Human Suction Blister Fluid Composition Determined Using High-Resolution Metabolomics.
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ABSTRACT:
SUBMITTER: Niedzwiecki MM
PROVIDER: S-EPMC5863097 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundBiomarker analysis allows for the detection and prediction of disease as well as health monitoring. The use of interstitial fluid (ISF) as a matrix for biomarkers has recently gained interest. This study aimed to compare levels of inflammatory markers in ISF from suction blister fluid (SBF) and plasma.MethodsPlasma and SBF were collected from 18 healthy individuals. Samples were analyzed for 92 inflammation-related protein biomarkers by Proximity Extension Assay (PEA). Protein profiles in the two matrices were compared using traditional and multivariate statistics.ResultsOut of 92 targeted proteins, 70 were successfully quantified in both plasma and SBF. Overall, plasma and SBF displayed distinct protein profiles with up to 40-fold difference in abundance of specific proteins. The levels of 25 proteins were significantly correlated between plasma and SBF and several of these were recognized as potential markers to monitor health using ISF.ConclusionsSkin ISF and plasma have unique protein profiles but many inflammatory markers are proportionally related between the matrices at the individual level. ISF is a promising biofluid for the monitoring of biomarkers in clinical studies and routine analyses.
| S-EPMC7670055 | biostudies-literature
Project description:BackgroundRecent technical advances in the extraction of dermal interstitial fluid (ISF) have stimulated interest in using this rather unexploited biofluid as an alternative to blood for detection and prediction of disease. However, knowledge about the presence of useful biomarkers for health monitoring in ISF is still limited. In this study, we characterized the lipidome of human suction blister fluid (SBF) as a surrogate for pure ISF and compared it to that of plasma.MethodsPlasma and SBF samples were obtained from 18 healthy human volunteers after an overnight fast. Total lipids were extracted and analyzed by liquid chromatography-tandem mass spectrometry. One hundred ninety-three lipid species covering 10 complex lipid classes were detected and quantified in both plasma and SBF using multiple reaction monitoring. A fraction of the lipid extract was subjected to alkaline transesterification and fatty acid methyl esters were analyzed by gas chromatography-mass spectrometry.ResultsThe total concentration of lipids in SBF was 17% of the plasma lipid concentration. The molar fraction of lipid species within lipid classes, as well as total fatty acids, showed a generally high correlation between plasma and SBF. However, SBF had larger fractions of lysophospholipids and diglycerides relative to plasma, and consequently less diacylphospholipids and triglycerides. Principal component analysis revealed that the interindividual variation in SBF lipid profiles was considerably larger than the within-subject variation between plasma and SBF.ConclusionsPlasma and SBF lipid profiles show high correlation and SBF could be used interchangeably with blood for the analysis of major lipids used in health monitoring.
| S-EPMC6708155 | biostudies-literature
Project description:To detect time of day dependent gene expression in human epidermis suction blister samples from 20 healthy subjects were obtained at three different time points throughout the day. RNA from 20 subjects were used to perform whole genome microarray analysis. Microarrays from 19 subjects showed sufficient quality to perform analysis for differential gene expression. We detected significant differential expression levels for several canonical clock genes such as Per1, Per2, Per3, Bmal1 and Rev-Erb_alpha throughout the day. In total we identified 294 genes that showed significant circadian gene expression including several transcription factors and rate limiting enzymes. To our knowledge this is the first study to investigate genome wide circadian gene expression in human epidermis.
2012-07-31 | GSE35635 | GEO
Project description:To detect time of day dependent gene expression in human epidermis suction blister samples from 20 healthy subjects were obtained at three different time points throughout the day. RNA from 20 subjects were used to perform whole genome microarray analysis. Microarrays from 19 subjects showed sufficient quality to perform analysis for differential gene expression. We detected significant differential expression levels for several canonical clock genes such as Per1, Per2, Per3, Bmal1 and Rev-Erb_alpha throughout the day. In total we identified 294 genes that showed significant circadian gene expression including several transcription factors and rate limiting enzymes. To our knowledge this is the first study to investigate genome wide circadian gene expression in human epidermis. Suction blisters of human epidermis were obtained by applying a vacuum for approximately 2.5 h. Suction blisters for each subject (19 subjects in total) were harvested at 9.30 am, 2.30 pm and 7.30 pm.
2012-07-30 | E-GEOD-35635 | biostudies-arrayexpress
Project description:Gas chromatography-mass spectrometry (GC-MS) platforms are typically run in electron ionization (EI) mode for mass spectral matching and metabolite annotation. With the advent of high resolution mass spectrometry (HRMS), soft ionization techniques such as chemical ionization (CI) may provide additional coverage for compound identification. We evaluated NIST SRM 1950 pooled plasma reference sample using a HRGC-MS instrument [GC-Orbitrap-MS with electron ionization (EI), positive chemical ionization (PCI), and negative CI (NCI) capabilities] for metabolite annotation and quantification to assess the suitability of the platform for routine discovery metabolomics. Using both open source and vendor workflows, we validated the spectral matches with an in-house spectral library (Wake Forest CPM GC-MS spectral and retention time libraries) of EI-MS and CI-MS/MS spectra obtained from chemical standards. We confidently [metabolomics standards initiative (MSI) confidence level 2] identified 263, 93, and 65 metabolites using EI, PCI, and NCI modes, respectively, of which 270 metabolites (64%) were validated using our Wake Forest CPM GC-MS spectral libraries. When compared to published LC-MS-based efforts using the same NIST SRM 1950 plasma sample, there was only 17% overlap between the two platforms. In addition, the metabolomics analysis of community approved standard human plasma demonstrated the ability of EI- and CI-MS modes of analysis using a HRGC-MS platform to enable reproducible and interoperable spectral matching.
| S-EPMC9701538 | biostudies-literature
Project description:RATIONALE:Metabolomics analyses using gas chromatography/mass spectrometry (GC/MS)-based metabolomics are heavily impeded by the lack of high-resolution mass spectrometers and limited spectral libraries to complement the excellent chromatography that GC platforms offer, a challenge that is being addressed with the implementation of high-resolution (HR) platforms such as 1D-GC/Orbitrap-MS. METHODS:We used serum samples from a non-human primate (NHP), a baboon (Papio hamadryas), with suitable quality controls to quantify the chemical space using an advanced HRMS platform for confident metabolite identification and robust quantification to assess the suitability of the platform for routine clinical metabolomics research. In a complementary approach, we also analyzed the same serum samples using two-dimensional gas chromatography/time-of-flight mass spectrometry (2D-GC/TOF-MS) for metabolite identification and quantification following established standard protocols. RESULTS:Overall, the 2D-GC/TOF-MS (~5000 peaks per sample) and 1D-GC/Orbitrap-MS (~500 peaks per sample) analyses enabled identification and quantification of a total of 555 annotated metabolites from the NHP serum with a spectral similarity score Rsim ? 900 and signal-to-noise (S/N) ratio of >25. A common set of 30 metabolites with HMDB and KEGG IDs was quantified in the serum samples by both platforms where 2D-GC/TOF-MS enabled quantification of a total 384 metabolites (118 HMDB IDs) and 1D-GC/Orbitrap-MS analysis quantification of a total 200 metabolites (47 HMDB IDs). Thus, roughly 30-70% of the peaks remain unidentified or un-annotated across both platforms. CONCLUSIONS:Our study provides insights into the benefits and limitations of the use of a higher mass resolution and mass accuracy instrument for untargeted GC/MS-based metabolomics with multi-dimensional chromatography in future studies addressing clinical conditions or exposome studies.
| S-EPMC6395519 | biostudies-literature
Project description:Feature detection is a critical step in the preprocessing of liquid chromatography-mass spectrometry (LC-MS) metabolomics data. Currently, the predominant approach is to detect features using noise filters and peak shape models based on the data at hand alone. Databases of known metabolites and historical data contain information that could help boost the sensitivity of feature detection, especially for low-concentration metabolites. However, utilizing such information in targeted feature detection may cause large number of false positives because of the high levels of noise in LC-MS data. With high-resolution mass spectrometry such as liquid chromatograph-Fourier transform mass spectrometry (LC-FTMS), high-confidence matching of peaks to known features is feasible. Here we describe a computational approach that serves two purposes. First it boosts feature detection sensitivity by using a hybrid procedure of both untargeted and targeted peak detection. New algorithms are designed to reduce the chance of false-positives by nonparametric local peak detection and filtering. Second, it can accumulate information on the concentration variation of metabolites over large number of samples, which can help find rare features and/or features with uncommon concentration in future studies. Information can be accumulated on features that are consistently found in real data even before their identities are found. We demonstrate the value of the approach in a proof-of-concept study. The method is implemented as part of the R package apLCMS at http://www.sph.emory.edu/apLCMS/ .
| S-EPMC3624888 | biostudies-literature
Project description:IntroductionDue to its proximity with the brain, cerebrospinal fluid (CSF) could be a medium of choice for the discovery of biomarkers of neurological and psychiatric diseases using untargeted analytical approaches.ObjectivesThis study explored the CSF lipidome in order to generate a robust mass spectral database using an untargeted lipidomic approach.MethodsCerebrospinal fluid samples from 45 individuals were analyzed by liquid chromatography coupled to high-resolution mass spectrometry method (LC-HRMS). A dedicated data processing workflow was implemented using XCMS software and adapted filters to select reliable features. In addition, an automatic annotation using an in silico lipid database and several MS/MS experiments were performed to identify CSF lipid species.ResultsUsing this complete workflow, 771 analytically relevant monoisotopic lipid species corresponding to 550 unique lipids which represent five major lipid families (i.e., free fatty acids, sphingolipids, glycerophospholipids, glycerolipids, and sterol lipids) were detected and annotated. In addition, MS/MS experiments enabled to improve the annotation of 304 lipid species. Thanks to LC-HRMS, it was possible to discriminate between isobaric and also isomeric lipid species; and interestingly, our study showed that isobaric ions represent about 50 % of the total annotated lipid species in the human CSF.ConclusionThis work provides an extensive LC/HRMS database of the human CSF lipidome which constitutes a relevant foundation for future studies aimed at finding biomarkers of neurological disorders.
| S-EPMC4824825 | biostudies-literature
Project description:ObjectTo investigate the distribution characteristics of gut microbiota in children with tic disorder (TD) and the possible role of these characteristics in the pathogenesis of TD.MethodsThe medical records of 28 children with TD treated at Wuxi Children's Hospital from January 1 to October 31, 2020, and 21 age-matched healthy children (controls) were included. The relative quantification of bacterial taxa was performed using 16S ribosomal RNA gene amplicon sequencing.ResultsThere was no significant difference in the alpha diversity of gut microbiota between the TD and control groups. Analyses of beta diversity were able to differentiate the TD patients from the healthy controls based on their gut microbiota. At the phylum level, the two groups were mainly composed of four phyla, Firmicutes, Actinobacteria, Bacteroidetes, and Proteobacteria. There were significant differences in Firmicutes and Actinobacteria between the two groups (P <0.05). At the level of genera, the abundance of Bifidobacterium and Collinsella reduced while that of Ruminococcaceae unclassified, Prevotella, Faecalibacterium, Coprobacillus, and Odoribacter increased in the TD group compared to that in the control group. The intergroup differences were significant (P < 0.05).ConclusionThe abnormal composition of gut microbiota in children with TD suggests that the change in gut microbiota may play an important role in TD development.
| S-EPMC9114666 | biostudies-literature
Project description:Mitochondrial phenotype is complex and difficult to define at the level of individual cell types. Newer metabolic profiling methods provide information on dozens of metabolic pathways from a relatively small sample. This pilot study used "top-down" metabolic profiling to determine the spectrum of metabolites present in liver mitochondria. High resolution mass spectral analyses and multivariate statistical tests provided global metabolic information about mitochondria and showed that liver mitochondria possess a significant phenotype based on gender and genotype. The data also show that mitochondria contain a large number of unidentified chemicals.
| S-EPMC3295783 | biostudies-literature