pawFLIM: reducing bias and uncertainty to enable lower photon count in FLIM experiments

Abstract

Förster resonant energy transfer measured by fluorescence lifetime imaging microscopy (FRET-FLIM) is the method of choice for monitoring the spatio-temporal dynamics of protein interactions in living cells. To obtain an accurate estimate of the molecular fraction of interacting proteins requires a large number of photons, which usually precludes the observation of a fast process, particularly with time correlated single photon counting (TCSPC) based FLIM. In this work, we propose a novel method named pawFLIM (phasor analysis via wavelets) that allows the denoising of FLIM datasets by adaptively and selectively adjusting the desired compromise between spatial and molecular resolution. The method operates by applying a weighted translational-invariant Haar-wavelet transform denoising algorithm to phasor images. This results in significantly less bias and mean square error than other existing methods. We also present a new lifetime estimator (named normal lifetime) with a smaller mean squared error and overall bias as compared to frequency domain phase and modulation lifetimes. Overall, we present an approach that will enable the observation of the dynamics of biological processes at the molecular level with better temporal and spatial resolution.

Exploring protein–protein interactions with large differences in protein expression levels using FLIM-FRET

“…To obtain robust fitting parameters for bi-exponential decay, it is recommended to use global analysis with a prior knowledge that only a limited number of fluorescent molecule species whose lifetimes do not vary spatially are present in the sample [14][15][16]. Some efforts for alternative analysis [17], like phasor based analysis [18,19] are also available, including attempts for denoising FLIM datasets and reducing bias and uncertainty in parameters estimations [20,21]. In the particular context of PPIs, non-interacting species (donor only decay) or interacting proteins where donors and acceptors are too far apart to undergo FRET can be discriminated from interacting ones in bi-molecular interactions assuming for example spatial invariance of the donor lifetime components across the data set.…”

Co-imaging extrinsic, intrinsic and effector caspase activity by fluorescence anisotropy microscopy

“…Genetically encoded proteins have been designed to implement this technique for detection of protein clustering [5], measuring protein conformations [37] and protease activity [33]. Unlike their heteroFRET counterparts in which analytical methods to derive quantitative biological information have been broadly demonstrated [31], information derived from anisotropy FRET-based sensors has mostly remained at the qualitative level. However, it is of interest to pursue this family of biosensors as their narrower spectral window per sensor would in principle allow to co-measure a larger number of them [32].…”

Evaluation of Caspase‐3 Activity During Apoptosis with Fluorescence Lifetime‐Based Cytometry Measurements and Phasor Analyses

“…Image construction Data points result from Fourier transform of waveforms correspond to each cell; resulting point cloud represents entire cell population.Data points result from intensity stack of images (homodyne frequency domain, time gated, or time-correlated single photon count); resulting point cloud represents a pixel population. Application and utility(35,39,46,(51)(52)(53)(54)(55) Sorting and segmentation…”