Animals learn both whether and when a reward will occur. Neural models of timing posit that animals learn the mean time until reward perturbed by a fixed relative uncertainty. Nonetheless, animals can learn to perform actions for reward even in highly variable natural environments. Optimal inference in the presence of variable information requires probabilistic models, yet it is unclear whether animals can infer such models for reward timing. Here, we develop a behavioral paradigm in which optimal performance required knowledge of the distribution from which reward delays were chosen. We found that mice were able to accurately adjust their behavior to the SD of the reward delay distribution. Importantly, mice were able to flexibly adjust the amount of prior information used for inference according to the moment-by-moment demands of the task. The ability to infer probabilistic models for timing may allow mice to adapt to complex and dynamic natural environments.

For the information content of microscopy images to be appropriately interpreted, reproduced, and meet FAIR (Findable Accessible Interoperable and Reusable) principles, they should be accompanied by detailed descriptions of microscope hardware, image acquisition settings, image pixel and dimensional structure, and instrument performance. Nonetheless, the thorough documentation of imaging experiments is significantly impaired by the lack of community-sanctioned easy-to-use software tools to facilitate the extraction and collection of relevant microscopy metadata. Here we present Micro-Meta App, an intuitive open-source software designed to tackle these issues that was developed in the context of nascent global bioimaging community organizations, including BioImaging North America (BINA) and QUAlity Assessment and REProducibility in Light Microscopy (QUAREP-LiMi), whose goal is to improve reproducibility, data quality and sharing value for imaging experiments. The App provides a user-friendly interface for building comprehensive descriptions of the conditions utilized to produce individual microscopy datasets as specified by the recently proposed 4DN-BINA-OME tiered-system of Microscopy Metadata model. To achieve this goal the App provides a visual guide for a microscope-user to: 1) interactively build diagrammatic representations of hardware configurations of given microscopes that can be easily reused and shared with colleagues needing to document similar instruments. 2) Automatically extracts relevant metadata from image files and facilitates the collection of missing image acquisition settings and calibration metrics associated with a given experiment. 3) Output all collected Microscopy Metadata to interoperable files that can be used for documenting imaging experiments and shared with the community. In addition to significantly lowering the burden of quality assurance, the visual nature of Micro-Meta App makes it particularly suited for training users that have limited knowledge of the intricacies of light microscopy experiments. To ensure wide-adoption by microscope-users with different needs Micro-Meta App closely interoperates with MethodsJ2 and OMERO.mde, two complementary tools described in parallel manuscripts.

For quality, interpretation, reproducibility and sharing value, microscopy images should be accompanied by detailed descriptions of the conditions that were used to produce them. Micro-Meta App is an intuitive, highly interoperable, open-source software tool that was developed in the context of the 4D Nucleome (4DN) consortium and is designed to facilitate the extraction and collection of relevant microscopy metadata as specified by the recent 4DN-BINA-OME tiered-system of Microscopy Metadata specifications. In addition to substantially lowering the burden of quality assurance, the visual nature of Micro-Meta App makes it particularly suited for training purposes.

Extracellular vesicles (EVs) are important mediators of cell-to-cell communication and have been implicated in several pathologies including those of the central nervous system. They are released by all cell types, including neurons, and are highly heterogenous in size and composition. Yet much remains unknown regarding the biophysical characteristics of different EVs. Here, using cryo-electron microscopy (cryoEM), we analyzed the size distribution and morphology of EVs released from primary cortical neurons. We discovered massive macromolecular clusters on the luminal face of EV membranes. These clusters are predominantly found on medium-sized vesicles, suggesting that they may be specific to microvesicles as opposed to exosomes. We propose that these clusters serve as microdomains for EV signaling and play an important role in EV physiology.

MicroED, a method at the intersection of X-ray crystallography and electron cryo-microscopy, has rapidly progressed by exploiting advances in both fields and has already been successfully employed to determine the atomic structures of several proteins from sub-micron-sized, three-dimensional crystals. A major limiting factor in X-ray crystallography is the requirement for large and well ordered crystals. By permitting electron diffraction patterns to be collected from much smaller crystals, or even single well ordered domains of large crystals composed of several small mosaic blocks, MicroED has the potential to overcome the limiting size requirement and enable structural studies on difficult-to-crystallize samples. This communication details the steps for sample preparation, data collection and reduction necessary to obtain refined, high-resolution, three-dimensional models by MicroED, and presents some of its unique challenges.

The cryoEM method Microcrystal Electron Diffraction (MicroED) involves transmission electron microscope (TEM) and electron detector working in synchrony to collect electron diffraction data by continuous rotation. We previously reported several protein, peptide, and small molecule structures by MicroED using manual control of the microscope and detector to collect data. Here we present a procedure to automate this process using a script developed for the popular open-source software package SerialEM. With this approach, SerialEM coordinates stage rotation, microscope operation, and camera functions for automated continuous-rotation MicroED data collection. Depending on crystal and substrate geometry, more than 300 datasets can be collected overnight in this way, facilitating high-throughput MicroED data collection for large-scale data analyses.

Solving the atomic structure of metallic clusters is fundamental to understanding their optical, electronic, and chemical properties. We report the structure of Au146(p-MBA)57 at subatomic resolution (0.85 {\AA}) using electron diffraction (MicroED) and atomic resolution by X-ray diffraction. The 146 gold atoms may be decomposed into two constituent sets consisting of 119 core and 27 peripheral atoms. The core atoms are organized in a twinned FCC structure whereas the surface gold atoms follow a C2 rotational symmetry about an axis bisecting the twinning plane. The protective layer of 57 p-MBAs fully encloses the cluster and comprises bridging, monomeric, and dimeric staple motifs. Au146(p-MBA)57 is the largest cluster observed exhibiting a bulk-like FCC structure as well as the smallest gold particle exhibiting a stacking fault.

Sodium (Na) is a ubiquitous and important inorganic salt mediating many critical biological processes such as neuronal excitation, signaling, and facilitation of various transporters. The hydration states of Na are proposed to play critical roles in determining the conductance and the selectivity of Na channels, yet they are rarely captured by conventional structural biology means. Here we use the emerging cryo-electron microscopy (cryoEM) method micro-electron diffraction (MicroED) to study the structure of a prototypical tetrameric Na-conducting channel, NaK, to 2.5 Å resolution from nano-crystals. Two new conformations at the external site of NaK are identified, allowing us to visualize a partially hydrated Na ion at the entrance of the channel pore. A process of dilation coupled with Na movement is identified leading to valuable insights into the mechanism of ion conduction and gating. This study lays the ground work for future studies using MicroED in membrane protein biophysics.

Theoretical calculations suggest that crystals exceeding 100 nm thickness are excluded by dynamical scattering from successful structure determination using microcrystal electron diffraction (MicroED). These calculations are at odds with experimental results where MicroED structures have been determined from significantly thicker crystals. Here we systematically evaluate the influence of thickness on the accuracy of MicroED intensities and the ability to determine structures from protein crystals one micrometer thick. To do so, we compare ab initio structures of a human prion protein segment determined from thin crystals to those determined from crystals up to one micrometer thick. We also compare molecular replacement solutions from crystals of varying thickness for a larger globular protein, proteinase K. Our results indicate that structures can be reliably determined from crystals at least an order of magnitude thicker than previously suggested by simulation, opening the possibility for an even broader range of MicroED experiments.