriminant analysis (LDA) classification model using principle component analysis (PCA) results were evaluated between the two groups: controls (lean) and cases (obese). Receiver operator characteristic (ROC) curves were alternatively used to calculate the variability of the identified peptides. The discriminating peptides were also independently identified and related to their corresponding proteins by using liquid chromatography and tandem mass spectrometry (LC-MS/MS) analyses. Eight peptides mainly from thyroglobulin were found to be upregulated whereas 10 others were found to be downregulated in the lean compared to the obese group. Through this technique, we will be able to better understand the relationship between the disease entity and obesity.

On-Tissue Derivatization of Lipopolysaccharide for Detection of Lipid A Using MALDI-MSI

We developed a method to directly detect and map the Gram-negative bacterial virulence factor lipid A derived from lipopolysaccharide (LPS) by coupling acid hydrolysis with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). As the structure of lipid A (endotoxin) determines the innate immune outcome during infection, the ability to map its location within an infected organ or animal is needed to understand localized inflammatory responses that results during host-pathogen interactions. We previously demonstrated detection of free lipid A from infected tissue; however detection of lipid A derived from intact (smooth) LPS from host-pathogen MSI studies, proved elusive. Here, we detected LPS-derived lipid A from the Gram-negative pathogens, Escherichia coli (Ec, m/z 1797) and Pseudomonas aeruginosa (Pa, m/z 1446) using on-tissue acid hydrolysis to cleave the glycosidic linkage between the polysaccharide (core and O-antigen) and lipid A moieties of LPS. Using accurate mass methods, the ion corresponding to the major Ec and Pa lipid A species (m/z 1797 and 1446, respectively) were unambiguously discriminated from complex tissue substrates. Further, we evaluated potential delocalization and signal loss of other tissue lipids and found no evidence for either, making this LPS-to-Lipid A-MSI (LLA-MSI) method, compatible with simultaneous host-pathogen lipid imaging following acid hydrolysis. This spatially sensitive technique is the first step in mapping host-influenced de novo lipid A modifications, such as those associated with antimicrobial resistance phenotypes, during Gram-negative bacterial infection and will advance our understanding of the host-pathogen interface.

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.

Antigen Retrieval and Its Effect on the MALDI-MSI of Lipids in Formalin-Fixed Paraffin-Embedded Tissue

Formalin-fixed paraffin-embedded (FFPE) tissue represents the primary source of clinical tissue and is routinely used in MALDI-MSI studies. However, it is not particularly suitable for lipidomics imaging given that many species are depleted during tissue processing. Irrespective, a number of solvent-resistant lipids remain, but their extraction may be hindered by the cross-link between proteins. Therefore, an antigen retrieval step could enable the extraction of a greater number of lipids and may provide information that is complementary to that which can be obtained from other biomolecules, such as proteins. In this short communication, we aim to address the effect of performing antigen retrieval prior to MALDI-MSI of lipids in FFPE tissue. As a result, an increased number of lipid signals could be detected and may have derived from lipid species that are known to be implicated in the lipid-protein cross-linking that is formed as a result of formalin fixation. Human renal cancer tissue was used as a proof of concept to determine whether using these detected lipid signals were also able to highlight the histopathological regions that were present. These preliminary findings may highlight the potential to enhance the clinical relevance of the lipidomic information obtained from FFPE tissue.

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

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

Mass spectrometry imaging of free-floating brain sections detects pathological lipid distribution in a mouse model of Alzheimer's-like pathology

Mass spectrometry imaging (MSI) is a modern analytical technique capable of monitoring the spatial distribution of compounds within target tissues. Collection and storage are important steps in sample preparation. The recommended and most widely used preservation procedure for MSI is freezing samples in isopentane and storing them at temperatures below -80 °C. On the other hand, the most common and general method for preserving biological samples in clinical practice is fixation in paraformaldehyde. Special types of samples prepared from these fixed tissues that are used for histology and immunohistochemistry are free-floating sections. It would be very beneficial if the latter procedure could also be applicable for the samples intended for subsequent MSI analysis. In the present work, we optimized and evaluated paraformaldehyde-fixed free-floating sections for the analysis of lipids in mouse brains and used the sections for the study of lipid changes in double transgenic APP/PS1 mice, a model of Alzheimer's-like pathology. Moreover, we examined the neuroprotective properties of palm¹¹-PrRP31, an anorexigenic and glucose-lowering analog of prolactin-releasing peptide, and liraglutide, a type 2 diabetes drug. From the free-floating sections, we obtained lipid images without interference or delocalization, and we demonstrated that free-floating sections can be used for the MSI of lipids. In the APP/PS1 mice, we observed a changed distribution of various lipids compared to the controls. The most significant changes in lipids in the brains of APP/PS1 mice compared to wild-type controls were related to gangliosides (GM2 36:1, GM3 36:1) and phosphatidylinositols (PI 38:4, 36:4) in regions where the accumulation of senile plaques occurred. In APP/PS1 mice peripherally treated with palm¹¹-PrRP31 or liraglutide for 2 months, we found that both peptides reduced the amount and space occupied by lipids, which were linked to the senile plaques. These results indicate that palm¹¹-PrRP31 as well as liraglutide might be potentially useful in the treatment of neurodegenerative diseases.

Sample preparation of formalin-fixed paraffin-embedded tissue sections for MALDI-mass spectrometry imaging

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MALDI MSI) has become a powerful tool with a high potential relevance for the analysis of biomolecules in tissue samples in the context of diseases like cancer and cardiovascular or cardiorenal diseases. In recent years, significant progress has been made in the technology of MALDI MSI. However, a more systematic optimization of sample preparation would likely achieve an increase in the molecular information derived from MALDI MSI. Therefore, we have employed a systematic approach to develop, establish and validate an optimized "standard operating protocol" (SOP) for sample preparation in MALDI MSI of formalin-fixed paraffin-embedded (FFPE) tissue sample analyses within this study. The optimized parameters regarding the impact on the resulting signal-to-noise (S/N) ratio were as follows: (i) trypsin concentration, solvents, deposition method, and incubation time; (ii) tissue washing procedures and drying processes; and (iii) spray flow rate, number of layers of trypsin deposition, and grid size. The protocol was evaluated on interday variability and its applicability for analyzing the mouse kidney, aorta, and heart FFPE tissue samples. In conclusion, an optimized SOP for MALDI MSI of FFPE tissue sections was developed to generate high sensitivity, to enhance spatial resolution and reproducibility, and to increase its applicability for various tissue types. This optimized SOP will further increase the molecular information content and intensify the use of MSI in future basic research and diagnostic applications. Graphical Abstract.

Maintenance of Deep Lung Architecture and Automated Airway Segmentation for 3D Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a technique for mapping the spatial distributions of molecules in sectioned tissue. Histology-preserving tissue preparation methods are central to successful MSI studies. Common fixation methods, used to preserve tissue morphology, can result in artifacts in the resulting MSI experiment including delocalization of analytes, altered adduct profiles, and loss of key analytes due to irreversible cross-linking and diffusion. This is especially troublesome in lung and airway samples, in which histology and morphology is best interpreted from 3D reconstruction, requiring the large and small airways to remain inflated during analysis. Here, we developed an MSI-compatible inflation containing as few exogenous components as possible, forgoing perfusion, fixation, and addition of salt solutions upon inflation that resulted in an ungapped 3D molecular reconstruction through more than 300 microns. We characterized a series of polyunsaturated phospholipids (PUFA-PLs), specifically phosphatidylinositol (-PI) lipids linked to lethal inflammation in bacterial infection and mapped them in serial sections of inflated mouse lung. PUFA-PIs were identified using spatial lipidomics and determined to be determinant markers of major airway features using unsupervised hierarchical clustering. Deep lung architecture was preserved using this inflation approach and the resulting sections are compatible with multiple MSI modalities, automated interpretation software, and serial 3D reconstruction.

[Mass spectrometry-applications in pathology]

BACKGROUND

Currently, more complex and extensive diagnostic pathology work-up of sometimes only limited sample material is necessary to ensure optimal patient treatment. This often includes genomic analyses. However, dynamic changes within an organism or tumor can be better reflected at the protein level. Therefore, proteomic technologies would seem to be the solution.

OBJECTIVES

To evaluate the application of different proteomic techniques to analyze body fluids and tissue samples with regards to implementation in diagnostics.

MATERIALS AND METHODS

All studies utilized mass spectrometry-based methods in order to achieve differentiation of a number of different patient groups in various diseases.

RESULTS

Whereas classical proteomic methods are particularly suitable for analyzing serum samples in order to diagnose bladder cancer or chronic hepatitis C, tissue analyses would require prior tissue lyses, thus erasing possible information to be obtained from histology. Imaging mass spectrometry offers a solution as it allows for the analysis of an intact tissue section. Possible applications and the added benefit of this method could be shown using various examples of tumors (prostate cancer, Hodgkin's lymphoma, cervical cancer, and different types of adenocarcinomas).

CONCLUSIONS

Mass spectrometry-based technologies allow diagnostic confirmation with high sensitivity and specificity. In comparison to routine diagnostic approaches, results can be achieved faster, using less sample material, and with comparable accuracy.

On-slide tissue digestion for mass spectrometry based glycomic and proteomic profiling

We describe a protocol for glycomic and proteomic profiling that uses serial enzyme digestions from the surface of fresh frozen or fixed tissue slides. The abundances of the extracted glycans and peptides are determined using liquid chromatography-mass spectrometry. In a typical experiment, our method quantifies 14 heparan sulfate disaccharides, 11 chondroitin sulfate disaccharides, 50 N-glycan compositions and approximately 1200 proteins from a 1.8 mm circle, on the surface of a fresh frozen tissue slide from rat brain. Each enzymatic digestion is incubated overnight with direct application of enzyme on the tissue surface. Overall, the sample preparation process for multiple tissue slides takes a day per biomolecule class. This protocol saves time by simultaneous digestion of large N-glycans and small HS disaccharides and subsequent separation using size exclusion chromatography. Compared to wet tissue analysis, this method requires less time by a factor of two. By comparison, MALDI-imaging provides higher spatial resolution of glycans and proteins but lower depth of coverage. MALDI dissociates fragile glycan substituents including sulfates and is not recommended for analysis of glycosaminoglycans (GAGs).

A recommended and verified procedure for in situ tryptic digestion of formalin-fixed paraffin-embedded tissues for analysis by matrix-assisted laser desorption/ionization imaging mass spectrometry

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a molecular imaging technology uniquely capable of untargeted measurement of proteins, lipids, and metabolites while retaining spatial information about their location in situ. This powerful combination of capabilities has the potential to bring a wealth of knowledge to the field of molecular histology. Translation of this innovative research tool into clinical laboratories requires the development of reliable sample preparation protocols for the analysis of proteins from formalin-fixed paraffin-embedded (FFPE) tissues, the standard preservation process in clinical pathology. Although ideal for stained tissue analysis by microscopy, the FFPE process cross-links, disrupts, or can remove proteins from the tissue, making analysis of the protein content challenging. To date, reported approaches differ widely in process and efficacy. This tutorial presents a strategy derived from systematic testing and optimization of key parameters, for reproducible in situ tryptic digestion of proteins in FFPE tissue and subsequent MALDI IMS analysis. The approach describes a generalized method for FFPE tissues originating from virtually any source.

Mass Spectrometry for Proteomics and Recent Developments in ESI, MALDI and other Ionization Methodologies

Background:

Mass spectrometry is a tool used in analytical chemistry to identify components in a chemical compound and it is of tremendous importance in the field of biology for high throughput analysis of biomolecules, among which protein is of great interest.

Objective:

Advancement in proteomics based on mass spectrometry has led the way to quantify multiple protein complexes, and proteins interactions with DNA/RNA or other chemical compounds which is a breakthrough in the field of bioinformatics.

Methods:

Many new technologies have been introduced in electrospray ionization (ESI) and Matrixassisted Laser Desorption/Ionization (MALDI) techniques which have enhanced sensitivity, resolution and many other key features for the characterization of proteins.

Results:

The advent of ambient mass spectrometry and its different versions like Desorption Electrospray Ionization (DESI), DART and ELDI has brought a huge revolution in proteomics research. Different imaging techniques are also introduced in MS to map proteins and other significant biomolecules. These drastic developments have paved the way to analyze large proteins of >200kDa easily.

Conclusion:

Here, we discuss the recent advancement in mass spectrometry, which is of great importance and it could lead us to further deep analysis of the molecules from different perspectives and further advancement in these techniques will enable us to find better ways for prediction of molecules and their behavioral properties.

Discriminating normal regions within cancerous hen ovarian tissue using multivariate hyperspectral image analysis

RATIONALE

Identification of subregions under different pathological conditions on cancerous tissue is of great significance for understanding cancer progression and metastasis. Infrared matrix-assisted laser desorption electrospray ionization mass spectrometry (IR-MALDESI-MS) can be potentially used for diagnostic purposes since it can monitor spatial distribution and abundance of metabolites and lipids in biological tissues. However, the large size and high dimensionality of hyperspectral data make analysis and interpretation challenging. To overcome these barriers, multivariate methods were applied to IR-MALDESI data for the first time, aiming at efficiently resolving mass spectral images, from which these results were then used to identify normal regions within cancerous tissue.

METHODS

Molecular profiles of healthy and cancerous hen ovary tissues were generated by IR-MALDESI-MS. Principal component analysis (PCA) combined with color-coding built a single tissue image which summarizes the high-dimensional data features. Pixels with similar color indicated similar composition. PCA results from healthy tissue were further used to test each pixel in cancerous tissue to determine if it is healthy. Multivariate curve resolution-alternating least squares (MCR-ALS) was used to obtain major spatial features existing in ovary tissues, and group molecules with the same distribution patterns simultaneously.

RESULTS

PCA as the predominating dimensionality reduction approach captured over 90% spectral variances by the first three PCs. The PCA images show the cancerous tissue is more chemically heterogeneous than healthy tissue, where at least four regions with different m/z profiles can be differentiated. PCA modeling assigns top regions of cancerous tissue as healthy-like. MCR-ALS extracted three and four major compounds from healthy and cancerous tissue, respectively. Evaluating similarities of resolved spectra uncovered the chemical components that were distinct in some regions on cancerous tissue, serving as a supplementary way to differentiate healthy and cancerous regions.

CONCLUSIONS

Two unsupervised chemometric methods including PCA and MCR-ALS were applied for resolving and visualizing IR-MALDESI-MS data acquired from hen ovary tissues, improving the interpretation of mass spectrometry imaging results. Then possible normal regions were differentiated from cancerous tissue sections. No prior knowledge is required using either chemometric method, so our approach is readily suitable for unstained tissue samples, which allows one to reveal the molecular events happening during disease progression.

Protein identification strategies in MALDI imaging mass spectrometry: a brief review

Matrix assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is a powerful technology used to investigate the spatial distributions of thousands of molecules throughout a tissue section from a single experiment. As proteins represent an important group of functional molecules in tissue and cells, the imaging of proteins has been an important point of focus in the development of IMS technologies and methods. Protein identification is crucial for the biological contextualization of molecular imaging data. However, gas-phase fragmentation efficiency of MALDI generated proteins presents significant challenges, making protein identification directly from tissue difficult. This review highlights methods and technologies specifically related to protein identification that have been developed to overcome these challenges in MALDI IMS experiments.

High throughput screening of complex biological samples with mass spectrometry – from bulk measurements to single cell analysis

We review the state of the art in HTS using mass spectrometry with minimal sample preparation from complex biological matrices. We focus on industrial and biotechnological applications.

Site-to-Site Reproducibility and Spatial Resolution in MALDI-MSI of Peptides from Formalin-Fixed Paraffin-Embedded Samples

PURPOSE

To facilitate the transition of MALDI-MS Imaging (MALDI-MSI) from basic science to clinical application, it is necessary to analyze formalin-fixed paraffin-embedded (FFPE) tissues. The aim is to improve in situ tryptic digestion for MALDI-MSI of FFPE samples and determine if similar results would be reproducible if obtained from different sites.

EXPERIMENTAL DESIGN

FFPE tissues (mouse intestine, human ovarian teratoma, tissue microarray of tumor entities sampled from three different sites) are prepared for MALDI-MSI. Samples are coated with trypsin using an automated sprayer then incubated using deliquescence to maintain a stable humid environment. After digestion, samples are sprayed with CHCA using the same spraying device and analyzed with a rapifleX MALDI Tissuetyper at 50 µm spatial resolution. Data are analyzed using flexImaging, SCiLS, and R.

RESULTS

Trypsin application and digestion are identified as sources of variation and loss of spatial resolution in the MALDI-MSI of FFPE samples. Using the described workflow, it is possible to discriminate discrete histological features in different tissues and enabled different sites to generate images of similar quality when assessed by spatial segmentation and PCA.

CONCLUSIONS AND CLINICAL RELEVANCE

Spatial resolution and site-to-site reproducibility can be maintained by adhering to a standardized MALDI-MSI workflow.

Mass spectrometry imaging for clinical research - latest developments, applications, and current limitations

Mass spectrometry is being used in many clinical research areas ranging from toxicology to personalized medicine. Of all the mass spectrometry techniques, mass spectrometry imaging (MSI), in particular, has continuously grown towards clinical acceptance. Significant technological and methodological improvements have contributed to enhance the performance of MSI recently, pushing the limits of throughput, spatial resolution, and sensitivity. This has stimulated the spread of MSI usage across various biomedical research areas such as oncology, neurological disorders, cardiology, and rheumatology, just to name a few. After highlighting the latest major developments and applications touching all aspects of translational research (i.e. from early pre-clinical to clinical research), we will discuss the present challenges in translational research performed with MSI: data management and analysis, molecular coverage and identification capabilities, and finally, reproducibility across multiple research centers, which is the largest remaining obstacle in moving MSI towards clinical routine.

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 Situ Imaging of Tryptic Peptides by MALDI Imaging Mass Spectrometry Using Fresh-Frozen or Formalin-Fixed, Paraffin-Embedded Tissue

Tryptic peptide imaging is a primary workflow for matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) and has led to new information reporting highly multiplexed protein localization. Technological advances within the last few years have produced robust tools for automated spraying of both matrix and enzymes. When combined with high-mass-resolution and high-mass-accuracy instrumentation, studies now generally result in two-dimensional mapping of well over 1,000 peptide peaks. This protocol describes sample preparation, spraying, and application of enzymes and matrices, and MALDI FT-ICR instrumental considerations for two-dimensional mapping of tryptic peptides from fresh-frozen or formalin-fixed, paraffin-embedded tissue sections. Procedures for extraction of tryptic peptides from tissue sections for LC-MS/MS identification are also described. © 2018 by John Wiley & Sons, Inc.

Discerning the Primary Carcinoma in Malignant Peritoneal and Pleural Effusions Using Imaging Mass Spectrometry-A Feasibility Study

PURPOSE

Malignant effusions challenge diagnostic accuracy due to cytomorphologic overlaps between various malignant primaries. Workup of this material to establish a correct diagnosis is time consuming and limited by the sparsity of material. In order to circumvent these drawbacks, the use of MALDI imaging MS (IMS) as a diagnostic platform has been explored.

EXPERIMENTAL DESIGN

Cytology cell blocks from malignant effusions (serous ovarian carcinoma and several non-ovarian carcinomas including gastric adenocarcinoma) containing at least 30% neoplastic cells are selected for generation of cytology microarrays (CMA). CMA sections are transferred to conductive glass slides, subjected to on-tissue tryptic digestion, and matrix application for MALDI-IMS analysis.

RESULTS

Supervised classification analysis identifies serous ovarian carcinomas as the source of malignant effusions with a sensitivity of 85.7% when compared to samples from all other included primary sites. When compared to gastric adenocarcinoma, serous ovarian carcinoma samples can be delineated with a sensitivity of 97.3%.

CONCLUSION AND CLINICAL RELEVANCE

These preliminary results highlight that MALDI-IMS allows subtyping of malignant effusions to identify the precise origin of neoplastic cells. While achieving similar results compared to classical approaches such as immunocytology, more material is conserved that will be available for further tests.

Strategies for managing multi-patient 3D mass spectrometry imaging data

Mass spectrometry imaging (MSI) has emerged as a powerful tool in biomedical research to reveal the localization of a broad scale of compounds ranging from metabolites to proteins in diseased tissues, such as malignant tumors. MSI is most commonly used for the two-dimensional imaging of tissues from multiple patients or for the three-dimensional (3D) imaging of tissue from a single patient. These applications are potentially introducing a sampling bias on a sample or patient level, respectively. The aim of this study is therefore to investigate the consequences of sampling bias on sample representativeness and on the precision of biomarker discovery for histological grading of human bladder cancers by MSI. We therefore submitted formalin-fixed paraffin-embedded tissues from 14 bladder cancer patients with varying histological grades to 3D analysis by matrix-assisted laser desorption/ionization (MALDI) MSI. We found that, after removing 20% of the data based on novel outlier detection routines for 3D-MSI data based on the evaluation of digestion efficacy and z-directed regression, on average 33% of a sample has to be measured in order to obtain sufficient coverage of the existing biological variance within a tissue sample. SIGNIFICANCE: In this study, 3D MALDI-MSI is applied for the first time on a cohort of bladder cancer patients using formalin-fixed paraffin-embedded (FFPE) tissue of bladder cancer resections. This work portrays the reproducibility that can be achieved when employing an optimized sample preparation and subsequent data evaluation approach. Our data shows the influence of sampling bias on the variability of the results, especially for a small patient cohort. Furthermore, the presented data analysis workflow can be used by others as a 3D FFPE data-analysis pipeline working on multi-patient 3D-MSI studies.