Neural circuits carry out complex computations that allow animals to evaluate food, select mates, move toward attractive stimuli, and move away from threats. In insects, the subesophageal zone (SEZ) is a brain region that receives gustatory, pheromonal, and mechanosensory inputs and contributes to the control of diverse behaviors, including feeding, grooming, and locomotion. Despite its importance in sensorimotor transformations, the study of SEZ circuits has been hindered by limited knowledge of the underlying diversity of SEZ neurons. Here, we generate a collection of split-GAL4 lines that provides precise genetic targeting of 138 different SEZ cell types in adult , comprising approximately one third of all SEZ neurons. We characterize the single cell anatomy of these neurons and find that they cluster by morphology into six supergroups that organize the SEZ into discrete anatomical domains. We find that the majority of local SEZ interneurons are not classically polarized, suggesting rich local processing, whereas SEZ projection neurons tend to be classically polarized, conveying information to a limited number of higher brain regions. This study provides insight into the anatomical organization of the SEZ and generates resources that will facilitate further study of SEZ neurons and their contributions to sensory processing and behavior.

The definition of neuronal type and how it relates to the transcriptome are open questions. Drosophila olfactory projection neurons (PNs) are among the best-characterized neuronal types: different PN classes target dendrites to distinct olfactory glomeruli, while PNs of the same class exhibit indistinguishable anatomical and physiological properties. Using single-cell RNA sequencing, we comprehensively characterized the transcriptomes of most PN classes and unequivocally mapped transcriptomes to specific olfactory function for six classes. Transcriptomes of closely related PN classes exhibit the largest differences during circuit assembly but become indistinguishable in adults, suggesting that neuronal subtype diversity peaks during development. Transcription factors and cell-surface molecules are the most differentially expressed genes between classes and are highly informative in encoding cell identity, enabling us to identify a new lineage-specific transcription factor that instructs PN dendrite targeting. These findings establish that neuronal transcriptomic identity corresponds with anatomical and physiological identity defined by connectivity and function.

BACKGROUND: Among the most prominent molecular constituents of a recycling synaptic vesicle is the clathrin triskelion, composed of clathrin light chain (Clc) and clathrin heavy chain (Chc). Remarkably, it remains unknown whether clathrin is strictly necessary for the stimulus-dependent re-formation of a synaptic vesicle and, conversely, whether clathrin-independent vesicle endocytosis exists at the neuronal synapse.

RESULTS: We employ FlAsH-FALI-mediated protein photoinactivation to rapidly (3 min) and specifically disrupt Clc function at the Drosophila neuromuscular junction. We first demonstrate that Clc photoinactivation does not impair synaptic-vesicle fusion. We then provide electrophysiological and ultrastructural evidence that synaptic vesicles, once fused with the plasma membrane, cannot be re-formed after Clc photoinactivation. Finally, we demonstrate that stimulus-dependent membrane internalization occurs after Clc photoinactivation. However, newly internalized membrane fails to resolve into synaptic vesicles. Rather, newly internalized membrane forms large and extensive internal-membrane compartments that are never observed at a wild-type synapse.

CONCLUSIONS: We make three major conclusions. (1) FlAsH-FALI-mediated protein photoinactivation rapidly and specifically disrupts Clc function with no effect on synaptic-vesicle fusion. (2) Synaptic-vesicle re-formation does not occur after Clc photoinactivation. By extension, clathrin-independent "kiss-and-run" endocytosis does not sustain synaptic transmission during a stimulus train at this synapse. (3) Stimulus-dependent, clathrin-independent membrane internalization exists at this synapse, but it is unable to generate fusion-competent, small-diameter synaptic vesicles.

Preclinical animal models have provided strong evidence that estrogen (E) therapy (ET) enhances cognition and induces spinogenesis in neuronal circuits. However, clinical studies have been inconsistent, with some studies revealing adverse effects of ET, including an increased risk of dementia. In an effort to bridge this disconnect between the preclinical and clinical data, we have developed a nonhuman primate (NHP) model of ET combined with high-resolution dendritic spine analysis of dorsolateral prefrontal cortical (dlPFC) neurons. Previously, we reported cyclic ET in aged, ovariectomized NHPs increased spine density on dlPFC neurons. Here, we report that monkeys treated with cyclic E treatment paired with cyclic progesterone (P), continuous E combined with P (either cyclic or continuous), or unopposed continuous E failed to increase spines on dlPFC neurons. Given that the most prevalent form of ET prescribed to women is a combined and continuous E and P, these data bring into convergence the human neuropsychological findings and preclinical neurobiological evidence that standard hormone therapy in women is unlikely to yield the synaptic benefit presumed to underlie the cognitive enhancement reported in animal models.

Detecting meaningful structure in neural activity and connectivity data is challenging in the presence of hidden nonlinearities, where traditional eigenvalue-based methods may be misleading. We introduce a novel approach to matrix analysis, called clique topology, that extracts features of the data invariant under nonlinear monotone transformations. These features can be used to detect both random and geometric structure, and depend only on the relative ordering of matrix entries. We then analyzed the activity of pyramidal neurons in rat hippocampus, recorded while the animal was exploring a 2D environment, and confirmed that our method is able to detect geometric organization using only the intrinsic pattern of neural correlations. Remarkably, we found similar results during nonspatial behaviors such as wheel running and rapid eye movement (REM) sleep. This suggests that the geometric structure of correlations is shaped by the underlying hippocampal circuits and is not merely a consequence of position coding. We propose that clique topology is a powerful new tool for matrix analysis in biological settings, where the relationship of observed quantities to more meaningful variables is often nonlinear and unknown.

The antennal lobe (AL) is the primary structure in the Drosophila brain that relays odor information from the antennae to higher brain centers. The characterization of uniglomerular projection neurons (PNs) and some local interneurons has facilitated our understanding of olfaction; however, many other AL neurons remain unidentified. Because neuron types are mostly specified by lineage and temporal origins, we use the MARCM techniques with a set of enhancer-trap GAL4 lines to perform systematical lineage analysis to characterize neuron morphologies, lineage origin and birth timing in the three AL neuron lineages that contain GAL4-GH146-positive PNs: anterodorsal, lateral and ventral lineages. The results show that the anterodorsal lineage is composed of pure uniglomerular PNs that project through the inner antennocerebral tract. The ventral lineage produces uniglomerular and multiglomerular PNs that project through the middle antennocerebral tract. The lateral lineage generates multiple types of neurons, including uniglomeurlar PNs, diverse atypical PNs, various types of AL local interneurons and the neurons that make no connection within the ALs. Specific neuron types in all three lineages are produced in specific time windows, although multiple neuron types in the lateral lineage are made simultaneously. These systematic cell lineage analyses have not only filled gaps in the olfactory map, but have also exemplified additional strategies used in the brain to increase neuronal diversity.

BACKGROUND: The insect brain can be divided into neuropils that are formed by neurites of both local and remote origin. The complexity of the interconnections obscures how these neuropils are established and interconnected through development. The Drosophila central brain develops from a fixed number of neuroblasts (NBs) that deposit neurons in regional clusters. RESULTS: By determining individual NB clones and pursuing their projections into specific neuropils, we unravel the regional development of the brain neural network. Exhaustive clonal analysis revealed 95 stereotyped neuronal lineages with characteristic cell-body locations and neurite trajectories. Most clones show complex projection patterns, but despite the complexity, neighboring clones often coinnervate the same local neuropil or neuropils and further target a restricted set of distant neuropils. CONCLUSIONS: These observations argue for regional clonal development of both neuropils and neuropil connectivity throughout the Drosophila central brain.

We describe the isolation of a cloned DNA segment carrying unique sequences from the white locus of Drosophila melanogaster. Sequences within the cloned segment are shown to hybridize in situ to the white locus region on the polytene chromosomes of both wild-type strains and strains carrying chromosomal rearrangements whose breakpoints bracket the white locus. We further show that two small deficiency mutations, deleting white locus genetic elements but not those of complementation groups contiguous to white, delete the genomic sequences corresponding to a portion of the cloned segment. The strategy we have employed to isolate this cloned segment exploits the existence of an allele at the white locus containing a copy of a previously cloned transposable, reiterated DNA sequence element. We describe a simple, rapid method for retrieving cloned segments carrying a copy of the transposable element together with contiguous sequences corresponding to this allele. The strategy described is potentially general and we discuss its application to the cloning of the DNA sequences of other genes in Drosophila, including those identified only by genetic analysis and for which no RNA product is known.

Multipath propagation in shallow water can lead to mismatch losses when single-path replicas are usedfor horizontal array beamforming.Matched field processing(MFP) seeks to remedy this by using full-fieldacoustic propagationmodels to predict the multipath arrival structure. Ideally MFP can give source localization in range and depth as well as detection gains but robustly estimating range and depth is difficult in practice. The approach described here seeks to collapse full-field replica outputs to bearing which is robustly estimated while retaining any signal gains provided by the full-field model.Clusteranalysis is used to group together full-field replicas with similar responses. This yields a less redundant “sampled field” describing a set of representative multipath structures for each bearing. A detection algorithm is introduced that uses clustering to collapse beamformer outputs to bearing such that signal gains are retained while increases in the noise floor are minimized. Horizontal array data from SWELLEX-96 are used to demonstrate the detection benefits of sampled field as compared to single-pathbeamforming.

Advances in single-cell RNA-sequencing technology have resulted in a wealth of studies aiming to identify transcriptomic cell types in various biological systems. There are multiple experimental approaches to isolate and profile single cells, which provide different levels of cellular and tissue coverage. In addition, multiple computational strategies have been proposed to identify putative cell types from single-cell data. From a data generation perspective, recent single-cell studies can be classified into two groups: those that distribute reads shallowly over large numbers of cells and those that distribute reads more deeply over a smaller cell population. Although there are advantages to both approaches in terms of cellular and tissue coverage, it is unclear whether different computational cell type identification methods are better suited to one or the other experimental paradigm. This study reviews three cell type clustering algorithms, each representing one of three broad approaches, and finds that PCA-based algorithms appear most suited to low read depth data sets, whereas gene clustering-based and biclustering algorithms perform better on high read depth data sets. In addition, highly related cell classes are better distinguished by higher-depth data, given the same total number of reads; however, simultaneous discovery of distinct and similar types is better served by lower-depth, higher cell number data. Overall, this study suggests that the depth of profiling should be determined by initial assumptions about the diversity of cells in the population, and that the selection of clustering algorithm(s) is subsequently based on the depth of profiling will allow for better identification of putative transcriptomic cell types.