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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 徐丞志 | zh_TW |
| dc.contributor.advisor | Cheng-Chih Hsu | en |
| dc.contributor.author | 陳宇萱 | zh_TW |
| dc.contributor.author | Yu-Hsuan Chen | en |
| dc.date.accessioned | 2024-08-16T17:25:45Z | - |
| dc.date.available | 2024-08-31 | - |
| dc.date.copyright | 2024-08-16 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-09 | - |
| dc.identifier.citation | 1 Buchberger, A. R.; Delaney, K.; Johnson, J.; Li, L. Mass Spectrometry Imaging: A Review of Emerging Advancements and Future Insights. Analytical Chemistry 2018, 90 (1), 240-265. DOI: 10.1021/acs.analchem.7b04733.
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Commun. 2006, (28), 2968-2970. DOI: 10.1039/b606020j. 1 Buchberger, A. R.; Delaney, K.; Johnson, J.; Li, L. Mass Spectrometry Imaging: A Review of Emerging Advancements and Future Insights. Analytical Chemistry 2018, 90 (1), 240-265. DOI: 10.1021/acs.analchem.7b04733. 2 McDonnell, L. A.; Heeren, R. M. A. Imaging mass spectrometry. Mass Spectrometry Reviews 2007, 26 (4), 606-643. DOI: 10.1002/mas.20124. 3 Amstalden van Hove, E. R.; Smith, D. F.; Heeren, R. M. A. A concise review of mass spectrometry imaging. Journal of Chromatography A 2010, 1217 (25), 3946-3954. DOI: https://doi.org/10.1016/j.chroma.2010.01.033. 4 Spengler, B. Mass Spectrometry Imaging of Biomolecular Information. Analytical Chemistry 2015, 87 (1), 64-82. DOI: 10.1021/ac504543v. 5 Ifa, D. R.; Eberlin, L. S. Ambient Ionization Mass Spectrometry for Cancer Diagnosis and Surgical Margin Evaluation. Clinical Chemistry 2016, 62 (1), 111-123. DOI: 10.1373/clinchem.2014.237172 (acccessed 4/13/2024). 6 Ma, X.; Fernández, F. 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Cancers 2020;12(11):3147. 50 Simiczyjew, A., Wądzyńska, J., Pietraszek-Gremplewicz, K., Kot, M., Ziętek, M., Matkowski, R., et al. Melanoma cells induce dedifferentiation and metabolic changes in adipocytes present in the tumor niche. Cellular & Molecular Biology Letters 2023;28. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94669 | - |
| dc.description.abstract | 質譜影像 (MSI) 為一種高靈敏並且可以同時提供空間資訊及非標的式分析之強大工具。近年來，常壓游離方法因為可以於常壓環境下進行游離與其簡易樣品準備步驟而廣為人知。然而質譜影像技術因涉及空間上的資訊，其產生之數據通常複雜程度較高，讓其實際應用有些阻礙。為了解決這個問題，我們首先介紹MSI Tale軟體，這是一款基於 MATLAB 的軟體，該軟體提供圖片對齊、圈選感興趣區域 (ROI) 選擇、標記和匯出等功能用以簡化 MSI 數據分析過程。此外，我們應用質譜影像技術於黑色素瘤，透過將附式電噴灑游離法 (DESI)-MSI 與機器學習相結合，目的開發出黑色素瘤分類平台以及尋找與黑色素瘤有相關之代謝產物。使用質譜影像方法能夠精確地描繪腫瘤區域，並提供黑色素瘤代謝體空間分布資訊，幫助我們尋找潛在生物標誌物的可能性。總結來說，我們的研究結果強調了 MSI Tale 的重要性以及質譜影像與機器學習結合在黑色素瘤研究中之應用。 | zh_TW |
| dc.description.abstract | Mass spectrometry imaging (MSI) provides spatial distribution and allows untargeted analysis of molecules. Recently, ambient ionization mass spectrometry has become popular due to its operation at atmospheric pressure with minimal sample preparation, greatly shortening analysis time. Despite its promise, MSI's large and complex datasets often hinder practical application. To address this, we introduce MSI Tale (Mass Spectrometry Imaging Tool for Tissue Alignment, Labeling, and Export), a MATLAB-based software designed to simplify MSI data analysis by offering image alignment, region of interest (ROI) selection, labeling, and exporting. Additionally, we apply MSI to melanoma by integrating desorption electrospray ionization (DESI)-MSI with machine learning to develop a melanoma classification platform. This approach enables precise tumor region mapping and provides insights into melanoma metabolism, highlighting metabolites as potential biomarkers. Our findings underscore the significance of MSI Tale and the integration of MSI with machine learning for melanoma research and diagnosis. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-16T17:25:44Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-16T17:25:45Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定 i
誌謝 ii 摘要 iv Abstract v Chapter 1. Introductory chapter 1 1-1 Mass spectrometry imaging 1 1-2 Overview of this study 6 1-3 References 8 Chapter 2. MSI data processing software: MSI Tale 10 2-1 Abstract 10 2-2 Introduction 11 2-3 Results and Discussions 15 2-3-1 Overview of MSI Tale 15 2-3-2 Data preprocessing and visualization 18 2-3-3 Image registration and ROI selection 20 2-3-4 Spectra labeling and exporting 22 2-3-5 Evaluation of MSI data processing pipeline for MSI Tale 24 2-4 Conclusion 25 2-5 Material &Method 26 2-5-1 Materials 26 2-5-2 DESI-MSI 26 2-5-3 MSI Tale 26 2-5-4 Metabolite identification 27 2-6 Reference 28 Chapter 3. Exploring cutaneous melanoma through spatially-resolved metabolomics via mass spectrometry imaging and machine learning algorithms 32 3-1 Abstract 32 3-2 Introduction 34 3-3 Results 39 3-3-1 Overview for the study design 39 3-3-2 Study design for exploring melanoma-related metabolites 41 3-3-3 ML model construction 43 3-3-4 Feature selection and model performance evaluation 44 3-3-5 Mapping lipidomic melanoma signatures 52 3-3-6 Identification of RFE-selected features 54 3-3-7 Exploration of disease-related candidates 64 3-4 Discussion 72 3-5 Conclusion 77 3-6 Material and Methods 78 3-6-1 Materials 78 3-6-2 Participants 78 3-6-3 Skin specimen collection and preparation. 79 3-6-4 Desorption electrospray ionization (DESI) MSI analysis. 81 3-6-5 Histopathology 81 3-6-6 MSI data processing 82 3-6-7 ML model construction 83 3-6-8 Feature selection 83 3-6-9 Lipid extraction from skin specimen 84 3-6-10 Highly-weighted compounds identification with LC-HR-MS/MS 84 3-6-11 Selected-candidate identification 86 3-6-12 Statistical analysis 86 3-7 Reference 88 | - |
| dc.language.iso | en | - |
| dc.subject | 黑色素瘤 | zh_TW |
| dc.subject | 質譜影像 | zh_TW |
| dc.subject | 皮膚癌 | zh_TW |
| dc.subject | 脂類組學 | zh_TW |
| dc.subject | 生物標誌物 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 常壓游離法 | zh_TW |
| dc.subject | Lipidomics | en |
| dc.subject | Mass spectrometry imaging | en |
| dc.subject | Ambient ionization | en |
| dc.subject | Machine learning | en |
| dc.subject | Biomarker discovery | en |
| dc.subject | Skin cancer | en |
| dc.subject | Melanoma | en |
| dc.title | 應用常壓游離質譜影像技術探索黑色素瘤之生物標記與開發質譜影像數據前處理軟體 | zh_TW |
| dc.title | Applying Ambient Mass Spectrometry Imaging to Discover Cutaneous Melanoma Biomarkers and Developing Supporting Data Processing Software | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 廖怡華;謝建台 | zh_TW |
| dc.contributor.oralexamcommittee | Yi-Hua Liao;Jentaie Shiea | en |
| dc.subject.keyword | 質譜影像,常壓游離法,機器學習,生物標誌物,脂類組學,皮膚癌,黑色素瘤, | zh_TW |
| dc.subject.keyword | Mass spectrometry imaging,Ambient ionization,Machine learning,Biomarker discovery,Lipidomics,Skin cancer,Melanoma, | en |
| dc.relation.page | 93 | - |
| dc.identifier.doi | 10.6342/NTU202402544 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-08-12 | - |
| dc.contributor.author-college | 理學院 | - |
| dc.contributor.author-dept | 化學系 | - |
| dc.date.embargo-lift | 2029-08-05 | - |
| 顯示於系所單位： | 化學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-112-2.pdf 未授權公開取用 | 6.48 MB | Adobe PDF | 檢視/開啟 |
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