Recent advances in brain-wide recordings of small animals such as worms, fish, and flies have revealed complex activity involving large populations of neurons. In the Drosophila brain, with about 140,000 neurons, brain-wide recordings have been critical to uncovering widespread sensory and motor activity. However, current limitations in volumetric imaging rates hinder the accurate capture of fast neural dynamics. To improve the speed of volumetric imaging in Drosophila , we leverage the recently introduced light beads microscopy (LBM) method. We built a microscope and a LBM module tailored to fly brain experiments and used it to record brain-wide calcium signals in adult behaving flies at either 28 volumes per second or at 60 volumes per second (when selecting the central brain alone). We uncover fast-timescale auditory responses that are missed with standard volumetric imaging. We also demonstrate how temporal super-resolution can be combined with LBM data to uncover responses to single Drosophila courtship song pulses. This establishes LBM as a viable tool for capturing whole-brain activity at high spatial and temporal resolution in the fly.

Many laboratories use two-photon microscopy through commercial suppliers, or homemade designs of considerable complexity. The integrated nature of these systems complicates customization, troubleshooting, and training on the principles of two-photon microscopy. Here, we present "Twinkle": a microscope for Two-photon Imaging in Neuroscience, and Kit for Learning and Education. It is a fully open, high performing and easy-to-set-up microscope that can effectively be used for both education and research. The instrument features a > 1 mm field of view, using a modern objective with 3 mm working distance and 2 inch diameter optics combined with GaAsP photomultiplier tubes to maximize the fluorescence signal. We document our experiences using this system as a teaching tool in several two week long workshops, exemplify scientific use cases, and conclude with a broader note on the place of our work in the growing space of open scientific instrumentation.

Detailed maps, or connectomes, of the fly brain and nervous system reveal how circuit architecture (wiring) shapes both neural activity and behavior. These maps already demonstrate that it is possible to predict function from structure, and provide the essential foundation for biologically realistic models of circuit function.

Imaging methods that span both functional measures in living tissue and anatomical measures in fixed tissue have played critical roles in advancing our understanding of the brain. However, making direct comparisons between different imaging modalities, particularly spanning living and fixed tissue, has remained challenging. For example, comparing brain-wide neural dynamics across experiments and aligning such data to anatomical resources, such as gene expression patterns or connectomes, requires precise alignment to a common set of anatomical coordinates. However, reaching this goal is difficult because registering in vivo functional imaging data to ex vivo reference atlases requires accommodating differences in imaging modality, microscope specification, and sample preparation. We overcome these challenges in Drosophila by building an in vivo reference atlas from multiphoton-imaged brains, called the Functional Drosophila Atlas. We then develop a registration pipeline, BrIdge For Registering Over Statistical Templates (BIFROST), for transforming neural imaging data into this common space and for importing ex vivo resources such as connectomes. Using genetically labeled cell types as ground truth, we demonstrate registration with a precision of less than 10 microns. Overall, BIFROST provides a pipeline for registering functional imaging datasets in the fly, both within and across experiments.

Brains comprise complex networks of neurons and connections. Network analysis applied to the wiring diagrams of brains can offer insights into how brains support computations and regulate information flow. The completion of the first whole-brain connectome of an adult Drosophila, the largest connectome to date, containing 130,000 neurons and millions of connections, offers an unprecedented opportunity to analyze its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We discovered that the network of the fly brain displays rich club organization, with a large population (30% percent of the connectome) of highly connected neurons. We identified subsets of rich club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex and will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5×10 7 chemical synapses between ∼130,000 neurons reconstructed from a female Drosophila melanogaster . The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.

Many animals perceive odorant molecules by collecting information from ensembles of olfactory neurons, where each neuron uses receptors that are tuned to recognize certain odorant molecules with different binding affinity. Olfactory systems are able, in principle, to detect and discriminate diverse odorants using combinatorial coding strategies. We have combined microfluidics and multineuronal imaging to study the ensemble-level olfactory representations at the sensory periphery of the nematode Caenorhabditis elegans . The collective activity of C. elegans chemosensory neurons reveals high-dimensional representations of olfactory information across a broad space of odorant molecules. We reveal diverse tuning properties and dose-response curves across chemosensory neurons and across odorants. We describe the unique contribution of each sensory neuron to an ensemble-level code for volatile odorants. We show that a natural stimuli, a set of nematode pheromones, are also encoded by the sensory ensemble. The integrated activity of the C. elegans chemosensory neurons contains sufficient information to robustly encode the intensity and identity of diverse chemical stimuli.

Until now, most brain studies have focused on small numbers of neurons that interact in limited circuits, allowing analysis of individual computations or steps of neural processing. During behaviour, however, brain activity must integrate multiple circuits in different brain regions. Whole-brain recording with cellular resolution provides a new opportunity to dissect the neural basis of behaviour, but whole-brain activity is mutually contingent on behaviour itself, especially for natural behaviours such as navigation, mating or hunting, which require dynamic interaction between the animal, its environment and other animals. Many of the signalling and feedback pathways that animals use to guide behaviour only occur in freely moving animals. Recent technological advances have enabled whole-brain recording in small behaving animals including the nematode Caenorhabditis elegans , the fruit fly Drosophila melanogaster and the larval zebrafish Danio rerio . These whole-brain experiments capture neural activity with cellular resolution spanning sensory, decision-making and motor circuits, and thereby demand new theoretical approaches that integrate brain dynamics with behavioural dynamics. We review the experimental and theoretical methods used to understand animal behaviour and whole-brain activity, and the opportunities for physics to contribute to this emerging field of systems neuroscience.

The availability of both anatomical connectivity and brain-wide neural activity measurements in C. elegans make the worm a promising system for learning detailed, mechanistic models of an entire nervous system in a data-driven way. However, one faces several challenges when constructing such a model. We often do not have direct experimental access to important modeling details such as single-neuron dynamics and the signs and strengths of the synaptic connectivity. Further, neural activity can only be measured in a subset of neurons, often indirectly via calcium imaging, and significant trial-to-trial variability has been observed. To address these challenges, we introduce a connectome-constrained latent variable model (CC-LVM) of the unobserved voltage dynamics of the entire C. elegans nervous system and the observed calcium signals. We used the framework of variational autoencoders to fit parameters of the mechanistic simulation constituting the generative model of the LVM to calcium imaging observations. A variational approximate posterior distribution over latent voltage traces for all neurons is efficiently inferred using an inference network, and constrained by a prior distribution given by the biophysical simulation of neural dynamics. We applied this model to an experimental whole-brain dataset, and found that connectomic constraints enable our LVM to predict the activity of neurons whose activity were withheld significantly better than models unconstrained by a connectome. We explored models with different degrees of biophysical detail, and found that models with realistic conductance-based synapses provide markedly better predictions than current-based synapses for this system.

Hydrogen peroxide (H 2 O 2 ) is the most common chemical threat that organisms face. Here, we show that H 2 O 2 alters the bacterial food preference of Caenorhabditis elegans , enabling the nematodes to find a safe environment with food. H 2 O 2 induces the nematodes to leave food patches of laboratory and microbiome bacteria when those bacterial communities have insufficient H 2 O 2 -degrading capacity. The nematode’s behavior is directed by H 2 O 2 -sensing neurons that promote escape from H 2 O 2 and by bacteria-sensing neurons that promote attraction to bacteria. However, the input for H 2 O 2 -sensing neurons is removed by bacterial H 2 O 2 -degrading enzymes and the bacteria-sensing neurons’ perception of bacteria is prevented by H 2 O 2 . The resulting cross-attenuation provides a general mechanism that ensures the nematode’s behavior is faithful to the lethal threat of hydrogen peroxide, increasing the nematode’s chances of finding a niche that provides both food and protection from hydrogen peroxide.

Microfluidic chip experiments for the NeuroPAL publications "NeuroPAL: A Multicolor Atlas for Whole-Brain Neuronal Identification in C. elegans" and "Extracting neural signals from semi-immobilized animals with deformable non-negative matrix factorization" (dNMF). This dataset contains image volumes, GCaMP recordings, and neuronal activity. Please see the included README for more information. The image volumes are best viewed using the NeuroPAL ID software available at: https://www.hobertlab.org/neuropal/ OR https://github.com/amin-nejat/CELL_ID An in-depth description of NeuroPAL is available at: https://www.cell.com/cell/fulltext/S0092-8674(20)31682-2 OR https://www.biorxiv.org/content/10.1101/676312v2 An in-depth description of dNMF is available at: https://www.biorxiv.org/content/10.1101/2020.07.07.192120v1

Extracting calcium traces from the neurons of C. elegans is an important problem, enabling the study of individual neuronal activity and the large-scale dynamics that govern behavior. Traditionally, non-negative matrix factorization (NMF) methods have been successful in demixing and denoising cellular calcium activity in relatively motionless or pre-registered videos. However, in the case of C. elegans or other animal models where motion compensation methods fail to stabilize the effect of even mild motion in the imaging data, standard NMF methods fail to capture cellular footprints since these footprints are variable in time. In this work, we introduce deformable non-negative matrix factorization (dNMF), which models the motion trajectory of the underlying image space using a polynomial basis function. Spatial footprints and neural activity are optimized jointly with motion trajectories in a matrix tri-factorization setting. On simulated data, dNMF is demonstrated to outperform currently available demixing methods as well as methods that account for motion and demixing separately. Furthermore, we display the practical utility of our approach in extracting calcium traces from C. elegans microscopy videos. The extracted traces elucidate spontaneous neural activity as well as responses to stimuli. Open source code implementing this pipeline is available at https://github.com/amin-nejat/dNMF

NeuroPAL Datasets The following datasets are provided for the publication "NeuroPAL: A Multicolor Atlas for Whole-Brain Neuronal Identification in C. elegans". The paper is available here: https://www.cell.com/cell/fulltext/S0092-8674(20)31682-2 See the included README for more information.

Extracting calcium traces from populations of neurons is a critical step in the study of the large-scale neural dynamics that govern behavior. Accurate activity extraction requires the correction of motion and movement-induced deformations as well as demixing of signals that may overlap spatially due to limitations in optical resolution. Traditionally, non-negative matrix factorization (NMF) methods have been successful in demixing and denoising cellular calcium activity in relatively motionless or pre-registered videos. However, standard NMF methods fail in animals undergoing significant non-rigid motion; similarly, standard image registration methods based on template matching can fail when large changes in activity lead to mismatches with the image template. To address these issues simultaneously, we introduce a deformable non-negative matrix factorization (dNMF) framework that jointly optimizes registration with signal demixing. On simulated data and real semi-immobilized C. elegans microscopy videos, dNMF outperforms traditional demixing methods that account for motion and demixing separately. Finally, following the extraction of neural traces from multiple imaging experiments, we develop a quantile regression time-series normalization technique to account for varying neural signal intensity baselines across different animals or different imaging setups. Open source code implementing this pipeline is available at https://github.com/amin-nejat/dNMF .

ABSTRACTComprehensively resolving single neurons and their cellular identities from whole-brain fluorescent images is a major challenge. We achieve this inC. elegansthrough the engineering and use of a multicolor transgene called NeuroPAL (aNeuronalPolychromaticAtlas ofLandmarks). NeuroPAL worms share a stereotypical multicolor fluorescence map for the entire hermaphrodite nervous system that allows comprehensive determination of neuronal identities. Neurons labeled with NeuroPAL do not exhibit fluorescence in the green, cyan, or yellow emission channels, allowing the transgene to be used with numerous reporters of gene expression or neuronal dynamics. Here we showcase three studies that leverage NeuroPAL for nervous-system-wide neuronal identification. First, we determine the brainwide expression patterns of all metabotropic receptors for acetylcholine, GABA, and glutamate, completing a map of this communication network. Second, we uncover novel changes in cell fate caused by transcription factor mutations. Third, we record brainwide activity in response to attractive and repulsive chemosensory cues, characterizing multimodal coding and novel neuronal asymmetries for these stimuli. We present a software package that enables semi-automated determination of all neuronal identities based on color and positional information. The NeuroPAL framework and software provide a means to design landmark atlases for other tissues and organisms. In conclusion, we expect NeuroPAL to serve as an invaluable tool for gene expression analysis, neuronal fate studies, and for mapping whole-brain activity patterns.

Comprehensively resolving neuronal identities in whole-brain images is a major challenge. We achieve this in C. elegans by engineering a multicolor transgene called NeuroPAL (a neuronal polychromatic atlas of landmarks). NeuroPAL worms share a stereotypical multicolor fluorescence map for the entire hermaphrodite nervous system that resolves all neuronal identities. Neurons labeled with NeuroPAL do not exhibit fluorescence in the green, cyan, or yellow emission channels, allowing the transgene to be used with numerous reporters of gene expression or neuronal dynamics. We showcase three applications that leverage NeuroPAL for nervous-system-wide neuronal identification. First, we determine the brainwide expression patterns of all metabotropic receptors for acetylcholine, GABA, and glutamate, completing a map of this communication network. Second, we uncover changes in cell fate caused by transcription factor mutations. Third, we record brainwide activity in response to attractive and repulsive chemosensory cues, characterizing multimodal coding for these stimuli.

Spatiotemporal patterns of gene expression are fundamental to every developmental program. The resulting macroscopic domains have been mainly characterized by their levels of gene products [1–3]. However, the establishment of such patterns results from differences in the dynamics of microscopic events in individual cells such as transcription. It is unclear how these microscopic decisions lead to macroscopic patterns, as measurements in fixed tissue cannot access the underlying transcriptional dynamics [4–7]. In vivo transcriptional dynamics have long been approached in single-celled organisms [8–12], but never in a multicellular developmental context. Here, we directly address howboundaries of gene expression emerge in theDrosophila embryo bymeasuring the absolute number of actively transcribing polymerases in real time in individual nuclei. Specifically, we show that the formation of a boundary cannot be quantitatively explained by the rate of mRNA production in each cell, but instead requires amplification of the dynamic range of the expression boundary. This amplification is accomplished by nuclei randomly adopting active or inactive states of transcription, leading to a collective effect where the fraction of active nuclei is modulated in space. Thus, developmental patterns are not just the consequence of reproducible transcriptional dynamics in individual nuclei, but are the result of averaging expression over space and time.

Noise in gene expression has been shown to play an important role in various organisms. In particular, transcriptional bursts are thought to be a common mode for mRNA output. Yet, the final patterns of gene expression, especially in the case of multicellular organisms such as Drosophila melanogaster, are highly precise and reproducible. In order to address this apparent contradiction, we present a strategy to measure transcriptional dynamics in living fly embryos by monitoring the production of mRNA at their sites of transcription on DNA loci in real time simultaneously in hundreds of individual nuclei. In particular, we measure the transcriptional activity of the hunchback promoter as a function of the position along the embryo throughout the first three hours of development. We observe "waves" of transcription associated with the progressing mitotic cycles, where transcription rises steadily in the beginning of the nuclear cycle, peaks mid-way through it and disappears during mitosis. Although the overall shape of these transcription waves is similar, we see a high degree of variability among nuclei. However, we see no evidence of transcriptional bursting. Single molecule mRNA FISH reveals that this variability in transcription rate does not translate into noise in the cytoplasmic mRNA distribution suggesting the need for a mechanism of noise rectification. In fact, we observe an overall 4-fold noise reduction between nuclear activity and cytoplasmic mRNA levels. using a simple stochastic model we demonstrate that our noise measurements can only be explained through a combination of both temporal and spatial averaging.

Spatiotemporal patterns of gene expression are fundamental to every developmental program. The resulting macroscopic domains have been mainly characterized by their levels of gene products. However, the establishment of such patterns results from differences in the dynamics of microscopic events in individual cells such as transcription. It is unclear how these microscopic decisions lead to macroscopic patterns, as measurements in fixed tissue cannot access the underlying transcriptional dynamics. In vivo transcriptional dynamics have long been approached in single-celled organisms, but never in a multicellular developmental context. Here, we directly address how boundaries of gene expression emerge in the Drosophila embryo by measuring the absolute number of actively transcribing polymerases in real time in individual nuclei. Specifically, we show that the formation of a boundary cannot be quantitatively explained by the rate of mRNA production in each cell, but instead requires amplification of the dynamic range of the expression boundary. This amplification is accomplished by nuclei randomly adopting active or inactive states of transcription, leading to a collective effect where the fraction of active nuclei is modulated in space. Thus, developmental patterns are not just the consequence of reproducible transcriptional dynamics in individual nuclei, but are the result of averaging expression over space and time.