1
|
Thachil A, Wang L, Mandal R, Wishart D, Blydt-Hansen T. An Overview of Pre-Analytical Factors Impacting Metabolomics Analyses of Blood Samples. Metabolites 2024; 14:474. [PMID: 39330481 DOI: 10.3390/metabo14090474] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2024] [Revised: 08/10/2024] [Accepted: 08/12/2024] [Indexed: 09/28/2024] Open
Abstract
Discrepant sample processing remains a significant challenge within blood metabolomics research, introducing non-biological variation into the measured metabolome and biasing downstream results. Inconsistency during the pre-analytical phase can influence experimental processes, producing metabolome measurements that are non-representative of in vivo composition. To minimize variation, there is a need to create and adhere to standardized pre-analytical protocols for blood samples intended for use in metabolomics analyses. This will allow for reliable and reproducible findings within blood metabolomics research. In this review article, we provide an overview of the existing literature pertaining to pre-analytical factors that influence blood metabolite measurements. Pre-analytical factors including blood tube selection, pre- and post-processing time and temperature conditions, centrifugation conditions, freeze-thaw cycles, and long-term storage conditions are specifically discussed, with recommendations provided for best practices at each stage.
Collapse
Affiliation(s)
- Amy Thachil
- Department of Pediatrics, BC Children's Hospital, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Li Wang
- Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- BC Children's Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada
| | - Rupasri Mandal
- Faculty of Science-Biological Sciences, The Metabolomics Innovation Centre, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - David Wishart
- Department of Laboratory Medicine & Pathology, Faculty of Science-Biological Sciences, The Metabolomics Innovation Centre, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Tom Blydt-Hansen
- Division of Nephrology, Department of Pediatrics, BC Children's Hospital, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| |
Collapse
|
2
|
Cavaleri D, Riboldi I, Crocamo C, Paglia G, Carrà G, Bartoli F. Evidence from preclinical and clinical metabolomics studies on the antidepressant effects of ketamine and esketamine. Neurosci Lett 2024; 831:137791. [PMID: 38670523 DOI: 10.1016/j.neulet.2024.137791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/15/2024] [Accepted: 04/23/2024] [Indexed: 04/28/2024]
Abstract
The antidepressant effects of ketamine and esketamine are well-documented. Nonetheless, most of the underlying molecular mechanisms have to be uncovered yet. In the last decade, metabolomics has emerged as a useful means to investigate the metabolic phenotype associated with depression as well as changes induced by antidepressant treatments. This mini-review aims at summarizing the main findings from preclinical and clinical studies that used metabolomics to investigate the metabolic effects of subanesthetic, antidepressant doses of ketamine and esketamine and their relationship with clinical response. Both animal and human studies report alterations in several metabolic pathways - including the tricarboxylic acid cycle, glycolysis, the pentose phosphate pathway, lipid metabolism, amino acid metabolism, the kynurenine pathway, and the urea cycle - following the administration of ketamine or its enantiomers. Although more research is needed to clarify commonalities and differences in molecular mechanisms of action between the racemic compound and its enantiomers, these findings comprehensively support an influence of ketamine and esketamine on mitochondrial and cellular energy production, membrane homeostasis, neurotransmission, and signaling. Metabolomics may thus represent a promising strategy to clarify molecular mechanisms underlying treatment-resistant depression and related markers of clinical response to ketamine and esketamine. This body of preclinical and clinical evidence, if further substantiated, has the potential to guide clinicians towards personalized approaches, contributing to new paradigms in the clinical management of depression.
Collapse
Affiliation(s)
- Daniele Cavaleri
- Department of Medicine and Surgery, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, Italy
| | - Ilaria Riboldi
- Department of Medicine and Surgery, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, Italy
| | - Cristina Crocamo
- Department of Medicine and Surgery, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, Italy
| | - Giuseppe Paglia
- Department of Medicine and Surgery, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, Italy
| | - Giuseppe Carrà
- Department of Medicine and Surgery, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, Italy; Division of Psychiatry, University College London, 149 Tottenham Ct Rd, London W1T 7NF, United Kingdom
| | - Francesco Bartoli
- Department of Medicine and Surgery, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, Italy.
| |
Collapse
|
3
|
Willency JA, Lin Y, Pirro V. Targeted metabolomics in human and animal biofluids and tissues using liquid chromatography coupled with tandem mass spectrometry. STAR Protoc 2024; 5:102884. [PMID: 38367229 PMCID: PMC10882138 DOI: 10.1016/j.xpro.2024.102884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 08/07/2023] [Accepted: 01/26/2024] [Indexed: 02/19/2024] Open
Abstract
Here, we present a targeted polar metabolomics protocol for the analysis of biofluids and frozen tissue biopsies using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We describe steps for sample pretreatment, liquid-liquid extraction, and isolation of polar metabolites. We then detail procedures for target LC-MS/MS analysis. In this protocol, we focus on the analysis of plasma and serum samples. We also provide brief instructions on how to process other biological matrices as supplemental information. For complete details on the use and execution of this protocol, please refer to Coskun et al. (2022).1.
Collapse
Affiliation(s)
- Jill A Willency
- Technologies and Operations Group, Eli Lilly and Company, Indianapolis, IN 46225, USA
| | - Yanzhu Lin
- Discovery Statistics, Eli Lilly and Company, Indianapolis, IN 46225, USA
| | - Valentina Pirro
- Technologies and Operations Group, Eli Lilly and Company, Indianapolis, IN 46225, USA.
| |
Collapse
|
4
|
Zhang Z, Cui X, Zhou N, Zhu L, Zhi Y, Zhang S. Influence of plasma collection tubes on N-glycome in human blood samples. Pract Lab Med 2024; 39:e00383. [PMID: 38463195 PMCID: PMC10924059 DOI: 10.1016/j.plabm.2024.e00383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 02/20/2024] [Accepted: 02/27/2024] [Indexed: 03/12/2024] Open
Abstract
Background and aims Quantitative analysis of plasma N-glycome is a promising method for identifying disease biomarkers. This study aimed to investigate the impact of using blood collection tubes with different anticoagulants on plasma N-glycome. Materials and methods We used a robust mass spectrometry method to profile plasma N-glycomes in two cohorts of healthy volunteers (cohort 1, n = 16; cohort 2, n = 53). The influence of three commonly used blood collection tubes on fully characterized N-glycomic profiles were explored. Results Principal component analysis revealed distinct clustering of blood samples based on the collection tubes. Pairwise comparisons demonstrated significant differences between EDTA and heparin plasma in 55 out of 82 quantified N-glycan traits, and between EDTA and citrate plasma in 62 out of 82 traits. These differences encompassed various N-glycan features, including glycan type, sialylation, galactosylation, fucosylation, and bisection. Trends in N-glycan variations in citrate and heparin plasma were largely consistent compared to EDTA plasma. In correlation analysis (EDTA vs. heparin; EDTA vs. citrate), Pearson's correlation coefficients were consistently higher than 0.7 for the majority of N-glycan traits. Conclusion Sample matrix variations impact plasma N-glycome measurements. Caution is crucial when comparing samples from different plasma collection tubes in glycomics projects.
Collapse
Affiliation(s)
- Zejian Zhang
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan Wangfujing, Dongcheng District, Beijing, 100730, China
| | - Xiangyi Cui
- Department of Allergy & Clinical Immunology, National Clinical Research Center for Immunologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan Wangfujing, Dongcheng District, Beijing, 100730, China
| | - Nan Zhou
- Department of Allergy & Clinical Immunology, National Clinical Research Center for Immunologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan Wangfujing, Dongcheng District, Beijing, 100730, China
| | - Lisi Zhu
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan Wangfujing, Dongcheng District, Beijing, 100730, China
| | - Yuxiang Zhi
- Department of Allergy & Clinical Immunology, National Clinical Research Center for Immunologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan Wangfujing, Dongcheng District, Beijing, 100730, China
| | - Shuyang Zhang
- Department of Cardiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan Wangfujing, Dongcheng District, Beijing, 100730, China
| |
Collapse
|
5
|
Wu B, Xu Y, Tang M, Jiang Y, Zhang T, Huang L, Wang S, Hu Y, Zhou K, Zhang X, Chen M. A Metabolome and Microbiome Analysis of Acute Myeloid Leukemia: Insights into the Carnosine-Histidine Metabolic Pathway. TOXICS 2023; 12:14. [PMID: 38250970 PMCID: PMC10821349 DOI: 10.3390/toxics12010014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/17/2023] [Accepted: 12/21/2023] [Indexed: 01/23/2024]
Abstract
Metabolism underlies the pathogenesis of acute myeloid leukemia (AML) and can be influenced by gut microbiota. However, the specific metabolic changes in different tissues and the role of gut microbiota in AML remain unclear. In this study, we analyzed the metabolome differences in blood samples from patients with AML and healthy controls using UPLC-Q-Exactive. Additionally, we examined the serum, liver, and fecal metabolome of AML model mice and control mice using UPLC-Q-Exactive. The gut microbiota of the mice were analyzed using 16S rRNA sequencing. Our UPLC-MS analysis revealed significant differences in metabolites between the AML and control groups in multiple tissue samples. Through cross-species validation in humans and animals, as well as reverse validation of Celastrol, we discovered that the Carnosine-Histidine metabolic pathway may play a potential role in the occurrence and progression of AML. Furthermore, our analysis of gut microbiota showed no significant diversity changes, but we observed a significant negative correlation between the key metabolite Carnosine and Peptococcaceae and Campylobacteraceae. In conclusion, the Carnosine-Histidine metabolic pathway influences the occurrence and progression of AML, while the gut microbiota might play a role in this process.
Collapse
Affiliation(s)
- Binxiong Wu
- Department of Hygienic Analysis and Detection, School of Public Health, Nanjing Medical University, Nanjing 211166, China;
| | - Yuntian Xu
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; (Y.X.); (M.T.); (Y.J.); (L.H.); (S.W.); (K.Z.)
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Miaomiao Tang
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; (Y.X.); (M.T.); (Y.J.); (L.H.); (S.W.); (K.Z.)
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Yingtong Jiang
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; (Y.X.); (M.T.); (Y.J.); (L.H.); (S.W.); (K.Z.)
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Ting Zhang
- Women’s Hospital of Jiangnan University, Wuxi 214002, China;
| | - Lei Huang
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; (Y.X.); (M.T.); (Y.J.); (L.H.); (S.W.); (K.Z.)
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Shuyang Wang
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; (Y.X.); (M.T.); (Y.J.); (L.H.); (S.W.); (K.Z.)
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Yanhui Hu
- Sir Run Run Hospital of Nanjing Medical University, Nanjing 211166, China;
| | - Kun Zhou
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; (Y.X.); (M.T.); (Y.J.); (L.H.); (S.W.); (K.Z.)
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Xiaoling Zhang
- Department of Hygienic Analysis and Detection, School of Public Health, Nanjing Medical University, Nanjing 211166, China;
| | - Minjian Chen
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; (Y.X.); (M.T.); (Y.J.); (L.H.); (S.W.); (K.Z.)
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| |
Collapse
|
6
|
Bovo S, Schiavo G, Galimberti G, Fanelli F, Bertolini F, Dall'Olio S, Pagotto U, Fontanesi L. Comparative targeted metabolomic profiles of porcine plasma and serum. Animal 2023; 17:101029. [PMID: 38064856 DOI: 10.1016/j.animal.2023.101029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 11/01/2023] [Accepted: 11/02/2023] [Indexed: 12/23/2023] Open
Abstract
Metabolomics has been used to characterise many biological matrices and obtain detailed pictures of biological systems based on many metabolites. Plasma and serum are two blood-derived biofluids commonly used to assess and monitor the organismal metabolism and obtain information on the physiological and health conditions of an animal. Plasma is the supernatant that is separated from the cellular components after centrifugation of the blood that is first added with an anticoagulant. Serum is obtained after centrifugation of the blood that has been coagulated. The choice of one or the other biofluid for metabolomic analyses is related to specific analytical needs and technical issues, to problems derived by the collection and preparation steps, in particular when specimens are sampled from animals involved in field studies. Thus far, most of the metabolomic studies that compared plasma and serum have been carried out in humans and very little is known on the pigs. In this study, we used a targeted metabolomic platform that can detect about 180 metabolites of five biochemical classes to compare plasma and serum profiles of samples collected from 24 pigs. To also obtain a cross-species comparative metabolomic analysis, information for human plasma and serum derived from the same platform was retrieved from previous studies. Statistical analyses included univariate and multivariate approaches aimed at identifying stable and/or differentially abundant metabolites between the two porcine biofluids. A total of 154 (∼83%) metabolites passed the initial quality control, indicating a good repeatability of the analytical platform in pigs. Discarded metabolites included aspartate and biogenic amines that were already reported to be unstable in human studies. More than 80% of the metabolites had similar profiles in both porcine biofluids (average correlation was 0.75). Concentrations were usually higher in serum than in plasma, in agreement with what was already reported in humans. The univariate analysis identified 44 metabolites that had statistically different concentrations between porcine plasma and serum, of which 28 metabolites were also confirmed by the multivariate analysis. The obtained picture described similarities and differences between these two biofluids in pigs and the related human-pig comparisons. The obtained information can be useful for the choice of one or the other matrix for the implementation of metabolomic studies in this livestock species. The results can also provide useful hints to valuing the pig as animal model, in particular when metabolite-derived physiological states are relevant.
Collapse
Affiliation(s)
- Samuele Bovo
- Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, 40127 Bologna, Italy
| | - Giuseppina Schiavo
- Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, 40127 Bologna, Italy
| | - Giuliano Galimberti
- Department of Statistical Sciences "Paolo Fortunati", University of Bologna, 40126 Bologna, Italy
| | - Flaminia Fanelli
- Department of Surgical and Medical Sciences, Endocrinology Unit, University of Bologna, 40138 Bologna, Italy
| | - Francesca Bertolini
- Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, 40127 Bologna, Italy
| | - Stefania Dall'Olio
- Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, 40127 Bologna, Italy
| | - Uberto Pagotto
- Department of Surgical and Medical Sciences, Endocrinology Unit, University of Bologna, 40138 Bologna, Italy
| | - Luca Fontanesi
- Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, 40127 Bologna, Italy.
| |
Collapse
|
7
|
Michels KA, Weinstein SJ, Albert PS, Black A, Brotzman M, Diaz-Mayoral NA, Gerlanc N, Huang WY, Sampson JN, Shreves A, Ueland PM, Wyatt K, Wentzensen N, Abnet CC. The Influence of Preanalytical Biospecimen Handling on the Measurement of B Vitamers, Amino Acids, and Other Metabolites in Blood. Biopreserv Biobank 2023; 21:467-476. [PMID: 36622937 PMCID: PMC10616936 DOI: 10.1089/bio.2022.0053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Introduction: Sample handling can influence biomarker measurement and introduce variability when combining data from multiple studies or study sites. To inform the development of blood collection protocols within a multisite cohort study, we directly quantified concentrations of 54 biomarkers in blood samples subjected to different handling conditions. Materials and Methods: We obtained serum, lithium heparin plasma, and EDTA plasma from 20 adult volunteers. Tubes of chilled whole blood were either centrifuged and processed within 2 hours of collection (the "reference standard") or were stored with cool packs for 24 or 48 hours; centrifuged before and/or after this delay; or collected in tubes with/without gel separators. We used linear mixed models with random intercepts to estimate geometric mean concentrations and relative percent differences across the conditions. Results: Compared to the reference standard tubes, concentrations of many biomarkers changed after processing delays, but changes were often small. In serum, we observed large differences for B vitamers, glutamic acid (37% and 73% increases with 24- and 48-hour delays, respectively), glycine (12% and 23% increases), serine (16% and 27% increases), and acetoacetate (-19% and -26% decreases). Centrifugation timing and separator tube use did not affect concentrations of most biomarkers. Conclusion: Sample handling should be consistent across samples within an analysis. The length of processing delays should be recorded and accounted for when this is not feasible.
Collapse
Affiliation(s)
- Kara A. Michels
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Stephanie J. Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Paul S. Albert
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Amanda Black
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Michelle Brotzman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Norma A. Diaz-Mayoral
- BioProcessing Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland, USA
| | - Nicole Gerlanc
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Wen-Yi Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Joshua N. Sampson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Alaina Shreves
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | | | - Kathleen Wyatt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Nicolas Wentzensen
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Christian C. Abnet
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| |
Collapse
|
8
|
Yuan Y, Song H, Zhou J, Yin Y. Evaluation of Small Molecules in Blank EDTA Plasma Tubes and Optimization of Metabolomic Workflow for Biomarker Studies Using Plasma Samples. Anal Chem 2023; 95:10859-10863. [PMID: 37428854 DOI: 10.1021/acs.analchem.3c01864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2023]
Abstract
As the first step of metabolomic analysis in biomarker identification studies, various types of blood collection tubes are used in clinical practice. However, little attention is paid to potential contamination caused by the blank tube itself. Here, we evaluated small molecules in blank EDTA plasma tubes through LC-MS-based untargeted metabolomic analysis and identified small molecules with markedly varied levels among different production batches or specifications. Our data demonstrate possible contamination and data interference caused by blank EDTA plasma tubes when employing large clinical cohorts for biomarker identification. Therefore, we propose a workflow of filtering metabolites in blank tubes prior to statistical analysis to improve the fidelity of biomarker identification.
Collapse
Affiliation(s)
- Yuyao Yuan
- Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Huajie Song
- Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Juntuo Zhou
- Beijing Boyuan Precision Medicine, Beijing 102206, China
| | - Yuxin Yin
- Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, China
| |
Collapse
|
9
|
Safari F, Kehelpannala C, Safarchi A, Batarseh AM, Vafaee F. Biomarker Reproducibility Challenge: A Review of Non-Nucleotide Biomarker Discovery Protocols from Body Fluids in Breast Cancer Diagnosis. Cancers (Basel) 2023; 15:2780. [PMID: 37345117 DOI: 10.3390/cancers15102780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/02/2023] [Accepted: 05/10/2023] [Indexed: 06/23/2023] Open
Abstract
Breast cancer has now become the most commonly diagnosed cancer, accounting for one in eight cancer diagnoses worldwide. Non-invasive diagnostic biomarkers and associated tests are superlative candidates to complement or improve current approaches for screening, early diagnosis, or prognosis of breast cancer. Biomarkers detected from body fluids such as blood (serum/plasma), urine, saliva, nipple aspiration fluid, and tears can detect breast cancer at its early stages in a minimally invasive way. The advancements in high-throughput molecular profiling (omics) technologies have opened an unprecedented opportunity for unbiased biomarker detection. However, the irreproducibility of biomarkers and discrepancies of reported markers have remained a major roadblock to clinical implementation, demanding the investigation of contributing factors and the development of standardised biomarker discovery pipelines. A typical biomarker discovery workflow includes pre-analytical, analytical, and post-analytical phases, from sample collection to model development. Variations introduced during these steps impact the data quality and the reproducibility of the findings. Here, we present a comprehensive review of methodological variations in biomarker discovery studies in breast cancer, with a focus on non-nucleotide biomarkers (i.e., proteins, lipids, and metabolites), highlighting the pre-analytical to post-analytical variables, which may affect the accurate identification of biomarkers from body fluids.
Collapse
Affiliation(s)
- Fatemeh Safari
- School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Sydney, NSW 2052, Australia
| | - Cheka Kehelpannala
- BCAL Diagnostics Ltd., Suite 506, 50 Clarence St, Sydney, NSW 2000, Australia
- BCAL Dx, The University of Sydney, Sydney Knowledge Hub, Merewether Building, Sydney, NSW 2006, Australia
| | - Azadeh Safarchi
- School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Sydney, NSW 2052, Australia
- Microbiomes for One Systems Health, Health and Biosecurity, CSIRO, Westmead, NSW 2145, Australia
| | - Amani M Batarseh
- BCAL Diagnostics Ltd., Suite 506, 50 Clarence St, Sydney, NSW 2000, Australia
- BCAL Dx, The University of Sydney, Sydney Knowledge Hub, Merewether Building, Sydney, NSW 2006, Australia
| | - Fatemeh Vafaee
- School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Sydney, NSW 2052, Australia
- UNSW Data Science Hub (uDASH), University of New South Wales (UNSW Sydney), Sydney, NSW 2052, Australia
- OmniOmics.ai Pty Ltd., Sydney, NSW 2035, Australia
| |
Collapse
|
10
|
Garwolińska D, Kot-Wasik A, Hewelt-Belka W. Pre-analytical aspects in metabolomics of human biofluids - sample collection, handling, transport, and storage. Mol Omics 2023; 19:95-104. [PMID: 36524542 DOI: 10.1039/d2mo00212d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Metabolomics is the field of omics research that offers valuable insights into the complex composition of biological samples. It has found wide application in clinical diagnostics, disease investigation, therapy prediction, monitoring of treatment efficiency, drug discovery, or in-depth analysis of sample composition. A suitable study design constitutes the fundamental requirements to ensure robust and reliable results from the study data. The study design process should include a careful selection of conditions for each experimental step, from sample collection to data analysis. The pre-analytical variability that can introduce bias to the subsequent analytical process, decrease the outcome reliability, and confuse the final results of the metabolomics research, should also be considered. Herein, we provide key information regarding the pre-analytical variables affecting the metabolomics studies of biological fluids that are the most desirable type of biological samples. Our work offers a practical review that can serve and guide metabolomics pre-analytical design. It indicates pre-analytical factors, which can introduce artificial data variation and should be identified and understood during experimental design (through literature overview or analytical experiments).
Collapse
Affiliation(s)
- Dorota Garwolińska
- Department of Analytical Chemistry, Faculty of Chemistry, Gdańsk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gdańsk, Poland.
| | - Agata Kot-Wasik
- Department of Analytical Chemistry, Faculty of Chemistry, Gdańsk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gdańsk, Poland.
| | - Weronika Hewelt-Belka
- Department of Analytical Chemistry, Faculty of Chemistry, Gdańsk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gdańsk, Poland.
| |
Collapse
|
11
|
Pharmacometabolomics for the Study of Lipid-Lowering Therapies: Opportunities and Challenges. Int J Mol Sci 2023; 24:ijms24043291. [PMID: 36834701 PMCID: PMC9960554 DOI: 10.3390/ijms24043291] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/30/2023] [Accepted: 02/02/2023] [Indexed: 02/11/2023] Open
Abstract
Lipid-lowering therapies are widely used to prevent the development of atherosclerotic cardiovascular disease (ASCVD) and related mortality worldwide. "Omics" technologies have been successfully applied in recent decades to investigate the mechanisms of action of these drugs, their pleiotropic effects, and their side effects, aiming to identify novel targets for future personalized medicine with an improvement of the efficacy and safety associated with the treatment. Pharmacometabolomics is a branch of metabolomics that is focused on the study of drug effects on metabolic pathways that are implicated in the variation of response to the treatment considering also the influences from a specific disease, environment, and concomitant pharmacological therapies. In this review, we summarized the most significant metabolomic studies on the effects of lipid-lowering therapies, including the most commonly used statins and fibrates to novel drugs or nutraceutical approaches. The integration of pharmacometabolomics data with the information obtained from the other "omics" approaches could help in the comprehension of the biological mechanisms underlying the use of lipid-lowering drugs in view of defining a precision medicine to improve the efficacy and reduce the side effects associated with the treatment.
Collapse
|
12
|
Volani C, Malfertheiner C, Caprioli G, Fjelstrup S, Pramstaller PP, Rainer J, Paglia G. VAMS-Based Blood Capillary Sampling for Mass Spectrometry-Based Human Metabolomics Studies. Metabolites 2023; 13:metabo13020146. [PMID: 36837765 PMCID: PMC9958641 DOI: 10.3390/metabo13020146] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/14/2023] [Accepted: 01/15/2023] [Indexed: 01/20/2023] Open
Abstract
Volumetric absorptive microsampling (VAMS) is a recently developed sample collection method that enables single-drop blood collection in a minimally invasive manner. Blood biomolecules can then be extracted and processed for analysis using several analytical platforms. The integration of VAMS with conventional mass spectrometry (MS)-based metabolomics approaches is an attractive solution for human studies representing a less-invasive procedure compared to phlebotomy with the additional potential for remote sample collection. However, as we recently demonstrated, VAMS samples require long-term storage at -80 °C. This study investigated the stability of VAMS samples during short-term storage and compared the metabolome obtained from capillary blood collected from the fingertip to those of plasma and venous blood from 22 healthy volunteers. Our results suggest that the blood metabolome collected by VAMS samples is stable at room temperature only for up to 6 h requiring subsequent storage at -80 °C to avoid significant changes in the metabolome. We also demonstrated that capillary blood provides better coverage of the metabolome compared to plasma enabling the analysis of several intracellular metabolites presented in red blood cells. Finally, this work demonstrates that with the appropriate pre-analytical protocol capillary blood can be successfully used for untargeted metabolomics studies.
Collapse
Affiliation(s)
- Chiara Volani
- Institute for Biomedicine, Affiliated to the University of Lübeck, Eurac Research, 39100 Bolzano, Italy
| | - Christa Malfertheiner
- Institute for Biomedicine, Affiliated to the University of Lübeck, Eurac Research, 39100 Bolzano, Italy
| | - Giulia Caprioli
- Institute for Biomedicine, Affiliated to the University of Lübeck, Eurac Research, 39100 Bolzano, Italy
| | - Søren Fjelstrup
- Interdisciplinary Nanoscience Center, Aarhus University, 8000 Aarhus, Denmark
| | - Peter P. Pramstaller
- Institute for Biomedicine, Affiliated to the University of Lübeck, Eurac Research, 39100 Bolzano, Italy
| | - Johannes Rainer
- Institute for Biomedicine, Affiliated to the University of Lübeck, Eurac Research, 39100 Bolzano, Italy
| | - Giuseppe Paglia
- School of Medicine and Surgery, University of Milano-Bicocca, 20854 Vedano al Lambro, Italy
- Correspondence:
| |
Collapse
|
13
|
Bishop LM, Fiehn O. Comprehensive lipidomic profiling by plasma separation cards. Anal Bioanal Chem 2023; 415:193-201. [PMID: 36316462 PMCID: PMC10448968 DOI: 10.1007/s00216-022-04399-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 10/04/2022] [Accepted: 10/21/2022] [Indexed: 01/07/2023]
Abstract
Large-scale lipidomic analyses have been limited by the cost and accessibility of traditional venipuncture sampling. Microsampling techniques offer a less-invasive and more accessible alternative. From a single drop of blood, plasma separation cards (PSC) deliver two volumetric dried plasma samples which are studied here for profiling endogenous blood lipids. Six lots of EDTA-treated human whole blood were used to compare PSC, dried blood spot analyses (DBS), and classic wet plasma extractions. Six replicate extractions were performed for each lot. Nontargeted lipidomics was performed by liquid chromatography-high resolution tandem mass spectrometry. Lipids were annotated by accurate mass/retention time matching and MS/MS spectral library matching using peak intensities for quantitation. Four hundred ninety-eight compounds covering 24 lipid subclasses were annotated. Inter-lot repeatability was evaluated by the percent relative standard deviation (%RSD) for each lot, giving median %RSD values across the lots at 14.6% for PSC, 9.3% for DBS, and 8.6% for wet plasma. Strong correlations of lipid peak intensities between wet plasma and PSCs were observed, but less for DBS. Lipid recovery and stability were comparable between the PSC and DBS samples, with roughly 60% of annotated lipids stable at room temperature after 28 days. Overall, PSCs provide a better alternative for quantitative blood lipidomic analyses compared to dried blood spots. However, problems with lipid stability for samples handled and shipped at room temperature are currently unavoidable outside of a clinical setting. Data transferability and comparability to standard plasma is lipid and lipid class dependent.
Collapse
Affiliation(s)
- Lauren M Bishop
- Department of Chemistry, University of California Davis, Davis, CA, USA
- West Coast Metabolomics Center, University of California Davis, Davis, CA, USA
| | - Oliver Fiehn
- Department of Chemistry, University of California Davis, Davis, CA, USA.
- West Coast Metabolomics Center, University of California Davis, Davis, CA, USA.
| |
Collapse
|
14
|
Vasilakopoulou PB, Gousgouni AT, Yanni AE, Kostomitsopoulos N, Karathanos VT, Chiou A. Polar Phenol Detection in Plasma and Serum: Insights on Sample Pre-Treatment for LC/MS Analysis and Application on the Serum of Corinthian Currant-Fed Rats. Biomolecules 2022; 12:biom12121838. [PMID: 36551268 PMCID: PMC9775334 DOI: 10.3390/biom12121838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/02/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022] Open
Abstract
Analysis of plasma and serum provides valuable information on the amounts of polar phenols' circulating after ingestion. In the present study, protein precipitation (PPT), liquid-liquid extraction (LLE), solid phase extraction (SPE), enzymatic hydrolysis and their combinations were meticulously evaluated for the extraction of a variety of polar phenolic moieties from plasma and serum. The recovery values of the above methods were compared; satisfactory recoveries (>60%) were attained for most analytes. Polar phenol aglycones undergo degradation with enzymatic hydrolysis; however, their extended phase II metabolism makes enzymatic hydrolysis a mandated process for their analysis in such biofluids. Hence, enzymatic hydrolysis followed by LLE was used for the identification of polar phenols in rats' serum, after the long-term oral consumption of Corinthian Currant. Corinthian Currant is a Greek dried vine product rich in bioactive polar phenolics. Flavonoids and phenolic acids, detected as aglycones, ranged from 0.57 ± 0.08 to 181.66 ± 48.95 and 3.45 ± 1.20 to 897.81 ± 173.96 ng/mL, respectively. The majority of polar phenolics were present as phase II metabolites, representing their fasting state in the blood stream. This is the first study evaluating the presence of polar phenolics in the serum of rats following a long-term diet supplemented with Corinthian Currant as a whole food.
Collapse
Affiliation(s)
- Paraskevi B. Vasilakopoulou
- Laboratory of Chemistry-Biochemistry-Physical Chemistry of Foods, Department of Nutrition and Dietetics, Harokopio University, 70 El. Venizelou Ave., 176 76 Kallithea, Greece
| | - Aimilia-Tatiana Gousgouni
- Laboratory of Chemistry-Biochemistry-Physical Chemistry of Foods, Department of Nutrition and Dietetics, Harokopio University, 70 El. Venizelou Ave., 176 76 Kallithea, Greece
| | - Amalia E. Yanni
- Laboratory of Chemistry-Biochemistry-Physical Chemistry of Foods, Department of Nutrition and Dietetics, Harokopio University, 70 El. Venizelou Ave., 176 76 Kallithea, Greece
| | - Nikolaos Kostomitsopoulos
- Laboratory Animal Facility, Biomedical Research Foundation of the Academy of Athens, 115 27 Athens, Greece
| | - Vaios T. Karathanos
- Laboratory of Chemistry-Biochemistry-Physical Chemistry of Foods, Department of Nutrition and Dietetics, Harokopio University, 70 El. Venizelou Ave., 176 76 Kallithea, Greece
- Agricultural Cooperatives’ Union of Aeghion, Corinthou 201, 251 00 Aeghion, Greece
| | - Antonia Chiou
- Laboratory of Chemistry-Biochemistry-Physical Chemistry of Foods, Department of Nutrition and Dietetics, Harokopio University, 70 El. Venizelou Ave., 176 76 Kallithea, Greece
- Correspondence: ; Tel.: +30-210-9549-157; Fax: +30-210-9577050
| |
Collapse
|
15
|
A Comprehensive Mass Spectrometry-Based Workflow for Clinical Metabolomics Cohort Studies. Metabolites 2022; 12:metabo12121168. [PMID: 36557207 PMCID: PMC9782571 DOI: 10.3390/metabo12121168] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/14/2022] [Accepted: 11/16/2022] [Indexed: 11/27/2022] Open
Abstract
As a comprehensive analysis of all metabolites in a biological system, metabolomics is being widely applied in various clinical/health areas for disease prediction, diagnosis, and prognosis. However, challenges remain in dealing with the metabolomic complexity, massive data, metabolite identification, intra- and inter-individual variation, and reproducibility, which largely limit its widespread implementation. This study provided a comprehensive workflow for clinical metabolomics, including sample collection and preparation, mass spectrometry (MS) data acquisition, and data processing and analysis. Sample collection from multiple clinical sites was strictly carried out with standardized operation procedures (SOP). During data acquisition, three types of quality control (QC) samples were set for respective MS platforms (GC-MS, LC-MS polar, and LC-MS lipid) to assess the MS performance, facilitate metabolite identification, and eliminate contamination. Compounds annotation and identification were implemented with commercial software and in-house-developed PAppLineTM and UlibMS library. The batch effects were removed using a deep learning model method (NormAE). Potential biomarkers identification was performed with tree-based modeling algorithms including random forest, AdaBoost, and XGBoost. The modeling performance was evaluated using the F1 score based on a 10-times repeated trial for each. Finally, a sub-cohort case study validated the reliability of the entire workflow.
Collapse
|
16
|
Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research. Metabolites 2022; 12:metabo12100963. [PMID: 36295865 PMCID: PMC9609461 DOI: 10.3390/metabo12100963] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/10/2022] [Accepted: 10/10/2022] [Indexed: 12/02/2022] Open
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.
Collapse
|
17
|
Paglia G, Smith AJ, Astarita G. Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics. MASS SPECTROMETRY REVIEWS 2022; 41:722-765. [PMID: 33522625 DOI: 10.1002/mas.21686] [Citation(s) in RCA: 90] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 01/17/2021] [Accepted: 01/17/2021] [Indexed: 06/12/2023]
Abstract
Researchers worldwide are taking advantage of novel, commercially available, technologies, such as ion mobility mass spectrometry (IM-MS), for metabolomics and lipidomics applications in a variety of fields including life, biomedical, and food sciences. IM-MS provides three main technical advantages over traditional LC-MS workflows. Firstly, in addition to mass, IM-MS allows collision cross-section values to be measured for metabolites and lipids, a physicochemical identifier related to the chemical shape of an analyte that increases the confidence of identification. Second, IM-MS increases peak capacity and the signal-to-noise, improving fingerprinting as well as quantification, and better defining the spatial localization of metabolites and lipids in biological and food samples. Third, IM-MS can be coupled with various fragmentation modes, adding new tools to improve structural characterization and molecular annotation. Here, we review the state-of-the-art in IM-MS technologies and approaches utilized to support metabolomics and lipidomics applications and we assess the challenges and opportunities in this growing field.
Collapse
Affiliation(s)
- Giuseppe Paglia
- School of Medicine and Surgery, University of Milano-Bicocca, Vedano al Lambro (MB), Italy
| | - Andrew J Smith
- School of Medicine and Surgery, University of Milano-Bicocca, Vedano al Lambro (MB), Italy
| | - Giuseppe Astarita
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, Washington, District of Columbia, USA
| |
Collapse
|
18
|
Zhang D, Lu S. Human exposure to neonicotinoids and the associated health risks: A review. ENVIRONMENT INTERNATIONAL 2022; 163:107201. [PMID: 35344909 DOI: 10.1016/j.envint.2022.107201] [Citation(s) in RCA: 56] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 03/08/2022] [Accepted: 03/21/2022] [Indexed: 06/14/2023]
Abstract
Neonicotinoids (NEOs) are a class of broad-spectrum insecticides dominant in the global market. They were distributed extensively in the environment and occurred frequently in humans. Potential health effects of NEOs, such as neurological toxicity and diabetes to non-targeted mammals, have raised concerns. This review summarizes analytical methods of NEOs in human samples, their internal exposure levels and composition profiles in urine, blood, hair, breast milk, saliva and tooth samples with global comparisons, and daily NEOs exposure dose and relative health risks.Urinary NEOs levels in Asian populations were substantially higher than those in the U.S. and Europe, which may be due to different dietary patterns and insecticide applications across regions. N-desmethyl acetamiprid, 5-hydroxy-imidacloprid and olefin-imidacloprid were dominant among detected NEOs. NEO metabolites exhibited higher detection frequencies and levels than their parent compounds in humans, while investigations on NEO metabolites remain much limited. Current exposure assessments mainly focused on short-term urine analysis, while biomaterials for long-term monitoring, such as hair, nail and other alternatives, should also be considered. Large-scale epidemiological studies are critically needed to elucidate potential health outcomes associated with NEOs exposure.
Collapse
Affiliation(s)
- Duo Zhang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, China
| | - Shaoyou Lu
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, China.
| |
Collapse
|
19
|
Knutti N, Neugebauer S, Scherr F, Mathay C, Marchese M, Henry E, Palm J, Betsou F, Kiehntopf M. Introduction of BD Vacutainer ® Barricor™ tubes in clinical biobanking and application of amino acid and cytokine quality indicators to Barricor plasma. Clin Chem Lab Med 2022; 60:689-700. [PMID: 35073617 DOI: 10.1515/cclm-2021-0899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 01/10/2022] [Indexed: 01/04/2024]
Abstract
OBJECTIVES The use of BD Vacutainer® Barricor™ tubes (BAR) can reduce turnaround time (TAT) and improve separation of plasma from cellular components using a specific mechanical separator. Concentrations of amino acids (AAs) and cytokines, known to be labile during pre-analytical time delays, were compared in heparin (BAR, BD Heparin standard tube [PST]), EDTA and serum gel tubes (SER) to validate previously identified quality indicators (QIs) in BAR. METHODS Samples of healthy individuals (n=10) were collected in heparin, EDTA and SER tubes and exposed to varying pre- and post-centrifugation delays at room temperature (RT). Cytokines (interleukin [IL]-8, IL-16 and sCD40L) were analyzed by enzyme-linked immunosorbent assay (ELISA) and AAs were characterized by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). RESULTS All QIs, AAs/AA ratio and cytokines increased during prolonged blood storage in heparin plasma (PST, BAR) and SER tubes. Comparison of 53 h/1 h pre-centrifugation delay resulted in an increase in taurine (Tau) and glutamic acid (Glu) concentrations by more than three times, soluble CD40L increased by 13.6, 9.2 and 4.3 fold in PST, BAR-CTRL and BAR-FAST, and IL-8 increased even more by 112.8 (PST), 266.1 (BAR-CTRL), 268.1 (BAR-FAST) and 70.0 (SER) fold, respectively. Overall, compared to prolonged blood storage, effects of post-centrifugation delays were less pronounced in all tested materials. CONCLUSIONS BAR tubes are compatible with the use of several established QIs and can therefore be used in clinical biobanking to reduce pre-analytical TAT without compromising QIs and thus pre-analytical sample quality analysis.
Collapse
Affiliation(s)
- Nadine Knutti
- Institute of Clinical Chemistry and Laboratory Diagnostics, Jena University Hospital, Jena, Germany
| | - Sophie Neugebauer
- Institute of Clinical Chemistry and Laboratory Diagnostics, Jena University Hospital, Jena, Germany
| | - Franziska Scherr
- Institute of Clinical Chemistry and Laboratory Diagnostics, Jena University Hospital, Jena, Germany
| | - Conny Mathay
- Integrated BioBank of Luxembourg (IBBL), Dudelange, Luxembourg
| | - Monica Marchese
- Integrated BioBank of Luxembourg (IBBL), Dudelange, Luxembourg
| | - Estelle Henry
- Integrated BioBank of Luxembourg (IBBL), Dudelange, Luxembourg
| | - Julia Palm
- Institute of Medical Statistics, Computer and Data Science, Jena University Hospital, Jena, Germany
| | - Fay Betsou
- Integrated BioBank of Luxembourg (IBBL), Dudelange, Luxembourg
- Laboratoire National de Santé (LNS), Dudelange, Luxembourg
| | - Michael Kiehntopf
- Institute of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), Jena University Hospital, Jena, Germany
| |
Collapse
|
20
|
Vignoli A, Tenori L, Morsiani C, Turano P, Capri M, Luchinat C. Serum or Plasma (and Which Plasma), That Is the Question. J Proteome Res 2022; 21:1061-1072. [PMID: 35271285 PMCID: PMC8981325 DOI: 10.1021/acs.jproteome.1c00935] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
Blood
derivatives
are the biofluids of choice for metabolomic clinical
studies since blood can be collected with low invasiveness and is
rich in biological information. However, the choice of the blood collection
tubes has an undeniable impact on the plasma and serum metabolic content.
Here, we compared the metabolomic and lipoprotein profiles of blood
samples collected at the same time and place from six healthy volunteers
but using different collection tubes (each enrolled volunteer provided
multiple blood samples at a distance of a few weeks/months): citrate
plasma, EDTA plasma, and serum tubes. All samples were analyzed via
nuclear magnetic resonance spectroscopy. Several metabolites showed
statistically significant alterations among the three blood matrices,
and also metabolites’ correlations were shown to be affected.
The effects of blood collection tubes on the lipoproteins’
profiles are relevant too, but less marked. Overcoming the issue associated
with different blood collection tubes is pivotal to scale metabolomics
and lipoprotein analysis at the level of epidemiological studies based
on samples from multicenter cohorts. We propose a statistical solution,
based on regression, that is shown to be efficient in reducing the
alterations induced by the different collection tubes for both the
metabolomic and lipoprotein profiles.
Collapse
Affiliation(s)
- Alessia Vignoli
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, 50019 Sesto Fiorentino, Italy
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, 50019 Sesto Fiorentino, Italy.,Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), 50019 Sesto Fiorentino, Italy
| | - Cristina Morsiani
- DIMES - Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40126 Bologna, Italy
| | - Paola Turano
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, 50019 Sesto Fiorentino, Italy.,Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), 50019 Sesto Fiorentino, Italy
| | - Miriam Capri
- DIMES - Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40126 Bologna, Italy
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, 50019 Sesto Fiorentino, Italy.,Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), 50019 Sesto Fiorentino, Italy
| |
Collapse
|
21
|
Ghini V, Abuja PM, Polasek O, Kozera L, Laiho P, Anton G, Zins M, Klovins J, Metspalu A, Wichmann HE, Gieger C, Luchinat C, Zatloukal K, Turano P. Impact of the pre-examination phase on multicenter metabolomic studies. N Biotechnol 2022; 68:37-47. [PMID: 35066155 DOI: 10.1016/j.nbt.2022.01.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 01/17/2022] [Accepted: 01/19/2022] [Indexed: 01/23/2023]
Abstract
The development of metabolomics in clinical applications has been limited by the lack of validation in large multicenter studies. Large population cohorts and their biobanks are a valuable resource for acquiring insights into molecular disease mechanisms. Nevertheless, most of their collections are not tailored for metabolomics and have been created without specific attention to the pre-analytical requirements for high-quality metabolome assessment. Thus, comparing samples obtained by different pre-analytical procedures remains a major challenge. Here, 1H NMR-based analyses are used to demonstrate how human serum and plasma samples collected with different operating procedures within several large European cohort studies from the Biobanking and Biomolecular Resources Infrastructure - Large Prospective Cohorts (BBMRI-LPC) consortium can be easily revealed by supervised multivariate statistical analyses at the initial stages of the process, to avoid biases in the downstream analysis. The inter-biobank differences are discussed in terms of deviations from the validated CEN/TS 16945:2016 / ISO 23118:2021 norms. It clearly emerges that biobanks must adhere to the evidence-based guidelines in order to support wider-scale application of metabolomics in biomedicine, and that NMR spectroscopy is informative in comparing the quality of different sample sources in multi cohort/center studies.
Collapse
Affiliation(s)
- Veronica Ghini
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), via Luigi Sacconi 6, 50019, Sesto Fiorentino (FI), Italy; Center of Magnetic Resonance (CERM), University of Florence, via Luigi Sacconi 6, 50019, Sesto Fiorentino (FI), Italy; Department of Chemistry, University of Florence, via della Lastruccia 3, 50019, Sesto Fiorentino (FI), Italy
| | - Peter M Abuja
- Institute of Pathology, Medical University of Graz, Neue Stiftingtalstrasse 6, A-8010, Graz, Austria
| | - Ozren Polasek
- Department for Large Population Studies, University of Split, Šoltanska 2, HR-21000, Split, Croatia; Gen-info Ltd, Ružmarinka ul. 17, 10000, Zagreb, Croatia
| | - Lukasz Kozera
- BBMRI-ERIC, Neue Stiftingtalstrasse 2/B/6, 8010, Graz, Austria
| | - Päivi Laiho
- Institute for Molecular Medicine Finland, National Institute for Health and Welfare, THL, University of Helsinki, 00290, Helsinki, Finland
| | - Gabriele Anton
- Molecular Epidemiology, Helmholtz-Zentrum München, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Marie Zins
- Population-based Epidemiological Cohorts Unit-UMS 11, Inserm, 16 Avenue Paul Vaillant Couturier, 94800, Villejuif, France
| | - Janis Klovins
- Latvian Biomedical Research and Study Centre, Rātsupītes iela 1, Kurzemes rajons, Rīga, LV-1067, Latvia
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia
| | - H-Erich Wichmann
- Institute of Epidemiology, Helmholtz Center Munich, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Center Munich, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Claudio Luchinat
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), via Luigi Sacconi 6, 50019, Sesto Fiorentino (FI), Italy; Center of Magnetic Resonance (CERM), University of Florence, via Luigi Sacconi 6, 50019, Sesto Fiorentino (FI), Italy; Department of Chemistry, University of Florence, via della Lastruccia 3, 50019, Sesto Fiorentino (FI), Italy
| | - Kurt Zatloukal
- Institute of Pathology, Medical University of Graz, Neue Stiftingtalstrasse 6, A-8010, Graz, Austria.
| | - Paola Turano
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), via Luigi Sacconi 6, 50019, Sesto Fiorentino (FI), Italy; Center of Magnetic Resonance (CERM), University of Florence, via Luigi Sacconi 6, 50019, Sesto Fiorentino (FI), Italy; Department of Chemistry, University of Florence, via della Lastruccia 3, 50019, Sesto Fiorentino (FI), Italy.
| |
Collapse
|
22
|
D’Amora P, Silva IDCG, Budib MA, Ayache R, Silva RMS, Silva FC, Appel RM, Júnior SS, Pontes HBD, Alvarenga AC, Arima EC, Martins WG, Silva NLF, Diaz RS, Salzgeber MB, Palma AM, Evans SS, Nagourney RA. Towards risk stratification and prediction of disease severity and mortality in COVID-19: Next generation metabolomics for the measurement of host response to COVID-19 infection. PLoS One 2021; 16:e0259909. [PMID: 34851990 PMCID: PMC8635335 DOI: 10.1371/journal.pone.0259909] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Accepted: 10/28/2021] [Indexed: 12/14/2022] Open
Abstract
This study investigated the association between COVID-19 infection and host metabolic signatures as prognostic markers for disease severity and mortality. We enrolled 82 patients with RT-PCR confirmed COVID-19 infection who were classified as mild, moderate, or severe/critical based upon their WHO clinical severity score and compared their results with 31 healthy volunteers. Data on demographics, comorbidities and clinical/laboratory characteristics were obtained from medical records. Peripheral blood samples were collected at the time of clinical evaluation or admission and tested by quantitative mass spectrometry to characterize metabolic profiles using selected metabolites. The findings in COVID-19 (+) patients reveal changes in the concentrations of glutamate, valeryl-carnitine, and the ratios of Kynurenine/Tryptophan (Kyn/Trp) to Citrulline/Ornithine (Cit/Orn). The observed changes may serve as predictors of disease severity with a (Kyn/Trp)/(Cit/Orn) Receiver Operator Curve (ROC) AUC = 0.95. Additional metabolite measures further characterized those likely to develop severe complications of their disease, suggesting that underlying immune signatures (Kyn/Trp), glutaminolysis (Glutamate), urea cycle abnormalities (Cit/Orn) and alterations in organic acid metabolism (C5) can be applied to identify individuals at the highest risk of morbidity and mortality from COVID-19 infection. We conclude that host metabolic factors, measured by plasma based biochemical signatures, could prove to be important determinants of Covid-19 severity with implications for prognosis, risk stratification and clinical management.
Collapse
Affiliation(s)
- Paulo D’Amora
- Department of Gynecology, Molecular Gynecology and Metabolomics Lab, College of Medicine of the Federal University of São Paulo (EPM-UNIFESP), São Paulo, São Paulo, Brazil
- Nagourney Institute, Long Beach, California, United States of America
- Metabolomycs, Inc., Long Beach, California, United States of America
| | - Ismael Dale C. G. Silva
- Department of Gynecology, Molecular Gynecology and Metabolomics Lab, College of Medicine of the Federal University of São Paulo (EPM-UNIFESP), São Paulo, São Paulo, Brazil
- Nagourney Cancer Institute, Long Beach, California, United States of America
| | - Maria Auxiliadora Budib
- Department of Emergency and Intensive Care Unit, CASSEMS Hospital, Campo Grande, Mato Grosso do Sul, Brazil
| | - Ricardo Ayache
- Department of Emergency and Intensive Care Unit, CASSEMS Hospital, Campo Grande, Mato Grosso do Sul, Brazil
| | - Rafaela Moraes Siufi Silva
- Department of Emergency and Intensive Care Unit, CASSEMS Hospital, Campo Grande, Mato Grosso do Sul, Brazil
| | - Fabricio Colacino Silva
- Department of Emergency and Intensive Care Unit, CASSEMS Hospital, Campo Grande, Mato Grosso do Sul, Brazil
| | - Robson Mateus Appel
- Department of Emergency and Intensive Care Unit, CASSEMS Hospital, Campo Grande, Mato Grosso do Sul, Brazil
| | - Saturnino Sarat Júnior
- Department of Emergency and Intensive Care Unit, CASSEMS Hospital, Campo Grande, Mato Grosso do Sul, Brazil
| | | | - Ana Carolina Alvarenga
- Department of Emergency and Intensive Care Unit, CASSEMS Hospital, Campo Grande, Mato Grosso do Sul, Brazil
| | - Emilli Carvalho Arima
- Department of Emergency and Intensive Care Unit, CASSEMS Hospital, Campo Grande, Mato Grosso do Sul, Brazil
| | - Wellington Galhano Martins
- Department of Emergency and Intensive Care Unit, CASSEMS Hospital, Campo Grande, Mato Grosso do Sul, Brazil
| | - Nakal Laurenço F. Silva
- Department of Emergency and Intensive Care Unit, CASSEMS Hospital, Campo Grande, Mato Grosso do Sul, Brazil
| | - Ricardo Sobhie Diaz
- Department of Medicine, Infectious Disease Division, Retrovirology Laboratory, College of Medicine of the Federal University of São Paulo (EPM-UNIFESP), São Paulo, São Paulo, Brazil
| | - Marcia B. Salzgeber
- Department of Gynecology, Molecular Gynecology and Metabolomics Lab, College of Medicine of the Federal University of São Paulo (EPM-UNIFESP), São Paulo, São Paulo, Brazil
| | - Anton M. Palma
- Institute for Clinical and Translational Science (ICTS), University of California Irvine (UCI), Irvine, California, United States of America
| | - Steven S. Evans
- Nagourney Institute, Long Beach, California, United States of America
- Metabolomycs, Inc., Long Beach, California, United States of America
- Nagourney Cancer Institute, Long Beach, California, United States of America
| | - Robert A. Nagourney
- Nagourney Institute, Long Beach, California, United States of America
- Metabolomycs, Inc., Long Beach, California, United States of America
- Nagourney Cancer Institute, Long Beach, California, United States of America
- Todd Cancer Institute, Memorial Medical Center of Long Beach, Long Beach, California, United States of America
- Department of Obstetrics and Gynecology, University of California Irvine (UCI), Orange, California, United States of America
| |
Collapse
|
23
|
Amino Acid and Phospholipid Metabolism as an Indicator of Inflammation and Subtle Cardiomyopathy in Patients with Marfan Syndrome. Metabolites 2021; 11:metabo11120805. [PMID: 34940564 PMCID: PMC8707072 DOI: 10.3390/metabo11120805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/19/2021] [Accepted: 11/26/2021] [Indexed: 11/16/2022] Open
Abstract
Patients with Marfan syndrome (MFS) have an increased risk of aortic aneurysm formation, dissection and development of a subtle cardiomyopathy. We analyzed amino acid and lipid metabolic pathways in MFS patients, seeking biomarker patterns as potential monitoring tools of cardiovascular risk with deterioration of myocardial function. We assessed myocardial function in 24 adult MFS patients and compared traditional laboratory values and mass spectrometry-based amino acid, phospholipid and acylcarnitine metabolomes in patients with those in healthy controls. Analytes for which values differed between patients and controls were subjected to regression analysis. A high proportion of patients had signs of impaired diastolic function and elevated serum levels of NT-proBNP. Patients had lower serum levels of taurine, histidine and PCaeC42:3 than controls. The evidence of diastolic dysfunction, aortic root dimensions and history of aortic root surgery correlated with NT-proBNP and taurine levels. Alterations in serum levels of metabolism derived analytes link MFS pathophysiology with inflammation, oxidative stress and incipient cardiomyopathy.
Collapse
|
24
|
Pattaro C, Barbieri G, Foco L, Weichenberger CX, Biasiotto R, De Grandi A, Fuchsberger C, Egger C, Amon VSC, Hicks AA, Mian M, Mahlknecht A, Lombardo S, Meier H, Weiss H, Rainer R, Dejaco C, Weiss G, Lavezzo E, Crisanti A, Pizzato M, Domingues FS, Mascalzoni D, Gögele M, Melotti R, Pramstaller PP. Prospective epidemiological, molecular, and genetic characterization of a novel coronavirus disease in the Val Venosta/Vinschgau: the CHRIS COVID-19 study protocol. Pathog Glob Health 2021; 116:128-136. [PMID: 34637685 PMCID: PMC8515786 DOI: 10.1080/20477724.2021.1978225] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
The COVID-19 pandemic has been threatening the healthcare and socioeconomic systems of entire nations. While population-based surveys to assess the distribution of SARS-CoV-2 infection have become a priority, pre-existing longitudinal studies are ideally suited to assess the determinants of COVID-19 onset and severity.The Cooperative Health Research In South Tyrol (CHRIS) study completed the baseline recruitment of 13,393 adults from the Venosta/Vinschgau rural district in 2018, collecting extensive phenotypic and biomarker data, metabolomic data, densely imputed genotype and whole-exome sequencing data.Based on CHRIS, we designed a prospective study, called CHRIS COVID-19, aimed at: 1) estimating the incidence of SARS-CoV-2 infections; 2) screening for and investigating the determinants of incident infection among CHRIS participants and their household members; 3) monitoring the immune response of infected participants prospectively.An online screening questionnaire was sent to all CHRIS participants and their household members. A random sample of 1450 participants representative of the district population was invited to assess active (nasopharyngeal swab) or past (serum antibody test) infections. We prospectively invited for complete SARS-CoV-2 testing all questionnaire completers gauged as possible cases of past infection and their household members. In positive tested individuals, antibody response is monitored quarterly for one year. Untested and negative participants receive the screening questionnaire every four weeks until gauged as possible incident cases or till the study end.Originated from a collaboration between researchers and community stakeholders, the CHRIS COVID-19 study aims at generating knowledge about the epidemiological, molecular, and genetic characterization of COVID-19 and its long-term sequelae.
Collapse
Affiliation(s)
- Cristian Pattaro
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Giulia Barbieri
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Luisa Foco
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Christian X Weichenberger
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Roberta Biasiotto
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Alessandro De Grandi
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Christian Fuchsberger
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Clemens Egger
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Vera S C Amon
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Andrew A Hicks
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Michael Mian
- Department of Haematology, Hospital of Bolzano SABES-ASDAA, Bolzano, Italy.,College of Health-Care Professions, Claudiana, Bolzano, Italy
| | - Angelika Mahlknecht
- Institute of General Medicine, Provincial School of Health (Claudiana), Bolzano, Italy.,Institute of General Practice, Family Medicine and Preventive Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Stefano Lombardo
- Provincial Institute of Statistics (ASTAT), Administration of the Autonomous Province of Bolzano, Italy
| | - Horand Meier
- Clinical Governance Unit, Administration of the Autonomous Province of Bolzano, Italy
| | - Helmuth Weiss
- Hospital of Silandro, Healthcare System of the Autonomous Province of Bolzano, Italy
| | - Robert Rainer
- Hospital of Silandro, Healthcare System of the Autonomous Province of Bolzano, Italy
| | - Christian Dejaco
- Department of Rheumatology, Medical University Graz, Austria.,Department of Rheumatology, Hospital of Bruneck (SABES-ASDAA), Italy
| | - Günter Weiss
- Department of Internal Medicine II, Medical University of Innsbruck, Innsbruck, Austria
| | - Enrico Lavezzo
- Department of Molecular Medicine, University of Padova, Italy
| | - Andrea Crisanti
- Department of Molecular Medicine, University of Padova, Italy
| | - Massimo Pizzato
- Laboratory of Virus-Cell Interaction, Centre for Integrative Biology, University of Trento, Italy
| | - Francisco S Domingues
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Deborah Mascalzoni
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy.,Center for Research Ethics and Bioethics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala Sweden
| | - Martin Gögele
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Roberto Melotti
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Peter P Pramstaller
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| |
Collapse
|
25
|
Metabolomics in asthma: A platform for discovery. Mol Aspects Med 2021; 85:100990. [PMID: 34281719 DOI: 10.1016/j.mam.2021.100990] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 06/21/2021] [Accepted: 07/06/2021] [Indexed: 12/28/2022]
Abstract
Asthma, characterized by airway hyperresponsiveness, inflammation and remodeling, is a chronic airway disease with complex etiology. Severe asthma is characterized by frequent exacerbations and poor therapeutic response to conventional asthma therapy. A clear understanding of cellular and molecular mechanisms of asthma is critical for the discovery of novel targets for optimal therapeutic control of asthma. Metabolomics is emerging as a powerful tool to elucidate novel disease mechanisms in a variety of diseases. In this review, we summarize the current status of knowledge in asthma metabolomics at systemic and cellular levels. The findings demonstrate that various metabolic pathways, related to energy metabolism, macromolecular biosynthesis and redox signaling, are differentially modulated in asthma. Airway smooth muscle cell plays pivotal roles in asthma by contributing to airway hyperreactivity, inflammatory mediator release and remodeling. We posit that metabolomic profiling of airway structural cells, including airway smooth muscle cells, will shed light on molecular mechanisms of asthma and airway hyperresponsiveness and help identify novel therapeutic targets.
Collapse
|
26
|
Sotelo-Orozco J, Chen SY, Hertz-Picciotto I, Slupsky CM. A Comparison of Serum and Plasma Blood Collection Tubes for the Integration of Epidemiological and Metabolomics Data. Front Mol Biosci 2021; 8:682134. [PMID: 34307452 PMCID: PMC8295687 DOI: 10.3389/fmolb.2021.682134] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 06/23/2021] [Indexed: 02/04/2023] Open
Abstract
Blood is a rich biological sample routinely collected in clinical and epidemiological studies. With advancements in high throughput -omics technology, such as metabolomics, epidemiology can now delve more deeply and comprehensively into biological mechanisms involved in the etiology of diseases. However, the impact of the blood collection tube matrix of samples collected needs to be carefully considered to obtain meaningful biological interpretations and understand how the metabolite signatures are affected by different tube types. In the present study, we investigated whether the metabolic profile of blood collected as serum differed from samples collected as ACD plasma, citrate plasma, EDTA plasma, fluoride plasma, or heparin plasma. We identified and quantified 50 metabolites present in all samples utilizing nuclear magnetic resonance (NMR) spectroscopy. The heparin plasma tubes performed the closest to serum, with only three metabolites showing significant differences, followed by EDTA which significantly differed for five metabolites, and fluoride tubes which differed in eleven of the fifty metabolites. Most of these metabolite differences were due to higher levels of amino acids in serum compared to heparin plasma, EDTA plasma, and fluoride plasma. In contrast, metabolite measurements from ACD and citrate plasma differed significantly for approximately half of the metabolites assessed. These metabolite differences in ACD and citrate plasma were largely due to significant interfering peaks from the anticoagulants themselves. Blood is one of the most banked samples and thus mining and comparing samples between studies requires understanding how the metabolite signature is affected by the different media and different tube types.
Collapse
Affiliation(s)
- Jennie Sotelo-Orozco
- Department of Public Health Sciences, University of California Davis, Davis, CA, United States
| | - Shin-Yu Chen
- Department of Food Science and Technology, University of California Davis, Davis, CA, United States
| | - Irva Hertz-Picciotto
- Department of Public Health Sciences, University of California Davis, Davis, CA, United States
| | - Carolyn M Slupsky
- Department of Food Science and Technology, University of California Davis, Davis, CA, United States.,Department of Nutrition, University of California Davis, Davis, CA, United States
| |
Collapse
|
27
|
From bedside to bench-practical considerations to avoid pre-analytical pitfalls and assess sample quality for high-resolution metabolomics and lipidomics analyses of body fluids. Anal Bioanal Chem 2021; 413:5567-5585. [PMID: 34159398 PMCID: PMC8410705 DOI: 10.1007/s00216-021-03450-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 05/24/2021] [Accepted: 05/31/2021] [Indexed: 11/22/2022]
Abstract
The stability of lipids and other metabolites in human body fluids ranges from very stable over several days to very unstable within minutes after sample collection. Since the high-resolution analytics of metabolomics and lipidomics approaches comprise all these compounds, the handling of body fluid samples, and thus the pre-analytical phase, is of utmost importance to obtain valid profiling data. This phase consists of two parts, sample collection in the hospital (“bedside”) and sample processing in the laboratory (“bench”). For sample quality, the apparently simple steps in the hospital are much more critical than the “bench” side handling, where (bio)analytical chemists focus on highly standardized processing for high-resolution analysis under well-controlled conditions. This review discusses the most critical pre-analytical steps for sample quality from patient preparation; collection of body fluids (blood, urine, cerebrospinal fluid) to sample handling, transport, and storage in freezers; and subsequent thawing using current literature, as well as own investigations and practical experiences in the hospital. Furthermore, it provides guidance for (bio)analytical chemists to detect and prevent potential pre-analytical pitfalls at the “bedside,” and how to assess the quality of already collected body fluid samples. A knowledge base is provided allowing one to decide whether or not the sample quality is acceptable for its intended use in distinct profiling approaches and to select the most suitable samples for high-resolution metabolomics and lipidomics investigations.
Collapse
|
28
|
Volani C, Rainer J, Hernandes VV, Meraviglia V, Pramstaller PP, Smárason SV, Pompilio G, Casella M, Sommariva E, Paglia G, Rossini A. Metabolic Signature of Arrhythmogenic Cardiomyopathy. Metabolites 2021; 11:metabo11040195. [PMID: 33805952 PMCID: PMC8064316 DOI: 10.3390/metabo11040195] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 03/06/2021] [Accepted: 03/23/2021] [Indexed: 02/07/2023] Open
Abstract
Arrhythmogenic cardiomyopathy (ACM) is a genetic-based cardiac disease accompanied by severe ventricular arrhythmias and a progressive substitution of the myocardium with fibro-fatty tissue. ACM is often associated with sudden cardiac death. Due to the reduced penetrance and variable expressivity, the presence of a genetic defect is not conclusive, thus complicating the diagnosis of ACM. Recent studies on human induced pluripotent stem cells-derived cardiomyocytes (hiPSC-CMs) obtained from ACM individuals showed a dysregulated metabolic status, leading to the hypothesis that ACM pathology is characterized by an impairment in the energy metabolism. However, despite efforts having been made for the identification of ACM specific biomarkers, there is still a substantial lack of information regarding the whole metabolomic profile of ACM patients. The aim of the present study was to investigate the metabolic profiles of ACM patients compared to healthy controls (CTRLs). The targeted Biocrates AbsoluteIDQ® p180 assay was used on plasma samples. Our analysis showed that ACM patients have a different metabolome compared to CTRLs, and that the pathways mainly affected include tryptophan metabolism, arginine and proline metabolism and beta oxidation of fatty acids. Altogether, our data indicated that the plasma metabolomes of arrhythmogenic cardiomyopathy patients show signs of endothelium damage and impaired nitric oxide (NO), fat, and energy metabolism.
Collapse
Affiliation(s)
- Chiara Volani
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Via Galvani 31, 39100 Bolzano, Italy; (J.R.); (V.V.H.); (V.M.); (P.P.P.); (S.V.S.); (A.R.)
- Correspondence:
| | - Johannes Rainer
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Via Galvani 31, 39100 Bolzano, Italy; (J.R.); (V.V.H.); (V.M.); (P.P.P.); (S.V.S.); (A.R.)
| | - Vinicius Veri Hernandes
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Via Galvani 31, 39100 Bolzano, Italy; (J.R.); (V.V.H.); (V.M.); (P.P.P.); (S.V.S.); (A.R.)
| | - Viviana Meraviglia
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Via Galvani 31, 39100 Bolzano, Italy; (J.R.); (V.V.H.); (V.M.); (P.P.P.); (S.V.S.); (A.R.)
| | - Peter Paul Pramstaller
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Via Galvani 31, 39100 Bolzano, Italy; (J.R.); (V.V.H.); (V.M.); (P.P.P.); (S.V.S.); (A.R.)
| | - Sigurður Vidir Smárason
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Via Galvani 31, 39100 Bolzano, Italy; (J.R.); (V.V.H.); (V.M.); (P.P.P.); (S.V.S.); (A.R.)
| | - Giulio Pompilio
- Unit of Vascular Biology and Regenerative Medicine, Centro Cardiologico Monzino IRCCS, Via Parea 4, 20138 Milan, Italy; (G.P.); (E.S.)
- Department of Biomedical, Surgical and Dental Sciences, Università degli Studi di Milano, 20138 Milan, Italy
| | - Michela Casella
- Heart Rhythm Center, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy;
- Cardiology and Arrhythmology Clinic, University Hospital Ospedali Riuniti Umberto I-Lancisi-Salesi, 60126 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, Marche Polytechnic University, 60126 Ancona, Italy
| | - Elena Sommariva
- Unit of Vascular Biology and Regenerative Medicine, Centro Cardiologico Monzino IRCCS, Via Parea 4, 20138 Milan, Italy; (G.P.); (E.S.)
| | - Giuseppe Paglia
- School of Medicine and Surgery, Università degli Studi di Milano-Bicocca, 20854 Vedano al Lambro, Italy;
| | - Alessandra Rossini
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Via Galvani 31, 39100 Bolzano, Italy; (J.R.); (V.V.H.); (V.M.); (P.P.P.); (S.V.S.); (A.R.)
| |
Collapse
|
29
|
Roca M, Alcoriza MI, Garcia-Cañaveras JC, Lahoz A. Reviewing the metabolome coverage provided by LC-MS: Focus on sample preparation and chromatography-A tutorial. Anal Chim Acta 2020; 1147:38-55. [PMID: 33485584 DOI: 10.1016/j.aca.2020.12.025] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 12/11/2020] [Accepted: 12/14/2020] [Indexed: 12/11/2022]
Abstract
Metabolomics has become an invaluable tool for both studying metabolism and biomarker discovery. The great technical advances in analytical chemistry and bioinformatics have considerably increased the number of measurable metabolites, yet an important part of the human metabolome remains uncovered. Among the various MS hyphenated techniques available, LC-MS stands out as the most used. Here, we aimed to show the capabilities of LC-MS to uncover part of the metabolome and how to best proceed with sample preparation and LC to maximise metabolite detection. The analyses of various open metabolite databases served us to estimate the size of the already detected human metabolome, the expected metabolite composition of most used human biospecimens and which part of the metabolome can be detected when LC-MS is used. Based on an extensive review and on our experience, we have outlined standard procedures for LC-MS analysis of urine, cells, serum/plasma, tissues and faeces, to guide in the selection of the sample preparation method that best matches with one or more LC techniques in order to get the widest metabolome coverage. These standard procedures may be a useful tool to explore, at a glance, the wide spectrum of possibilities available, which can be a good starting point for most of the LC-MS metabolomic studies.
Collapse
Affiliation(s)
- Marta Roca
- Analytical Unit, Medical Research Institute-Hospital La Fe, Av. Fernando Abril Martorell 106, Valencia, 46026, Spain
| | - Maria Isabel Alcoriza
- Biomarkers and Precision Medicine Unit, Medical Research Institute-Hospital La Fe, Av. Fernando Abril Martorell 106, Valencia, 46026, Spain
| | - Juan Carlos Garcia-Cañaveras
- Biomarkers and Precision Medicine Unit, Medical Research Institute-Hospital La Fe, Av. Fernando Abril Martorell 106, Valencia, 46026, Spain
| | - Agustín Lahoz
- Analytical Unit, Medical Research Institute-Hospital La Fe, Av. Fernando Abril Martorell 106, Valencia, 46026, Spain; Biomarkers and Precision Medicine Unit, Medical Research Institute-Hospital La Fe, Av. Fernando Abril Martorell 106, Valencia, 46026, Spain.
| |
Collapse
|
30
|
Wojcicki AV, Kasowski MM, Sakamoto KM, Lacayo N. Metabolomics in acute myeloid leukemia. Mol Genet Metab 2020; 130:230-238. [PMID: 32457018 DOI: 10.1016/j.ymgme.2020.05.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 05/12/2020] [Accepted: 05/13/2020] [Indexed: 12/16/2022]
Abstract
Acute myeloid leukemia (AML) is a complex, heterogenous hematological malignancy caused by mutations in myeloid differentiation and proliferation. Response to therapy and long-term outcomes vary widely based on chromosomal and molecular aberrations. Many platforms have been used to characterize and stratify AML. Metabolomics, the global profiling of small molecules in a biological sample, has emerged in the last decade as an important tool for studying the metabolic dependency of cancer cells. Metabolic reprogramming is not only an important manifestation of AML but clinically relevant for diagnosis, risk stratification and targeted drug development. In this review, we discuss notable metabolic studies of the last decade and their application to novel therapies.
Collapse
Affiliation(s)
- Anna V Wojcicki
- Division of Hematology/Oncology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Maya M Kasowski
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kathleen M Sakamoto
- Division of Hematology/Oncology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
| | - Norman Lacayo
- Division of Hematology/Oncology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
| |
Collapse
|
31
|
McGee EE, Kiblawi R, Playdon MC, Eliassen AH. Nutritional Metabolomics in Cancer Epidemiology: Current Trends, Challenges, and Future Directions. Curr Nutr Rep 2020; 8:187-201. [PMID: 31129888 DOI: 10.1007/s13668-019-00279-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE OF REVIEW Metabolomics offers several opportunities for advancement in nutritional cancer epidemiology; however, numerous research gaps and challenges remain. This narrative review summarizes current research, challenges, and future directions for epidemiologic studies of nutritional metabolomics and cancer. RECENT FINDINGS Although many studies have used metabolomics to investigate either dietary exposures or cancer, few studies have explicitly investigated diet-cancer relationships using metabolomics. Most studies have been relatively small (≤ ~ 250 cases) or have assessed a limited number of nutritional metabolites (e.g., coffee or alcohol-related metabolites). Nutritional metabolomic investigations of cancer face several challenges in study design; biospecimen selection, handling, and processing; diet and metabolite measurement; statistical analyses; and data sharing and synthesis. More metabolomics studies linking dietary exposures to cancer risk, prognosis, and survival are needed, as are biomarker validation studies, longitudinal analyses, and methodological studies. Despite the remaining challenges, metabolomics offers a promising avenue for future dietary cancer research.
Collapse
Affiliation(s)
- Emma E McGee
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Rama Kiblawi
- Division of Cancer Population Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Mary C Playdon
- Division of Cancer Population Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Nutrition and Integrative Physiology, University of Utah, Salt Lake City, UT, USA
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| |
Collapse
|
32
|
González-Domínguez R, González-Domínguez Á, Sayago A, Fernández-Recamales Á. Recommendations and Best Practices for Standardizing the Pre-Analytical Processing of Blood and Urine Samples in Metabolomics. Metabolites 2020; 10:metabo10060229. [PMID: 32503183 PMCID: PMC7344701 DOI: 10.3390/metabo10060229] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 05/29/2020] [Accepted: 05/31/2020] [Indexed: 12/11/2022] Open
Abstract
Metabolomics can be significantly influenced by a range of pre-analytical factors, such as sample collection, pre-processing, aliquoting, transport, storage and thawing. This therefore shows the crucial need for standardizing the pre-analytical phase with the aim of minimizing the inter-sample variability driven by these technical issues, as well as for maintaining the metabolic integrity of biological samples to ensure that metabolomic profiles are a direct expression of the in vivo biochemical status. This review article provides an updated literature revision of the most important factors related to sample handling and pre-processing that may affect metabolomics results, particularly focusing on the most commonly investigated biofluids in metabolomics, namely blood plasma/serum and urine. Finally, we also provide some general recommendations and best practices aimed to standardize and accurately report all these pre-analytical aspects in metabolomics research.
Collapse
Affiliation(s)
- Raúl González-Domínguez
- AgriFood Laboratory, Faculty of Experimental Sciences, University of Huelva, 21007 Huelva, Spain; (A.S.); (Á.F.-R.)
- International Campus of Excellence CeiA3, University of Huelva, 21007 Huelva, Spain
- Correspondence: ; Tel.: +34-959219975
| | - Álvaro González-Domínguez
- Department of Pediatrics, Hospital Universitario Puerta del Mar, 11009 Cádiz, Spain;
- Institute of Research and Innovation in Biomedical Sciences of the Province of Cádiz (INiBICA), 11009 Cádiz, Spain
| | - Ana Sayago
- AgriFood Laboratory, Faculty of Experimental Sciences, University of Huelva, 21007 Huelva, Spain; (A.S.); (Á.F.-R.)
- International Campus of Excellence CeiA3, University of Huelva, 21007 Huelva, Spain
| | - Ángeles Fernández-Recamales
- AgriFood Laboratory, Faculty of Experimental Sciences, University of Huelva, 21007 Huelva, Spain; (A.S.); (Á.F.-R.)
- International Campus of Excellence CeiA3, University of Huelva, 21007 Huelva, Spain
| |
Collapse
|
33
|
Bi H, Guo Z, Jia X, Liu H, Ma L, Xue L. The key points in the pre-analytical procedures of blood and urine samples in metabolomics studies. Metabolomics 2020; 16:68. [PMID: 32451742 DOI: 10.1007/s11306-020-01666-2] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 03/14/2020] [Indexed: 10/25/2022]
Abstract
BACKGROUND Metabolomics provides measurement of numerous metabolites in human samples, which can be a useful tool in clinical research. Blood and urine are regarded as preferred subjects of study because of their minimally invasive collection and simple preprocessing methods. Adhering to standard operating procedures is an essential factor in ensuring excellent sample quality and reliable results. AIM OF REVIEW In this review, we summarize the studies about the impacts of various preprocessing factors on metabolomics studies involving clinical blood and urine samples in order to provide guidance for sample collection and preprocessing. KEY SCIENTIFIC CONCEPTS OF REVIEW Clinical information is important for sample grouping and data analysis which deserves attention before sample collection. Plasma and serum as well as urine samples are appropriate for metabolomics analysis. Collection tubes, hemolysis, delay at room temperature, and freeze-thaw cycles may affect metabolic profiles of blood samples. Collection time, time between sampling and examination, contamination, normalization strategies, and storage conditions may alter analysis results of urine samples. Taking these collection and preprocessing factors into account, this review provides suggestions of standard sample preprocessing.
Collapse
Affiliation(s)
- Hai Bi
- Department of Urology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, People's Republic of China
| | - Zhengyang Guo
- Medical Research Center, Peking University Third Hospital, Haidian District, 49 Huayuan North Road, Beijing, People's Republic of China
| | - Xiao Jia
- Medical Research Center, Peking University Third Hospital, Haidian District, 49 Huayuan North Road, Beijing, People's Republic of China
- Biobank, Peking University Third Hospital, Beijing, People's Republic of China
| | - Huiying Liu
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Peking University, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Beijing, People's Republic of China
| | - Lulin Ma
- Department of Urology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, People's Republic of China.
| | - Lixiang Xue
- Medical Research Center, Peking University Third Hospital, Haidian District, 49 Huayuan North Road, Beijing, People's Republic of China.
- Biobank, Peking University Third Hospital, Beijing, People's Republic of China.
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, People's Republic of China.
| |
Collapse
|
34
|
Pezzatti J, Boccard J, Codesido S, Gagnebin Y, Joshi A, Picard D, González-Ruiz V, Rudaz S. Implementation of liquid chromatography-high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial. Anal Chim Acta 2020; 1105:28-44. [PMID: 32138924 DOI: 10.1016/j.aca.2019.12.062] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 11/18/2019] [Accepted: 12/20/2019] [Indexed: 12/23/2022]
Abstract
Untargeted metabolomics is now widely recognized as a useful tool for exploring metabolic changes taking place in biological systems under different conditions. By its nature, this is a highly interdisciplinary field of research, and mastering all of the steps comprised in the pipeline can be a challenging task, especially for those researchers new to the topic. In this tutorial, we aim to provide an overview of the most widely adopted methods of performing LC-HRMS-based untargeted metabolomics of biological samples. A detailed protocol is provided in the Supplementary Information for rapidly implementing a basic screening workflow in a laboratory setting. This tutorial covers experimental design, sample preparation and analysis, signal processing and data treatment, and, finally, data analysis and its biological interpretation. Each section is accompanied by up-to-date literature to guide readers through the preparation and optimization of such a workflow, as well as practical information for avoiding or fixing some of the most frequently encountered pitfalls.
Collapse
Affiliation(s)
- Julian Pezzatti
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, 1211, Geneva, Switzerland
| | - Julien Boccard
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, 1211, Geneva, Switzerland; Swiss Centre for Applied Human Toxicology (SCAHT), Switzerland
| | - Santiago Codesido
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, 1211, Geneva, Switzerland
| | - Yoric Gagnebin
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, 1211, Geneva, Switzerland
| | - Abhinav Joshi
- Department of Cell Biology, Faculty of Science, University of Geneva, 1211, Geneva, Switzerland
| | - Didier Picard
- Department of Cell Biology, Faculty of Science, University of Geneva, 1211, Geneva, Switzerland
| | - Víctor González-Ruiz
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, 1211, Geneva, Switzerland; Swiss Centre for Applied Human Toxicology (SCAHT), Switzerland
| | - Serge Rudaz
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, 1211, Geneva, Switzerland; Swiss Centre for Applied Human Toxicology (SCAHT), Switzerland.
| |
Collapse
|
35
|
Diet Alters Serum Metabolomic Profiling in the Mouse Model of Chronic Chagas Cardiomyopathy. DISEASE MARKERS 2019; 2019:4956016. [PMID: 31949545 PMCID: PMC6948343 DOI: 10.1155/2019/4956016] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 08/21/2019] [Indexed: 12/14/2022]
Abstract
Chagas disease is caused by Trypanosoma cruzi which is endemic in Latin America. T. cruzi infection results in a latent infection with approximately a third of latently infected patients developing chronic Chagas cardiomyopathy (CCM). CCM is a common cause of cardiomyopathy in endemic regions and has a poor prognosis compared to other cardiomyopathies. The factors responsible for the transition from the asymptomatic indeterminate latent stage of infection to CCM are poorly understood. Our previous studies demonstrated that lipid metabolism and diet are important determinants of disease progression. In the present study, we analyzed various serum metabolomic biomarkers such as acylcarnitines, amino acids, biogenic amines, glycerophospholipids, and sphingolipids in murine models of CCM, where the mice specifically develop either left or right ventricular cardiomyopathy based on the diets fed during the indeterminate stage in a murine model of Chagas disease. Our data provide new insights into the metabolic changes that may predispose patients to CCM and biomarkers that may help predict the risk of developing cardiomyopathy from T. cruzi infection. Author Summary. Chronic Chagas cardiomyopathy (CCM) is a parasitic disease prevalent in Latin America. Currently, no effective drugs or vaccines are available to prevent or cure CCM. The factors involved in the disease severity and progression are poorly understood to design new therapeutic interventions. In order to rapidly identify Chagas patients with a higher risk to develop CCM, a new set of biomarkers specific to Chagas disease is needed. We performed serum metabolomic analyses in chronic T. cruzi-infected mice fed on different diets and identified cardiac ventricular-specific metabolite biomarkers that could define CCM severity. In this paper, we present the results of serum metabolomic analyses and discuss its correlations to the diet-induced metabolic regulations in the pathogenesis of CCM in a murine model of Chagas disease.
Collapse
|
36
|
González-Riano C, Dudzik D, Garcia A, Gil-de-la-Fuente A, Gradillas A, Godzien J, López-Gonzálvez Á, Rey-Stolle F, Rojo D, Ruperez FJ, Saiz J, Barbas C. Recent Developments along the Analytical Process for Metabolomics Workflows. Anal Chem 2019; 92:203-226. [PMID: 31625723 DOI: 10.1021/acs.analchem.9b04553] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Carolina González-Riano
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Danuta Dudzik
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain.,Department of Biopharmaceutics and Pharmacodynamics, Faculty of Pharmacy , Medical University of Gdańsk , 80-210 Gdańsk , Poland
| | - Antonia Garcia
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Alberto Gil-de-la-Fuente
- Department of Information Technology, Escuela Politécnica Superior , Universidad San Pablo-CEU , 28003 Madrid , Spain
| | - Ana Gradillas
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Joanna Godzien
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain.,Clinical Research Centre , Medical University of Bialystok , 15-089 Bialystok , Poland
| | - Ángeles López-Gonzálvez
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Fernanda Rey-Stolle
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - David Rojo
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Francisco J Ruperez
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Jorge Saiz
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Coral Barbas
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| |
Collapse
|
37
|
Ignjatovic V, Geyer PE, Palaniappan KK, Chaaban JE, Omenn GS, Baker MS, Deutsch EW, Schwenk JM. Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data. J Proteome Res 2019; 18:4085-4097. [PMID: 31573204 DOI: 10.1021/acs.jproteome.9b00503] [Citation(s) in RCA: 110] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The proteomic analysis of human blood and blood-derived products (e.g., plasma) offers an attractive avenue to translate research progress from the laboratory into the clinic. However, due to its unique protein composition, performing proteomics assays with plasma is challenging. Plasma proteomics has regained interest due to recent technological advances, but challenges imposed by both complications inherent to studying human biology (e.g., interindividual variability) and analysis of biospecimens (e.g., sample variability), as well as technological limitations remain. As part of the Human Proteome Project (HPP), the Human Plasma Proteome Project (HPPP) brings together key aspects of the plasma proteomics pipeline. Here, we provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, mass spectrometry (MS) data acquisition, data processing, and bioinformatic analysis. With exciting opportunities in studying human health and disease though this plasma proteomics pipeline, a more informed analysis of human plasma will accelerate interest while enhancing possibilities for the incorporation of proteomics-scaled assays into clinical practice.
Collapse
Affiliation(s)
- Vera Ignjatovic
- Haematology Research , Murdoch Children's Research Institute , Parkville , VIC 3052 , Australia.,Department of Paediatrics , The University of Melbourne , Parkville , VIC 3052 , Australia
| | - Philipp E Geyer
- NNF Center for Protein Research, Faculty of Health Sciences , University of Copenhagen , 2200 Copenhagen , Denmark.,Department of Proteomics and Signal Transduction , Max Planck Institute of Biochemistry , 82152 Martinsried , Germany
| | - Krishnan K Palaniappan
- Freenome , 259 East Grand Avenue , South San Francisco , California 94080 , United States
| | - Jessica E Chaaban
- Haematology Research , Murdoch Children's Research Institute , Parkville , VIC 3052 , Australia
| | - Gilbert S Omenn
- Departments of Computational Medicine & Bioinformatics, Human Genetics, and Internal Medicine and School of Public Health , University of Michigan , 100 Washtenaw Avenue , Ann Arbor , Michigan 48109-2218 , United States
| | - Mark S Baker
- Department of Biomedical Sciences, Faculty of Medicine & Health Sciences , Macquarie University , 75 Talavera Road , North Ryde , NSW 2109 , Australia
| | - Eric W Deutsch
- Institute for Systems Biology , 401 Terry Avenue North , Seattle , Washington 98109 , United States
| | - Jochen M Schwenk
- Affinity Proteomics, SciLifeLab , KTH Royal Institute of Technology , 171 65 Stockholm , Sweden
| |
Collapse
|
38
|
Stevens VL, Hoover E, Wang Y, Zanetti KA. Pre-Analytical Factors that Affect Metabolite Stability in Human Urine, Plasma, and Serum: A Review. Metabolites 2019; 9:metabo9080156. [PMID: 31349624 PMCID: PMC6724180 DOI: 10.3390/metabo9080156] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 07/18/2019] [Accepted: 07/19/2019] [Indexed: 01/01/2023] Open
Abstract
Metabolomics provides a comprehensive assessment of numerous small molecules in biological samples. As it integrates the effects of exogenous exposures, endogenous metabolism, and genetic variation, metabolomics is well-suited for studies examining metabolic profiles associated with a variety of chronic diseases. In this review, we summarize the studies that have characterized the effects of various pre-analytical factors on both targeted and untargeted metabolomic studies involving human plasma, serum, and urine and were published through 14 January 2019. A standardized protocol was used for extracting data from full-text articles identified by searching PubMed and EMBASE. For plasma and serum samples, metabolomic profiles were affected by fasting status, hemolysis, collection time, processing delays, particularly at room temperature, and repeated freeze/thaw cycles. For urine samples, collection time and fasting, centrifugation conditions, filtration and the use of additives, normalization procedures and multiple freeze/thaw cycles were found to alter metabolomic findings. Consideration of the effects of pre-analytical factors is a particularly important issue for epidemiological studies where samples are often collected in nonclinical settings and various locations and are subjected to time and temperature delays prior being to processed and frozen for storage.
Collapse
Affiliation(s)
- Victoria L Stevens
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA 30303, USA.
| | - Elise Hoover
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD 20850, USA
- PKD Foundation, Kansas City, MO 64131, USA
| | - Ying Wang
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA 30303, USA
| | - Krista A Zanetti
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD 20850, USA.
| |
Collapse
|
39
|
Volani C, Paglia G, Smarason SV, Pramstaller PP, Demetz E, Pfeifhofer-Obermair C, Weiss G. Metabolic Signature of Dietary Iron Overload in a Mouse Model. Cells 2018; 7:cells7120264. [PMID: 30544931 PMCID: PMC6315421 DOI: 10.3390/cells7120264] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 12/03/2018] [Accepted: 12/07/2018] [Indexed: 12/28/2022] Open
Abstract
Iron is an essential co-factor for several metabolic processes, including the Krebs cycle and mitochondrial oxidative phosphorylation. Therefore, maintaining an appropriate iron balance is essential to ensure sufficient energy production and to avoid excessive reactive oxygen species formation. Iron overload impairs mitochondrial fitness; however, little is known about the associated metabolic changes. Here we aimed to characterize the metabolic signature triggered by dietary iron overload over time in a mouse model, where mice received either a standard or a high-iron diet. Metabolic profiling was assessed in blood, plasma and liver tissue. Peripheral blood was collected by means of volumetric absorptive microsampling (VAMS). Extracted blood and tissue metabolites were analyzed by liquid chromatography combined to high resolution mass spectrometry. Upon dietary iron loading we found increased glucose, aspartic acid and 2-/3-hydroxybutyric acid levels but low lactate and malate levels in peripheral blood and plasma, pointing to a re-programming of glucose homeostasis and the Krebs cycle. Further, iron loading resulted in the stimulation of the urea cycle in the liver. In addition, oxidative stress was enhanced in circulation and coincided with increased liver glutathione and systemic cysteine synthesis. Overall, iron supplementation affected several central metabolic circuits over time. Hence, in vivo investigation of metabolic signatures represents a novel and useful tool for getting deeper insights into iron-dependent regulatory circuits and for monitoring of patients with primary and secondary iron overload, and those ones receiving iron supplementation therapy.
Collapse
Affiliation(s)
- Chiara Volani
- Department of Internal Medicine II, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria.
- Christian Doppler Laboratory for Iron Metabolism and Anemia Research, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria.
- Institute for Biomedicine, Eurac Research, Via Galvani 31, 39100 Bolzano, Italy.
| | - Giuseppe Paglia
- Institute for Biomedicine, Eurac Research, Via Galvani 31, 39100 Bolzano, Italy.
| | - Sigurdur V Smarason
- Institute for Biomedicine, Eurac Research, Via Galvani 31, 39100 Bolzano, Italy.
| | - Peter P Pramstaller
- Institute for Biomedicine, Eurac Research, Via Galvani 31, 39100 Bolzano, Italy.
| | - Egon Demetz
- Department of Internal Medicine II, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria.
| | - Christa Pfeifhofer-Obermair
- Department of Internal Medicine II, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria.
| | - Guenter Weiss
- Department of Internal Medicine II, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria.
- Christian Doppler Laboratory for Iron Metabolism and Anemia Research, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria.
| |
Collapse
|
40
|
Impact of Blood Collection Tubes and Sample Handling Time on Serum and Plasma Metabolome and Lipidome. Metabolites 2018; 8:metabo8040088. [PMID: 30518126 PMCID: PMC6316012 DOI: 10.3390/metabo8040088] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 11/26/2018] [Accepted: 11/29/2018] [Indexed: 01/06/2023] Open
Abstract
Background: Metabolomics is emerging as a valuable tool in clinical science. However, one major challenge in clinical metabolomics is the limited use of standardized guidelines for sample collection and handling. In this study, we conducted a pilot analysis of serum and plasma to determine the effects of processing time and collection tube on the metabolome. Methods: Blood was collected in 3 tubes: Vacutainer serum separator tube (SST, serum), EDTA (plasma) and P100 (plasma) and stored at 4 degrees for 0, 0.5, 1, 2, 4 and 24 h prior to centrifugation. Compounds were extracted using liquid-liquid extraction to obtain a hydrophilic and a hydrophobic fraction and analyzed using liquid chromatography mass spectrometry. Differences among the blood collection tubes and sample processing time were evaluated (ANOVA, Bonferroni FWER ≤ 0.05 and ANOVA, Benjamini Hochberg FDR ≤ 0.1, respectively). Results: Among the serum and plasma tubes 93.5% of compounds overlapped, 382 compounds were unique to serum and one compound was unique to plasma. There were 46, 50 and 86 compounds affected by processing time in SST, EDTA and P100 tubes, respectively, including many lipids. In contrast, 496 hydrophilic and 242 hydrophobic compounds differed by collection tube. Forty-five different chemical classes including alcohols, sugars, amino acids and prenol lipids were affected by the choice of blood collection tube. Conclusion: Our results suggest that the choice of blood collection tube has a significant effect on detected metabolites and their overall abundances. Perhaps surprisingly, variation in sample processing time has less of an effect compared to collection tube; however, a larger sample size is needed to confirm this.
Collapse
|