无监督的机器学习用于在体内标记同位素的质谱成像数据分析
Raven L Buckman Johnson1, Vy T Tat1, Young Jin Lee1
1Department of Chemistry, Iowa State University, Ames, IA, USA. yjlee@iastate.edu.
The Analyst
|September 3, 2025
概括
机器学习应用于体谱成像与体内同位素标记 (MSI) 增强空间代谢. 无监督的机器学习显著减少了分析时间,并提高了草中的同位素分布的清晰度.
科学领域:
- 代谢学
- 空间生物学
- 生物物理
背景情况:
- 质谱成像 (MSI) 对空间代谢学至关重要.
- 不针对性MSI数据分析和MSI与体内同位素标记 (MSIi) 存在重大挑战.
- 对于MSI分析的机器学习 (ML) 应用仍然未被探索.
研究的目的:
- 探索用于MSI数据分析的无监督机器学习应用.
- 利用ML区分代谢物局部化和研究非目标代谢物中的同位素标记.
- 评估ML对分析时间,吞吐量和空间同位素分布的清晰度的影响.
主要方法:
- 使用Cardinal软件处理MSIi数据集从13CO2和D2O标记的草.
- 应用空间缩小中心体 (SSC) 分段,无监督的ML算法,用于数据分析.
- 计算了新生生物合成的分量,以了解组织特异性代谢物流量.
主要成果:
- 根据脂质同位素分布,SSC细分在C标记的草中确定了五个不同的空间细分,超过了手工分类.
- 标记为D的草的SSC细分显示了五个空间细分,其特征是独特的代谢物和同位素配置.
- 不针对性细分分析提供了关于组织特异性代谢物相对流量的见解.
结论:
- 无监督的机器学习显著提高了复杂的MSI数据集的分析.
- ML的应用缩短了分析时间,增加了空间代谢量.
- 这种方法提高了理解空间同位素分布和代谢动态的清晰度和深度.
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