2020
DOI: 10.1038/s41467-020-18726-9
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Unscrambling fluorophore blinking for comprehensive cluster detection via photoactivated localization microscopy

Abstract: Determining nanoscale protein distribution via Photoactivated Localization Microscopy (PALM) mandates precise knowledge of the applied fluorophore’s blinking properties to counteract overcounting artifacts that distort the resulting biomolecular distributions. Here, we present a readily applicable methodology to determine, optimize and quantitatively account for the blinking behavior of any PALM-compatible fluorophore. Using a custom-designed platform, we reveal complex blinking of two photoswitchable fluoresc… Show more

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Cited by 24 publications
(52 citation statements)
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“…However, Voronoї tessellation can be applied to a set of sub-pixel localizations obtained for a robust, non-photo-switchable emitter (e.g., Qdot) in a tracking and localization microscopy (TALM) concept [ 75 ] that complements sptPALM, wherein longer trajectories yield hidden information about subcellular morphology. One should note that a potential caveat is false-positive cluster detection due to emitter overcounting that arises from repetitive on-off blinking (especially true for immobile targets) [ 76 ]. The first step in our analysis was estimating individual Voronoї cell areas for mEos2-PH-PLCδ and Qdot-D2L localization maps, as it allows assessment of changes in emitter localization density in a parameter-free manner when compared to subjective cluster detection [ 41 ].…”
Section: Resultsmentioning
confidence: 99%
“…However, Voronoї tessellation can be applied to a set of sub-pixel localizations obtained for a robust, non-photo-switchable emitter (e.g., Qdot) in a tracking and localization microscopy (TALM) concept [ 75 ] that complements sptPALM, wherein longer trajectories yield hidden information about subcellular morphology. One should note that a potential caveat is false-positive cluster detection due to emitter overcounting that arises from repetitive on-off blinking (especially true for immobile targets) [ 76 ]. The first step in our analysis was estimating individual Voronoї cell areas for mEos2-PH-PLCδ and Qdot-D2L localization maps, as it allows assessment of changes in emitter localization density in a parameter-free manner when compared to subjective cluster detection [ 41 ].…”
Section: Resultsmentioning
confidence: 99%
“…Correction can also be made by measuring blinking behaviour in a separate sample of well-isolated fluorophores 7 , but this again adds complexity and experimental effort and requires the assumption that probe photophysics is maintained between the sample and the calibration. It is also possible to measure or simulate multiple-blinking using realistic photophysical models and use these to derive new CSR confidence intervals for the L-function curves 30 . It should be noted however that while all of these competing methods can be used to account for multiple-blinking, none produce a new set of corrected positions and therefore none can be used to extract rich descriptors such as those output by a clustering algorithm.…”
Section: Discussionmentioning
confidence: 99%
“…Overcounting due to multiple blinking makes quantifying small clusters challenging (64,65). Labeling methodology generally limited to proteins.…”
Section: Methods Limitations Required Developmentsmentioning
confidence: 99%