Molecular organization of neuronal cell types and neuromodulatory systems in the zebrafish telencephalon.

The function of neuronal networks is determined not only by synaptic connectivity but also by neuromodulatory systems that broadcast information via distributed connections and volume transmission. To understand the molecular constraints that organize neuromodulatory signaling in the telencephalon of adult zebrafish, we used transcriptomics and additional approaches to delineate cell types, to determine their phylogenetic conservation, and to map the expression of marker genes at high granularity. The combinatorial expression of GPCRs and cell-type markers indicates that all neuronal cell types are subject to modulation by multiple monoaminergic systems and distinct combinations of neuropeptides. Individual cell types were associated with multiple (typically >30) neuromodulatory signaling networks but expressed only a few diagnostic GPCRs at high levels, suggesting that different neuromodulatory systems act in combination, albeit with unequal weights. These results provide a detailed map of cell types and brain areas in the zebrafish telencephalon, identify core components of neuromodulatory networks, highlight the cell-type specificity of neuropeptides and GPCRs, and begin to decipher the logic of combinatorial neuromodulation.

A viral toolbox for conditional and transneuronal gene expression in zebrafish.

The zebrafish is an important model in systems neuroscience but viral tools to dissect the structure and function of neuronal circuitry are not established. We developed methods for efficient gene transfer and retrograde tracing in adult and larval zebrafish by herpes simplex viruses (HSV1). HSV1 was combined with the Gal4/UAS system to target cell types with high spatial, temporal, and molecular specificity. We also established methods for efficient transneuronal tracing by modified rabies viruses in zebrafish. We demonstrate that HSV1 and rabies viruses can be used to visualize and manipulate genetically or anatomically identified neurons within and across different brain areas of adult and larval zebrafish. An expandable library of viruses is provided to express fluorescent proteins, calcium indicators, optogenetic probes, toxins and other molecular tools. This toolbox creates new opportunities to interrogate neuronal circuits in zebrafish through combinations of genetic and viral approaches.

Exposure of Individuals in Europe to Air Pollution and Related Health Effects.

Air pollutants, especially PM2.5 and NO2, are associated with adverse health impacts, as shown by numerous epidemiological studies. In these studies, the observed health impacts have been correlated with ambient concentrations, mainly taken from air pollution monitoring stations. However, individuals are harmed by the pollutants in the inhaled air at the places where they stay, and thus, the concentration of pollutants in the inhaled air is obviously a better indicator for health impacts than the ambient concentration at a monitoring station. Furthermore, the current method for estimating the occurrence of chronic diseases uses annual average concentrations as indicator. However, according to current hypotheses, chronic diseases, especially chronic mortality, develop through the exposure to pollutants over many years, maybe up to a full lifetime. Thus in this study, a methodology and a computer-aided probabilistic model system are described for calculating the exposure of a person to PM2.5 and NO2 over the whole lifetime where the person is characterized by attributes such as age, gender, place of residence and work as well as socioeconomic status. The model system contains a "life course trajectory model", which estimates the course of the education and professional development for the past lifetime of a person, whose present socioeconomic status is known. Furthermore, a "time-activity model" estimates at which places (so-called micro-environments) a person with a certain socioeconomic status stayed and how long he stayed there within a certain year. The concentrations of air pollutants in indoor environments are calculated with a "mass-balance model", the outdoor concentrations with "atmospheric models". Finally, the results of these models are combined to estimate the annual average exposure for the life years of individuals and population subgroups. The exposure is then used to estimate and monetize health impacts. The exposures and health impacts for a number of population subgroups in Europe are presented. For instance, a European citizen, who was 70 years old in 2015, has been exposed to around 25 µg/m3 of PM2.5 during his lifetime above the age of 30, which is associated with a reduction of life expectancy of 13.4 days per year of exposure above 30.

Whole-body integration of gene expression and single-cell morphology.

Animal bodies are composed of cell types with unique expression programs that implement their distinct locations, shapes, structures, and functions. Based on these properties, cell types assemble into specific tissues and organs. To systematically explore the link between cell-type-specific gene expression and morphology, we registered an expression atlas to a whole-body electron microscopy volume of the nereid Platynereis dumerilii. Automated segmentation of cells and nuclei identifies major cell classes and establishes a link between gene activation, chromatin topography, and nuclear size. Clustering of segmented cells according to gene expression reveals spatially coherent tissues. In the brain, genetically defined groups of neurons match ganglionic nuclei with coherent projections. Besides interneurons, we uncover sensory-neurosecretory cells in the nereid mushroom bodies, which thus qualify as sensory organs. They furthermore resemble the vertebrate telencephalon by molecular anatomy. We provide an integrated browser as a Fiji plugin for remote exploration of all available multimodal datasets.

A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging.

Inference of action potentials ('spikes') from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentials and calcium signals ('ground truth'). In this study, we compiled a large, diverse ground truth database from publicly available and newly performed recordings in zebrafish and mice covering a broad range of calcium indicators, cell types and signal-to-noise ratios, comprising a total of more than 35 recording hours from 298 neurons. We developed an algorithm for spike inference (termed CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level; therefore, no parameters need to be adjusted by the user. In addition, we developed systematic performance assessments for unseen data, openly released a resource toolbox and provide a user-friendly cloud-based implementation.

Multimodal patterns of inhibitory activity in cerebellar cortex.

In this issue of Neuron, Gurnani and Silver (2021) report that activity across Golgi cells, a major type of inhibitory interneuron in the cerebellar cortex, is multidimensional and modulated by behavior. These results suggest multiple functions for inhibition in cerebellar computations.

Dense Circuit Reconstruction to Understand Neuronal Computation: Focus on Zebrafish.

The dense reconstruction of neuronal wiring diagrams from volumetric electron microscopy data has the potential to generate fundamentally new insights into mechanisms of information processing and storage in neuronal circuits. Zebrafish provide unique opportunities for dynamical connectomics approaches that combine reconstructions of wiring diagrams with measurements of neuronal population activity and behavior. Such approaches have the power to reveal higher-order structure in wiring diagrams that cannot be detected by sparse sampling of connectivity and that is essential for neuronal computations. In the brain stem, recurrently connected neuronal modules were identified that can account for slow, low-dimensional dynamics in an integrator circuit. In the spinal cord, connectivity specifies functional differences between premotor interneurons. In the olfactory bulb, tuning-dependent connectivity implements a whitening transformation that is based on the selective suppression of responses to overrepresented stimulus features. These findings illustrate the potential of dynamical connectomics in zebrafish to analyze the circuit mechanisms underlying higher-order neuronal computations.

High resolution temporal profiles in the Emissions Database for Global Atmospheric Research.

Emissions into the atmosphere from human activities show marked temporal variations, from inter-annual to hourly levels. The consolidated practice of calculating yearly emissions follows the same temporal allocation of the underlying annual statistics. However, yearly emissions might not reflect heavy pollution episodes, seasonal trends, or any time-dependant atmospheric process. This study develops high-time resolution profiles for air pollutants and greenhouse gases co- emitted by anthropogenic sources in support of atmospheric modelling, Earth observation communities and decision makers. The key novelties of the Emissions Database for Global Atmospheric Research (EDGAR) temporal profiles are the development of (i) country/region- and sector- specific yearly profiles for all sources, (ii) time dependent yearly profiles for sources with inter-annual variability of their seasonal pattern, (iii) country- specific weekly and daily profiles to represent hourly emissions, (iv) a flexible system to compute hourly emissions including input from different users. This work creates a harmonized emission temporal distribution to be applied to any emission database as input for atmospheric models, thus promoting homogeneity in inter-comparison exercises.

A virtual reality system to analyze neural activity and behavior in adult zebrafish.

Virtual realities are powerful tools to analyze and manipulate interactions between animals and their environment and to enable measurements of neuronal activity during behavior. In many species, however, optical access to the brain and/or the behavioral repertoire are limited. We developed a high-resolution virtual reality for head-restrained adult zebrafish, which exhibit cognitive behaviors not shown by larvae. We noninvasively measured activity throughout the dorsal telencephalon by multiphoton calcium imaging. Fish in the virtual reality showed regular swimming patterns and were attracted to animations of conspecifics. Manipulations of visuo-motor feedback revealed neurons that responded selectively to the mismatch between the expected and the actual visual consequences of motor output. Such error signals were prominent in multiple telencephalic areas, consistent with models of predictive processing. A virtual reality system for adult zebrafish therefore provides opportunities to analyze neuronal processing mechanisms underlying higher brain functions including decision making, associative learning, and social interactions.

Whitening of odor representations by the wiring diagram of the olfactory bulb.

Neuronal computations underlying higher brain functions depend on synaptic interactions among specific neurons. A mechanistic understanding of such computations requires wiring diagrams of neuronal networks. In this study, we examined how the olfactory bulb (OB) performs 'whitening', a fundamental computation that decorrelates activity patterns and supports their classification by memory networks. We measured odor-evoked activity in the OB of a zebrafish larva and subsequently reconstructed the complete wiring diagram by volumetric electron microscopy. The resulting functional connectome revealed an over-representation of multisynaptic connectivity motifs that mediate reciprocal inhibition between neurons with similar tuning. This connectivity suppressed redundant responses and was necessary and sufficient to reproduce whitening in simulations. Whitening of odor representations is therefore mediated by higher-order structure in the wiring diagram that is adapted to natural input patterns.

Associative conditioning remaps odor representations and modifies inhibition in a higher olfactory brain area.

Intelligent behavior involves associations between high-dimensional sensory representations and behaviorally relevant qualities such as valence. Learning of associations involves plasticity of excitatory connectivity, but it remains poorly understood how information flow is reorganized in networks and how inhibition contributes to this process. We trained adult zebrafish in an appetitive odor discrimination task and analyzed odor representations in a specific compartment of the posterior zone of the dorsal telencephalon (Dp), the homolog of mammalian olfactory cortex. Associative conditioning enhanced responses with a preference for the positively conditioned odor. Moreover, conditioning systematically remapped odor representations along an axis in coding space that represented attractiveness (valence). Interindividual variations in this mapping predicted variations in behavioral odor preference. Photoinhibition of interneurons resulted in specific modifications of odor representations that mirrored effects of conditioning and reduced experience-dependent, interindividual variations in odor-valence mapping. These results reveal an individualized odor-to-valence map that is shaped by inhibition and reorganized during learning.

A model for estimating the lifelong exposure to PM2.5 and NO2 and the application to population studies.

Numerous epidemiological studies have confirmed the negative influences of air pollutants on human health, where fine particles (PM2.5) and nitrogen dioxide (NO2) cause the highest health risks. However, the traditional studies have only involved the ambient concentration for a short to medium time period, which ignores the influence of indoor sources, the individual time-activity pattern, and the fact that the health status is impacted by the long-term accumulated exposure. The aim of this paper is to develop a methodology to simulate the lifelong exposure (rather than outdoor concentration) to PM2.5 and NO2 for individuals in Europe. This method is realized by developing a probabilistic model that integrates an outdoor air quality model, a model estimating indoor air pollution, an exposure model, and a life course trajectory model for predicting retrospectively the employment status. This approach has been applied to samples of two population studies in the frame of the European Commission FP7-ENVIRONMENT research project HEALS (Health and Environment-wide Associations based on Large Population Surveys), where socioeconomic data of the participants have been collected. Results show that the simulated exposures to both pollutants for the samples are influenced by socio-demographic characteristics, including age, gender, residential location, employment status and smoking habits. Both outdoor concentrations and indoor sources play an important role in the total exposure. Moreover, large variances have been observed among countries and cities. The application of this methodology provides valuable insights for the exposure modelling, as well as important input data for exploring the correlation between exposure and health impacts.

Lifelong exposure to multiple stressors through different environmental pathways for European populations.

Traditional exposure studies provide valuable insights for epidemiology, toxicology, and risk assessment. Throughout their lives, individuals are exposed to thousands of stressors in the environment which are not static, but influenced by environmental, temporal, spatial, and even socio-demographic factors. Existing exposure studies have usually focused on specific stressors for a constrained period of time. In response, the concept of the exposome has been raised, which is defined as the totality of exposure experienced from conception until death. The EU FP7-ENVIRONMENT research project HEALS was launched with the aim of incorporating a series of novel technologies, data analysis, and modelling tools to efficiently support exposome studies in Europe. The authors have developed a framework of modelling tools for estimating the long-term external exposure of selected population groups to multiple stressors through different pathways. As the starting point, the stressors, including electromagnetic fields (EMF) and ultraviolet light (UV) through dermal uptake, phthalates (DEHP, DIDP, and DINP) through inhalation, as well as chromium, mercury, and lead through food intake, have been selected. The simulation for multiple stressors has been realised by developing a probabilistic model that integrates the micro-environment approach, time-activity patterns, and a life course trajectory model. The methodology has been applied to a selected sample of subjects enrolled in the Italian Twin Registry (ITR). The results show that long-term exposures to multiple stressors are affected by factors including age, gender, geographical location, and education level. The methods developed in this paper extended the temporal and spatial scales of exposure modelling in Europe. Moreover, the application of our methods provided a novel approach and crucial input data for future work on environment-wide association studies.

Fast Homogeneous En Bloc Staining of Large Tissue Samples for Volume Electron Microscopy.

Fixation and staining of large tissue samples are critical for the acquisition of volumetric electron microscopic image datasets and the subsequent reconstruction of neuronal circuits. Efficient protocols exist for the staining of small samples, but uniform contrast is often difficult to achieve when the sample diameter exceeds a few hundred micrometers. Recently, a protocol (BROPA, brain-wide reduced-osmium staining with pyrogallol-mediated amplification) was developed that achieves homogeneous staining of the entire mouse brain but requires very long sample preparation times. By exploring modifications of this protocol we developed a substantially faster procedure, fBROPA, that allows for reliable high-quality staining of tissue blocks on the millimeter scale. Modifications of the original BROPA protocol include drastically reduced incubation times and a lead aspartate incubation to increase sample conductivity. Using this procedure, whole brains from adult zebrafish were stained within 4 days. Homogenous high-contrast staining was achieved throughout the brain. High-quality image stacks with voxel sizes of 10 × 10 × 25 nm3 were obtained by serial block-face imaging using an electron dose of ~15 e-/nm2. No obvious reduction in staining quality was observed in comparison to smaller samples stained by other state-of-the-art procedures. Furthermore, high-quality images with minimal charging artifacts were obtained from non-neural tissues with low membrane density. fBROPA is therefore likely to be a versatile and efficient sample preparation protocol for a wide range of applications in volume electron microscopy.

Precise Synaptic Balance in the Zebrafish Homolog of Olfactory Cortex.

Neuronal computations critically depend on the connectivity rules that govern the convergence of excitatory and inhibitory synaptic signals onto individual neurons. To examine the functional synaptic organization of a distributed memory network, we performed voltage clamp recordings in telencephalic area Dp of adult zebrafish, the homolog of olfactory cortex. In neurons of posterior Dp, odor stimulation evoked large, recurrent excitatory and inhibitory inputs that established a transient state of high conductance and synaptic balance. Excitation and inhibition in individual neurons were co-tuned to different odors and correlated on slow and fast timescales. This precise synaptic balance implies specific connectivity among Dp neurons, despite the absence of an obvious topography. Precise synaptic balance stabilizes activity patterns in different directions of coding space and in time while preserving high bandwidth. The coordinated connectivity of excitatory and inhibitory subnetworks in Dp therefore supports fast recurrent memory operations.

SBEMimage: Versatile Acquisition Control Software for Serial Block-Face Electron Microscopy.

We present SBEMimage, an open-source Python-based application to operate serial block-face electron microscopy (SBEM) systems. SBEMimage is designed for complex, challenging acquisition tasks, such as large-scale volume imaging of neuronal tissue or other biological ultrastructure. Advanced monitoring, process control, and error handling capabilities improve reliability, speed, and quality of acquisitions. Debris detection, autofocus, real-time image inspection, and various other quality control features minimize the risk of data loss during long-term acquisitions. Adaptive tile selection allows for efficient imaging of large tissue volumes of arbitrary shape. The software's graphical user interface is optimized for remote operation. In its user-friendly viewport, tile grids covering the region of interest to be acquired are overlaid on previously acquired overview images of the sample surface. Images from other sources, e.g., light microscopes, can be imported and superimposed. SBEMimage complements existing DigitalMicrograph (Gatan Microscopy Suite) installations on 3View systems but permits higher acquisition rates by interacting directly with the microscope's control software. Its modular architecture and the use of Python/PyQt make SBEMimage highly customizable and extensible, which allows for fast prototyping and will permit adaptation to a wide range of SBEM systems and applications.

Rapid olfactory discrimination learning in adult zebrafish.

The zebrafish is a model organism to study olfactory information processing, but efficient behavioral procedures to analyze olfactory discrimination and memory are lacking. We devised an automated odor discrimination task for adult zebrafish based on olfactory conditioning of feeding behavior. Presentation of a conditioned odor (CS+), but not a neutral odor (CS-) was followed by food delivery at a specific location. Fish developed differential behavioral responses to CS+ and CS- within a few trials. The behavioral response to the CS+ was complex and included components reminiscent of food search such as increased swimming speed and water surface sampling. Appetitive behavior was therefore quantified by a composite score that combined measurements of multiple behavioral parameters. Robust discrimination behavior was observed in different strains, even when odors were chemically similar, and learned preferences could overcome innate odor preferences. These results confirm that zebrafish can rapidly learn to make fine odor discriminations. The procedure is efficient and provides novel opportunities to dissect the neural mechanisms underlying olfactory discrimination and memory.

Community-based benchmarking improves spike rate inference from two-photon calcium imaging data.

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

Experience-Dependent Plasticity of Odor Representations in the Telencephalon of Zebrafish.

Sensory systems balance stability and plasticity to optimize stimulus representations in dynamic environments. We studied these processes in the olfactory system of adult zebrafish. Activity patterns evoked by repeated odor stimulation were measured by multiphoton calcium imaging in the olfactory bulb (OB) and in telencephalic area Dp, the homolog of olfactory cortex. Whereas odor responses in the OB were highly reproducible, responses of Dp neurons adapted over trials and exhibited substantial variability that could be attributed to ongoing activity and to systematic changes in neuronal representations following each stimulus. An NMDA receptor antagonist did not affect the magnitude of odor responses but strongly reduced the variability and experience-dependent modification of odor responses in Dp. As a consequence, odor representations became stable over trials. These results demonstrate that odor representations in higher brain areas are continuously modified by experience, supporting the view that olfactory processing is inseparable from memory, even in the absence of reinforcement.

3-dimensional electron microscopic imaging of the zebrafish olfactory bulb and dense reconstruction of neurons.

Large-scale reconstructions of neuronal populations are critical for structural analyses of neuronal cell types and circuits. Dense reconstructions of neurons from image data require ultrastructural resolution throughout large volumes, which can be achieved by automated volumetric electron microscopy (EM) techniques. We used serial block face scanning EM (SBEM) and conductive sample embedding to acquire an image stack from an olfactory bulb (OB) of a zebrafish larva at a voxel resolution of 9.25×9.25×25 nm3. Skeletons of 1,022 neurons, 98% of all neurons in the OB, were reconstructed by manual tracing and efficient error correction procedures. An ergonomic software package, PyKNOSSOS, was created in Python for data browsing, neuron tracing, synapse annotation, and visualization. The reconstructions allow for detailed analyses of morphology, projections and subcellular features of different neuron types. The high density of reconstructions enables geometrical and topological analyses of the OB circuitry. Image data can be accessed and viewed through the neurodata web services (http://www.neurodata.io). Raw data and reconstructions can be visualized in PyKNOSSOS.