Nanoscopic Stoichiometry and Single-Molecule Counting

Nino, Daniel; Djayakarsana, Daniel; Milstein, Joshua N.

doi:10.1101/524801

Cited by 4 publications

(6 citation statements)

References 44 publications

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“…Learning the number of molecules located within a molecular cluster, while simultaneously and self-consistently determining the dynamics of the cluster’s constituent members, is a key step toward unraveling life’s processes occurring well below light’s diffraction limit [ 27 , 15 , 2 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 12 , 13 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 56 ]. In order to do so, we introduced a Bayesian nonparametric framework that accurately models the photophysics, shot noise, and detector noise that gives rise to the data, along with sampling methods capable of exploring this high dimensional probability space.…”

Section: Discussionmentioning

confidence: 99%

“…Traditionally, Photobleaching step analysis (PBSA) methods were developed to enumerate the number of fluorophores within a spot [ 12 , 13 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Such methods proceed in many ways; for example, by exploiting hidden Markov models [ 23 , 25 ], data filtering to identify steps [ 17 ], statistical measures to identify expected violations of statistics characteristic of steps [ 21 , 18 , 20 , 19 ], or neural nets [ 22 ]. In a recently submitted manuscript, additional ways of automating and improving upon PBSA methods listed above have also been explored [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

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Diffraction-limited molecular cluster quantification with Bayesian nonparametrics

2022

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Life’s fundamental processes involve multiple molecules operating in close proximity within cells. To probe the composition and kinetics of molecular clusters confined within small (diffraction-limited) regions, experiments often report on the total fluorescence intensity simultaneously emitted from labeled molecules confined to such regions. Methods exist to enumerate total fluorophore numbers (e.g., step counting by photobleaching). However, methods aimed at step counting by photobleaching cannot treat photophysical dynamics in counting nor learn their associated kinetic rates. Here we propose a method to simultaneously enumerate fluorophores and determine their individual photophysical state trajectories. As the number of active (fluorescent) molecules at any given time is unknown, we rely on Bayesian nonparametrics and use specialized Monte Carlo algorithms to derive our estimates. Our formulation is benchmarked on synthetic and real data sets. While our focus here is on photophysical dynamics (in which labels transition between active and inactive states), such dynamics can also serve as a proxy for other types of dynamics such as assembly and disassembly kinetics of clusters. Similarly, while we focus on the case where all labels are initially fluorescent, other regimes, more appropriate to photoactivated localization microscopy, where fluorophores are instantiated in a non-fluorescent state, fall within the scope of the framework. As such, we provide a complete and versatile framework for the interpretation of complex time traces arising from the simultaneous activity of up to 100 fluorophores.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diffraction-limited molecular cluster quantification with Bayesian nonparametrics

2022

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“…An active effort to advance quantitative SMLM (qSMLM) by developing methods to accurately count molecules and their complex subunits is still in its infancy ( Annibale et al , 2011 ; Cella Zanacchi et al , 2017 ; Cui et al , 2018 ; Ehmann et al , 2014 ; Herrera Reyes, 2019 ; Jungmann et al , 2016 ; Karathanasis et al , 2017 ; Lando et al , 2012 ; Lee et al , 2012 ; Nieuwenhuizen et al , 2015 ; Nino et al , 2017 , 2019 ; Patel et al , 2021 ; Puchner et al , 2013 ; Rollins et al , 2015 ). These methods attempt to correct for counting artifacts introduced by the stochastic blinking required for super-resolved imaging.…”

Section: Introductionmentioning

confidence: 99%

“…Utilizing the empirical fact that many photswitchable fluorophores emit a geometrically (i.e. discrete exponentially) distributed number of blinks before photobleaching ( Fricke et al , 2015 ; Lee et al , 2012 ; Nieuwenhuizen et al , 2015 ; Wang et al , 2016 ), we previously developed a statistical model for molecular counting with SMLM ( Nino et al , 2017 , 2019 ). The advantage of this approach is that one only needs to calibrate for the characteristic number of blinks λ defining the blink distribution for a particular fluorophore.…”

Section: Introductionmentioning

confidence: 99%

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An expectation–maximization approach to quantifying protein stoichiometry with single-molecule imaging

Boonkird

Nino

Milstein

2021

Bioinformatics Advances

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Motivation Single-molecule localization microscopy (SMLM) is a super-resolution technique capable of rendering nanometer scale images of cellular structures. Recently, much effort has gone into developing algorithms for extracting quantitative features from SMLM datasets such as the abundance and stoichiometry of macromolecular complexes. These algorithms often require knowledge of the complicated photophysical properties of photoswitchable flourophores. Results Here we develop a calibration-free approach to quantitative SMLM built upon the observation that most photswitchable fluorophores emit a geometrically distributed number of blinks before photobleaching. From a statistical model of a mixture of monomers, dimers and trimers, the method employs an adapted Expectation-Maximization (EM) algorithm to learn the protomer fractions while simultaneously determining the single-fluorophore blinking distribution. To illustrate the utility of our approach, we benchmark it on both simulated datasets and experimental datasets assembled from SMLM images of fluorescently labeled DNA nanostructures. Availability An implementation of our algorithm written in Python is available at: https://www.utm.utoronto.ca/milsteinlab/resources/Software/MMCode/ Supplementary information Supplementary data are available at Bioinformatics Advances online.

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Microampere electric currents caused bacterial membrane damage and two-way leakage in short time

Krishnamurthi

Rogers

Peifer

et al. 2020

Preprint

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Physical agents such as low electric voltages and currents have recently gained attention for antimicrobial treatment due to their bactericidal capability. Although microampere electric currents were shown to suppress the growth of bacteria, it remains unclear to what extent the microampere currents damage bacterial membrane. Here, we investigated the membrane damage and two-way leakage caused by microampere electric currents (≤ 100 µA) in a short time (30 min). Based on MitoTracker staining, propidium iodide staining, filtration assays, and quantitative single-molecule localization microscopy, we found that microampere electric currents caused significant membrane damages and allowed two-way leakages of ions, small molecules and proteins. This study paves the way to new development and antibiotic applications of ultra-low electric voltages and currents. Statement of SignificancePrevious studies showed that treating bacteria with milliampere electric currents for 72 hours led to significant damages of the bacterial membrane. However, it remains unclear to what extent membrane damages and two-way (i.e. inward and outward) leakages are caused by lower electric currents in a shorter time. In this work, we set out to answer this question. We carried out several assays on the bacteria treated by microampere electric currents of ≤ 100 µA for 30 min, including MitoTracker staining, propidium iodide staining, filtration assays, and quantitative single-molecule localization microscopy. We found and quantified that the membrane damages were caused by microampere electric currents in half an hour and allowed two-way leakages of ions, small molecules, and proteins.

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Nanoscopic Stoichiometry and Single-Molecule Counting

Cited by 4 publications

References 44 publications

Diffraction-limited molecular cluster quantification with Bayesian nonparametrics

Diffraction-limited molecular cluster quantification with Bayesian nonparametrics

An expectation–maximization approach to quantifying protein stoichiometry with single-molecule imaging

Microampere electric currents caused bacterial membrane damage and two-way leakage in short time

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