SIGNIFICANCE: The potential of fluorescence lifetime imaging microscopy (FLIM) is recently being recognized, especially in biological studies. However, FLIM does not directly measure the lifetimes, rather it records the fluorescence decay traces. The lifetimes and/or abundances have to be estimated from these traces during the phase of data processing. To precisely estimate these parameters is challenging and requires a well-designed computer program. Conventionally employed methods, which are based on curve fitting, are computationally expensive and limited in performance especially for highly noisy FLIM data. The graphical analysis, while free of fit, requires calibration samples for a quantitative analysis. AIM: We propose to extract the lifetimes and abundances directly from the decay traces through machine learning (ML). APPROACH: The ML-based approach was verified with simulated testing data in which the lifetimes and abundances were known exactly. Thereafter, we compared its performance with the commercial software SPCImage based on datasets measured from biological samples on a time-correlated single photon counting system. We reconstructed the decay traces using the lifetime and abundance values estimated by ML and SPCImage methods and utilized the root-mean-squared-error (RMSE) as marker. RESULTS: The RMSE, which represents the difference between the reconstructed and measured decay traces, was observed to be lower for ML than for SPCImage. In addition, we could demonstrate with a three-component analysis the high potential and flexibility of the ML method to deal with more than two lifetime components. CONCLUSIONS: The ML-based approach shows great performance in FLIM data analysis.
SIGNIFICANCE: The potential of fluorescence lifetime imaging microscopy (FLIM) is recently being recognized, especially in biological studies. However, FLIM does not directly measure the lifetimes, rather it records the fluorescence decay traces. The lifetimes and/or abundances have to be estimated from these traces during the phase of data processing. To precisely estimate these parameters is challenging and requires a well-designed computer program. Conventionally employed methods, which are based on curve fitting, are computationally expensive and limited in performance especially for highly noisy FLIM data. The graphical analysis, while free of fit, requires calibration samples for a quantitative analysis. AIM: We propose to extract the lifetimes and abundances directly from the decay traces through machine learning (ML). APPROACH: The ML-based approach was verified with simulated testing data in which the lifetimes and abundances were known exactly. Thereafter, we compared its performance with the commercial software SPCImage based on datasets measured from biological samples on a time-correlated single photon counting system. We reconstructed the decay traces using the lifetime and abundance values estimated by ML and SPCImage methods and utilized the root-mean-squared-error (RMSE) as marker. RESULTS: The RMSE, which represents the difference between the reconstructed and measured decay traces, was observed to be lower for ML than for SPCImage. In addition, we could demonstrate with a three-component analysis the high potential and flexibility of the ML method to deal with more than two lifetime components. CONCLUSIONS: The ML-based approach shows great performance in FLIM data analysis.
Entities:
Keywords:
chemometrics; fit-free; fluorescence lifetime imaging microscopy; life time extraction; machine learning
Authors: K C Lee; J Siegel; S E Webb; S Lévêque-Fort; M J Cole; R Jones; K Dowling; M J Lever; P M French Journal: Biophys J Date: 2001-09 Impact factor: 4.033
Authors: Thomas S Blacker; Zoe F Mann; Jonathan E Gale; Mathias Ziegler; Angus J Bain; Gyorgy Szabadkai; Michael R Duchen Journal: Nat Commun Date: 2014-05-29 Impact factor: 14.919