Recent advances in fluorescence and electron microscopy (FM and EM) are closing the resolution gap between the two technologies, allowing cellular structures to be studied across the scales between structural and cell biology. FM and EM not only cover different scales, they provide complementary information. FM provides dynamic information, allows the imaging of large volumes, and is specific for the fluorescently labeled protein. EM, on the other hand, while imaging static and smaller volumes, offers contextual information, because the nonlabeled components of the cell are also seen. This working group presented methods and applications that bridge the gaps in resolution and integrate information from different FM and EM imaging modalities. John Briggs discussed motivations and methods for correlating FM and EM. He introduced sample preparation for transmission EM, comparing high-contrast overviews available after plastic-embedding with low-contrast, high-resolution structural views from cryo-EM. When correlative FM/EM is required, FM can be carried out before or after EM sample preparation. FM before sample preparation allows live imaging, but movements or distortions may occur between the FM and EM images. FM after sample preparation requires preservation of the FM signal during preparation. Methods and applications from different laboratories were presented (e.g., Muller-Reichert et al., 2007 ; Nixon et al., 2009 ; van Driel et al., 2009 ; Jun et al., 2011 ), as was the application of a sensitive, precise correlative FM–EM method (Kukulski et al., 2011 ) to study spatiotemporal dynamics of membrane bending during yeast endocytosis. Melike Lakadamyali presented an overview of superresolution FM techniques, such as stimulated emission depletion microscopy (STED), stochastic optical reconstruction microscopy (STORM), and photoactivation localization microscopy (PALM), and their extension to three-dimensional, multicolor, and live-cell imaging (Hell, 2009 ; Huang et al., 2010 ; Patterson et al., 2010 ). She also discussed their limitations, particularly in imaging living cells (e.g., trade-off between temporal and spatial resolution). Finally, she gave two examples of biological applications of superresolution: 1) using three-dimensional, multicolor STORM provides improved accuracy in neuronal tracing in the context of connectomics (Lakadamyali et al., 2012 ), and 2) correlating fast organelle dynamics with superresolution images of the cytoskeleton can reveal the biophysical mechanisms of organelle transport (Balint and Lakadamyali, unpublished). Stephen Smith of Stanford University's Department of Molecular and Cellular Physiology highlighted array tomography, a versatile method that can potentially reveal the synaptic connectivity of neurons at the light and electron microscopic scale (Micheva and Smith, 2007 ). Cutting ultrathin tissue sections and immunostaining them with multiple rounds of antibodies allow imaging of a large number of synaptic molecules. Using array tomography, the Smith laboratory is getting the first glimpse into the rich molecular diversity of synapses and searching for principles that govern the complex molecular architectures of synapses and synaptic circuits (Micheva et al., 2010 ). Array tomography is also compatible with EM and superresolution FM and allows correlating molecular and ultrastructural information across different scales. Finally, Erik Jorgensen of the Biology Department at the University of Utah gave an example of how molecular content of cells captured by superresolution FM can be correlated with ultrastructural information derived using scanning EM. Having previously correlated STED-based methods with EM, he described the correlation of PALM-based superresolution methods with scanning EM (Watanabe et al., 2011 ). This method allowed the imaging of specific ultrastructural features of synapses within Caenorhabditis elegans. He also introduced a three-dimensional PALM-based system capable of generating images with resolution of 20 nm in the plane of the image and 50 nm in the axial dimension.