ReviewProteomics2013
Two birds with one stone: doing metabolomics with your proteomics kit.
Review in Proteomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Microbial communities and their role in enhancing hemp fiber quality through field retting.Applied microbiology and biotechnology · 2024Review
- Personalized Medicine Approach to Proteomics and Metabolomics of Cytochrome P450 Enzymes: A Narrative Review.European journal of drug metabolism and pharmacokinetics · 2024Review
- PANAMA-enabled high-sensitivity dual nanoflow LC-MS metabolomics and proteomics analysis.Cell reports methods · 2024Article
- Optimizing MS-Based Multi-Omics: Comparative Analysis of Protein, Metabolite, and Lipid Extraction Techniques.Metabolites · 2024Article
- Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite-Protein Physical Interaction Subnetworks Altered in Cancer.Molecular & cellular proteomics : MCP · 2022Article
- ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis.Scientific data · 2021Article
- Scalable Data Analysis in Proteomics and Metabolomics Using BioContainers and Workflows Engines.Proteomics · 2020Review
- Corona Isolation Method Matters: Capillary Electrophoresis Mass Spectrometry Based Comparison of Protein Corona Compositions Following On-Particle versus In-Solution or In-Gel Digestion.Nanomaterials (Basel, Switzerland) · 2019Article
- Digging into the low molecular weight peptidome with the OligoNet web server.Scientific reports · 2017Article
- Changes in fecal microbiota and metabolomics in a child with juvenile idiopathic arthritis (JIA) responding to two treatment periods with exclusive enteral nutrition (EEN).Clinical rheumatology · 2016Article
- Urinary proteomics and metabolomics studies to monitor bladder health and urological diseases.BMC urology · 2016Review
- A nano ultra-performance liquid chromatography-high resolution mass spectrometry approach for global metabolomic profiling and case study on drug-resistant multiple myeloma.Analytical chemistry · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Proteomic research facilities and laboratories are facing increasing demands for the integration of biological data from multiple '-OMICS' approaches. The aim to fully understand biological processes requires the integrated study of genomes, proteomes and metabolomes. While genomic and proteomic workflows are different, the study of the metabolome overlaps significantly with the latter, both in instrumentation and methodology. However, chemical diversity complicates an easy and direct access to the metabolome by mass spectrometry (MS). The present review provides an introduction into metabolomics workflows from the viewpoint of proteomic researchers. We compare the physicochemical properties of proteins and peptides with metabolites/small molecules to establish principle differences between these analyte classes based on human data. We highlight the implications this may have on sample preparation, separation, ionisation, detection and data analysis. We argue that a typical proteomic workflow (nLC-MS) can be exploited for the detection of a number of aliphatic and aromatic metabolites, including fatty acids, lipids, prostaglandins, di/tripeptides, steroids and vitamins, thereby providing a straightforward entry point for metabolomics-based studies. Limitations and requirements are discussed as well as extensions to the LC-MS workflow to expand the range of detectable molecular classes without investing in dedicated instrumentation such as GC-MS, CE-MS or NMR.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.