Intelligent behavior involves associations between high-dimensional sensory representations and behaviorally relevant qualities such as valence. Learning of associations involves plasticity of excitatory connectivity, but it remains poorly understood how information flow is reorganized in networks and how inhibition contributes to this process. We trained adult zebrafish in an appetitive odor discrimination task and analyzed odor representations in a specific compartment of the posterior zone of the dorsal telencephalon (Dp), the homolog of mammalian olfactory cortex. Associative conditioning enhanced responses with a preference for the positively conditioned odor. Moreover, conditioning systematically remapped odor representations along an axis in coding space that represented attractiveness (valence). Interindividual variations in this mapping predicted variations in behavioral odor preference. Photoinhibition of interneurons resulted in specific modifications of odor representations that mirrored effects of conditioning and reduced experience-dependent, interindividual variations in odor-valence mapping. These results reveal an individualized odor-to-valence map that is shaped by inhibition and reorganized during learning.

C. elegans is the premier system for the systematic analysis of cell fate specification and morphogenetic events during embryonic development. One challenge is that embryogenesis dynamically unfolds over a period of about 13 h; this half day-long timescale has constrained the scope of experiments by limiting the number of embryos that can be imaged. Here, we describe a semi-high-throughput protocol that allows for the simultaneous 3D time-lapse imaging of development in 80–100 embryos at moderate time resolution, from up to 14 different conditions, in a single overnight run. The protocol is straightforward and can be implemented by any laboratory with access to a microscope with point visiting capacity. The utility of this protocol is demonstrated by using it to image two custom-built strains expressing fluorescent markers optimized to visualize key aspects of germ-layer specification and morphogenesis. To analyze the data, a custom program that crops individual embryos out of a broader field of view in all channels, z-steps, and timepoints and saves the sequences for each embryo into a separate tiff stack was built. The program, which includes a user-friendly graphical user interface (GUI), streamlines data processing by isolating, pre-processing, and uniformly orienting individual embryos in preparation for visualization or automated analysis. Also supplied is an ImageJ macro that compiles individual embryo data into a multi-panel file that displays maximum intensity fluorescence projection and brightfield images for each embryo at each time point. The protocols and tools described herein were validated by using them to characterize embryonic development following knock-down of 40 previously described developmental genes; this analysis visualized previously annotated developmental phenotypes and revealed new ones. In summary, this work details a semi-high-throughput imaging method coupled with a cropping program and ImageJ visualization tool that, when combined with strains expressing informative fluorescent markers, greatly accelerates experiments to analyze embryonic development.

Recent work has highlighted that many types of variables are represented in each neocortical area. How can these many neural representations be organized together without interference, and coherently maintained/updated through time? We recorded from large neural populations in posterior cortices as mice performed a complex, dynamic task involving multiple interrelated variables. The neural encoding implied that correlated task variables were represented by uncorrelated modes in an information-coding subspace. We show via theory that this can enable optimal decoding directions to be insensitive to neural noise levels. Across posterior cortex, principles of efficient coding thus applied to task-specific information, with neural-population modes as the encoding unit. Remarkably, this encoding function was multiplexed with rapidly changing, sequential neural dynamics, yet reliably followed slow changes in task-variable correlations through time. We can explain this as due to a mathematical property of high-dimensional spaces that the brain might exploit as a temporal scaffold.

Our understanding of the mechanisms of neural circuit assembly is far from complete. Identification of wiring molecules with novel mechanisms of action will provide insights into how complex and heterogeneous neural circuits assemble during development. In the olfactory system, 50 classes of olfactory receptor neurons (ORNs) make precise synaptic connections with 50 classes of partner projection neurons (PNs). Here, we performed an RNA interference screen for cell surface molecules and identified the leucine-rich repeat-containing transmembrane protein known as Fish-lips (Fili) as a novel wiring molecule in the assembly of the olfactory circuit. Fili contributes to the precise axon and dendrite targeting of a small subset of ORN and PN classes, respectively. Cell-type-specific expression and genetic analyses suggest that Fili sends a transsynaptic repulsive signal to neurites of nonpartner classes that prevents their targeting to inappropriate glomeruli in the antennal lobe.

Fiber-optic epifluorescence imaging with one-photon excitation benefits from its ease of use, cheap light sources, and full-frame acquisition, which enables it for favorable temporal resolution of image acquisition. However, it suffers from a lack of robustness against autofluorescence and light scattering. Moreover, it cannot easily eliminate the out-of-focus background, which generally results in low-contrast images. In order to overcome these limitations, we have implemented fast out-of-phase imaging after optical modulation (Speed OPIOM) for dynamic contrast in fluorescence endomicroscopy. Using a simple and cheap optical-fiber bundle-based endomicroscope integrating modulatable light sources, we first showed that Speed OPIOM provides intrinsic optical sectioning, which restricts the observation of fluorescent labels at targeted positions within a sample. We also demonstrated that this imaging protocol efficiently eliminates the interference of autofluorescence arising from both the fiber bundle and the specimen in several biological samples. Finally, we could perform multiplexed observations of two spectrally similar fluorophores differing by their photoswitching dynamics. Such attractive features of Speed OPIOM in fluorescence endomicroscopy should find applications in bioprocessing, clinical diagnostics, plant observation, and surface imaging.

Neuromodulation plays a critical role in brain function in both health and disease, and new tools that capture neuromodulation with high spatial and temporal resolution are needed. Here, we introduce a synthetic catecholamine nanosensor with fluorescent emission in the near infrared range (1000–1300 nm), near infrared catecholamine nanosensor (nIRCat). We demonstrate that nIRCats can be used to measure electrically and optogenetically evoked dopamine release in brain tissue, revealing hotspots with a median size of 2 µm. We also demonstrated that nIRCats are compatible with dopamine pharmacology and show D2 autoreceptor modulation of evoked dopamine release, which varied as a function of initial release magnitude at different hotspots. Together, our data demonstrate that nIRCats and other nanosensors of this class can serve as versatile synthetic optical tools to monitor neuromodulatory neurotransmitter release with high spatial resolution.

Understanding circuit organization depends on identification of cell types. Recent advances in transcriptional profiling methods have enabled classification of cell types by their gene expression. While exceptionally powerful and high throughput, the ground-truth validation of these methods is difficult: if cell type is unknown, how does one assess whether a given analysis accurately captures neuronal identity? To shed light on the capabilities and limitations of solely using transcriptional profiling for cell type classification, we performed two forms of transcriptional profiling – RNA-seq and quantitative RT-PCR, in single, unambiguously identified neurons from two small crustacean networks: the stomatogastric and cardiac ganglia. We then combined our knowledge of cell type with unbiased clustering analyses and supervised machine learning to determine how accurately functionally-defined neuron types can be classified by expression profile alone. Our results demonstrate that expression profile is able to capture neuronal identity most accurately when combined with multimodal information that allows for post-hoc grouping so analysis can proceed from a supervised perspective. Solely unsupervised clustering can lead to misidentification and an inability to distinguish between two or more cell types. Therefore, our study supports the general utility of cell identification by transcriptional profiling, but adds a caution: it is difficult or impossible to know under what conditions transcriptional profiling alone is capable of assigning cell identity. Only by combining multiple modalities of information such as physiology, morphology or innervation target can neuronal identity be unambiguously determined.SIGNIFICANCE STATEMENT Single cell transcriptional profiling has become a widespread tool in cell identification, particularly in the nervous system, based on the notion that genomic information determines cell identity. However, many cell type classification studies are unconstrained by other cellular attributes (e.g., morphology, physiology). Here, we systematically test how accurately transcriptional profiling can assign cell identity to well-studied anatomically- and functionally-identified neurons in two small neuronal networks. While these neurons clearly possess distinct patterns of gene expression across cell types, their expression profiles are not sufficient to unambiguously confirm their identity. We suggest that true cell identity can only be determined by combining gene expression data with other cellular attributes such as innervation pattern, morphology, or physiology.

Interactions between proteins play an essential role in metabolic and signaling pathways, cellular processes and organismal systems. We report the development of splitFAST, a fluorescence complementation system for the visualization of transient protein-protein interactions in living cells. Engineered from the fluorogenic reporter FAST (Fluorescence-Activating and absorption-Shifting Tag), which specifically and reversibly binds fluorogenic hydroxybenzylidene rhodanine (HBR) analogs, splitFAST displays rapid and reversible complementation, allowing the real-time visualization of both the formation and the dissociation of a protein assembly.

Plexins exhibit multitudinous, evolutionarily conserved functions in neural development. How Plexins employ their diverse structural motifs in vivo to perform distinct roles is unclear. We previously reported that Plexin B (PlexB) controls multiple steps during the assembly of the olfactory circuit (Li et al., 2018b). Here, we systematically mutagenized structural motifs of PlexB and examined the function of these variants in these multiple steps: axon fasciculation, trajectory choice, and synaptic partner selection. We found that the extracellular Sema domain is essential for all three steps, the catalytic site of the intracellular RapGAP is engaged in none, and the intracellular GTPase-binding motifs are essential for trajectory choice and synaptic partner selection, but are dispensable for fasciculation. Moreover, extracellular PlexB cleavage serves as a regulatory mechanism of PlexB signaling. Thus, the divergent roles of PlexB motifs in distinct steps of neural development contribute to its functional versatility in neural circuit assembly.

Single neurons in visual cortex provide unreliable measurements of visual features due to their high trial-to-trial variability. It is not known if this “noise” extends its effects over large neural populations to impair the global encoding of sensory stimuli. We recorded simultaneously from ∼20,000 neurons in mouse visual cortex and found that the neural population had discrimination thresholds of 0.3° in an orientation decoding task. These thresholds are ∼100 times smaller than those reported behaviorally in mice. The discrepancy between neural and behavioral discrimination could not be explained by the types of stimuli we used, by behavioral states or by the sequential nature of trial-by-trial perceptual learning tasks. These results imply that the limits of sensory perception in mice are not set by neural noise in sensory cortex, but by the limitations of downstream decoders.