Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo

Fazel, Mohamadreza; Wester, Michael J.; Mazloom-Farsibaf, Hanieh; Meddens, Marjolein B.M.; Eklund, Alexandra S.; Schlichthaerle, Thomas; Schueder, Florian; Jungmann, Ralf; Lidke, Keith A.

doi:10.1038/s41598-019-50232-x

Cited by 20 publications

(15 citation statements)

References 26 publications

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“…As the posterior does not assume an analytic form and cannot be directly sampled from, we invoke a Markov chain Monte Carlo (MCMC) procedure. 39 , 48 , 60 , 72 − 75 In particular, we opt for the following Gibbs sampling 60 , 76 strategy sketched here and discussed further in the Supporting Information, Note 2.2 .…”

Section: Methodsmentioning

confidence: 99%

“…With the posterior at hand, we are in a position to draw reasonable values for our parameters of interest from the posterior. As the posterior does not assume an analytic form and cannot be directly sampled from, we invoke a Markov chain Monte Carlo (MCMC) procedure. ,,,− In particular, we opt for the following Gibbs sampling , strategy sketched here and discussed further in the Supporting Information, Note 2.2.…”

Section: Methodsmentioning

confidence: 99%

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High Resolution Fluorescence Lifetime Maps from Minimal Photon Counts

et al. 2022

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

High Resolution Fluorescence Lifetime Maps from Minimal Photon Counts

et al. 2022

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“…A given numerical PSF was used in the GPU 3D fitting algorithm to localize emitters. The algorithm fit the single emitters using maximum likelihood estimation (MLE) and assuming a Poisson noise model 41 , 42 . The algorithm finds the MLE employing the Newton-Raphson approach to iteratively update parameters, including x , y , z -locations, intensity and background.…”

Section: Methodsmentioning

confidence: 99%

Robust, fiducial-free drift correction for super-resolution imaging

Wester

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Mazloom-Farsibaf

et al. 2021

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We describe a robust, fiducial-free method of drift correction for use in single molecule localization-based super-resolution methods. The method combines periodic 3D registration of the sample using brightfield images with a fast post-processing algorithm that corrects residual registration errors and drift between registration events. The method is robust to low numbers of collected localizations, requires no specialized hardware, and provides stability and drift correction for an indefinite time period.

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“…A given numerical PSF was used in the GPU 3D fitting algorithm to localize emitters. The algorithm fit the single emitters using maximum likelihood estimation (MLE) and assuming a Poisson noise model 34,35 . The algorithm finds the MLE employing the Newton-Raphson approach to iteratively update parameters, including x, y, z-locations, intensity and background.…”

Section: Super-resolution Fittingmentioning

confidence: 99%