光终身预测的深度学习使生物成像中的高通量成为可能
Sofia Kapsiani1, Nino F Läubli1, Edward N Ward1
1Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, U.K.
Journal of the American Chemical Society
|June 14, 2025
概括
一个深度学习模型FLIMngo准确地量化了来自光子缺乏环境的光终身成像显微镜 (FLIM) 数据. 这种进步大大缩短了数据采集时间,使FLIM成为活样分析的高吞吐量工具.
科学领域:
- 生物医学光学
- 微观学中的机器学习
- 光光谱学
背景情况:
- 光终身成像显微镜 (FLIM) 对于研究生物医学研究中的微环境变化至关重要.
- 传统的FLIM方法需要高的光子计数,导致采集时间长,活样的吞吐量有限.
- 目前的技术与缺乏光子的数据作斗争, 阻碍FLIM在动态实体研究中的应用.
研究的目的:
- 推出FLIMngo,一个深度学习模型,用于在光子缺乏条件下精确量化FLIM数据.
- 为了实现高通量FLIM分析,减少数据采集时间和光毒性.
- 提高FLIM对活体生物标本的适用性.
主要方法:
- 开发FLIMngo,一个深度学习模型,利用原始FLIM数据中的时间和空间信息.
- 从每像素少于50个光子的衰变曲线获得FLIM数据的量化.
- 与使用模拟和实验数据的传统相位图分析和其他深度学习方法进行比较.
主要成果:
- FLIMngo从缺乏光子的FLIM数据中准确预测光寿命, 性能优于现有的方法.
- 该模型将FLIM数据采集时间缩短到几秒钟,从而实现更高的吞吐量和最小化的光毒性.
- 已被证明可以在活体"Caenorhabditis elegans"中量化蛋白质聚合物.
结论:
- FLIMngo显著提高了FLIM作为活生物样本的高吞吐量工具的实用性.
- 该模型能够分析光子缺乏的数据,为像C. elegans这样的生物进行纵向研究开辟了新的途径.
- FLIMngo是开源的,易于实现的,不需要对各种应用进行再培训.
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