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:In this work, we demonstrate the use of grid-aided, parafilm-assisted microdissection to perform MALDI MS imaging and shotgun proteomics and metabolomics in a combined workflow and using only a single tissue section. The grid is generated by microspotting of acid dye 25 using a piezoelectric microspotter. In the gas phase, the dye is detectable as a free radical species, and its distribution can be superimposed with ion images generated by tissue components, then used as a guide to locate regions of interest and aid during manual microdissection. Subjecting the dissected pieces to the modified Folch method allows to separate the metabolic components from the proteins. The proteins can then be subjected to overnight digestion under controlled conditions to improve protein identification yields. The proof of concept experiment on rat brain generated 162 and 140 metabolite assignments from three ROIs (cerebellum, hippocampus and midbrain/hypothalamus) in positive and negative mode, respectively, and 890, 1,303 and 1,059 unique protein accessions. Integrated metabolite and protein overrepresentation analysis identified pathways associated with the biological functions of each ROI, most of which were not identified when looking at the protein and metabolite lists individually. This combined MALDI MS imaging and multi-omics approach benefits from the advantages of both methods (molecular mapping from MSI and increased depth of coverage from shotgun proteomics and metabolomics), and further extends the amount of information that can be generated from single tissue sections.
Project description:Mass spectrometry imaging, which quantifies molecules in a tag-free, spatially-resolved manner, is a powerful tool for the understanding of underlying biochemical mechanisms of biological phenomena. When analyzing MSI data, it is essential to delineate Regions-of-Interest (ROIs) that correspond to tissue areas of different anatomical or pathological labels. Spatial segmentation, obtained by clustering MSI pixels according to their mass spectral similarities, is a popular approach to automate ROI definition. However, how to select the number of clusters (\#Clusters), which determines the granularity of segmentation, remains to be resolved, and an inappropriate \#Clusters may lead to ROIs not biologically real. Here we report a multimodal fusion strategy to enable an objective and trustworthy selection of #Clusters by utilizing additional information from corresponding histology images. A Deep Learning-based algorithm is proposed to extract "histomorphological feature spectra" across an entire H\&E image. Clustering is then similarly performed to produce Histology-segmentation. Since ROIs originating from instrumental noise or artifacts wouldn't be reproduced cross-modally, the consistency between histology- and MSI-segmentation becomes an effective measure of the biological validity of the results. So, \#Clusters that maximizes the consistency is deemed as most probable. We validated our strategy on mouse kidney and renal tumor specimens by producing multimodally corroborated ROIs that agreed excellently with ground truths. Downstream analysis based on the said ROIs revealed lipid molecules highly specific to tissue anatomy or pathology. Our work will greatly facilitate MSI-mediated spatial lipidomics, metabolomics, and proteomics research by providing intelligent software to automatically and reliably generate ROIs.
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:Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) combines information-rich chemical detection with spatial localization of analytes. For a given instrumental platform and analyte class, the data acquired can represent a compromise between analyte extraction and spatial information. Here, we introduce an improvement to the spatial resolution achievable with MALDI MSI conducted with standard mass spectrometric systems that also reduces analyte migration during matrix application. Tissue is placed directly on a stretchable membrane that, when stretched, fragments the tissue into micrometer-sized pieces. Scanning electron microscopy analysis shows that this process produces fairly homogeneous distributions of small tissue fragments separated and surrounded by areas of hydrophobic membrane surface. MALDI matrix is then applied by either a robotic microspotter or an artist's airbrush. Rat spinal cord samples imaged with an instrumental resolution of 50-250 μm demonstrate lipid distributions with a 5-fold high spatial resolution (a 25-fold increase in pixel density) after stretching compared to tissues that were not stretched.
Project description:Matrix-assisted laser desorption/ionization mass spectrometry imaging allows for the study of metabolic activity in the tumor microenvironment of brain cancers. The detectable metabolites within these tumors are contingent upon the choice of matrix, deposition technique, and polarity setting. In this study, we compared the performance of three different matrices, two deposition techniques, and the use of positive and negative polarity in two different brain cancer types and across two species. Optimal combinations were confirmed by a comparative analysis of lipid and small-molecule abundance by using liquid chromatography-mass spectrometry and RNA sequencing to assess differential metabolites and enzymes between normal and tumor regions. Our findings indicate that in the tumor-bearing brain, the recrystallized α-cyano-4-hydroxycinnamic acid matrix with positive polarity offered superior performance for both detected metabolites and consistency with other techniques. Beyond these implications for brain cancer, our work establishes a workflow to identify optimal matrices for spatial metabolomics studies.
Project description:Retinoblastoma (RB) is an intraocular childhood tumor which, if left untreated, leads to blindness and mortality. Nucleolin (NCL) protein which is differentially expressed on the tumor cell surface, binds ligands and regulates carcinogenesis and angiogenesis. We found that NCL is over expressed in RB tumor tissues and cell lines compared to normal retina. We studied the effect of nucleolin-aptamer (NCL-APT) to reduce proliferation in RB tumor cells. Aptamer treatment on the RB cell lines (Y79 and WERI-Rb1) led to significant inhibition of cell proliferation. Locked nucleic acid (LNA) modified NCL-APT administered subcutaneously (s.c.) near tumor or intraperitoneally (i.p.) in Y79 xenografted nude mice resulted in 26 and 65% of tumor growth inhibition, respectively. Downregulation of inhibitor of apoptosis proteins, tumor miRNA-18a, altered serum cytokines, and serum miRNA-18a levels were observed upon NCL-APT treatment. Desorption electrospray ionization mass spectrometry (DESI MS)-based imaging of cell lines and tumor tissues revealed changes in phosphatidylcholines levels upon treatment. Thus, our study provides proof of concept illustrating NCL-APT-based targeted therapeutic strategy and use of DESI MS-based lipid imaging in monitoring therapeutic responses in RB.
Project description:Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a standard tool used for absolute quantification of drugs in pharmacokinetic (PK) studies. However, all spatial information is lost during the extraction and elucidation of a drugs biodistribution within the tissue is impossible. In the study presented here we used a sample embedding protocol optimized for mass spectrometry imaging (MSI) to prepare up to 15 rat intestine specimens at once. Desorption electrospray ionization (DESI) and matrix assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) were employed to determine the distributions and relative abundances of four benchmarking compounds in the intestinal segments. High resolution MALDI-MSI experiments performed at 10 µm spatial resolution allowed to determine the drug distribution in the different intestinal histological compartments to determine the absorbed and tissue bound fractions of the drugs. The low tissue bound drug fractions, which were determined to account for 56-66% of the total drug, highlight the importance to understand the spatial distribution of drugs within the histological compartments of a given tissue to rationalize concentration differences found in PK studies. The mean drug abundances of four benchmark compounds determined by MSI were correlated with the absolute drug concentrations. Linear regression resulted in coefficients of determination (R2) ranging from 0.532 to 0.926 for MALDI-MSI and R2 values ranging from 0.585 to 0.945 for DESI-MSI, validating a quantitative relation of the imaging data. The good correlation of the absolute tissue concentrations of the benchmark compounds and the MSI data provides a bases for relative quantification of compounds within and between tissues, without normalization to an isotopically labelled standard, provided that the compared tissues have inherently similar ion suppression effects.
Project description:Ambient ionization imaging mass spectrometry is uniquely suited for detailed spatially resolved chemical characterization of biological samples in their native environment. However, the spatial resolution attainable using existing approaches is limited by the ion transfer efficiency from the ionization region into the mass spectrometer. Here, we present a first study of ambient imaging of biological samples using nanospray desorption ionization (nano-DESI). Nano-DESI is a new ambient pressure ionization technique that uses minute amounts of solvent confined between two capillaries comprising the nano-DESI probe and the solid analyte for controlled desorption of molecules present on the substrate followed by ionization through self-aspirating nanospray. We demonstrate highly sensitive spatially resolved analysis of tissue samples without sample preparation. Our first proof-of-principle experiments indicate the potential of nano-DESI for ambient imaging with a spatial resolution of better than 12 μm. The significant improvement of the spatial resolution offered by nano-DESI imaging combined with high detection efficiency will enable new imaging mass spectrometry applications in clinical diagnostics, drug discovery, molecular biology, and biochemistry.