| 1. |
- Forshed, Jenny, et al.
(författare)
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Enhanced multivariate analysis by correlation scaling and fusion of LC/MS and 1H-NMR data
- 2007
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier B.V. - 0169-7439 .- 1873-3239. ; 85:2, s. 179-185
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Tidskriftsartikel (refereegranskat)abstract
- A method to enhance the multivariate data interpretation of, for instance, metabolic profiles is presented. This was done by correlation scaling of 1H NMR data by the time pattern of drug metabolite peaks identified by LC/MS, followed by parallel factor analysis (PARAFAC). The variables responsible for the discrimination between the dosed and control rats in this model were then eliminated in both data sets. Next, an additional PARAFAC analysis was performed with both LC/MS and 1H NMR data, fused by outer product analysis (OPA), to obtain sufficient class separation. The loadings from this second PARAFAC analysis showed new peaks discriminating between the classes. The time trajectories of these peaks did not agree with the drug metabolites and were detected as possible candidates for markers. These data analyses were also compared with the PARAFAC analysis of raw data, which showed very much the same loading peaks as for the correlation-scaled data, although the intensities differed. Elimination of the variables correlated with the drug metabolites was therefore necessary to be able to select the peaks which were not drug metabolites and which discriminated between the classes.1
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| 2. |
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| 3. |
- Forshed, Jenny, et al.
(författare)
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Evaluation of different techniques for fusion of LC/MS and 1HNMR data
- 2007
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - 0169-7439 .- 1873-3239. ; 85:1, s. 102-109
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Tidskriftsartikel (refereegranskat)abstract
- In the analyses of highly complex samples (for example, metabolic fingerprinting), the data might not suffice for classification when using only a single analytical technique. Hence, the use of two complementary techniques, e.g., LUMS and H-1-NMR, might be advantageous. Another possible advantage from using two different techniques is the ability to verify the results (for instance, by verifying a time trend of a metabolic pattern). In this work, both LC/MS and H-1-NMR data from analysis of rat urine have been used to obtain metabolic fingerprints. A comparison of three different methods for data fusion of the two data sets was performed and the possibilities and difficulties associated with data fusion were discussed. When comparing concatenated data, full hierarchical modeling, and batch modeling, the first two approaches were found to be the most successful. Different types of block scaling and variable scaling were evaluated and the optimal scaling for each case was found by cross validation. Validations of the final models were performed by means of an external test set.(2)
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| 4. |
- Idborg, Helena, 1976-
(författare)
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Analysis of Metabolites in Complex Biological Samples Using LC/MS and Multivariate Data Analysis : Metabolic Fingerprinting and Detection of Biomarkers
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- To facilitate early diagnosis of diseases and elucidation of the processes involved in their development and progression, various specific compounds or ‘biomarkers’ are often monitored. The first step is to decide which compounds to analyze. An untargeted approach within the field of metabolomics, can provide a unique chemical fingerprint representing the biological state of the studied organism. Fingerprints can be used for classification and thus facilitate the identification of biomarkers and investigation of drug metabolism.In this work a method was developed for the general detection of metabolites using liquid chromatography/mass spectrometry (LC/MS) to obtain data for metabolic fingerprinting. One part of the project focused on generating the data and the other on analyzing the acquired data. First part: Solid phase extraction was applied to rat urine samples and protein precipitation to human plasma samples. Several LC/MS variants were used, i.e. reversed phase LC, hydrophilic interaction LC and ultrahigh pressure LC (UHPLC) coupled to a triple quadrupole MS or time of flight (ToF) MS. Second part: Methods for handling LC/MS data and extracting information, e.g. curve resolution, were evaluated. In addition data fusion methods were investigated. Principal Component Analysis (PCA) and partial least squares (PLS), were applied for pattern recognition. Furthermore, 3-way classification methods, such as parallel factor analysis (PARAFAC) and N-way PLS, were also explored.The developed method was applied to the early detection of phospholipi-dosis in drug development and to search for indicators of successful treatment in breast cancer therapy. Potential biomarkers were suggested, but have not yet been fully evaluated. In addition, new metabolites of an antidepressant drug were discovered and identified using this approach.
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| 5. |
- Idborg, Helena, et al.
(författare)
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Metabolic fingerprinting of rat urine by LC/MS. : Part1. Analysis by hydrophilic interaction liquid chromatography-electrospray ionization mass spectrometry
- 2005
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Ingår i: Journal of Chromatography B. - : Elsevier BV. - 1387-2273 .- 1878-5603 .- 1570-0232 .- 1873-376X. ; 828:1-2, s. 9-13
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Tidskriftsartikel (refereegranskat)abstract
- Complex biological samples, such as urine, contain a very large number of endogenous metabolites reflecting the metabolic state of an organism. Metabolite patterns can provide a comprehensive signature of the physiological state of an organism as well as insights into specific biochemical processes. Although the metabolites excreted in urine are commonly highly polar, the samples are generally analyzed using reversed-phase liquid chromatography mass spectrometry (RP-LC/MS). In Part I of this work, a method for detecting highly polar metabolites by hydrophilic interaction liquid chromatography-electrospray ionization mass spectrometry (HILIC/ESI-MS) is described as a complement to RP-LC/ESI-MS. In addition, in an accompanying paper (Part 2), different multivariate approaches to extracting information from the resulting complex data are described to enable metabolic fingerprints to be obtained. The coverage of the method for the screening of as many metabolites as possible is highly improved by analyzing the urine samples using both a C-18 column and a ZIC (R)-HILIC column. The latter was found to be a good alternative when analyzing highly polar compounds, e.g., hydroxyproline and creatinine, to columns typically used for reversed-phase liquid chromatography. (c) 2005 Elsevier B.V. All rights reserved.
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| 6. |
- Idborg, Helena, et al.
(författare)
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Metabolic fingerprinting of rat urine by LC/MS. : Part 2. Data pretreatment methods for handling of complex data
- 2005
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Ingår i: Journal of Chromatography B. - : Elsevier BV. - 1387-2273 .- 1878-5603 .- 1570-0232. ; 828:1-2, s. 14-20
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Tidskriftsartikel (refereegranskat)abstract
- Metabolic fingerprinting of biofluids like urine is a useful technique for detecting differences between individuals. With this approach, it might be possible to classify samples according to their biological relevance. In Part I of this work a method for the comprehensive screening of metabolites was described [H. Idborg, L. Zamani, P-O. Edlund, I. Schuppe-Koistinen, S.P. Jacobsson, Part 1, J. Chromatogr. B 828 (2005) 9], using two different liquid chromatography (LC) column set-ups and detection by electrospray ionization mass spectrometry (ESI-MS). Data pretreatment of the resulting data described in [H. Idborg, L. Zamani, P-O. Edlund, 1. Schuppe-Koistinen, S.P. Jacobsson, Part 1, J. Chromatogr. B 828 (2005) 9] is needed to reduce the complexity of the data and to obtain useful metabolic fingerprints. Three different approaches, i.e., reduced dimensionality (RD), MarkerLynx (TM), and MS Resolver (TM), were compared for the extraction of information. The pretreated data were then subjected to multivariate data analysis by partial least squares discriminant analysis (PLS-DA) for classification. By combining two different chromatographic procedures and data analysis, the detection of metabolites was enhanced as well as the finding of metabolic fingerprints that govern classification. Additional potential biomarkers or xenobiotic metabolites were detected in the fraction containing highly polar compounds that are normally discarded when using reversed-phase liquid chromatography.
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