Precast Gelatin-Based Molds for Tissue Embedding Compatible with Mass Spectrometry Imaging

Preparation of tissue for matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) generally involves embedding the tissue followed by freezing and cryosectioning, usually between 5 and 25 μm thick, depending on the tissue type and the analyte(s) of interest. The brain is approximately 60% fat; it therefore lacks rigidity and poses structural preservation challenges during sample preparation. Histological sample preparation procedures are generally transferable to MALDI-MSI; however, there are various limitations. Optimal cutting temperature compound (OCT) is commonly used to embed and mount fixed tissue onto the chuck inside the cryostat during cryosectioning. However, OCT contains potential interferences that are detrimental to MALDI-MSI, while fixation is undesirable for the analysis of some analytes either due to extraction or chemical modification (i.e., polar metabolites). Therefore, a method for both fixed and fresh tissue compatible with MALDI-MSI and histology is desirable to increase the breadth of analyte(s), maintain the topographies of the brain, and provide rigidity to the fragile tissue while eliminating background interference. The method we introduce uses precast gelatin-based molds in which a whole mouse brain is embedded, flash frozen, and cryosectioned in preparation for mass spectrometry imaging (MSI)

l‑Carnitine Inhibits Lipopolysaccharide-Induced Nitric Oxide Production of SIM-A9 Microglia Cells

Microglia are the resident immune effector cells of the central nervous system. They account for approximately 10–15% of all cells found in the brain and spinal cord, acting as macrophages, sensing and engaging in phagocytosis to eliminate toxic proteins. Microglia are dynamic and can change their morphology in response to cues from their milieu. Parkinson’s disease is a neurodegenerative disease, associated with reactive gliosis, neuroinflammation, and oxidative stress. It is thought that Parkinson’s disease is caused by the accumulation of abnormally folded alpha-synuclein protein, accompanied by persistent neuroinflammation, oxidative stress, and subsequent neuronal injury/death. There is evidence in the literature for mitochondrial dysfunction in Parkinson’s disease as well as fatty acid beta-oxidation, involving l-carnitine. Here we investigate l-carnitine in the context of microglial activation, suggesting a potential new strategy of supplementation for PD patients. Preliminary results from our studies suggest that the treatment of activated microglia with the endogenous antioxidant l-carnitine can reverse the effects of detrimental neuroinflammation in vitro

Flow-chart of integrative scheme between genomics and metabolomics to identify bacterial OTUs associated with cholesterol and coprostanol.

<p>Genomic DNA (gDNA) from longitudinal fecal samples emanating from Healthy or CDI subjects (over 90 days) was isolated and deep sequenced on the V1V3 hypervariable 16s rRNA gene before being classified to 2395 refOTUs (Right). The same longitudinal fecal sample was extracted with dichloromethane and injected on a GC-MS instrument where the retention time of discriminatory peaks were determined based on PLS-DA VIP scores (Left). Discriminatory peaks cholesterol and coprostanol were Spearman correlated to refOTUs based on NMDS and ANOVA. As a further step, ISA was used to determine whether refOTUs associated with high coprostanol or cholesterol were enriched in Healthy or CDI cohorts. Red arrows represent feedback and integration between chart items whereas black arrows are directional flow of the pipeline. Abbreviations: ANOVA: analysis of variance, ISA: indicator species analysis, NMDS: non-metric multidimensional scaling, PLS-DA: partial least squares discriminant analysis refOTUs: reference operational taxonomic units, RT: retention time, VIP scores: Variable importance in projection scores, CH2Cl2: dichloromethane.</p

Inverse relationship of cholesterol and coprostanol levels in fecal extracts from subjects with CDI and Healthy controls.

<p><b>(A)</b> The relationship between retention times for cholesterol and coprostanol as determined by mass spectrometry (x-axis) and their relative abundance (y-axis). The inverse relationship between the two compounds based on fold change in fecal composition is highlighted in blue and red circles. <b>(B)</b> Box-plots showing distribution of average total ion current of coprostanol (left) and cholesterol (right) for all fecal samples from the Healthy or the CDI group. The TIC of the two metabolites was normalized by auto-scaling before plotting. <b>(C)</b> Percentage of coprostanol TIC relative to the sum of coprostanol and cholesterol TIC for each subgroup. ANOVA on the ranked Coprostanol TIC values indicated a significant difference among the four cohorts (<i>F</i>_{3, 9} = 9.797, <i>p</i> < 0.01). For the 13 subjects, ranks were highest for Healthy (10) and HAbx (10), followed by Met (6) and Vanc (2); numbers in parentheses indicate mean ranks. Letters above whiskers indicate similar groups based on ranks according to the Tukey HSD test. Fecal samples from a Healthy, HAbx, or Metronidazole origin could be grouped together according to coprostanol levels. Likewise, Metronidazole and Vancomycin treated fecal derived samples could be grouped together based on coprostanol levels.</p

Subject characteristics used in this study.

<p>The number of fecal samples analyzed is shown in parenthesis.</p

PLS-DA plots using a (left) two-state model, and (right) 4-state model.

<p>Antibiotic therapy for CDI (Met: Metronidazole, Vanc: Vancomycin) and antibiotic exposure history (HAbx: antibiotic exposure; Healthy: no antibiotic exposure) were used to distinguish groups. A matrix of retention time intensities were sum normalized and auto-scaled to generate both plots using a metabolomics pipeline established by Xia, et al. Each sphere represents a fecal chromatographic sample.</p

Flow-chart of integrative scheme between genomics and metabolomics to identify bacterial OTUs associated with cholesterol and coprostanol.

<p>Genomic DNA (gDNA) from longitudinal fecal samples emanating from Healthy or CDI subjects (over 90 days) was isolated and deep sequenced on the V1V3 hypervariable 16s rRNA gene before being classified to 2395 refOTUs (Right). The same longitudinal fecal sample was extracted with dichloromethane and injected on a GC-MS instrument where the retention time of discriminatory peaks were determined based on PLS-DA VIP scores (Left). Discriminatory peaks cholesterol and coprostanol were Spearman correlated to refOTUs based on NMDS and ANOVA. As a further step, ISA was used to determine whether refOTUs associated with high coprostanol or cholesterol were enriched in Healthy or CDI cohorts. Red arrows represent feedback and integration between chart items whereas black arrows are directional flow of the pipeline. Abbreviations: ANOVA: analysis of variance, ISA: indicator species analysis, NMDS: non-metric multidimensional scaling, PLS-DA: partial least squares discriminant analysis refOTUs: reference operational taxonomic units, RT: retention time, VIP scores: Variable importance in projection scores, CH2Cl2: dichloromethane.</p

Indicator species analysis (ISA) of the remaining 32 OTUs that were not previously assigned to one of the four individual cohorts.

<p>Groups were designated as “Health” for healthy volunteers (combining the two groups with and without prior antibiotic exposure, and as “Disease” for subjects with CDI (combining the metronidazole and vancomycin groups). A representative sequence accession number from Silva (release 108) database is shown for all uncultured species as a reference OTU. Indicator value of species <i>j</i> in group <i>k</i> is the product of the percent relative abundance of each organism to a specific cohort along with its percent relative frequency. Those species that are significant after 100,000 Monte-Carlo randomizations of ecological communities are listed below.</p

Correlation between coprostanol total ion current (TIC) and 16S rRNA taxonomic sequences.

<p><b>(A)</b>: Spearman’s rank of 65 bacteria significantly correlated to coprostanol and cholesterol total ion current. Each taxon was grouped according to an indicator cohort (HAbx, Healthy, or Vancomycin) using indicator species analysis. No phylotypes were identified as an indicator for the Metronidazole (Met) cohort. <b>(B)</b> Nonmetric Multidimensional Scaling (NMDS) analysis of bacterial OTUs and relative coprostanol TICs. Fecal samples were assigned as either “High” or “Low” coprostanol formers. Data was reduced by the NMDS approach using Bray-Curtis distances, followed by Spearman rank correlation to identify OTUs associated with coprostanol TIC levels. Dimension 1 represents coprostanol levels; Dimension 2 represents CDI treatment or antibiotics exposure for each subject.</p

LipidQC: Method Validation Tool for Visual Comparison to SRM 1950 Using NIST Interlaboratory Comparison Exercise Lipid Consensus Mean Estimate Values

As advances in analytical separation techniques, mass spectrometry instrumentation, and data processing platforms continue to spur growth in the lipidomics field, more structurally unique lipid species are detected and annotated. The lipidomics community is in need of benchmark reference values to assess the validity of various lipidomics workflows in providing accurate quantitative measurements across the diverse lipidome. LipidQC addresses the harmonization challenge in lipid quantitation by providing a semiautomated process, independent of analytical platform, for visual comparison of experimental results of National Institute of Standards and Technology Standard Reference Material (SRM) 1950, “Metabolites in Frozen Human Plasma”, against benchmark consensus mean concentrations derived from the NIST Lipidomics Interlaboratory Comparison Exercise