Zhang Jiaxin, Wallrabe Horst, Siller Karsten, Mbogo Brian, Cassidy Thomas, Alam Shagufta Rehman, Periasamy Ammasi
The W.M. Keck Center for Cellular Imaging, University of Virginia, Charlottesville, Virginia, USA.
Department of Research Computing, University of Virginia, Charlottesville, Virginia, USA.
J Biophotonics. 2025 Jan;18(1):e202400426. doi: 10.1002/jbio.202400426. Epub 2024 Nov 25.
Two-photon (2P) fluorescence lifetime imaging microscopy (FLIM) was used to track cellular metabolism with drug treatment of auto-fluorescent coenzymes NAD(P)H and FAD in living cancer cells. Simultaneous excitation at 800 nm of both coenzymes was compared with traditional sequential 740/890 nm plus another alternative of 740/800 nm, before and after adding doxorubicin in an imaging time course. Changes of redox states at single cell resolution were compared by three analysis methods: our recently introduced fluorescence lifetime redox ratio (FLIRR: NAD(P)H-a %/FAD-a %), machine-learning (ML) algorithms using principal component (PCA) and non-linear multi-Feature autoencoder (AE) analysis. While all three led to similar biological conclusions (early drug response), the ML models provided statistically the most robust significant results. The advantage of the single 800 nm excitation of both coenzymes for metabolic imaging in above mentioned analysis is demonstrated.
双光子（2P）荧光寿命成像显微镜（FLIM）用于通过对活癌细胞中自身荧光辅酶NAD（P）H和FAD进行药物处理来追踪细胞代谢。在成像时间进程中添加阿霉素之前和之后，将两种辅酶在800nm处的同时激发与传统的740/890nm顺序激发以及另一种740/800nm激发进行了比较。通过三种分析方法比较了单细胞分辨率下氧化还原状态的变化：我们最近引入的荧光寿命氧化还原比（FLIRR：NAD（P）H-a%/FAD-a%）、使用主成分（PCA）的机器学习（ML）算法和非线性多特征自动编码器（AE）分析。虽然这三种方法都得出了相似的生物学结论（早期药物反应），但ML模型在统计学上提供了最可靠的显著结果。证明了在上述分析中，两种辅酶在800nm处的单一激发用于代谢成像的优势。
