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Tracking single molecules continues to provide new insights into the fundamental rules governing biological function. Despite continued technical advances in fluorescent and non-fluorescent labeling as well as data analysis, direct observations of trajectories and interactions of multiple molecules in dense environments remain aspirational goals. While confocal methods provide a means to deduce dynamical parameters with high temporal resolution, such as diffusion coefficients, they do so at the expense of spatial resolution. Indeed, on account of a confocal volume's symmetry, typically only distances from the center of the confocal spot can be deduced. Motivated by the need for true three dimensional high speed tracking in densely labeled environments, we propose a computational tool for tracking many fluorescent molecules traversing multiple, closely spaced, confocal measurement volumes providing independent observations. Various realizations of this multiple confocal volumes strategy have previously been used for long term, large area, tracking of one fluorescent molecule in three dimensions. What is more, we achieve tracking by directly using single photon arrival times to inform our likelihood and exploit Hamiltonian Monte Carlo to efficiently sample trajectories from our posterior within a Bayesian nonparametric paradigm. A nonparametric paradigm here is warranted as the number of molecules present are, themselves

Single-molecule fluorescence resonance energy transfer (FRET) is a powerful technique based on dipole-dipole interaction between donor and acceptor fluorophores to observe inter- and intra-molecular dynamics in realtime with sensitivity to macro-molecular distances (∼ 2.5-10 nm). That said, some fluorophores have an inherent characteristic known as protein induced fluorescence enhancement (PIFE). PIFE is a photo-physical feature of dyes undergoing cis-trans transitions and occurs for protein-dye interactions closer than 3 nm. Here, the challenge is uncoupling the PIFE effect in the FRET data. Ignoring the PIFE effect in the analysis of the FRET data may lead to misinterpretation of the system under investigation. As a solution to this problem, we develop a computational framework based on Bayesian statistics to analyze the fluorescence lifetime signals of the donor and acceptor channels which allows us to uncouple the PIFE effects from the FRET. Our framework can extract any changes in the FRET efficiency simultaneously with any changes in the fluorescence lifetimes of the donor and acceptor due to the PIFE effect. In addition, our framework can provide other parameters, such as the donor and acceptor excitation rates, background photon rates, and detectors’ cross-talk ratios. Our framework extracts all these parameters by analyzing a single photon arrival time trace with only a few thousand photons.

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.

Fluorescence lifetime imaging microscopy (FLIM) has become a standard tool in the quantitative analysis of sub-cellular environments. However, quantitative FLIM analyses face several challenges. First, spatial correlations between pixels are often ignored as signal from individual pixels is analyzed independently thereby limiting spatial resolution. Second, existing methods deduce photon ratios instead of absolute lifetime maps. Next, the number of lifetime components contributing to the signal is unknown, while excited state lifetimes with

Tracking single molecules continues to provide new insights into the fundamental rules governing biological function. Despite continued technical advances in fluorescent and non-fluorescent labeling as well as data analysis, direct observations of trajectories and interactions of multiple molecules in dense environments remain aspirational goals. While confocal methods provide a means to deduce dynamical parameters with high temporal resolution, such as diffusion coefficients, they do so at the expense of spatial resolution. Indeed, on account of a confocal volume’s symmetry, typically only distances from the center of the confocal spot can be deduced. Motivated by the need for true three dimensional high speed tracking in densely labeled environments, we propose a computational tool for tracking many fluorescent molecules traversing multiple, closely spaced, confocal measurement volumes providing independent observations. Various realizations of this multiple confocal volumes strategy have previously been used for long term, large area, tracking of one fluorescent molecule in three dimensions. What is more, we achieve tracking by directly using single photon arrival times to inform our likelihood and exploit Hamiltonian Monte Carlo to efficiently sample trajectories from our posterior within a Bayesian nonparametric paradigm. A nonparametric paradigm here is warranted as the number of molecules present are, themselves, a priori unknown. Taken together, we provide a computational framework to infer trajectories of multiple molecules at once, below the diffraction limit (the width of a confocal spot), in three dimensions at sub-millisecond or faster time scales.

SUMMARY Lifetimes of chemical species are typically estimated by either fitting time-correlated single-photon counting (TCSPC) histograms or phasor analysis from time-resolved photon arrivals. While both methods yield lifetimes in a computationally efficient manner, their performance is limited by choices made on the number of distinct chemical species contributing photons. However, the number of species is encoded in the photon arrival times collected for each illuminated spot and need not be set by hand a priori. Here, we propose a direct photon-by-photon analysis of data drawn from pulsed excitation experiments to infer, simultaneously and self-consistently, the number of species and their associated lifetimes from a few thousand photons. We do so by leveraging new mathematical tools within the Bayesian nonparametric. We benchmark our method for both simulated and experimental data for 1–4 species., Graphical abstract Here, Tavakoli et al. propose the direct photon-by-photon analysis of data drawn from pulsed excitation experiments to infer, simultaneously and self-consistently, the number of species and their associated lifetimes from as little as a few thousand photons. This is achieved by leveraging new mathematical tools within the Bayesian nonparametric (BNP) paradigm.

Imaging methods by using computer techniques provide doctors assistance at any time and relieve their workload, especially for iterative processes like identifying objects of interest such as lesions and anatomical structures from the image. Detection of microaneurysms (MAs) as one of the lesions in the retina is considered to be a crucial step in some retinal image analysis algorithms for the identification of diabetic retinopathy (DR) as the second-largest eye diseases in developed countries. The objective of this study is to compare the effect of two preprocessing methods, Illumination Equalization, and Top-hat transformation, on retinal images to detect MAs using a combination of Matching based approach and deep learning methods either in the normal fundus images or in the presence of DR. The steps for the detection are as following: 1) applying preprocessing, 2) vessel segmentation and masking, and 3) MAs detection using a combination of Matching based approach and deep learning. From the accuracy viewpoint, we compared the method to manual detection performed by ophthalmologists for our big retinal image databases (more than 2200 images). Using first preprocessing method, Illumination equalization and contrast enhancement, the accuracy of MAs detection was about 90% for all databases (one local and two publicly retinal databases). The performance of the MAs detection methods using top-hat preprocessing (the second preprocessing method) was more than 80% for all databases.

One way to achieve spatial resolution using fluorescence imaging—and track single molecules—is to use wide-field illumination and collect measurements over multiple sensors (camera pixels). Here we propose another way that uses confocal measurements and a single sensor. Traditionally, confocal microscopy has been used to achieve high temporal resolution at the expense of spatial resolution. This is because it utilizes very few, and commonly just one, sensors to collect data. Yet confocal data encode spatial information. Here we show that non-uniformities in the shape of the confocal excitation volume can be exploited to achieve spatial resolution. To achieve this, we formulate a specialized hidden Markov model and adapt a forward filtering-backward sampling Markov chain Monte Carlo scheme to efficiently handle molecular motion within a symmetric confocal volume characteristically used in fluorescence correlation spectroscopy. Our method can be used for single confocal volume applications or incorporated into larger computational schemes for specialized, multi-confocal volume, optical setups.

Fluorescence correlation spectroscopy (FCS), is a flexible and widely used tool routinely exploited in vivo. While FCS provides estimates of dynamical quantities, such as diffusion coefficients, it demands high molecular concentrations, high signal to noise ratio and long time traces typically in the minute range. This is by contrast to data acquisition which, in principle, provides information on the micro second timescales that has, thus far, evaded analysis. To overcome these limitations, we adapt novel tools inspired by Bayesian non-parametrics. Using this framework, which starts from the direct analysis of photon counts, we achieve the same accuracy as FCS, but with much shorter traces, even at nearly single molecule concentrations. Our new analysis extends the capability of confocal microscopy based approaches, to probe processes several orders of magnitude faster in time and permits a reduction of phototoxic effects on living samples induced by long periods of light exposure.