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SNSMIL, a real-time single molecule identification and localization algorithm for super-resolution fluorescence microscopy

Single molecule localization based super-resolution fluorescence microscopy offers significantly higher spatial resolution than predicted by Abbe’s resolution limit for far field optical microscopy. Such super-resolution images are reconstructed from wide-field or total internal reflection single mo...

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Detalles Bibliográficos
Autores principales: Tang, Yunqing, Dai, Luru, Zhang, Xiaoming, Li, Junbai, Hendriks, Johnny, Fan, Xiaoming, Gruteser, Nadine, Meisenberg, Annika, Baumann, Arnd, Katranidis, Alexandros, Gensch, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4476421/
https://www.ncbi.nlm.nih.gov/pubmed/26098742
http://dx.doi.org/10.1038/srep11073
Descripción
Sumario:Single molecule localization based super-resolution fluorescence microscopy offers significantly higher spatial resolution than predicted by Abbe’s resolution limit for far field optical microscopy. Such super-resolution images are reconstructed from wide-field or total internal reflection single molecule fluorescence recordings. Discrimination between emission of single fluorescent molecules and background noise fluctuations remains a great challenge in current data analysis. Here we present a real-time, and robust single molecule identification and localization algorithm, SNSMIL (Shot Noise based Single Molecule Identification and Localization). This algorithm is based on the intrinsic nature of noise, i.e., its Poisson or shot noise characteristics and a new identification criterion, Q(SNSMIL), is defined. SNSMIL improves the identification accuracy of single fluorescent molecules in experimental or simulated datasets with high and inhomogeneous background. The implementation of SNSMIL relies on a graphics processing unit (GPU), making real-time analysis feasible as shown for real experimental and simulated datasets.