Connectomics is a subfield of neuroscience that aims to map the brain’s intricate wiring diagram. Accurate neuron segmentation from microscopy volumes is essential for automating connectome reconstruction. However, current state-of-the-art algorithms use image-based convolutional neural networks that are limited to local neuron shape context. Thus, we introduce a new framework that reasons over global neuron shape with a novel point affinity transformer. Our framework embeds a (multi-)neuron point cloud into a fixed-length feature set from which we can decode any point pair affinities, enabling clustering neuron point clouds for automatic proofreading. We also show that the learned feature set can easily be mapped to a contrastive embedding space that enables neuron type classification using a simple KNN classifier. Our approach excels in two demanding connectomics tasks: proofreading segmentation errors and classifying neuron types. Evaluated on three benchmark datasets derived from state-of-the-art connectomes, our method outperforms point transformers, graph neural networks, and unsupervised clustering baselines.

The fluorescent glutamate indicator iGluSnFR enables imaging of neurotransmission with genetic and molecular specificity. However, existing iGluSnFR variants exhibit low in vivo signal-to-noise ratios, saturating activation kinetics and exclusion from postsynaptic densities. Using a multiassay screen in bacteria, soluble protein and cultured neurons, we generated variants with improved signal-to-noise ratios and kinetics. We developed surface display constructs that improve iGluSnFR's nanoscopic localization to postsynapses. The resulting indicator iGluSnFR3 exhibits rapid nonsaturating activation kinetics and reports synaptic glutamate release with decreased saturation and increased specificity versus extrasynaptic signals in cultured neurons. Simultaneous imaging and electrophysiology at individual boutons in mouse visual cortex showed that iGluSnFR3 transients report single action potentials with high specificity. In vibrissal sensory cortex layer 4, we used iGluSnFR3 to characterize distinct patterns of touch-evoked feedforward input from thalamocortical boutons and both feedforward and recurrent input onto L4 cortical neuron dendritic spines.

Identifying the input-output operations of neurons requires measurements of synaptic transmission simultaneously at many of a neuron’s thousands of inputs in the intact brain. To facilitate this goal, we engineered and screened 3365 variants of the fluorescent protein glutamate indicator iGluSnFR3 in neuron culture, and selected variants in the mouse visual cortex. Two variants have high sensitivity, fast activation (< 2 ms) and deactivation times tailored for recording large populations of synapses (iGluSnFR4s, 153 ms) or rapid dynamics (iGluSnFR4f, 26 ms). By imaging action-potential evoked signals on axons and visually-evoked signals on dendritic spines, we show that iGluSnFR4s/4f primarily detect local synaptic glutamate with single-vesicle sensitivity. The indicators detect a wide range of naturalistic synaptic transmission, including in the vibrissal cortex layer 4 and in hippocampal CA1 dendrites. iGluSnFR4 increases the sensitivity and scale (4s) or speed (4f) of tracking information flow in neural networks in vivo.

Understanding how neurons integrate signals from thousands of input synapses requires methods to monitor neurotransmission across many sites simultaneously. The fluorescent protein glutamate indicator iGluSnFR enables visualization of synaptic signaling, but the sensitivity, scale and speed of such measurements are limited by existing variants. Here we developed two highly sensitive fourth-generation iGluSnFR variants with fast activation and tailored deactivation rates: iGluSnFR4f for tracking rapid dynamics, and iGluSnFR4s for recording from large populations of synapses. These indicators detect glutamate with high spatial specificity and single-vesicle sensitivity in vivo. We used them to record natural patterns of synaptic transmission across multiple experimental contexts in mice, including two-photon imaging in cortical layers 1–4 and hippocampal CA1, and photometry in the midbrain. The iGluSnFR4 variants extend the speed, sensitivity and scalability of glutamate imaging, enabling direct observation of information flow through neural networks in the intact brain.

Localization of mRNA is required for protein synthesis to occur within discrete intracellular compartments. Neurons represent an ideal system for studying the precision of mRNA trafficking because of their polarized structure and the need for synapse-specific targeting. To investigate this targeting, we derived a quantitative and analytical approach. Dendritic spines were stimulated by glutamate uncaging at a diffraction-limited spot, and the localization of single β-actin mRNAs was measured in space and time. Localization required NMDA receptor activity, a dynamic actin cytoskeleton, and the transacting RNA-binding protein, Zipcode-binding protein 1 (ZBP1). The ability of the mRNA to direct newly synthesized proteins to the site of localization was evaluated using a Halo-actin reporter so that RNA and protein were detected simultaneously. Newly synthesized Halo-actin was enriched at the site of stimulation, required NMDA receptor activity, and localized preferentially at the periphery of spines. This work demonstrates that synaptic activity can induce mRNA localization and local translation of β-actin where the new actin participates in stabilizing the expanding synapse in dendritic spines.

The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them.

Genetically encoded calcium indicators (GECIs) permit imaging intracellular calcium transients. Among GECIs, the GFP-based GCaMPs are the most widely used because of their high sensitivity and rapid response to changes in intracellular calcium concentrations. Here we report that the fluorescence of GCaMPs-including GCaMP3, GCaMP5 and GCaMP6-can be converted from green to red following exposure to blue-green light (450-500 nm). This photoconversion occurs in both insect and mammalian cells and is enhanced in a low oxygen environment. The red fluorescent GCaMPs retained calcium responsiveness, albeit with reduced sensitivity. We identified several amino acid residues in GCaMP important for photoconversion and generated a GCaMP variant with increased photoconversion efficiency in cell culture. This light-induced spectral shift allows the ready labeling of specific, targeted sets of GCaMP-expressing cells for functional imaging in the red channel. Together, these findings indicate the potential for greater utility of existing GCaMP reagents, including transgenic animals.

A polarized arrangement of neuronal microtubule arrays is the foundation of membrane trafficking and subcellular compartmentalization. Conserved among both invertebrates and vertebrates, axons contain exclusively 'plus-end-out' microtubules while dendrites contain a high percentage of 'minus-end-out' microtubules, the origins of which have been a mystery. Here we show that in the dendritic growth cone contains a non-centrosomal microtubule organizing center, which generates minus-end-out microtubules along outgrowing dendrites and plus-end-out microtubules in the growth cone. RAB-11-positive endosomes accumulate in this region and co-migrate with the microtubule nucleation complex γ-TuRC. The MTOC tracks the extending growth cone by kinesin-1/UNC-116-mediated endosome movements on distal plus-end-out microtubules and dynein clusters this advancing MTOC. Critically, perturbation of the function or localization of the MTOC causes reversed microtubule polarity in dendrites. These findings unveil the endosome-localized dendritic MTOC as a critical organelle for establishing axon-dendrite polarity.

The histone variant H2A.Z is a universal mark of gene promoters, enhancers, and regulatory elements in eukaryotic chromatin. The chromatin remodeler SWR1 mediates site-specific incorporation of H2A.Z by a multi-step histone replacement reaction, evicting histone H2A-H2B from the canonical nucleosome and depositing the H2A.Z-H2B dimer. Binding of both substrates, the canonical nucleosome and the H2A.Z-H2B dimer, is essential for activation of SWR1. We found that SWR1 primarily recognizes key residues within the α2 helix in the histone-fold of nucleosomal histone H2A, a region not previously known to influence remodeler activity. Moreover, SWR1 interacts preferentially with nucleosomal DNA at superhelix location 2 on the nucleosome face distal to its linker-binding site. Our findings provide new molecular insights on recognition of the canonical nucleosome by a chromatin remodeler and have implications for ATP-driven mechanisms of histone eviction and deposition.

Animals can discriminate myriad sensory stimuli but can also generalize from learned experience. You can probably distinguish the favorite teas of your colleagues while still recognizing that all tea pales in comparison to coffee. Tradeoffs between detection, discrimination, and generalization are inherent at every layer of sensory processing. During development, specific quantitative parameters are wired into perceptual circuits and set the playing field on which plasticity mechanisms play out. A primary goal of systems neuroscience is to understand how material properties of a circuit define the logical operations— computations--that it makes, and what good these computations are for survival. A cardinal method in biology—and the mechanism of evolution--is to change a unit or variable within a system and ask how this affects organismal function. Here, we make use of our knowledge of developmental wiring mechanisms to modify hard-wired circuit parameters in the Drosophila melanogaster mushroom body and assess the functional and behavioral consequences. By altering the number of expansion layer neurons (Kenyon cells) and their dendritic complexity, we find that input number, but not cell number, tunes odor selectivity. Simple odor discrimination performance is maintained when Kenyon cell number is reduced and augmented by Kenyon cell expansion.