一个机器学习驱动的对象图像的比较,通过MALDI和MALDI-2质谱成像获得的离子图像
Tassiani Sarretto1, Wil Gardner2, Daniel Brungs3
1Molecular Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, Australia, 2522.
Journal of the American Society for Mass Spectrometry
|February 26, 2024
概括
使用MALDI-2后电离的矩阵辅助激光脱离电离质谱成像 (MALDI-MSI) 显示了比传统的MALDI-MSI更多的分子特征. 这种先进的技术增强了生物分子检测,并在组织成像中提供了独特的空间分布见解.
科学领域:
- 分析化学 分析化学
- 生物技术是生物技术.
- 图像质谱仪的成像质谱仪
背景情况:
- 矩阵辅助激光吸附离子化质谱成像 (MALDI-MSI) 是一种无标签的技术,用于可视化组织中的生物分子.
- 由于传统的MALDI-MSI具有较低的电离效率,许多生物分子无法检测到.
- MALDI-2 (激光诱导后电离) 是一种提高电离效率的方法.
研究的目的:
- 为了比较MALDI和MALDI-2在质谱成像 (MSI) 中产生的离子图像.
- 研究两种技术产生的离子空间分布的相似性和差异.
- 评估MALDI-2能够揭示以前未被检测到的分子特征的能力.
主要方法:
- 使用相关性分析来比较来自MALDI和MALDI-2的离子图像在相同的组织部分.
- 采用深度学习方法,使用Xception卷积神经网络来分析离子图像的相似性.
- 适应自然图像分类网络用于MSI数据分析.
主要成果:
- 相关性分析显示,常见离子的空间分布相似,差异归因于信号强度或MALDI-2的额外信号.
- 深度学习方法确定了来自MALDI和MALDI-2的类似离子图像的区域.
- 确定了具有独特空间分布的不同区域,仅可见于MALDI-2,证明了其揭示不可见分子特征的能力.
结论:
- 与MSI中的传统MALDI相比,MALDI-2显著提高了生物分子的检测.
- 这项研究强调了MALDI-2发现新型分子模式和组织学区域的能力.
- 深度学习分析有效地区分离子图像,展示了MALDI-2在MSI中的优势.
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