What are we imaging? Software tools and experimental strategies for annotation and identification of small molecules in mass spectrometry imaging.

Mass spectrometry imaging (MSI) has become a widespread analytical technique to perform nonlabeled spatial molecular identification. The Achilles' heel of MSI is the annotation and identification of molecular species due to intrinsic limitations of the technique (lack of chromatographic separation and the difficulty to apply tandem MS). Successful strategies to perform annotation and identification combine extra analytical steps, like using orthogonal analytical techniques to identify compounds; with algorithms that integrate the spectral and spatial information. In this review, we discuss different experimental strategies and bioinformatics tools to annotate and identify compounds in MSI experiments. We target strategies and tools for small molecule applications, such as lipidomics and metabolomics. First, we explain how sample preparation and the acquisition process influences annotation and identification, from sample preservation to the use of orthogonal techniques. Then, we review twelve software tools for annotation and identification in MSI. Finally, we offer perspectives on two current needs of the MSI community: the adaptation of guidelines for communicating confidence levels in identifications; and the creation of a standard format to store and exchange annotations and identifications in MSI.

Lipidomics Reveals Myocardial Lipid Composition in a Murine Model of Insulin Resistance Induced by a High-Fat Diet.

Ectopic fat accumulation in non-adipose tissues is closely related to diabetes-related myocardial dysfunction. Nevertheless, the complete picture of the lipid metabolites involved in the metabolic-related myocardial alterations is not fully characterized. The aim of this study was to characterize the specific lipid profile in hearts in an animal model of obesity/insulin resistance induced by a high-fat diet (HFD). The cardiac lipidome profiles were assessed via liquid chromatography-mass spectrometry (LC-MS)/MS-MS and laser desorption/ionization-mass spectrometry (LDI-MS) tissue imaging in hearts from C57BL/6J mice fed with an HFD or standard-diet (STD) for 12 weeks. Targeted lipidome analysis identified a total of 63 lipids (i.e., 48 triacylglycerols (TG), 5 diacylglycerols (DG), 1 sphingomyelin (SM), 3 phosphatidylcholines (PC), 1 DihydroPC, and 5 carnitines) modified in hearts from HFD-fed mice compared to animals fed with STD. Whereas most of the TG were up-regulated in hearts from animals fed with an HFD, most of the carnitines were down-regulated, thereby suggesting a reduction in the mitochondrial ß-oxidation. Roughly 30% of the identified metabolites were oxidated, pointing to an increase in lipid peroxidation. Cardiac lipidome was associated with a specific biochemical profile and a specific liver TG pattern. Overall, our study reveals a specific cardiac lipid fingerprint associated with metabolic alterations induced by HFD.

Gradual Increase in Inflammation-Linked Glycoproteins and a Proatherogenic Lipoprotein Profile in the Early Stages of Psychosis as Characterized by 1H NMR Blood Analysis.

Minimally invasive prognostic markers of inflammation and dyslipidemia in individuals with a risk of psychosis, also called "at-risk mental state" (ARMS), or in the first episode of psychosis (FEP) are of utmost clinical importance to prevent cardiovascular disorders. We analyzed the plasma concentration of inflammation-linked glycoproteins (Glycs) and lipoprotein subclasses by proton nuclear magnetic resonance (1H NMR) in a single acquisition. Study participants were healthy controls (HCs, N = 67) and patients with ARMS (N = 58), FEP (N = 110), or early psychosis diagnosis with ≥2 episodes (critical period (CP), N = 53). Clinical biomarkers such as high-sensitivity C-reactive protein, interleukin 6, fibrinogen, insulin, and lipoproteins were also measured. Although all participants had normal lipoprotein profiles and no inflammation according to conventional biomarkers, a gradual increase in the Glyc 1H NMR levels was observed from HCs to CP patients; this increase was statistically significant for GlycA (CP vs HC). In parallel, a progressive and significant proatherogenic 1H NMR lipoprotein profile was also identified across stages of psychosis (ARMS and CP vs HC). These findings highlight the potential of using 1H NMR Glyc and lipoprotein profiling to identify blood changes in individuals with ARMS or FEP and pave the way for applications using this technology to monitor metabolic and cardiovascular risks in clinical psychiatry.

LC/MS-Based Untargeted Metabolomics Analysis in Women with Morbid Obesity and Associated Type 2 Diabetes Mellitus.

Obesity is a chronic and complex disease, with an increasing incidence worldwide that is associated with metabolic disorders such as type 2 diabetes mellitus (T2DM). Thus, it is important to determine the differences between metabolically healthy obese individuals and those with metabolic disorders. The aim of this study was to perform an untargeted metabolomics assay in women with morbid obesity (MO) compared to a normal weight group, and to differentiate the metabolome of these women with MO who present with T2DM. We carried out a liquid chromatography-mass spectrometry-based untargeted metabolomics assay using serum samples of 209 Caucasian women: 73 with normal weight and 136 with MO, of which 71 had T2DM. First, we found increased levels of choline and acylglycerols and lower levels of bile acids, steroids, ceramides, glycosphingolipids, lysophosphatidylcholines, and lysophosphatidylethanolamines in MO women than in the control group. Then, in MO women with T2DM, we found increased levels of glutamate, propionyl-carnitine, bile acids, ceramides, lysophosphatidylcholine 14:0, phosphatidylinositols and phosphoethanolamines, and lower levels of Phe-Ile/Leu. Thus, we found metabolites with opposite trends of concentration in the two metabolomic analyses. These metabolites could be considered possible new factors of study in the pathogenesis of MO and associated T2DM in women.

LC/MS-Based Untargeted Metabolomics Study in Women with Nonalcoholic Steatohepatitis Associated with Morbid Obesity.

This study investigated the importance of a metabolomic analysis in a complex disease such as nonalcoholic steatohepatitis (NASH) associated with obesity. Using an untargeted metabolomics technique, we studied blood metabolites in 216 morbidly obese women with liver histological diagnosis. A total of 172 patients were diagnosed with nonalcoholic fatty liver disease (NAFLD), and 44 were diagnosed with normal liver (NL). Patients with NAFLD were classified into simple steatosis (n = 66) and NASH (n = 106) categories. A comparative analysis of metabolites levels between NASH and NL demonstrated significant differences in lipid metabolites and derivatives, mainly from the phospholipid group. In NASH, there were increased levels of several phosphatidylinositols and phosphatidylethanolamines, as well as isolated metabolites such as diacylglycerol 34:1, lyso-phosphatidylethanolamine 20:3 and sphingomyelin 38:1. By contrast, there were decreased levels of acylcarnitines, sphingomyelins and linoleic acid. These findings may facilitate identification studies of the main pathogenic metabolic pathways related to NASH and may also have a possible applicability in a panel of metabolites to be used as biomarkers in future algorithms of the disease diagnosis and its follow-up. Further confirmatory studies in groups with different ages and sexes are necessary.

Muscular carnosine is a marker for cardiorespiratory fitness and cardiometabolic risk factors in men with type 1 diabetes.

PURPOSE: Muscle is an essential organ for glucose metabolism and can be influenced by metabolic disorders and physical activity. Elevated muscle carnosine levels have been associated with insulin resistance and cardiometabolic risk factors. Little is known about muscle carnosine in type 1 diabetes (T1D) and how it is influenced by physical activity. The aim of this study was to characterize muscle carnosine in vivo by proton magnetic resonance spectroscopy (1H MRS) and evaluate the relationship with physical activity, clinical characteristics and lipoprotein subfractions. METHODS: 16 men with T1D (10 athletes/6 sedentary) and 14 controls without diabetes (9/5) were included. Body composition by DXA, cardiorespiratory capacity (VO2peak) and serum lipoprotein profile by proton nuclear magnetic resonance (1H NMR) were obtained. Muscle carnosine scaled to water (carnosineW) and to creatine (carnosineCR), creatine and intramyocellular lipids (IMCL) were quantified in vivo using 1H MRS in a 3T MR scanner in soleus muscle. RESULTS: Subjects with T1D presented higher carnosine CR levels compared to controls. T1D patients with a lower VO2peak presented higher carnosineCR levels compared to sedentary controls, but both T1D and control groups presented similar levels of carnosineCR at high VO2peak levels. CarnosineW followed the same trend. Integrated correlation networks in T1D demonstrated that carnosineW and carnosineCR were associated with cardiometabolic risk factors including total and abdominal fat, pro-atherogenic lipoproteins (very low-density lipoprotein subfractions), low VO2peak, and IMCL. CONCLUSIONS: Elevated muscle carnosine levels in persons with T1D and their effect on atherogenic lipoproteins can be modulated by physical activity.

rMSIproc: an R package for mass spectrometry imaging data processing.

SUMMARY: Mass spectrometry imaging (MSI) can reveal biochemical information directly from a tissue section. MSI generates a large quantity of complex spectral data which is still challenging to translate into relevant biochemical information. Here, we present rMSIproc, an open-source R package that implements a full data processing workflow for MSI experiments performed using TOF or FT-based mass spectrometers. The package provides a novel strategy for spectral alignment and recalibration, which allows to process multiple datasets simultaneously. This enables to perform a confident statistical analysis with multiple datasets from one or several experiments. rMSIproc is designed to work with files larger than the computer memory capacity and the algorithms are implemented using a multi-threading strategy. rMSIproc is a powerful tool able to take full advantage of modern computer systems to completely develop the whole MSI potential. AVAILABILITY AND IMPLEMENTATION: rMSIproc is freely available at https://github.com/prafols/rMSIproc. CONTACT: pere.rafols@urv.cat. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Analysis of LDL and HDL size and number by nuclear magnetic resonance in a healthy working population: The LipoLab Study.

BACKGROUND AND AIM: Atherosclerosis is the underlying process in cardiovascular disease (CVD), the first cause of death in developed countries. We aimed to identify people with no known CVD and normal values of LDL-C and HDL-C, but with alterations in the number and size of lipoprotein particles (as measured by nuclear magnetic resonance [NMR]) and to analyse their sociodemographic, clinical and biochemical characteristics. METHODS: Cross-sectional study in occupational risks prevention centre in Castellón (Spain) in 2017 and 2018, in consecutively recruited adults (18-65 years) with no known CVD. Sociodemographic, clinical and biochemical variables were collected. Lipid profiles were analysed (Liposcale test), along with the concentration, size and number of the main types of lipoprotein particles, determined by 2D diffusion-ordered NMR spectroscopy. Using contingency tables, we analysed the characteristics of people with normal LDL and HDL cholesterol but abnormal levels of LDL and HDL particles. The magnitude of association between explanatory variables and abnormal levels of each kind of lipoprotein was assessed with multivariable logistic regression models. RESULTS: Of the 400 total participants (31.3% women; age 46.4 ± 4.3 years), 169 had normal LDL and HDL cholesterol. Abnormal lipoprotein particle values depended on the subtype: prevalence of abnormal LDL levels ranged from 8.3% to 36.7%; and of HDL, from 28.4% to 42.6%. High systolic blood pressure and total cholesterol were significantly associated with abnormal LDL levels. Male sex and high systolic blood pressure were associated with abnormalities in HDL. CONCLUSIONS: An extended lipids profile, obtained by NMR, enables the identification of people with normal HDL-C and LDL-C levels who present abnormal levels of LDL-P and/or HDL-P. Higher total cholesterol, systolic blood pressure, BMI and male sex were significantly associated with these abnormal values.

Raman2imzML converts Raman imaging data into the standard mass spectrometry imaging format.

BACKGROUND: Multimodal imaging that combines mass spectrometry imaging (MSI) with Raman imaging is a rapidly developing multidisciplinary analytical method used by a growing number of research groups. Computational tools that can visualize and aid the analysis of datasets by both techniques are in demand. RESULTS: Raman2imzML was developed as an open-source converter that transforms Raman imaging data into imzML, a standardized common data format created and adopted by the mass spectrometry community. We successfully converted Raman datasets to imzML and visualized Raman images using open-source software designed for MSI applications. CONCLUSION: Raman2imzML enables both MSI and Raman images to be visualized using the same file format and the same software for a straightforward exploratory imaging analysis.

Gelsolin: a new biomarker of disease activity in SLE patients associated with HDL-c.

OBJECTIVES: To identify potential biomarkers of disease activity analysing the proteome of high-density lipoprotein (HDL) particles from SLE patients in clinical remission and when they develop a flare compared with a healthy control group. METHODS: Quantitative proteomic analyses of purified HDL were performed using Tandem Mass Tag isobaric tag-labelling and nanoLC-Orbitrap (nLC-MS/MS) from nine SLE patients in clinical remission when they developed a flare and from nine healthy controls (9-9-9). We verified the identified proteins by Western blot and ELISA in a cohort of 104 SLE women patients, 46 healthy women and 14 SLE patients when a flare developed. RESULTS: We found 17 proteins with a significant fold-change (>1.1) compared with the control group. In lupus patients experiencing a flare compared with those in remission, we identified four proteins with a significant fold-change (C4, Indian Hedgehog protein, S100A8 and gelsolin). Plasma gelsolin (pGSN) levels were decreased in the 104 SLE patients (176.02(74.9) mcg/l) compared with the control group (217.13(86.7) mcg/l); P=0.005 and when they developed a clinical flare (104.84(41.7) mcg/l); P=0.002). pGSN levels were associated with HDL cholesterol levels (r = 0.316, P<0.001). Antimalarial treated patients showed significant higher levels of pGSN (214.56(88.94) mcg/l regarding 170.35(66.36) mcg/l); P = 0.017. CONCLUSION: Decreased pGSN are associated with clinical disease activity in SLE patients. Antimalarial treatment and HDL cholesterol are associated with higher levels of pGSN.

Signal preprocessing, multivariate analysis and software tools for MA(LDI)-TOF mass spectrometry imaging for biological applications.

Mass spectrometry imaging (MSI) is a label-free analytical technique capable of molecularly characterizing biological samples, including tissues and cell lines. The constant development of analytical instrumentation and strategies over the previous decade makes MSI a key tool in clinical research. Nevertheless, most MSI studies are limited to targeted analysis or the mere visualization of a few molecular species (proteins, peptides, metabolites, or lipids) in a region of interest without fully exploiting the possibilities inherent in the MSI technique, such as tissue classification and segmentation or the identification of relevant biomarkers from an untargeted approach. MSI data processing is challenging due to several factors. The large volume of mass spectra involved in a MSI experiment makes choosing the correct computational strategies critical. Furthermore, pixel to pixel variation inherent in the technique makes choosing the correct preprocessing steps critical. The primary aim of this review was to provide an overview of the data-processing steps and tools that can be applied to an MSI experiment, from preprocessing the raw data to the more advanced strategies for image visualization and segmentation. This review is particularly aimed at researchers performing MSI experiments and who are interested in incorporating new data-processing features, improving their computational strategy, and/or desire access to data-processing tools currently available. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:281-306, 2018.

HDL Triglycerides: A New Marker of Metabolic and Cardiovascular Risk.

While cholesterol content in high-density lipoproteins (HDLs) is a well-established inverse marker of cardiovascular risk, the importance of HDL-triglyceride (HDL-TG) concentration is not well known. We aim to examine plasma HDL-TG concentrations, assessed by 1H-NMR, in patients with metabolic diseases and their association with classical biomarkers. In this cross-sectional study, we included 502 patients with type 2 diabetes or metabolic syndrome attending the lipid unit of our University Hospital. The presence of arteriosclerotic plaques was assessed by ultrasonography. A complete lipoprotein profile was performed by 1H-NMR (Liposcale test). HDL-TG was strongly positively correlated with total triglycerides, glycerol, and fatty liver index, while a strong negative correlation was observed with HDL-cholesterol (HDL-C) and HDL-particle number (HDL-P). HDL-TG was associated with all triglyceride-rich lipoprotein parameters and had an opposite association with HDL-C and HDL-P. It was also significantly correlated with circulating cholesterol ester transfer protein (CETP). HDL-TG concentrations were higher as metabolic syndrome components increased. HDL-TG was also higher with worsening glucose metabolism. Patients with carotid plaques also showed higher HDL-TG. In contrast to HDL-C, HDL-TG is directly associated with metabolism and arteriosclerotic vascular alterations. HDL-TG should be considered a biomarker of metabolic and cardiovascular risk and could be a marker of HDL dysfunction.

LipSpin: A New Bioinformatics Tool for Quantitative 1H NMR Lipid Profiling.

The structural similarity among lipid species and the low sensitivity and spectral resolution of nuclear magnetic resonance (NMR) have traditionally hampered the routine use of 1H NMR lipid profiling of complex biological samples in metabolomics, which remains mostly manual and lacks freely available bioinformatics tools. However, 1H NMR lipid profiling provides fast quantitative screening of major lipid classes (fatty acids, glycerolipids, phospholipids, and sterols) and some individual species and has been used in several clinical and nutritional studies, leading to improved risk prediction models. In this Article, we present LipSpin, a free and open-source bioinformatics tool for quantitative 1H NMR lipid profiling. LipSpin implements a constrained line shape fitting algorithm based on voigt profiles and spectral templates from spectra of lipid standards, which automates the analysis of severely overlapped spectral regions and lipid signals with complex coupling patterns. LipSpin provides the most detailed quantification of fatty acid families and choline phospholipids in serum lipid samples by 1H NMR to date. Moreover, analytical and clinical results using LipSpin quantifications conform with other techniques commonly used for lipid analysis.

rMSI: an R package for MS imaging data handling and visualization.

SUMMARY: R platform provides some packages that are useful to process mass spectrometry imaging (MSI) data; however, none of them provide an easy to use graphical user interface (GUI). Here, we introduce rMSI, an R package for MSI data analysis focused on providing an efficient way to manage MSI data together with a GUI integrated in R environment. MS data is loaded in rMSI custom format optimized to minimize the memory footprint yet maintaining a fast spectra access. The rMSI GUI is designed for simple and effective data exploration and visualization. Moreover, rMSI is designed to be integrated in the R environment through a library of functions that can be used to share MS data across others R packages. The release of rMSI for R environment establishes a novel and flexible platform for MSI data analysis, completely free and open-source. AVAILABILITY AND IMPLEMENTATION: The code, the documentation, a tutorial and example data are available open-source at: github.com/prafols/rMSI. CONTACT: jesus.brezmes@urv.cat. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

rDolphin: a GUI R package for proficient automatic profiling of 1D 1H-NMR spectra of study datasets.

INTRODUCTION: Adoption of automatic profiling tools for 1H-NMR-based metabolomic studies still lags behind other approaches in the absence of the flexibility and interactivity necessary to adapt to the properties of study data sets of complex matrices. OBJECTIVES: To provide an open source tool that fully integrates these needs and enables the reproducibility of the profiling process. METHODS: rDolphin incorporates novel techniques to optimize exploratory analysis, metabolite identification, and validation of profiling output quality. RESULTS: The information and quality achieved in two public datasets of complex matrices are maximized. CONCLUSION: rDolphin is an open-source R package ( http://github.com/danielcanueto/rDolphin ) able to provide the best balance between accuracy, reproducibility and ease of use.

Unravelling and Quantifying the "NMR-Invisible" Metabolites Interacting with Human Serum Albumin by Binding Competition and T2 Relaxation-Based Decomposition Analysis.

Quantitative profiling of low-molecular-weight metabolites (LMWMs) by 1H NMR is routinely used in high-throughput serum metabolomics. First, the protein background is attenuated using a T2 filter; then, the LMWM signals are resolved by line-shape fitting. However, protein-binding modifies the motional properties of LMWM, and their signal partially attenuates with the T2 filter, along with the protein background. Consequently, the quantified LMWM signals do not reflect the total concentration in serum but the nonbinding part. Here we present a novel strategy based on binding competition to promote the release of the "NMR-invisible" metabolites from serum proteins and achieve quantifications closer to total concentrations. The study focuses on five clinically relevant amino acids with different binding properties (valine, isoleucine, leucine, tyrosine, and phenylalanine). We analyzed their binding affinity to human serum albumin (HSA) in serum mimic samples and promoted the release of their bound fraction by TSP titration. Furthermore, we used a novel combination of pseudo-2D CPMG and multivariate curve resolution analysis, allowing the separation of LMWM and protein signals and providing LMWM quantifications corrected for transverse relaxation effects. We found that TSP concentrations larger than 3 mM released most of the bound fraction and validated these findings in real serum/plasma samples.

eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC/MS-Based Metabolomics.

Gas chromatography coupled to mass spectrometry (GC/MS) has been a long-standing approach used for identifying small molecules due to the highly reproducible ionization process of electron impact ionization (EI). However, the use of GC-EI MS in untargeted metabolomics produces large and complex data sets characterized by coeluting compounds and extensive fragmentation of molecular ions caused by the hard electron ionization. In order to identify and extract quantitative information on metabolites across multiple biological samples, integrated computational workflows for data processing are needed. Here we introduce eRah, a free computational tool written in the open language R composed of five core functions: (i) noise filtering and baseline removal of GC/MS chromatograms, (ii) an innovative compound deconvolution process using multivariate analysis techniques based on compound match by local covariance (CMLC) and orthogonal signal deconvolution (OSD), (iii) alignment of mass spectra across samples, (iv) missing compound recovery, and (v) identification of metabolites by spectral library matching using publicly available mass spectra. eRah outputs a table with compound names, matching scores and the integrated area of compounds for each sample. The automated capabilities of eRah are demonstrated by the analysis of GC-time-of-flight (TOF) MS data from plasma samples of adolescents with hyperinsulinaemic androgen excess and healthy controls. The quantitative results of eRah are compared to centWave, the peak-picking algorithm implemented in the widely used XCMS package, MetAlign, and ChromaTOF software. Significantly dysregulated metabolites are further validated using pure standards and targeted analysis by GC-triple quadrupole (QqQ) MS, LC-QqQ, and NMR. eRah is freely available at http://CRAN.R-project.org/package=erah .

Urine metabolome profiling of immune-mediated inflammatory diseases.

BACKGROUND: Immune-mediated inflammatory diseases (IMIDs) are a group of complex and prevalent diseases where disease diagnostic and activity monitoring is highly challenging. The determination of the metabolite profiles of biological samples is becoming a powerful approach to identify new biomarkers of clinical utility. In order to identify new metabolite biomarkers of diagnosis and disease activity, we have performed the first large-scale profiling of the urine metabolome of the six most prevalent IMIDs: rheumatoid arthritis, psoriatic arthritis, psoriasis, systemic lupus erythematosus, Crohn's disease, and ulcerative colitis. METHODS: Using nuclear magnetic resonance, we analyzed the urine metabolome in a discovery cohort of 1210 patients and 100 controls. Within each IMID, two patient subgroups were recruited representing extreme disease activity (very high vs. very low). Metabolite association analysis with disease diagnosis and disease activity was performed using multivariate linear regression in order to control for the effects of clinical, epidemiological, or technical variability. After multiple test correction, the most significant metabolite biomarkers were validated in an independent cohort of 1200 patients and 200 controls. RESULTS: In the discovery cohort, we identified 28 significant associations between urine metabolite levels and disease diagnosis and three significant metabolite associations with disease activity (P FDR < 0.05). Using the validation cohort, we validated 26 of the diagnostic associations and all three metabolite associations with disease activity (P FDR < 0.05). Combining all diagnostic biomarkers using multivariate classifiers we obtained a good disease prediction accuracy in all IMIDs and particularly high in inflammatory bowel diseases. Several of the associated metabolites were found to be commonly altered in multiple IMIDs, some of which can be considered as hub biomarkers. The analysis of the metabolic reactions connecting the IMID-associated metabolites showed an over-representation of citric acid cycle, phenylalanine, and glycine-serine metabolism pathways. CONCLUSIONS: This study shows that urine is a source of biomarkers of clinical utility in IMIDs. We have found that IMIDs show similar metabolic changes, particularly between clinically similar diseases and we have found, for the first time, the presence of hub metabolites. These findings represent an important step in the development of more efficient and less invasive diagnostic and disease monitoring methods in IMIDs.

Liposcale: a novel advanced lipoprotein test based on 2D diffusion-ordered 1H NMR spectroscopy.

Determination of lipoprotein particle size and number using advanced lipoprotein tests (ALTs) is of particular importance to improve cardiovascular risk prediction. Here we present the Liposcale test, a novel ALT based on 2D diffusion-ordered (1)H NMR spectroscopy. Our method uses diffusion coefficients to provide a direct measure of the mean particle sizes and numbers. Using 177 plasma samples from healthy individuals and the concentration of ApoB and ApoA from isolated lipoprotein fractions, our test showed a stronger correlation between the NMR-derived lipoprotein particle numbers and apolipoprotein concentrations than the LipoProfile(®) test commercialized by Liposcience. We also converted LDL particle numbers to ApoB equivalents (milligrams per deciliter) and our test yielded similar values of LDL-ApoB to the LipoProfile(®) test (absolute mean bias of 8.5 and 7.4 mg/dl, respectively). In addition, our HDL particle number values were more concordant with the calibrated values determined recently using ion mobility. Finally, principal component analysis distinguished type 2 diabetic patients with and without atherogenic dyslipidemia (AD) on a second cohort of 307 subjects characterized using the Liposcale test (area under the curve = 0.88) and showed concordant relationships between variables explaining AD. Altogether, our method provides reproducible and reliable characterization of lipoprotein particles and it is applicable to pathological states such as AD.

Integrative analysis reveals novel pathways mediating the interaction between adipose tissue and pancreatic islets in obesity in rats.

AIMS/HYPOTHESIS: Comprehensive characterisation of the interrelation between the peripancreatic adipose tissue and the pancreatic islets promises novel insights into the mechanisms that regulate beta cell adaptation to obesity. Here, we sought to determine the main pathways and key molecules mediating the crosstalk between these two tissues during adaptation to obesity by the way of an integrated inter-tissue, multi-platform analysis. METHODS: Wistar rats were fed a standard or cafeteria diet for 30 days. Transcriptomic variations by diet in islets and peripancreatic adipose tissue were examined through microarray analysis. The secretome from peripancreatic adipose tissue was subjected to a non-targeted metabolomic and proteomic analysis. Gene expression variations in islets were integrated with changes in peripancreatic adipose tissue gene expression and protein and metabolite secretion using an integrated inter-tissue pathway and network analysis. RESULTS: The highest level of data integration, linking genes differentially expressed in both tissues with secretome variations, allowed the identification of significantly enriched canonical pathways, such as the activation of liver/retinoid X receptors, triacylglycerol degradation, and regulation of inflammatory and immune responses, and underscored interaction network hubs, such as cholesterol and the fatty acid binding protein 4, which were unpredicted through single-tissue analysis and have not been previously implicated in the peripancreatic adipose tissue crosstalk with beta cells. CONCLUSIONS/INTERPRETATION: The integrated analysis reported here allowed the identification of novel mechanisms and key molecules involved in peripancreatic adipose tissue interrelation with beta cells during the development of obesity; this might help the development of novel strategies to prevent type 2 diabetes.