Single Photon smFRET. II. Application to Continuous Illumination

Saurabh, Ayush; Safar, Matthew; Fazel, Mohamadreza; Sgouralis, Ioannis; Pressé, Steve

doi:10.1101/2022.07.20.500888

Cited by 4 publications

(8 citation statements)

References 58 publications

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“…In this section, we present results using our BNP-FRET sampler described above. Specifically, here we benchmark the parametric (fixed number of system states) version of our sampler using synthetic data, while the two subsequent manuscripts [55, 68] focus on the nonparametric (unknown number of system states) analysis of experimental data.…”

Section: Resultsmentioning

confidence: 99%

“…To conclude, we have presented a general framework and demonstrated the importance of incorporating various features into the likelihood while learning full distributions over all unknowns including system states. In the following two companion papers [55, 68], we specialize our method, and computational scheme, to continuous and pulsed illumination. We then apply our method to interactions of the intrinsically disordered proteins NCBD and ACTR [68] under continuous illumination, and the dynamics of the Holliday junction under pulsed illumination [55].…”

Section: Discussionmentioning

confidence: 99%

“…In the following two companion papers [55, 68], we specialize our method, and computational scheme, to continuous and pulsed illumination. We then apply our method to interactions of the intrinsically disordered proteins NCBD and ACTR [68] under continuous illumination, and the dynamics of the Holliday junction under pulsed illumination [55].…”

Section: Discussionmentioning

confidence: 99%

“…In the end, the chain of generated samples can be used for subsequent statistical analysis. We will show implementation of this method for synthetic and experimental data in the second manuscript of this series [68].…”

Section: Beta-bernoulli Process For Continuous Illuminationmentioning

confidence: 99%

“…We will show implementation of this method for synthetic and experimental data in the second manuscript of this series [68].…”

Section: Inverse Strategymentioning

confidence: 99%

See 4 more Smart Citations

Single Photon smFRET. I. Theory and Conceptual Basis

Saurabh

Safar

Sgouralis

et al. 2022

Preprint

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We present a unified conceptual framework and associated software package for single molecule Förster Resonance Energy Transfer (smFRET) analysis from single photon arrivals leveraging Bayesian nonparametrics, BNP-FRET. This unified framework addresses the following key physical complexities of an smFRET experiment, including: 1) fluorophore photophysics; 2) continuous time dynamics of the labeled system with large timescale separations between photophysical phenomena such as excited photophysical state lifetimes and events such as transition between system states; 3) unavoidable detector artefacts; 4) background emissions; 5) unknown number of system states; and 6) both continuous and pulsed illumination. These physical features necessarily demand a novel framework that extends beyond existing tools. In particular, we propose a second order hidden Markov model (HMM) and Bayesian nonparametrics (BNP) on account of items 1, 2 and 5 on the list, respectively. In companion manuscripts II and III, we discuss the direct effects of these key complexities on the inference of parameters for continuous and pulsed illumination, respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Beta-bernoulli Process For Continuous Illuminationmentioning

confidence: 99%

“…We will show implementation of this method for synthetic and experimental data in the second manuscript of this series [68].…”

Section: Inverse Strategymentioning

confidence: 99%

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Single Photon smFRET. I. Theory and Conceptual Basis

Saurabh

Safar

Sgouralis

et al. 2022

Preprint

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Fluorescence Lifetime: Beating the IRF and interpulse window

Fazel

Vallmitjana

Scipioni

et al. 2022

Preprint

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Fluorescence lifetime imaging (FLIM) has been essential in capturing spatial distributions of chemical species across cellular environments employing pulsed illumination confocal setups. However, quantitative interpretation of lifetime data continues to face critical challenges. For instance, fluorescent species with known in vitro excited state lifetimes may split into multiple species with unique lifetimes when introduced into complex living environments. What is more, mixtures of species, that may be both endogenous and introduced into the sample, may exhibit: 1) very similar lifetimes; as well as 2) wide ranges of lifetimes including lifetimes shorter than the instrumental response function (IRF) or whose duration may be long enough to be comparable to the interpulse window. By contrast, existing methods of analysis are optimized for well separated and intermediate lifetimes. Here we broaden the applicability of fluorescence lifetime analysis by simultaneously treating unknown mixtures of arbitrary lifetimes, outside the intermediate, goldilocks, zone, for data drawn from a single confocal spot leveraging the tools of Bayesian nonparametrics (BNP). We benchmark our algorithm, termed BNP lifetime analysis of BNPLA, using a range of synthetic and experimental data. Moreover, we show that the BNPLA method can distinguish and deduce lifetimes using photon counts as small as 500.

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Learning Continuous Potentials from smFRET

Bryan

Pressé

2022

Preprint

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Potential energy landscapes are useful models in describing events such as protein folding and binding. While single molecule fluorescence resonance energy transfer (smFRET) experiments encode information on continuous potentials for the system probed, including rarely visited barriers between putative potential minima, this information is rarely decoded from the data. This is because existing analysis methods often model smFRET output assuming, from the onset, that the system probed evolves in a discretized state-space to be analyzed within a Hidden Markov Model (HMM) paradigm. By contrast, here we infer continuous potentials from smFRET data without discretely approximating the state-space. We do so by operating within a Bayesian nonparametric paradigm by placing priors on the family of all possible potential curves. As our inference accounts for a number of required experimental features raising computational cost (such as incorporating discrete photon shot noise), the framework leverages a Structured-Kernel-Interpolation Gaussian Process prior to help curtail computational cost. We show that our Structured-Kernel-Interpolation Priors for Potential Energy Reconstruction from smFRET (SKIPPER-FRET) analysis accurately infers the potential energy landscape from a smFRET binding experiment. We then illustrate advantages of SKIPPER-FRET over standard HMM approaches by providing information, such as barrier heights and friction coefficients, otherwise inaccessible to HMMs.

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Single Photon smFRET. II. Application to Continuous Illumination

Cited by 4 publications

References 58 publications

Single Photon smFRET. I. Theory and Conceptual Basis

Single Photon smFRET. I. Theory and Conceptual Basis

Fluorescence Lifetime: Beating the IRF and interpulse window

Learning Continuous Potentials from smFRET

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