The connectome of an insect brain.

Brains contain networks of interconnected neurons and so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an entire insect brain (Drosophila larva) with rich behavior, including learning, value computation, and action selection, comprising 3016 neurons and 548,000 synapses. We characterized neuron types, hubs, feedforward and feedback pathways, as well as cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain's most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled state-of-the-art deep learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.

A global timing mechanism regulates cell-type-specific wiring programmes.

The assembly of neural circuits is dependent on precise spatiotemporal expression of cell recognition molecules1-5. Factors controlling cell type specificity have been identified6-8, but how timing is determined remains unknown. Here we describe induction of a cascade of transcription factors by a steroid hormone (ecdysone) in all fly visual system neurons spanning target recognition and synaptogenesis. We demonstrate through single-cell sequencing that the ecdysone pathway regulates the expression of a common set of targets required for synaptic maturation and cell-type-specific targets enriched for cell-surface proteins regulating wiring specificity. Transcription factors in the cascade regulate the expression of the same wiring genes in complex ways, including activation in one cell type and repression in another. We show that disruption of the ecdysone pathway generates specific defects in dendritic and axonal processes and synaptic connectivity, with the order of transcription factor expression correlating with sequential steps in wiring. We also identify shared targets of a cell-type-specific transcription factor and the ecdysone pathway that regulate specificity. We propose that neurons integrate a global temporal transcriptional module with cell-type-specific transcription factors to generate different cell-type-specific patterns of cell recognition molecules regulating wiring.

Circuits for integrating learned and innate valences in the insect brain.

Animal behavior is shaped both by evolution and by individual experience. Parallel brain pathways encode innate and learned valences of cues, but the way in which they are integrated during action-selection is not well understood. We used electron microscopy to comprehensively map with synaptic resolution all neurons downstream of all mushroom body (MB) output neurons (encoding learned valences) and characterized their patterns of interaction with lateral horn (LH) neurons (encoding innate valences) in Drosophila larva. The connectome revealed multiple convergence neuron types that receive convergent MB and LH inputs. A subset of these receives excitatory input from positive-valence MB and LH pathways and inhibitory input from negative-valence MB pathways. We confirmed functional connectivity from LH and MB pathways and behavioral roles of two of these neurons. These neurons encode integrated odor value and bidirectionally regulate turning. Based on this, we speculate that learning could potentially skew the balance of excitation and inhibition onto these neurons and thereby modulate turning. Together, our study provides insights into the circuits that integrate learned and innate valences to modify behavior.

Comparative Connectomics Reveals How Partner Identity, Location, and Activity Specify Synaptic Connectivity in Drosophila.

The mechanisms by which synaptic partners recognize each other and establish appropriate numbers of connections during embryonic development to form functional neural circuits are poorly understood. We combined electron microscopy reconstruction, functional imaging of neural activity, and behavioral experiments to elucidate the roles of (1) partner identity, (2) location, and (3) activity in circuit assembly in the embryonic nerve cord of Drosophila. We found that postsynaptic partners are able to find and connect to their presynaptic partners even when these have been shifted to ectopic locations or silenced. However, orderly positioning of axon terminals by positional cues and synaptic activity is required for appropriate numbers of connections between specific partners, for appropriate balance between excitatory and inhibitory connections, and for appropriate functional connectivity and behavior. Our study reveals with unprecedented resolution the fine connectivity effects of multiple factors that work together to control the assembly of neural circuits.

Transcriptional Programs of Circuit Assembly in the Drosophila Visual System.

Precise patterns of synaptic connections between neurons are encoded in their genetic programs. Here, we use single-cell RNA sequencing to profile neuronal transcriptomes at multiple stages in the developing Drosophila visual system. We devise an efficient strategy for profiling neurons at multiple time points in a single pool, thereby minimizing batch effects and maximizing the reliability of time-course data. A transcriptional atlas spanning multiple stages is generated, including more than 150 distinct neuronal populations; of these, 88 are followed through synaptogenesis. This analysis reveals a common (pan-neuronal) program unfolding in highly coordinated fashion in all neurons, including genes encoding proteins comprising the core synaptic machinery and membrane excitability. This program is overlaid by cell-type-specific programs with diverse cell recognition molecules expressed in different combinations and at different times. We propose that a pan-neuronal program endows neurons with the competence to form synapses and that cell-type-specific programs control synaptic specificity.

Recurrent architecture for adaptive regulation of learning in the insect brain.

Dopaminergic neurons (DANs) drive learning across the animal kingdom, but the upstream circuits that regulate their activity and thereby learning remain poorly understood. We provide a synaptic-resolution connectome of the circuitry upstream of all DANs in a learning center, the mushroom body of Drosophila larva. We discover afferent sensory pathways and a large population of neurons that provide feedback from mushroom body output neurons and link distinct memory systems (aversive and appetitive). We combine this with functional studies of DANs and their presynaptic partners and with comprehensive circuit modeling. We find that DANs compare convergent feedback from aversive and appetitive systems, which enables the computation of integrated predictions that may improve future learning. Computational modeling reveals that the discovered feedback motifs increase model flexibility and performance on learning tasks. Our study provides the most detailed view to date of biological circuit motifs that support associative learning.

Hybridization-based antibody cDNA recovery for the production of recombinant antibodies identified by repertoire sequencing.

High-throughput sequencing of the antibody repertoire is enabling a thorough analysis of B cell diversity and clonal selection, which may improve the novel antibody discovery process. Theoretically, an adequate bioinformatic analysis could allow identification of candidate antigen-specific antibodies, requiring their recombinant production for experimental validation of their specificity. Gene synthesis is commonly used for the generation of recombinant antibodies identified in silico. Novel strategies that bypass gene synthesis could offer more accessible antibody identification and validation alternatives. We developed a hybridization-based recovery strategy that targets the complementarity-determining region 3 (CDRH3) for the enrichment of cDNA of candidate antigen-specific antibody sequences. Ten clonal groups of interest were identified through bioinformatic analysis of the heavy chain antibody repertoire of mice immunized with hen egg white lysozyme (HEL). cDNA from eight of the targeted clonal groups was recovered efficiently, leading to the generation of recombinant antibodies. One representative heavy chain sequence from each clonal group recovered was paired with previously reported anti-HEL light chains to generate full antibodies, later tested for HEL-binding capacity. The recovery process proposed represents a simple and scalable molecular strategy that could enhance antibody identification and specificity assessment, enabling a more cost-efficient generation of recombinant antibodies.