Statistical deconvolution for superresolution fluorescence microscopy.

ABSTRACT: Superresolution microscopy techniques based on the sequential activation of fluorophores can achieve image resolution of ∼10 nm but require a sparse distribution of simultaneously activated fluorophores in the field of view. Image analysis procedures for this approach typically discard data from crowded molecules with overlapping images, wasting valuable image information that is only partly degraded by overlap. A data analysis method that exploits all available fluorescence data, regardless of overlap, could increase the number of molecules processed per frame and thereby accelerate superresolution imaging speed, enabling the study of fast, dynamic biological processes. Here, we present a computational method, referred to as deconvolution-STORM (deconSTORM), which uses iterative image dec

SUBMITTER: Mukamel EA 

PROVIDER: S-EPMC3353000 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature