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- Fernández-Ochoa, Álvaro, et al.
(författare)
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A case report of switching from specific vendor-based to R-based pipelines for untargeted LC-MS metabolomics
- 2020
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Ingår i: Metabolites. - : MDPI AG. - 2218-1989 .- 2218-1989. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Data pre-processing of the LC-MS data is a critical step in untargeted metabolomics studies in order to achieve correct biological interpretations. Several tools have been developed for pre-processing, and these can be classified into either commercial or open source software. This case report aims to compare two specific methodologies, Agilent Profinder vs. R pipeline, for a metabolomic study with a large number of samples. Specifically, 369 plasma samples were analyzed by HPLC-ESI-QTOF-MS. The collected data were pre-processed by both methodologies and later evaluated by several parameters (number of peaks, degree of missingness, quality of the peaks, degree of misalignments, and robustness in multivariate models). The vendor software was characterized by ease of use, friendly interface and good quality of the graphs. The open source methodology could more effectively correct the drifts due to between and within batch effects. In addition, the evaluated statistical methods achieved better classification results with higher parsimony for the open source methodology, indicating higher data quality. Although both methodologies have strengths and weaknesses, the open source methodology seems to be more appropriate for studies with a large number of samples mainly due to its higher capacity and versatility that allows combining different packages, functions, and methods in a single environment.
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| 2. |
- Fernandez-Ochoa, Alvaro, et al.
(författare)
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Metabolic Disturbances in Urinary and Plasma Samples from Seven Different Systemic Autoimmune Diseases Detected by HPLC-ESI-QTOF-MS
- 2020
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3907 .- 1535-3893. ; 19:8, s. 3220-3229
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Tidskriftsartikel (refereegranskat)abstract
- Systemic autoimmune diseases (SADs) are characterized by dysfunctioning of the immune system, which causes damage in several tissues and organs. Among these pathologies are systemic lupus erythematosus (SLE), systemic sclerosis or scleroderma, Sjogren's syndrome, rheumatoid arthritis, primary antiphospholipid syndrome (PAPS), mixed connective tissue disease (MCTD), and undifferentiated connective tissue disease (UCTD). Early diagnosis is difficult due to similarity in symptoms, signs, and clinical test results. Hence, our aim was to search for differentiating metabolites of these diseases in plasma and urine samples. We performed metabolomic profiling by liquid chromatography-mass spectrometry (LC-MS) of samples from 228 SADs patients and 55 healthy volunteers. Multivariate PLS models were applied to investigate classification accuracies and identify metabolites differentiating SADs and healthy controls. Furthermore, we specifically investigated UCTD against the other SADs. PLS models were able to classify most SADs vs healthy controls (area under the roc curve (AUC) > 0.7), with the exception of MCTD and PAPS. Differentiating metabolites consisted predominantly of unsaturated fatty acids, acylglycines, acylcarnitines, and amino acids. In accordance with the difficulties in defining UCTD, the UCTD metabolome did not differentiate well from the other SADs. However, most UCTD cases were dassified as SLE, suggesting that metabolomics may provide a tool to reassess UCTD diagnosis into other conditions for more well-informed therapeutic strategies.
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