Chemotaxis is a powerful paradigm to investigate how nervous systems represent and integrate changes in sensory signals to direct navigational decisions. In the Drosophila melanogaster larva, chemotaxis mainly consists of an alternation of distinct behavioral modes: runs and directed turns. During locomotion, turns are triggered by the integration of temporal changes in the intensity of the stimulus. Upon completion of a turning maneuver, the direction of motion is typically realigned toward the odor gradient. While the anatomy of the peripheral olfactory circuits and the locomotor system of the larva are reasonably well documented, the neural circuits connecting the sensory neurons to the motor neurons remain unknown. We combined a loss-of-function behavioral screen with optogenetics-based clonal gain-of-function manipulations to identify neurons that are necessary and sufficient for the initiation of reorientation maneuvers in odor gradients. Our results indicate that a small subset of neurons residing in the subesophageal zone controls the rate of transition from runs to turns-a premotor function compatible with previous observations made in other invertebrates. After having shown that this function pertains to the processing of inputs from different sensory modalities (olfaction, vision, thermosensation), we conclude that the subesophageal zone operates as a general premotor center that regulates the selection of different behavioral programs based on the integration of sensory stimuli. The present analysis paves the way for a systematic investigation of the neural computations underlying action selection in a miniature brain amenable to genetic manipulations.

Fluorescent in-situ hybridization (FISH)-based methods are powerful tools to study molecular processes with subcellular resolution, relying on accurate identification and localization of diffraction-limited spots in microscopy images. We developed the Radial Symmetry-FISH (RS-FISH) software that accurately, robustly, and quickly detects single-molecule spots in two and three dimensions, making it applicable to several key assays, including single-molecule FISH (smFISH), spatial transcriptomics, and spatial genomics. RS-FISH allows interactive parameter tuning and scales to large sets of images as well as tera-byte sized image volumes such as entire brain scans using straight-forward distributed processing on workstations, clusters, and in the cloud.

Optogenetic activators with red-shifted excitation spectra, such as Chrimson, have significantly advanced Drosophila neuroscience. However, until recently, available optogenetic inhibitors required shorter activation wavelengths, which don’t penetrate tissue as effectively and are stronger visual stimuli to the animal, potentially confounding behavioral results. Here, we assess the efficacy of two newly identified anion-conducting channelrhodopsins with spectral sensitivities similar to Chrimson: A1ACR and HfACR (RubyACRs). Electrophysiology and functional imaging confirmed that RubyACRs effectively hyperpolarize neurons, with stronger and faster effects than the widely used inhibitor GtACR1. Activation of RubyACRs led to circuit-specific behavioral changes in three different neuronal groups. In glutamatergic motor neurons, activating RubyACRs suppressed adult locomotor activity. In PPL1-γ1pedc dopaminergic neurons, pairing odors with RubyACR activation during learning produced odor responses consistent with synaptic silencing. Finally, activation of RubyACRs in the pIP10 neuron suppressed pulse song during courtship. Together, these results demonstrate that RubyACRs are effective and reliable tools for neuronal inhibition in Drosophila, expanding the optogenetic toolkit for circuit dissection in freely behaving animals.

Preprint: https://www.biorxiv.org/content/early/2025/06/15/2025.06.13.659144

The salivary gland undergoes branching morphogenesis to elaborate into a tree-like structure with numerous saliva-secreting acinar units, all joined by a hierarchical ductal system. The expansive epithelial surface generated by branching morphogenesis serves as the structural basis for the efficient production and delivery of saliva. Here, we elucidate the process of salivary gland morphogenesis, emphasizing the role of mechanics. Structurally, the developing salivary gland is characterized by a stratified epithelium tightly encased by the basement membrane, which is in turn surrounded by a mesenchyme consisting of a dense network of interstitial matrix and mesenchymal cells. Diverse cell types and extracellular matrices bestow this developing organ with organized, yet spatially varied mechanical properties. For instance, the surface epithelial sheet of the bud is highly fluidic due to its high cell motility and weak cell-cell adhesion, rendering it highly pliable. In contrast, the inner core of the bud is more rigid, characterized by reduced cell motility and strong cell-cell adhesion, which likely provide structural support for the tissue. The interactions between the surface epithelial sheet and the inner core give rise to budding morphogenesis. Furthermore, the basement membrane and the mesenchyme offer mechanical constraints that could play a pivotal role in determining the higher-order architecture of a fully mature salivary gland.

Although mesenchyme is essential for inducing the epithelium of ectodermal organs, its precise role in organ-specific epithelial fate determination remains poorly understood. To elucidate the roles of tissue interactions in cellular differentiation, we performed single-cell RNA sequencing and imaging analyses on recombined tissues, where mesenchyme and epithelium were switched ex vivo between two types of embryonic mouse salivary glands: the parotid gland (a serous gland) and the submandibular gland (a predominantly mucous gland). We found partial induction of molecules that define gland-specific acinar and myoepithelial cells in recombined salivary epithelium. The parotid epithelium recombined with submandibular mesenchyme began to express mucous acinar genes not intrinsic to the parotid gland. While myoepithelial cells do not normally line parotid acini, newly induced myoepithelial cells densely populated recombined parotid acini. However, mucous acinar and myoepithelial markers continued to be expressed in submandibular epithelial cells recombined with parotid mesenchyme. Consequently, some epithelial cells appeared to be plastic, such that their fate could still be modified in response to mesenchymal signaling, whereas other epithelial cells appeared to be already committed to a specific fate. We also discovered evidence for bidirectional induction: transcriptional changes were observed not only in the epithelium but also in the mesenchyme after heterotypic tissue recombination. For example, parotid epithelium induced the expression of muscle-related genes in submandibular fibroblasts that began to mimic parotid fibroblast gene expression. These studies provide the first comprehensive unbiased molecular characterization of tissue recombination approaches exploring the regulation of cell fate.

Metric learning seeks a transformation of the feature space that enhances prediction quality for a given task. In this work we provide PAC-style sample complexity rates for supervised metric learning. We give matching lower- and upper-bounds showing that sample complexity scales with the representation dimension when no assumptions are made about the underlying data distribution. In addition, by leveraging the structure of the data distribution, we provide rates fine-tuned to a specific notion of the intrinsic complexity of a given dataset, allowing us to relax the dependence on representation dimension. We show both theoretically and empirically that augmenting the metric learning optimization criterion with a simple norm-based regularization is important and can help adapt to a dataset’s intrinsic complexity yielding better generalization, thus partly explaining the empirical success of similar regularizations reported in previous works.

The generation of four-dimensional (4D) confocal datasets; consisting of 3D image sequences over time; provides an excellent methodology to capture cellular behaviors involved in developmental processes.  The ability to track and follow cell movements is limited by sample movements that occur due to drift of the sample or, in some cases, growth during image acquisition. Tracking cells in datasets affected by drift and/or growth will incorporate these movements into any analysis of cell position. This may result in the apparent movement of static structures within the sample. Therefore prior to cell tracking, any sample drift should be corrected. Using the open source Fiji distribution (1)  of ImageJ (2,3) and the incorporated LOCI tools (4), we developed the Correct 3D drift plug-in to remove erroneous sample movement in confocal datasets. This protocol effectively compensates for sample translation or alterations in focal position by utilizing phase correlation to register each time-point of a four-dimensional confocal datasets while maintaining the ability to visualize and measure cell movements over extended time-lapse experiments.

We consider the problem of estimating the parameters of a linear autoregressive model with sub-Gaussian innovations from a limited sequence of consecutive observations. Assuming that the parameters are compressible, we analyze the performance of the ℓ1-regularized least squares as well as a greedy estimator of the parameters and characterize the sampling trade-offs required for stable recovery in the non-asymptotic regime. Our results extend those of compressed sensing for linear models where the covariates are i.i.d. and independent of the observation history to autoregressive processes with highly inter-dependent covariates. We also derive sufficient conditions on the sparsity level that guarantee the minimax optimality of the ℓ1-regularized least squares estimate. Applying these techniques to simulated data as well as real-world datasets from crude oil prices and traffic speed data confirm our predicted theoretical performance gains in terms of estimation accuracy and model selection.

The nociceptive flexor withdrawal reflex has an august place in the history of neuroscience. In this issue of Neuron, Hilde et al. (2016) advance our understanding of this reflex by characterizing the molecular identity and circuit connectivity of component interneurons. They assess how a DNA-binding factor Satb2 controls cell position, molecular identity, pre-and postsynaptic targeting, and function of a population of inhibitory sensory relay interneurons that serve to integrate both proprioceptive and nociceptive afferent information.

The nociceptive flexor withdrawal reflex has an august place in the history of neuroscience. In this issue of Neuron, Hilde et al. (2016) advance our understanding of this reflex by characterizing the molecular identity and circuit connectivity of component interneurons. They assess how a DNA-binding factor Satb2 controls cell position, molecular identity, pre-and postsynaptic targeting, and function of a population of inhibitory sensory relay interneurons that serve to integrate both proprioceptive and nociceptive afferent information.