Evaluation of Thin-Layer Chromatography–Laser Desorption Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometric Imaging for Visualization of Crude Oil Interactions

A light oil was separated into four chromatographic fractions that serve as proxy for SARA fractions. The fractions were (semi)­quantified on a rod by TLC-flame ionization detection and characterized on a plate with laser desorption ionization–mass spectrometry imaging (TLC-LDI-MS). Comparisons of (semi)­quantitative TLC-FID and qualitative TLC-LDI-MS results showed that LDI-MS was most sensitive for detection of molecules in the polar P1 fraction, and, to some extent, for the aromatics fraction, while no signal was observed for the most polar P2 and saturates fractions. Based on these results, limits of the compositional space, as observed by the laser ionization technique, were evaluated. The molecular speciation between and within the spots of the aromatics and the P1 fractions were analyzed and interpreted in terms of oil–SiO₂ versus oil–solvent interactions, as a function of molecular characteristics such as DBE, aromaticity (H/C ratio), heteroatom content, degree of alkylation, and shielding of heteroatoms. In addition, the high oil loading resulted in an interesting bifurcation of the aromatics spot, which implies that oil–oil interactions can be enforced and studied in the TLC model system

Gold Sputtered Fiducial Markers for Combined Secondary Ion Mass Spectrometry and MALDI Imaging of Tissue Samples

Mass spectrometry imaging (MSI) is a label free technique capable of providing simultaneous identification and localization of biomolecules. A multimodal approach is required that allows for the study of the complexity of biological tissue samples to overcome the limitations of a single MSI technique. Secondary ion mass spectrometry (SIMS) allows for high spatial resolution imaging while matrix-assisted laser desorption (MALDI) offers a significantly wider mass range. The combination of coregistered SIMS and MALDI images results in detailed and unique biomolecular information. In this Technical Note, we describe how gold sputtered/implanted fiducial markers (FM) are created and can be used to ensure a proper overlay and coregistration of the two-dimensional images provided by the two MSI modalities

Characterization of Phosphatidylcholine Oxidation Products by MALDI MS^<i>n</i>

Phospholipid oxidation has been implicated in the pathogenesis and progression of numerous age-related and neurodegenerative diseases. Despite these implications, this broad class of biomolecules remains poorly characterized. In this work, the fragmentation patterns of [M + H]⁺ and [M + Na]⁺ ions of intact phosphatidylcholine oxidation products (OxPCs) were characterized by matrix-assisted laser desorption/ionization tandem mass spectrometry (MALDI MS^<i>n</i>, <i>n</i> = 2, 3, and 4). MS² of both the [M + H]⁺ and [M + Na]⁺ ions of short-chain OxPCs yielded product ions related to the PC headgroup and the fatty acid substituents. MS³ of the [M + Na – N­(CH₃)₃]⁺ ions yielded fragmentation indicative of the OxPC modification; specifically, a product ion corresponding to the neutral loss of CO₂ (NL of 44) was observed for OxPCs containing a terminal carboxylic acid rather than an aldehyde. Furthermore, MS⁴ of the [M + Na – HPO₄(CH₂)₂N­(CH₃)₃]⁺ ions resulted in fragmentation pathways dependent on the <i>sn</i>-2 fatty acid chain length and type of functional group(s). Specifically, CHO-containing OxPCs with palmitic acid esterified to the <i>sn</i>-1 position of the glycerol backbone yielded a NL of 254, 2 u less than the nominal mass of palmitic acid, whereas the analogous terminal COOH-containing OxPCs demonstrated a NL of 256. Finally, the presence of a γ-ketone relative to the terminal carboxyl group resulted in C–C bond cleavages along the <i>sn</i>-2 substituent, providing diagnostic product ions for keto-containing OxPCs. This work illustrates the enhanced selectivity afforded by MS^<i>n</i> on the linear ion trap and develops a method for the identification of individual products of PC oxidation

Time-of-Flight Secondary Ion Mass Spectrometry-Based Molecular Distribution Distinguishing Healthy and Osteoarthritic Human Cartilage

Osteoarthritis (OA) is a pathology that ultimately causes joint destruction. The cartilage is one of the principal affected tissues. Alterations in the lipid mediators and an imbalance in the metabolism of cells that form the cartilage (chondrocytes) have been described as contributors to the OA development. In this study, we have studied the distribution of lipids and chemical elements in healthy and OA human cartilage. Time of flight-secondary ion mass spectrometry (TOF-SIMS) allows us to study the spatial distribution of molecules at a high resolution on a tissue section. TOF-SIMS revealed a specific peak profile that distinguishes healthy from OA cartilages. The spatial distribution of cholesterol-related peaks exhibited a remarkable difference between healthy and OA cartilages. A distinctive colocalization of cholesterol and other lipids in the superficial area of the cartilage was found. A higher intensity of oleic acid and other fatty acids in the OA cartilages exhibited a similar localization. On the other hand, CN^– was observed with a higher intensity in the healthy samples. Finally, we observed an accumulation of calcium and phosphate ions exclusively in areas surrounding the chondrocyte in OA tissues. To our knowledge, this is the first time that TOF-SIMS revealed combined changes in the molecular distribution in the OA human cartilage

Active Learning for Convenient Annotation and Classification of Secondary Ion Mass Spectrometry Images

Digital staining for the automated annotation of mass spectrometry imaging (MSI) data has previously been achieved using state-of-the-art classifiers such as random forests or support vector machines (SVMs). However, the training of such classifiers requires an expert to label exemplary data in advance. This process is time-consuming and hence costly, especially if the tissue is heterogeneous. In theory, it may be sufficient to only label a few highly representative pixels of an MS image, but it is not known a priori which pixels to select. This motivates <i>active learning</i> strategies in which the algorithm itself queries the expert by automatically suggesting promising candidate pixels of an MS image for labeling. Given a suitable querying strategy, the number of required training labels can be significantly reduced while maintaining classification accuracy. In this work, we propose active learning for convenient annotation of MSI data. We generalize a recently proposed active learning method to the multiclass case and combine it with the random forest classifier. Its superior performance over random sampling is demonstrated on secondary ion mass spectrometry data, making it an interesting approach for the classification of MS images

Digestion-Free Analysis of Peptides from 30-year-old Formalin-Fixed, Paraffin-Embedded Tissue by Mass Spectrometry Imaging

Formalin-fixed neuroendocrine tissues from American cockroaches (Periplaneta americana) embedded in paraffin more than 30 years ago were recently analyzed by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), to reveal the histological localization of more than 20 peptide ions. These represented protonated, and other cationic species of, at least, 14 known neuropeptides. The characterization of peptides in such historical samples was made possible by a novel sample preparation protocol rendering the endogenous peptides readily amenable to MSI analysis. The protocol comprises brief deparaffinization steps involving xylene and ethanol, and is further devoid of conventional aqueous washing, buffer incubations, or antigen retrieval steps. Endogenous secretory peptides that are typically highly soluble are therefore retained in-tissue with this protocol. The method is fully “top-down”, that is, without laborious in situ enzymatic digestion that typically disturbs the detection of low-abundance endogenous peptides by MSI. Peptide identifications were supported by accurate mass, on-tissue tandem MS analyses, and by earlier MALDI-MSI results reported for freshly prepared P. americana samples. In contrast to earlier literature accounts stating that MALDI-MSI detection of endogenous peptides is possible only in fresh or freshly frozen tissues, or exceptionally, in formalin-fixed, paraffin-embedded (FFPE) material of less than 1 year old, we demonstrate that MALDI-MSI works for endogenous peptides in FFPE tissue of up to 30 years old. Our findings put forward a useful method for digestion-free, high-throughput analysis of endogenous peptides from FFPE samples and offer the potential for reinvestigating archived and historically interesting FFPE material, such as those stored in hospital biobanks

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

Integration of Ion Mobility MS^E after Fully Automated, Online, High-Resolution Liquid Extraction Surface Analysis Micro-Liquid Chromatography

Direct analysis by mass spectrometry (imaging) has become increasingly deployed in preclinical and clinical research due to its rapid and accurate readouts. However, when it comes to biomarker discovery or histopathological diagnostics, more sensitive and in-depth profiling from localized areas is required. We developed a comprehensive, fully automated online platform for high-resolution liquid extraction surface analysis (HR-LESA) followed by micro–liquid chromatography (LC) separation and a data-independent acquisition strategy for untargeted and low abundant analyte identification directly from tissue sections. Applied to tissue sections of rat pituitary, the platform demonstrated improved spatial resolution, allowing sample areas as small as 400 μm to be studied, a major advantage over conventional LESA. The platform integrates an online buffer exchange and washing step for removal of salts and other endogenous contamination that originates from local tissue extraction. Our carry over–free platform showed high reproducibility, with an interextraction variability below 30%. Another strength of the platform is the additional selectivity provided by a postsampling gas-phase ion mobility separation. This allowed distinguishing coeluted isobaric compounds without requiring additional separation time. Furthermore, we identified untargeted and low-abundance analytes, including neuropeptides deriving from the pro-opiomelanocortin precursor protein and localized a specific area of the pituitary gland (i.e., adenohypophysis) known to secrete neuropeptides and other small metabolites related to development, growth, and metabolism. This platform can thus be applied for the in-depth study of small samples of complex tissues with histologic features of ∼400 μm or more, including potential neuropeptide markers involved in many diseases such as neurodegenerative diseases, obesity, bulimia, and anorexia nervosa

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