Nanoscopic Stoichiometry and Single‐Molecule Counting

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

doi:10.1002/smtd.201900082

Cited by 11 publications

(18 citation statements)

References 59 publications

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“…Learning the number of fluorophores within an ROI is a key step toward unraveling processes below the diffraction limit [5,12,2,23,24,25,26,27,28,29,30,31,32,33,34,10,11,12,13,14,15,16,17,18,19,20,21,56]. In order to go beyond the state of the art and enumerate as many as five times more fluorophores, we must accurately account for all the following: 1) fluorophore photophysics; 2) models for shot and detector noise; 3) provide full credible intervals (error bars) about the estimates; and 4) exploit every data point available without pre-or post-processing.…”

Section: Discussionmentioning

confidence: 99%

“…A number of methods exist to enumerate fluorophores within an ROI [10,11,12,13,14,15,16,17,18,19,20,21,22]. In the absence of photobleaching altogether, and by simply comparing background to the brightness of an ROI, one can use ruler methods that approximate the number of fluorophores by relating the maximum brightness of the trace to some reference with known brightness.…”

mentioning

confidence: 99%

“…A cartoon depicting this process is laid out in figure 2. Algorithms for PBSA have been developed by many groups [10,11,12,13,14,15,16,17,18,19,20,21,22]. These methods exploit, for example, hidden Markov models [20,22], data filtering in order to see steps more clearly [14], statistical measures used to identify violations of statistics where steps are located [18,15,17,16], or neural nets to count fluorophores [19].…”

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confidence: 99%

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Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics

Sgouralis

Bryan

Pressé

2020

Preprint

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Determining the number of biomolecules within a small, diffraction-limited spot, is key to understanding intermolecular interactions that form the basis of all of information processing relevant to life. To enumerate the number of biomolecules within such a small region of interest (ROI), it is possible to illuminate an ROI containing a collection of fluorescently labeled biomolecules and observe the ROI's brightness over time. As most fluorescent labels (fluorophores) are initially in a fluorescently active state, the brightness diminishes in a step-like pattern as fluorophores photobleach one at a time. Naively, by counting steps, one could determine the number of fluorophores present. However, this analysis is complicated by photon shot noise, camera noise that amplifies intrinsic photon shot noise and fluorophore photophysics (i.e., states with variable emission visited by fluorophores). Current state of the art can typically enumerate as many as 20 fluorophores reliably. Yet in many biophysics problems, fluorophore numbers can vastly exceed 20 within an ROI. For this reason, we develop a method to learn the number of fluorophores alongside other parameters required to achieve high counts simultaneously and self-consistently (e.g., fluorophore photophysical rate parameters and camera parameters). As the number of fluorophores is initially unknown and may be very large, and as not all fluorophores may initially be emitting photons, we cannot rely on a traditional (parametric) Bayesian framework. As such, to achieve our goal of exceeding the state of the art by a factor of about 5, we must abandon the parametric Bayesian paradigm and invoke novel tools within Bayesian nonparametrics never previously used in step counting by photobleaching.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics

Sgouralis

Bryan

Pressé

2020

Preprint

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“…This is because all photoswitchable or photoactivatable fluorophores tend to blink, with the same dye giving rise to multiple localizations. In quantitative SMLM, which attempts to quantify the abundance of nucleic acids or proteins from SMLM data, blinking gives rise to an overcounting problem [24][25][26][27][28]. In a clustering analysis, unclustered molecules with a single fluorophore label may appear as small clusters, of a size determined by the localization precision, due to blinking.…”

Section: Discussionmentioning

confidence: 99%

FOCAL3D: A 3-dimensional clustering package for single-molecule localization microscopy

2020

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Single-molecule localization microscopy (SMLM) is a powerful tool for studying intracellular structure and macromolecular organization at the nanoscale. The increasingly massive pointillistic data sets generated by SMLM require the development of new and highly efficient quantification tools. Here we present FOCAL3D, an accurate, flexible and exceedingly fast (scaling linearly with the number of localizations) density-based algorithm for quantifying spatial clustering in large 3D SMLM data sets. Unlike DBSCAN, which is perhaps the most commonly employed density-based clustering algorithm, an optimum set of parameters for FOCAL3D may be objectively determined. We initially validate the performance of FOCAL3D on simulated datasets at varying noise levels and for a range of cluster sizes. These simulated datasets are used to illustrate the parametric insensitivity of the algorithm, in contrast to DBSCAN, and clustering metrics such as the F1 and Silhouette score indicate that FOCAL3D is highly accurate, even in the presence of significant background noise and mixed populations of variable sized clusters, once optimized. We then apply FOCAL3D to 3D astigmatic dSTORM images of the nuclear pore complex (NPC) in human osteosaracoma cells, illustrating both the validity of the parameter optimization and the ability of the algorithm to accurately cluster complex, heterogeneous 3D clusters in a biological dataset. FOCAL3D is provided as an open source software package written in Python.

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“…SMLM is one type of super-resolution fluorescence microscopy that localizes individual molecules of interest with a precision of ≤10 nm (60); therefore, it not only produces super-resolved images with high spatial resolution, but also provides a convenient way to count the number of molecules. Without applying sophisticated algorithms (61)(62)(63), the number of molecules of interest is on average proportional to the intensity in the super-resolved images or the number of localizations obtained by SMLM (39).…”

Section: Quantification Of Protein Leakage By Single-molecule Localizmentioning

confidence: 99%

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 11 publications

References 59 publications

Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics

Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics

FOCAL3D: A 3-dimensional clustering package for single-molecule localization microscopy

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

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