PySOFI: an open source Python package for SOFI

Miao, Yuting; Weiss, Shimon; Yi, Xiyu

doi:10.1016/j.bpr.2022.100052

Cited by 1 publication

(2 citation statements)

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“… 44 bSOFI can be executed as a MATLAB script 36 ; and recently, open‐source Python implementations of SOFI have been published (PySOFI). 45 For microscopists interested in delving deeper into the principles and applications of SOFI, we suggest reading a recent and exhaustive SOFI review. 32 …”

Section: Stochastic Optical Fluctuation Imaging (Sofi)mentioning

confidence: 99%

“…Localizer is an open‐source software optimised with the most recent version of XC‐SOFI 43 ; it is accessible as a MATLAB script or a user‐friendly plug‐in for Igor pro 44 . bSOFI can be executed as a MATLAB script 36 ; and recently, open‐source Python implementations of SOFI have been published (PySOFI) 45 . For microscopists interested in delving deeper into the principles and applications of SOFI, we suggest reading a recent and exhaustive SOFI review 32 …”

Section: Stochastic Optical Fluctuation Imaging (Sofi)mentioning

confidence: 99%

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Fluorescence fluctuation‐based super‐resolution microscopy: Basic concepts for an easy start

Alva

Brito‐Alarcón

Linares

et al. 2022

Journal of Microscopy

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Due to the wave nature of light, optical microscopy has a lower-bound lateral resolution limit of about half of the wavelength of the detected light, i.e., within the range of 200 to 300 nm. The Fluorescence Fluctuation based Super Resolution Microscopy (FF-SRM) encompases a collection of image analysis techniques which rely on the statistical processing of temporal variations of fluorescence to reduce the uncertainty about the fluorophore positions within a sample, hence, bringing spatial resolution down to several tens of nm. The FF-SRM is known to be suitable for live-cell imaging due to its compatibility with most fluorescent probes and lower instrumental and experimental requirements, which are mostly camera-based epifluorescence instruments. Each FF-SRM approach has strengths and weaknesses, which depend directly on the underlying statistical principles through which enhanced spatial resolution is achieved. In this review, the basic concepts and principles behind a range of FF-SRM methods published to date are revisited. Their operational parameters are explained and guidance for its selection is provided.

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