MALDI Imaging Mass Spectrometry of Neuropeptides in Parkinson's Disease

MALDI imaging mass spectrometry (IMS) is a powerful approach that facilitates the spatial analysis of molecular species in biological tissue samples² (Fig.1). A 12 μm thin tissue section is covered with a MALDI matrix, which facilitates desorption and ionization of intact peptides and proteins that can be detected with a mass analyzer, typically using a MALDI TOF/TOF mass spectrometer. Generally hundreds of peaks can be assessed in a single rat brain tissue section. In contrast to commonly used imaging techniques, this approach does not require prior knowledge of the molecules of interest and allows for unsupervised and comprehensive analysis of multiple molecular species while maintaining high molecular specificity and sensitivity². Here we describe a MALDI IMS based approach for elucidating region-specific distribution profiles of neuropeptides in the rat brain of an animal model Parkinson''s disease (PD). PD is a common neurodegenerative disease with a prevalence of 1% for people over 65 of age^3,4. The most common symptomatic treatment is based on dopamine replacement using L-DOPA⁵. However this is accompanied by severe side effects including involuntary abnormal movements, termed L-DOPA-induced dyskinesias (LID)^1,3,6. One of the most prominent molecular change in LID is an upregulation of the opioid precursor prodynorphin mRNA⁷. The dynorphin peptides modulate neurotransmission in brain areas that are essentially involved in movement control^7,8. However, to date the exact opioid peptides that originate from processing of the neuropeptide precursor have not been characterized. Therefore, we utilized MALDI IMS in an animal model of experimental Parkinson''s disease and L-DOPA induced dyskinesia. MALDI imaging mass spectrometry proved to be particularly advantageous with respect to neuropeptide characterization, since commonly used antibody based approaches targets known peptide sequences and previously observed post-translational modifications. By contrast MALDI IMS can unravel novel peptide processing products and thus reveal new molecular mechanisms of neuropeptide modulation of neuronal transmission. While the absolute amount of neuropeptides cannot be determined by MALDI IMS, the relative abundance of peptide ions can be delineated from the mass spectra, giving insights about changing levels in health and disease. In the examples presented here, the peak intensities of dynorphin B, alpha-neoendorphin and substance P were found to be significantly increased in the dorsolateral, but not the dorsomedial, striatum of animals with severe dyskinesia involving facial, trunk and orolingual muscles (Fig. 5). Furthermore, MALDI IMS revealed a correlation between dyskinesia severity and levels of des-tyrosine alpha-neoendorphin, representing a previously unknown mechanism of functional inactivation of dynorphins in the striatum as the removal of N-terminal tyrosine reduces the dynorphin''s opioid-receptor binding capacity⁹. This is the first study on neuropeptide characterization in LID using MALDI IMS and the results highlight the potential of the technique for application in all fields of biomedical research.     

Primer on agar-based microbial imaging mass spectrometry

Matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) imaging mass spectrometry (IMS) applied directly to microbes on agar-based medium captures global information about microbial molecules, allowing for direct correlation of chemotypes to phenotypes. This tool was developed to investigate metabolic exchange factors of intraspecies, interspecies, and polymicrobial interactions. Based on our experience of the thousands of images we have generated in the laboratory, we present five steps of microbial IMS: culturing, matrix application, dehydration of the sample, data acquisition, and data analysis/interpretation. We also address the common challenges encountered during sample preparation, matrix selection and application, and sample adherence to the MALDI target plate. With the practical guidelines described herein, microbial IMS use can be extended to bio-based agricultural, biofuel, diagnostic, and therapeutic discovery applications.     

Quantitative imaging mass spectrometry of renal sulfatides: validation by classical mass spectrometric methods

Owing to its capability of discriminating subtle mass-altering structural differences such as double bonds or elongated acyl chains, MALDI-based imaging MS (IMS) has emerged as a powerful technique for analysis of lipid distribution in tissue at moderate spatial resolution of about 50 μm. However, it is still unknown if MS¹-signals and ion intensity images correlate with the corresponding apparent lipid concentrations. Analyzing renal sulfated glycosphingolipids, sulfatides, we validate for the first time IMS-signal identities using corresponding sulfatide-deficient kidneys. To evaluate the extent of signal quenching effects interfering with lipid quantification, we surgically dissected the three major renal regions (papillae, medulla, and cortex) and systematically compared MALDI IMS of renal sulfatides with quantitative analyses of corresponding lipid extracts by on-target MALDI TOF-MS and by ultra-performance LC-ESI-(triple-quadrupole)tandem MS. Our results demonstrate a generally strong correlation (R² > 0.9) between the local relative sulfatide signal intensity in MALDI IMS and absolute sulfatide quantities determined by the other two methods. However, high concentrations of sulfatides in the papillae and medulla result in an up to 4-fold signal suppression. In conclusion, our study suggests that MALDI IMS is useful for semi-quantitative dissection of relative local changes of sulfatides and possibly other lipids in tissue.     

Molecular histoproteomy by MALDI mass spectrometry imaging to uncover markers of the impact of Nosema on Apis mellifera

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a powerful technology used to investigate the spatio-temporal distribution of a huge number of molecules throughout a body/tissue section. In this paper, we report the use of MALDI IMS to follow the molecular impact of an experimental infection of Apis mellifera with the microsporidia Nosema ceranae. We performed representative molecular mass fingerprints of selected tissues obtained by dissection. This was followed by MALDI IMS workflows optimization including specimen embedding and positioning as well as washing and matrix application. We recorded the local distribution of peptides/proteins within different tissues from experimentally infected versus non infected honeybees. As expected, a distinction in these molecular profiles between the two conditions was recorded from different anatomical sections of the gut tissue. More importantly, we observed differences in the molecular profiles in the brain, thoracic ganglia, hypopharyngeal glands, and hemolymph. We introduced MALDI IMS as an effective approach to monitor the impact of N. ceranae infection on A. mellifera. This opens perspectives for the discovery of molecular changes in peptides/proteins markers that could contribute to a better understanding of the impact of stressors and toxicity on different tissues of a bee in a single experiment.     

Penalized likelihood and multiple testing

The classical multiple testing model remains an important practical area of statistics with new approaches still being developed. In this paper we develop a new multiple testing procedure inspired by a method sometimes used in a problem with a different focus. Namely, the inference after model selection problem. We note that solutions to that problem are often accomplished by making use of a penalized likelihood function. A classic example is the Bayesian information criterion (BIC) method. In this paper we construct a generalized BIC method and evaluate its properties as a multiple testing procedure. The procedure is applicable to a wide variety of statistical models including regression, contrasts, treatment versus control, change point, and others. Numerical work indicates that, in particular, for sparse models the new generalized BIC would be preferred over existing multiple testing procedures.     

Trials and tribulations of statistical significance in biochemistry and omics

Over recent years many statisticians and researchers have highlighted that statistical inference would benefit from a better use and understanding of hypothesis testing, p-values, and statistical significance. We highlight three recommendations in the context of biochemical sciences. First recommendation: to improve the biological interpretation of biochemical data, do not use p-values (or similar test statistics) as thresholded values to select biomolecules. Second recommendation: to improve comparison among studies and to achieve robust knowledge, perform complete reporting of data. Third recommendation: statistical analyses should be reported completely with exact numbers (not as asterisks or inequalities). Owing to the high number of variables, a better use of statistics is of special importance in omic studies.     

Comprehensive Identification of Proteins from MALDI Imaging

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a powerful tool for the visualization of proteins in tissues and has demonstrated considerable diagnostic and prognostic value. One main challenge is that the molecular identity of such potential biomarkers mostly remains unknown. We introduce a generic method that removes this issue by systematically identifying the proteins embedded in the MALDI matrix using a combination of bottom-up and top-down proteomics. The analyses of ten human tissues lead to the identification of 1400 abundant and soluble proteins constituting the set of proteins detectable by MALDI IMS including >90% of all IMS biomarkers reported in the literature. Top-down analysis of the matrix proteome identified 124 mostly N- and C-terminally fragmented proteins indicating considerable protein processing activity in tissues. All protein identification data from this study as well as the IMS literature has been deposited into MaTisse, a new publically available database, which we anticipate will become a valuable resource for the IMS community.Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS)¹ is an emerging technique that can be described as a multi-color molecular microscope as it allows visualizing the distribution of many molecules as mass to charge (m/z) signals in parallel in situ (1). Originally described some 15 years ago (2) the method has been successfully adapted to different analyte classes including small molecule drugs (3), metabolites (4), lipids (5), proteins (6), and peptides (7) using e.g. formalin fixed paraffin embedded (FFPE) as well as fresh frozen tissue (8). Because the tissue stays intact in the process, MALDI IMS is compatible with histochemistry (9) as well as immunohistochemistry and thus adds an additional dimension of molecular information to classical microscopy based tissue analysis (10). Imaging of proteins is appealing as it conceptually allows determining the localization and abundance of proteoforms (11) that naturally occur in the tissue under investigation including modifications such as phosphorylation, acetylation, or ubiquitination, protease mediated cleavage or truncation (12). Therefore a proteinous m/z species detected by MALDI IMS can be viewed as an in situ molecular probe of a particular biological process. In turn, m/z abundance patterns that discriminate different physiological or pathological conditions might be used as diagnostic or even prognostic markers (13, 14). In recent years, MALDI IMS of proteins has been successfully applied to different cancer types from the brain (15), breast (16, 17), kidney (18), prostate (19), and skin (20). Furthermore, the technique has been applied in the context of colon inflammation (21), embryonic development (22), Alzheimer''s disease (23), and amyotrophic lateral sclerosis (24). With a few notable exceptions (13, 14, 16–18, 20, 24–30), the identity of the proteins constituting the observed characteristic m/z patters has generally remained elusive. This not only precludes the validation of the putative biomarkers by, for example, immunohistochemistry, but also the elucidation of the biological processes that might underlie the observed phenotype.Here, we introduce a straightforward extraction and identification method for proteins embedded in the MALDI matrix layer that represent the molecular species amenable to MALDI IMS. Using a bottom-up proteomics approach including tryptic digestion and liquid chromatography tandem mass spectrometry (LC-MS/MS), we first created an inventory list of proteins derived from this layer, which we term the MALDI matrix proteome. Although the bottom-up approach breaks the link between the identified proteins and the m/z species detected in MALDI IMS, the list of identified proteins serves as the pool of proteins from which all potential biomarkers are most likely derived. Indeed we detected >90% of all human MALDI IMS biomarkers reported in the literature by analyzing just ten human tissues. In addition, the results demonstrate that the same inventory can be used as a focused database for direct top-down sequencing and identification of proteins extracted from the MALDI matrix layer. The proposed method is generic and can be applied to any MALDI IMS study, which is why we believe that one of the major challenges in identifying MALDI IMS biomarkers has now been overcome. In addition, we provide a list of all proteins and peptides identified in the MALDI matrices and tissues studied here as well as a comprehensive list of m/z species identified in the literature dealing with MALDI imaging of humans and rodents. This information has been compiled in MaTisse (http://www.wzw.tum.de/bioanalytik/matisse), a new publically available and searchable database, which we believe will become a valuable tool for the MALDI imaging community.     

Spatial and temporal alterations of phospholipids determined by mass spectrometry during mouse embryo implantation

Molecular events involved in successful embryo implantation are not well understood. In this study, we used MALDI imaging mass spectrometry (IMS) technologies to characterize the spatial and temporal distribution of phospholipid species associated with mouse embryo implantation. Molecular images showing phospholipid distribution within implantation sites changed markedly between distinct cellular areas during days 4–8 of pregnancy. For example, by day 8, linoleate- and docosahexaenoate-containing phospholipids localized to regions destined to undergo cell death, whereas oleate-containing phospholipids localized to angiogenic regions. Arachidonate-containing phospholipids showed different segregation patterns depending on the lipid class, revealing a strong correlation of phosphatidylethanolamines and phosphatidylinositols with cytosolic phospholipase A_2α and cyclooxygenase-2 during embryo implantation. LC-ESI-MS/MS was used to validate MALDI IMS phospholipid distribution patterns. Overall, molecular images revealed the dynamic complexity of lipid distributions in early pregnancy, signifying the importance of complex interplay of lipid molecules in uterine biology and implantation.     

Imaging mass spectrometry: principle and application

Imaging mass spectrometry (IMS) is two-dimensional mass spectrometry to visualize the spatial distribution of biomolecules, which does not need either separation or purification of target molecules, and enables us to monitor not only the identification of unknown molecules but also the localization of numerous molecules simultaneously. Among the ionization techniques, matrix assisted laser desorption/ionization (MALDI) is one of the most generally used for IMS, which allows the analysis of numerous biomolecules ranging over wide molecular weights. Proper selection and preparation of matrix is essential for successful imaging using IMS. Tandem mass spectrometry, which is referred to MSⁿ, enables the structural analysis of a molecule detected by the first step of IMS. Applications of IMS were initially developed for studying proteins or peptides. At present, however, targets of IMS research have expanded to the imaging of small endogenous metabolites such as lipids, exogenous drug pharmacokinetics, exploring new disease markers, and other new scientific fields. We hope that this new technology will open a new era for biophysics.     

Statistical Inference for Hardy-Weinberg Proportions in the Presence of Missing Genotype Information

In genetic association studies, tests for Hardy-Weinberg proportions are often employed as a quality control checking procedure. Missing genotypes are typically discarded prior to testing. In this paper we show that inference for Hardy-Weinberg proportions can be biased when missing values are discarded. We propose to use multiple imputation of missing values in order to improve inference for Hardy-Weinberg proportions. For imputation we employ a multinomial logit model that uses information from allele intensities and/or neighbouring markers. Analysis of an empirical data set of single nucleotide polymorphisms possibly related to colon cancer reveals that missing genotypes are not missing completely at random. Deviation from Hardy-Weinberg proportions is mostly due to a lack of heterozygotes. Inbreeding coefficients estimated by multiple imputation of the missings are typically lowered with respect to inbreeding coefficients estimated by discarding the missings. Accounting for missings by multiple imputation qualitatively changed the results of 10 to 17% of the statistical tests performed. Estimates of inbreeding coefficients obtained by multiple imputation showed high correlation with estimates obtained by single imputation using an external reference panel. Our conclusion is that imputation of missing data leads to improved statistical inference for Hardy-Weinberg proportions.     

Global changes in phospholipids identified by MALDI MS in rats with focal cerebral ischemia

Neuronal membrane phospholipids are highly affected by oxidative stress caused by ischemic injury. Thus, it is necessary to identify key lipid components that show changes during ischemia to develop an effective approach to prevent brain damage from ischemic injury. The recent development of MALDI imaging MS (MALDI IMS) makes it possible to identify phospholipids that change between damaged and normal regions directly from tissues. In this study, we conducted IMS on rat brains damaged by ischemic injury and detected various phospholipids that showed unique distributions between normal and damaged areas of the brain. Among them, we confirmed changes in phospholipids such as lysophosphatidylcholine, phosphatidylcholine, phosphatidylethanolamine, and sphingomyelin by MALDI IMS followed by MS/MS analysis. These lipids were present in high concentrations in the brain and are important for maintenance of cellular structure as well as production of second messengers for cellular signal transduction. Our results emphasize the identification of phospholipid markers for ischemic injury and successfully identified several distinctly located phospholipids in ischemic brain tissue.     

Imaging mass spectrometry of proteins and peptides: 3D volume reconstruction

As large genomic and proteomic datasets are generated from homogenates of various tissues, the need for information on the spatial localization of their encoded products has become more pressing. Matrix-assisted laser desorption-ionization (MALDI) imaging mass spectrometry (IMS) offers investigators the means with which to unambiguously study peptides and proteins with molecular specificity, and to determine their distribution in two and three dimensions. In the past few years, several parameters have been optimized for IMS, including sample preparation, matrix application and instrumental acquisition parameters (Box 1). These developments have resulted in a high degree of reproducibility in mass accuracy and peak intensities (Supplementary Fig. 1 online). Recently, we have optimized our protocol to be able to increase the number of molecular species analyzed by collecting two sets of sections, covering one set of sections with sinapinic acid for optimal detection of proteins and adjacent sections with 2,5-dihydroxybenzoic acid (DHB) matrix for the optimal detection of low-mass species, including peptides. Approximately 1,000 peaks can be observed in each dataset (Fig. 1). Furthermore, the sections are collected at an equal distance, 200 mum instead of 400-500 mum used previously, thus enabling the use of virtual z-stacks and three-dimensional (3D) volume renderings to investigate differential localization patterns in much smaller brain structures such as the substantia nigra and the interpeduncular nucleus. Here we present our optimized step-by-step procedure based on previous work in our laboratory, describing how to make 3D volume reconstructions of MALDI IMS data, as applied to the rat brain.     

Assessment of LD matrix measures for the analysis of biological pathway association

Complex diseases will have multiple functional sites, and it will be invaluable to understand the cross-locus interaction in terms of linkage disequilibrium (LD) between those sites (epistasis) in addition to the haplotype-LD effects. We investigated the statistical properties of a class of matrix-based statistics to assess this epistasis. These statistical methods include two LD contrast tests (Zaykin et al., 2006) and partial least squares regression (Wang et al., 2008). To estimate Type 1 error rates and power, we simulated multiple two-variant disease models using the SIMLA software package. SIMLA allows for the joint action of up to two disease genes in the simulated data with all possible multiplicative interaction effects between them. Our goal was to detect an interaction between multiple disease-causing variants by means of their linkage disequilibrium (LD) patterns with other markers. We measured the effects of marginal disease effect size, haplotype LD, disease prevalence and minor allele frequency have on cross-locus interaction (epistasis). In the setting of strong allele effects and strong interaction, the correlation between the two disease genes was weak (r=0.2). In a complex system with multiple correlations (both marginal and interaction), it was difficult to determine the source of a significant result. Despite these complications, the partial least squares and modified LD contrast methods maintained adequate power to detect the epistatic effects; however, for many of the analyses we often could not separate interaction from a strong marginal effect. While we did not exhaust the entire parameter space of possible models, we do provide guidance on the effects that population parameters have on cross-locus interaction.     

L-DOPA-induced dyskinesia is associated with regional increase of striatal dynorphin peptides as elucidated by imaging mass spectrometry

Opioid peptides are involved in various pathophysiological processes, including algesia, epilepsy, and drug dependence. A strong association between L-DOPA-induced dyskinesia (LID) and elevated prodynorphin mRNA levels has been established in both patients and in animal models of Parkinson's disease, but to date the endogenous prodynorphin peptide products have not been determined. Here, matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) was used for characterization, localization, and relative quantification of striatal neuropeptides in a rat model of LID in Parkinson's disease. MALDI IMS has the unique advantage of high sensitivity and high molecular specificity, allowing comprehensive detection of multiple molecular species in a single tissue section. Indeed, several dynorphins and enkephalins could be detected in the present study, including dynorphin A(1-8), dynorphin B, α-neoendorphin, MetEnkRF, MetEnkRGL, PEnk (198-209, 219-229). IMS analysis revealed elevated levels of dynorphin B, α-neoendorphin, substance P, and PEnk (220-229) in the dorsolateral striatum of high-dyskinetic animals compared with low-dyskinetic and lesion-only control rats. Furthermore, the peak-intensities of the prodynorphin derived peptides, dynorphin B and α-neoendorphin, were strongly and positively correlated with LID severity. Interestingly, these LID associated dynorphin peptides are not those with high affinity to κ opioid receptors, but are known to bind and activate also μ- and Δ-opioid receptors. In addition, the peak intensities of a novel endogenous metabolite of α-neoendorphin lacking the N-terminal tyrosine correlated positively with dyskinesia severity. MALDI IMS of striatal sections from Pdyn knockout mice verified the identity of fully processed dynorphin peptides and the presence of endogenous des-tyrosine α-neoendorphin. Des-tyrosine dynorphins display reduced opioid receptor binding and this points to possible novel nonopioid receptor mediated changes in the striatum of dyskinetic rats. Because des-tyrosine dynorphins can only be detected by mass spectrometry, as no antibodies are available, these findings highlight the importance of MALDI IMS analysis for the study of molecular dynamics in neurological diseases.     

Inference for nonparanormal partial correlation via regularized rank-based nodewise regression

Partial correlation is a common tool in studying conditional dependence for Gaussian distributed data. However, partial correlation being zero may not be equivalent to conditional independence under non-Gaussian distributions. In this paper, we propose a statistical inference procedure for partial correlations under the high-dimensional nonparanormal (NPN) model where the observed data are normally distributed after certain monotone transformations. The NPN partial correlation is the partial correlation of the normal transformed data under the NPN model, which is a more general measure of conditional dependence. We estimate the NPN partial correlations by regularized nodewise regression based on the empirical ranks of the original data. A multiple testing procedure is proposed to identify the nonzero NPN partial correlations. The proposed method can be carried out by a simple coordinate descent algorithm for lasso optimization. It is easy-to-implement and computationally more efficient compared to the existing methods for estimating NPN graphical models. Theoretical results are developed to show the asymptotic normality of the proposed estimator and to justify the proposed multiple testing procedure. Numerical simulations and a case study on brain imaging data demonstrate the utility of the proposed procedure and evaluate its performance compared to the existing methods. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.     

Revisiting rat spermatogenesis with MALDI imaging at 20-microm resolution

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

Improved two‐stage group sequential procedures for testing a secondary endpoint after the primary endpoint achieves significance

In two‐stage group sequential trials with a primary and a secondary endpoint, the overall type I error rate for the primary endpoint is often controlled by an α‐level boundary, such as an O'Brien‐Fleming or Pocock boundary. Following a hierarchical testing sequence, the secondary endpoint is tested only if the primary endpoint achieves statistical significance either at an interim analysis or at the final analysis. To control the type I error rate for the secondary endpoint, this is tested using a Bonferroni procedure or any α‐level group sequential method. In comparison with marginal testing, there is an overall power loss for the test of the secondary endpoint since a claim of a positive result depends on the significance of the primary endpoint in the hierarchical testing sequence. We propose two group sequential testing procedures with improved secondary power: the improved Bonferroni procedure and the improved Pocock procedure. The proposed procedures use the correlation between the interim and final statistics for the secondary endpoint while applying graphical approaches to transfer the significance level from the primary endpoint to the secondary endpoint. The procedures control the familywise error rate (FWER) strongly by construction and this is confirmed via simulation. We also compare the proposed procedures with other commonly used group sequential procedures in terms of control of the FWER and the power of rejecting the secondary hypothesis. An example is provided to illustrate the procedures.     

MALDI Mass Spectrometry Imaging: A Novel Tool for the Identification and Classification of Amyloidosis

Amyloidosis is a group of diseases caused by extracellular accumulation of fibrillar polypeptide aggregates. So far, diagnosis is performed by Congo red staining of tissue sections in combination with polarization microscopy. Subsequent identification of the causative protein by immunohistochemistry harbors some difficulties regarding sensitivity and specificity. Mass spectrometry based approaches have been demonstrated to constitute a reliable method to supplement typing of amyloidosis, but still depend on Congo red staining. In the present study, we used matrix‐assisted laser desorption/ionization mass spectrometry imaging coupled with ion mobility separation (MALDI‐IMS MSI) to investigate amyloid deposits in formalin‐fixed and paraffin‐embedded tissue samples. Utilizing a novel peptide filter method, we found a universal peptide signature for amyloidoses. Furthermore, differences in the peptide composition of ALλ and ATTR amyloid were revealed and used to build a reliable classification model. Integrating the peptide filter in MALDI‐IMS MSI analysis, we developed a bioinformatics workflow facilitating the identification and classification of amyloidosis in a less time and sample‐consuming experimental setup. Our findings demonstrate also the feasibility to investigate the amyloid's protein composition, thus paving the way to establish classification models for the diverse types of amyloidoses and to shed further light on the complex process of amyloidogenesis.     

Direct profiling and identification of peptide expression differences in the pancreas of control and ob/ob mice by imaging mass spectrometry

Imaging mass spectrometry (IMS) technology utilizes MALDI MS to map molecules of interest in thin tissue sections. In this study, we have evaluated the potential of MALDI IMS to study peptide expression patterns in the mouse pancreas under normal and pathological conditions, and to in situ identify peptides of interest using MS/MS. Different regions of the pancreas of both control and ob/ob mice were imaged, resulting in peptide-specific profiles. The distribution of ions of m/z 3120 and 3439 displayed a striking resemblance with Langerhans islet's histology and, following MS/MS fragmentation and database searching were identified as C-peptide of insulin and glicentin-related polypeptide, respectively. In addition, a significant increase of the 3120 peak intensity in the obese mice was observed. This study underscores the potential of MALDI IMS to study the contribution of peptides to pancreas pathology.