Fast fluorescence lifetime imaging techniques: A review on challenge and development

Abstract

Fluorescence lifetime imaging microscopy (FLIM) is increasingly used in biomedicine, material science, chemistry, and other related research fields, because of its advantages of high specificity and sensitivity in monitoring cellular microenvironments, studying interaction between proteins, metabolic state, screening drugs and analyzing their efficacy, characterizing novel materials, and diagnosing early cancers. Understandably, there is a large interest in obtaining FLIM data within an acquisition time as short as possible. Consequently, there is currently a technology that advances towards faster and faster FLIM recording. However, the maximum speed of a recording technique is only part of the problem. The acquisition time of a FLIM image is a complex function of many factors. These include the photon rate that can be obtained from the sample, the amount of information a technique extracts from the decay functions, the efficiency at which it determines fluorescence decay parameters from the recorded photons, the demands for the accuracy of these parameters, the number of pixels, and the lateral and axial resolutions that are obtained in biological materials. Starting from a discussion of the parameters which determine the acquisition time, this review will describe existing and emerging FLIM techniques and data analysis algorithms, and analyze their performance and recording speed in biological and biomedical applications.

Introduction to the Special Issue on Fluorescence Lifetime Imaging Microscopy (FLIM)

“…For example, Liu et al reviewed the recent progress on fast FLIM technique from the following aspects: the biophysical and electronic characteristics limiting FLIM speed, di®erent imaging techniques for breaking the \speed limit" and di®erent analytical tools for fast imaging. 1 Wang et al comprehensively introduced the FRET-FLIM technology including the principle, detection method and data processing, which enables the study of dynamic proteinprotein interactions in biological¯eld. 2 Liu et al and Li et al reviewed the progress of FLIM-based skin cancer diagnosis 3 and the application of TP-FLIM in tumor detection, 4 respectively.…”

Quantitative real-time imaging of intracellular FRET biosensor dynamics using rapid multi-beam confocal FLIM

“…Fluorescence lifetime imaging (FLIM) is a well-established, robust technique for quantitative intracellular measurements of protein-protein interactions by Förster resonance energy transfer (FRET) [1][2][3][4] , and highly photon efficient FRET measurements are achieved using FLIM with time-correlated single photon counting (TCSPC) 5 . In order to follow intracellular interactions using FLIM of biosensors based on FRET, measurements must often be made on the timescale of seconds, thus it is highly desirable to increase FLIM frame rates 6 whilst preserving the spatial and temporal accuracy and retaining a modest light dose at the sample. Time-domain widefield FLIM based on gated cameras 7 , optical intensifiers [8][9][10] , and more recently TCSPC detection using crossed delay line anode detection 11 and single photon avalanche diode (SPAD) arrays 12 , have been demonstrated with high frame rates.…”

Wide-field Fluorescence Lifetime Imaging Microscopy with a High-Speed Mega-pixel SPAD Camera

“…Lifetime retrieval. Irrespective of the imaging modality used for FLIM measurements, extracting lifetime information is not a trivial matter [32]. The measured signal, f (t), is the convolution of the impulse response function (IRF) and the fluorescence decay of the fluorophore, g(t), can be expressed as:…”

“…where t i is the time of the i-th sampling of the signal. A plethora of algorithms have been developed in order to tackle the problem of retrieving fluorescence decay (reviewed in [32]), with perhaps the most common being least-squares (LSQ) deconvolution (sometimes referred to as 'reconvolution'). In this approach a model of the fluorescence decay is convolved with the IRF and compared to the measured data using LSQ minimisation.…”

“…These could be scaled to even larger fields of view and shorter acquisition times by using bespoke and rapid translation stages. In this work we retrieve mono-exponential lifetimes as this has less strin-gent SNR requirements compared to multi-exponential fitting [32,43]. However, future work could of course extend this multi-exponential decays.…”