通过图像融合增强空间分辨率在并列质谱仪离子/离子反应成像实验中的空间分辨率
Zhongling Liang1, Yingchan Guo1, Xizheng Diao1
1Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States.
概括
我们开发了一种使用计算图像融合的新方法,以提高脂质成像质谱的空间分辨率. 这种技术通过结合来自不同实验的数据来增强脂素脂质图像的细节性.
科学领域:
- 利皮多米克 (Lipidomics) 是一种消化剂.
- 质谱仪成像成像 质谱仪成像
- 计算生物学 计算生物学
背景情况:
- 带有电荷逆转离子/离子反应的双重质谱 (MS/MS) 能够实现特定的脂质衍生.
- 目前的MS/MS脂质成像具有较差的空间分辨率,限制了详细分析.
- 计算图像融合提供了一种结合互补图像数据的方法.
研究的目的:
- 展示一种概念验证工作流程,用于提高脂质成像中的空间分辨率.
- 在MS/MS成像中克服化学特异性和空间分辨率之间的权衡.
- 改进组织样本中特定脂质的检测和定位.
主要方法:
- 开发了一种电荷逆转离子/离子反应,用于氨基脂衍生物化.
- 化低分辨率 (125微米) 离子/离子反应产品离子图像与高分辨率 (25微米) 全扫描图像.
- 测试了线性回归,随机森林回归和2D CNN图像融合预测模型.
主要成果:
- 在预测的离子/离子反应产品离子图像的空间分辨率上实现了5倍的改进.
- 线性回归模型对于脂胺素 (PS 40:6) 图像是最优的.
- 二维CNN模型对硫酸盐基胺 (SHexCer d38:1) 图像是最优的.
结论:
- 计算图像融合显著提高脂质MS/MS成像中的空间分辨率.
- 开发的工作流改善了在组织中定位和分析特定脂质的能力.
- 不同的预测模型根据脂类物种显示不同的最佳性能.
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