The Use of Mass Spectrometry Imaging to Predict Treatment Response of Patient-Derived Xenograft Models of Triple-Negative Breast Cancer

In recent years, mass spectrometry imaging (MSI) has been shown to be a promising technique in oncology. The effective application of MSI, however, is hampered by the complexity of the generated data. Bioinformatic approaches that reduce the complexity of these data are needed for the effective use in a (bio)­medical setting. This holds especially for the analysis of tissue microarrays (TMA), which consist of hundreds of small tissue cores. Here we present an approach that combines MSI on tissue microarrays with principal component linear discriminant analysis (PCA-LDA) to predict treatment response. The feasibility of such an approach was evaluated on a set of patient-derived xenograft models of triple-negative breast cancer (TNBC). PCA-LDA was used to classify TNBC tumor tissues based on the proteomic information obtained with matrix-assisted laser desorption ionization (MALDI) MSI from the TMA surface. Classifiers based on two different tissue microarrays from the same tumor models showed overall classification accuracies between 59 and 77%, as determined by cross-validation. Reproducibility tests revealed that the two models were similar. A clear effect of intratumor heterogeneity of the classification scores was observed. These results demonstrate that the analysis of MALDI-MSI data by PCA-LDA is a valuable approach for the classification of treatment response and tumor heterogeneity in breast cancer

Oxygen-Dependent Lipid Profiles of Three-Dimensional Cultured Human Chondrocytes Revealed by MALDI-MSI

Articular cartilage is exposed to a gradient of oxygen levels ranging from 5% at the surface to 1% in the deepest layers. While most cartilage research is performed in supraphysiological oxygen levels (19–21%), culturing chondrocytes under hypoxic oxygen levels (≤8%) promotes the chondrogenic phenotype. Exposure of cells to various oxygen levels alters their lipid metabolism, but detailed studies examining how hypoxia affects lipid metabolism in chondrocytes are lacking. To better understand the chondrocyte’s behavior in response to oxygen, we cultured 3D pellets of human primary chondrocytes in normoxia (20% oxygen) and hypoxia (2.5% oxygen) and employed matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) in order to characterize the lipid profiles and their spatial distribution. In this work we show that chondrocytes cultured in hypoxia and normoxia can be differentiated by their lipid profiles. Among other species, phosphatidylglycerol species were increased in normoxic pellets, whereas phosphatidylinositol species were the most prominent lipids in hypoxic pellets. Moreover, spatial mapping revealed that phospahtidylglyycerol species were less prominent in the center of pellets where the oxygen level is lower. Additional analysis revealed a higher abundance of the mitochondrial-specific lipids, cardiolipins, in normoxic conditions. In conclusion MALDI-MSI described specific lipid profiles that could be used as sensors of oxygen level changes and may especially be relevant for retaining the chondrogenic phenotype, which has important implications for the treatment of bone and cartilage diseases

Multiorder Correction Algorithms to Remove Image Distortions from Mass Spectrometry Imaging Data Sets

Time-of-flight secondary ion mass spectrometry imaging is a rapidly evolving technology. Its main application is the study of the distribution of small molecules on biological tissues. The sequential image acquisition process remains susceptible to measurement distortions that can render imaging data less analytically useful. Most of these artifacts show a repetitive nature from tile to tile. Here we statistically describe these distortions and derive two different algorithms to correct them. Both a generalized linear model approach and the linear discriminant analysis approach are able to increase image quality for negative and positive ion mode data sets. Additionally, performing simulation studies with repetitive and nonrepetitive tiling error we show that both algorithms are only removing repetitive distortions. It is further shown that the spectral component of the data set is not altered by the use of these correction methods. Both algorithms presented in this work greatly increase the image quality and improve the analytical usefulness of distorted images dramatically

Design and Performance of a Novel Interface for Combined Matrix-Assisted Laser Desorption Ionization at Elevated Pressure and Electrospray Ionization with Orbitrap Mass Spectrometry

Matrix-Assisted Laser Desorption Ionization, MALDI, has been increasingly used in a variety of biomedical applications, including tissue imaging of clinical tissue samples, and in drug discovery and development. These studies strongly depend on the performance of the analytical instrumentation and would drastically benefit from improved sensitivity, reproducibility, and mass/spatial resolution. In this work, we report on a novel combined MALDI/ESI interface, which was coupled to different Orbitrap mass spectrometers (Elite and Q Exactive Plus) and extensively characterized with peptide and protein standards, and in tissue imaging experiments. In our approach, MALDI is performed in the elevated pressure regime (5–8 Torr) at a spatial resolution of 15–30 μm, while ESI-generated ions are injected orthogonally to the interface axis. We have found that introduction of the MALDI-generated ions into an electrodynamic dual-funnel interface results in increased sensitivity characterized by a limit of detection of ∼400 zmol, while providing a mass measurement accuracy of 1 ppm and a mass resolving power of 120 000 in analysis of protein digests. In tissue imaging experiments, the MALDI/ESI interface has been employed in experiments with rat brain sections and was shown to be capable of visualizing and spatially characterizing very low abundance analytes separated only by 20 mDa. Comparison of imaging data has revealed excellent agreement between the MALDI and histological images

Ingres 1780-1867 / Lambros Liaropoulos, réal. ; Maryse Perrin, aut.

Résumé : Par la découverte d'une exposition (Exposition Petit Palais 27 Octobre 1967 au 29 Janvier 1968 ??), on aborde l'oeuvre du peintre, ses influences, son apprentissage et l'évolution de la critique à l'égard de sa peinture. Analyse de tableaux et dessins : L'apothéose d'Homère (commande officielle), Jésus au milieu des docteurs, Le martyre de Saint-Symphorien, Le songe d'Ossian, Le voeu de Louis XIII, Madame Rivière et autres portraits, La vierge, Roger délivrant Angélique, Les deux Odalisques, La Baigneuse de Valpinçon, Le bain turc. (source : Canopé)Durée : 00:28:54Thème : Peintur

Spatial Systems Lipidomics Reveals Nonalcoholic Fatty Liver Disease Heterogeneity

Hepatocellular lipid accumulation characterizes nonalcoholic fatty liver disease (NAFLD). However, the types of lipids associated with disease progression are debated, as is the impact of their localization. Traditional lipidomics analysis using liver homogenates or plasma dilutes and averages lipid concentrations, and does not provide spatial information about lipid distribution. We aimed to characterize the distribution of specific lipid species related to NAFLD severity by performing label-free molecular analysis by mass spectrometry imaging (MSI). Fresh frozen liver biopsies from obese subjects undergoing bariatric surgery (<i>n</i> = 23) with various degrees of NAFLD were cryosectioned and analyzed by matrix-assisted laser desorption/ionization (MALDI)-MSI. Molecular identification was verified by tandem MS. Tissue sections were histopathologically stained, annotated according to the Kleiner classification, and coregistered with the MSI data set. Lipid pathway analysis was performed and linked to local proteome networks. Spatially resolved lipid profiles showed pronounced differences between nonsteatotic and steatotic tissues. Lipid identification and network analyses revealed phosphatidylinositols and arachidonic acid metabolism in nonsteatotic regions, whereas low–density lipoprotein (LDL) and very low–density lipoprotein (VLDL) metabolism was associated with steatotic tissue. Supervised and unsupervised discriminant analysis using lipid based classifiers outperformed simulated analysis of liver tissue homogenates in predicting steatosis severity. We conclude that lipid composition of steatotic and nonsteatotic tissue is highly distinct, implying that spatial context is important for understanding the mechanisms of lipid accumulation in NAFLD. MSI combined with principal component–linear discriminant analysis linking lipid and protein pathways represents a novel tool enabling detailed, comprehensive studies of the heterogeneity of NAFLD