Genetically encoded calcium indicators (GECIs) are powerful tools for systems neuroscience. Recent efforts in protein engineering have significantly increased the performance of GECIs. The state-of-the art single-wavelength GECI, GCaMP3, has been deployed in a number of model organisms and can reliably detect three or more action potentials (APs) in short bursts in several systems in vivo. Through protein structure determination, targeted mutagenesis, high-throughput screening, and a battery of in vitro assays, we have increased the dynamic range of GCaMP3 by several-fold, creating a family of “GCaMP5” sensors. We tested GCaMP5s in several systems: cultured neurons and astrocytes, mouse retina, and in vivo in Caenorhabditis chemosensory neurons, Drosophila larval neuromuscular junction and adult antennal lobe, zebrafish retina and tectum, and mouse visual cortex. Signal-to-noise ratio was improved by at least 2–3-fold. In the visual cortex, two GCaMP5 variants detected twice as many visual stimulus-responsive cells as GCaMP3. By combining in vivo imaging with electrophysiology we show that GCaMP5 fluorescence provides a more reliable measure of neuronal activity than its predecessor GCaMP3. GCaMP5 allows more sensitive detection of neural activity in vivo and may find widespread applications for cellular imaging in general.

We describe an intensity-based glutamate-sensing fluorescent reporter (“iGluSnFR”) with signal-to-noise ratio and kinetics appropriate for in vivo imaging. We engineered iGluSnFR in vitro to maximize its fluorescence change, and validated its utility for visualizing glutamate release by neurons and astrocytes in increasingly intact neurological systems. In hippocampal culture, iGluSnFR detected single field stimulus-evoked glutamate release events. In pyramidal neurons in acute brain slices, glutamate uncaging at single spines showed that iGluSnFR responds robustly and specifically to glutamate in situ, and responses correlate with voltage changes. In mouse retina, iGluSnFR-expressing neurons showed intact light-evoked excitatory currents, and the sensor revealed tonic glutamate signaling in response to light stimuli. In worms, glutamate signals preceded and predicted post-synaptic calcium transients. In zebrafish, iGluSnFR revealed spatial organization of direction-selective synaptic activity in the optic tectum. Finally, in mouse forelimb motor cortex, iGluSnFR expression in layer V pyramidal neurons revealed task-dependent single-spine activity during running.

The photopigment melanopsin confers photosensitivity upon a minority of retinal output neurons. These intrinsically photosensitive retinal ganglion cells (ipRGCs) are more diverse than once believed, comprising five morphologically distinct types, M1 through M5. Here, in mouse retina, we provide the first in-depth characterization of M4 cells, including their structure, function and central projections. M4 cells apparently correspond to ON alpha cells of earlier reports, and are easily distinguished from other ipRGCs by their very large somata. Their dendritic arbors are more radiate and highly branched than those of M1, M2 or M3 cells. The melanopsin-based intrinsic photocurrents of M4 cells are smaller than those of M1 and M2 cells, presumably because melanopsin is more weakly expressed; we can detect it immunohistochemically only with strong amplification. Like M2 cells, M4 cells exhibit robust, sustained synaptically-driven ON responses and dendritic stratification in the ON-sublamina of the IPL. However, their stratification patterns are subtly different, with M4 dendrites positioned just distal to those of M2 cells and just proximal to the ON cholinergic band. M4 receptive fields are large, with an ON center, antagonistic OFF surround, and non-linear spatial summation. Their synaptically-driven photoresponses lack direction selectivity and show higher ultraviolet sensitivity in the ventral retina than in the dorsal retina, echoing the topographic gradient in S- and M-cone opsin expression. M4 cells are readily labeled by retrograde transport from the dorsal lateral geniculate nucleus, and thus likely contribute to the pattern vision that persists in mice lacking functional rods and cones.

Fluorescent calcium indicator proteins, such as GCaMP3, allow imaging of activity in genetically defined neuronal populations. GCaMP3 can be expressed using various gene delivery methods, such as viral infection or electroporation. However, these methods are invasive and provide inhomogeneous and non-stationary expression. Here we developed a genetic reporter mouse, Ai38, which expresses GCaMP3 in a Cre-dependent manner from the ROSA26 locus, driven by a strong CAG promoter. Crossing Ai38 with appropriate Cre mice produced robust GCaMP3 expression in defined cell populations in the retina, cortex and cerebellum. In the primary visual cortex, visually-evoked GCaMP3 signals showed normal orientation and direction selectivity. GCaMP3 signals were rapid, compared to virally expressed GCaMP3 and synthetic calcium indicators. In the retina, Ai38 allowed imaging spontaneous calcium waves in starburst amacrine cells during development, and light-evoked responses in ganglion cells in adult tissue. Our results show that the Ai38 reporter mouse provides a flexible method for targeted expression of GCaMP3.

Single-wavelength fluorescent reporters allow visualization of specific neurotransmitters with high spatial and temporal resolution. We report variants of the glutamate sensor iGluSnFR that are functionally brighter, detect sub-micromolar to millimolar glutamate, and have blue, cyan, green, or yellow emission profiles. These variants allow in vivo imaging where original iGluSnFR was too dim, can resolve glutamate transients in dendritic spines and axonal boutons, and permit kilohertz imaging.

Retinal ganglion cells that respond selectively to a dark spot on a brighter background (OFF cells) have smaller dendritic fields than their ON counterparts and are more numerous. OFF cells also branch more densely, and thus collect more synapses per visual angle. That the retina devotes more resources to processing dark contrasts predicts that natural images contain more dark information. We confirm this across a range of spatial scales and trace the origin of this phenomenon to the statistical structure of natural scenes. We show that the optimal mosaics for encoding natural images are also asymmetric, with OFF elements smaller and more numerous, matching retinal structure. Finally, the concentration of synapses within a dendritic field matches the information content, suggesting a simple principle to connect a concrete fact of neuroanatomy with the abstract concept of information: equal synapses for equal bits.ganglion cells | neural coding | vision T he brain separates light from dark unequally. Psychophysical studies and measurements of visually evoked potentials show greater sensitivity to light decrements and dark spots in images (1, 2). Also, more cortical cells respond to negative than positive contrasts (3). In fact, this asymmetry begins with the second order (bipolar) neurons in the retina, which rectify the local contrast signal from the cone array (Fig. 1A). OFF cone bipolar cells (responding mostly to negative contrasts) outnumber the ON cells (responding mostly to positive contrasts) by 2-fold (4); thus, right from the start, the brain provides more resources for signaling negative contrasts.This aspect of retinal structure continues through the ganglion-cell level, where some ganglion-cell types have paired ON and OFF polarities (e.g., P and M in monkey, brisk-transient and brisk-sustained in rabbit and guinea pig, and X and Y in cat). Of these, the OFF cells have narrower dendritic fields and correspondingly narrower receptive-field centers than their ON partners [guinea pig (Fig. 1B), rat (5), rabbit (6), monkey (7), human (8), and smaller differences in cat (9)]. Thus, to cover the retina, OFF cells outnumber their ON partners. OFF arbors are narrower across cell classes with different spatiotemporal bandwidths (e.g., they are narrower for both midget and parasol cells in humans) (8). Specifically, whereas in fovea, midget cells are paired (one ON and one OFF per cone), beyond fovea, where midget cells collect from many bipolar cells and cones, OFF cells have smaller arbors and hence, are more numerous.While OFF cells distribute more densely than their counterpart ON cells, both types have similar receptive-field overlap (spacing is about two times the SD of a Gaussian fit to the central receptive field) (6, 10, 11). Furthermore, OFF arbors (as we quantify here) branch more densely (5) and provide similar dendritic membrane areas as ON arbors. Because the membrane density of excitatory synapses (synapses/μm 2 ) is constant across an arbor and across cell types (12, 13), OFF cells receive simi...

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Retinal ganglion cells of a given type overlap their dendritic fields such that every point in space is covered by three to four cells. We investigated what function is served by such extensive overlap. Recording from pairs of ON or OFF brisk-transient ganglion cells at photopic intensities, we confirmed that this overlap causes the Gaussian receptive field centers to be spaced at ϳ2 SDs (). This, together with response nonlinearities and variability, was just sufficient to provide an ideal observer with uniform contrast sensitivity across the retina for both threshold and suprathreshold stimuli. We hypothesized that overlap might maximize the information represented from natural images, thereby optimizing retinal performance for many tasks. Indeed, tested with natural images (which contain statistical correlations), a model ganglion cell array maximized information represented in its population responses with ϳ2 spacing, i.e., the overlap observed in the retina. Yet, tested with white noise (which lacks statistical correlations), an array maximized its information by minimizing overlap. In both cases, optimal overlap balanced greater signal-to-noise ratio (from larger receptive fields) against greater redundancy (because of larger receptive field overlap). Thus, dendritic overlap improves vision by taking optimal advantage of the statistical correlations of natural scenes.