TY - JOUR T1 - Sub-diffraction Limit Localization of Proteins in Volumetric Space Using Bayesian Restoration of Fluorescence Images from Ultrathin Specimens A1 - Wang, Gordon A1 - Smith, Stephen J. Y1 - 2012/08/30 N2 - Author Summary Biological function at its fundamental level involves molecular interactions on a nanometer scale, and it is this reason that biological imaging has pushed for increasingly better resolution. Light microscopy is highly prevalent in biology due to its combination of large field of view, simple sample preparation, cost effective usage and relatively high tolerance by biological samples. The problem with light microscopy is that diffraction of light limits the resolution of achievable images to hundreds of nanometers in volumetric space, which is much too low for the accurate localization of proteins in subcellular organelle or structures, such as the synapse of a neuron. Super-resolution light microscopy is now available, but its implementation usually requires technically complex and expensive imaging systems. In this paper, we demonstrate a method that combines physical thin sectioning of tissue with Bayesian based deconvolution of conventional, fluorescent microscopy to achieve volumetric resolution well below the diffraction limit, and that using this method we are able to greatly improve the computational segmentation and localization of labeled proteins in a reconstructed volume of brain tissue. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 8 IS - 8 UR - https://doi.org/10.1371/journal.pcbi.1002671 SP - e1002671 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1002671 ER -