We address the problem of inferring the number of independently blinking fluorescent light emitters, when only their combined intensity contributions can be observed. This problem occurs regularly in light microscopy of objects smaller than the diffraction limit, where one wishes to count the number of fluorescently labeled subunits. Our proposed solution directly models the photophysics of the system, as well as the blinking kinetics of the fluorescent emitters as a fully differentiable hidden Markov model, estimating a posterior distribution of the total number of emitters. We show that our model is more accurate and increases the range of countable subunits by a factor of 2 compared to current state-of-the-art methods. Furthermore, we demonstrate that our model can be used to investigate the effect of blinking kinetics on counting ability and therefore can inform optimal experimental conditions.

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. Here, we introduce activity quantification and analysis (AQuA2), a fast, accurate, and versatile data analysis platform built upon advanced machine-learning techniques. It decomposes complex live-imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a wide range of biosensors, cell types, organs, animal models, microscopy techniques, and imaging approaches. As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, as well as distinct sensorimotor signal propagation patterns in the mouse spinal cord.

Preprint: https://doi.org/10.1101/2024.05.02.592259

High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. While wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement, and slow when serially mapping spatially varying aberrations across large fields of view. Here, we introduce AOViFT (Adaptive Optical Vision Fourier Transformer)---a machine learning-based aberration sensing framework built around a 3D multistage Vision Transformer that operates on Fourier domain embeddings. AOViFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time, and memory footprint compared to conventional architectures or real-space networks. We validated AOViFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or post-acquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOViFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples.

Hedonic eating is defined as food consumption driven by palatability without physiological need. However, neural control of palatable food intake is poorly understood. We discovered that hedonic eating is controlled by a neural pathway from the peri-locus ceruleus to the ventral tegmental area (VTA). Using photometry-calibrated optogenetics, we found that VTA dopamine (VTA) neurons encode palatability to bidirectionally regulate hedonic food consumption. VTA neuron responsiveness was suppressed during food consumption by semaglutide, a glucagon-like peptide receptor 1 (GLP-1R) agonist used as an antiobesity drug. Mice recovered palatable food appetite and VTA neuron activity during repeated semaglutide treatment, which was reversed by consumption-triggered VTA neuron inhibition. Thus, hedonic food intake activates VTA neurons, which sustain further consumption, a mechanism that opposes appetite reduction by semaglutide.

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal and abstract relationships that can be used to shape thought, planning and behaviour. Cognitive maps have been observed in the hippocampus1, but their algorithmic form and learning mechanisms remain obscure. Here we used large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different linear tracks in virtual reality. Throughout learning, both animal behaviour and hippocampal neural activity progressed through multiple stages, gradually revealing improved task representation that mirrored improved behavioural efficiency. The learning process involved progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. This decorrelation process was driven by individual neurons acquiring task-state-specific responses (that is, 'state cells'). Although various standard artificial neural networks did not naturally capture these dynamics, the clone-structured causal graph, a hidden Markov model variant, uniquely reproduced both the final orthogonalized states and the learning trajectory seen in animals. The observed cellular and population dynamics constrain the mechanisms underlying cognitive map formation in the hippocampus, pointing to hidden state inference as a fundamental computational principle, with implications for both biological and artificial intelligence.

Vimentin intermediate filaments (VIFs) form complex, tightly packed networks; due to this density, traditional imaging approaches cannot discern single-filament behavior. To address this, we developed and validated a sparse vimentin-SunTag labeling strategy, enabling single-particle tracking of individual VIFs and providing a sensitive, unbiased, and quantitative method for measuring global VIF motility. Using this approach, we define the steady-state VIF motility rate, showing a constant ∼8% of VIFs undergo directed microtubule-based motion irrespective of subcellular location or local filament density. Significantly, our single-particle tracking approach revealed uncorrelated motion of individual VIFs within bundles, an observation seemingly at odds with conventional models of tightly cross-linked bundles. To address this, we acquired high-resolution focused ion beam scanning electron microscopy volumes of vitreously frozen cells and reconstructed three-dimensional VIF bundles, finding that they form only loosely organized, semi-coherent structures from which single VIFs frequently emerge to locally engage neighboring microtubules. Overall, this work demonstrates single VIF dynamics and organization in the cellular milieu for the first time.

bioRxiv Preprint: https://doi.org/10.1101/2024.06.10.598346

Synchronous neuronal ensembles play a pivotal role in the consolidation of long-term memory in the hippocampus. However, their organization during the acquisition of spatial memory remains less clear. In this study, we used neuronal population voltage imaging to investigate the synchronization patterns of CA1 pyramidal neuronal ensembles during the exploration of a new environment, a critical phase for spatial memory acquisition. We found synchronous ensembles comprising approximately 40% of CA1 pyramidal neurons, firing simultaneously in brief windows (∼25ms) during immobility and locomotion in novel exploration. Notably, these synchronous ensembles were not associated with ripple oscillations but were instead phase-locked to local field potential theta waves. Specifically, the subthreshold membrane potentials of neurons exhibited coherent theta oscillations with a depolarizing peak at the moment of synchrony. Among newly formed place cells, pairs with more robust synchronization during locomotion displayed more distinct place-specific activities. These findings underscore the role of synchronous ensembles in coordinating place cells of different place fields.

Synaptic plasticity alters neuronal connections in response to experience, which is thought to underlie learning and memory. However, the loci of learning-related synaptic plasticity, and the degree to which plasticity is localized or distributed, remain largely unknown. Here we describe a new method, DELTA, for mapping brain-wide changes in synaptic protein turnover with single-synapse resolution, based on Janelia Fluor dyes and HaloTag knock-in mice. During associative learning, the turnover of the ionotropic glutamate receptor subunit GluA2, an indicator of synaptic plasticity, was enhanced in several brain regions, most markedly hippocampal area CA1. More broadly distributed increases in the turnover of synaptic proteins were observed in response to environmental enrichment. In CA1, GluA2 stability was regulated in an input-specific manner, with more turnover in layers containing input from CA3 compared to entorhinal cortex. DELTA will facilitate exploration of the molecular and circuit basis of learning and memory and other forms of plasticity at scales ranging from single synapses to the entire brain.

A tool to map changes in synaptic strength during a defined time window could provide powerful insights into the mechanisms of learning and memory. Here we developed a technique, Extracellular Protein Surface Labeling in Neurons (EPSILON), to map α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) exocytosis in vivo by sequential pulse-chase labeling of surface AMPARs with membrane-impermeable dyes. This approach yields synaptic-resolution maps of AMPAR exocytosis, a proxy for synaptic potentiation, in genetically targeted neurons during memory formation. In mice undergoing contextual fear conditioning, we investigated the relationship between synapse-level AMPAR exocytosis in CA1 pyramidal neurons and cell-level expression of the immediate early gene product cFos, a frequently used marker of engram neurons. We observed a strong correlation between AMPAR exocytosis and cFos expression, suggesting a synaptic mechanism for the association of cFos expression with memory engrams. The EPSILON technique is a useful tool for mapping synaptic plasticity and may be extended to investigate trafficking of other transmembrane proteins.

Internal signals from the body and external signals from the environment are processed by brain-wide circuits to guide behavior. However, the complete brain-wide circuit activity underlying interoception—the perception of bodily signals—and its interactions with sensorimotor circuits remain unclear due to technical barriers to accessing whole-brain activity at the cellular level during organ physiology perturbations. We developed an all-optical system for whole-brain neuronal imaging in behaving larval zebrafish during optical uncaging of gut-targeted nutrients and visuo-motor stimulation. Widespread neural activity throughout the brain encoded nutrient delivery, unfolding on multiple timescales across many specific peripheral and central regions. Evoked activity depended on delivery location and occurred with amino acids and D-glucose, but not L-glucose. Many gut-sensitive neurons also responded to swimming and visual stimuli, with brainstem areas primarily integrating gut and motor signals and midbrain regions integrating gut and visual signals. This platform links body-brain communication studies to brain-wide neural computation in awake, behaving vertebrates.