Sculpting a flat patch of membrane into an endocytic vesicle requires curvature generation on the cell surface, which is the primary function of the endocytosis machinery. Using super-resolved live cell fluorescence imaging, we demonstrate that curvature generation by individual clathrin-coated pits can be detected in real time within cultured cells and tissues of developing organisms. Our analyses demonstrate that the footprint of clathrin coats increases monotonically during the formation of pits at different levels of plasma membrane tension. These findings are only compatible with models that predict curvature generation at the early stages of endocytic clathrin pit formation. We also found that CALM adaptors associated with clathrin plaques form clusters, whereas AP2 distribution is more homogenous. Considering the curvature sensing and driving roles of CALM, we propose that CALM clusters may increase the strain on clathrin lattices locally, eventually giving rise to rupture and subsequent pit completion at the edges of plaques.

Understanding molecular mechanisms of cellular pathways requires knowledge of the identities of participating proteins, their cellular localization and their 3D structures. Contemporary workflows typically require multiple techniques to identify target proteins, track their localization using fluorescence microscopy, followed by in vitro structure determination. To identify mammal-specific sperm proteins and understand their functions, we developed a visual proteomics workflow to directly address these challenges. Our in situ cryo-electron tomography and subtomogram averaging provided 6.0 Å resolution reconstructions of axonemal microtubules and their associated proteins. The well-resolved secondary and tertiary structures allowed us to computationally match, in an unbiased manner, novel densities in our 3D reconstruction maps with 21,615 AlphaFold2-predicted protein models of the mouse proteome. We identified Tektin 5, CCDC105 and SPACA9 as novel microtubule inner proteins that form an extensive network crosslinking the lumen of microtubule and existing proteins. Additional biochemical and mass spectrometry analyses helped validate potential candidates. The novel axonemal sperm structures identified by this approach form an extensive interaction network within the lumen of microtubules, suggesting they have a role in the mechanical and elastic properties of the microtubule filaments required for the vigorous beating motions of flagella.

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.

This special feature of , titled 'Advances in Quantitative Bioimaging', proposes an overview of the latest advancements in quantitative bioimaging techniques and their wide-ranging applications. The articles cover various topics, including modern imaging methods that enable visualization on a nanoscale, such as super-resolution microscopy and single-particle analysis. These techniques offer unparalleled insights into complex molecular structures and dynamic cellular processes , such as mapping nuclear pore proteins or tracking single histone deposition events throughout the cell cycle. The articles presented in this edition showcase cutting-edge quantitative imaging techniques coupled with advanced computational analysis capable of precisely measuring biological structures and processes. Examples range from correlating calcium release events to underlying protein organization in heart cells to pioneering tools for categorizing changes in microglia morphology under various conditions. This editorial highlights how these advancements are revolutionizing our understanding of living systems, while acknowledging challenges that must be addressed to fully exploit the potential of these emerging technologies, such as improving molecular probes, algorithms and correlation protocols.

Natural physical, chemical, and biological dynamical systems are often complex, with heterogeneous components interacting in diverse ways. We show that graph neural networks can be designed to jointly learn the interaction rules and the structure of the heterogeneity from data alone. The learned latent structure and dynamics can be used to virtually decompose the complex system which is necessary to parameterize and infer the underlying governing equations. We tested the approach with simulation experiments of moving particles and vector fields that interact with each other. While our current aim is to better understand and validate the approach with simulated data, we anticipate it to become a generally applicable tool to uncover the governing rules underlying complex dynamics observed in nature.

Behavior has molecular, cellular, and circuit determinants. However, because many proteins are broadly expressed, their acute manipulation within defined cells has been difficult. Here, we combined the speed and molecular specificity of pharmacology with the cell type specificity of genetic tools. DART (drugs acutely restricted by tethering) is a technique that rapidly localizes drugs to the surface of defined cells, without prior modification of the native target. We first developed an AMPAR antagonist DART, with validation in cultured neuronal assays, in slices of mouse dorsal striatum, and in behaving mice. In parkinsonian animals, motor deficits were causally attributed to AMPARs in indirect spiny projection neurons (iSPNs) and to excess phasic firing of tonically active interneurons (TANs). Together, iSPNs and TANs (i.e., D2 cells) drove akinesia, whereas movement execution deficits reflected the ratio of AMPARs in D2 versus D1 cells. Finally, we designed a muscarinic antagonist DART in one iteration, demonstrating applicability of the method to diverse targets.

Hunger is a complex behavioural state that elicits intense food seeking and consumption. These behaviours are rapidly recapitulated by activation of starvation-sensitive AGRP neurons, which present an entry point for reverse-engineering neural circuits for hunger. Here we mapped synaptic interactions of AGRP neurons with multiple cell populations in mice and probed the contribution of these distinct circuits to feeding behaviour using optogenetic and pharmacogenetic techniques. An inhibitory circuit with paraventricular hypothalamus (PVH) neurons substantially accounted for acute AGRP neuron-evoked eating, whereas two other prominent circuits were insufficient. Within the PVH, we found that AGRP neurons target and inhibit oxytocin neurons, a small population that is selectively lost in Prader-Willi syndrome, a condition involving insatiable hunger. By developing strategies for evaluating molecularly defined circuits, we show that AGRP neuron suppression of oxytocin neurons is critical for evoked feeding. These experiments reveal a new neural circuit that regulates hunger state and pathways associated with overeating disorders.

Serine hydrolases have diverse intracellular substrates, biological functions, and structural plasticity, and are thus important for biocatalyst design. Amongst serine hydrolases, the recently described ybfF enzyme family are promising novel biocatalysts with an unusual bifurcated substrate-binding cleft and the ability to recognize commercially relevant substrates. We characterized in detail the substrate selectivity of a novel ybfF enzyme from Vibrio cholerae (Vc-ybfF) by using a 21-member library of fluorogenic ester substrates. We assigned the roles of the two substrate-binding clefts in controlling the substrate selectivity and folded stability of Vc-ybfF by comprehensive substitution analysis. The overall substrate preference of Vc-ybfF was for short polar chains, but it retained significant activity with a range of cyclic and extended esters. This broad substrate specificity combined with the substitutional analysis demonstrates that the larger binding cleft controls the substrate specificity of Vc-ybfF. Key selectivity residues (Tyr116, Arg120, Tyr209) are also located at the larger binding pocket and control the substrate specificity profile. In the structure of ybfF the narrower binding cleft contains water molecules prepositioned for hydrolysis, but based on substitution this cleft showed only minimal contribution to catalysis. Instead, the residues surrounding the narrow binding cleft and at the entrance to the binding pocket contributed significantly to the folded stability of Vc-ybfF. The relative contributions of each cleft of the binding pocket to the catalytic activity and folded stability of Vc-ybfF provide a valuable map for designing future biocatalysts based on the ybfF scaffold.

To integrate changing environmental cues with high spatial and temporal resolution is critical for animals to orient themselves. Drosophila larvae show an effective motor program to navigate away from light sources. How the larval visual circuit processes light stimuli to control navigational decision remains unknown. The larval visual system is composed of two sensory input channels, Rhodopsin5 (Rh5) and Rhodopsin6 (Rh6) expressing photoreceptors (PRs). We here characterize how spatial and temporal information are used to control navigation. Rh6-PRs are required to perceive temporal changes of light intensity during head casts, while Rh5-PRs are required to control behaviors that allow navigation in response to spatial cues. We characterize how distinct behaviors are modulated and identify parallel acting and converging features of the visual circuit. Functional features of the larval visual circuit highlight the principle of how early in a sensory circuit distinct behaviors may be computed by partly overlapping sensory pathways.

Single-molecule localization microscopy (SMLM) has had remarkable success in imaging cellular structures with nanometer resolution, but the need for activating only single isolated emitters limits imaging speed and labeling density. Here, we overcome this major limitation using deep learning. We developed DECODE, a computational tool that can localize single emitters at high density in 3D with highest accuracy for a large range of imaging modalities and conditions. In a public software benchmark competition, it outperformed all other fitters on 12 out of 12 data-sets when comparing both detection accuracy and localization error, often by a substantial margin. DECODE allowed us to take live-cell SMLM data with reduced light exposure in just 3 seconds and to image microtubules at ultra-high labeling density. Packaged for simple installation and use, DECODE will enable many labs to reduce imaging times and increase localization density in SMLM.Competing Interest StatementThe authors have declared no competing interest.