We describe an engineered family of highly antigenic molecules based on GFP-like fluorescent proteins. These molecules contain numerous copies of peptide epitopes and simultaneously bind IgG antibodies at each location. These “spaghetti monster” fluorescent proteins (smFPs) distribute well in neurons, notably into small dendrites, spines and axons. smFP immunolabeling localizes weakly expressed proteins not well resolved with traditional epitope tags. By varying epitope and scaffold, we generated a diverse family of mutually orthogonal antigens. In cultured neurons and mouse and fly brains, smFP probes allow robust, orthogonal multi-color visualization of proteins, cell populations and neuropil. smFP variants complement existing tracers, greatly increase the number of simultaneous imaging channels, and perform well in advanced preparations such as array tomography, super-resolution fluorescence imaging and electron microscopy. In living cells, the probes improve single-molecule image tracking and increase yield for RNA-Seq. These probes facilitate new experiments in connectomics, transcriptomics and protein localization.

Summary As a basic functional unit in neural circuits, each neuron integrates input signals from hundreds to thousands of synapses. Knowledge of the synaptic input fields of individual neurons, including the identity, strength and location of each synapse, is essential for understanding how neurons compute. Here we developed a volumetric super-resolution reconstruction platform for large-volume imaging and automated segmentation of neurons and synapses with molecular identity information. We used this platform to map inhibitory synaptic input fields of On-Off direction-selective ganglion cells (On-Off DSGCs), which are important for computing visual motion direction in the mouse retina. The reconstructions of On-Off DSGCs showed a GABAergic, receptor subtype-specific input field for generating direction selective responses without significant glycinergic inputs for mediating monosynaptic crossover inhibition. These results demonstrate unique capabilities of this super-resolution platform for interrogating neural circuitry.

The mechanisms that give rise to ocular dominance columns (ODCs) during development are controversial. Early experiments indicated a key role for retinal activity in ODC formation. However, later studies showed that in those early experiments, the retinal activity perturbation was initiated after ODCs had already formed. Moreover, recent studies concluded that early eye removals do not impact ODC segregation. Here we blocked spontaneous retinal activity during the very early stages of ODC development. This permanently disrupted the anatomical organization of ODCs and led to a dramatic increase in receptive field size for binocular cells in primary visual cortex. Our data suggest that early spontaneous retinal activity conveys crucial information about whether thalamocortical axons represent one or the other eye and that this activity mediates binocular competition important for shaping receptive fields in primary visual cortex.

Cocaine abusers may experience drug craving upon exposure to environmental contexts where cocaine was experienced. The dorsal hippocampus (DHC) is important for contextual conditioning, therefore the authors examined the specific role of the DHC in cocaine conditioned place preference (CPP). Muscimol was used to temporarily inhibit the DHC and was infused before conditioning sessions or tests for CPP to investigate acquisition and expression of cocaine CPP, respectively. To investigate consolidation, rats received intra-DHC muscimol either immediately or 6 hr after conditioning sessions. Inhibition of DHC, but not the overlying cortex, disrupted acquisition and expression of cocaine CPP. It is interesting to note that there was no effect of post-conditioning DHC inhibition. The findings suggest that the DHC is important for both acquisition and recall, but not consolidation, of context-cocaine associations.

Epibatidine (EPI), a potent cholinergic agonist, disrupts acetylcholine-dependent spontaneous retinal activity. Early patch-clamp recordings in juvenile ferrets suggested that EPI blocks all retinal ganglion cell (RGC) action potentials when applied to the retina. In contrast, recent experiments on the developing mouse that relied on multielectrode array (MEA) recordings reported that EPI application decorrelates the activity of neighboring RGCs and eliminates retinal waves while preserving the spiking activity of many neurons. The different techniques used in previous studies raise the question of whether EPI has different effects on RGC activity in mouse compared with that in ferret. A resolution of this issue is essential for interpreting the results of developmental studies that relied on EPI to manipulate retinal activity. Our goal was to compare the effects of EPI on the spontaneous discharges of RGCs in mouse and ferret using 60-electrode MEA as well as patch-clamp recordings during the developmental stage when retinal waves are driven by acetylcholine in both species. We found that in both mouse and ferret EPI decorrelates RGC activity and eliminates retinal waves. However, EPI does not block all spontaneous activity in either species. Instead, our whole cell recordings reveal that EPI silences more than half of all RGCs while significantly increasing the activity of the remainder. These results have important implications for interpreting the results of previous studies that relied on this cholinergic agonist to perturb retinal activity.

Eye-specific projections to the dorsal lateral geniculate nucleus (dLGN) serve as a model for exploring how precise patterns of circuitry form during development in the mammalian central nervous system. Using a combination of dual-label anterograde retinogeniculate tracing and Nissl-staining, we studied the patterns of eye-specific afferents and cellular laminae in the dLGN of the pigmented sable ferret at eight developmental timepoints between birth and adulthood. Each time point was investigated in the three standard orthogonal planes of section, allowing us to generate a complete anatomical map of eye-specific development in this species. We find that eye-specific retinal ganglion cell axon segregation varies according to location in the dLGN, with the principle contralateral (A) and ipsilateral layers (A1) maturing first, followed by the contralateral and ipsilateral C laminae. Cytoarchitectural lamination lags behind eye-specific segregation, except in the C laminae where underlying cellular layers never develop to accompany eye-specific afferent domains. The emergence of On/Off sublaminae occurs following eye-specific segregation in this species. On the basis of these findings, we constructed a threedimensional map of eye-specific channels in the developing and mature ferret dLGN. Anat Rec, 293:1-24,

Summary Here, we present a protocol for collecting large-volume, four-color, single-molecule localization imaging data from neural tissue. We have applied this technique to map the location and identities of chemical synapses across whole cells in mouse retinae. Our sample preparation approach improves 3D STORM image quality by reducing tissue scattering, photobleaching, and optical distortions associated with deep imaging. This approach can be extended for use on other tissue types enabling life scientists to perform volumetric super-resolution imaging in diverse biological models. For complete details on the use and execution of this protocol, please refer to Sigal et al. (2015) .

Optimal analysis of single molecule localization microscopy (SMLM) data acquired with a scientific Complementary Metal-Oxide-Semiconductor (sCMOS) camera relies on statistical compensation for its pixel-dependent gain, offset and readout noise. In this work we show that it is also necessary to compensate for differences in the relative quantum efficiency (RQE) of each pixel. We found differences in RQE on the order of 4% in our tested sCMOS sensors. These differences were large enough to have a noticeable effect on analysis algorithm results, as seen both in simulations and biological imaging data. We discuss how the RQE differences manifest themselves in the analysis results and present the modifications to the Poisson maximum likelihood estimation (MLE) sCMOS analysis algorithm that are needed to correct for the RQE differences.