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

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。

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.