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.

Recent advances in super-resolution microscopy have pushed the resolution limit of light microscopy closer to that of electron microscopy. However, as they invariably rely on fluorescence, light microscopy techniques only visualize whatever gets labeled. On the other hand, while electron microscopy reveals cellular structures at the highest resolution, it offers no specificity. The information gap between the two imaging modalities can only be bridged by correlative light and electron microscopy (CLEM). Previously we have developed a probe (mEos4) whose fluorescence and photoconversion survive 0.5-1% OsO4 fixation, allowing super-resolution visualization of organelles and fused proteins in the context of resinembedded ultrastructure in both transmission EM (TEM) and scanning EM (SEM) [1,2].

The worldwide COVID-19 pandemic has had devastating effects on health, healthcare infrastructure, social structure, and economics. One of the limiting factors in containing the spread of this virus has been the lack of widespread availability of fast, inexpensive, and reliable methods for testing of individuals. Frequent screening for infected and often asymptomatic people is a cornerstone of pandemic management plans. Here, we introduce two pH sensitive ‘LAMPshade’ dyes as novel readouts in an isothermal RT- LAMP amplification assay for SARS-CoV-2 RNA. The resulting JaneliaLAMP (jLAMP) assay is robust, simple, inexpensive, has low technical requirements and we describe its use and performance in direct testing of contrived and clinical samples without RNA extraction.

Serotonin plays a central role in cognition and is the target of most pharmaceuticals for psychiatric disorders. Existing drugs have limited efficacy; creation of improved versions will require better understanding of serotonergic circuitry, which has been hampered by our inability to monitor serotonin release and transport with high spatial and temporal resolution. We developed and applied a binding-pocket redesign strategy, guided by machine learning, to create a high-performance, soluble, fluorescent serotonin sensor (iSeroSnFR), enabling optical detection of millisecond-scale serotonin transients. We demonstrate that iSeroSnFR can be used to detect serotonin release in freely behaving mice during fear conditioning, social interaction, and sleep/wake transitions. We also developed a robust assay of serotonin transporter function and modulation by drugs. We expect that both machine-learning-guided binding-pocket redesign and iSeroSnFR will have broad utility for the development of other sensors and in vitro and in vivo serotonin detection, respectively.

To control reaching, the nervous system must generate large changes in muscle activation to drive the limb toward the target, and must also make smaller adjustments for precise and accurate behavior. Motor cortex controls the arm through projections to diverse targets across the central nervous system, but it has been challenging to identify the roles of cortical projections to specific targets. Here, we selectively disrupt cortico-cerebellar communication in the mouse by optogenetically stimulating the pontine nuclei in a cued reaching task. This perturbation did not typically block movement initiation, but degraded the precision, accuracy, duration, or success rate of the movement. Correspondingly, cerebellar and cortical activity during movement were largely preserved, but differences in hand velocity between control and stimulation conditions predicted from neural activity were correlated with observed velocity differences. These results suggest that while the total output of motor cortex drives reaching, the cortico-cerebellar loop makes small adjustments that contribute to the successful execution of this dexterous movement.

Sensory cues that precede reward acquire predictive (expected value) and incentive (drive reward-seeking action) properties. Mesolimbic dopamine neurons' responses to sensory cues correlate with both expected value and reward-seeking action. This has led to the proposal that phasic dopamine responses may be sufficient to inform value-based decisions, elicit actions, and/or induce motivational states; however, causal tests are incomplete. Here, we show that direct dopamine neuron stimulation, both calibrated to physiological and greater intensities, at the time of reward can be sufficient to induce and maintain reward seeking (reinforcing) although replacement of a cue with stimulation is insufficient to induce reward seeking or act as an informative cue. Stimulation of descending cortical inputs, one synapse upstream, are sufficient for reinforcement and cues to future reward. Thus, physiological activation of mesolimbic dopamine neurons can be sufficient for reinforcing properties of reward without being sufficient for the predictive and incentive properties of cues.

Pigmentation divergence between Drosophila species has emerged as a model trait for studying the genetic basis of phenotypic evolution, with genetic changes contributing to pigmentation differences often mapping to genes in the pigment synthesis pathway and their regulators. These studies of Drosophila pigmentation have tended to focus on pigmentation changes in one body part for a particular pair of species, but changes in pigmentation are often observed in multiple body parts between the same pair of species. The similarities and differences of genetic changes responsible for divergent pigmentation in different body parts of the same species thus remain largely unknown. Here we compare the genetic basis of pigmentation divergence between Drosophila elegans and D. gunungcola in the wing, legs, and thorax. Prior work has shown that regions of the genome containing the pigmentation genes yellow and ebony influence the size of divergent male-specific wing spots between these two species. We find that these same two regions of the genome underlie differences in leg and thorax pigmentation; however, divergent alleles in these regions show differences in allelic dominance and epistasis among the three body parts. These complex patterns of inheritance can be explained by a model of evolution involving tissue-specific changes in the expression of Yellow and Ebony between D. elegans and D. gunungcola.

Changes in walking speed are characterized by changes in both the animal's gait and the mechanics of its interaction with the ground. Here we study these changes in walking . We measured the fly's center of mass (CoM) movement with high spatial resolution and the position of its footprints. Flies predominantly employ a modified tripod gait that only changes marginally with speed. The mechanics of a tripod gait can be approximated with a simple model - angular and radial spring-loaded inverted pendulum (ARSLIP) - which is characterized by two springs of an effective leg that become stiffer as the speed increases. Surprisingly, the change in the stiffness of the spring is mediated by the change in tripod shape rather than a change in stiffness of the individual leg. The effect of tripod shape on mechanics can also explain the large variation in kinematics among insects, and ARSLIP can model these variations.

In the central nervous system (CNS), functional tasks are often allocated to distinct compartments. This is also evident in the Drosophila CNS where synapses and dendrites are clustered in distinct neuropil regions. The neuropil is separated from neuronal cell bodies by ensheathing glia, which as we show using dye injection experiments, contribute to the formation of an internal diffusion barrier. We find that ensheathing glia are polarized with a basolateral plasma membrane rich in phosphatidylinositol-(3,4,5)-triphosphate (PIP) and the Na/K-ATPase Nervana2 (Nrv2) that abuts an extracellular matrix formed at neuropil-cortex interface. The apical plasma membrane is facing the neuropil and is rich in phosphatidylinositol-(4,5)-bisphosphate (PIP) that is supported by a sub-membranous ß-Spectrin cytoskeleton. ß-spectrin mutant larvae affect ensheathing glial cell polarity with delocalized PIP and Nrv2 and exhibit an abnormal locomotion which is similarly shown by ensheathing glia ablated larvae. Thus, polarized glia compartmentalizes the brain and is essential for proper nervous system function.

Determining the spatial organization and morphological characteristics of molecularly defined cell types is a major bottleneck for characterizing the architecture underpinning brain function. We developed Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to survey gene expression in brain tissue, as well as a turnkey computational pipeline to rapidly process large EASI-FISH image datasets. EASI-FISH was optimized for thick brain sections (300 μm) to facilitate reconstruction of spatio-molecular domains that generalize across brains. Using the EASI-FISH pipeline, we investigated the spatial distribution of dozens of molecularly defined cell types in the lateral hypothalamic area (LHA), a brain region with poorly defined anatomical organization. Mapping cell types in the LHA revealed nine spatially and molecularly defined subregions. EASI-FISH also facilitates iterative reanalysis of scRNA-seq datasets to determine marker-genes that further dissociated spatial and morphological heterogeneity. The EASI-FISH pipeline democratizes mapping molecularly defined cell types, enabling discoveries about brain organization.