代謝體學是以生物系統中的小分子代謝物進行全面表徵，近年來，代謝體學已應用於環境毒理學和臨床上的研究，目前代謝體學研究常用的生物樣本是血液和尿液，然而，這些代謝物在生物樣本容易受日常活動、壓力或飲食變化等影響且僅僅反映了短時間的變化。頭髮具有長期和回顧性分析的特性，能夠保留內生性代謝物變化和外在環境暴露。此外，頭髮取樣是非侵入性的並且易於收集和儲存。在本研究中，利用超高效液相層析質譜法並使用多種條件，例如梯度、碰撞能量和排除清單等方法的調整對頭髮代謝組成進行分析。利用MS-DIAL和Compound Discoverer軟體處理原始數據以進行峰挑選和篩選，進一步利用精確質量和串聯圖譜並經由線上資料庫比對後，在頭髮中總共鑑定出1215種代謝物，並根據Metabolomics Standards Initiative（MSI）分為2級和3級來證明代謝物鑑定的可信度。利用ClassyFire將所有頭髮代謝物分為12個superclasses並進一步區分為109個classes，其中有30.5%是lipids and lipid-like molecules，21.3%是organoheterocyclic compounds，18.0%是benzenoids，11.9%是organic acids and derivatives等類型。在此，我們開發了人類頭髮代謝體資料庫，該資料庫可以提供在一般人群中的頭髮代謝物組成重要見解。在未來，頭髮代謝體也可用於臨床和流行病學研究並提供長期和回顧性資訊。

Metabolomics is a comprehensive characterization of small molecule metabolites in biological systems, applied in environmental toxicology and clinical research over recent years. For metabolomics research, the common biological samples are blood and urine. However, the metabolome of body fluids easily affected by daily activities, dietary variations, stress, or circadian variation. Blood and urine reflect short-term monitoring of metabolomic changes. Hair analysis presents several advantages, which reflects long-term endogenous compounds accumulation and retrospective environmental exposure. Moreover, hair sampling is non-invasive, and the storage of hair is much simpler and more efficient than blood and urine. Here, this study aimed to profile hair metabolome using ultra-high performance liquid chromatography-high resolution mass spectrometry approach of multiple conditions, such as LC gradient, collision energy, and exclusion list. The data was processed with MS-DIAL and Compound Discoverer for peak picking and filtering. Accurate mass and MS/MS spectra of metabolites were used to match online databases, such as Massbank of North America (MoNA), mzCloud, LipidBlast. A total of 1215 metabolites were profiled in the hair as classified level 2, and 3 according to the Metabolomics Standards Initiative (MSI) to demonstrate the strength of metabolite identification. All of the hair metabolites were classified using the ClassyFire ontology into 12 superclasses and further discriminated into 109 chemical classes. Of these, 30.5% were lipids and lipid-like molecules, 21.3% were organoheterocyclic compounds, 18.0% were benzenoids, 11.9% were organic acids and derivatives, and so on. In conclusion, the human hair metabolome database was constructed and could provide important insight to understand hair metabolites in the general population. In the future, hair metabolome can be useful to provide long-term and retrospective information to applications for clinical and epidemiology studies.

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