| 研究実績の概要 |
1. Untargeted high-throughput serum HILIC metabolomics and lipidomics method was optimized using automation sample handler Bravo (Agilent) system in a 96-well plate format. Sample preparation method, internal standards, Bravo workflow and metabolite identification were done. In addition, we evaluate metabolite stability across the whole workflow and provide guidelines as when and how to freeze unprocessed and processed samples. Overall, the automation results in a more than two-fold gain in person-time (from 7.5 h to 3h), decreased operator fatigue and fatigue-related errors, and increased reproducibility for large-scale studies.
2. Methods were validated by several small cohorts. The coefficient variance of internal standards in quality control samples were all lower than 10% and in samples lower than 15%, demonstrating that our methods are robust.
3. Samples were randomized, sorted, transported to deep 96-well plates from original tubes and aliquoted into small volume aliquots.
4. Information of exposures associated with the etiology of asthema were collected and submitted to biobank.
5. Clinical parameters especially lung function parameters were pulled out from electronic medical records and pre-analyzed.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
The project is progressing basically following our initial research plans. Ethic documents were submitted and approved by the ethics committee at Gunma University (No. 2018-018). Untargeted metabolomics methods were developed and validated. Samples were transported and sorted, aliquoted. Due to the instrument maintenance, data acquisition is still ongoing.
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| 今後の研究の推進方策 |
After data acquisition, we will analyze the data utilizing multiple methods and software in collaboration with other researchers.
1. Metabolomics data pre-processing. Data acquired by HILIC and lipidomics methods will be processed using MS-DIAL software which is developed by collaborators from NIG. Due to the nature of HILIC column, retention time should be corrected or aligned first in MS-DIAL. After that, intensity correction will be performed using quality control correction algorithms developed by our collaborators from Australia. 2. Metabolite identification. Metabolite annotation is very critical and will be done by comparing with our in-house database as well as online metabolomics databases. 3. Correlation analysis. All metabolites as well as clinical parameters will be clustered into several modules and the correlation relationship among all the modules will be calculated. Based on the significance of each module and lung function parameters, metabolites that have close relationship with asthma will be screened out. 4. Data integration. Metabolomics data will be integrated with GWAS data which has already been acquired by our collaborators in Harvard Medical School. 5. Validation. Findings from this cohort will be validated in other independent cohort(s). 6. Available exposures that can potentially intrigue asthma will be collected. The relationship between exposures, GWAS and metabolome will be analyzed.
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