Journal: Molecular cancer research : MCR

Article Title: Metabolic Profiling in Formalin-Fixed and Paraffin-Embedded Prostate Cancer Tissues

doi: 10.1158/1541-7786.MCR-16-0262

Figure Lengend Snippet: Isogenic cell lines. A, Schematic overview of the protocol used to prepare frozen and FFPE cell samples. The steps are discussed in Supplementary Methods. B, Venn diagram showing the intersection between frozen and FFPE metabolomic data in the experimental settings. C, Box-and-whisker plot representing the relative signal intensity of all shared metabolites found in frozen and FFPE samples. D, Bar plot of the metabolite number found in frozen and FFPE samples. The metabolites are categorized according to the class membership. The percentage above each bar represents the number of detectable metabolites (of each class) found in FFPE compared with frozen samples. E, Correlation plots between FFPE cell replicates and between frozen and FFPE cell samples. F, Box-and-whisker plots of the correlation coefficients, categorized to the class membership, between frozen replicates, FFPE replicates, and frozen and FFPE samples. G, Heatmap of selected metabolites from cell line samples. Hierarchical clustering (Ward method) based on KODAMA dissimilarity matrix is used for unsupervised classification. The phenotypic labels of the samples (i.e., LNCaP and LNCaP-Abl) are indicated as a colored band on top of the heatmap.

Article Snippet: Metabolite profiling Metabolite profiling was conducted by the company Metabolon Inc. as previously described by Evans and colleagues ( 13 ).

Techniques: Whisker Assay

Journal: Molecular cancer research : MCR

Article Title: Metabolic Profiling in Formalin-Fixed and Paraffin-Embedded Prostate Cancer Tissues

doi: 10.1158/1541-7786.MCR-16-0262

Figure Lengend Snippet: Human prostate. A, Schematic diagram of the human samples used. B, Venn diagram showing the intersection between frozen and FFPE metabolomic data in the experimental settings. C, Bar plot of the metabolite number found in frozen and FFPE samples. The metabolites are categorized according to the class membership. The percentage above each bar represents the number of detectable metabolites (for each class) found in FFPE compared with frozen samples. D, Correlation plots between FFPE cell replicates and between frozen and FFPE cell samples. E, Heatmap of selected metabolites from cell line samples. Hierarchical clustering (Ward method) based on KODAMA dissimilarity matrix is used for unsupervised/semisupervised classification. The phenotypic labels of the samples (i.e., normal and tumor tissue) are indicated as a colored band on top of the heatmap.

Article Snippet: Metabolite profiling Metabolite profiling was conducted by the company Metabolon Inc. as previously described by Evans and colleagues ( 13 ).

Techniques:

Journal: Nutrients

Article Title: NMR-Based Multi Parametric Quality Control of Fruit Juices: SGF Profiling

doi: 10.3390/nu1020148

Figure Lengend Snippet: SGF Profiling quantification results of an orange juice. The colored flags show agreements with or deviations from reference intervals, which are provided by the European Fruit Juice Association A.I.J.N. (N/Q: Not quantified due to non-detectable signal or insufficient signal assignment).

Article Snippet: One example of the successful application of this technique is the Bruker JuiceScreener™ for SGF Profiling™ [ , ].

Techniques:

Journal: Circulation

Article Title: Comprehensive metabolic phenotyping refines cardiovascular risk in young adults

doi: 10.1161/CIRCULATIONAHA.120.047689

Figure Lengend Snippet: Figure 2A displays magnitude and significance in linear models of associations with four key parameters reflecting myocardial phenotypes, vascular phenotypes, and fitness (Year 25 coronary calcification [Y25 CAC]; Year 20 exercise tolerance time [Y20 ETT]; Year 25 echocardiographic left ventricular mass by M-mode [Y25 MM LV Mass, indexed as described]; Year 25 global longitudinal strain). Associations for both HILIC and C8 metabolite modes displayed. Beta coefficients displayed here are adjusted for age, sex and race. Red dots signify metabolites statistically significant at a 5% false discovery rate (Benjamini-Hochberg). Of note, metabolites associated with greater strain (positive beta coefficient), greater LV mass, greater calcification, and lower exercise duration (negative beta coefficient) are unfavorable. For example, glutamate was associated with adverse phenotypes across four phenotypes. The full set of associations across the full range of the measured cardiovascular phenome are tabulated in Supplemental Data File. Figure 2B displays a Venn diagram of metabolites significantly associated with four groups of phenotypes: fitness (treadmill time), vascular calcification (ln(CAC + 1) at year 25 or ln(AAC + 1) at year 25), LV mass (by either 2D or M-mode) and LV strain (longitudinal or circumferential).

Article Snippet: Metabolite profiling Metabolite profiling in CARDIA was performed as described in the Expanded Methods (Broad Institute, Cambridge, MA) via standard liquid chromatography-mass spectrometric (LC-MS) techniques 13 , 14 .

Techniques: Hydrophilic Interaction Liquid Chromatography

Journal: Circulation

Article Title: Comprehensive metabolic phenotyping refines cardiovascular risk in young adults

doi: 10.1161/CIRCULATIONAHA.120.047689

Figure Lengend Snippet: Figure 3A displays the regression coefficients of elastic nets specified for each phenotypic outcome in CARDIA, with outcomes in columns and metabolites in rows. Elastic net regressions included all metabolites measured in the HILIC and C8 platforms (separately). Metabolites with any non-zero coefficient for any outcome are displayed here in heatmap visualization, with the heatmap color key representing the magnitude of the regression coefficient. Coefficients for all subclinical endpoints except exercise duration were inverted such that a positive coefficient would be associated with a prognostically better value of the subclinical endpoint. Metabolites were ordered by complete-linkage clustering and subclinical CVD endpoints were ordered based on pathophysiology. Figure 3B shows loadings from a principal component analysis (PCA) of the elastic net regression coefficients from Figure 3A. This approach organizes the metabolite-phenome associations observed in the elastic net into those metabolites that are jointly related in a similar fashion to each set of phenotypes (“elastic net-PCA” approach described in Figure 1). This process yielded two PCs: the first PC loaded on the vascular phenome, and the second PC loaded on the myocardial phenome. The absolute value of the loading on each phenotype signifies how closely the underlying metabolite may be related to that phenotype. Figure 3C shows the relative weightings of each metabolite in each PC that are subsequently used in the construction of specific, independent myocardial and vascular health “scores.” Here, the blue color represents positive weighting (related to greater myocardial or vascular health), and the red color represents negative weighting (relative to poorer myocardial or vascular health). As noted, several metabolites in elastic net were similar to those observed in single metabolite-phenotype association (Figure 2A; e.g., glutamine, urate). Figure 3D shows Spearman correlation between the subsequent score (derived from summing the product of weightings in Figure 3C and individual metabolite levels, as described in Methods) and each phenotype. Figure 3E demonstrates the independence of vascular and myocardial health metabolite scores. The dashed line represents the 95% ellipse for the bivariate distribution. The cloud density and marginal histograms represent the fraction of CARDIA at each metabolomic score. Individual points outlying the 95% ellipse are also shown. Figure 3F shows the distribution of each score by sex and race, with a lower myocardial health metabolite score in Blacks (vs. whites) and a lower vascular health metabolite score in females (vs. males), consistent with epidemiologic observations of a higher heart failure risk in Blacks and greater atherosclerotic CVD risk in men.

Article Snippet: Metabolite profiling Metabolite profiling in CARDIA was performed as described in the Expanded Methods (Broad Institute, Cambridge, MA) via standard liquid chromatography-mass spectrometric (LC-MS) techniques 13 , 14 .

Techniques: Hydrophilic Interaction Liquid Chromatography, Derivative Assay

Journal: Circulation

Article Title: Comprehensive metabolic phenotyping refines cardiovascular risk in young adults

doi: 10.1161/CIRCULATIONAHA.120.047689

Figure Lengend Snippet: Figure 4A depicts the results of fully adjusted multivariable survival analysis for hard CVD (defined in Methods) for models containing the myocardial and vascular metabolite score and their multiplicative interaction (top three rows). Given their significant interaction, the myocardial and vascular metabolite scores were added together to produce a composite “myocardial-vascular health score.” The bottom three rows in Figure 4A shows the results of each score considered in separate fully adjusted models. Point estimates and confidence limits for reclassification (net reclassification index, NRI), discrimination (C-index), and fit statistics (R2) are depicted (and described in Methods). Base model adjustments are listed in Figure 4A. Figures 4B and 4C depict Kaplan-Meier (unadjusted) curves for survival free of hard CVD by tertiles of vascular and myocardial health metabolite score, with trend P value. Figure 4D is a visual depiction of the statistically significant interaction between myocardial and vascular health metabolite scores and long-term CVD. Here, the contours (solid black lines) represent the hazard ratio for hard CVD, and the cloud density represents the distribution of CARDIA participants across each score. The dashed line represents the 95% ellipse and individuals outside are plotted (to avoid overplotting within the cloud). Hazard (contour lines) for CVD increases most steeply (e.g., most “rapidly” crossing contours of hazard) across a diagonal from the top right to the bottom left, corresponding to jointly more negative myocardial and vascular metabolic health, suggesting the greatest risk is via a joint increase in both of them concurrently in young adulthood (evidence of interaction). Figure 4E is a Kaplan-Meier (unadjusted) curve for survival free of hard CVD by tertiles of the myocardial-vascular health score. The term tft indicates test for trend.

Article Snippet: Metabolite profiling Metabolite profiling in CARDIA was performed as described in the Expanded Methods (Broad Institute, Cambridge, MA) via standard liquid chromatography-mass spectrometric (LC-MS) techniques 13 , 14 .

Techniques:

Journal: Circulation

Article Title: Comprehensive metabolic phenotyping refines cardiovascular risk in young adults

doi: 10.1161/CIRCULATIONAHA.120.047689

Figure Lengend Snippet: Figure 5A-C shows Kaplan-Meier (unadjusted) survival curves for CVD (defined in Methods) for each score in FHS, alongside a fully adjusted multivariable survival analysis (Figure 5D) analogous to Figure 4A. Figure 5E demonstrates the expected marginal hazard for a 1-SD decrease (“poorer” metabolic health) in each metabolomic score across age for CVD and mortality. This figure demonstrates that the hazard of CVD or mortality in young adults for a change in metabolic health is higher relative to the same change later in life. Figure 5F shows the results of the multivariable survival models for CVD, including interactions. Of note, age is modeled in years (not standardized) for this analysis for clarity. The hazard ratios for each score are expressed per 1 SD increase (better health), and the hazard for metabolite-based score is expressed at an age of “0 years” for this table. The hazard for a 1-SD increase in metabolite-based score at any given age is calculated by adding effects from interaction to the base score term. The term tft indicates test for trend.

Article Snippet: Metabolite profiling Metabolite profiling in CARDIA was performed as described in the Expanded Methods (Broad Institute, Cambridge, MA) via standard liquid chromatography-mass spectrometric (LC-MS) techniques 13 , 14 .

Techniques: