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.

bioRxiv preprint: 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.

Maintaining proper tension is critical for the organization and function of the plasma membrane. To study the mechanisms by which yeast restores normal plasma membrane tension, we used a microfluidics device to expose yeast to hyperosmotic conditions, which reduced cell volume and caused a ∼20% drop in cell surface area. The resulting low tension plasma membrane exhibited large clusters of negatively-charged glycerophospholipids together with nutrient transporters, suggesting phase segregation of the membrane. We found that endocytosis was blocked by the phase segregation and thus was not involved in removing excess membrane. In contrast, rapid recovery of plasma membrane tension was dependent on 1) eisosome morphology changes that were able to absorb most of the excess surface area and 2) lipid transport from the plasma membrane to the endoplasmic reticulum, where lipids were shunted into newly formed lipid droplets.

Systemic lipid homeostasis requires hepatic autophagy, a major cellular program for intracellular fat recycling. Here, we find melanocortin 3 receptor (MC3R) regulates hepatic autophagy in addition to its previously established CNS role in systemic energy partitioning and puberty. Mice with Mc3r deficiency develop obesity with hepatic triglyceride accumulation and disrupted hepatocellular autophagosome turnover. Mice with partially inactive human MC3R due to obesogenic variants demonstrate similar hepatic autophagic dysfunction. In vitro and in vivo activation of hepatic MC3R upregulates autophagy through LC3II activation, TFEB cytoplasmic-to-nuclear translocation, and subsequent downstream gene activation. MC3R-deficient hepatocytes had blunted autophagosome-lysosome docking and lipid droplet clearance. Finally, the liver-specific rescue of Mc3r was sufficient to restore hepatocellular autophagy, improve hepatocyte mitochondrial function and systemic energy expenditures, reduce adipose tissue lipid accumulation, and partially restore body weight in both male and female mice. We thus report a role for MC3R in regulating hepatic autophagy and systemic adiposity.

Inside the cell, proteins essential for signaling, morphogenesis, and migration navigate the complex, ever-changing environment through vesicular trafficking or microtubule-driven mechanisms. However, the mechanisms by which soluble proteins reach their target destinations remain unknown. Here, we show that soluble proteins are directed toward the cell’s advancing edge by advection, diffusion facilitated by fluid flow. The advective transport mechanism operates in a compartment at the front of the cell isolated from the rest of the cytoplasm by a semi-permeable actin-myosin barrier that restricts protein mixing between the compartment and the rest of the cytoplasm. Contraction at the barrier generates a molecularly non-specific fluid flow that propels treadmilling actin monomer, actin-binding, adhesion, and even inert proteins forward. Changes in the dynamic local curvature of the barrier direct the flow, targeting proteins toward the protruding regions of the leading edge, effectively coordinating the distribution of proteins needed for local changes in cellular dynamics. Outside the compartment, diffusion is the primary mode of soluble protein transport. Our findings suggest that cells possess previously unrecognized organizational strategies for managing soluble protein concentration and distributing them efficiently for activities such as protrusion and adhesion.

Motor control in mammals is traditionally viewed as a hierarchy of descending spinal-targeting pathways, with frontal cortex at the top 1–3. Many redundant muscle patterns can solve a given task, and this high dimensionality allows flexibility but poses a problem for efficient learning 4. Although a feasible solution invokes subcortical innate motor patterns, or primitives, to reduce the dimensionality of the control problem, how cortex learns to utilize such primitives remains an open question 5–7. To address this, we studied cortical and subcortical interactions as head-fixed mice learned contextual control of innate hindlimb extension behavior. Naïve mice performed reactive extensions to turn off a cold air stimulus within seconds and, using predictive cues, learned to avoid the stimulus altogether in tens of trials. Optogenetic inhibition of large areas of rostral cortex completely prevented avoidance behavior, but did not impair hindlimb extensions in reaction to the cold air stimulus. Remarkably, mice covertly learned to avoid the cold stimulus even without any prior experience of successful, cortically-mediated avoidance. These findings support a dynamic, heterarchical model in which the dominant locus of control can change, on the order of seconds, between cortical and subcortical brain areas. We propose that cortex can leverage periods when subcortex predominates as demonstrations, to learn parameterized control of innate behavioral primitives.