The ability to localize proteins precisely within subcellular space is crucial to understanding the functioning of biological systems. Recently, we described a protocol that correlates a precise map of fluorescent fusion proteins localized using three-dimensional super-resolution optical microscopy with the fine ultrastructural context of three-dimensional electron micrographs. While it achieved the difficult simultaneous objectives of high photoactivated fluorophore preservation and ultrastructure preservation, it required a super-resolution optical and specialized electron microscope that is not available to many researchers. We present here a faster and more practical protocol with the advantage of a simpler two-dimensional optical (Photoactivated Localization Microscopy (PALM)) and scanning electron microscope (SEM) system that retains the often mutually exclusive attributes of fluorophore preservation and ultrastructure preservation. As before, cryosections were prepared using the Tokuyasu protocol, but the staining protocol was modified to be amenable for use in a standard SEM without the need for focused ion beam ablation. We show the versatility of this technique by labeling different cellular compartments and structures including mitochondrial nucleoids, peroxisomes, and the nuclear lamina. We also demonstrate simultaneous two-color PALM imaging with correlated electron micrographs. Lastly, this technique can be used with small-molecule dyes as demonstrated with actin labeling using phalloidin conjugated to a caged dye. By retaining the dense protein labeling expected for super-resolution microscopy combined with ultrastructural preservation, simplifying the tools required for correlative microscopy, and expanding the number of useful labels we expect this method to be accessible and valuable to a wide variety of researchers.

A central problem in neuroscience is reconstructing neuronal circuits on the synapse level. Due to a wide range of scales in brain architecture such reconstruction requires imaging that is both high-resolution and high-throughput. Existing electron microscopy (EM) techniques possess required resolution in the lateral plane and either high-throughput or high depth resolution but not both. Here, we exploit recent advances in unsupervised learning and signal processing to obtain high depth-resolution EM images computationally without sacrificing throughput. First, we show that the brain tissue can be represented as a sparse linear combination of localized basis functions that are learned using high-resolution datasets. We then develop compressive sensing-inspired techniques that can reconstruct the brain tissue from very few (typically 5) tomographic views of each section. This enables tracing of neuronal processes and, hence, high throughput reconstruction of neural circuits on the level of individual synapses.