MALDI-imaging segmentation is a powerful tool for spatial functional proteomic analysis of human larynx carcinoma

The Application of a Random Forest Classifier to ToF-SIMS Imaging Data

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is a potent analytical tool that provides spatially resolved chemical information on surfaces at the microscale. However, the hyperspectral nature of ToF-SIMS datasets can be challenging to analyze and interpret. Both supervised and unsupervised machine learning (ML) approaches are increasingly useful to help analyze ToF-SIMS data. Random Forest (RF) has emerged as a robust and powerful algorithm for processing mass spectrometry data. This machine learning approach offers several advantages, including accommodating nonlinear relationships, robustness to outliers in the data, managing the high-dimensional feature space, and mitigating the risk of overfitting. The application of RF to ToF-SIMS imaging facilitates the classification of complex chemical compositions and the identification of features contributing to these classifications. This tutorial aims to assist nonexperts in either machine learning or ToF-SIMS to apply Random Forest to complex ToF-SIMS datasets.

Retinoid X Receptor Signaling Mediates Cancer Cell Lipid Metabolism in the Leptomeninges

Cancer cells metastatic to the leptomeninges encounter a metabolically-challenging extreme microenvironment. To understand adaptations to this space, we subjected leptomeningeal-metastatic (LeptoM) mouse breast and lung cancers isolated from either the leptomeninges or orthotopic primary sites to ATAC-and RNA-sequencing. When inhabiting the leptomeninges, the LeptoM cells demonstrated transcription downstream of retinoid-X-receptors (RXRs). We found evidence of local retinoic acid (RA) generation in both human leptomeningeal metastasis and mouse models in the form of elevated spinal fluid retinol and expression of RA-generating dehydrogenases within the leptomeningeal microenvironment. Stimulating LeptoM cells with RA induced expression of transcripts encoding de novo fatty acid synthesis pathway enzymes in vitro . In vivo , while deletion of Stra6 did not alter cancer cell leptomeningeal growth, knockout of Rxra/b/g interrupted cancer cell lipid biosynthesis and arrested cancer growth. These observations illustrate a mechanism whereby metastatic cancer cells awake locally-generated developmental cues for metabolically reprograming, suggesting novel therapeutic approaches.

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

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

The enhancer landscape predetermines the skeletal regeneration capacity of stromal cells

Multipotent stromal cells are considered attractive sources for cell therapy and tissue engineering. Despite numerous experimental and clinical studies, broad application of stromal cell therapeutics is not yet emerging. A major challenge is the functional diversity of available cell sources. Here, we investigated the regenerative potential of clinically relevant human stromal cells from bone marrow (BMSCs), white adipose tissue, and umbilical cord compared with mature chondrocytes and skin fibroblasts in vitro and in vivo. Although all stromal cell types could express transcription factors related to endochondral ossification, only BMSCs formed cartilage discs in vitro that fully regenerated critical-size femoral defects after transplantation into mice. We identified cell type-specific epigenetic landscapes as the underlying molecular mechanism controlling transcriptional stromal differentiation networks. Binding sites of commonly expressed transcription factors in the enhancer and promoter regions of ossification-related genes, including Runt and bZIP families, were accessible only in BMSCs but not in extraskeletal stromal cells. This suggests an epigenetically predetermined differentiation potential depending on cell origin that allows common transcription factors to trigger distinct organ-specific transcriptional programs, facilitating forward selection of regeneration-competent cell sources. Last, we demonstrate that viable human BMSCs initiated defect healing through the secretion of osteopontin and contributed to transient mineralized bone hard callus formation after transplantation into immunodeficient mice, which was eventually replaced by murine recipient bone during final tissue remodeling.

Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks

Despite numerous diagnostic and therapeutic advances, pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate, and is the fourth leading cause of cancer death in developing countries. Besides its increasing prevalence, pancreatic malignancies are characterized by poor prognosis. Omics technologies have potential relevance for PDAC assessment but are time-intensive and relatively cost-intensive and limited by tissue heterogeneity. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can obtain spatially distinct peptide-signatures and enables tumor classification within a feasible time with relatively low cost. While MALDI-MSI data sets are inherently large, machine learning methods have the potential to greatly decrease processing time. We present a pilot study investigating the potential of MALDI-MSI in combination with neural networks, for classification of pancreatic ductal adenocarcinoma. Neural-network models were trained to distinguish between pancreatic ductal adenocarcinoma and other pancreatic cancer types. The proposed methods are able to correctly classify the PDAC types with an accuracy of up to 86% and a sensitivity of 82%. This study demonstrates that machine learning tools are able to identify different pancreatic carcinoma from complex MALDI data, enabling fast prediction of large data sets. Our results encourage a more frequent use of MALDI-MSI and machine learning in histopathological studies in the future.

Evaluation and comparison of unsupervised methods for the extraction of spatial patterns from mass spectrometry imaging data (MSI)

For the extraction of spatially important regions from mass spectrometry imaging (MSI) data, different clustering methods have been proposed. These clustering methods are based on certain assumptions and use different criteria to assign pixels into different classes. For high-dimensional MSI data, the curse of dimensionality also limits the performance of clustering methods which are usually overcome by pre-processing the data using dimension reduction techniques. In summary, the extraction of spatial patterns from MSI data can be done using different unsupervised methods, but the robust evaluation of clustering results is what is still missing. In this study, we have performed multiple simulations on synthetic and real MSI data to validate the performance of unsupervised methods. The synthetic data were simulated mimicking important spatial and statistical properties of real MSI data. Our simulation results confirmed that K-means clustering with correlation distance and Gaussian Mixture Modeling clustering methods give optimal performance in most of the scenarios. The clustering methods give efficient results together with dimension reduction techniques. From all the dimension techniques considered here, the best results were obtained with the minimum noise fraction (MNF) transform. The results were confirmed on both synthetic and real MSI data. However, for successful implementation of MNF transform the MSI data requires to be of limited dimensions.

Prognostic Value of Molecular Intratumor Heterogeneity in Primary Oral Cancer and Its Lymph Node Metastases Assessed by Mass Spectrometry Imaging

Different aspects of intra-tumor heterogeneity (ITH), which are associated with the development of cancer and its response to treatment, have postulated prognostic value. Here we searched for potential association between phenotypic ITH analyzed by mass spectrometry imaging (MSI) and prognosis of head and neck cancer. The study involved tissue specimens resected from 77 patients with locally advanced oral squamous cell carcinoma, including 37 patients where matched samples of primary tumor and synchronous lymph node metastases were analyzed. A 3-year follow-up was available for all patients which enabled their separation into two groups: with no evidence of disease (NED, n = 41) and with progressive disease (PD, n = 36). After on-tissue trypsin digestion, peptide maps of all cancer regions were segmented using an unsupervised approach to reveal their intrinsic heterogeneity. We found that intra-tumor similarity of spectra was higher in the PD group and diversity of clusters identified during image segmentation was higher in the NED group, which indicated a higher level of ITH in patients with more favorable outcomes. Signature of molecular components that correlated with long-term outcomes could be associated with proteins involved in the immune functions. Furthermore, a positive correlation between ITH and histopathological lymphocytic host response was observed. Hence, we proposed that a higher level of ITH revealed by MSI in cancers with a better prognosis could reflect the presence of heterotypic components of tumor microenvironment such as infiltrating immune cells enhancing the response to the treatment.

Furosemide-induced systemic dehydration alters the proteome of rabbit vocal folds

Whole-body dehydration (i.e., systemic dehydration) leads to vocal fold tissue dehydration. Furosemide, a common diuretic prescribed to treat hypertension and edema-associated conditions, induces systemic dehydration. Furosemide also causes voice changes in human speakers, making this method of systemic dehydration particularly interesting for vocal fold dehydration studies. Our objective was to obtain a comprehensive proteome of vocal folds following furosemide-induced systemic dehydration. New Zealand White rabbits were used as the animal model and randomly assigned to euhydrated (control) or furosemide-dehydrated groups. Systemic dehydration, induced by injectable furosemide, was verified by an average body weight loss of -5.5% and significant percentage changes in blood analytes in the dehydrated rabbits compared to controls. Vocal fold specimens, including mucosa and muscle, were processed for proteomic analysis using label-free quantitation LC-MS/MS. Over 1600 proteins were successfully identified across all vocal fold samples; and associated with a variety of cellular components and ubiquitous cell functions. Protein levels were compared between groups showing 32 proteins differentially regulated (p ≤ 0.05) in the dehydrated vocal folds. These are mainly involved with mitochondrial translation and metabolism. The downregulation of proteins involved in mitochondrial metabolism in the vocal folds suggests a mechanism to prevent oxidative stress associated with systemic dehydration. SIGNIFICANCE: Voice disorders affect different population demographics worldwide with one in 13 adults in the United States reporting voice problems annually. Vocal fold systemic hydration is clinically recognized for preventing and treating voice problems and depends on optimal body hydration primarily achieved by water intake. Herein, we use the rabbit as a translatable animal model, and furosemide as a translatable method of systemic dehydration, to reveal a comprehensive proteomic profile of vocal fold mucosa and muscle in response to systemic dehydration. The significant subset of proteins differentially regulated due to furosemide-induced dehydration offer novel insights into the molecular mechanisms of systemic dehydration in the vocal folds. These findings also deepen our understanding of changes to tissue biology after diuretic administration.

Peptide Signatures for Prognostic Markers of Pancreatic Cancer by MALDI Mass Spectrometry Imaging

Despite the overall poor prognosis of pancreatic cancer there is heterogeneity in clinical courses of tumors not assessed by conventional risk stratification. This yields the need of additional markers for proper assessment of prognosis and multimodal clinical management. We provide a proof of concept study evaluating the feasibility of Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to identify specific peptide signatures linked to prognostic parameters of pancreatic cancer. On 18 patients with exocrine pancreatic cancer after tumor resection, MALDI imaging analysis was performed additional to histopathological assessment. Principal component analysis (PCA) was used to explore discrimination of peptide signatures of prognostic histopathological features and receiver operator characteristic (ROC) to identify which specific m/z values are the most discriminative between the prognostic subgroups of patients. Out of 557 aligned m/z values discriminate peptide signatures for the prognostic histopathological features lymphatic vessel invasion (pL, 16 m/z values, eight proteins), nodal metastasis (pN, two m/z values, one protein) and angioinvasion (pV, 4 m/z values, two proteins) were identified. These results yield proof of concept that MALDI-MSI of pancreatic cancer tissue is feasible to identify peptide signatures of prognostic relevance and can augment risk assessment.

Intra-Tumor Heterogeneity Revealed by Mass Spectrometry Imaging Is Associated with the Prognosis of Breast Cancer

Intra-tumor heterogeneity (ITH) results from the coexistence of genetically distinct cancer cell (sub)populations, their phenotypic plasticity, and the presence of heterotypic components of the tumor microenvironment (TME). Here we addressed the potential association between phenotypic ITH revealed by mass spectrometry imaging (MSI) and the prognosis of breast cancer. Tissue specimens resected from 59 patients treated radically due to the locally advanced HER2-positive invasive ductal carcinoma were included in the study. After the on-tissue trypsin digestion of cellular proteins, peptide maps of all cancer regions (about 380,000 spectra in total) were segmented by an unsupervised approach to reveal their intrinsic heterogeneity. A high degree of similarity between spectra was observed, which indicated the relative homogeneity of cancer regions. However, when the number and diversity of the detected clusters of spectra were analyzed, differences between patient groups were observed. It is noteworthy that a higher degree of heterogeneity was found in tumors from patients who remained disease-free during a 5-year follow-up (n = 38) compared to tumors from patients with progressive disease (distant metastases detected during the follow-up, n = 21). Interestingly, such differences were not observed between patients with a different status of regional lymph nodes, cancer grade, or expression of estrogen receptor at the time of the primary treatment. Subsequently, spectral components with different abundance in cancer regions were detected in patients with different outcomes, and their hypothetical identity was established by assignment to measured masses of tryptic peptides identified in corresponding tissue lysates. Such differentiating components were associated with proteins involved in immune regulation and hemostasis. Further, a positive correlation between the level of tumor-infiltrating lymphocytes and heterogeneity revealed by MSI was observed. We postulate that a higher heterogeneity of tumors with a better prognosis could reflect the presence of heterotypic components including infiltrating immune cells, that facilitated the response to treatment.

Use of MALDI Mass Spectrometry Imaging to Identify Proteomic Signatures in Aortic Aneurysms after Endovascular Repair

Endovascular repair (EVAR) has become the standard procedure in treating thoracic (TAA) or abdominal aortic aneurysms (AAA). Not entirely free of complications, a persisting perfusion of the aneurysm after EVAR, called Endoleak (EL), leads to reintervention and risk of secondary rupture. How the aortic wall responds to the implantation of a stentgraft and EL is mostly uncertain. We present a pilot study to identify peptide signatures and gain new insights in pathophysiological alterations of the aortic wall after EVAR using matrix-assisted laser desorption or ionization mass spectrometry imaging (MALDI-MSI). In course of or accompanying an open aortic repair, tissue sections from 15 patients (TAA = 5, AAA = 5, EVAR = 5) were collected. Regions of interest (tunica media and tunica adventitia) were defined and univariate (receiver operating characteristic analysis) statistical analysis for subgroup comparison was used. This proof-of-concept study demonstrates that MALDI-MSI is feasible to identify discriminatory peptide signatures separating TAA, AAA and EVAR. Decreased intensity distributions for actin, tropomyosin, and troponin after EVAR suggest impaired contractility in vascular smooth muscle cells. Furthermore, inability to provide energy caused by impaired respiratory chain function and continuous degradation of extracellular matrix components (collagen) might support aortic wall destabilization. In case of EL after EVAR, this mechanism may result in a weakened aortic wall with lacking ability to react on reinstating pulsatile blood flow.

Discovery of Spatial Peptide Signatures for Neuroblastoma Risk Assessment by MALDI Mass Spectrometry Imaging

Simple Summary

The childhood tumor, neuroblastoma, has a broad clinical presentation. Risk assessment at diagnosis is particularly difficult in molecularly heterogeneous high-risk cases. Here we investigate the potential of imaging mass spectrometry to directly detect intratumor heterogeneity on the protein level in tissue sections. We show that this approach can produce discriminatory peptide signatures separating high- from low- and intermediate-risk tumors, identify 8 proteins aassociated with these signatures and validate two marker proteins using tissue immunostaining that have promise for further basic and translational research in neuroblastoma. We provide proof-of-concept that mass spectrometry-based technology could assist early risk assessment in neuroblastoma and provide insights into peptide signature-based detection of intratumor heterogeneity.

Abstract

Risk classification plays a crucial role in clinical management and therapy decisions in children with neuroblastoma. Risk assessment is currently based on patient criteria and molecular factors in single tumor biopsies at diagnosis. Growing evidence of extensive neuroblastoma intratumor heterogeneity drives the need for novel diagnostics to assess molecular profiles more comprehensively in spatial resolution to better predict risk for tumor progression and therapy resistance. We present a pilot study investigating the feasibility and potential of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to identify spatial peptide heterogeneity in neuroblastoma tissues of divergent current risk classification: high versus low/intermediate risk. Univariate (receiver operating characteristic analysis) and multivariate (segmentation, principal component analysis) statistical strategies identified spatially discriminative risk-associated MALDI-based peptide signatures. The AHNAK nucleoprotein and collapsin response mediator protein 1 (CRMP1) were identified as proteins associated with these peptide signatures, and their differential expression in the neuroblastomas of divergent risk was immunohistochemically validated. This proof-of-concept study demonstrates that MALDI-MSI combined with univariate and multivariate analysis strategies can identify spatially discriminative risk-associated peptide signatures in neuroblastoma tissues. These results suggest a promising new analytical strategy improving risk classification and providing new biological insights into neuroblastoma intratumor heterogeneity.

Implications of Peak Selection in the Interpretation of Unsupervised Mass Spectrometry Imaging Data Analyses

Mass spectrometry imaging can produce large amounts of complex spectral and spatial data. Such data sets are often analyzed with unsupervised machine learning approaches, which aim at reducing their complexity and facilitating their interpretation. However, choices made during data processing can impact the overall interpretation of these analyses. This work investigates the impact of the choices made at the peak selection step, which often occurs early in the data processing pipeline. The discussion is done in terms of visualization and interpretation of the results of two commonly used unsupervised approaches: t-distributed stochastic neighbor embedding and k-means clustering, which differ in nature and complexity. Criteria considered for peak selection include those based on hypotheses (exemplified herein in the analysis of metabolic alterations in genetically engineered mouse models of human colorectal cancer), particular molecular classes, and ion intensity. The results suggest that the choices made at the peak selection step have a significant impact in the visual interpretation of the results of either dimensionality reduction or clustering techniques and consequently in any downstream analysis that relies on these. Of particular significance, the results of this work show that while using the most abundant ions can result in interesting structure-related segmentation patterns that correlate well with histological features, using a smaller number of ions specifically selected based on prior knowledge about the biochemistry of the tissues under investigation can result in an easier-to-interpret, potentially more valuable, hypothesis-confirming result. Findings presented will help researchers understand and better utilize unsupervised machine learning approaches to mine high-dimensionality data.

Evaluation of the Aortopathy in the Ascending Aorta: The Novelty of Using Matrix-Assisted Laser Desorption/Ionization Imaging

PURPOSE

Histopathological evaluation presents conflicting reports regarding aortic abnormalities. The authors aim to present proof-of-concept study to explore the feasibility of matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) in combination with histopathology for characterizing alterations in the aneurysmal ascending formalin-fixed paraffin-embedded (FFPE) aorta tissue.

EXPERIMENTAL DESIGN

The authors assess FFPE specimens from patients with a dilated aorta and bicuspid aortic valve (BAV), those with a standard tricuspid aortic valve (TAV), and those with Marfan syndrome (MFS) via histopathology and grade the conditions for elastic fiber fragmentation (EFF) and MALDI-IMS. The proteins using liquid chromatographic-mass spectrometry are identified and the results are confirmed by immunohistochemistry.

RESULTS

There is significant difference in terms of EFF between MFS and BAV, and TAV and BAV. Characteristic peptide signatures and m/z values in the EFF facilitate the characterization among the aortic specimens of BAV, MFS, and TAV. The m/z values from the aortic alpha smooth muscle actin and myosin heavy chains significantly increase in BAV compared with MFS and TAV. These findings are confirmed by immunohistochemistry.

CONCLUSION

The results represent a strategy that uses MALDI-IMS in combination with histopathology as promising approaches to characterize spatial alteration in the structure of the aneurysmal ascending aorta.

MALDI-IMS as a Tool to Determine the Myocardial Response to Syndecan-2-Selected Mesenchymal Stromal Cell Application in an Experimental Model of Diabetic Cardiomyopathy

PURPOSE

Mesenchymal stromal cells (MSC) are an attractive tool for treatment of diabetic cardiomyopathy. Syndecan-2/CD362 has been identified as a functional marker for MSC isolation. Imaging mass spectrometry (IMS) allows for the characterization of therapeutic responses in the left ventricle. This study aims to investigate whether IMS can assess the therapeutic effect of CD362+ -selected MSC on early onset experimental diabetic cardiomyopathy.

EXPERIMENTAL DESIGN

1 × 106 wild type (WT), CD362- , or CD362+ MSC are intravenously injected into db/db mice. Four weeks later, mice are hemodynamically characterized and subsequently sacrificed for IMS combined with bottom-up mass spectrometry, and isoform and phosphorylation analyses of cardiac titin.

RESULTS

Overall alterations of the cardiac proteome signatures, especially titin, are observed in db/db compared to control mice. Interestingly, only CD362+ MSC can overcome the reduced titin intensity distribution and shifts the isoform ratio toward the more compliant N2BA form. In contrast, WT and CD362- MSCs improve all-titin phosphorylation and protein kinase G activity, which is reflected in an improvement in diastolic performance.

CONCLUSIONS AND CLINICAL RELEVANCE

IMS enables the characterization of differences in titin intensity distribution following MSC application. However, further analysis of titin phosphorylation is needed to allow for the assessment of the therapeutic efficacy of MSC.

Multiplex enzyme activity imaging by MALDI-IMS of substrate library conversions

Enzymes are fundamental to biological processes and involved in most pathologies. Here we demonstrate the concept of simultaneously mapping multiple enzyme activities (EA) by applying enzyme substrate libraries to tissue sections and analyzing their conversion by matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS). To that end, we spray-applied a solution of 20 naturally derived peptides that are known substrates for proteases, kinases, and phosphatases to zinc-fixed paraffin tissue sections of mouse kidneys. After enzyme conversion for 5 to 120 min at 37 °C and matrix application, the tissue sections were imaged by MALDI-IMS. We could image incubation time-dependently 16 of the applied substrates with differing signal intensities and 12 masses of expected products. Utilizing inherent enzyme amplification, EA-IMS can become a powerful tool to locally study multiple, potentially even lowly expressed, enzyme activities, networks, and their pharmaceutical modulation. Differences in the substrate detectability highlight the need for future optimizations.

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.

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high-dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data-driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry-based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1-47, 2019.

Identification of Proteomic Markers in Head and Neck Cancer Using MALDI-MS Imaging, LC-MS/MS, and Immunohistochemistry

PURPOSE

The heterogeneity of squamous cell carcinoma tissue greatly complicates diagnosis and individualized therapy. Therefore, characterizing the heterogeneity of tissue spatially and identifying appropriate biomarkers is crucial. MALDI-MS imaging (MSI) is capable of analyzing spatially resolved tissue biopsies on a molecular level.

EXPERIMENTAL DESIGN

MALDI-MSI is used on snap frozen and formalin-fixed and paraffin-embedded (FFPE) tissue samples from patients with head and neck cancer (HNC) to analyze m/z values localized in tumor and nontumor regions. Peptide identification is performed using LC-MS/MS and immunohistochemistry (IHC).

RESULTS

In both FFPE and frozen tissue specimens, eight characteristic masses of the tumor's epithelial region are found. Using LC-MS/MS, the peaks are identified as vimentin, keratin type II, nucleolin, heat shock protein 90, prelamin-A/C, junction plakoglobin, and PGAM1. Lastly, vimentin, nucleolin, and PGAM1 are verified with IHC.

CONCLUSIONS AND CLINICAL RELEVANCE

The combination of MALDI-MSI, LC-MS/MS, and subsequent IHC furnishes a tool suitable for characterizing the molecular heterogeneity of tissue. It is also suited for use in identifying new representative biomarkers to enable a more individualized therapy.

Discrimination of normal oral mucosa from oral cancer by mass spectrometry imaging of proteins and lipids

Identification of biomarkers for molecular classification of cancer and for differentiation between cancerous and normal epithelium remains a vital issue in the field of head and neck cancer. Here we aimed to compare the ability of proteome and lipidome components to discriminate oral cancer from normal mucosa. Tissue specimens including squamous cell cancer and normal epithelium were analyzed by MALDI mass spectrometry imaging. Two molecular domains of tissue components were imaged in serial sections-peptides (resulting from trypsin-processed proteins) and lipids (primarily zwitterionic phospholipids), then regions of interest corresponding to cancer and normal epithelium were compared. Heterogeneity of cancer regions was higher than the heterogeneity of normal epithelium, and the distribution of peptide components was more heterogeneous than the distribution of lipid components. Moreover, there were more peptide components than lipid components that showed significantly different abundance between cancer and normal epithelium (median of the Cohen's effect was 0.49 and 0.31 in case of peptide and lipid components, respectively). Multicomponent cancer classifier was tested (vs. normal epithelium) using tissue specimens from three patients and then validated with a tissue specimen from the fourth patient. Peptide-based signature and lipid-based signature allowed cancer classification with a weighted accuracy of 0.85 and 0.69, respectively. Nevertheless, both classifiers had very high precision (0.98 and 0.94, respectively). We concluded that though molecular differences between cancerous and normal mucosa were higher in the proteome domain than in the analyzed lipidome subdomain, imaging of lipidome components also enabled discrimination of oral cancer and normal epithelium. Therefore, both cancer proteome and lipidome are promising sources of biomarkers of oral malignancies.

Advanced Registration and Analysis of MALDI Imaging Mass Spectrometry Measurements through Autofluorescence Microscopy

The correlation of imaging mass spectrometry (IMS) with histopathology can help relate novel molecular findings obtained through IMS to the well-characterized and validated histopathology knowledge base. The quality of correlation between these two modalities is limited by the quality of the spatial mapping that is obtained by registration of the two image types. In this work, we develop novel workflows for MALDI IMS-to-microscopy data registration and analysis using nondestructive IMS-compatible wide field autofluorescence (AF) microscopy combined with computational image registration. First, a substantially automated procedure for high-accuracy registration between IMS and microscopy data of the same section is described that explicitly links the MALDI laser ablation pattern imaged by microscopy to its corresponding IMS pixel. Subsequent examination of the registered data allows for high-confidence colocalization of image features between the two modalities, down to single-cell scales within tissue. Building on this IMS-microscopy spatial mapping, we furthermore demonstrate the automated spatial correlation between IMS measurements from serial sections. This AF-registration-driven inter-section analysis, using a combination of nonlinear AF-to-AF and IMS-to-AF image registrations, can be applied to tissue sections that are prepared and imaged with different sample preparations (e.g., lipids vs proteins) and/or that are measured using different spatial resolutions. Importantly, all registrations, whether within a single section or across serial sections, are entirely independent of the IMS intensity signal content and thus unbiased by it.

Pharmacometabolic response to pirfenidone in pulmonary fibrosis detected by MALDI-FTICR-MSI

Idiopathic pulmonary fibrosis (IPF) is a fatal condition that reduces life expectancy and shows a limited response to available therapies. Pirfenidone has been approved for treatment of IPF, but little is known about the distinct metabolic changes that occur in the lung upon pirfenidone administration.Here, we performed a proof-of-concept study using high-resolution quantitative matrix-assisted laser desorption/ionisation Fourier-transform ion cyclotron resonance mass spectrometry imaging (MALDI-FTICR-MSI) to simultaneously detect, visualise and quantify in situ endogenous and exogenous metabolites in lungs of mice subjected to experimental fibrosis and human patients with IPF, and to assess the effect of pirfenidone treatment on metabolite levels.Metabolic pathway analysis and endogenous metabolite quantification revealed that pirfenidone treatment restores redox imbalance and glycolysis in IPF tissues, and downregulates ascorbate and aldarate metabolism, thereby likely contributing to in situ modulation of collagen processing. As such, we detected specific alterations in metabolite pathways in fibrosis and, importantly, metabolic recalibration following pirfenidone treatment.Together, these results highlight the suitability of high-resolution MALDI-FTICR-MSI for deciphering the therapeutic effects of pirfenidone and provide a preliminary analysis of the metabolic changes that occur during pirfenidone treatment in vivo These data may therefore contribute to improvement of currently available therapies for IPF.

Unraveling local tissue changes within severely injured skeletal muscles in response to MSC-based intervention using MALDI Imaging mass spectrometry

Pre-clinical and clinical studies are now beginning to demonstrate the high potential of cell therapies in enhancing muscle regeneration. We previously demonstrated functional benefit after the transplantation of autologous bone marrow mesenchymal stromal cells (MSC-TX) into a severe muscle crush trauma model. Despite our increasing understanding of the molecular and cellular mechanisms underlying MSC's regenerative function, little is known about the local molecular alterations and their spatial distribution within the tissue after MSC-TX. Here, we used MALDI imaging mass spectrometry (MALDI-IMS) in combination with multivariate statistical strategies to uncover previously unknown peptide alterations within severely injured skeletal muscles. Our analysis revealed that very early molecular alterations in response to MSC-TX occur largely in the region adjacent to the trauma and only to a small extent in the actual trauma region. Using "bottom up" mass spectrometry, we subsequently identified the proteins corresponding to the differentially expressed peptide intensity distributions in the specific muscle regions and used immunohistochemistry to validate our results. These findings extend our current understanding about the early molecular processes of muscle healing and highlights the critical role of trauma adjacent tissue during the early therapeutic response upon treatment with MSC.

High-Resolution Tissue Mass Spectrometry Imaging Reveals a Refined Functional Anatomy of the Human Adult Adrenal Gland

In the adrenal gland, neuroendocrine cells that synthesize catecholamines and epithelial cells that produce steroid hormones are united beneath a common organ capsule to function as a single stress-responsive organ. The functional anatomy of the steroid hormone-producing adrenal cortex and the catecholamine-producing medulla is ill defined at the level of small molecules. Here, we report a comprehensive high-resolution mass spectrometry imaging (MSI) map of the normal human adrenal gland. A large variety of biomolecules was accessible by matrix-assisted laser desorption/ionization-Fourier transform-ion cyclotron resonance MSI, including nucleoside phosphates indicative of oxidative phosphorylation, sterol and steroid metabolites, intermediates of glycolysis and the tricarboxylic acid cycle, lipids, and fatty acids. Statistical clustering analyses yielded a molecularly defined adrenal anatomy of 10 distinct molecular zones including a highly structured corticomedullary interface. By incorporating pathway information, activities of carbohydrate, amino acid, and lipid metabolism as well as endocrine bioactivity were revealed to be highly spatially organized, which could be visualized as different molecularly defined zones. Together, these findings provide a molecular definition of human adult adrenal gland structure beyond classical histological anatomy.

MALDI Mass Spectrometry Imaging for Evaluation of Therapeutics in Colorectal Tumor Organoids

Patient-derived colorectal tumor organoids (CTOs) closely recapitulate the complex morphological, phenotypic, and genetic features observed in in vivo tumors. Therefore, evaluation of drug distribution and metabolism in this model system can provide valuable information to predict the clinical outcome of a therapeutic response in individual patients. In this report, we applied matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to examine the spatial distribution of the drug irinotecan and its metabolites in CTOs from two patients. Irinotecan is a prodrug and is often prescribed as part of therapeutic regimes for patients with advanced colorectal cancer. Irinotecan shows a time-dependent and concentration-dependent permeability and metabolism in the CTOs. More interestingly, the active metabolite SN-38 does not co-localize well with the parent drug irinotecan and the inactive metabolite SN-38G. The phenotypic effect of irinotecan metabolism was also confirmed by a viability study showing significantly reduced proliferation in the drug treated CTOs. MALDI-MSI can be used to investigate various pharmaceutical compounds in CTOs derived from different patients. By analyzing multiple CTOs from a patient, this method could be used to predict patient-specific drug responses and help to improve personalized dosing regimens. Graphical Abstract ᅟ.

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.

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.

MALDI-imaging reveals thymosin beta-4 as an independent prognostic marker for colorectal cancer

DNA aneuploidy has been identified as a prognostic factor for epithelial malignancies. Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is a powerful tool for direct analysis of multiple proteins in tissue sections while maintaining the cellular and molecular integrity. We compared diploid and aneuploid colon cancer tissues against normal mucosa of the colon by means of IMS.

DNA image cytometry determined the ploidy status of tissue samples that were subsequently subjected to MALDI-IMS. After obtaining protein profiles through direct analysis of tissue sections, a discovery and independent validation set were used to predict ploidy status by applying proteomic classification algorithms [Supervised Neural Network (SNN) and Receiver Operating Characteristic (ROC)]. Five peaks (m/z 2,395 and 4,977 for diploid vs. aneuploid comparison as well as m/z 3,376, 6,663, and 8,581 for normal mucosa vs. carcinoma comparison) were significant in both SNN and ROC analysis. Among these, m/z 4,977 was identified as thymosin beta 4 (Tβ-4). Tβ-4 was subsequently validated in clinical samples using a tissue microarray to predict overall survival in colon cancer patients.

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.

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.

Deconvolving molecular signatures of interactions between microbial colonies

Motivation: The interactions between microbial colonies through chemical signaling are not well understood. A microbial colony can use different molecules to inhibit or accelerate the growth of other colonies. A better understanding of the molecules involved in these interactions could lead to advancements in health and medicine. Imaging mass spectrometry (IMS) applied to co-cultured microbial communities aims to capture the spatial characteristics of the colonies’ molecular fingerprints. These data are high-dimensional and require computational analysis methods to interpret.

Results: Here, we present a dictionary learning method that deconvolves spectra of different molecules from IMS data. We call this method MOLecular Dictionary Learning (MOLDL). Unlike standard dictionary learning methods which assume Gaussian-distributed data, our method uses the Poisson distribution to capture the count nature of the mass spectrometry data. Also, our method incorporates universally applicable information on common ion types of molecules in MALDI mass spectrometry. This greatly reduces model parameterization and increases deconvolution accuracy by eliminating spurious solutions. Moreover, our method leverages the spatial nature of IMS data by assuming that nearby locations share similar abundances, thus avoiding overfitting to noise. Tests on simulated datasets show that this method has good performance in recovering molecule dictionaries. We also tested our method on real data measured on a microbial community composed of two species. We confirmed through follow-up validation experiments that our method recovered true and complete signatures of molecules. These results indicate that our method can discover molecules in IMS data reliably, and hence can help advance the study of interaction of microbial colonies.

Availability and implementation: The code used in this paper is available at: https://github.com/frizfealer/IMS_project.

Contact:vjojic@cs.unc.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

The mutational landscape of cutaneous T cell lymphoma and Sézary syndrome

Sézary syndrome is a leukemic and aggressive form of cutaneous T cell lymphoma (CTCL) resulting from the malignant transformation of skin-homing central memory CD4(+) T cells. Here we performed whole-exome sequencing of tumor-normal sample pairs from 25 patients with Sézary syndrome and 17 patients with other CTCLs. These analyses identified a distinctive pattern of somatic copy number alterations in Sézary syndrome, including highly prevalent chromosomal deletions involving the TP53, RB1, PTEN, DNMT3A and CDKN1B tumor suppressors. Mutation analysis identified a broad spectrum of somatic mutations in key genes involved in epigenetic regulation (TET2, CREBBP, KMT2D (MLL2), KMT2C (MLL3), BRD9, SMARCA4 and CHD3) and signaling, including MAPK1, BRAF, CARD11 and PRKG1 mutations driving increased MAPK, NF-κB and NFAT activity upon T cell receptor stimulation. Collectively, our findings provide new insights into the genetics of Sézary syndrome and CTCL and support the development of personalized therapies targeting key oncogenically activated signaling pathways for the treatment of these diseases.

Comparison of clustering pipelines for the analysis of mass spectrometry imaging data

Mass spectrometry imaging (MSI) is valuable for biomedical applications because it links molecular and morphological information. However, MSI datasets can be very large, and analyzing them to identify important biological patterns is a challenging computational problem. Many types of unsupervised analysis have been applied to MSI data, and in particular, clustering has recently gained attention for this application. In this paper, we present an exploratory study of the performance of different analysis pipelines using k-means and fuzzy k-means clustering. The results indicate the effects of different pre-processing and parameter selections on identifying biologically relevant patterns in MSI data.

Molecular and chemical dialogues in bacteria-protozoa interactions

Protozoan predation of bacteria can significantly affect soil microbial community composition and ecosystem functioning. Bacteria possess diverse defense strategies to resist or evade protozoan predation. For soil-dwelling Pseudomonas species, several secondary metabolites were proposed to provide protection against different protozoan genera. By combining whole-genome transcriptome analyses with (live) imaging mass spectrometry (IMS), we observed multiple changes in the molecular and chemical dialogues between Pseudomonas fluorescens and the protist Naegleria americana. Lipopeptide (LP) biosynthesis was induced in Pseudomonas upon protozoan grazing and LP accumulation transitioned from homogeneous distributions across bacterial colonies to site-specific accumulation at the bacteria-protist interface. Also putrescine biosynthesis was upregulated in P. fluorescens upon predation. We demonstrated that putrescine induces protozoan trophozoite encystment and adversely affects cyst viability. This multifaceted study provides new insights in common and strain-specific responses in bacteria-protozoa interactions, including responses that contribute to bacterial survival in highly competitive soil and rhizosphere environments.

Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry

Background

Three-dimensional (3D) imaging mass spectrometry (MS) is an analytical chemistry technique for the 3D molecular analysis of a tissue specimen, entire organ, or microbial colonies on an agar plate. 3D-imaging MS has unique advantages over existing 3D imaging techniques, offers novel perspectives for understanding the spatial organization of biological processes, and has growing potential to be introduced into routine use in both biology and medicine. Owing to the sheer quantity of data generated, the visualization, analysis, and interpretation of 3D imaging MS data remain a significant challenge. Bioinformatics research in this field is hampered by the lack of publicly available benchmark datasets needed to evaluate and compare algorithms.

Findings

High-quality 3D imaging MS datasets from different biological systems at several labs were acquired, supplied with overview images and scripts demonstrating how to read them, and deposited into MetaboLights, an open repository for metabolomics data. 3D imaging MS data were collected from five samples using two types of 3D imaging MS. 3D matrix-assisted laser desorption/ionization imaging (MALDI) MS data were collected from murine pancreas, murine kidney, human oral squamous cell carcinoma, and interacting microbial colonies cultured in Petri dishes. 3D desorption electrospray ionization (DESI) imaging MS data were collected from a human colorectal adenocarcinoma.

Conclusions

With the aim to stimulate computational research in the field of computational 3D imaging MS, selected high-quality 3D imaging MS datasets are provided that could be used by algorithm developers as benchmark datasets.

Electronic supplementary material

The online version of this article (doi:10.1186/s13742-015-0059-4) contains supplementary material, which is available to authorized users.

Spatial segmentation of MALDI FT-ICR MSI data: a powerful tool to explore the head and neck tumor in situ lipidome

Matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI MSI) is a well-established analytical technique for determining spatial localization of lipids in biological samples. The use of Fourier-transform ion cyclotron resonance (FT-ICR) mass spectrometers for the molecular imaging of endogenous compounds is gaining popularity, since the high mass accuracy and high mass resolving power enables accurate determination of exact masses and, consequently, a more confident identification of these molecules. The high mass resolution FT-ICR imaging datasets are typically large in size. In order to analyze them in an appropriate timeframe, the following approach has been employed: the FT-ICR imaging datasets were spatially segmented by clustering all spectra by their similarity. The resulted spatial segmentation maps were compared with the histologic annotation. This approach facilitates interpretation of the full datasets by providing spatial regions of interest. The application of this approach, which has originally been developed for MALDI-TOF MSI datasets, to the lipidomic analysis of head and neck tumor tissue revealed new insights into the metabolic organization of the carcinoma tissue.

High-resolution metabolite imaging of light and dark treated retina using MALDI-FTICR mass spectrometry

MS imaging (MSI) is a valuable tool for diagnostics and systems biology studies, being a highly sensitive, label-free technique capable of providing comprehensive spatial distribution of different classes of biomolecules. The application of MSI to the study of endogenous compounds has received considerable attention because metabolites are the result of the interactions of a biosystem with its environment. MSI can therefore enhance understanding of disease mechanisms and elucidate mechanisms for biological variation. We present the in situ comparative metabolomics imaging data for analyses of light- and dark-treated retina using MALDI-FTICR. A wide variety of tissue metabolites were imaged at a high spatial resolution. These include nucleotides, central carbon metabolism pathway intermediates, 2-oxocarboxylic acid metabolism, oxidative phosphorylation, glycerophospholipid metabolism, and cysteine and methionine metabolites. The high lateral resolution enabled the differentiation of retinal layers, allowing determination of the spatial distributions of different endogenous compounds. A number of metabolites demonstrated differences between light and dark conditions. These findings add to the understanding of metabolic activity in the retina.

MALDI imaging mass spectrometry: discrimination of pathophysiological regions in traumatized skeletal muscle by characteristic peptide signatures

Due to formation of fibrosis and the loss of contractile muscle tissue, severe muscle injuries often result in insufficient healing marked by a significant reduction of muscle force and motor activity. Our previous studies demonstrated that the local transplantation of mesenchymal stromal cells into an injured skeletal muscle of the rat improves the functional outcome of the healing process. Since, due to the lack of sufficient markers, the accurate discrimination of pathophysiological regions in injured skeletal muscle is inadequate, underlying mechanisms of the beneficial effects of mesenchymal stromal cell transplantation on primary trauma and trauma adjacent muscle area remain elusive. For discrimination of these pathophysiological regions, formalin-fixed injured skeletal muscle tissue was analyzed by MALDI imaging MS. By using two computational evaluation strategies, a supervised approach (ClinProTools) and unsupervised segmentation (SCiLS Lab), characteristic m/z species could be assigned to primary trauma and trauma adjacent muscle regions. Using "bottom-up" MS for protein identification and validation of results by immunohistochemistry, we could identify two proteins, skeletal muscle alpha actin and carbonic anhydrase III, which discriminate between the secondary damage on adjacent tissue and the primary traumatized muscle area. Our results underscore the high potential of MALDI imaging MS to describe the spatial characteristics of pathophysiological changes in muscle.

MALDI-TOF MS as evolving cancer diagnostic tool: a review

Recent developments in mass spectrometry have introduced clinical proteomics to the forefront of diseases diagnosis, offering reliable, robust and efficient analytical method for biomarker discovery and monitoring. MALDI-TOF is a powerful tool for surveying proteins and peptides comprising the realm for clinical analysis. MALDI-TOF has the potential to revolutionize cancer diagnostics by facilitating biomarker discovery, enabling tissue imaging and quantifying biomarker levels. Healthy (control) and cancerous tissues can be analyzed on the basis of mass spectrometry (MALDI-TOF) imaging to identify cancer-specific changes that may prove to be clinically useful. We review MALDI-TOF profiling techniques as tools for detection of cancer biomarkers in various cancers. We mainly discuss recent advances including period from 2011 to 2013.

Deeper understanding of biological tissue: quantitative correlation of MALDI-TOF and Raman imaging

In order to achieve a comprehensive description of biological tissue, spectral information about proteins, lipids, nucleic acids, and other biochemical components need to be obtained concurrently. Different analytical techniques may be combined to record complementary information of the same sample. Established techniques, which can be utilized to elucidate the biochemistry of tissue samples are, for instance, MALDI-TOF-MS and Raman microscopic imaging. With this contribution, we combine these two techniques for the first time. The combination of both techniques allows the utilization and interpretation of complementary information (i.e., the information about the protein composition derived from the Raman spectra with data of the lipids analyzed by the MALDI-TOF measurements). Furthermore, we demonstrate how spectral information from MALDI-TOF experiments can be utilized to interpret Raman spectra.

OpenMSI: a high-performance web-based platform for mass spectrometry imaging

Mass spectrometry imaging (MSI) enables researchers to directly probe endogenous molecules directly within the architecture of the biological matrix. Unfortunately, efficient access, management, and analysis of the data generated by MSI approaches remain major challenges to this rapidly developing field. Despite the availability of numerous dedicated file formats and software packages, it is a widely held viewpoint that the biggest challenge is simply opening, sharing, and analyzing a file without loss of information. Here we present OpenMSI, a software framework and platform that addresses these challenges via an advanced, high-performance, extensible file format and Web API for remote data access (http://openmsi.nersc.gov). The OpenMSI file format supports storage of raw MSI data, metadata, and derived analyses in a single, self-describing format based on HDF5 and is supported by a large range of analysis software (e.g., Matlab and R) and programming languages (e.g., C++, Fortran, and Python). Careful optimization of the storage layout of MSI data sets using chunking, compression, and data replication accelerates common, selective data access operations while minimizing data storage requirements and are critical enablers of rapid data I/O. The OpenMSI file format has shown to provide >2000-fold improvement for image access operations, enabling spectrum and image retrieval in less than 0.3 s across the Internet even for 50 GB MSI data sets. To make remote high-performance compute resources accessible for analysis and to facilitate data sharing and collaboration, we describe an easy-to-use yet powerful Web API, enabling fast and convenient access to MSI data, metadata, and derived analysis results stored remotely to facilitate high-performance data analysis and enable implementation of Web based data sharing, visualization, and analysis.