MALDI MSI of MeLiM melanoma: Searching for differences in protein profiles

Multimodal MALDI imaging mass spectrometry for improved diagnosis of melanoma

Imaging mass spectrometry (IMS) provides promising avenues to augment histopathological investigation with rich spatio-molecular information. We have previously developed a classification model to differentiate melanoma from nevi lesions based on IMS protein data, a task that is challenging solely by histopathologic evaluation. Most IMS-focused studies collect microscopy in tandem with IMS data, but this microscopy data is generally omitted in downstream data analysis. Microscopy, nevertheless, forms the basis for traditional histopathology and thus contains invaluable morphological information. In this work, we developed a multimodal classification pipeline that uses deep learning, in the form of a pre-trained artificial neural network, to extract the meaningful morphological features from histopathological images, and combine it with the IMS data. To test whether this deep learning-based classification strategy can improve on our previous results in classification of melanocytic neoplasia, we utilized MALDI IMS data with collected serial H&E stained sections for 331 patients, and compared this multimodal classification pipeline to classifiers using either exclusively microscopy or IMS data. The multimodal pipeline achieved the best performance, with ROC-AUCs of 0.968 vs. 0.938 vs. 0.931 for the multimodal, unimodal microscopy and unimodal IMS pipelines respectively. Due to the use of a pre-trained network to perform the morphological feature extraction, this pipeline does not require any training on large amounts of microscopy data. As such, this framework can be readily applied to improve classification performance in other experimental settings where microscopy data is acquired in tandem with IMS experiments.

Application of clustering strategy for automatic segmentation of tissue regions in mass spectrometry imaging

RATIONALE

Mass spectrometry imaging (MSI) has been widely used in biomedical research fields. Each pixel in MSI consists of a mass spectrum that reflects the molecule feature of the tissue spot. Because MSI contains high-dimensional datasets, it is highly desired to develop computational methods for data mining and constructing tissue segmentation maps.

METHODS

To visualize different tissue regions based on mass spectrum features and improve the efficiency in processing enormous data, we proposed a computational strategy that consists of four procedures including preprocessing, data reduction, clustering, and quantitative validation.

RESULTS

In this study, we examined the combination of t-distributed stochastic neighbor embedding (t-SNE) and hierarchical clustering (HC) for MSI data analysis. Using publicly available MSI datasets, one dataset of mouse urinary bladder, and one dataset of human colorectal cancer, we demonstrated that the generated tissue segmentation maps from this combination were superior to other data reduction and clustering algorithms. Using the staining image as a reference, we assessed the performance of clustering algorithms with external and internal clustering validation measures, including purity, adjusted Rand index (ARI), Davies-Bouldin index (DBI), and spatial aggregation index (SAI). The result indicated that SAI delivered excellent performance for automatic segmentation of tissue regions in MSI.

CONCLUSIONS

We used a clustering algorithm to construct tissue automatic segmentation in MSI datasets. The performance was evaluated by comparing it with the stained image and calculating clustering validation indexes. The results indicated that SAI is important for automatic tissue segmentation in MSI, different from traditional clustering validation measures. Compared to the reports that used internal clustering validation measures such as DBI, our method offers more effective evaluation of clustering results for MSI segmentation. We envision that the proposed automatic image segmentation strategy can facilitate deep learning in molecular feature extraction and biomarker discovery for the biomedical applications of MSI.

Advances in mass spectrometry imaging for spatial cancer metabolomics

Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.

Quantitative mass spectrometry imaging: therapeutics & biomolecules

Mass spectrometry imaging (MSI) has become increasingly utilized in the analysis of biological molecules. MSI grants the ability to spatially map thousands of molecules within one experimental run in a label-free manner. While MSI is considered by most to be a qualitative method, recent advancements in instrumentation, sample preparation, and development of standards has made quantitative MSI (qMSI) more common. In this feature article, we present a tailored review of recent advancements in qMSI of therapeutics and biomolecules such as lipids and peptides/proteins. We also provide detailed experimental considerations for conducting qMSI studies on biological samples, aiming to advance the methodology.

Applications of MALDI-MS/MS-Based Proteomics in Biomedical Research

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) is one of the most widely used techniques in proteomics to achieve structural identification and characterization of proteins and peptides, including their variety of proteoforms due to post-translational modifications (PTMs) or protein–protein interactions (PPIs). MALDI-MS and MALDI tandem mass spectrometry (MS/MS) have been developed as analytical techniques to study small and large molecules, offering picomole to femtomole sensitivity and enabling the direct analysis of biological samples, such as biofluids, solid tissues, tissue/cell homogenates, and cell culture lysates, with a minimized procedure of sample preparation. In the last decades, structural identification of peptides and proteins achieved by MALDI-MS/MS helped researchers and clinicians to decipher molecular function, biological process, cellular component, and related pathways of the gene products as well as their involvement in pathogenesis of diseases. In this review, we highlight the applications of MALDI ionization source and tandem approaches for MS for analyzing biomedical relevant peptides and proteins. Furthermore, one of the most relevant applications of MALDI-MS/MS is to provide “molecular pictures”, which offer in situ information about molecular weight proteins without labeling of potential targets. Histology-directed MALDI-mass spectrometry imaging (MSI) uses MALDI-ToF/ToF or other MALDI tandem mass spectrometers for accurate sequence analysis of peptide biomarkers and biological active compounds directly in tissues, to assure complementary and essential spatial data compared with those obtained by LC-ESI-MS/MS technique.

Dysbiosis of skin microbiome and gut microbiome in melanoma progression

Background

The microbiome alterations are associated with cancer growth and may influence the immune system and response to therapy. Particularly, the gut microbiome has been recently shown to modulate response to melanoma immunotherapy. However, the role of the skin microbiome has not been well explored in the skin tumour microenvironment and the link between the gut microbiome and skin microbiome has not been investigated in melanoma progression. Therefore, the aim of the present study was to examine associations between dysbiosis in the skin and gut microbiome and the melanoma growth using MeLiM porcine model of melanoma progression and spontaneous regression.

Results

Parallel analysis of cutaneous microbiota and faecal microbiota of the same individuals was performed in 8 to 12 weeks old MeLiM piglets. The bacterial composition of samples was analysed by high throughput sequencing of the V4-V5 region of the 16S rRNA gene. A significant difference in microbiome diversity and richness between melanoma tissue and healthy skin and between the faecal microbiome of MeLiM piglets and control piglets were observed. Both Principal Coordinate Analysis and Non-metric multidimensional scaling revealed dissimilarities between different bacterial communities. Linear discriminant analysis effect size at the genus level determined different potential biomarkers in multiple bacterial communities. Lactobacillus, Clostridium sensu stricto 1 and Corynebacterium 1 were the most discriminately higher genera in the healthy skin microbiome, while Fusobacterium, Trueperella, Staphylococcus, Streptococcus and Bacteroides were discriminately abundant in melanoma tissue microbiome. Bacteroides, Fusobacterium and Escherichia-Shigella were associated with the faecal microbiota of MeLiM piglets. Potential functional pathways analysis based on the KEGG database indicated significant differences in the predicted profile metabolisms between the healthy skin microbiome and melanoma tissue microbiome. The faecal microbiome of MeLiM piglets was enriched by genes related to membrane transports pathways allowing for the increase of intestinal permeability and alteration of the intestinal mucosal barrier.

Conclusion

The associations between melanoma progression and dysbiosis in the skin microbiome as well as dysbiosis in the gut microbiome were identified. Results provide promising information for further studies on the local skin and gut microbiome involvement in melanoma progression and may support the development of new therapeutic approaches.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-022-02458-5.

Mapping of MeLiM melanoma combining ICP-MS and MALDI-MSI methods

Here we developed a powerful tool for comprehensive data collection and mapping of molecular and elemental signatures in the Melanoma-bearing Libechov Minipig (MeLiM) model. The combination of different mass spectrometric methods allowed for detail investigation of specific melanoma markers and elements and their spatial distribution in tissue sections. MALDI-MSI combined with HPLC-MS/MS analyses resulted in identification of seven specific proteins, S100A12, CD163, MMP-2, galectin-1, tenascin, resistin and PCNA that were presented in the melanoma signatures. Furthermore, the ICP-MS method allowed for spatial detection of zinc, calcium, copper, and iron elements linked with the allocation of the specific binding proteins.

Applications of spatially resolved omics in the field of endocrine tumors

Endocrine tumors derive from endocrine cells with high heterogeneity in function, structure and embryology, and are characteristic of a marked diversity and tissue heterogeneity. There are still challenges in analyzing the molecular alternations within the heterogeneous microenvironment for endocrine tumors. Recently, several proteomic, lipidomic and metabolomic platforms have been applied to the analysis of endocrine tumors to explore the cellular and molecular mechanisms of tumor genesis, progression and metastasis. In this review, we provide a comprehensive overview of spatially resolved proteomics, lipidomics and metabolomics guided by mass spectrometry imaging and spatially resolved microproteomics directed by microextraction and tandem mass spectrometry. In this regard, we will discuss different mass spectrometry imaging techniques, including secondary ion mass spectrometry, matrix-assisted laser desorption/ionization and desorption electrospray ionization. Additionally, we will highlight microextraction approaches such as laser capture microdissection and liquid microjunction extraction. With these methods, proteins can be extracted precisely from specific regions of the endocrine tumor. Finally, we compare applications of proteomic, lipidomic and metabolomic platforms in the field of endocrine tumors and outline their potentials in elucidating cellular and molecular processes involved in endocrine tumors.

[Mass spectrometry imaging technology and its application in breast cancer research]

乳腺癌是女性最常见的恶性肿瘤,其发病率在世界范围内呈现上升趋势,是威胁女性健康的重要疾病之一。随着现代医学技术的快速发展,早期有效的诊断和筛查方法能够改善乳腺癌患者生存率和提高其生活质量。由于乳腺癌肿瘤具有非常显著的异质性,这对于诊断和筛查带来了较大困难,亟须在肿瘤演进时间信息中,继续引入生物分子的空间信息,从而对其异质性、肿瘤微环境等进行准确的追踪。质谱成像技术,可在免标记的前提下利用离子质荷比的特性发现生物组织中的各种分子,并研究这些分子的时间和空间信息,对其进行准确的定性、定量和空间定位。目前,通过质谱成像技术可直接获取药物及其代谢物、内源性代谢物、脂质、多肽和蛋白质等在组织中的空间分布信息,为肿瘤分子分型诊断和确认以及相关抗肿瘤药物的筛选提供了新的思路和研究方向。该综述以乳腺癌相关的生物样品制备和研究进展为主要内容,从小分子样本、大分子样本、石蜡包埋样本、基质喷涂方式、常用离子源等方面阐述质谱成像中样本制备的重要性以及样品制备过程中存在的难点问题。同时,以细胞模型、动物模型和临床肿瘤标本为研究对象,汇总了质谱成像技术在乳腺癌方面的应用进展,并进行了展望,为开展癌症精准分型研究和药物药效的快速筛查提供了重要依据。

Diagnosis of melanoma by imaging mass spectrometry: Development and validation of a melanoma prediction model

BACKGROUND

The definitive diagnosis of melanocytic neoplasia using solely histopathologic evaluation can be challenging. Novel techniques that objectively confirm diagnoses are needed. This study details the development and validation of a melanoma prediction model from spatially resolved multivariate protein expression profiles generated by imaging mass spectrometry (IMS).

METHODS

Three board-certified dermatopathologists blindly evaluated 333 samples. Samples with triply concordant diagnoses were included in this study, divided into a training set (n = 241) and a test set (n = 92). Both the training and test sets included various representative subclasses of unambiguous nevi and melanomas. A prediction model was developed from the training set using a linear support vector machine classification model.

RESULTS

We validated the prediction model on the independent test set of 92 specimens (75 classified correctly, 2 misclassified, and 15 indeterminate). IMS detects melanoma with a sensitivity of 97.6% and a specificity of 96.4% when evaluating each unique spot. IMS predicts melanoma at the sample level with a sensitivity of 97.3% and a specificity of 97.5%. Indeterminate results were excluded from sensitivity and specificity calculations.

CONCLUSION

This study provides evidence that IMS-based proteomics results are highly concordant to diagnostic results obtained by careful histopathologic evaluation from a panel of expert dermatopathologists.

Metallothionein-3 promotes cisplatin chemoresistance remodelling in neuroblastoma

Metallothionein-3 has poorly characterized functions in neuroblastoma. Cisplatin-based chemotherapy is a major regimen to treat neuroblastoma, but its clinical efficacy is limited by chemoresistance. We investigated the impact of human metallothionein-3 (hMT3) up-regulation in neuroblastoma cells and the mechanisms underlying the cisplatin-resistance. We confirmed the cisplatin-metallothionein complex formation using mass spectrometry. Overexpression of hMT3 decreased the sensitivity of neuroblastoma UKF-NB-4 cells to cisplatin. We report, for the first time, cisplatin-sensitive human UKF-NB-4 cells remodelled into cisplatin-resistant cells via high and constitutive hMT3 expression in an in vivo model using chick chorioallantoic membrane assay. Comparative proteomic analysis demonstrated that several biological pathways related to apoptosis, transport, proteasome, and cellular stress were involved in cisplatin-resistance in hMT3 overexpressing UKF-NB-4 cells. Overall, our data confirmed that up-regulation of hMT3 positively correlated with increased cisplatin-chemoresistance in neuroblastoma, and a high level of hMT3 could be one of the causes of frequent tumour relapses.

Metabolomic characterisation of progression and spontaneous regression of melanoma in the melanoma-bearing Libechov minipig model

Melanoma-bearing Libechov minipig (MeLiM) represents a large animal model for melanoma research. This model shows a high incidence of complete spontaneous regression of melanoma - a phenomenon uncommon in humans. Here, we present the first metabolomic characterisation of the MeLiM model comparing animals with progressing and spontaneously regressing melanomas. Plasma samples of 19 minipigs with progression and 27 minipigs with evidence of regression were analysed by a targeted metabolomic assay based on mass spectrometry detection. Differences in plasma metabolomics patterns were investigated by univariate and multivariate statistical analyses. Overall, 185 metabolites were quantified in each plasma sample. Significantly altered metabolomic profile was found, and 42 features were differentially regulated in plasma. Besides, the machine learning approach was used to create a predictive model utilising Arg/Orn and Arg/ADMA ratios to discriminate minipigs with progressive disease development from minipigs with regression evidence. Our results suggest that progression of melanoma in the MeLiM model is associated with alteration of arginine, glycerophospholipid and acylcarnitines metabolism. Moreover, this study provides targeted metabolomics characterisation of an animal model of melanoma with progression and spontaneous regression of tumours.

MALDI MSI Reveals the Spatial Distribution of Protein Markers in Tracheobronchial Lymph Nodes and Lung of Pigs after Respiratory Infection

Respiratory infections are a real threat for humans, and therefore the pig model is of interest for studies. As one of a case for studies, Actinobacillus pleuropneumoniae (APP) caused infections and still worries many pig breeders around the world. To better understand the influence of pathogenic effect of APP on a respiratory system-lungs and tracheobronchial lymph nodes (TBLN), we aimed to employ matrix-assisted laser desorption/ionization time-of-flight mass spectrometry imaging (MALDI-TOF MSI). In this study, six pigs were intranasally infected by APP and two were used as non-infected control, and 48 cryosections have been obtained. MALDI-TOF MSI and immunohistochemistry (IHC) were used to study spatial distribution of infectious markers, especially interleukins, in cryosections of porcine tissues of lungs (necrotic area, marginal zone) and tracheobronchial lymph nodes (TBLN) from pigs infected by APP. CD163, interleukin 1β (IL‑1β) and a protegrin-4 precursor were successfully detected based on their tryptic fragments. CD163 and IL‑1β were confirmed also by IHC. The protegrin-4 precursor was identified by MALDI-TOF/TOF directly on the tissue cryosections. CD163, IL‑1β and protegrin‑4 precursor were all significantly (p < 0.001) more expressed in necrotic areas of lungs infected by APP than in marginal zone, TBLN and in control lungs.

Comparison of Dietary Oils with Different Polyunsaturated Fatty Acid n-3 and n-6 Content in the Rat Model of Cutaneous Wound Healing

Dietary supplementation with polyunsaturated fatty acids (PUFA) n-3 can affect cutaneous wound healing; however, recent findings demonstrate the variable extent of their influence on the quality of healing. Here, we compare the effect of several dietary oils, containing different levels of PUFA n-3 and PUFA n-6, on wound healing in the rat model. Rats were fed the feed mixture with 8% palm oil (P), safflower oil (S), fish oil (F) or Schizochytrium microalga extract (Sch) and compared to the animals fed by control feed mixture (C). Dorsal full-thickness cutaneous excisions were performed after 52 days of feeding and skin was left to heal for an additional 12 days. Histopathological analysis of skin wounds was performed, including immune cells immunolabeling and the determination of hydroxyproline amount as well as gene expression analyses of molecules contributing to different steps of the healing. Matrix-assisted-laser-desorption-ionization mass-spectrometry-imaging (MALDI-MSI) was used to determine the amount of collagen α-1(III) chain fragment in healing samples. Treatment by Schizochytrium extract resulted in decrease in the total wound area, in contrast to the safflower oil group where the size of the wound was larger when comparing to control animals. Diet with Schizochytrium extract and safflower oils displayed a tendency to increase the number of new vessels. The number of MPO-positive cells was diminished following any of oil treatment in comparison to the control, but their highest amount was found in animals with a fish oil diet. On the other hand, the number of CD68-positive macrophages was increased, with the most significant enhancement in the fish oil and safflower oil group. Hydroxyproline concentration was the highest in the safflower oil group but it was also enhanced in all other analyzed treatments in comparison to the control. MALDI-MSI signal intensity of a collagen III fragment decreased in the sequence C > S > Sch > P > F treatment. In conclusion, we observed differences in tissue response during healing between dietary oils, with the activation of inflammation observed following the treatment with oil containing high eicosapentaenoic acid (EPA) level (fish oil) and enhanced healing features were induced by the diet with high content of docosahexaenoic acid (DHA, Schizochytrium extract).

Mass Spectrometric (MS) Analysis of Proteins and Peptides

The human genome is sequenced and comprised of ~30,000 genes, making humans just a little bit more complicated than worms or flies. However, complexity of humans is given by proteins that these genes code for because one gene can produce many proteins mostly through alternative splicing and tissue-dependent expression of particular proteins. In addition, post-translational modifications (PTMs) in proteins greatly increase the number of gene products or protein isoforms. Furthermore, stable and transient interactions between proteins, protein isoforms/proteoforms and PTM-ed proteins (protein-protein interactions, PPI) add yet another level of complexity in humans and other organisms. In the past, all of these proteins were analyzed one at the time. Currently, they are analyzed by a less tedious method: mass spectrometry (MS) for two reasons: 1) because of the complexity of proteins, protein PTMs and PPIs and 2) because MS is the only method that can keep up with such a complex array of features. Here, we discuss the applications of mass spectrometry in protein analysis.

The effect of different fatty acid sources on wound healing in rats assessed by matrix-assisted-laser-desorption-ionization mass-spectroscopy-imaging

The objective of the present study was to compare the effects of dietary oils containing polyunsaturated fatty acids (PUFA) n-3 and n-6, respectively, on cutaneous wound healing in rats, and to demonstrate the usefulness of the matrix-assisted-laser-desorption-ionization mass spectroscopy-imaging (MALDI MSI) method in this type of experiment. Superiority of PUFA n-3 in this context was the tested hypothesis. Four groups of male Wistar rats by twelve animals each were fed a diet with added 5% of palm oil (P; control), fish oil (F), Schizochytrium microalga oil (Sch) and safflower oil (S), respectively, for eight weeks. Consequently, dorsal full-thickness cutaneous excisions were performed, and selected markers of wound healing were evaluated 18 days post excision. The median of signal intensity corresponding to an amount of collagen α-1 (III) fragment, quantified using MALDI MSI, decreased in a sequence P > F > Sch > S (P < 0.001). Using haematoxylin-eosin staining of the histological preparations, semi-quantitatively assessed epithelium height tended to decrease in the order of P > S > Sch > F; the wound extent in the sequence of P > Sch > S > F; and the sequence of the progress of neo-angiogenesis was assessed as S > P > F ≈ Sch. It was concluded that the tested hypothesis was confirmed only partially: PUFA n-3 showed better results regarding the wound extent, but were inferior in terms of epithelium height and progress of neo-angiogenesis. This was the first time MALDI MSI was successfully employed for evaluating skin wound healing in a rat model.

Melanoma-Bearing Libechov Minipig (MeLiM): The Unique Swine Model of Hereditary Metastatic Melanoma

National cancer databases document that melanoma is the most aggressive and deadly cutaneous malignancy with worldwide increasing incidence in the Caucasian population. Around 10% of melanomas occur in families. Several germline mutations were identified that might help to indicate individuals at risk for preventive interventions and early disease detection. More than 50% of sporadic melanomas carry mutations in Ras/Raf/mitogen-activated protein kinase (MAPK/MEK) pathway, which may represent aims of novel targeted therapies. Despite advances in targeted therapies and immunotherapies, the outcomes in metastatic tumor are still unsatisfactory. Here, we review animal models that help our understanding of melanoma development and treatment, including non-vertebrate, mouse, swine, and other mammal models, with an emphasis on those with spontaneously developing melanoma. Special attention is paid to the melanoma-bearing Libechov minipig (MeLiM). This original swine model of hereditary metastatic melanoma enables studying biological processes underlying melanoma progression, as well as spontaneous regression. Current histological, immunohistochemical, biochemical, genetic, hematological, immunological, and skin microbiome findings in the MeLiM model are summarized, together with development of new therapeutic approaches based on tumor devitalization. The ongoing study of molecular and immunological base of spontaneous regression in MeLiM model has potential to bring new knowledge of clinical importance.

Unsupervised machine learning using an imaging mass spectrometry dataset automatically reassembles grey and white matter

Current histological and anatomical analysis techniques, including fluorescence in situ hybridisation, immunohistochemistry, immunofluorescence, immunoelectron microscopy and fluorescent fusion protein, have revealed great distribution diversity of mRNA and proteins in the brain. However, the distributional pattern of small biomolecules, such as lipids, remains unclear. To this end, we have developed and optimised imaging mass spectrometry (IMS), a combined technique incorporating mass spectrometry and microscopy, which is capable of comprehensively visualising biomolecule distribution. We demonstrated the differential distribution of phospholipids throughout the cell body and axon of neuronal cells using IMS analysis. In this study, we used solarix XR, a high mass resolution and highly sensitive MALDI-FT-ICR-MS capable of detecting higher number of molecules than conventional MALDI-TOF-MS instruments, to create a molecular distribution dataset. We examined the diversity of biomolecule distribution in rat brains using IMS and hypothesised that unsupervised machine learning reconstructs brain structures such as the grey and white matters. We have demonstrated that principal component analysis (PCA) can reassemble the grey and white matters without assigning brain anatomical regions. Hierarchical clustering allowed us to classify the 10 groups of observed molecules according to their distributions. Furthermore, the group of molecules specifically localised in the cerebellar cortex was estimated to be composed of phospholipids.

Sample Treatment for Tissue Proteomics in Cancer, Toxicology, and Forensics

Since the birth of proteomics science in the 1990, the number of applications and of sample preparation methods has grown exponentially, making a huge contribution to the knowledge in life science disciplines. Continuous improvements in the sample treatment strategies unlock and reveal the fine details of disease mechanisms, drug potency, and toxicity as well as enable new disciplines to be investigated such as forensic science.This chapter will cover the most recent developments in sample preparation strategies for tissue proteomics in three areas, namely, cancer, toxicology, and forensics, thus also demonstrating breath of application within the domain of health and well-being, pharmaceuticals, and secure societies.In particular, in the area of cancer (human tumor biomarkers), the most efficient and multi-informative proteomic strategies will be covered in relation to the subsequent application of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and liquid extraction surface analysis (LESA), due to their ability to provide molecular localization of tumor biomarkers albeit with different spatial resolution.With respect to toxicology, methodologies applied in toxicoproteomics will be illustrated with examples from its use in two important areas: the study of drug-induced liver injury (DILI) and studies of effects of chemical and environmental insults on skin, i.e., the effects of irritants, sensitizers, and ionizing radiation. Within this chapter, mainly tissue proteomics sample preparation methods for LC-MS/MS analysis will be discussed as (i) the use of LC-MS/MS is majorly represented in the research efforts of the bioanalytical community in this area and (ii) LC-MS/MS still is the gold standard for quantification studies.Finally, the use of proteomics will also be discussed in forensic science with respect to the information that can be recovered from blood and fingerprint evidence which are commonly encountered at the scene of the crime. The application of proteomic strategies for the analysis of blood and fingerprints is novel and proteomic preparation methods will be reported in relation to the subsequent use of mass spectrometry without any hyphenation. While generally yielding more information, hyphenated methods are often more laborious and time-consuming; since forensic investigations need quick turnaround, without compromising validity of the information, the prospect to develop methods for the application of quick forensic mass spectrometry techniques such as MALDI-MS (in imaging or profiling mode) is of great interest.

Proteome Imaging: From Classic to Modern Mass Spectrometry-Based Molecular Histology

In order to overcome the limitations of classic imaging in Histology during the actually era of multiomics, the multi-color "molecular microscope" by its emerging "molecular pictures" offers quantitative and spatial information about thousands of molecular profiles without labeling of potential targets. Healthy and diseased human tissues, as well as those of diverse invertebrate and vertebrate animal models, including genetically engineered species and cultured cells, can be easily analyzed by histology-directed MALDI imaging mass spectrometry. The aims of this review are to discuss a range of proteomic information emerging from MALDI mass spectrometry imaging comparative to classic histology, histochemistry and immunohistochemistry, with applications in biology and medicine, concerning the detection and distribution of structural proteins and biological active molecules, such as antimicrobial peptides and proteins, allergens, neurotransmitters and hormones, enzymes, growth factors, toxins and others. The molecular imaging is very well suited for discovery and validation of candidate protein biomarkers in neuroproteomics, oncoproteomics, aging and age-related diseases, parasitoproteomics, forensic, and ecotoxicology. Additionally, in situ proteome imaging may help to elucidate the physiological and pathological mechanisms involved in developmental biology, reproductive research, amyloidogenesis, tumorigenesis, wound healing, neural network regeneration, matrix mineralization, apoptosis and oxidative stress, pain tolerance, cell cycle and transformation under oncogenic stress, tumor heterogeneity, behavior and aggressiveness, drugs bioaccumulation and biotransformation, organism's reaction against environmental penetrating xenobiotics, immune signaling, assessment of integrity and functionality of tissue barriers, behavioral biology, and molecular origins of diseases. MALDI MSI is certainly a valuable tool for personalized medicine and "Eco-Evo-Devo" integrative biology in the current context of global environmental challenges.

Mass spectrometric imaging of cysteine rich proteins in human skin

Looking insight pathological processes, metallothioneins (MTs) are considered to be potential biomarkers for monitoring of a development of various types of diseases, such as cancer. The early identification of the MTs in biological tissues could be important tool for the estimation of appropriate clinical therapy. Therefore, here we investigated the application of matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) together with immunohistochemical analyses (IHC) using MT-1/2 antibody for MT detection in formalin-fixed paraffin-embedded (FFPE) biopsy specimens of human skin. Principal component analyses revealed differences in the peptide/protein profiles separating healthy skin from the carcinoma specimens. Statistically significant ion peaks at m/z 6038, 6300, 6676, and 7026 were more frequently detected in squamous cell carcinoma (SCC), basal cell carcinoma (BCC) and melanoma. Using IHC, we found that MT-1/2 was significantly higher in SCC and melanoma compared to healthy skin. Surprisingly, significantly low levels of MT-1/2 were found in BCC. On one side, the results indicate important role of MTs in melanoma occurrence and progression, as on the second side, there are hidden processes associated with MTs based on differences of the occurrence of the MS peaks, which could be associated with cycling of MTs isoforms.