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.

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.

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.

Enhanced Coverage of Insect Neuropeptides in Tissue Sections by an Optimized Mass-Spectrometry-Imaging Protocol.

Mass spectrometry imaging (MSI) of neuropeptides has become a well-established method with the ability to combine spatially resolved information from immunohistochemistry with peptidomics information from mass spectrometric analysis. Several studies have conducted MSI of insect neural tissues; however, these studies did not detect neuropeptide complements in manners comparable to those of conventional peptidomics. The aim of our study was to improve sample preparation so that MSI could provide comprehensive and reproducible neuropeptidomics information. Using the cockroach retrocerebral complex, the presented protocol produces enhanced coverage of neuropeptides at 15 µm spatial resolution, which was confirmed by parallel analysis of tissue extracts using electrospray-ionization MS. Altogether, more than 100 peptide signals from 15 neuropeptide-precursor genes could be traced with high spatial resolution. In addition, MSI spectra confirmed differential prohormone processing and distinct neuropeptide-based compartmentalization of the retrocerebral complex. We believe that our workflow facilitates incorporation of MSI in neuroscience-related topics, including the study of complex neuropeptide interactions within the CNS.

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.

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.

Development of a Class Prediction Model to Discriminate Pancreatic Ductal Adenocarcinoma from Pancreatic Neuroendocrine Tumor by MALDI Mass Spectrometry Imaging.

PURPOSE: To define proteomic differences between pancreatic ductal adenocarcinoma (pDAC) and pancreatic neuroendocrine tumor (pNET) by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI). EXPERIMENTAL DESIGN: Ninety-three pDAC and 126 pNET individual tissues are assembled in tissue microarrays and analyzed by MALDI MSI. The cohort is separated in a training (52 pDAC and 83 pNET) and validation set (41 pDAC and 43 pNET). Subsequently, a linear discriminant analysis (LDA) model based on 46 peptide ions is performed on the training set and evaluated on the validation cohort. Additionally, two liver metastases and a whole slide of pDAC are analyzed by the same LDA algorithm. RESULTS: Classification of pDAC and pNET by the LDA model is correct in 95% (39/41) and 100% (43/43) of patients in the validation cohort, respectively. The two liver metastases and the whole slide of pDAC are also correctly classified in agreement with the histopathological diagnosis. CONCLUSION AND CLINICAL RELEVANCE: In the present study, a large dataset of pDAC and pNET by MALDI MSI is investigated, a class prediction model that allowed separation of both entities with high accuracy is developed, and differential peptide peaks with potential diagnostic, prognostic, and predictive values are highlighted.

MALDI imaging mass spectrometry analysis-A new approach for protein mapping in multiple sclerosis brain lesions.

Multiple sclerosis is a disease of the central nervous system characterized by recurrent inflammatory demyelinating lesions in the early disease stage. Lesion formation and mechanisms leading to lesion remyelination are not fully understood. Matrix Assisted Laser Desorption Ionisation Mass Spectrometry imaging (MALDI-IMS) is a technology which analyses proteins and peptides in tissue, preserves their spatial localization, and generates molecular maps within the tissue section. In a pilot study we employed MALDI imaging mass spectrometry to profile and identify peptides and proteins expressed in normal-appearing white matter, grey matter and multiple sclerosis brain lesions with different extents of remyelination. The unsupervised clustering analysis of the mass spectra generated images which reflected the tissue section morphology in luxol fast blue stain and in myelin basic protein immunohistochemistry. Lesions with low remyelination extent were defined by compounds with molecular weight smaller than 5300Da, while more completely remyelinated lesions showed compounds with molecular weights greater than 15,200Da. An in-depth analysis of the mass spectra enabled the detection of cortical lesions which were not seen by routine luxol fast blue histology. An ion mass, mainly distributed at the rim of multiple sclerosis lesions, was identified by liquid chromatography and tandem mass spectrometry as thymosin beta-4, a protein known to be involved in cell migration and in restorative processes. The ion mass of thymosin beta-4 was profiled by MALDI imaging mass spectrometry in brain slides of 12 multiple sclerosis patients and validated by immunohistochemical analysis. In summary, our results demonstrate the ability of the MALDI-IMS technology to map proteins within the brain parenchyma and multiple sclerosis lesions and to identify potential markers involved in multiple sclerosis pathogenesis and/or remyelination.

Imaging mass spectrometry analysis of renal amyloidosis biopsies reveals protein co-localization with amyloid deposits.

Amyloidosis is a heterogeneous group of protein misfolding diseases characterized by deposition of amyloid proteins. The kidney is frequently affected, especially by immunoglobulin light chain (AL) and serum amyloid A (SAA) amyloidosis as the most common subgroups. Current diagnosis relies on histopathological examination, Congo red staining, or electron microscopy. Subtyping is done by immunohistochemistry; however, commercially available antibodies lack specificity. The purpose of this study was to identify and map amyloid proteins in formalin-fixed paraffin-embedded tissue sections using matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis in an integrated workflow. Renal amyloidosis and non-amyloidosis biopsies were processed for histological and MS analysis. Mass spectra corresponding to the congophilic areas were directly linked to the histological and MS images for correlation studies. Peptides for SAA and AL were detected by MALDI IMS associated to Congo red-positive areas. Sequence determination of amyloid peptides by LC-MS/MS analysis provided protein distribution and identification. Serum amyloid P component, apolipoprotein E, and vitronectin proteins were identified in both AA and AL amyloidosis, showing a strong correlation with Congo red-positive regions. Our findings highlight the utility of MALDI IMS as a new method to type amyloidosis in histopathological routine material and characterize amyloid-associated proteins that may provide insights into the pathogenetic process of amyloid formation.

Imaging mass spectrometry to discriminate breast from pancreatic cancer metastasis in formalin-fixed paraffin-embedded tissues.

Diagnosis of the origin of metastasis is mandatory for adequate therapy. In the past, classification of tumors was based on histology (morphological expression of a complex protein pattern), while supportive immunohistochemical investigation relied only on few "tumor specific" proteins. At present, histopathological diagnosis is based on clinical information, morphology, immunohistochemistry, and may include molecular methods. This process is complex, expensive, requires an experienced pathologist and may be time consuming. Currently, proteomic methods have been introduced in various clinical disciplines. MALDI imaging MS combines detection of numerous proteins with morphological features, and seems to be the ideal tool for objective and fast histopathological tumor classification. To study a special tumor type and to identify predictive patterns that could discriminate metastatic breast from pancreatic carcinoma MALDI imaging MS was applied to multitissue paraffin blocks. A statistical classification model was created using a training set of primary carcinoma biopsies. This model was validated on two testing sets of different breast and pancreatic carcinoma specimens. We could discern breast from pancreatic primary tumors with an overall accuracy of 83.38%, a sensitivity of 85.95% and a specificity of 76.96%. Furthermore, breast and pancreatic liver metastases were tested and classified correctly.

Imaging mass spectrometry statistical analysis.

Imaging mass spectrometry is increasingly used to identify new candidate biomarkers. This clinical application of imaging mass spectrometry is highly multidisciplinary: expertise in mass spectrometry is necessary to acquire high quality data, histology is required to accurately label the origin of each pixel's mass spectrum, disease biology is necessary to understand the potential meaning of the imaging mass spectrometry results, and statistics to assess the confidence of any findings. Imaging mass spectrometry data analysis is further complicated because of the unique nature of the data (within the mass spectrometry field); several of the assumptions implicit in the analysis of LC-MS/profiling datasets are not applicable to imaging. The very large size of imaging datasets and the reporting of many data analysis routines, combined with inadequate training and accessible reviews, have exacerbated this problem. In this paper we provide an accessible review of the nature of imaging data and the different strategies by which the data may be analyzed. Particular attention is paid to the assumptions of the data analysis routines to ensure that the reader is apprised of their correct usage in imaging mass spectrometry research.

Normalization in MALDI-TOF imaging datasets of proteins: practical considerations.

Normalization is critically important for the proper interpretation of matrix-assisted laser desorption/ionization (MALDI) imaging datasets. The effects of the commonly used normalization techniques based on total ion count (TIC) or vector norm normalization are significant, and they are frequently beneficial. In certain cases, however, these normalization algorithms may produce misleading results and possibly lead to wrong conclusions, e.g. regarding to potential biomarker distributions. This is typical for tissues in which signals of prominent abundance are present in confined areas, such as insulin in the pancreas or ß-amyloid peptides in the brain. In this work, we investigated whether normalization can be improved if dominant signals are excluded from the calculation. Because manual interaction with the data (e.g., defining the abundant signals) is not desired for routine analysis, we investigated two alternatives: normalization on the spectra noise level or on the median of signal intensities in the spectrum. Normalization on the median and the noise level was found to be significantly more robust against artifact generation compared to normalization on the TIC. Therefore, we propose to include these normalization methods in the standard "toolbox" of MALDI imaging for reliable results under conditions of automation.

Revisiting rat spermatogenesis with MALDI imaging at 20-microm resolution.

Matrix-assisted laser desorption/ionization (MALDI) molecular imaging technology attracts increasing attention in the field of biomarker discovery. The unambiguous correlation between histopathology and MALDI images is a key feature for success. MALDI imaging mass spectrometry (MS) at high definition thus calls for technological developments that were established by a number of small steps. These included tissue and matrix preparation steps, dedicated lasers for MALDI imaging, an increase of the robustness against cell debris and matrix sublimation, software for precision matching of molecular and microscopic images, and the analysis of MALDI imaging data using multivariate statistical methods. The goal of these developments is to approach single cell resolution with imaging MS. Currently, a performance level of 20-µm image resolution was achieved with an unmodified and commercially available instrument for proteins detected in the 2-16-kDa range. The rat testis was used as a relevant model for validating and optimizing our technological developments. Indeed, testicular anatomy is among the most complex found in mammalian bodies. In the present study, we were able to visualize, at 20-µm image resolution level, different stages of germ cell development in testicular seminiferous tubules; to provide a molecular correlate for its well established stage-specific classification; and to identify proteins of interest using a top-down approach and superimpose molecular and immunohistochemistry images.

Approaching MALDI molecular imaging for clinical proteomic research: current state and fields of application.

MALDI imaging mass spectrometry ('MALDI imaging') is an increasingly recognized technique for biomarker research. After years of method development in the scientific community, the technique is now increasingly applied in clinical research. In this article, we discuss the use of MALDI imaging in clinical proteomics and put it in context with classical proteomics techniques. We also highlight a number of upcoming challenges for personalized medicine, development of targeted therapies and diagnostic molecular pathology where MALDI imaging could help.

Tutorial: multivariate statistical treatment of imaging data for clinical biomarker discovery.

Cancer research is one of the most promising application areas for the new technology of MALDI tissue imaging. Cancerous tissue can easily be distinguished from healthy tissue by dramatically changed metabolism, growth, and apoptotic processes. Of even higher interest is the fact that MALDI imaging allows to unveil molecular differentiation undetectable by classical histological techniques. Thus, MALDI imaging has tremendous potential as a tool to characterize the therapeutic susceptibility of tumors in biopsies as well as to predict tumor progression in endpoint studies. However, some aspects are important to consider for a successful MALDI imaging-based cancer research. Cancer sections are usually very heterogeneous - different biochemical pathways can be active in individual tumor clones, at different development stages or in various tumor microenvironments. Understanding tissue at this level is only possible for experienced histopathologists working on high-resolution optical images. Therefore, the largest benefit from the use of MALDI imaging results in histopathology will arise if molecular images are related to classical high-resolution histological images in a simple way without the need to interpret mass spectra directly. Each MALDI imaging data set effectively provides information on hundreds of molecules and permits the generation of molecular images displaying the relative abundance of these molecules across the tissue. The interpretation of these in the histological context is a major challenge in terms of expert analysis time. This is true especially for clinical work with hundreds of tissue specimens to be analyzed by MALDI, interpreted, and compared. Therefore, a MALDI imaging workflow is described here that enables fast and unambiguous interpretation of the MALDI imaging data in the histological context. Preprocessing of the image data using statistical tools allows efficient and straightforward interpretation by the histopathologist. In this chapter, we explain the use of principal component analysis (PCA) and hierarchical clustering (HC) for the efficient interpretation of MALDI imaging data. We also outline how these methods can be used to compare specific disease states between patients in the search for biomarkers.

Classification of HER2 receptor status in breast cancer tissues by MALDI imaging mass spectrometry.

Clinical laboratory testing for HER2 status in breast cancer tissues is critically important for therapeutic decision making. Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is a powerful tool for investigating proteins through the direct and morphology-driven analysis of tissue sections. We hypothesized that MALDI-IMS may determine HER2 status directly from breast cancer tissues. Breast cancer tissues (n = 48) predefined for HER2 status were subjected to MALDI-IMS, and protein profiles were obtained through direct analysis of tissue sections. Protein identification was performed by tissue microextraction and fractionation followed by top-down tandem mass spectrometry. A discovery and an independent validation set were used to predict HER2 status by applying proteomic classification algorithms. We found that specific protein/peptide expression changes strongly correlated with the HER2 overexpression. Among these, we identified m/z 8404 as cysteine-rich intestinal protein 1. The proteomic signature was able to accurately define HER2-positive from HER2-negative tissues, achieving high values for sensitivity of 83%, for specificity of 92%, and an overall accuracy of 89%. Our results underscore the potential of MALDI-IMS proteomic algorithms for morphology-driven tissue diagnostics such as HER2 testing and show that MALDI-IMS can reveal biologically significant molecular details from tissues which are not limited to traditional high-abundance proteins.

MALDI imaging mass spectrometry for direct tissue analysis: a new frontier for molecular histology.

Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is a powerful tool for investigating the distribution of proteins and small molecules within biological systems through the in situ analysis of tissue sections. MALDI-IMS can determine the distribution of hundreds of unknown compounds in a single measurement and enables the acquisition of cellular expression profiles while maintaining the cellular and molecular integrity. In recent years, a great many advances in the practice of imaging mass spectrometry have taken place, making the technique more sensitive, robust, and ultimately useful. In this review, we focus on the current state of the art of MALDI-IMS, describe basic technological developments for MALDI-IMS of animal and human tissues, and discuss some recent applications in basic research and in clinical settings.

LC MALDI-TOF MS/MS and LC ESI FTMS analyses of HLA-B27 associated peptides isolated from peripheral blood cells.

Peptides eluted from peripheral blood cells of HLA-B*2705 healthy donor were analyzed by LC MALDI MS/MS and LC ESI FTMS techniques. The sequences of 92 peptide ligands identified from one healthy blood donor by LC MALDI-TOF MS/MS were compared with those previously published from in vitro long-term cell cultures available in SYFPEITHI database and splenocytes. It was found that 18 sequences confirmed within 1ppm mass error by LC ESI FTMS were already described and 3 of them matched with those previously reported from HLA-B*2705 splenocytes. Another 38 sequences validated within the same mass error were not found in SYFPEITHI database and are identified here for the first time. Finally, 36 sequences (5 sequences already published in SYFPEITHI database) were evaluated by LC MALDI-TOF MS/MS but no matches in the list of monoisotopic masses obtained from LC ESI FTMS were found.

MALDI imaging combined with hierarchical clustering as a new tool for the interpretation of complex human cancers.

Proteomics analyses have been exploited for the discovery of novel biomarkers for the early recognition and prognostic stratification of cancer patients. These analyses have now been extended to whole tissue sections by using a new tool, that is, MALDI imaging. This allows the spatial resolution of protein and peptides and their allocation to histoanatomical structures. Each MALDI imaging data set contains a large number of proteins and peptides, and their analysis can be quite tedious. We report here a new approach for the analysis of MALDI imaging results. Mass spectra are classified by hierarchical clustering by similarity and the resulting tissue classes are compared with the histology. The same approach is used to compare data sets of different patients. Tissue sections of gastric cancer and non-neoplastic mucosa obtained from 10 patients were forwarded to MALDI-Imaging. The in situ proteome expression was analyzed by hierarchical clustering and by principal component analysis (PCA). The reconstruction of images based on principal component scores allowed an unsupervised feature extraction of the data set. Generally, these images were in good agreement with the histology of the samples. The hierarchical clustering allowed a quick and intuitive access to the multidimensional information in the data set. It allowed a quick selection of spectra classes representative for different tissue features. The use of PCA for the comparison of MALDI spectra from different patients showed that the tumor and non-neoplastic mucosa are separated in the first three principal components. MALDI imaging in combination with hierarchical clustering allows the comprehensive analysis of the in situ cancer proteome in complex human cancers. On the basis of this cluster analysis, classification of complex human tissues is possible and opens the way for specific and cancer-related in situ biomarker analysis and identification.