A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the format itself – OME-Zarr – along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain — the file format that underlies so many personal, institutional, and global data management and analysis tasks.

Advances in microscopy hold great promise for allowing quantitative and precise readouts of morphological and molecular phenomena at the single cell level in bacteria. However, the potential of this approach is ultimately limited by the availability of methods to perform unbiased cell segmentation, defined as the ability to faithfully identify cells independent of their morphology or optical characteristics. In this study, we present a new algorithm, Omnipose, which accurately segments samples that present significant challenges to current algorithms, including mixed bacterial cultures, antibiotic-treated cells, and cells of extended or branched morphology. We show that Omnipose achieves generality and performance beyond leading algorithms and its predecessor, Cellpose, by virtue of unique neural network outputs such as the gradient of the distance field. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism and on the segmentation of non-bacterial objects. Our results distinguish Omnipose as a uniquely powerful tool for answering diverse questions in bacterial cell biology.

Advances in microscopy hold great promise for allowing quantitative and precise measurement of morphological and molecular phenomena at the single-cell level in bacteria; however, the potential of this approach is ultimately limited by the availability of methods to faithfully segment cells independent of their morphological or optical characteristics. Here, we present Omnipose, a deep neural network image-segmentation algorithm. Unique network outputs such as the gradient of the distance field allow Omnipose to accurately segment cells on which current algorithms, including its predecessor, Cellpose, produce errors. We show that Omnipose achieves unprecedented segmentation performance on mixed bacterial cultures, antibiotic-treated cells and cells of elongated or branched morphology. Furthermore, the benefits of Omnipose extend to non-bacterial subjects, varied imaging modalities and three-dimensional objects. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism. Our results distinguish Omnipose as a powerful tool for characterizing diverse and arbitrarily shaped cell types from imaging data.

Memory guides the choices an animal makes across widely varying conditions in dynamic environments. Consequently, the most adaptive choice depends on the options available. How can a single memory support optimal behavior across different sets of choice options? We address this using olfactory learning in Drosophila. Even when we restrict an odor-punishment association to a single set of synapses using optogenetics, we find that flies still show choice behavior that depends on the options it encounters. Here we show that how the odor choices are presented to the animal influences memory recall itself. Presenting two similar odors in sequence enabled flies to not only discriminate them behaviorally but also at the level of neural activity. However, when the same odors were encountered as solitary stimuli, no such differences were detectable. These results show that memory recall is not simply a comparison to a static learned template, but can be adaptively modulated by stimulus dynamics.

Behavioral neuroscience faces two conflicting demands: long-duration recordings from large neural populations and unimpeded animal behavior. To meet this challenge we developed ONIX, an open-source data acquisition system with high data throughput (2 GB s) and low closed-loop latencies (<1 ms) that uses a 0.3-mm thin tether to minimize behavioral impact. Head position and rotation are tracked in three dimensions and used to drive active commutation without torque measurements. ONIX can acquire data from combinations of passive electrodes, Neuropixels probes, head-mounted microscopes, cameras, three-dimensional trackers and other data sources. We performed uninterrupted, long (~7 h) neural recordings in mice as they traversed complex three-dimensional terrain, and multiday sleep-tracking recordings (~55 h). ONIX enabled exploration with similar mobility as nonimplanted animals, in contrast to conventional tethered systems, which have restricted movement. By combining long recordings with full mobility, our technology will enable progress on questions that require high-quality neural recordings during ethologically grounded behaviors.

Insect neural systems are a promising source of inspiration for new navigation algorithms, especially on low size, weight, and power platforms. There have been unprecedented recent neuroscience breakthroughs with Drosophila in behavioral and neural imaging experiments as well as the mapping of detailed connectivity of neural structures. General mechanisms for learning orientation in the central complex (CX) of Drosophila have been investigated previously; however, it is unclear how these underlying mechanisms extend to cases where there is translation through an environment (beyond only rotation), which is critical for navigation in robotic systems. Here, we develop a CX neural connectivity-constrained model that performs sensor fusion, as well as unsupervised learning of visual features for path integration; we demonstrate the viability of this circuit for use in robotic systems in simulated and physical environments. Furthermore, we propose a theoretical understanding of how distributed online unsupervised network weight modification can be leveraged for learning in a trajectory through an environment by minimizing orientation estimation error. Overall, our results may enable a new class of CX-derived low power robotic navigation algorithms and lead to testable predictions to inform future neuroscience experiments.

A group leader decided that his lab would share the fluorescent dyes they create, for free and without authorship requirements. Nearly 12,000 aliquots later, he reveals what has happened since.

New technologies for monitoring and manipulating the nervous system promise exciting biology but pose challenges for analysis and computation. Solutions can be found in the form of modern approaches to distributed computing, machine learning, and interactive visualization. But embracing these new technologies will require a cultural shift: away from independent efforts and proprietary methods and toward an open source and collaborative neuroscience.

Laboratory behavioural tasks are an essential research tool. As questions asked of behaviour and brain activity become more sophisticated, the ability to specify and run richly structured tasks becomes more important. An increasing focus on reproducibility also necessitates accurate communication of task logic to other researchers. To these ends, we developed pyControl, a system of open-source hardware and software for controlling behavioural experiments comprising a simple yet flexible Python-based syntax for specifying tasks as extended state machines, hardware modules for building behavioural setups, and a graphical user interface designed for efficiently running high-throughput experiments on many setups in parallel, all with extensive online documentation. These tools make it quicker, easier, and cheaper to implement rich behavioural tasks at scale. As important, pyControl facilitates communication and reproducibility of behavioural experiments through a highly readable task definition syntax and self-documenting features. Here, we outline the system's design and rationale, present validation experiments characterising system performance, and demonstrate example applications in freely moving and head-fixed mouse behaviour.

To smooth the academic-to-industry transition, one institution is experimenting with offering biomedical researchers pre-commercial open access to new optical imaging systems still under development. The approach, the authors of this case study suggest, can be a win on both sides.