Animals retain some but not all experiences in long-term memory (LTM). Sleep supports LTM retention across animal species. It is well established that learning experiences enhance post-learning sleep. However, the underlying mechanisms of how learning mediates sleep for memory retention are not clear. Drosophila males display increased amounts of sleep after courtship learning. Courtship learning depends on Mushroom Body (MB) neurons, and post-learning sleep is mediated by the sleep-promoting ventral Fan-Shaped Body neurons (vFBs). We show that post-learning sleep is regulated by two opposing output neurons (MBONs) from the MB, which encode a measure of learning. Excitatory MBONs-γ2α'1 becomes increasingly active upon increasing time of learning, whereas inhibitory MBONs-β'2mp is activated only by a short learning experience. These MB outputs are integrated by SFS neurons, which excite vFBs to promote sleep after prolonged but not short training. This circuit may ensure that only longer or more intense learning experiences induce sleep and are thereby consolidated into LTM.

The last three decades have brought a revolution in fluorescence microscopy. The development of new microscopes, fluorescent labels and analysis techniques has pushed the frontiers of biological imaging forward, moving from fixed to live cells, from diffraction-limited to super-resolution imaging and from simple cell culture systems to experiments in vivo. The large and ever-evolving collection of tools can be daunting for biologists, who must invest substantial time and effort in adopting new technologies to answer their specific questions. This is particularly relevant when working with small-molecule fluorescent labels, where users must navigate the jargon, idiosyncrasies and caveats of chemistry. Here, we present an overview of chemical dyes used in biology and provide frank advice from a chemist's perspective.

Individuals vary in their innate behaviours, even when they have the same genome and have been reared in the same environment. The extent of individuality in plastic behaviours, like learning, is less well characterized. Also unknown is the extent to which intragenotypic differences in learning generalize: if an individual performs well in one assay, will it perform well in other assays? We investigated this using the fruit fly , an organism long-used to study the mechanistic basis of learning and memory. We found that isogenic flies, reared in identical laboratory conditions, and subject to classical conditioning that associated odorants with electric shock, exhibit clear individuality in their learning responses. Flies that performed well when an odour was paired with shock tended to perform well when the odour was paired with bitter taste or when other odours were paired with shock. Thus, individuality in learning performance appears to be prominent in isogenic animals reared identically, and individual differences in learning performance generalize across some aversive sensory modalities. Establishing these results in flies opens up the possibility of studying the genetic and neural circuit basis of individual differences in learning in a highly suitable model organism.

The protein folding paradigm asserts that the three-dimensional structure of a protein is determined by its amino acid sequence. Here we show that a substantial population of proteins from the NusG superfamily of transcription factors do not adhere to this paradigm. Previous work demonstrated that one member of this superfamily has a regulatory domain that completely switches between α-helical and β-sheet folds, but the pervasiveness of this fold-switching mechanism is uncertain. To address this question, we developed a sequence-based predictor, which revealed that thousands of proteins from this superfamily switch folds. Circular dichroism and nuclear magnetic resonance spectroscopies of 10 sequence-diverse variants confirmed our predictions. By contrast, state-of-the-art methods based on the protein folding paradigm assume that related sequences adopt the same fold and thus predicted that the regulatory domains of all variants adopt only the β-sheet fold. Removal of this bias revealed that residue-residue contacts from both α-helical and β-sheet folds are conserved in a large subpopulation of fold-switching domains, poising them to assume disparate conformations. Our results suggest that fold switching is a pervasive mechanism of transcriptional regulation in all kingdoms of life and indicate that expanding the protein folding paradigm may reveal the involvement of fold-switching proteins in diverse biological processes.

Unraveling the complexity of the brain requires sophisticated methods to probe and perturb neurobiological processes with high spatiotemporal control. The field of chemical biology has produced general strategies to combine the molecular specificity of small-molecule tools with the cellular specificity of genetically encoded reagents. Here, we survey the application, refinement, and extension of these hybrid small-molecule:protein methods to problems in neuroscience, which yields powerful reagents to precisely measure and manipulate neural systems. Expected final online publication date for the , Volume 45 is July 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Unraveling the complexity of the brain requires sophisticated methods to probe and perturb neurobiological processes with high spatiotemporal control. The field of chemical biology has produced general strategies to combine the molecular specificity of small-molecule tools with the cellular specificity of genetically encoded reagents. Here, we survey the application, refinement, and extension of these hybrid small-molecule:protein methods to problems in neuroscience, which yields powerful reagents to precisely measure and manipulate neural systems. Expected final online publication date for the , Volume 45 is July 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Understanding live-cell behavior in part requires high precision mapping of molecular species in 3-D dynamic environments. Approaches like single-molecule localization microscopy (SMLM) offer high promise for challenges posed by molecular cartography. Effectively, the precision of these approaches is dependent on the how many photons / second a fluorescent marker is capable of emitting. For this reason, many SRLM experiments are typically done using fluorescent organic dyes (such as Alexa Fluors) in reducing chemical environments which cause some organic dyes to stochastically cycle through dark states, allowing single-molecule localization (e.g. (d)STORM). The need to couple these dyes to antibodies and the harsh reducing conditions makes their application to live cell work problematic. To overcome these limitations, we made use of modifications to Janelia Fluor-based dyes which make them spontaneously cycle through dark states (blink) under physiological imaging conditions. The dyes are spectrally compatible with photo-activatable fluorescent proteins such as mEos and allow for simultaneous 2-color superresolution microscopy. When conjugated to a HaloTag, these artificial dyes can bind genetically encodable targets in live samples, allowing subsequent measurement in a live-cell environment. To correct for nanoscale chromatic aberrations we developed a new machine-learning based approach with reconstruction errors below achievable localization precisions. We show that these methods allow the reconstruction of live synapse surfaces and a variety of the associated molecular machineries with up to 50 nm accuracy in 3 dimensions.

The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits. Expected final online publication date for the , Volume 45 is July 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

While insects like Drosophila are flying, aerodynamic instabilities require that they make millisecond-timescale adjustments to their wing motion to stay aloft and on course. These stabilization reflexes can be modeled as a proportional-integral (PI) controller; however, it is unclear how such control might be instantiated in insects at the level of muscles and neurons. Here, we show that the b1 and b2 motor units—prominent components of the fly's steering muscles system—modulate specific elements of the PI controller: the angular displacement (integral, I) and angular velocity (proportional, P), respectively. Moreover, these effects are observed only during the stabilization of pitch. Our results provide evidence for an organizational principle in which each muscle contributes to a specific functional role in flight control, a finding that highlights the power of using top-down behavioral modeling to guide bottom-up cellular manipulation studies.

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.