Proteomic Analysis Identifies FNDC1, A1BG, and Antigen Processing Proteins Associated with Tumor Heterogeneity and Malignancy in a Canine Model of Breast Cancer.

New insights into the underlying biological processes of breast cancer are needed for the development of improved markers and treatments. The complex nature of mammary cancer in dogs makes it a great model to study cancer biology since they present a high degree of tumor heterogeneity. In search of disease-state biomarkers candidates, we applied proteomic mass spectrometry imaging in order to simultaneously detect histopathological and molecular alterations whilst preserving morphological integrity, comparing peptide expression between intratumor populations in distinct levels of differentiation. Peptides assigned to FNDC1, A1BG, and double-matching keratins 18 and 19 presented a higher intensity in poorly differentiated regions. In contrast, we observed a lower intensity of peptides matching calnexin, PDIA3, and HSPA5 in poorly differentiated cells, which enriched for protein folding in the endoplasmic reticulum and antigen processing, assembly, and loading of class I MHC. Over-representation of collagen metabolism, coagulation cascade, extracellular matrix components, cadherin-binding and cell adhesion pathways also distinguished cell populations. Finally, an independent validation showed FNDC1, A1BG, PDIA3, HSPA5, and calnexin as significant prognostic markers for human breast cancer patients. Thus, through a spatially correlated characterization of spontaneous carcinomas, we described key proteins which can be further validated as potential prognostic biomarkers.

Linkage-Specific in Situ Sialic Acid Derivatization for N-Glycan Mass Spectrometry Imaging of Formalin-Fixed Paraffin-Embedded Tissues.

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging is a rapidly evolving field in which mass spectrometry techniques are applied directly on tissues to characterize the spatial distribution of various molecules such as lipids, protein/peptides, and recently also N-glycans. Glycans are involved in many biological processes and several glycan changes have been associated with different kinds of cancer, making them an interesting target group to study. An important analytical challenge for the study of glycans by MALDI mass spectrometry is the labile character of sialic acid groups which are prone to in-source/postsource decay, thereby biasing the recorded glycan profile. We therefore developed a linkage-specific sialic acid derivatization by dimethylamidation and subsequent amidation and transferred this onto formalin-fixed paraffin-embedded (FFPE) tissues for MALDI imaging of N-glycans. Our results show (i) the successful stabilization of sialic acids in a linkage specific manner, thereby not only increasing the detection range, but also adding biological meaning, (ii) that no noticeable lateral diffusion is induced during to sample preparation, (iii) the potential of mass spectrometry imaging to spatially characterize the N-glycan expression within heterogeneous tissues.

Lipid and protein maps defining arterial layers in atherosclerotic aorta.

Subclinical atherosclerosis cannot be predicted and novel therapeutic targets are needed. The molecular anatomy of healthy and atherosclerotic tissue is pursued to identify ongoing molecular changes in atherosclerosis development. Mass Spectrometry Imaging (MSI) accounts with the unique advantage of analyzing proteins and metabolites (lipids) while preserving their original localization; thus two dimensional maps can be obtained. Main molecular alterations were investigated in a rabbit model in response to early development of atherosclerosis. Aortic arterial layers (intima and media) and calcified regions were investigated in detail by MALDI-MSI and proteins and lipids specifically defining those areas of interest were identified. These data further complement main findings previously published in J Proteomics (M. Martin-Lorenzo et al., J. Proteomics. (In press); M. Martin-Lorenzo et al., J. Proteomics 108 (2014) 465-468.) [1,2].

Molecular anatomy of ascending aorta in atherosclerosis by MS Imaging: Specific lipid and protein patterns reflect pathology.

The molecular anatomy of healthy and atherosclerotic tissue is pursued here to identify ongoing molecular changes in atherosclerosis development. Subclinical atherosclerosis cannot be predicted and novel therapeutic targets are needed. Mass spectrometry imaging (MSI) is a novel unexplored ex vivo imaging approach in CVD able to provide in-tissue molecular maps. A rabbit model of early atherosclerosis was developed and high-spatial-resolution MALDI-MSI was applied to comparatively analyze histologically-based arterial regions of interest from control and early atherosclerotic aortas. Specific protocols were applied to identify lipids and proteins significantly altered in response to atherosclerosis. Observed protein alterations were confirmed by immunohistochemistry in rabbit tissue, and additionally in human aortas. Molecular features specifically defining different arterial regions were identified. Localized in the intima, increased expression of SFA and lysolipids and intimal spatial organization showing accumulation of PI, PG and SM point to endothelial dysfunction and triggered inflammatory response. TG, PA, SM and PE-Cer were identified specifically located in calcified regions. Thymosin ß4 (TMSB4X) protein was upregulated in intima versus media layer and also in response to atherosclerosis. This overexpression and localization was confirmed in human aortas. In conclusion, molecular histology by MS Imaging identifies spatial organization of arterial tissue in response to atherosclerosis.

Large-scale mass spectrometry imaging investigation of consequences of cortical spreading depression in a transgenic mouse model of migraine.

Cortical spreading depression (CSD) is the electrophysiological correlate of migraine aura. Transgenic mice carrying the R192Q missense mutation in the Cacna1a gene, which in patients causes familial hemiplegic migraine type 1 (FHM1), exhibit increased propensity to CSD. Herein, mass spectrometry imaging (MSI) was applied for the first time to an animal cohort of transgenic and wild type mice to study the biomolecular changes following CSD in the brain. Ninety-six coronal brain sections from 32 mice were analyzed by MALDI-MSI. All MSI datasets were registered to the Allen Brain Atlas reference atlas of the mouse brain so that the molecular signatures of distinct brain regions could be compared. A number of metabolites and peptides showed substantial changes in the brain associated with CSD. Among those, different mass spectral features showed significant (t-test, P < 0.05) changes in the cortex, 146 and 377 Da, and in the thalamus, 1820 and 1834 Da, of the CSD-affected hemisphere of FHM1 R192Q mice. Our findings reveal CSD- and genotype-specific molecular changes in the brain of FHM1 transgenic mice that may further our understanding about the role of CSD in migraine pathophysiology. The results also demonstrate the utility of aligning MSI datasets to a common reference atlas for large-scale MSI investigations.

Towards imaging metabolic pathways in tissues.

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging using 9-aminoacridine as the matrix leads to the detection of low mass metabolites and lipids directly from cancer tissues. These included lactate and pyruvate for studying the Warburg effect, as well as succinate and fumarate, metabolites whose accumulation is associated with specific syndromes. By using the pathway information present in the human metabolome database, it was possible to identify regions within tumor tissue samples with distinct metabolic signatures that were consistent with known tumor biology. We present a data analysis workflow for assessing metabolic pathways in their histopathological context.

30µm spatial resolution protein MALDI MSI: In-depth comparison of five sample preparation protocols applied to human healthy and atherosclerotic arteries.

Tissue preparation is the key to a successful MALDI mass spectrometry imaging experiment. A number of different tissue preparations methods have recently been reported for increased sensitivity and/or high spatial resolution analysis. In order to better benchmark these methods in terms of the information content and their suitability for analyzing small tissues containing small but distinct regions, we have performed an extensive comparison using technical and biological repeats as well as a fully randomized measuring sequence. We then demonstrate how the optimized tissue preparation method enables 30µm spatial resolution analysis of proteins from atherosclerotic arterial tissues, revealing proteins specific to the intima and media layers.

Automatic registration of mass spectrometry imaging data sets to the Allen brain atlas.

Mass spectrometry imaging holds great potential for understanding the molecular basis of neurological disease. Several key studies have demonstrated its ability to uncover disease-related biomolecular changes in rodent models of disease, even if highly localized or invisible to established histological methods. The high analytical reproducibility necessary for the biomedical application of mass spectrometry imaging means it is widely developed in mass spectrometry laboratories. However, many lack the expertise to correctly annotate the complex anatomy of brain tissue, or have the capacity to analyze the number of animals required in preclinical studies, especially considering the significant variability in sizes of brain regions. To address this issue, we have developed a pipeline to automatically map mass spectrometry imaging data sets of mouse brains to the Allen Brain Reference Atlas, which contains publically available data combining gene expression with brain anatomical locations. Our pipeline enables facile and rapid interanimal comparisons by first testing if each animal's tissue section was sampled at a similar location and enabling the extraction of the biomolecular signatures from specific brain regions.

Imaging mass spectrometry-based molecular histology differentiates microscopically identical and heterogeneous tumors.

Many tumors display significant cellular heterogeneity as well as molecular heterogeneity. Sensitive biomarkers that differentiate between diagnostically challenging tumors must contend with this heterogeneity. Mass spectrometry-based molecular histology of a patient series of heterogeneous, microscopically identical bone tumors highlighted the tumor cell types that could be characterized by a single profile and led to the identification of specific peptides that differentiate between the tumors.

Imaging mass spectrometry to visualize biomolecule distributions in mouse brain tissue following hemispheric cortical spreading depression.

MALDI mass spectrometry can simultaneously measure hundreds of biomolecules directly from tissue. Using essentially the same technique but different sample preparation strategies, metabolites, lipids, peptides and proteins can be analyzed. Spatially correlated analysis, imaging MS, enables the distributions of these biomolecular ions to be simultaneously measured in tissues. A key advantage of imaging MS is that it can annotate tissues based on their MS profiles and thereby distinguish biomolecularly distinct regions even if they were unexpected or are not distinct using established histological and histochemical methods e.g. neuropeptide and metabolite changes following transient electrophysiological events such as cortical spreading depression (CSD), which are spreading events of massive neuronal and glial depolarisations that occur in one hemisphere of the brain and do not pass to the other hemisphere , enabling the contralateral hemisphere to act as an internal control. A proof-of-principle imaging MS study, including 2D and 3D datasets, revealed substantial metabolite and neuropeptide changes immediately following CSD events which were absent in the protein imaging datasets. The large high dimensionality 3D datasets make even rudimentary contralateral comparisons difficult to visualize. Instead non-negative matrix factorization (NNMF), a multivariate factorization tool that is adept at highlighting latent features, such as MS signatures associated with CSD events, was applied to the 3D datasets. NNMF confirmed that the protein dataset did not contain substantial contralateral differences, while these were present in the neuropeptide dataset.

High speed data processing for imaging MS-based molecular histology using graphical processing units.

Imaging MS enables the distributions of hundreds of biomolecular ions to be determined directly from tissue samples. The application of multivariate methods, to identify pixels possessing correlated MS profiles, is referred to as molecular histology as tissues can be annotated on the basis of the MS profiles. The application of imaging MS-based molecular histology to larger tissue series, for clinical applications, requires significantly increased computational capacity in order to efficiently analyze the very large, highly dimensional datasets. Such datasets are highly suited to processing using graphical processor units, a very cost-effective solution for high speed processing. Here we demonstrate up to 13× speed improvements for imaging MS-based molecular histology using off-the-shelf components, and demonstrate equivalence with CPU based calculations. It is then discussed how imaging MS investigations may be designed to fully exploit the high speed of graphical processor units.

Multiple statistical analysis techniques corroborate intratumor heterogeneity in imaging mass spectrometry datasets of myxofibrosarcoma.

MALDI mass spectrometry can generate profiles that contain hundreds of biomolecular ions directly from tissue. Spatially-correlated analysis, MALDI imaging MS, can simultaneously reveal how each of these biomolecular ions varies in clinical tissue samples. The use of statistical data analysis tools to identify regions containing correlated mass spectrometry profiles is referred to as imaging MS-based molecular histology because of its ability to annotate tissues solely on the basis of the imaging MS data. Several reports have indicated that imaging MS-based molecular histology may be able to complement established histological and histochemical techniques by distinguishing between pathologies with overlapping/identical morphologies and revealing biomolecular intratumor heterogeneity. A data analysis pipeline that identifies regions of imaging MS datasets with correlated mass spectrometry profiles could lead to the development of novel methods for improved diagnosis (differentiating subgroups within distinct histological groups) and annotating the spatio-chemical makeup of tumors. Here it is demonstrated that highlighting the regions within imaging MS datasets whose mass spectrometry profiles were found to be correlated by five independent multivariate methods provides a consistently accurate summary of the spatio-chemical heterogeneity. The corroboration provided by using multiple multivariate methods, efficiently applied in an automated routine, provides assurance that the identified regions are indeed characterized by distinct mass spectrometry profiles, a crucial requirement for its development as a complementary histological tool. When simultaneously applied to imaging MS datasets from multiple patient samples of intermediate-grade myxofibrosarcoma, a heterogeneous soft tissue sarcoma, nodules with mass spectrometry profiles found to be distinct by five different multivariate methods were detected within morphologically identical regions of all patient tissue samples. To aid the further development of imaging MS based molecular histology as a complementary histological tool the Matlab code of the agreement analysis, instructions and a reduced dataset are included as supporting information.

MALDI imaging and profiling MS of higher mass proteins from tissue.

MALDI imaging and profiling mass spectrometry of proteins typically leads to the detection of a large number of peptides and small proteins but is much less successful for larger proteins: most ion signals correspond to proteins of m/z < 25,000. This is a severe limitation as many proteins, including cytokines, growth factors, enzymes, and receptors have molecular weights exceeding 25 kDa. The detector technology typically used for protein imaging, a microchannel plate, is not well suited to the detection of high m/z ions and is prone to detector saturation when analyzing complex mixtures. Here we report increased sensitivity for higher mass proteins by using the CovalX high mass HM1 detector (Zurich, Switzerland), which has been specifically designed for the detection of high mass ions and which is much less prone to detector saturation. The results demonstrate that a range of different sample preparation strategies enable higher mass proteins to be analyzed if the detector technology maintains high detection efficiency throughout the mass range. The detector enables proteins up to 70 kDa to be imaged, and proteins up to 110 kDa to be detected, directly from tissue, and indicates new directions by which the mass range amenable to MALDI imaging MS and MALDI profiling MS may be extended.

Imaging mass spectrometry data reduction: automated feature identification and extraction.

Imaging MS now enables the parallel analysis of hundreds of biomolecules, spanning multiple molecular classes, which allows tissues to be described by their molecular content and distribution. When combined with advanced data analysis routines, tissues can be analyzed and classified based solely on their molecular content. Such molecular histology techniques have been used to distinguish regions with differential molecular signatures that could not be distinguished using established histologic tools. However, its potential to provide an independent, complementary analysis of clinical tissues has been limited by the very large file sizes and large number of discrete variables associated with imaging MS experiments. Here we demonstrate data reduction tools, based on automated feature identification and extraction, for peptide, protein, and lipid imaging MS, using multiple imaging MS technologies, that reduce data loads and the number of variables by >100×, and that highlight highly-localized features that can be missed using standard data analysis strategies. It is then demonstrated how these capabilities enable multivariate analysis on large imaging MS datasets spanning multiple tissues.

Peptide and protein imaging mass spectrometry in cancer research.

MALDI mass spectrometry is able to acquire protein profiles directly from tissue that can describe the levels of hundreds of distinct proteins. MALDI imaging MS can simultaneously reveal how each of these proteins varies in heterogeneous tissues. Numerous studies have now demonstrated how MALDI imaging MS can generate different protein profiles from the different cell types in a tumor, which can act as biomarker profiles or enable specific candidate protein biomarkers to be identified. MALDI imaging MS can be directly applied to patient samples where its utility is to accomplish untargeted multiplex analysis of the tissue's protein content, enabling the different regions of the tissue to be differentiated on the basis of previously unknown protein profiles/biomarkers. The technique continues to rapidly develop and is now approaching the cusp whereby its potential to provide new diagnostic/prognostic tools for cancer patients can be routinely investigated. Here the latest methodological developments are summarized and its application to a range of tumors is reported in detail. The prospects of MALDI imaging MS are then described from the perspectives of modern pathological practice and MS-based proteomics, to ensure the outlook addresses real clinical needs and reflects the real capabilities of MS-based proteomics of complex tissue samples.

Automated imaging MS: Toward high throughput imaging mass spectrometry.

The term molecular histology has been used to convey the potential of imaging mass spectrometry to describe tissue by its constituent peptides and proteins, and to link this with established histological features. The low throughput of imaging mass spectrometry has been one of the factors inhibiting a full investigation of the clinical potential of molecular histology. Here we report the development of an automated set-up, consisting of a controlled environment sample storage chamber, a sample loading robot, and a MALDI-TOF/TOF mass spectrometer, all controlled by a single user interface. The automated set-up is demonstrated to have the positional stability and experimental reproducibility necessary for its clinical application.

Mass spectrometry image correlation: quantifying colocalization.

A typical imaging mass spectrometry data set can contain 100+ images, each describing the distribution of a specific biomolecule. Multivariate and hierarchical clustering techniques have been developed to investigate the correlations within a data set, and have revealed the differential patterns associated with different organs/anatomical features. These methods do not quantify the correlations between the hundreds of molecular distributions produced in an imaging mass spectrometry experiment, and are extremely difficult to apply to multiple tissue section investigations. This latter aspect includes quantifying the correlation between the results of repeat imaging mass spectrometry experiments, a crucial aspect for determining the significance of any measured changes in distribution. To date, the large chemical background and pixel-to-pixel variation in the images has limited the quantification of correlation between imaging mass spectrometry results. Here, we demonstrate how to quantify the correlations between imaging mass spectrometry images, both within a data set and between data sets.