Project description:In order to research the variation in protein distribution in teeth, proteins were extracted from archaeological (15-18th century, Netherlands) and modern teeth and identified using LC-MS/MS. Of the recovered proteins we then visualised the distribution of collagen type I (both the alpha-1 and -2 chains), alpha-2-HS-glycoprotein, haemoglobin subunit alpha and myosin light polypeptide 6 using MALDI-MSI. We found distinct differences in the spatial distributions of different proteins as well as between some peptides of the same protein. The reason for these differences in protein spatial distribution remain unclear, yet this study highlights the ability of MALDI-MSI for visualisng the spatial distribution of proteins in archaeological biomineralised tissues. Therefore MALDI-MSI might prove a useful tool to improve our understanding of protein preservation as well as aid in deciding sampling strategies.
Project description:The human prostate tissue sample was analyzed by 2D MALDI FT ICR MSI. For detailed information we refer to the msiPL manuscript by Abdelmoula et al.
Project description:INTRODUCTION: Mass Spectrometry Imaging (MSI) is a hybrid mass spectrometry technique that integrates aspects of traditional microscopy and mass spectrometry-based omics analysis. The traditional MALDI TOF/TOF instrument still remains the dominant platform for this type of anal-ysis. However, with reduced mass resolution compared to other platforms it is insufficient to rely on mass resolution alone for peptide identification. Here we propose a hybrid method of data analysis that integrates both image-based analysis and a parallel protein identification workflow using peptide mass fingerprinting, and its successful application to the detection and validation of viral biomarkers. METHODS: FFPE samples were imaged as described previously in an UltrafleXtreme MALDI TOF/TOF. Total mass spectra were exported and searched against the mouse FASTA da-tabase, while companion images were exported and processed with image J. RESULTS: Peptide mass fingerprinting (PMF) revealed 14 target peptides that were successfully assigned to viral proteins while a pixel based correlational analysis revealed a very high R2 correlation (>0.81) be-tween those same peptides assigned to the NS1 and VP1 viral proteins. CONCLUSIONS: We successfully identified and validated the presence of viral biomarkers to a high degree of confidence using MALDI MSI.
Project description:With the current clinical available technologies, not all cancers are staged accurately. Because of this a large percentage of patients are misclassified before treatment, leading to under or over treatment. Therefore, a classification system based on the molecular feature is required to deter-mine the tumour behavior and metastatic potential. Here, we have shown the diagnostic potential of MALDI MSI using supervised machine learning approach in distinguishing cancerous colorectal tissue from normal with an overall accuracy of 98%. Also, shown is the capability of the technique in predicting the presence of metastasis in endometrial cancer with an overall accuracy of 80%. The development of such a model can help in determining the optimum treatment for cancerous pa-tients, reduce morbidity and better patient outcome.
Project description:The goal of the project is the detection of region-specific lipid features that discriminate between symptomatic and asymptomatic human carotid atherosclerotic plaques by MALDI MSI.
Project description:To facilitate analysis of protein expression changes in in situ tumors and stroma, we took advantage of a mouse model that permits conditional activation of the Ser-Thr kinase ROCK within mammary tumor cells. In this study, we undertook MALDI-MSI analysis of tissue samples derived from our conditional ROCK mammary tumor model, to quantify in an unbiased manner, the proteomic changes occurring during the progression of mammary cancers in their specific spatial contexts.
Project description:An integrated diagnosis using molecular features is recommended in the updated World Health Organization (WHO) classification. Our aim was to explore the role of MALDI-Mass spectrometry imaging (MSI) coupled to microproteomics in order to classify anaplastic glioma by integration of clinical data.
Project description:In osteoarthritis (OA), impairment of cartilage regeneration can be related to a defective chondrogenic differentiation of mesenchymal stromal cells (MSCs). Therefore, understanding the proteomic- and metabolomic-associated molecular events during the chondrogenesis of MSCs could provide alternative targets for therapeutic intervention. Here, a SILAC-based proteomic analysis identified 43 proteins related with metabolic pathways whose abundance was significantly altered during the chondrogenesis of OA human bone marrow MSCs (hBMSCs). Then, the level and distribution of metabolites was analyzed in these cells and healthy controls by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), leading to the recognition of characteristic metabolomic profiles at the early stages of differentiation. Finally, integrative pathway analysis showed that UDP-glucuronic acid synthesis and amino sugar metabolism were downregulated in OA hBMSCs during chondrogenesis compared to healthy cells. Alterations in these metabolic pathways may disturb the production of hyaluronic acid (HA) and other relevant cartilage extracellular matrix (ECM) components. This work provides a novel integrative insight into the molecular alterations of osteoarthritic MSCs and potential therapeutic targets for OA drug development through the enhancement of chondrogenesis.
Project description:Here we present an approach to identify N-linked glycoproteins and deduce their spatial localization using a combination of MALDI N-glycan MSI and spatially-resolved glycoproteomics. We subjected glioma biopsies to on-tissue PNGaseF digestion and MALDI-MSI and found that the glycan HexNAc4-Hex5-NeuAc2 was predominantly expressed in necrotic regions of high-grade canine gliomas. To determine the underlying sialo-glycoprotein, various regions in adjacent tissue sections were subjected to microdigestion and manual glycoproteomic analysis. Results identified haptoglobin as the protein associated with HexNAc4-Hex5-NeuAc2, making our study the first report that directly links glycan imaging with intact glycopeptide identification. In total, our spatially-resolved glycoproteomics technique identified over 400 N-, O-, and S- glycopeptides from over 30 proteins, demonstrating the diverse array of glycosylation present on the tissue slides and the sensitivity of our technique. Ultimately, this proof-of-principle work demonstrates that spatially-resolved glycoproteomics greatly complement MALDI-MSI in understanding dysregulated glycosylation.