2018
DOI: 10.1117/1.jbo.23.10.106501
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Method to identify and minimize artifacts induced by fluorescent impurities in single-molecule localization microscopy

Abstract: . The existence of fluorescent impurities has been a long-standing obstacle in single-molecule imaging, which results in sample misidentification and higher localization uncertainty. Spectroscopic single-molecule localization microscopy can record the full fluorescent spectrum of every stochastic single-molecule emission event. This capability allows us to quantify the spatial and spectral characteristics of fluorescent impurities introduced by sample preparation steps, based on which we developed a… Show more

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Cited by 14 publications
(15 citation statements)
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“…Autofluorescence is stronger at shorter wavelengths and can hence be alleviated using fluorophores with longer emission wavelengths and/or appropriate filters. Impurities can be reduced, although not entirely removed, by appropriate cleaning of coverslips, for example using plasma cleaners 206 .…”
Section: Sample Driftmentioning
confidence: 99%
“…Autofluorescence is stronger at shorter wavelengths and can hence be alleviated using fluorophores with longer emission wavelengths and/or appropriate filters. Impurities can be reduced, although not entirely removed, by appropriate cleaning of coverslips, for example using plasma cleaners 206 .…”
Section: Sample Driftmentioning
confidence: 99%
“…We developed sSMLM systems that utilize diffraction gratings to obtain the spatial and spectral information of the single-molecule blinking events simultaneously [16, 2124]. Using sSMLM, we reported that Rhodamine dyes tagged microtubules assembled in vitro showed significant spectral heterogeneity.…”
Section: Introductionmentioning
confidence: 99%
“…Future updates will make RainbowSTORM compatible with a wider range of spatial analysis platforms. Further, additional spectroscopic analysis methods such as spectral unmixing (Davis, et al, 2018), machine-learning based spectral classification (Zhang, et al, 2019), and cluster analysis (Bongiovanni, et al, 2016) will be added to RainbowSTORM.…”
Section: Discussionmentioning
confidence: 99%