During brain development, synapses are initially formed in excess and are later eliminated in an activity-dependent manner, with weak synapses being preferentially removed. Previous studies identified glia as mediators of synapse removal, but it is unclear how glia specifically target weak synapses. Here we show that, in the developing mouse visual pathway, inhibiting synaptic transmission induces postsynaptic activation of caspase-3. Caspase-3 is essential for synapse elimination driven by both spontaneous and experience-dependent neural activity. Synapse weakening-induced caspase-3 activation determines the specificity of synapse elimination mediated by microglia but not astrocytes. Furthermore, in a mouse model of Alzheimer’s disease, caspase-3 deficiency protects against synapse loss induced by amyloid-β deposition. Our results reveal caspase-3 activation as a key step in activity-dependent synapse elimination during development and synapse loss in neurodegeneration.

bioRxiv preprint: https://doi.org/10.1101/2024.08.02.606316

Reducing fibrous aggregates of the protein tau is a possible strategy for halting the progression of Alzheimer's disease (AD). Previously, we found that in vitro, the D-enantiomeric peptide (D-peptide) D-TLKIVWC disassembles ultra-stable tau fibrils extracted from the autopsied brains of individuals with AD (hereafter, these tau fibrils are referred to as AD-tau) into benign segments, with no energy source other than ambient thermal agitation. To consider D-peptide-mediated disassembly as a potential route to therapeutics for AD, it is essential to understand the mechanism and energy source of the disassembly action. Here, we show that the assembly of D-peptides into amyloid-like ('mock-amyloid') fibrils is essential for AD-tau disassembly. These mock-amyloid fibrils have a right-handed twist but are constrained to adopt a left-handed twist when templated in complex with AD-tau. The release of strain that accompanies the conversion of left-twisted to right-twisted, relaxed mock-amyloid produces a torque that is sufficient to break the local hydrogen bonding between tau molecules, and leads to the fragmentation of AD-tau. This strain-relief mechanism seems to operate in other examples of amyloid fibril disassembly, and could inform the development of first-in-class therapeutics for amyloid diseases.

Preprint: https://doi.org/10.1101/2024.03.25.586668

To successfully forage for food, animals must balance the energetic cost of searching for food sources with the energetic benefit of exploiting those sources. While the Marginal Value Theorem provides one normative account of this balance by specifying that a forager should leave a food patch when its energetic yield falls below the average yield of other patches in the environment, it assumes the presence of other readily reachable patches. In natural settings, however, a forager does not know whether it will encounter additional food patches, and it must balance potential energetic costs and benefits accordingly. Upon first encountering a patch of food, it faces a decision of whether and when to leave the patch in search of better options, and when to return if no better options are found. Here, we explore how a forager should structure its search for new food patches when the existence of those patches is unknown, and when searching for those patches requires energy that can only be harvested from a single known food patch. We identify conditions under which it is more favorable to explore the environment in several successive trips rather than in a single long exploration, and we show how the optimal sequence of trips depends on the forager’s beliefs about the distribution and nutritional content of food patches in the environment. This optimal strategy is well approximated by a local decision that can be implemented by a simple neural circuit architecture. Together, this work highlights how energetic constraints and prior beliefs shape optimal foraging strategies, and how such strategies can be approximated by simple neural networks that implement local decision rules.

Liposomes are essential vehicles for membrane protein reconstitution and drug delivery, making them vital tools in both in vivo and in vitro studies. However, the lack of robust techniques for the precise arrangement of these synthetic vesicles limits their potential applications. Here, we present a modular polymerization platform based on square DNA origami to template the formation and organization of liposomes. By programming the sequence, number, position, chirality, and flexibility of sticky ends on each square, we assemble uniformly sized liposomes into diverse two-dimensional (2D) arrays, as well as finite lattices and rings. Additionally, we demonstrate stepwise assembly and targeted disassembly, enabling dynamic structural control. These complex liposome architectures represent a significant advancement in the fields of biotechnology, nanotechnology, and bottom-up biology.

Artificial neural networks (ANNs) have been shown to predict neural responses in primary visual cortex (V1) better than classical models. However, this performance often comes at the expense of simplicity and interpretability. Here we introduce a new class of simplified ANN models that can predict over 70% of the response variance of V1 neurons. To achieve this high performance, we first recorded a new dataset of over 29,000 neurons responding to up to 65,000 natural image presentations in mouse V1. We found that ANN models required only two convolutional layers for good performance, with a relatively small first layer. We further found that we could make the second layer small without loss of performance, by fitting individual "minimodels" to each neuron. Similar simplifications applied for models of monkey V1 neurons. We show that the minimodels can be used to gain insight into how stimulus invariance arises in biological neurons.

Preprint: https://www.biorxiv.org/content/early/2024/07/02/2024.06.30.601394

Often referred to as a 'fight,' survival involves intense competition over resources. Threat displays and high-intensity attacks are just a few of the aggressive actions exhibited during these contests. Certain motor programs are species-specific, like the vibration of a rattlesnake tail. However, conserved behavioral features are found across species, which appear to be mirrored within the brain. Further parallels have been found across sexes between aggression-promoting contexts and the underlying neuronal circuits. Unraveling the complex web of conserved and variable circuit mechanisms has been considerably advanced by the generation of brain-wiring diagrams in adult female and male Drosophila melanogaster. Here, I will summarize current research, primarily in Drosophila, on how contexts, sensory cues, and internal states regulate aggression across sexes.

Sexual dimorphisms are present across brains. Male and female brains contain sets of cell types with differences in cell number, morphology, or synaptic connectivity between the two sexes. These differences are driven by differentially-expressed transcription factors, which set the stage for disparate sexual and social behaviors observed between males and females, such as courtship, aggression, receptivity, and mating. In the Drosophila brain, sexual dimorphisms result from differential expression of two transcription factors, Fruitless (Fru) and Doublesex (Dsx), and genetic reagents driven by enhancers for Fru and Dsx label sexually-dimorphic neurons in both male and female brains. The recent release of the first whole-brain connectome for Drosophila provides a unique opportunity to study the connectivity between these neurons as well as their integration into the larger brain network. Here, we identify 91 putative Fru or Dsx cell types, comprising ~1400 neurons, within the whole-brain connectome, using morphological similarity between electron microscopic (EM) reconstructions and light microscopic (LM) images of known Fru and Dsx neurons. We discover that while Fru and Dsx neurons are highly interconnected, each cell type typically receives more inputs from and sends more outputs to non-Fru/Dsx neurons. We characterize the connectivity in the Fru/Dsx networks to predict the function of cell types not previously characterized, we measure distances to the sensory periphery and uncover multisensory interactions, and we map connections to descending neurons that drive behavior. All Fru and Dsx labels reported here are shared within FlyWire Codex (codex.flywire.ai; gene==Fruitless or Doublesex); this work is a critical first step towards deciphering the neural basis of sexually-dimorphic behaviors and for making comparisons with future connectomes of the male brain.

De novo protein design has emerged as a powerful strategy with the promise to create new tools. The practical performance of designed fluorophore binders, however, has remained far from meeting fluorescence microscopy demands. Here, we design de novo Rhodamine Binder (Rhobin) tags that combine ideal properties including size, brightness, and now adding hyperstability. Rhobin allows live and fixed cell imaging of a wide range of subcellular targets in mammalian cells. Its reversible fluorophore binding further enables live super-resolution STED microscopy with low photobleaching, as well as PAINT-type single-molecule localization microscopy. We showcase Rhobin in the extremophile Sulfolobus acidocaldarius living at 75 degrees Celsius, an application previously inaccessible by existing tags. Rhobin will serve as the basis for a new class of live cell fluorescent tags and biosensors.

The growing channel count of silicon probes has substantially increased the number of neurons recorded in electrophysiology (ephys) experiments, rendering traditional manual spike sorting impractical. Instead, modern ephys recordings are processed with automated methods that use waveform template matching to isolate putative single neurons. While scalable, automated methods are subject to assumptions that often fail to account for biophysical changes in action potential waveforms, leading to systematic errors. Consequently, manual curation of these errors, which is both time-consuming and lacks reproducibility, remains necessary. To improve efficiency and reproducibility in the spike-sorting pipeline, we introduce here the Spike-sorting Lapse Amelioration System (SLAy), an algorithm that automatically merges oversplit spike clusters. SLAy employs two novel metrics: (1) a waveform similarity metric that uses a neural network to obtain spatially informed, time-shift invariant low-dimensional waveform representations, and (2) a cross-correlogram significance metric based on the earth-movers distance between the observed and null cross-correlograms. We demonstrate that SLAy achieves 85% agreement with human curators across a diverse set of animal models, brain regions, and probe geometries. To illustrate the impact of spike sorting errors on downstream analyses, we develop a new burst-detection algorithm and show that SLAy fixes spike sorting errors that preclude the accurate detection of bursts in neural data. SLAy leverages GPU parallelization and multithreading for computational efficiency, and is compatible with Phy and NeuroData Without Borders, making it a practical and flexible solution for large-scale ephys data analysis.

High-density silicon probes have transformed neuroscience by enabling large-scale neural recordings at single-cell resolution. However, existing technologies have provided limited functionality in nonhuman primates (NHPs) such as macaques. In the present report, we describe the design, fabrication and performance of Neuropixels 1.0 NHP, a high-channel electrode array designed to enable large-scale acute recording throughout large animal brains. The probe features 4,416 recording sites distributed along a 45-mm shank. Experimenters can programmably select 384 recording channels, enabling simultaneous multi-area recording from thousands of neurons with single or multiple probes. This technology substantially increases scalability and recording access relative to existing technologies and enables new classes of experiments that involve electrophysiological mapping of brain areas at single-neuron and single-spike resolution, measurement of spike-spike correlations between cells and simultaneous brain-wide recordings at scale.

Preprint: https://doi.org/10.1101/2023.02.01.526664