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      Metabolomics in transfusion medicine

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          Abstract

          Biochemical investigations on the regulatory mechanisms of red blood cell (RBC) and platelet (PLT) metabolism have fostered a century of advances in the field of transfusion medicine. Owing to these advances, storage of RBCs and PLT concentrates has become a lifesaving practice in clinical and military settings. There, however, remains room for improvement, especially with regard to the introduction of novel storage and/or rejuvenation solutions, alternative cell processing strategies (e.g., pathogen inactivation technologies), and quality testing (e.g., evaluation of novel containers with alternative plasticizers). Recent advancements in mass spectrometry–based metabolomics and systems biology, the bioinformatics integration of omics data, promise to speed up the design and testing of innovative storage strategies developed to improve the quality, safety, and effectiveness of blood products.

          Here we review the currently available metabolomics technologies and briefly describe the routine workflow for transfusion medicine–relevant studies. The goal is to provide transfusion medicine experts with adequate tools to navigate through the otherwise overwhelming amount of metabolomics data burgeoning in the field during the past few years.

          Descriptive metabolomics data have represented the first step omics researchers have taken into the field of transfusion medicine. However, to up the ante, clinical and omics experts will need to merge their expertise to investigate correlative and mechanistic relationships among metabolic variables and transfusion-relevant variables, such as 24-hour in vivo recovery for transfused RBCs. Integration with systems biology models will potentially allow for in silico prediction of metabolic phenotypes, thus streamlining the design and testing of alternative storage strategies and/or solutions.

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          The first comprehensive and quantitative analysis of human platelet protein composition allows the comparative analysis of structural and functional pathways.

          Antiplatelet treatment is of fundamental importance in combatting functions/dysfunction of platelets in the pathogenesis of cardiovascular and inflammatory diseases. Dysfunction of anucleate platelets is likely to be completely attributable to alterations in posttranslational modifications and protein expression. We therefore examined the proteome of platelets highly purified from fresh blood donations, using elaborate protocols to ensure negligible contamination by leukocytes, erythrocytes, and plasma. Using quantitative mass spectrometry, we created the first comprehensive and quantitative human platelet proteome, comprising almost 4000 unique proteins, estimated copy numbers for ∼ 3700 of those, and assessed intersubject (4 donors) as well as intrasubject (3 different blood samples from 1 donor) variations of the proteome. For the first time, our data allow for a systematic and weighted appraisal of protein networks and pathways in human platelets, and indicate the feasibility of differential and comprehensive proteome analyses from small blood donations. Because 85% of the platelet proteome shows no variation between healthy donors, this study represents the starting point for disease-oriented platelet proteomics. In the near future, comprehensive and quantitative comparisons between normal and well-defined dysfunctional platelets, or between platelets obtained from donors at various stages of chronic cardiovascular and inflammatory diseases will be feasible.
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            Is Open Access

            Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry

            Background Structure elucidation of unknown small molecules by mass spectrometry is a challenge despite advances in instrumentation. The first crucial step is to obtain correct elemental compositions. In order to automatically constrain the thousands of possible candidate structures, rules need to be developed to select the most likely and chemically correct molecular formulas. Results An algorithm for filtering molecular formulas is derived from seven heuristic rules: (1) restrictions for the number of elements, (2) LEWIS and SENIOR chemical rules, (3) isotopic patterns, (4) hydrogen/carbon ratios, (5) element ratio of nitrogen, oxygen, phosphor, and sulphur versus carbon, (6) element ratio probabilities and (7) presence of trimethylsilylated compounds. Formulas are ranked according to their isotopic patterns and subsequently constrained by presence in public chemical databases. The seven rules were developed on 68,237 existing molecular formulas and were validated in four experiments. First, 432,968 formulas covering five million PubChem database entries were checked for consistency. Only 0.6% of these compounds did not pass all rules. Next, the rules were shown to effectively reducing the complement all eight billion theoretically possible C, H, N, S, O, P-formulas up to 2000 Da to only 623 million most probable elemental compositions. Thirdly 6,000 pharmaceutical, toxic and natural compounds were selected from DrugBank, TSCA and DNP databases. The correct formulas were retrieved as top hit at 80–99% probability when assuming data acquisition with complete resolution of unique compounds and 5% absolute isotope ratio deviation and 3 ppm mass accuracy. Last, some exemplary compounds were analyzed by Fourier transform ion cyclotron resonance mass spectrometry and by gas chromatography-time of flight mass spectrometry. In each case, the correct formula was ranked as top hit when combining the seven rules with database queries. Conclusion The seven rules enable an automatic exclusion of molecular formulas which are either wrong or which contain unlikely high or low number of elements. The correct molecular formula is assigned with a probability of 98% if the formula exists in a compound database. For truly novel compounds that are not present in databases, the correct formula is found in the first three hits with a probability of 65–81%. Corresponding software and supplemental data are available for downloads from the authors' website.
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              An update on red blood cell storage lesions, as gleaned through biochemistry and omics technologies.

              Red blood cell (RBC) aging in the blood bank is characterized by the accumulation of a significant number of biochemical and morphologic alterations. Recent mass spectrometry and electron microscopy studies have provided novel insights into the molecular changes underpinning the accumulation of storage lesions to RBCs in the blood bank. Biochemical lesions include altered cation homeostasis, reprogrammed energy, and redox metabolism, which result in the impairment of enzymatic activity and progressive depletion of high-energy phosphate compounds. These factors contribute to the progressive accumulation of oxidative stress, which in turn promotes oxidative lesions to proteins (carbonylation, fragmentation, hemoglobin glycation) and lipids (peroxidation). Biochemical lesions negatively affect RBC morphology, which is marked by progressive membrane blebbing and vesiculation. These storage lesions contribute to the altered physiology of long-stored RBCs and promote the rapid clearance of up to one-fourth of long-stored RBCs from the recipient's bloodstream after 24 hours from administration. While prospective clinical evidence is accumulating, from the present review it emerges that biochemical, morphologic, and omics profiles of stored RBCs have observable changes after approximately 14 days of storage. Future studies will assess whether these in vitro observations might have clinically meaningful effects.
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                Author and article information

                Journal
                0417360
                7833
                Transfusion
                Transfusion
                Transfusion
                0041-1132
                1537-2995
                25 January 2018
                12 December 2015
                April 2016
                01 February 2018
                : 56
                : 4
                : 980-993
                Affiliations
                [1 ]Department of Biochemistry and Molecular Genetics, University of Colorado Denver–Anschutz Medical Campus, Aurora, Colorado
                [2 ]Department of Pathology, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
                Author notes
                Address reprint requests to: Angelo D’Alessandro, Department of Biochemistry and Molecular Genetics, University of Colorado Denver Anschutz Medical Campus, Aurora, CO 80045; angelo.dalessandro@ 123456ucdenver.edu
                Article
                PMC5794012 PMC5794012 5794012 nihpa936697
                10.1111/trf.13442
                5794012
                26662506
                aca29872-0a5b-488b-8976-38a55eb83f98
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