De novo discovery of phenotypic intratumour heterogeneity using imaging mass spectrometry

Integrating Analysis of Cellular Heterogeneity in High-Content Dose-Response Studies

Heterogeneity is a complex property of cellular systems and therefore presents challenges to the reliable identification and characterization. Large-scale biology projects may span many months, requiring a systematic approach to quality control to track reproducibility and correct for instrumental variation and assay drift that could mask biological heterogeneity and preclude comparisons of heterogeneity between runs or even between plates. However, presently there is no standard approach to the tracking and analysis of heterogeneity. Previously, we demonstrated the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in a screen and described the use of three heterogeneity indices as a means to characterize, filter, and browse cellular heterogeneity in big data sets (Gough et al., Methods 96:12-26, 2016). In this chapter, we present a detailed method for integrating the analysis of cellular heterogeneity in assay development, validation, screening, and post screen. Importantly, we provide a detailed method for quality control, to normalize cellular data, track heterogeneity over time, and analyze heterogeneity in big data sets, along with software tools to assist in that process. The example screen for this method is from an HCS project, but the approach applies equally to other experimental methods that measure populations of cells.

Recent advances and opportunities in proteomic analyses of tumour heterogeneity

Solid tumour malignancies comprise a highly variable admixture of tumour and non-tumour cellular populations, forming a complex cellular ecosystem and tumour microenvironment. This tumour heterogeneity is not incidental, and is known to correlate with poor patient prognosis for many cancer types. Indeed, non-malignant cell populations, such as vascular endothelial and immune cells, are known to play key roles supporting and, in some cases, driving aggressive tumour biology, and represent targets of emerging therapeutics, such as antiangiogenesis and immune checkpoint inhibitors. The biochemical interplay between these cellular populations and how they contribute to molecular tumour heterogeneity remains enigmatic, particularly from the perspective of the tumour proteome. This review focuses on recent advances in proteomic methods, namely imaging mass spectrometry, single-cell proteomic techniques, and preanalytical sample processing, that are uniquely positioned to enable detailed analysis of discrete cellular populations within tumours to improve our understanding of tumour proteomic heterogeneity. This review further emphasizes the opportunity afforded by the application of these techniques to the analysis of tumour heterogeneity in formalin-fixed paraffin-embedded archival tumour tissues, as these represent an invaluable resource for retrospective analyses that is now routinely accessible, owing to recent technological and methodological advances in tumour tissue proteomics. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Gut roundtable meeting paper: selected recent advances in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) ranks number three among the most frequent causes of death from solid tumors worldwide. With obesity and fatty liver diseases as risk factors on the rise, HCC represents an ever increasing challenge. While there is still no curative treatment for most patients numerous novel drugs have been proposed, but most ultimately failed in phase III trials. This manuscript targets therapeutic advances and most burning issues. Expert key point summaries and urgent research agenda are provided regarding risk factors, including microbiota, need for prognostic and predictive biomarkers and the equivocal role of liver biopsy. Therapeutic topics highlighted are locoregional techniques, combination therapies and the potential of immunotherapy. Finally the manuscript provides a critical evaluation of novel targets and strategies for personalized treatment of HCC.

Advances in mass spectrometry imaging coupled to ion mobility spectrometry for enhanced imaging of biological tissues

Tissues present complex biochemical and morphological composition associated with their various cell types and physiological functions. Mass spectrometry (MS) imaging technologies are powerful tools to investigate the molecular information from biological tissue samples and visualize their complex spatial distributions. Coupling of gas-phase ion mobility spectrometry (IMS) technologies to MS imaging has been increasingly explored to improve performance for biological tissue imaging. This approach allows improved detection of low abundance ions and separation of isobaric molecular species, thus resulting in more accurate determination of the spatial distribution of molecular ions. In this review, we highlight recent advances in the field focusing on promising applications of these technologies for metabolite, lipid and protein tissue imaging.

Spatial Tissue Proteomics Quantifies Inter- and Intratumor Heterogeneity in Hepatocellular Carcinoma (HCC)

The interpatient variability of tumor proteomes has been investigated on a large scale but many tumors display also intratumoral heterogeneity regarding morphological and genetic features. It remains largely unknown to what extent the local proteome of tumors intrinsically differs. Here, we used hepatocellular carcinoma as a model system to quantify both inter- and intratumor heterogeneity across human patient specimens with spatial resolution. We defined proteomic features that distinguish neoplastic from the directly adjacent nonneoplastic tissue, such as decreased abundance of NADH dehydrogenase complex I. We then demonstrated the existence of intratumoral variations in protein abundance that re-occur across different patient samples, and affect clinically relevant proteins, even in the absence of obvious morphological differences or genetic alterations. Our work demonstrates the suitability and the benefits of using mass spectrometry-based proteomics to analyze diagnostic tumor specimens with spatial resolution. Data are available via ProteomeXchange with identifier PXD007052.

Molecular similarities and differences from human pulmonary fibrosis and corresponding mouse model: MALDI imaging mass spectrometry in comparative medicine

Animal models can reproduce some model-specific aspects of human diseases, but some animal models translate poorly or fail to translate to the corresponding human disease. Here, we develop a strategy to systematically compare human and mouse tissues, and conduct a proof-of-concept experiment to identify molecular similarities and differences using patients with idiopathic pulmonary fibrosis and a bleomycin-induced fibrosis mouse model. Our novel approach employs high-throughput tissue microarrays (TMAs) of humans and mice, high-resolution matrix-assisted laser desorption/ionization-Fourier transform-ion cyclotron resonance-mass spectrometry imaging (MALDI-FT-ICR-MSI) to spatially resolve mass spectra at the level of specific metabolites, and hierarchical clustering and pathway enrichment analysis to identify functionally similar/different molecular patterns and pathways in pathological lesions of humans and mice. We identified a large number of common molecules (n=1366) and fewer exclusive molecules in humans (n=83) and mice (n=54). Among the common molecules, the 'ascorbate and aldarate metabolism' pathway had the highest similarity in human and mouse lesions. This proof-of-concept study demonstrates that our novel strategy employing a reliable and easy-to-perform experimental design accurately identifies pathways and factors that can be directly compared between animal models and human diseases.

MALDI Imaging Combined with Laser Microdissection-Based Microproteomics for Protein Identification: Application to Intratumor Heterogeneity Studies

Matrix-assisted laser desorption ionization (MALDI) imaging is widely used for in situ proteomic mapping and finds multiple applications in pathology. However, low fragmentation yields in MALDI avoid an optimal identification of peptides from tissues. On the other hand, LMD-based microproteomic analyses allow for the identification of hundreds to thousands of proteins from small tissue regions. Herein, we present the combination of MALDI imaging and LMD-based microproteomic approaches for parallel identification. We illustrate the workflow with an application to intratumor heterogeneity studies.

Automated Morphological and Morphometric Analysis of Mass Spectrometry Imaging Data: Application to Biomarker Discovery

Mass spectrometry imaging datasets are mostly analyzed in terms of average intensity in regions of interest. However, biological tissues have different morphologies with several sizes, shapes, and structures. The important biological information, contained in this highly heterogeneous cellular organization, could be hidden by analyzing the average intensities. Finding an analytical process of morphology would help to find such information, describe tissue model, and support identification of biomarkers. This study describes an informatics approach for the extraction and identification of mass spectrometry image features and its application to sample analysis and modeling. For the proof of concept, two different tissue types (healthy kidney and CT-26 xenograft tumor tissues) were imaged and analyzed. A mouse kidney model and tumor model were generated using morphometric - number of objects and total surface - information. The morphometric information was used to identify m/z that have a heterogeneous distribution. It seems to be a worthwhile pursuit as clonal heterogeneity in a tumor is of clinical relevance. This study provides a new approach to find biomarker or support tissue classification with more information. Graphical Abstract ᅟ.

Matrix-Assisted Laser Desorption/Ionisation Mass Spectrometry Imaging in the Study of Gastric Cancer: A Mini Review

Gastric cancer (GC) is one of the leading causes of cancer-related deaths worldwide and the disease outcome commonly depends upon the tumour stage at the time of diagnosis. However, this cancer can often be asymptomatic during the early stages and remain undetected until the later stages of tumour development, having a significant impact on patient prognosis. However, our comprehension of the mechanisms underlying the development of gastric malignancies is still lacking. For these reasons, the search for new diagnostic and prognostic markers for gastric cancer is an ongoing pursuit. Modern mass spectrometry imaging (MSI) techniques, in particular matrix-assisted laser desorption/ionisation (MALDI), have emerged as a plausible tool in clinical pathology as a whole. More specifically, MALDI-MSI is being increasingly employed in the study of gastric cancer and has already elucidated some important disease checkpoints that may help us to better understand the molecular mechanisms underpinning this aggressive cancer. Here we report the state of the art of MALDI-MSI approaches, ranging from sample preparation to statistical analysis, and provide a complete review of the key findings that have been reported in the literature thus far.

A plasma metabolomic signature discloses human breast cancer

PURPOSE

Metabolomics is the comprehensive global study of metabolites in biological samples. In this retrospective pilot study we explored whether serum metabolomic profile can discriminate the presence of human breast cancer irrespective of the cancer subtype.

METHODS

Plasma samples were analyzed from healthy women (n = 20) and patients with breast cancer after diagnosis (n = 91) using a liquid chromatography-mass spectrometry platform. Multivariate statistics and a Random Forest (RF) classifier were used to create a metabolomics panel for the diagnosis of human breast cancer.

RESULTS

Metabolomics correctly distinguished between breast cancer patients and healthy control subjects. In the RF supervised class prediction analysis comparing breast cancer and healthy control groups, RF accurately classified 100% both samples of the breast cancer patients and healthy controls. So, the class error for both group in and the out-of-bag error were 0. We also found 1269 metabolites with different concentration in plasma from healthy controls and cancer patients; and basing on exact mass, retention time and isotopic distribution we identified 35 metabolites. These metabolites mostly support cell growth by providing energy and building stones for the synthesis of essential biomolecules, and function as signal transduction molecules. The collective results of RF, significance testing, and false discovery rate analysis identified several metabolites that were strongly associated with breast cancer.

CONCLUSIONS

In breast cancer a metabolomics signature of cancer exists and can be detected in patient plasma irrespectively of the breast cancer type.

Integration of Ion Mobility MSE after Fully Automated, Online, High-Resolution Liquid Extraction Surface Analysis Micro-Liquid Chromatography

Direct analysis by mass spectrometry (imaging) has become increasingly deployed in preclinical and clinical research due to its rapid and accurate readouts. However, when it comes to biomarker discovery or histopathological diagnostics, more sensitive and in-depth profiling from localized areas is required. We developed a comprehensive, fully automated online platform for high-resolution liquid extraction surface analysis (HR-LESA) followed by micro–liquid chromatography (LC) separation and a data-independent acquisition strategy for untargeted and low abundant analyte identification directly from tissue sections. Applied to tissue sections of rat pituitary, the platform demonstrated improved spatial resolution, allowing sample areas as small as 400 μm to be studied, a major advantage over conventional LESA. The platform integrates an online buffer exchange and washing step for removal of salts and other endogenous contamination that originates from local tissue extraction. Our carry over–free platform showed high reproducibility, with an interextraction variability below 30%. Another strength of the platform is the additional selectivity provided by a postsampling gas-phase ion mobility separation. This allowed distinguishing coeluted isobaric compounds without requiring additional separation time. Furthermore, we identified untargeted and low-abundance analytes, including neuropeptides deriving from the pro-opiomelanocortin precursor protein and localized a specific area of the pituitary gland (i.e., adenohypophysis) known to secrete neuropeptides and other small metabolites related to development, growth, and metabolism. This platform can thus be applied for the in-depth study of small samples of complex tissues with histologic features of ∼400 μm or more, including potential neuropeptide markers involved in many diseases such as neurodegenerative diseases, obesity, bulimia, and anorexia nervosa.

High-grade sarcoma diagnosis and prognosis: Biomarker discovery by mass spectrometry imaging

The combination of high heterogeneity, both intratumoral and intertumoral, with their rarity has made diagnosis, prognosis of high-grade sarcomas difficult. There is an urgent need for more objective molecular biomarkers, to differentiate between the many different subtypes, and to also provide new treatment targets. Mass spectrometry imaging (MSI) has amply demonstrated its ability to identify potential new markers for patient diagnosis, survival, metastasis and response to therapy in cancer research. In this study, we investigated the ability of MALDI-MSI of proteins to distinguish between high-grade osteosarcoma (OS), leiomyosarcoma (LMS), myxofibrosarcoma (MFS) and undifferentiated pleomorphic sarcoma (UPS) (Ntotal = 53). We also investigated if there are individual proteins or protein signatures that are statistically associated with patient survival. Twenty diagnostic protein signals were found characteristic for specific tumors (p ≤ 0.05), amongst them acyl-CoA-binding protein (m/z 11 162), macrophage migration inhibitory factor (m/z 12 350), thioredoxin (m/z 11 608) and galectin-1 (m/z 14 633) were assigned. Another nine protein signals were found to be associated with overall survival (p ≤ 0.05), including proteasome activator complex subunit 1 (m/z 9753), indicative for non-OS patients with poor survival; and two histone H4 variants (m/z 11 314 and 11 355), indicative of poor survival for LMS patients.

Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer

Visual inspection of tumour tissues does not reveal the complex metabolic changes that differentiate cancer and its sub-types from healthy tissues. Mass spectrometry imaging, which quantifies the underlying chemistry, represents a powerful tool for the molecular exploration of tumour tissues. A 3-dimensional topological description of the chemical properties of the tumour permits the formulation of hypotheses about the biological composition and interactions and the possible causes of its heterogeneous structure. The large amount of information contained in such datasets requires powerful tools for its analysis, visualisation and interpretation. Linear methods for unsupervised dimensionality reduction, such as PCA, are inadequate to capture the complex non-linear relationships present in these data. For this reason, a deep unsupervised neural network based technique, parametric t-SNE, is adopted to map a 3D-DESI-MS dataset from a human colorectal adenocarcinoma biopsy onto a 2-dimensional manifold. This technique allows the identification of clusters not visible with linear methods. The unsupervised clustering of the tumour tissue results in the identification of sub-regions characterised by the abundance of identified metabolites, making possible the formulation of hypotheses to account for their significance and the underlying biological heterogeneity in the tumour.

Clinical applications of MALDI imaging technologies in cancer and neurodegenerative diseases

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) imaging mass spectrometry (IMS) enables localization of analytes of interest along with histology. More specifically, MALDI-IMS identifies the distributions of proteins, peptides, small molecules, lipids, and drugs and their metabolites in tissues, with high spatial resolution. This unique capacity to directly analyze tissue samples without the need for lengthy sample preparation reduces technical variability and renders MALDI-IMS ideal for the identification of potential diagnostic and prognostic biomarkers and disease gradation. MALDI-IMS has evolved rapidly over the last decade and has been successfully used in both medical and basic research by scientists worldwide. In this review, we explore the clinical applications of MALDI-IMS, focusing on the major cancer types and neurodegenerative diseases. In particular, we re-emphasize the diagnostic potential of IMS and the challenges that must be confronted when conducting MALDI-IMS in clinical settings. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Applications of Mass Spectrometry Imaging to Cancer

Pathologists play an essential role in the diagnosis and prognosis of benign and cancerous tumors. Clinicians provide tissue samples, for example, from a biopsy, which are then processed and thin sections are placed onto glass slides, followed by staining of the tissue with visible dyes. Upon processing and microscopic examination, a pathology report is provided, which relies on the pathologist's interpretation of the phenotypical presentation of the tissue. Targeted analysis of single proteins provide further insight and together with clinical data these results influence clinical decision making. Recent developments in mass spectrometry facilitate the collection of molecular information about such tissue specimens. These relatively new techniques generate label-free mass spectra across tissue sections providing nonbiased, nontargeted molecular information. At each pixel with spatial coordinates (x/y) a mass spectrum is acquired. The acquired mass spectrums can be visualized as intensity maps displaying the distribution of single m/z values of interest. Based on the sample preparation, proteins, peptides, lipids, small molecules, or glycans can be analyzed. The generated intensity maps/images allow new insights into tumor tissues. The technique has the ability to detect and characterize tumor cells and their environment in a spatial context and combined with histological staining, can be used to aid pathologists and clinicians in the diagnosis and management of cancer. Moreover, such data may help classify patients to aid therapy decisions and predict outcomes. The novel complementary mass spectrometry-based methods described in this chapter will contribute to the transformation of pathology services around the world.

Biologically Relevant Heterogeneity: Metrics and Practical Insights

Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high-throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell-level data, as well as the need for standard metrics of the spatial, temporal, and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high-throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics, and the design of optimal therapeutic strategies for individual patients.

Mass Spectrometry Imaging for the Investigation of Intratumor Heterogeneity

One of the big clinical challenges in the treatment of cancer is the different behavior of cancer patients under guideline therapy. An important determinant for this phenomenon has been identified as inter- and intratumor heterogeneity. While intertumor heterogeneity refers to the differences in cancer characteristics between patients, intratumor heterogeneity refers to the clonal and nongenetic molecular diversity within a patient. The deciphering of intratumor heterogeneity is recognized as key to the development of novel therapeutics or treatment regimens. The investigation of intratumor heterogeneity is challenging since it requires an untargeted molecular analysis technique that accounts for the spatial and temporal dynamics of the tumor. So far, next-generation sequencing has contributed most to the understanding of clonal evolution within a cancer patient. However, it falls short in accounting for the spatial dimension. Mass spectrometry imaging (MSI) is a powerful tool for the untargeted but spatially resolved molecular analysis of biological tissues such as solid tumors. As it provides multidimensional datasets by the parallel acquisition of hundreds of mass channels, multivariate data analysis methods can be applied for the automated annotation of tissues. Moreover, it integrates the histology of the sample, which enables studying the molecular information in a histopathological context. This chapter will illustrate how MSI in combination with statistical methods and histology has been used for the description and discovery of intratumor heterogeneity in different cancers. This will give evidence that MSI constitutes a unique tool for the investigation of intratumor heterogeneity, and could hence become a key technology in cancer research.

The Importance of Histology and Pathology in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) has become a valuable tool in cancer research. Even more, due to its capability to directly link molecular changes with histology, it holds the prospect to revolutionize tissue-based diagnostics. In order to learn to walk before running, however, information obtained through classical histology should not be neglected but rather used to its full capacity and integrated with mass spectrometry data to lead to a superior molecular histology synthesis. In order to achieve this, pathomorphological analyses have to be integrated into MSI analyses right from the beginning to avoid errors and pitfalls of MSI application possibly leading to incorrect or imprecise study outcomes. Such errors can be caused by different sample or tissue inherent factors or through factors in sample preparation. Future studies should, therefore, aim for a comprehensive incorporation of histology and pathology characteristics to ensure the generation of high-quality data in MSI to exploit its full capacity in tissue-based basic and translational research.

Invasive mouse gastric adenocarcinomas arising from Lgr5+ stem cells are dependent on crosstalk between the Hedgehog/GLI2 and mTOR pathways

Gastric adenocarcinoma is the third most common cause of cancer-related death worldwide. Here we report a novel, highly-penetrant mouse model of invasive gastric cancer arising from deregulated Hedgehog/Gli2 signaling targeted to Lgr5-expressing stem cells in adult stomach. Tumor development progressed rapidly: three weeks after inducing the Hh pathway oncogene GLI2A, 65% of mice harbored in situ gastric cancer, and an additional 23% of mice had locally invasive tumors. Advanced mouse gastric tumors had multiple features in common with human gastric adenocarcinomas, including characteristic histological changes, expression of RNA and protein markers, and the presence of major inflammatory and stromal cell populations. A subset of tumor cells underwent epithelial-mesenchymal transition, likely mediated by focal activation of canonical Wnt signaling and Snail1 induction. Strikingly, mTOR pathway activation, based on pS6 expression, was robustly activated in mouse gastric adenocarcinomas from the earliest stages of tumor development, and treatment with rapamycin impaired tumor growth. GLI2A-expressing epithelial cells were detected transiently in intestine, which also contains Lgr5+ stem cells, but they did not give rise to epithelial tumors in this organ. These findings establish that deregulated activation of Hedgehog/Gli2 signaling in Lgr5-expressing stem cells is sufficient to drive gastric adenocarcinoma development in mice, identify a critical requirement for mTOR signaling in the pathogenesis of these tumors, and underscore the importance of tissue context in defining stem cell responsiveness to oncogenic stimuli.

In Situ Metabolomics in Cancer by Mass Spectrometry Imaging

Metabolomics is a rapidly evolving and a promising research field with the expectation to improve diagnosis, therapeutic treatment prediction, and prognosis of particular diseases. Among all techniques used to assess the metabolome in biological systems, mass spectrometry imaging is the method of choice to qualitatively and quantitatively analyze metabolite distribution in tissues with a high spatial resolution, thus providing molecular data in relation to cancer histopathology. The technique is ideally suited to study tissues molecular content and is able to provide molecular biomarkers or specific mass signatures which can be used in classification or the prognostic evaluation of tumors. Recently, it was shown that FFPE tissue samples are also suitable for metabolic analyses. This progress in methodology allows access to a highly valuable resource of tissues believed to widen and strengthen metabolic discovery-driven studies.

Pointwise mutual information quantifies intratumor heterogeneity in tissue sections labeled with multiple fluorescent biomarkers

BACKGROUND

Measures of spatial intratumor heterogeneity are potentially important diagnostic biomarkers for cancer progression, proliferation, and response to therapy. Spatial relationships among cells including cancer and stromal cells in the tumor microenvironment (TME) are key contributors to heterogeneity.

METHODS

We demonstrate how to quantify spatial heterogeneity from immunofluorescence pathology samples, using a set of 3 basic breast cancer biomarkers as a test case. We learn a set of dominant biomarker intensity patterns and map the spatial distribution of the biomarker patterns with a network. We then describe the pairwise association statistics for each pattern within the network using pointwise mutual information (PMI) and visually represent heterogeneity with a two-dimensional map.

RESULTS

We found a salient set of 8 biomarker patterns to describe cellular phenotypes from a tissue microarray cohort containing 4 different breast cancer subtypes. After computing PMI for each pair of biomarker patterns in each patient and tumor replicate, we visualize the interactions that contribute to the resulting association statistics. Then, we demonstrate the potential for using PMI as a diagnostic biomarker, by comparing PMI maps and heterogeneity scores from patients across the 4 different cancer subtypes. Estrogen receptor positive invasive lobular carcinoma patient, AL13-6, exhibited the highest heterogeneity score among those tested, while estrogen receptor negative invasive ductal carcinoma patient, AL13-14, exhibited the lowest heterogeneity score.

CONCLUSIONS

This paper presents an approach for describing intratumor heterogeneity, in a quantitative fashion (via PMI), which departs from the purely qualitative approaches currently used in the clinic. PMI is generalizable to highly multiplexed/hyperplexed immunofluorescence images, as well as spatial data from complementary in situ methods including FISSEQ and CyTOF, sampling many different components within the TME. We hypothesize that PMI will uncover key spatial interactions in the TME that contribute to disease proliferation and progression.

An Improved Molecular Histology Method for Ion Suppression Monitoring and Quantification of Phosphatidyl Cholines During MALDI MSI Lipidomics Analyses

Tissue lipidomics is one of the latest omics approaches for biomarker discovery in pharmacology, pathology, and the life sciences at large. In this context, matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is the most versatile tool to map compounds within tissue sections. However, ion suppression events occurring during MALDI MSI analyses make it impossible to use this method for quantitative investigations without additional validation steps. This is especially true for lipidomics, since different lipid classes are responsible for important ion suppression events. We propose here an improved lipidomics method to assess local ion suppression of phospatidylcholines in tissues. Serial tissue sections were spiked with different amounts of PC(16:0 d31/18:1) using a nebulization device. Settings for standard nebulization were strictly controlled for a detection similar to when using spiked tissue homogenates. The sections were simultaneously analyzed by MALDI MSI using a Fourier transform ion cyclotron resonance analyzer. Such a spray-based approach allows taking into account the biochemical heterogeneity of the tissue for the detection of PC(16:0 d31/18:1). Thus, here we present the perspective to use this method for quantification purposes. The linear regression lines are considered as calibration curves and we calculate PC(16:0/18:1) quantification values for different ROIs. Although those values need to be validated by a using a different independent approach, the workflow offers an insight into new quantitative mass spectrometry imaging (q-MSI) methods. This approach of ion suppression monitoring of phosphocholines in tissues may be highly interesting for a large range of applications in MALDI MSI, particularly for pathology using translational science workflows.

Molecular profiles of thyroid cancer subtypes: Classification based on features of tissue revealed by mass spectrometry imaging

Determination of the specific type of thyroid cancer is crucial for the prognosis and selection of treatment of this malignancy. However, in some cases appropriate classification is not possible based on histopathological features only, and it might be supported by molecular biomarkers. Here we aimed to characterize molecular profiles of different thyroid malignancies using mass spectrometry imaging (MSI) which enables the direct annotation of molecular features with morphological pictures of an analyzed tissue. Fifteen formalin-fixed paraffin-embedded tissue specimens corresponding to five major types of thyroid cancer were analyzed by MALDI-MSI after in-situ trypsin digestion, and the possibility of classification based on the results of unsupervised segmentation of MALDI images was tested. Novel method of semi-supervised detection of the cancer region of interest (ROI) was implemented. We found strong separation of medullary cancer from malignancies derived from thyroid epithelium, and separation of anaplastic cancer from differentiated cancers. Reliable classification of medullary and anaplastic cancers using an approach based on automated detection of cancer ROI was validated with independent samples. Moreover, extraction of spectra from tumor areas allowed the detection of molecular components that differentiated follicular cancer and two variants of papillary cancer (classical and follicular). We concluded that MALDI-MSI approach is a promising strategy in the search for biomarkers supporting classification of thyroid malignant tumors. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Data-driven identification of prognostic tumor subpopulations using spatially mapped t-SNE of mass spectrometry imaging data

The identification of tumor subpopulations that adversely affect patient outcomes is essential for a more targeted investigation into how tumors develop detrimental phenotypes, as well as for personalized therapy. Mass spectrometry imaging has demonstrated the ability to uncover molecular intratumor heterogeneity. The challenge has been to conduct an objective analysis of the resulting data to identify those tumor subpopulations that affect patient outcome. Here we introduce spatially mapped t-distributed stochastic neighbor embedding (t-SNE), a nonlinear visualization of the data that is able to better resolve the biomolecular intratumor heterogeneity. In an unbiased manner, t-SNE can uncover tumor subpopulations that are statistically linked to patient survival in gastric cancer and metastasis status in primary tumors of breast cancer.

An experimental guideline for the analysis of histologically heterogeneous tumors by MALDI-TOF mass spectrometry imaging

Mass spectrometry imaging (MSI) has been widely used for the direct molecular assessment of tissue samples and has demonstrated great potential to complement current histopathological methods in cancer research. It is now well established that tissue preparation is key to a successful MSI experiment; for histologically heterogeneous tumor tissues, other parts of the workflow are equally important to the experiment's success. To demonstrate these facets here we describe a matrix-assisted laser desorption/ionization MSI biomarker discovery investigation of high-grade, complex karyotype sarcomas, which often have histological overlap and moderate response to chemo-/radio-therapy. Multiple aspects of the workflow had to be optimized, ranging from the tissue preparation and data acquisition protocols, to the post-MSI histological staining method, data quality control, histology-defined data selection, data processing and statistical analysis. Only as a result of developing every step of the biomarker discovery workflow was it possible to identify a panel of protein signatures that could distinguish between different subtypes of sarcomas or could predict patient survival outcome. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Determining protein biomarkers for DLBCL using FFPE tissues from HIV negative and HIV positive patients

DLBCL is the most common lymphoma subtype occurring in older populations as well as in younger HIV infected patients. The current treatment options for DLBCL are effective for most patients yet the relapse rate is high. While many biomarkers for DLBCL exist, they are not in clinical use due to low sensitivity and specificity. In addition, these biomarkers have not been studied in the HIV context. Therefore, the identification of new biomarkers for HIV negative and HIV positive DLBCL, may lead to a better understanding of the disease pathology and better therapeutic design. Protein biomarkers for DLBCL were determined using MALDI imaging mass spectrometry (IMS) and characterised using LC-MS. The expression of one of the biomarkers, heat shock protein (Hsp) 70, was confirmed on a separate cohort of samples using immunohistochemistry. The biomarkers identified in the study consisted of four protein clusters including glycolytic enzymes, ribosomal proteins, histones and collagen. These proteins could differentiate between control and tumour tissue, and the DLBCL immunohistochemical subtypes in both cohorts. The majority (41/52) of samples in the confirmation cohort were negative for Hsp70 expression. The HIV positive DLBCL cases had a higher percentage of cases expressing Hsp70 than their HIV negative counterparts. The non-GC subtype also frequently overexpressed Hsp70, confirming MALDI IMS data. The expression of Hsp70 did not correlate with survival in both the HIV negative and HIV positive cohort. This study identified potential biomarkers for HIV negative and HIV positive DLBCL from FFPE tissue sections. These may be used as diagnostic and prognostic markers complementary to current clinical management programmes for DLBCL.

Scores for standardization of on-tissue digestion of formalin-fixed paraffin-embedded tissue in MALDI-MS imaging

On-slide digestion of formalin-fixed and paraffin-embedded human biopsy tissue followed by mass spectrometry imaging of resulting peptides may have the potential to become an additional analytical modality in future ePathology. Multiple workflows have been described for dewaxing, antigen retrieval, digestion and imaging in the past decade. However, little is known about suitable statistical scores for method comparison and systematic workflow standardization required for development of processes that would be robust enough to be compatible with clinical routine. To define scores for homogeneity of tissue processing and imaging as well as inter-day repeatability for five different processing methods, we used human liver and gastrointestinal stromal tumor tissue, both judged by an expert pathologist to be >98% histologically homogeneous. For mean spectra-based as well as pixel-wise data analysis, we propose the coefficient of determination R², the natural fold-change (natFC) value and the digest efficiency DE% as readily accessible scores. Moreover, we introduce two scores derived from principal component analysis, the variance of the mean absolute deviation, MAD, and the interclass overlap, J_overlap, as computational scores that may help to avoid user bias during future workflow development. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Multimodal Mass Spectrometry Imaging of N-Glycans and Proteins from the Same Tissue Section

On-tissue digestion matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can be used to record spatially correlated molecular information from formalin-fixed, paraffin-embedded (FFPE) tissue sections. In this work, we present the in situ multimodal analysis of N-linked glycans and proteins from the same FFPE tissue section. The robustness and applicability of the method are demonstrated for several tumors, including epithelial and mesenchymal tumor types. Major analytical aspects, such as lateral diffusion of the analyte molecules and differences in measurement sensitivity due to the additional sample preparation methods, have been investigated for both N-glycans and proteolytic peptides. By combining the MSI approach with extract analysis, we were also able to assess which mass spectral peaks generated by MALDI-MSI could be assigned to unique N-glycan and peptide identities.

Machine learning approaches in MALDI-MSI: clinical applications

INTRODUCTION

Despite the unquestionable advantages of Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging in visualizing the spatial distribution and the relative abundance of biomolecules directly on-tissue, the yielded data is complex and high dimensional. Therefore, analysis and interpretation of this huge amount of information is mathematically, statistically and computationally challenging.

AREAS COVERED

This article reviews some of the challenges in data elaboration with particular emphasis on machine learning techniques employed in clinical applications, and can be useful in general as an entry point for those who want to study the computational aspects. Several characteristics of data processing are described, enlightening advantages and disadvantages. Different approaches for data elaboration focused on clinical applications are also provided. Practical tutorial based upon Orange Canvas and Weka software is included, helping familiarization with the data processing. Expert commentary: Recently, MALDI-MSI has gained considerable attention and has been employed for research and diagnostic purposes, with successful results. Data dimensionality constitutes an important issue and statistical methods for information-preserving data reduction represent one of the most challenging aspects. The most common data reduction methods are characterized by collecting independent observations into a single table. However, the incorporation of relational information can improve the discriminatory capability of the data.

MALDI mass spectrometry imaging: A cutting-edge tool for fundamental and clinical histopathology

Histopathological diagnoses have been done in the last century based on hematoxylin and eosin staining. These methods were complemented by histochemistry, electron microscopy, immunohistochemistry (IHC), and molecular techniques. Mass spectrometry (MS) methods allow the thorough examination of various biocompounds in extracts and tissue sections. Today, mass spectrometry imaging (MSI), and especially matrix-assisted laser desorption ionization (MALDI) imaging links classical histology and molecular analyses. Direct mapping is a major advantage of the combination of molecular profiling and imaging. MSI can be considered as a cutting edge approach for molecular detection of proteins, peptides, carbohydrates, lipids, and small molecules in tissues. This review covers the detection of various biomolecules in histopathological sections by MSI. Proteomic methods will be introduced into clinical histopathology within the next few years.

Proteomics in thyroid cytopathology: Relevance of MALDI-imaging in distinguishing malignant from benign lesions

Several proteomic strategies are used extensively for the purpose of biomarker discovery and in order to obtain insights into the molecular aspects of cancers, using either body fluids or tissue as samples. Among them, MALDI-imaging can be applied to cytological thyroid specimens to investigate the molecular signatures of different pathological conditions and highlight differences in the proteome that are of relevance for diagnostic and pathogenetic research. In this study, 26 ex-vivo fine needle aspirations from benign thyroid nodules (n = 13) and papillary thyroid carcinomas (n = 13) were analyzed by MALDI-imaging. Based on the specific protein signatures capable of distinguishing the aforementioned patients, MALDI-imaging was able to correctly assign, in blind, the specimens from ten additional FNABs to a malignant or benign class, as later confirmed by the morphological classification. Moreover, some proteins presented a progressive overexpression in malignant phenotypes when compared with Hashimoto's thyroiditis and hyperplastic/follicular adenoma. This data not only suggests that a MALDI-imaging based approach can be a valuable tool in the diagnosis of thyroid lesions but also in the detection of proteins that have a possible role in the promotion of tumorigenic activity.

Detection of molecular signatures of oral squamous cell carcinoma and normal epithelium - application of a novel methodology for unsupervised segmentation of imaging mass spectrometry data

Intra-tumor heterogeneity is a vivid problem of molecular oncology that could be addressed by imaging mass spectrometry. Here we aimed to assess molecular heterogeneity of oral squamous cell carcinoma and to detect signatures discriminating normal and cancerous epithelium. Tryptic peptides were analyzed by MALDI-IMS in tissue specimens from five patients with oral cancer. Novel algorithm of IMS data analysis was developed and implemented, which included Gaussian mixture modeling for detection of spectral components and iterative k-means algorithm for unsupervised spectra clustering performed in domain reduced to a subset of the most dispersed components. About 4% of the detected peptides showed significantly different abundances between normal epithelium and tumor, and could be considered as a molecular signature of oral cancer. Moreover, unsupervised clustering revealed two major sub-regions within expert-defined tumor areas. One of them showed molecular similarity with histologically normal epithelium. The other one showed similarity with connective tissue, yet was markedly different from normal epithelium. Pathologist's re-inspection of tissue specimens confirmed distinct features in both tumor sub-regions: foci of actual cancer cells or cancer microenvironment-related cells prevailed in corresponding areas. Hence, molecular differences detected during automated segmentation of IMS data had an apparent reflection in real structures present in tumor.

Application of imaging mass spectrometry for the molecular diagnosis of human breast tumors

Distinguishing breast invasive ductal carcinoma (IDC) and breast ductal carcinoma in situ (DCIS) is a key step in breast surgery, especially to determine whether DCIS is associated with tumor cell micro-invasion. However, there is currently no reliable method to obtain molecular information for breast tumor analysis during surgery. Here, we present a novel air flow-assisted ionization (AFAI) mass spectrometry imaging method that can be used in ambient environments to differentiate breast cancer by analyzing lipids. In this study, we demonstrate that various subtypes and histological grades of IDC and DCIS can be discriminated using AFAI-MSI: phospholipids were more abundant in IDC than in DCIS, whereas fatty acids were more abundant in DCIS than in IDC. The classification of specimens in the subtype and grade validation sets showed 100% and 78.6% agreement with the histopathological diagnosis, respectively. Our work shows the rapid classification of breast cancer utilizing AFAI-MSI. This work suggests that this method could be developed to provide surgeons with nearly real-time information to guide surgical resections.

Effective Use of Mass Spectrometry in the Clinical Laboratory

BACKGROUND

Historically the success of mass spectrometry in the clinical laboratory has focused on drugs of abuse confirmations, newborn screening, and steroid analysis. Clinical applications of mass spectrometry continue to expand, and mass spectrometry is now being used in almost all areas of laboratory medicine.

CONTENT

A brief background of the evolution of mass spectrometry in the clinical laboratory is provided with a discussion of future applications. Prominent examples of mass spectrometry are covered to illustrate how it has improved the practice of medicine and enabled physicians to provide better patient care. With increasing economic pressures and decreasing laboratory test reimbursement, mass spectrometry testing has been shown to provide cost-effective solutions. In addition to pointing out the numerous benefits, the challenges of implementing mass spectrometry in the clinical laboratory are also covered.

SUMMARY

Mass spectrometry continues to play a prominent role in the field of laboratory medicine. The advancement of this technology along with the development of new applications will only accelerate the incorporation of mass spectrometry into more areas of medicine.

A laser microdissection-based workflow for FFPE tissue microproteomics: Important considerations for small sample processing

Proteomic methods are today widely applied to formalin-fixed paraffin-embedded (FFPE) tissue samples for several applications in research, especially in molecular pathology. To date, there is an unmet need for the analysis of small tissue samples, such as for early cancerous lesions. Indeed, no method has yet been proposed for the reproducible processing of small FFPE tissue samples to allow biomarker discovery. In this work, we tested several procedures to process laser microdissected tissue pieces bearing less than 3000 cells. Combined with appropriate settings for liquid chromatography mass spectrometry-mass spectrometry (LC-MS/MS) analysis, a citric acid antigen retrieval (CAAR)-based procedure was established, allowing to identify more than 1400 proteins from a single microdissected breast cancer tissue biopsy. This work demonstrates important considerations concerning the handling and processing of laser microdissected tissue samples of extremely limited size, in the process opening new perspectives in molecular pathology. A proof of the proposed method for biomarker discovery, with respect to these specific handling considerations, is illustrated using the differential proteomic analysis of invasive breast carcinoma of no special type and invasive lobular triple-negative breast cancer tissues. This work will be of utmost importance for early biomarker discovery or in support of matrix-assisted laser desorption/ionization (MALDI) imaging for microproteomics from small regions of interest.

Using collective expert judgements to evaluate quality measures of mass spectrometry images

Motivation: Imaging mass spectrometry (IMS) is a maturating technique of molecular imaging. Confidence in the reproducible quality of IMS data is essential for its integration into routine use. However, the predominant method for assessing quality is visual examination, a time consuming, unstandardized and non-scalable approach. So far, the problem of assessing the quality has only been marginally addressed and existing measures do not account for the spatial information of IMS data. Importantly, no approach exists for unbiased evaluation of potential quality measures.

Results: We propose a novel approach for evaluating potential measures by creating a gold-standard set using collective expert judgements upon which we evaluated image-based measures. To produce a gold standard, we engaged 80 IMS experts, each to rate the relative quality between 52 pairs of ion images from MALDI-TOF IMS datasets of rat brain coronal sections. Experts’ optional feedback on their expertise, the task and the survey showed that (i) they had diverse backgrounds and sufficient expertise, (ii) the task was properly understood, and (iii) the survey was comprehensible. A moderate inter-rater agreement was achieved with Krippendorff’s alpha of 0.5. A gold-standard set of 634 pairs of images with accompanying ratings was constructed and showed a high agreement of 0.85. Eight families of potential measures with a range of parameters and statistical descriptors, giving 143 in total, were evaluated. Both signal-to-noise and spatial chaos-based measures performed highly with a correlation of 0.7 to 0.9 with the gold standard ratings. Moreover, we showed that a composite measure with the linear coefficients (trained on the gold standard with regularized least squares optimization and lasso) showed a strong linear correlation of 0.94 and an accuracy of 0.98 in predicting which image in a pair was of higher quality.

Availability and implementation: The anonymized data collected from the survey and the Matlab source code for data processing can be found at: https://github.com/alexandrovteam/IMS_quality.

Contact:theodore.alexandrov@embl.de

A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens

Heterogeneity is well recognized as a common property of cellular systems that impacts biomedical research and the development of therapeutics and diagnostics. Several studies have shown that analysis of heterogeneity: gives insight into mechanisms of action of perturbagens; can be used to predict optimal combination therapies; and can be applied to tumors where heterogeneity is believed to be associated with adaptation and resistance. Cytometry methods including high content screening (HCS), high throughput microscopy, flow cytometry, mass spec imaging and digital pathology capture cell level data for populations of cells. However it is often assumed that the population response is normally distributed and therefore that the average adequately describes the results. A deeper understanding of the results of the measurements and more effective comparison of perturbagen effects requires analysis that takes into account the distribution of the measurements, i.e. the heterogeneity. However, the reproducibility of heterogeneous data collected on different days, and in different plates/slides has not previously been evaluated. Here we show that conventional assay quality metrics alone are not adequate for quality control of the heterogeneity in the data. To address this need, we demonstrate the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in an SAR screen, describe a workflow for quality control in heterogeneity analysis. One major challenge in high throughput biology is the evaluation and interpretation of heterogeneity in thousands of samples, such as compounds in a cell-based screen. In this study we also demonstrate that three heterogeneity indices previously reported, capture the shapes of the distributions and provide a means to filter and browse big data sets of cellular distributions in order to compare and identify distributions of interest. These metrics and methods are presented as a workflow for analysis of heterogeneity in large scale biology projects.

The promise of metabolic phenotyping in gastroenterology and hepatology

Disease risk and treatment response are determined, at the individual level, by a complex history of genetic and environmental interactions, including those with our endogenous microbiomes. Personalized health care requires a deep understanding of patient biology that can now be measured using a range of '-omics' technologies. Patient stratification involves the identification of genetic and/or phenotypic disease subclasses that require different therapeutic strategies. Stratified medicine approaches to disease diagnosis, prognosis and therapeutic response monitoring herald a new dimension in patient care. Here, we explore the potential value of metabolic profiling as applied to unmet clinical needs in gastroenterology and hepatology. We describe potential applications in a number of diseases, with emphasis on large-scale population studies as well as metabolic profiling on the individual level, using spectrometric and imaging technologies that will leverage the discovery of mechanistic information and deliver novel health care solutions to improve clinical pathway management.