Sensory neurons innervating the lower airway provide essential feedback information that regulates respiratory physiology. These neurons synapse with second-order neurons in the central nervous system, which project directly or indirectly to the respiratory and autonomic centers. Both primary sensory neurons and second-order neurons within these circuits exhibit significant heterogeneity, and the precise roles of individual neuronal subtypes in coding the airway's internal states and modulating respiratory and autonomic outputs remain incompletely understood. In this review, we summarize recent advances in understanding the neuronal diversity along sensory circuits of the lower airway and their physiological functions. We also highlight the challenges in elucidating the roles of specific neuronal subtypes due to the extensive molecular and anatomical diversity among these neurons. Improving targeting specificity for neuronal manipulation, combined with the development of a comprehensive connectivity map, will be critical for revealing the coding and wiring logics that underlie the precise control of respiratory physiology.

Biological neural networks seem to efficiently select and represent task-relevant features of their inputs, an ability that is highly sought after also in artificial networks. A lot of work has gone into identifying such representations in both sensory and motor systems; however, less is understood about how representations form during complex learning conditions to support behavior, especially in higher associative brain areas. Our work shows that the hippocampus maintains a robust hierarchical representation of task variables and that this structure can support new learning through minimal changes to the neural representations.

Preprint: https://www.doi.org/10.1101/2024.08.21.608911

The courtship song of Drosophila melanogaster has long served as an excellent model system for studies of animal communication and differences in courtship song have been demonstrated among populations and between species. Here, we report that flies of African and European origin, which diverged approximately 13,000 years ago, show significant genetic differentiation in the use of slow versus fast pulse song. Using a combination of quantitative trait mapping and population genetic analysis we detected a single strong QTL underlying this trait and we identified candidate genes that may contribute to the evolution of this trait. Song trait variation between parental strains of our recombinant inbred panel enabled detection of genomic intervals associated with six additional song traits, some of which include known courtship-related genes. These findings improve the prospects for further genetic insights into the evolution of reproductive behavior and the biology underlying courtship song.

bioRxiv Preprint: https://www.biorxiv.org/content/early/2024/05/17/2024.05.14.594231

To survive, animals must be able quickly infer the state of their surroundings. For example, to successfully escape an approaching predator, prey must quickly estimate the direction of approach from incoming sensory stimuli and guide their behavior accordingly. Such rapid inferences are particularly challenging because the animal has only a brief window of time to gather sensory stimuli, and yet the accuracy of inference is critical for survival. Due to evolutionary pressures, nervous systems have likely evolved effective computational strategies that enable accurate inferences under strong time limitations. Traditionally, the relationship between the speed and accuracy of inference has been described by the “speed-accuracy tradeoff” (SAT), which quantifies how the average performance of an ideal observer improves as the observer has more time to collect incoming stimuli. While this trial-averaged description can reasonably account for individual inferences made over long timescales, it does not capture individual inferences on short timescales, when trial-to-trial variability gives rise to diverse patterns of error dynamics. We show that an ideal observer can exploit this single-trial structure by adaptively tracking the dynamics of its belief about the state of the environment, which enables it to speed its own inferences and more reliably track its own error, but also causes it to violate the SAT. We show that these features can be used to improve overall performance during rapid escape. The resulting behavior qualitatively reproduces features of escape behavior in the fruit fly Drosophila melanogaster, whose escapes have presumably been highly optimized by natural selection.

The past 20 years have seen a paradigm-shifting explosion of new optical microscopy technologies aimed at uncovering fundamental biological insights. Yet only a small portion 'cross the finish line' into wide adoption by the life science community. We contend that this is not primarily due to a lack of technical prowess or utility. Rather, many risks can conspire to derail the adoption of potentially disruptive technologies. One way to address these challenges is to de-risk paradigm-shifting inventions within open-access technology incubators. Here we detail the framework needed to shepherd innovative microscopy techniques through the often-treacherous adoption landscape to enable transformative scientific output.

Uncertainty is a fundamental aspect of the natural environment, requiring the brain to infer and integrate noisy signals to guide behavior effectively. Sampling-based inference has been proposed as a mechanism for dealing with uncertainty, particularly in early sensory processing. However, it is unclear how to reconcile sampling-based methods with operational principles of higher-order brain areas, such as attractor dynamics of persistent neural representations. In this study, we present a spiking neural network model for the head-direction (HD) system that combines sampling-based inference with attractor dynamics. To achieve this, we derive the required spiking neural network dynamics and interactions to perform sampling from a large family of probability distributions - including variables encoded with Poisson noise. We then propose a method that allows the network to update its estimate of the current head direction by integrating angular velocity samples - derived from noisy inputs - with a pull towards a circular manifold, thereby maintaining consistent attractor dynamics. This model makes specific, testable predictions about the HD system that can be examined in future neurophysiological experiments: it predicts correlated subthreshold voltage fluctuations; distinctive short- and long-term firing correlations among neurons; and characteristic statistics of the movement of the neural activity "bump" representing the head direction. Overall, our approach extends previous theories on probabilistic sampling with spiking neurons, offers a novel perspective on the computations responsible for orientation and navigation, and supports the hypothesis that sampling-based methods can be combined with attractor dynamics to provide a viable framework for studying neural dynamics across the brain.

Polarized fluorescence microscopy is a valuable tool for measuring molecular orientations in biological samples, but techniques for recovering three-dimensional orientations and positions of fluorescent ensembles are limited. We report a polarized dual-view light-sheet system for determining the diffraction-limited three-dimensional distribution of the orientations and positions of ensembles of fluorescent dipoles that label biological structures. We share a set of visualization, histogram, and profiling tools for interpreting these positions and orientations. We model the distributions based on the polarization-dependent efficiency of excitation and detection of emitted fluorescence, using coarse-grained representations we call orientation distribution functions (ODFs). We apply ODFs to create physics-informed models of image formation with spatio-angular point-spread and transfer functions. We use theory and experiment to conclude that light-sheet tilting is a necessary part of our design for recovering all three-dimensional orientations. We use our system to extend known two-dimensional results to three dimensions in FM1-43-labeled giant unilamellar vesicles, fast-scarlet-labeled cellulose in xylem cells, and phalloidin-labeled actin in U2OS cells. Additionally, we observe phalloidin-labeled actin in mouse fibroblasts grown on grids of labeled nanowires and identify correlations between local actin alignment and global cell-scale orientation, indicating cellular coordination across length scales.

Descending neurons (DNs) occupy a key position in the sensorimotor hierarchy, conveying signals from the brain to the rest of the body below the neck. In Drosophila melanogaster flies, approximately 480 DN cell types have been described from electron-microscopy image datasets. Genetic access to these cell types is crucial for further investigation of their role in generating behaviour. We previously conducted the first large-scale survey of Drosophila melanogaster DNs, describing 98 unique cell types from light microscopy and generating cell-type-specific split-Gal4 driver lines for 65 of them. Here, we extend our previous work, describing the morphology of 137 additional DN types from light microscopy, bringing the total number DN types identified in light microscopy datasets to 235, or nearly 50%. In addition, we produced 500 new sparse split-Gal4 driver lines and compiled a list of previously published DN lines from the literature for a combined list of 738 split-Gal4 driver lines targeting 171 DN types.

Limited color channels in fluorescence microscopy have long constrained spatial analysis in biological specimens. Here, we introduce cycle Hybridization Chain Reaction (HCR), a method that integrates multicycle DNA barcoding with HCR to overcome this limitation. cycleHCR enables highly multiplexed imaging of RNA and proteins using a unified barcode system. Whole-embryo transcriptomics imaging achieved precise three-dimensional gene expression and cell fate mapping across a specimen depth of ~310 μm. When combined with expansion microscopy, cycleHCR revealed an intricate network of 10 subcellular structures in mouse embryonic fibroblasts. In mouse hippocampal slices, multiplex RNA and protein imaging uncovered complex gene expression gradients and cell-type-specific nuclear structural variations. cycleHCR provides a quantitative framework for elucidating spatial regulation in deep tissue contexts for research and potentially diagnostic applications.

Preprint: https://doi.org/10.1101/2024.05.17.594641

Fluorescence is magical. Shine one color of light on a fluorophore and it glows in another color. This property allows imaging of biological systems with high sensitivity─we can visualize individual fluorescent molecules in an ocean of nonfluorescent ones.

Fluorescence microscopy has long been used to study isolated cells, both living and dead, but the development of newer, tailored fluorophores is swiftly expanding the use of fluorescence imaging to more complicated systems such as intact animals. In the latest in a long string of transformative work, Sletten and co-workers introduce dyes shrouded with multiple polymer chains─effectively star polymers with a bright fluorophore at the center.