We introduce a workflow to identify oligomeric structures that are recorded with single-molecule localization microscopy (SMLM) under cryogenic conditions. Typically, these oligomers are assumed to consist of protomers arranged as equilateral two-dimensional polygons and every protomer is labeled with a dye molecule for visualization. Unlike previous work, we consider scenarios in which the sample plane has an unknown orientation relative to the focal plane. Our contribution is a high-precision plane-fitting algorithm to determine the sample plane, combined with geometrical transformations and two circle-fitting algorithms to identify the oligomeric structures. Our simulations on synthetic data demonstrate that the proposed workflow achieves high accuracy in estimating both the unknown tilted plane and the oligomer size.

Sarcomeres, the basic repeating unit of striated muscle, are joined together by crosslinked actin filaments found at the boundaries of muscle sarcomeres, termed Z-discs. Z-discs play a key role in cardiac signalling and disease, however, the arrangement and function of many of the proteins present in the Z-disc remain to be understood. Here, we determined the organisation of 3 key proteins, ZASP, α-Actinin-2 and the Z1Z2 epitope of titin, located within the Z-disc. We fluorescently labelled these proteins in cardiac myofibrils using Adhirons specific to each protein and used interferometric photoactivated localization microscopy (iPALM) to obtain the 3D position of these proteins to a high precision (<10nm in x,y,z). We then used PERPL (Pattern Extraction from Relative Positions of Localisations) to analyse patterns in the relative positions of the proteins and reveal their underlying organisation. This analysis revealed that ZASP and α-Actinin-2 have a similar repeating organisation, but that the organisation of Z1Z2 is different.

Protein assemblies, including aggregates and condensates, are closely linked to health and diseases. We demonstrate boxcar-enhanced Fluorescence-detected mid-Infrared photothermaL Microscopy (FILM), using two model species, Caenorhabditis elegans and Saccharomyces cerevisiae, to quantitatively resolve these protein states in vivo by imaging β-sheet and α-helix secondary structures and analyzing their ratios. This method directly distinguishes polyglutamine (PolyQ) protein aggregates, α-synuclein protein condensates, and P-granule condensates implicated in neurodegenerative diseases and embryonic development in live organisms. It further enables the unraveling of protein assembly dynamics and their physio-pathological roles, such as age-related progression of PolyQ from condensates to aggregates.

Sleep is regulated by a homeostatic process and associated with an increased arousal threshold, but the genetic and neuronal mechanisms that implement these essential features of sleep remain poorly understood.To address these fundamental questions, we performed a zebrafish genetic screen informed by human genome-wide association studies.We found that mutation of serine/threonine kinase 32a (stk32a) results in increased sleep and impaired sleep homeostasis in both zebrafish and mice, and that stk32a acts downstream of neurotensin signaling and the serotonergic raphe in zebrafish. stk32a mutation reduces phosphorylation of neurofilament proteins, which are co-expressed with stk32a in neurons that regulate motor activity and in lateral line hair cells that detect environmental stimuli, and ablating these cells phenocopies stk32a mutation. Neurotensin signaling inhibits specific sensory and motor populations, and blocks stimulus-evoked responses of neurons that relay sensory information from hair cells to the brain.Our work thus shows that stk32a is an evolutionarily conserved sleep regulator that links neuropeptidergic and neuromodulatory systems to homeostatic sleep drive and changes in arousal threshold, which are implemented through suppression of specific sensory and motor systems.

In natural environments, animals must allocate choices across multiple concurrently available resources when foraging, a complex decision-making process not fully captured by existing models. To understand how rodents learn to navigate this challenge, we developed a novel paradigm in which naive, water-restricted mice freely sampled six options of varying quality arranged around a large (∼2 m) arena. Mice exhibited rapid learning, matching their choices to integrated reward probabilities across six options within tens of minutes. A reinforcement learning model with distinct states for staying vs. leaving an option, as well as a dynamic global learning rate, accurately reproduced behavior. Fiber photometry recordings revealed that dopamine in the nucleus accumbens core (NAcC), but not the dorsomedial striatum (DMS), reflected this learning rate. Moreover, optogenetic manipulation of NAcC dopamine bidirectionally altered learning in quantitative agreement with model predictions. Together, we identified a neural substrate of a learning algorithm enabling efficient multi-option foraging in large spatial environments.

From work emerging through the middle of the 20th century, the essence of meaning has become widely accepted as being described by the three orthogonal dimensions of valence, arousal, and dominance. These essential dimensions have become the cornerstone of sentiment analysis across many fields. By reexamining first types and then tokens for the English language, and through the use of automatically annotated histograms-"ousiograms"-we find here that the essence of meaning conveyed by words is instead best described by a goodness-power-aggression-danger-structure (GPADS) circumplex framework; that large-scale English language corpora reveal a systematic bias toward safe, low-danger words; and that the power-danger-structure framework is the minimal framework that represents essential meaning. We find remarkable congruences between the GPADS framework and other spaces including mental states and fictional archetypes, and we construct and demonstrate a prototype ousiometer.

Fluorescence microscopy is constrained by optical limits, fluorophore chemistry and finite photon budgets, imposing trade-offs between imaging speed, resolution and phototoxicity. Here we introduce MicroSplit, a deep learning-based computational multiplexing method that enables multiple cellular structures to be imaged simultaneously in a single fluorescent channel and then computationally unmixed. We show that MicroSplit separates up to four superimposed noisy structures into distinct, denoised image channels, enabling faster and more photon-efficient imaging. Built on Variational Splitting Encoder-Decoder networks,  MicroSplit models a posterior distribution over solutions, allowing uncertainty-aware predictions and the estimation of spatially resolved prediction errors from posterior variability. We demonstrate robust performance across diverse datasets, noise levels and imaging conditions, and show that  MicroSplit improves downstream analysis while reducing photon exposure. All methods, data and trained models are released as open resources, enabling immediate adoption of computational multiplexing in biological imaging.

Understanding how nervous systems generate coordinated movement requires precise measurement of body kinematics during natural behavior. The fruit fly, Drosophila, is a model organism with sophisticated behavior and well-studied neural circuits, but tracking fly movements in 3D remains challenging because of their teeny bodies, rapid movements, and frequent self-occlusions. Here we present a pipeline for markerless, full-body 3D pose estimation of fly terrestrial behavior, combining seven synchronized high-speed cameras to capture whole-body kinematics at 800 frames per second. We trained a hybrid 2D/3D deep learning model to track 50 keypoints, then refined them to produce anatomically feasible kinematic trajectories through a retargeting process that solved an inverse kinematics problem constrained by a biomechanical body model. Analysis of 3D kinematics revealed that flies perform grounded running across their full speed range, without transitioning between discrete gaits. Using multi-animal tracking, we found that courting males coordinate both wings during song and modulate body pitch to track the female’s vertical position. Our open-source pipeline and 3D kinematic dataset of fly behavior provide a foundation for neuromechanical modeling and mechanistic studies of motor control in a genetically tractable model organism.

Cells depend on the spatial organization of proteins, RNA, and DNA into discrete subcellular compartments. Previous methods have largely centered on measuring spatial organization based on only one of these biomolecular classes at a time. Here, we demonstrate that POCA photocatalytic proximity labeling can serve as a unified photosensitizer-based platform for profiling the proximal proteomes of protein, RNA, and DNA targets within a single experimental framework. We show that POCA can harness standard immunofluorescence or in situ hybridization workflows to specifically target organic fluorophore photosensitizers to intracellular targets for proximity labeling in fixed cells. POCA-targeted proximity labeling requires minimal cellular input and does not require genetic engineering. Additionally, POCA photosensitizers are selected to also be fluorescent, enabling direct confirmation of on-target localization by imaging prior to proteomic analysis. To demonstrate broad utility, we apply POCA across multiple molecular targets spanning protein, RNA, and genomic DNA, including components of the nuclear pore complex, nucleolus, nuclear speckles, telomeres, and pericentromeric heterochromatin. By anchoring proximity labeling to both a protein and an RNA within the same nuclear compartment, we resolve shared and distinct proximal proteomes from orthogonal molecular perspectives.Competing Interest StatementD.K.S. is a collaborator with Thermo Fisher Scientific, Genentech, Calico Labs, Matchpoint Therapeutics, and AI Proteins. K.M.B is a collaborator with Thermo Fisher Scientific and on the advisory board for Matchpoint Therapeutics. B.J.B. has filed a patent application covering aspects of this work (US Patent App. 18/728,937). B.J.B. is listed as an inventor on patent applications related to the SABER technology related to this work (US Patent 11,492,661; US Patent App. 18/607,269). E.L.H. also collaborates with Thermo Fisher Scientific, Genentech, and Xaira Therapeutics and consults for Calico Labs, Matchpoint Therapeutics, and Flagship Pioneering. Patents and patent applications covering azetidine-containing rhodamine dyes (with inventors J.B.G. and L.D.L.) are assigned to HHMI. L.D.L. is a scientific cofounder, consultant, and shareholder of Eikon Therapeutics. The other authors declare no conflicts.National Institutes of Health, R35GM137916, R35GM150919, DP2GM146246, P30 CA015704, U24HG006673, T32HL007093W. M. Keck Foundation, https://ror.org/000dswa46Pew Charitable Trusts, https://ror.org/02xhk2825Andy Hill CARE FoundationDavid and Lucile Packard Foundation, https://ror.org/032atxq54Damon Runyon Cancer Research Foundation, https://ror.org/01gd7b947

Fructose-1,6-bisphosphate (FBP) is the product of the first committed step of glycolysis, and its concentration is tightly correlated with glycolytic flux. Glycolytic activity varies across tissues and cell types: some tissues, such as the brain, dynamically regulate glycolysis in response to demand, while others, such as the liver have characterized spatial heterogeneity. Here, we report HYlight2, an improved sensor for FBP developed through random whole-gene mutagenesis in E. coli lysate. After four rounds of screening, we isolated HYlight2, which retains its binding affinity while displaying a ΔR/R \~9 in vitro, a three-fold improvement in mammalian cells, and a two-fold improvement in detecting glycolytic responses during stimulated neuronal activity. We further demonstrate its use in vivo to detect altered glycolytic activity in C. elegans neurons, zebrafish pancreatic islets, and mouse liver.