NeuroPAL: A Multicolor Atlas for Whole-Brain Neuronal Identification in C. elegans

A Markovian dynamics for Caenorhabditis elegans behavior across scales

How do we capture the breadth of behavior in animal movement, from rapid body twitches to aging? Using high-resolution videos of the nematode worm Caenorhabditis elegans, we show that a single dynamics connects posture-scale fluctuations with trajectory diffusion and longer-lived behavioral states. We take short posture sequences as an instantaneous behavioral measure, fixing the sequence length for maximal prediction. Within the space of posture sequences, we construct a fine-scale, maximum entropy partition so that transitions among microstates define a high-fidelity Markov model, which we also use as a means of principled coarse-graining. We translate these dynamics into movement using resistive force theory, capturing the statistical properties of foraging trajectories. Predictive across scales, we leverage the longest-lived eigenvectors of the inferred Markov chain to perform a top-down subdivision of the worm's foraging behavior, revealing both "runs-and-pirouettes" as well as previously uncharacterized finer-scale behaviors. We use our model to investigate the relevance of these fine-scale behaviors for foraging success, recovering a trade-off between local and global search strategies.

Functional analysis of conserved C. elegans bHLH family members uncovers lifespan control by a peptidergic hub neuron

Throughout the animal kingdom, several members of the basic helix-loop-helix (bHLH) family act as proneural genes during early steps of nervous system development. Roles of bHLH genes in specifying terminal differentiation of postmitotic neurons have been less extensively studied. We analyze here the function of five C. elegans bHLH genes, falling into three phylogenetically conserved subfamilies, which are continuously expressed in a very small number of postmitotic neurons in the central nervous system. We show (a) that two orthologs of the vertebrate bHLHb4/b5 genes, called hlh-17 and hlh-32, function redundantly to specify the identity of a single head interneuron (AUA), as well as an individual motor neuron (VB2), (b) that the PTF1a ortholog hlh-13 acts as a terminal selector to control terminal differentiation and function of the sole octopaminergic neuron class in C. elegans , RIC, and (c) that the NHLH1/2 ortholog hlh-15 controls terminal differentiation and function of the peptidergic AVK head interneuron class, a known neuropeptidergic signaling hub in the animal. Strikingly, through null mutant analysis and cell-specific rescue experiments, we find that loss of hlh-15/NHLH in the peptidergic AVK neurons and the resulting abrogation of neuropeptide secretion causes a substantially expanded lifespan of the animal, revealing an unanticipated impact of a central, peptidergic hub neuron in regulating lifespan, which we propose to be akin to hypothalamic control of lifespan in vertebrates. Taken together, our functional analysis reveals themes of bHLH gene function during terminal differentiation that are complementary to the earlier lineage specification roles of other bHLH family members. However, such late functions are much more sparsely employed by members of the bHLH transcription factor family, compared to the function of the much more broadly employed homeodomain transcription factor family.

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics into the imaging path. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained 'de-aberration' networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.

TWISP: a transgenic worm for interrogating signal propagation in Caenorhabditis elegans

Genetically encoded optical indicators and actuators of neural activity allow for all-optical investigations of signaling in the nervous system. But commonly used indicators, actuators, and expression strategies are poorly suited for systematic measurements of signal propagation at brain scale and cellular resolution. Large-scale measurements of the brain require indicators and actuators with compatible excitation spectra to avoid optical crosstalk. They must be highly expressed in every neuron but at the same time avoid lethality and permit the animal to reach adulthood. Their expression must also be compatible with additional fluorescent labels to locate and identify neurons, such as those in the NeuroPAL cell identification system. We present TWISP, a transgenic worm for interrogating signal propagation, that addresses these needs and enables optical measurements of evoked calcium activity at brain scale and cellular resolution in the nervous system of the nematode Caenorhabditis elegans. In every neuron we express a nonconventional optical actuator, the gustatory receptor homolog GUR-3 + PRDX-2, under the control of a drug-inducible system QF + hGR, and a calcium indicator GCAMP6s, in a background with additional fluorophores from the NeuroPAL cell ID system. We show that this combination, but not others tested, avoids optical crosstalk, creates strong expression in the adult, and generates stable transgenic lines for systematic measurements of signal propagation in the worm brain.

Exposure of quantum dots in the nervous system: Central nervous system risks and the blood-brain barrier interface

Quantum dots currently possess significant importance in the field of biomedical science. Upon introduction into the body, quantum dots exhibit a tendency to accumulate in diverse tissues including the central nervous system (CNS). Consequently, it becomes imperative to devote specific attention to their potential toxic effects. Moreover, the preservation of optimal CNS function relies heavily on blood-brain barrier (BBB) integrity, thereby necessitating its prioritization in neurotoxicological investigations. A more comprehensive understanding of the BBB and CNS characteristics, along with the underlying mechanisms that may contribute to neurotoxicity, will greatly aid researchers in the development of effective design strategies. This article offers an in-depth look at the methods used to reduce the harmful effects of quantum dots on the nervous system, alongside the progression of effective treatments for brain-related conditions. The focal point of this discussion is the BBB and its intricate association with the CNS and neurotoxicology. The discourse commences by recent advancements in the medical application of quantum dots are examined. Subsequently, elucidating the mechanisms through which quantum dots infiltrate the human body and traverse into the brain. Additionally, the discourse delves into the factors that facilitate the passage of quantum dots across the BBB, primarily encompassing the physicochemical properties of quantum dots and the BBB's inherent capacity for self-permeability alteration. Furthermore, a concluding summary is presented, emphasizing existing research deficiencies and identifying promising avenues for further investigation within this field.

Unifying community-wide whole-brain imaging datasets enables robust automated neuron identification and reveals determinants of neuron positioning in C. elegans

We develop a data harmonization approach for C. elegans volumetric microscopy data, still or video, consisting of a standardized format, data pre-processing techniques, and a set of human-in-the-loop machine learning based analysis software tools. We unify a diverse collection of 118 whole-brain neural activity imaging datasets from 5 labs, storing these and accompanying tools in an online repository called WormID (wormid.org). We use this repository to train three existing automated cell identification algorithms to, for the first time, enable accuracy in neural identification that generalizes across labs, approaching human performance in some cases. We mine this repository to identify factors that influence the developmental positioning of neurons. To facilitate communal use of this repository, we created open-source software, code, web-based tools, and tutorials to explore and curate datasets for contribution to the scientific community. This repository provides a growing resource for experimentalists, theorists, and toolmakers to (a) study neuroanatomical organization and neural activity across diverse experimental paradigms, (b) develop and benchmark algorithms for automated neuron detection, segmentation, cell identification, tracking, and activity extraction, and (c) inform models of neurobiological development and function.

C. elegans foraging as a model for understanding the neuronal basis of decision-making

Animals have evolved to seek, select, and exploit food sources in their environment. Collectively termed foraging, these ubiquitous behaviors are necessary for animal survival. As a foundation for understanding foraging, behavioral ecologists established early theoretical and mathematical frameworks which have been subsequently refined and supported by field and laboratory studies of foraging animals. These simple models sought to explain how animals decide which strategies to employ when locating food, what food items to consume, and when to explore the environment for new food sources. These foraging decisions involve integration of prior experience with multimodal sensory information about the animal's current environment and internal state. We suggest that the nematode Caenorhabditis elegans is well-suited for a high-resolution analysis of complex goal-oriented behaviors such as foraging. We focus our discussion on behavioral studies highlighting C. elegans foraging on bacteria and summarize what is known about the underlying neuronal and molecular pathways. Broadly, we suggest that this simple model system can provide a mechanistic understanding of decision-making and present additional avenues for advancing our understanding of complex behavioral processes.

A neurotransmitter atlas of C. elegans males and hermaphrodites

Mapping neurotransmitter identities to neurons is key to understanding information flow in a nervous system. It also provides valuable entry points for studying the development and plasticity of neuronal identity features. In the C. elegans nervous system, neurotransmitter identities have been largely assigned by expression pattern analysis of neurotransmitter pathway genes that encode neurotransmitter biosynthetic enzymes or transporters. However, many of these assignments have relied on multicopy reporter transgenes that may lack relevant cis-regulatory information and therefore may not provide an accurate picture of neurotransmitter usage. We analyzed the expression patterns of 16 CRISPR/Cas9-engineered knock-in reporter strains for all main types of neurotransmitters in C. elegans (glutamate, acetylcholine, GABA, serotonin, dopamine, tyramine, and octopamine) in both the hermaphrodite and the male. Our analysis reveals novel sites of expression of these neurotransmitter systems within both neurons and glia, as well as non-neural cells. The resulting expression atlas defines neurons that may be exclusively neuropeptidergic, substantially expands the repertoire of neurons capable of co-transmitting multiple neurotransmitters, and identifies novel neurons that uptake monoaminergic neurotransmitters. Furthermore, we also observed unusual co-expression patterns of monoaminergic synthesis pathway genes, suggesting the existence of novel monoaminergic transmitters. Our analysis results in what constitutes the most extensive whole-animal-wide map of neurotransmitter usage to date, paving the way for a better understanding of neuronal communication and neuronal identity specification in C. elegans.

Building and integrating brain-wide maps of nervous system function in invertebrates

The selection and execution of context-appropriate behaviors is controlled by the integrated action of neural circuits throughout the brain. However, how activity is coordinated across brain regions, and how nervous system structure enables these functional interactions, remain open questions. Recent technical advances have made it feasible to build brain-wide maps of nervous system structure and function, such as brain activity maps, connectomes, and cell atlases. Here, we review recent progress in this area, focusing on C. elegans and D. melanogaster, as recent work has produced global maps of these nervous systems. We also describe neural circuit motifs elucidated in studies of specific networks, which highlight the complexities that must be captured to build accurate models of whole-brain function.

Sex-specific developmental gene expression atlas unveils dimorphic gene networks in C. elegans

Sex-specific traits and behaviors emerge during development by the acquisition of unique properties in the nervous system of each sex. However, the genetic events responsible for introducing these sex-specific features remain poorly understood. In this study, we create a comprehensive gene expression atlas of pure populations of hermaphrodites and males of the nematode Caenorhabditis elegans across development. We discover numerous differentially expressed genes, including neuronal gene families like transcription factors, neuropeptides, and G protein-coupled receptors. We identify INS-39, an insulin-like peptide, as a prominent male-biased gene expressed specifically in ciliated sensory neurons. We show that INS-39 serves as an early-stage male marker, facilitating the effective isolation of males in high-throughput experiments. Through complex and sex-specific regulation, ins-39 plays pleiotropic sexually dimorphic roles in various behaviors, while also playing a shared, dimorphic role in early life stress. This study offers a comparative sexual and developmental gene expression database for C. elegans. Furthermore, it highlights conserved genes that may underlie the sexually dimorphic manifestation of different human diseases.

BIFROST: a method for registering diverse imaging datasets of the Drosophila brain

The heterogeneity of brain imaging methods in neuroscience provides rich data that cannot be captured by a single technique, and our interpretations benefit from approaches that enable easy comparison both within and across different data types. 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, this is challenging 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 (FDA). We then develop a two-step 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.

Significance

Large-scale functional imaging experiments in Drosophila have given us new insights into neural activity in various sensory and behavioral contexts. However, precisely registering volumetric images from different studies has proven challenging, limiting quantitative comparisons of data across experiments. Here, we address this limitation by developing BIFROST, a registration pipeline robust to differences across experimental setups and datasets. We benchmark this pipeline by genetically labeling cell types in the fly brain and demonstrate sub-10 micron registration precision, both across specimens and across laboratories. We further demonstrate accurate registration between in-vivo brain volumes and ultrastructural connectomes, enabling direct structure-function comparisons in future experiments.

Automated segmentation and recognition of C. elegans whole-body cells

MOTIVATION

Accurate segmentation and recognition of C.elegans cells are critical for various biological studies, including gene expression, cell lineages, and cell fates analysis at single-cell level. However, the highly dense distribution, similar shapes, and inhomogeneous intensity profiles of whole-body cells in 3D fluorescence microscopy images make automatic cell segmentation and recognition a challenging task. Existing methods either rely on additional fiducial markers or only handle a subset of cells. Given the difficulty or expense associated with generating fiducial features in many experimental settings, a marker-free approach capable of reliably segmenting and recognizing C.elegans whole-body cells is highly desirable.

RESULTS

We report a new pipeline, called automated segmentation and recognition (ASR) of cells, and applied it to 3D fluorescent microscopy images of L1-stage C.elegans with 558 whole-body cells. A novel displacement vector field based deep learning model is proposed to address the problem of reliable segmentation of highly crowded cells with blurred boundary. We then realize the cell recognition by encoding and exploiting statistical priors on cell positions and structural similarities of neighboring cells. To the best of our knowledge, this is the first method successfully applied to the segmentation and recognition of C.elegans whole-body cells. The ASR-segmentation module achieves an F1-score of 0.8956 on a dataset of 116 C.elegans image stacks with 64 728 cells (accuracy 0.9880, AJI 0.7813). Based on the segmentation results, the ASR recognition module achieved an average accuracy of 0.8879. We also show ASR's applicability to other cell types, e.g. platynereis and rat kidney cells.

AVAILABILITY AND IMPLEMENTATION

The code is available at https://github.com/reaneyli/ASR.

Phasic/tonic glial GABA differentially transduce for olfactory adaptation and neuronal aging

Phasic (fast) and tonic (sustained) inhibition of γ-aminobutyric acid (GABA) are fundamental for regulating day-to-day activities, neuronal excitability, and plasticity. However, the mechanisms and physiological functions of glial GABA transductions remain poorly understood. Here, we report that the AMsh glia in Caenorhabditis elegans exhibit both phasic and tonic GABAergic signaling, which distinctively regulate olfactory adaptation and neuronal aging. Through genetic screening, we find that GABA permeates through bestrophin-9/-13/-14 anion channels from AMsh glia, which primarily activate the metabolic GABAB receptor GBB-1 in the neighboring ASH sensory neurons. This tonic action of glial GABA regulates the age-associated changes of ASH neurons and olfactory responses via a conserved signaling pathway, inducing neuroprotection. In addition, the calcium-evoked, vesicular glial GABA release acts upon the ionotropic GABAA receptor LGC-38 in ASH neurons to regulate olfactory adaptation. These findings underscore the fundamental significance of glial GABA in maintaining healthy aging and neuronal stability.

C. elegans: a prominent platform for modeling and drug screening in neurological disorders

INTRODUCTION

Human neurodevelopmental and neurodegenerative diseases (NDevDs and NDegDs, respectively) encompass a broad spectrum of disorders affecting the nervous system with an increasing incidence. In this context, the nematode C. elegans, has emerged as a benchmark model for biological research, especially in the field of neuroscience.

AREAS COVERED

The authors highlight the numerous advantages of this tiny worm as a model for exploring nervous system pathologies and as a platform for drug discovery. There is a particular focus given to describing the existing models of C. elegans for the study of NDevDs and NDegDs. Specifically, the authors underscore their strong applicability in preclinical drug development. Furthermore, they place particular emphasis on detailing the common techniques employed to explore the nervous system in both healthy and diseased states.

EXPERT OPINION

Drug discovery constitutes a long and expensive process. The incorporation of invertebrate models, such as C. elegans, stands as an exemplary strategy for mitigating costs and expediting timelines. The utilization of C. elegans as a platform to replicate nervous system pathologies and conduct high-throughput automated assays in the initial phases of drug discovery is pivotal for rendering therapeutic options more attainable and cost-effective.

How neurobehavior and brain development in alternative whole-organism models can contribute to prediction of developmental neurotoxicity

Developmental neurotoxicity (DNT) is not routinely evaluated in chemical risk assessment because current test paradigms for DNT require the use of mammalian models which are ethically controversial, expensive, and resource demanding. Consequently, efforts have focused on revolutionizing DNT testing through affordable novel alternative methods for risk assessment. The goal is to develop a DNT in vitro test battery amenable to high-throughput screening (HTS). Currently, the DNT in vitro test battery consists primarily of human cell-based assays because of their immediate relevance to human health. However, such cell-based assays alone are unable to capture the complexity of a developing nervous system. Whole organismal systems that qualify as 3 R (Replace, Reduce and Refine) models are urgently needed to complement cell-based DNT testing. These models can provide the necessary organismal context and be used to explore the impact of chemicals on brain function by linking molecular and/or cellular changes to behavioural readouts. The nematode Caenorhabditis elegans, the planarian Dugesia japonica, and embryos of the zebrafish Danio rerio are all suited to low-cost HTS and each has unique strengths for DNT testing. Here, we review the strengths and the complementarity of these organisms in a novel, integrative context and highlight how they can augment current cell-based assays for more comprehensive and robust DNT screening of chemicals. Considering the limitations of all in vitro test systems, we discuss how a smart combinatory use of these systems will contribute to a better human relevant risk assessment of chemicals that considers the complexity of the developing brain.

Mitochondrial energy state controls AMPK-mediated foraging behavior in C. elegans

Organisms surveil and respond to their environment using behaviors entrained by metabolic cues that reflect food availability. Mitochondria act as metabolic hubs and at the center of mitochondrial energy production is the protonmotive force (PMF), an electrochemical gradient generated by metabolite consumption. The PMF serves as a central integrator of mitochondrial status, but its role in governing metabolic signaling is poorly understood. We used optogenetics to dissipate the PMF in Caenorhabditis elegans tissues to test its role in food-related behaviors. Our data demonstrate that PMF reduction in the intestine is sufficient to initiate locomotor responses to acute food deprivation. This behavioral adaptation requires the cellular energy regulator AMP-activated protein kinase (AMPK) in neurons, not in the intestine, and relies on mitochondrial dynamics and axonal trafficking. Our results highlight a role for intestinal PMF as an internal metabolic cue, and we identify a bottom-up signaling axis through which changes in the PMF trigger AMPK activity in neurons to promote foraging behavior.

A molecular atlas of adult C. elegans motor neurons reveals ancient diversity delineated by conserved transcription factor codes

Motor neurons (MNs) constitute an ancient cell type targeted by multiple adult-onset diseases. It is therefore important to define the molecular makeup of adult MNs in animal models and extract organizing principles. Here, we generate a comprehensive molecular atlas of adult Caenorhabditis elegans MNs and a searchable database. Single-cell RNA sequencing of 13,200 cells reveals that ventral nerve cord MNs cluster into 29 molecularly distinct subclasses. Extending C. elegans Neuronal Gene Expression Map and Network (CeNGEN) findings, all MN subclasses are delineated by distinct expression codes of either neuropeptide or transcription factor gene families. Strikingly, combinatorial codes of homeodomain transcription factor genes succinctly delineate adult MN diversity in both C. elegans and mice. Further, molecularly defined MN subclasses in C. elegans display distinct patterns of connectivity. Hence, our study couples the connectivity map of the C. elegans motor circuit with a molecular atlas of its constituent MNs and uncovers organizing principles and conserved molecular codes of adult MN diversity.

See Elegans: Simple-to-use, accurate, and automatic 3D detection of neural activity from densely packed neurons

In the emerging field of whole-brain imaging at single-cell resolution, which represents one of the new frontiers to investigate the link between brain activity and behavior, the nematode Caenorhabditis elegans offers one of the most characterized models for systems neuroscience. Whole-brain recordings consist of 3D time series of volumes that need to be processed to obtain neuronal traces. Current solutions for this task are either computationally demanding or limited to specific acquisition setups. Here, we propose See Elegans, a direct programming algorithm that combines different techniques for automatic neuron segmentation and tracking without the need for the RFP channel, and we compare it with other available algorithms. While outperforming them in most cases, our solution offers a novel method to guide the identification of a subset of head neurons based on position and activity. The built-in interface allows the user to follow and manually curate each of the processing steps. See Elegans is thus a simple-to-use interface aimed at speeding up the post-processing of volumetric calcium imaging recordings while maintaining a high level of accuracy and low computational demands. (Contact: enrico.lanza@iit.it).

LET-381/FoxF and its target UNC-30/Pitx2 specify and maintain the molecular identity of C. elegans mesodermal glia that regulate motor behavior

While most glial cell types in the central nervous system (CNS) arise from neuroectodermal progenitors, some, like microglia, are mesodermally derived. To understand mesodermal glia development and function, we investigated C. elegans GLR glia, which envelop the brain neuropil and separate it from the circulatory system cavity. Transcriptome analysis shows that GLR glia combine astrocytic and endothelial characteristics, which are relegated to separate cell types in vertebrates. Combined fate acquisition is orchestrated by LET-381/FoxF, a fate-specification/maintenance transcription factor also expressed in glia and endothelia of other animals. Among LET-381/FoxF targets, the UNC-30/Pitx2 transcription factor controls GLR glia morphology and represses alternative mesodermal fates. LET-381 and UNC-30 co-expression in naive cells is sufficient for GLR glia gene expression. GLR glia inactivation by ablation or let-381 mutation disrupts locomotory behavior and promotes salt-induced paralysis, suggesting brain-neuropil activity dysregulation. Our studies uncover mechanisms of mesodermal glia development and show that like neuronal differentiation, glia differentiation requires autoregulatory terminal selector genes that define and maintain the glial fate.

Network Statistics of the Whole-Brain Connectome of Drosophila

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.

Learning dynamic representations of the functional connectome in neurobiological networks

The static synaptic connectivity of neuronal circuits stands in direct contrast to the dynamics of their function. As in changing community interactions, different neurons can participate actively in various combinations to effect behaviors at different times. We introduce an unsupervised approach to learn the dynamic affinities between neurons in live, behaving animals, and to reveal which communities form among neurons at different times. The inference occurs in two major steps. First, pairwise non-linear affinities between neuronal traces from brain-wide calcium activity are organized by non-negative tensor factorization (NTF). Each factor specifies which groups of neurons are most likely interacting for an inferred interval in time, and for which animals. Finally, a generative model that allows for weighted community detection is applied to the functional motifs produced by NTF to reveal a dynamic functional connectome. Since time codes the different experimental variables (e.g., application of chemical stimuli), this provides an atlas of neural motifs active during separate stages of an experiment (e.g., stimulus application or spontaneous behaviors). Results from our analysis are experimentally validated, confirming that our method is able to robustly predict causal interactions between neurons to generate behavior.

Automated cell annotation in multi-cell images using an improved CRF_ID algorithm

Cell identification is an important yet difficult process in data analysis of biological images. Previously, we developed an automated cell identification method called CRF_ID and demonstrated its high performance in C. elegans whole-brain images (Chaudhary et al, 2021). However, because the method was optimized for whole-brain imaging, comparable performance could not be guaranteed for application in commonly used C. elegans multi-cell images that display a subpopulation of cells. Here, we present an advance CRF_ID 2.0 that expands the generalizability of the method to multi-cell imaging beyond whole-brain imaging. To illustrate the application of the advance, we show the characterization of CRF_ID 2.0 in multi-cell imaging and cell-specific gene expression analysis in C. elegans. This work demonstrates that high accuracy automated cell annotation in multi-cell imaging can expedite cell identification and reduce its subjectivity in C. elegans and potentially other biological images of various origins.

Unraveling the hierarchical structure of posture and muscle activity changes during mating of Caenorhabditis elegans

One goal of neurobiology is to explain how decision-making in neuromuscular circuits produces behaviors. However, two obstacles complicate such efforts: individual behavioral variability and the challenge of simultaneously assessing multiple neuronal activities during behavior. Here, we circumvent these obstacles by analyzing whole animal behavior from a library of Caenorhabditis elegans male mating recordings. The copulating males express the GCaMP calcium sensor in the muscles, allowing simultaneous recording of posture and muscle activities. Our library contains wild type and males with selective neuronal desensitization in serotonergic neurons, which include male-specific posterior cord motor/interneurons and sensory ray neurons that modulate mating behavior. Incorporating deep learning-enabled computer vision, we developed a software to automatically quantify posture and muscle activities. By modeling, the posture and muscle activity data are classified into stereotyped modules, with the behaviors represented by serial executions and transitions among the modules. Detailed analysis of the modules reveals previously unidentified subtypes of the male's copulatory spicule prodding behavior. We find that wild-type and serotonergic neurons-suppressed males had different usage preferences for those module subtypes, highlighting the requirement of serotonergic neurons in the coordinated function of some muscles. In the structure of the behavior, bi-module repeats coincide with most of the previously described copulation steps, suggesting a recursive "repeat until success/give up" program is used for each step during mating. On the other hand, the transition orders of the bi-module repeats reveal the sub-behavioral hierarchy males employ to locate and inseminate hermaphrodites.

A protocol to transform a fluorescent reporter from a nuclear to a cytoplasmic location

To facilitate cell identification for expression pattern analysis in C. elegans , an SL2::GFP::H2B fluorescent reporter cassette has become a popular and widely used choice to generate nuclear localized reporter alleles by CRISPR/Cas9 genome engineering. When added at the 3' end of a locus of interest, this cassette concentrates GFP into the nucleus and permits the identification of expressing cells, for example with the help of the NeuroPAL tool. However, there are instances in which it is desirable to visualize the complete morphology of a cell that expresses an SL2::GFP::H2B reporter cassette. We describe here a CRISPR/Cas9-engineering strategy to transform an endogenous SL2::GFP::H2B tag into a cytosolic tag by insertion of the self-cleaving T2A tag in between GFP and H2B.

A full-body transcription factor expression atlas with completely resolved cell identities in C. elegans

Invariant cell lineage in C. elegans enables spatiotemporal resolution of transcriptional regulatory mechanisms controlling the fate of each cell. Here, we develop RAPCAT (Robust-point-matching- And Piecewise-affine-based Cell Annotation Tool) to automate cell identity assignment in three-dimensional image stacks of L1 larvae and profile reporter expression of 620 transcription factors in every cell. Transcription factor profile-based clustering analysis defines 80 cell types distinct from conventional phenotypic cell types and identifies three general phenotypic modalities related to these classifications. First, transcription factors are broadly downregulated in quiescent stage Hermaphrodite Specific Neurons, suggesting stage- and cell type-specific variation in transcriptome size. Second, transcription factor expression is more closely associated with morphology than other phenotypic modalities in different pre- and post-differentiation developmental stages. Finally, embryonic cell lineages can be associated with specific transcription factor expression patterns and functions that persist throughout postembryonic life. This study presents a comprehensive transcription factor atlas for investigation of intra-cell type heterogeneity.

Neural mechanisms of dopamine function in learning and memory in Caenorhabditis elegans

Research into learning and memory over the past decades has revealed key neurotransmitters that regulate these processes, many of which are evolutionarily conserved across diverse species. The monoamine neurotransmitter dopamine is one example of this, with countless studies demonstrating its importance in regulating behavioural plasticity. However, dopaminergic neural networks in the mammalian brain consist of hundreds or thousands of neurons, and thus cannot be studied at the level of single neurons acting within defined neural circuits. The nematode Caenorhabditis elegans (C. elegans) has an experimentally tractable nervous system with a completely characterized synaptic connectome. This makes it an advantageous system to undertake mechanistic studies into how dopamine encodes lasting yet flexible behavioural plasticity in the nervous system. In this review, we synthesize the research to date exploring the importance of dopaminergic signalling in learning, memory formation, and forgetting, focusing on research in C. elegans. We also explore the potential for dopamine-specific fluorescent biosensors in C. elegans to visualize dopaminergic neural circuits during learning and memory formation in real-time. We propose that the use of these sensors in C. elegans, in combination with optogenetic and other light-based approaches, will further illuminate the detailed spatiotemporal requirements for encoding behavioural plasticity in an accessible experimental system. Understanding the key molecules and circuit mechanisms that regulate learning and forgetting in more compact invertebrate nervous systems may reveal new druggable targets for enhancing memory storage and delaying memory loss in bigger brains.

Communities in C. elegans connectome through the prism of non-backtracking walks

The fundamental relationship between the mesoscopic structure of neuronal circuits and organismic functions they subserve is one of the major challenges in contemporary neuroscience. Formation of structurally connected modules of neurons enacts the conversion from single-cell firing to large-scale behaviour of an organism, highlighting the importance of their accurate profiling in the data. While connectomes are typically characterized by significant sparsity of neuronal connections, recent advances in network theory and machine learning have revealed fundamental limitations of traditionally used community detection approaches in cases where the network is sparse. Here we studied the optimal community structure in the structural connectome of Caenorhabditis elegans, for which we exploited a non-conventional approach that is based on non-backtracking random walks, virtually eliminating the sparsity issue. In full agreement with the previous asymptotic results, we demonstrated that non-backtracking walks resolve the ground truth annotation into clusters on stochastic block models (SBM) with the size and density of the connectome better than the spectral methods related to simple random walks. Based on the cluster detectability threshold, we determined that the optimal number of modules in a recently mapped connectome of C. elegans is 10, which precisely corresponds to the number of isolated eigenvalues in the spectrum of the non-backtracking flow matrix. The discovered communities have a clear interpretation in terms of their functional role, which allows one to discern three structural compartments in the worm: the Worm Brain (WB), the Worm Movement Controller (WMC), and the Worm Information Flow Connector (WIFC). Broadly, our work provides a robust network-based framework to reveal mesoscopic structures in sparse connectomic datasets, paving way to further investigation of connectome mechanisms for different functions.

A cGAL-UAS bipartite expression toolkit for Caenorhabditis elegans sensory neurons

Animals integrate sensory information from the environment and display various behaviors in response to external stimuli. In Caenorhabditis elegans hermaphrodites, 33 types of sensory neurons are responsible for chemosensation, olfaction, and mechanosensation. However, the functional roles of all sensory neurons have not been systematically studied due to the lack of facile genetic accessibility. A bipartite cGAL-UAS system has been previously developed to study tissue- or cell-specific functions in C. elegans. Here, we report a toolkit of new cGAL drivers that can facilitate the analysis of a vast majority of the 60 sensory neurons in C. elegans hermaphrodites. We generated 37 sensory neuronal cGAL drivers that drive cGAL expression by cell-specific regulatory sequences or intersection of two distinct regulatory regions with overlapping expression (split cGAL). Most cGAL-drivers exhibit expression in single types of cells. We also constructed 28 UAS effectors that allow expression of proteins to perturb or interrogate sensory neurons of choice. This cGAL-UAS sensory neuron toolkit provides a genetic platform to systematically study the functions of C. elegans sensory neurons.

Distinct neurexin isoforms cooperate to initiate and maintain foraging activity

Neurexins are synaptic adhesion molecules that play diverse roles in synaptic development, function, maintenance, and plasticity. Neurexin genes have been associated with changes in human behavior, where variants in NRXN1 are associated with autism, schizophrenia, and Tourette syndrome. While NRXN1, NRXN2, and NRXN3 all encode major α and β isoforms, NRXN1 uniquely encodes a γ isoform, for which mechanistic roles in behavior have yet to be defined. Here, we show that both α and γ isoforms of neurexin/nrx-1 are required for the C. elegans behavioral response to food deprivation, a sustained period of hyperactivity upon food loss. We find that the γ isoform regulates initiation and the α isoform regulates maintenance of the behavioral response to food deprivation, demonstrating cooperative function of multiple nrx-1 isoforms in regulating a sustained behavior. The γ isoform alters monoamine signaling via octopamine, relies on specific expression of NRX-1 isoforms throughout the relevant circuit, and is independent of neuroligin/nlg-1, the canonical trans-synaptic partner of nrx-1. The α isoform regulates the pre-synaptic structure of the octopamine producing RIC neuron and its maintenance role is conditional on neuroligin/nlg-1. Collectively, these results demonstrate that neurexin isoforms can have separate behavioral roles and act cooperatively across neuronal circuits to modify behavior, highlighting the need to directly analyze and consider all isoforms when defining the contribution of neurexins to behavior.

The neuropeptidergic connectome of C. elegans

Efforts are ongoing to map synaptic wiring diagrams, or connectomes, to understand the neural basis of brain function. However, chemical synapses represent only one type of functionally important neuronal connection; in particular, extrasynaptic, "wireless" signaling by neuropeptides is widespread and plays essential roles in all nervous systems. By integrating single-cell anatomical and gene-expression datasets with biochemical analysis of receptor-ligand interactions, we have generated a draft connectome of neuropeptide signaling in the C. elegans nervous system. This network is characterized by high connection density, extended signaling cascades, autocrine foci, and a decentralized topology, with a large, highly interconnected core containing three constituent communities sharing similar patterns of input connectivity. Intriguingly, several key network hubs are little-studied neurons that appear specialized for peptidergic neuromodulation. We anticipate that the C. elegans neuropeptidergic connectome will serve as a prototype to understand how networks of neuromodulatory signaling are organized.

Comparing engineered nuclear-localized reporter cassettes

Recent single-cell transcriptome analysis has revealed a tremendous breadth and specificity of neuropeptide-encoding gene expression in the nervous system of C. elegans. To analyze the dynamics of neuropeptide gene expression, as well as to dissect the regulatory mechanism by which their expression is controlled, reporter genes remain an important tool. Using CRISPR/Cas9 genome-engineering, we generate here reporter alleles for 6 different neuropeptide encoding genes (3 flp genes, 1 nlp and 2 insulin genes). We find that different reporter cassettes result in different levels of reporter expression and recommend usage of an SL2::GFP::H2B or GFP::H2B::SL2 cassette.

Neural signal propagation atlas of Caenorhabditis elegans

Establishing how neural function emerges from network properties is a fundamental problem in neuroscience1. Here, to better understand the relationship between the structure and the function of a nervous system, we systematically measure signal propagation in 23,433 pairs of neurons across the head of the nematode Caenorhabditis elegans by direct optogenetic activation and simultaneous whole-brain calcium imaging. We measure the sign (excitatory or inhibitory), strength, temporal properties and causal direction of signal propagation between these neurons to create a functional atlas. We find that signal propagation differs from model predictions that are based on anatomy. Using mutants, we show that extrasynaptic signalling not visible from anatomy contributes to this difference. We identify many instances of dense-core-vesicle-dependent signalling, including on timescales of less than a second, that evoke acute calcium transients-often where no direct wired connection exists but where relevant neuropeptides and receptors are expressed. We propose that, in such cases, extrasynaptically released neuropeptides serve a similar function to that of classical neurotransmitters. Finally, our measured signal propagation atlas better predicts the neural dynamics of spontaneous activity than do models based on anatomy. We conclude that both synaptic and extrasynaptic signalling drive neural dynamics on short timescales, and that measurements of evoked signal propagation are crucial for interpreting neural function.

The effective enhancement of information in 3D small-world networks of biological neuronal cells

The cardiovascular system, the kidney, or the brain, are examples of complex systems - where the properties of the systems arise because of the layout of cells in those systems. One way to characterize systems is using networks, where elements and interactions of the systems are represented as nodes and links of a graph. Network's topology can be, in turn, measured by the small-world coefficient. Small world networks feature increased clustering and shorter paths compared to random or periodic networks of the same size. This suggests that systems with small world attributes can also efficiently transport signals, nutrients, or information within their body. While several reports in literature have illustrated that real biological systems are small-world, yet little is known about how information varies as a function of the small-world-ness (sw) of three dimensional graphs. Here, we used a model of the brain to estimate quantitatively the information processed in 3D networks. In the model, nodes of the graph are neuronal units capable to receive, integrate and transmit signals to other neurons of the system in parallel. The information encoded in the signals was then extracted using the techniques of information theory. In simulations where the topology of networks of400nodes was varied over large intervals, we found that in the0-9swrange information scales linearly with the small world coefficient, with a five-fold increase. Results of the paper and review of the existing literature on model organisms suggest that a small-world architecture may offer an evolutionary advantage to biological systems.

Electric shock causes a fleeing-like persistent behavioral response in the nematode Caenorhabditis elegans

Behavioral persistency reflects internal brain states, which are the foundations of multiple brain functions. However, experimental paradigms enabling genetic analyses of behavioral persistency and its associated brain functions have been limited. Here, we report novel persistent behavioral responses caused by electric stimuli in the nematode Caenorhabditis elegans. When the animals on bacterial food are stimulated by alternating current, their movement speed suddenly increases 2- to 3-fold, persisting for more than 1 minute even after a 5-second stimulation. Genetic analyses reveal that voltage-gated channels in the neurons are required for the response, possibly as the sensors, and neuropeptide signaling regulates the duration of the persistent response. Additional behavioral analyses implicate that the animal's response to electric shock is scalable and has a negative valence. These properties, along with persistence, have been recently regarded as essential features of emotion, suggesting that C. elegans response to electric shock may reflect a form of emotion, akin to fear.

Zebrafish brain atlases: a collective effort for a tiny vertebrate brain

In the past two decades, digital brain atlases have emerged as essential tools for sharing and integrating complex neuroscience datasets. Concurrently, the larval zebrafish has become a prominent vertebrate model offering a strategic compromise for brain size, complexity, transparency, optogenetic access, and behavior. We provide a brief overview of digital atlases recently developed for the larval zebrafish brain, intersecting neuroanatomical information, gene expression patterns, and connectivity. These atlases are becoming pivotal by centralizing large datasets while supporting the generation of circuit hypotheses as functional measurements can be registered into an atlas' standard coordinate system to interrogate its structural database. As challenges persist in mapping neural circuits and incorporating functional measurements into zebrafish atlases, we emphasize the importance of collaborative efforts and standardized protocols to expand these resources to crack the complex codes of neuronal activity guiding behavior in this tiny vertebrate brain.

Brain-wide representations of behavior spanning multiple timescales and states in C. elegans

Changes in an animal's behavior and internal state are accompanied by widespread changes in activity across its brain. However, how neurons across the brain encode behavior and how this is impacted by state is poorly understood. We recorded brain-wide activity and the diverse motor programs of freely moving C. elegans and built probabilistic models that explain how each neuron encodes quantitative behavioral features. By determining the identities of the recorded neurons, we created an atlas of how the defined neuron classes in the C. elegans connectome encode behavior. Many neuron classes have conjunctive representations of multiple behaviors. Moreover, although many neurons encode current motor actions, others integrate recent actions. Changes in behavioral state are accompanied by widespread changes in how neurons encode behavior, and we identify these flexible nodes in the connectome. Our results provide a global map of how the cell types across an animal's brain encode its behavior.

A molecular atlas of adult C. elegans motor neurons reveals ancient diversity delineated by conserved transcription factor codes

Motor neurons (MNs) constitute an ancient cell type targeted by multiple adult-onset diseases. It is therefore important to define the molecular makeup of adult MNs in animal models and extract organizing principles. Here, we generated a comprehensive molecular atlas of adult Caenorhabditis elegans MNs and a searchable database (http://celegans.spinalcordatlas.org). Single-cell RNA-sequencing of 13,200 cells revealed that ventral nerve cord MNs cluster into 29 molecularly distinct subclasses. All subclasses are delineated by unique expression codes of either neuropeptide or transcription factor gene families. Strikingly, we found that combinatorial codes of homeodomain transcription factor genes define adult MN diversity both in C. elegans and mice. Further, molecularly defined MN subclasses in C. elegans display distinct patterns of connectivity. Hence, our study couples the connectivity map of the C. elegans motor circuit with a molecular atlas of its constituent MNs, and uncovers organizing principles and conserved molecular codes of adult MN diversity.

TWISP: A Transgenic Worm for Interrogating Signal Propagation in C. elegans

Genetically encoded optical indicators and actuators of neural activity allow for all-optical investigations of signaling in the nervous system. But commonly used indicators, actuators and expression strategies are poorly suited for systematic measurements of signal propagation at brain scale and cellular resolution. Large scale measurements of the brain require indicators and actuators with compatible excitation spectra to avoid optical crosstalk. They must be highly expressed in every neuron but at the same time avoid lethality and permit the animal to reach adulthood. And finally, their expression must be compatible with additional fluorescent labels to locate and identify neurons, such as those in the NeuroPAL cell identification system. We present TWISP, a Transgenic Worm for Interrogating Signal Propagation, that address these needs and enables optical measurements of evoked calcium activity at brain scale and cellular resolution in the nervous system of the nematode Caenorhabditis elegans. We express in every neuron a non-conventional optical actuator, the gustatory receptor homolog GUR-3+PRDX-2 under the control of a drug-inducible system QF+hGR, and calcium indicator GCAMP6s, in a background with additional fluorophores of the NeuroPAL cell ID system. We show that this combination, but not others tested, avoids optical-crosstalk, creates strong expression in the adult, and generates stable transgenic lines for systematic measurements of signal propagation in the worm brain.

Systems neuroscience: Foraging through serotonin's tangled web

Serotonin signaling is conserved in regulating animal behaviors. A new paper decodes the nonlinear effects of all serotonin receptor combinations on foraging behaviors. The authors introduce a brain-wide multiscale method to dissect receptor dynamics, receptor effects on neural activity, and resulting behavioral changes.

Measures of Information Content during Anesthesia and Emergence in the Caenorhabditis elegans Nervous System

BACKGROUND

Suppression of behavioral and physical responses defines the anesthetized state. This is accompanied, in humans, by characteristic changes in electroencephalogram patterns. However, these measures reveal little about the neuron or circuit-level physiologic action of anesthetics nor how information is trafficked between neurons. This study assessed whether entropy-based metrics can differentiate between the awake and anesthetized state in Caenorhabditis elegans and characterize emergence from anesthesia at the level of interneuronal communication.

METHODS

Volumetric fluorescence imaging measured neuronal activity across a large portion of the C. elegans nervous system at cellular resolution during distinct states of isoflurane anesthesia, as well as during emergence from the anesthetized state. Using a generalized model of interneuronal communication, new entropy metrics were empirically derived that can distinguish the awake and anesthetized states.

RESULTS

This study derived three new entropy-based metrics that distinguish between stable awake and anesthetized states (isoflurane, n = 10) while possessing plausible physiologic interpretations. State decoupling is elevated in the anesthetized state (0%: 48.8 ± 3.50%; 4%: 66.9 ± 6.08%; 8%: 65.1 ± 5.16%; 0% vs. 4%, P < 0.001; 0% vs. 8%, P < 0.001), while internal predictability (0%: 46.0 ± 2.94%; 4%: 27.7 ± 5.13%; 8%: 30.5 ± 4.56%; 0% vs. 4%, P < 0.001; 0% vs. 8%, P < 0.001), and system consistency (0%: 2.64 ± 1.27%; 4%: 0.97 ± 1.38%; 8%: 1.14 ± 0.47%; 0% vs. 4%, P = 0.006; 0% vs. 8%, P = 0.015) are suppressed. These new metrics also resolve to baseline during gradual emergence of C. elegans from moderate levels of anesthesia to the awake state (n = 8). The results of this study show that early emergence from isoflurane anesthesia in C. elegans is characterized by the rapid resolution of an elevation in high frequency activity (n = 8, P = 0.032). The entropy-based metrics mutual information and transfer entropy, however, did not differentiate well between the awake and anesthetized states.

CONCLUSIONS

Novel empirically derived entropy metrics better distinguish the awake and anesthetized states compared to extant metrics and reveal meaningful differences in information transfer characteristics between states.

EDITOR’S PERSPECTIVE

WormTensor: a clustering method for time-series whole-brain activity data from C. elegans

BACKGROUND

In the field of neuroscience, neural modules and circuits that control biological functions have been found throughout entire neural networks. Correlations in neural activity can be used to identify such neural modules. Recent technological advances enable us to measure whole-brain neural activity with single-cell resolution in several species including [Formula: see text]. Because current neural activity data in C. elegans contain many missing data points, it is necessary to merge results from as many animals as possible to obtain more reliable functional modules.

RESULTS

In this work, we developed a new time-series clustering method, WormTensor, to identify functional modules using whole-brain activity data from C. elegans. WormTensor uses a distance measure, modified shape-based distance to account for the lags and the mutual inhibition of cell-cell interactions and applies the tensor decomposition algorithm multi-view clustering based on matrix integration using the higher orthogonal iteration of tensors (HOOI) algorithm (MC-MI-HOOI), which can estimate both the weight to account for the reliability of data from each animal and the clusters that are common across animals.

CONCLUSION

We applied the method to 24 individual C. elegans and successfully found some known functional modules. Compared with a widely used consensus clustering method to aggregate multiple clustering results, WormTensor showed higher silhouette coefficients. Our simulation also showed that WormTensor is robust to contamination from noisy data. WormTensor is freely available as an R/CRAN package https://cran.r-project.org/web/packages/WormTensor .

Dissecting the functional organization of the C. elegans serotonergic system at whole-brain scale

Serotonin influences many aspects of animal behavior. But how serotonin acts on its diverse receptors across the brain to modulate global activity and behavior is unknown. Here, we examine how serotonin release in C. elegans alters brain-wide activity to induce foraging behaviors, like slow locomotion and increased feeding. Comprehensive genetic analyses identify three core serotonin receptors (MOD-1, SER-4, and LGC-50) that induce slow locomotion upon serotonin release and others (SER-1, SER-5, and SER-7) that interact with them to modulate this behavior. SER-4 induces behavioral responses to sudden increases in serotonin release, whereas MOD-1 induces responses to persistent release. Whole-brain imaging reveals widespread serotonin-associated brain dynamics, spanning many behavioral networks. We map all sites of serotonin receptor expression in the connectome, which, together with synaptic connectivity, helps predict which neurons show serotonin-associated activity. These results reveal how serotonin acts at defined sites across a connectome to modulate brain-wide activity and behavior.

Learning Probabilistic Piecewise Rigid Atlases of Model Organisms via Generative Deep Networks

Atlases are crucial to imaging statistics as they enable the standardization of inter-subject and inter-population analyses. While existing atlas estimation methods based on fluid/elastic/diffusion registration yield high-quality results for the human brain, these deformation models do not extend to a variety of other challenging areas of neuroscience such as the anatomy of C. elegans worms and fruit flies. To this end, this work presents a general probabilistic deep network-based framework for atlas estimation and registration which can flexibly incorporate various deformation models and levels of keypoint supervision that can be applied to a wide class of model organisms. Of particular relevance, it also develops a deformable piecewise rigid atlas model which is regularized to preserve inter-observation distances between neighbors. These modeling considerations are shown to improve atlas construction and key-point alignment across a diversity of datasets with small sample sizes including neuron positions in C. elegans hermaphrodites, fluorescence microscopy of male C. elegans, and images of fruit fly wings. Code is accessible at https://github.com/amin-nejat/Deformable-Atlas.

Principles for coding associative memories in a compact neural network

A major goal in neuroscience is to elucidate the principles by which memories are stored in a neural network. Here, we have systematically studied how four types of associative memories (short- and long-term memories, each as positive and negative associations) are encoded within the compact neural network of Caenorhabditis elegans worms. Interestingly, sensory neurons were primarily involved in coding short-term, but not long-term, memories, and individual sensory neurons could be assigned to coding either the conditioned stimulus or the experience valence (or both). Moreover, when considering the collective activity of the sensory neurons, the specific training experiences could be decoded. Interneurons integrated the modulated sensory inputs and a simple linear combination model identified the experience-specific modulated communication routes. The widely distributed memory suggests that integrated network plasticity, rather than changes to individual neurons, underlies the fine behavioral plasticity. This comprehensive study reveals basic memory-coding principles and highlights the central roles of sensory neurons in memory formation.

Functional imaging and quantification of multineuronal olfactory responses in C. elegans

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.

A sleep-active neuron can promote survival while sleep behavior is disturbed

Sleep is controlled by neurons that induce behavioral quiescence and physiological restoration. It is not known, however, how sleep neurons link sleep behavior and survival. In Caenorhabditis elegans, the sleep-active RIS neuron induces sleep behavior and is required for survival of starvation and wounding. Sleep-active neurons such as RIS might hypothetically promote survival primarily by causing sleep behavior and associated conservation of energy. Alternatively, RIS might provide a survival benefit that does not depend on behavioral sleep. To probe these hypotheses, we tested how activity of the sleep-active RIS neuron in Caenorhabditis elegans controls sleep behavior and survival during larval starvation. To manipulate the activity of RIS, we expressed constitutively active potassium channel (twk-18gf and egl-23gf) or sodium channel (unc-58gf) mutant alleles in this neuron. Low levels of unc-58gf expression in RIS increased RIS calcium transients and sleep. High levels of unc-58gf expression in RIS elevated baseline calcium activity and inhibited calcium activation transients, thus locking RIS activity at a high but constant level. This manipulation caused a nearly complete loss of sleep behavior but increased survival. Long-term optogenetic activation also caused constantly elevated RIS activity and a small trend towards increased survival. Disturbing sleep by lethal blue-light stimulation also overactivated RIS, which again increased survival. FLP-11 neuropeptides were important for both, induction of sleep behavior and starvation survival, suggesting that FLP-11 might have divergent roles downstream of RIS. These results indicate that promotion of sleep behavior and survival are separable functions of RIS. These two functions may normally be coupled but can be uncoupled during conditions of strong RIS activation or when sleep behavior is impaired. Through this uncoupling, RIS can provide survival benefits under conditions when behavioral sleep is disturbed. Promoting survival in the face of impaired sleep might be a general function of sleep neurons.

A single-cell resolution gene expression atlas of the larval zebrafish brain

The advent of multimodal brain atlases promises to accelerate progress in neuroscience by allowing in silico queries of neuron morphology, connectivity, and gene expression. We used multiplexed fluorescent in situ RNA hybridization chain reaction (HCR) technology to generate expression maps across the larval zebrafish brain for a growing set of marker genes. The data were registered to the Max Planck Zebrafish Brain (mapzebrain) atlas, thus allowing covisualization of gene expression, single-neuron tracings, and expertly curated anatomical segmentations. Using post hoc HCR labeling of the immediate early gene cfos, we mapped responses to prey stimuli and food ingestion across the brain of freely swimming larvae. This unbiased approach revealed, in addition to previously described visual and motor areas, a cluster of neurons in the secondary gustatory nucleus, which express the marker calb2a, as well as a specific neuropeptide Y receptor, and project to the hypothalamus. This discovery exemplifies the power of this new atlas resource for zebrafish neurobiology.

A Novel and Functionally Diverse Class of Acetylcholine-Gated Ion Channels

Fast cholinergic neurotransmission is mediated by acetylcholine-gated ion channels; in particular, excitatory nicotinic acetylcholine receptors play well established roles in virtually all nervous systems. Acetylcholine-gated inhibitory channels have also been identified in some invertebrate phyla, yet their roles in the nervous system are less well understood. We report the existence of multiple new inhibitory ion channels with diverse ligand activation properties in Caenorhabditis elegans We identify three channels, LGC-40, LGC-57, and LGC-58, whose primary ligand is choline rather than acetylcholine, as well as the first evidence of a truly polymodal channel, LGC-39, which is activated by both cholinergic and aminergic ligands. Using our new ligand-receptor pairs we uncover the surprising extent to which single neurons in the hermaphrodite nervous system express both excitatory and inhibitory channels, not only for acetylcholine but also for the other major neurotransmitters. The results presented in this study offer new insight into the potential evolutionary benefit of a vast and diverse repertoire of ligand-gated ion channels to generate complexity in an anatomically compact nervous system.SIGNIFICANCE STATEMENT Here we describe the diversity of cholinergic signaling in the nematode Caenorhabditis elegans We identify and characterize a novel family of ligand-gated ion channels and show that they are preferentially gated by choline rather than acetylcholine and expressed broadly in the nervous system. Interestingly, we also identify one channel gated by chemically diverse ligands including acetylcholine and aminergic ligands. By using our new knowledge of these ligand-gated ion channels, we built a model to predict the synaptic polarity in the C. elegans connectome. This model can be used for generating hypotheses on neural circuit function.

Synaptogenesis: unmasking molecular mechanisms using Caenorhabditis elegans

The nematode Caenorhabditis elegans is a research model organism particularly suited to the mechanistic understanding of synapse genesis in the nervous system. Armed with powerful genetics, knowledge of complete connectomics, and modern genomics, studies using C. elegans have unveiled multiple key regulators in the formation of a functional synapse. Importantly, many signaling networks display remarkable conservation throughout animals, underscoring the contributions of C. elegans research to advance the understanding of our brain. In this chapter, we will review up-to-date information of the contribution of C. elegans to the understanding of chemical synapses, from structure to molecules and to synaptic remodeling.

Pharmacological Profiling of a Brugia malayi Muscarinic Acetylcholine Receptor as a Putative Antiparasitic Target

The diversification of anthelmintic targets and mechanisms of action will help ensure the sustainable control of nematode infections in response to the growing threat of drug resistance. G protein-coupled receptors (GPCRs) are established drug targets in human medicine but remain unexploited as anthelmintic substrates despite their important roles in nematode neuromuscular and physiological processes. Bottlenecks in exploring the druggability of parasitic nematode GPCRs include a limited helminth genetic toolkit and difficulties establishing functional heterologous expression. In an effort to address some of these challenges, we profile the function and pharmacology of muscarinic acetylcholine receptors in the human parasite Brugia malayi, an etiological agent of human lymphatic filariasis. While acetylcholine-gated ion channels are intensely studied as targets of existing anthelmintics, comparatively little is known about metabotropic receptor contributions to parasite cholinergic signaling. Using multivariate phenotypic assays in microfilariae and adults, we show that nicotinic and muscarinic compounds disparately affect parasite fitness traits. We identify a putative G protein-linked acetylcholine receptor of B. malayi (Bma-GAR-3) that is highly expressed across intramammalian life stages and adapt spatial RNA in situ hybridization to map receptor transcripts to critical parasite tissues. Tissue-specific expression of Bma-gar-3 in Caenorhabditis elegans (body wall muscle, sensory neurons, and pharynx) enabled receptor deorphanization and pharmacological profiling in a nematode physiological context. Finally, we developed an image-based feeding assay as a reporter of pharyngeal activity to facilitate GPCR screening in parasitized strains. We expect that these receptor characterization approaches and improved knowledge of GARs as putative drug targets will further advance the study of GPCR biology across medically important nematodes.