基于人工智能的实时生态人工智能与相对数据依赖获取扩大了对单细胞质谱蛋白质学的访问
Bowen Shen1, Fei Zhou1, Peter Nemes1
1Department of Chemistry & Biochemistry, University of Maryland, College Park, MD, 20742, USA.
Angewandte Chemie (International ed. in English)
|August 23, 2025
概括
这项研究引入了实时生态AI,一种使用毛细血管电泳和AI的成本效益高的单细胞蛋白质组学方法. 它可以从最小的细胞样本中实现高蛋白质识别,使高级分析更容易获得.
科学领域:
- 蛋白质组学
- 生物技术
- 分析化学
背景情况:
- 单细胞质谱 (MS) 提供敏感的细胞蛋白质组概况.
- 高的仪器成本限制了单细胞蛋白质组的广泛采用.
- 需要可访问和负担得起的单细胞蛋白质组分析.
研究的目的:
- 开发一个预算意识的单细胞蛋白质组学策略.
- 扩大对单细胞蛋白质基因分析的使用.
- 展示一个新的CE-MS工作流程的实用性.
主要方法:
- 将毛细电泳 (CE),数据依赖获取 (DDA) 与电泳相关 (Eco) 离子分类以及人工智能辅助的光谱解卷 (CHIMERYS) 结合到实时生态人工智能工作流程中.
- 在与传统的混合四极轨道质谱仪相结合的定制CE平台上实现工作流程.
- 使用实时Eco-DDA采样和CHIMERYS处理进行光谱分析.
主要成果:
- 每个频谱最多可识别15个,相当于现代高端系统.
- 从1 ng的HeLa消化物中确定了2142种蛋白质,超过了当代纳米LC Orbitrap Fusion Lumos的性能.
- 在<15分钟内确定了1799个蛋白质,从约250ppg (单细胞相当量) 开始,使得每天的理论吞吐量达到48个样本.
- 从单个 Xenopus laevis 芽细胞中分析了 1524 种蛋白质,显示出蛋白质组不对称.
结论:
- 实时生态人工智能为使用CE-MS的单细胞蛋白质组提供了强大而负担得起的方法.
- 工作流程提高了敏感蛋白质基因分析的可访问性.
- 在发育生物学背景下分析蛋白质组不对称的可行性.
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