High Resolution Fluorescence Lifetime Maps from Minimal Photon Counts

Abstract

Fluorescence lifetime imaging microscopy (FLIM) may reveal subcellular spatial lifetime maps of key molecular species. Yet, such a quantitative picture of life necessarily demands high photon budgets at every pixel under the current analysis paradigm, thereby increasing acquisition time and photodamage to the sample. Motivated by recent developments in computational statistics, we provide a direct means to update our knowledge of the lifetime maps of species of different lifetimes from direct photon arrivals, while accounting for experimental features such as arbitrary forms of the instrument response function (IRF) and exploiting information from empty laser pulses not resulting in photon detection. Our ability to construct lifetime maps holds for arbitrary lifetimes, from short lifetimes (comparable to the IRF) to lifetimes exceeding interpulse times. As our method is highly data efficient, for the same amount of data normally used to determine lifetimes and photon ratios, working within the Bayesian paradigm, we report direct blind unmixing of lifetimes with subnanosecond resolution and subpixel spatial resolution using standard raster scan FLIM images. We demonstrate our method using a wide range of simulated and experimental data.

Single Photon smFRET. III. Application to Pulsed Illumination

“…However, as the resulting posterior has a non-analytical form, it cannot be directly sampled. Therefore, we develop a Markov chain Monte Carlo sampling (MCMC) procedure [36,[44][45][46][47][48] to draw samples from the posterior.…”

“…In this paper, we adapt the general smFRET analysis framework presented in the first companion paper [22] for the case of pulsed illumination to learn full distributions over the system kinetics and photophysical rates, i.e., donor and acceptor relaxation and FRET rates, while 1) inferring full distributions over the number of system states; and while 2) taking into account experimental factors such as IRF and crosstalk. As our main concern is deducing system state numbers using single photon arrivals while incorporating detector effects, we leverage the formalism of infinite hidden Markov models (iHMM) [23][24][25][26][27][28] within the Bayesian nonparametric (BNP) paradigm [23,24,[29][30][31][32][33][34][35][36]. The iHMM framework assumes an a priori infinite number of system states with associated probabilities where the number of system states warranted by input data is enumerated by non-zero probabilities.…”

Single Photon smFRET. II. Application to Continuous Illumination

“…Equating the right hand sides of Eqs. 2 & 4 then allows us to write the following conditional posterior for as Since the conditional posterior above does not take a closed form that allows direct sampling, we use the Metropolis-Hastings (MH) step [28–34], where new samples are drawn from a proposal distribution q and accepted with the probability where the asterisk represents the proposed rate values from the proposal distribution q .…”

“…Since the conditional posterior above does not take a closed form that allows direct sampling, we use the Metropolis-Hastings (MH) step [28][29][30][31][32][33][34], where new samples are drawn from a proposal distribution q and accepted with the probability…”

Fluorescence Lifetime: Beating the IRF and interpulse window

“…To deduce weights (photon ratios) and lifetimes present from photon arrival time data, analysis methods employ either model free techniques, such as phasors 26,28 and deep learning 29,30 , or model based techniques, such as least-squares 31,32 , compressed sensing 33 , maximum likelihood 34,35 , and Bayesian methods 27,36–39 .…”