Neural signal propagation atlas of Caenorhabditis elegans

Adaptive robustness through incoherent signaling mechanisms in a regenerative brain

Animal behavior emerges from collective dynamics of neurons, making it vulnerable to damage. Paradoxically, many organisms exhibit a remarkable ability to maintain significant behavior even after large-scale neural injury. Molecular underpinnings of this extreme robustness remain largely unknown. Here, we develop a quantitative pipeline to measure long-lasting latent states in planarian flatworm behaviors during whole-brain regeneration. By combining >20,000 animal trials with neural network modeling, we show that long-range volumetric peptidergic signals allow the planarian to rapidly restore coarse behavior output after large perturbations to the nervous system, while slow restoration of small-molecule neuromodulator functions refines precision. This relies on the different time and length scales of neuropeptide and small-molecule transmission to generate incoherent patterns of neural activity that competitively regulate behavior. Controlling behavior through opposing communication mechanisms creates a more robust system than either alone and may serve as a generalizable approach for constructing robust neural networks.

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.

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.

Irene Salinas: The joy of continuous growth and learning

Irene Salinas is a professor in the Department of Biology, University of New Mexico. Her lab uses multiple animal models to study nasal immunity and neuroimmune interactions in the olfactory-central nervous system axis in response to microorganisms. We recently spoke with Irene about her current work, her diversity, equity, and inclusion (DEI) work, and how her leadership style has changed over the years.

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.

DNA repair deficiencies and neurodegeneration

Neurodegenerative diseases are the second most prevalent cause of death in industrialized countries. Alzheimer's Disease is the most widespread and also most acknowledged form of dementia today. Together with Parkinson's Disease they account for over 90 % cases of neurodegenerative disorders caused by proteopathies. Far less known are the neurodegenerative pathologies in DNA repair deficiency syndromes. Such diseases like Cockayne - or Werner Syndrome are described as progeroid syndromes - diseases that cause the premature ageing of the affected persons, and there are clear implications of such diseases in neurologic dysfunction and degeneration. In this review, we aim to draw the attention on commonalities between proteopathy-associated neurodegeneration and neurodegeneration caused by DNA repair defects and discuss how mitochondria are implicated in the development of both disorder classes. Furthermore, we highlight how nematodes are a valuable and indispensable model organism to study conserved neurodegenerative processes in a fast-forward manner.

Distinct mechanisms of non-autonomous UPRER mediated by GABAergic, glutamatergic, and octopaminergic neurons

The capacity to deal with stress declines during the aging process, and preservation of cellular stress responses is critical to healthy aging. The unfolded protein response of the endoplasmic reticulum (UPRER) is one such conserved mechanism, which is critical for the maintenance of several major functions of the ER during stress, including protein folding and lipid metabolism. Hyperactivation of the UPRER by overexpression of the major transcription factor, xbp-1s, solely in neurons drives lifespan extension as neurons send a neurotransmitter-based signal to other tissue to activate UPRER in a non-autonomous fashion. Previous work identified serotonergic and dopaminergic neurons in this signaling paradigm. To further expand our understanding of the neural circuitry that underlies the non-autonomous signaling of ER stress, we activated UPRER solely in glutamatergic, octopaminergic, and GABAergic neurons in C. elegans and paired whole-body transcriptomic analysis with functional assays. We found that UPRER-induced signals from glutamatergic neurons increased expression of canonical protein homeostasis pathways and octopaminergic neurons promoted pathogen response pathways, while minor, but statistically significant changes were observed in lipid metabolism-related genes with GABAergic UPRER activation. These findings provide further evidence for the distinct role neuronal subtypes play in driving the diverse response to ER stress.

Prediction of neural activity in connectome-constrained recurrent networks

We develop a theory of connectome-constrained neural networks in which a "student" network is trained to reproduce the activity of a ground-truth "teacher," representing a neural system for which a connectome is available. Unlike standard paradigms with unconstrained connectivity, here the two networks have the same connectivity but different biophysical parameters, reflecting uncertainty in neuronal and synaptic properties. We find that a connectome is often insufficient to constrain the dynamics of networks that perform a specific task, illustrating the difficulty of inferring function from connectivity alone. However, recordings from a small subset of neurons can remove this degeneracy, producing dynamics in the student that agree with the teacher. Our theory can also prioritize which neurons to record from to most efficiently predict unmeasured network activity. Our analysis shows that the solution spaces of connectome-constrained and unconstrained models are qualitatively different and provides a framework to determine when such models yield consistent dynamics.

Norepinephrine changes behavioral state via astroglial purinergic signaling

Both neurons and glia communicate via diffusible neuromodulatory substances, but the substrates of computation in such neuromodulatory networks are unclear. During behavioral transitions in the larval zebrafish, the neuromodulator norepinephrine drives fast excitation and delayed inhibition of behavior and circuit activity. We find that the inhibitory arm of this feedforward motif is implemented by astroglial purinergic signaling. Neuromodulator imaging, behavioral pharmacology, and perturbations of neurons and astroglia reveal that norepinephrine triggers astroglial release of adenosine triphosphate, extracellular conversion into adenosine, and behavioral suppression through activation of hindbrain neuronal adenosine receptors. This work, along with a companion piece by Lefton and colleagues demonstrating an analogous pathway mediating the effect of norepinephrine on synaptic connectivity in mice, identifies a computational and behavioral role for an evolutionarily conserved astroglial purinergic signaling axis in norepinephrine-mediated behavioral and brain state transitions.

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.

Astrocytes as a mechanism for contextually-guided network dynamics and function

Astrocytes are a ubiquitous and enigmatic type of non-neuronal cell and are found in the brain of all vertebrates. While traditionally viewed as being supportive of neurons, it is increasingly recognized that astrocytes play a more direct and active role in brain function and neural computation. On account of their sensitivity to a host of physiological covariates and ability to modulate neuronal activity and connectivity on slower time scales, astrocytes may be particularly well poised to modulate the dynamics of neural circuits in functionally salient ways. In the current paper, we seek to capture these features via actionable abstractions within computational models of neuron-astrocyte interaction. Specifically, we engage how nested feedback loops of neuron-astrocyte interaction, acting over separated time-scales, may endow astrocytes with the capability to enable learning in context-dependent settings, where fluctuations in task parameters may occur much more slowly than within-task requirements. We pose a general model of neuron-synapse-astrocyte interaction and use formal analysis to characterize how astrocytic modulation may constitute a form of meta-plasticity, altering the ways in which synapses and neurons adapt as a function of time. We then embed this model in a bandit-based reinforcement learning task environment, and show how the presence of time-scale separated astrocytic modulation enables learning over multiple fluctuating contexts. Indeed, these networks learn far more reliably compared to dynamically homogeneous networks and conventional non-network-based bandit algorithms. Our results fuel the notion that neuron-astrocyte interactions in the brain benefit learning over different time-scales and the conveyance of task-relevant contextual information onto circuit dynamics.

FUNCTIONS OF C. ELEGANS NEURONS FROM SYNAPTIC CONNECTIVITY

Despite decades of research on the C. elegans nervous system based on an anatomical description of synaptic connectivity, the circuits underlying behavior remain incompletely described and the functions of many neurons are still unknown. Updated and more complete chemical and gap junction connectomes of both adult sexes covering the entire animal including the muscle end organ have become available recently. Here these are analyzed to gain insight into the overall structure of the connectivity network and to suggest functions of individual neuron classes. Modularity analysis divides the connectome graph into ten communities that can be correlated with broad categories of behavior. A significant role of the body wall musculature end organ is emphasized as both a site of significant information convergence and as a source of sensory input in a feedback loop. Convergence of pathways for multisensory integration occurs throughout the network - most interneurons have similar indegrees and outdegrees and hence disperse information as much as they aggregate it. New insights include description of a set of high degree interneurons connected by many gap junctions running through the ventral cord that may represent a previously unrecognized locus of information processing. There is an apparent mechanosensory and proprioceptive field covering the entire body formed by connectivity of the many mechanosensory neurons of multiple types to two interneurons with output connections across the nervous system. Several additional significant, previously unrecognized circuits and pathways are uncovered, some involving unstudied neurons. The insights are valuable for guiding theoretical investigation of network properties as well as experimental studies of the functions of individual neurons, groups of neurons, and circuits.

Statistically inferred neuronal connections in subsampled neural networks strongly correlate with spike train covariances

Statistically inferred neuronal connections from observed spike train data are often skewed from ground truth by factors such as model mismatch, unobserved neurons, and limited data. Spike train covariances, sometimes referred to as "functional connections," are often used as a proxy for the connections between pairs of neurons, but reflect statistical relationships between neurons, not anatomical connections. Moreover, covariances are not causal: spiking activity is correlated in both the past and the future, whereas neurons respond only to synaptic inputs in the past. Connections inferred by maximum likelihood inference, however, can be constrained to be causal. However, we show in this work that the inferred connections in spontaneously active networks modeled by stochastic leaky integrate-and-fire networks strongly correlate with the covariances between neurons, and may reflect noncausal relationships, when many neurons are unobserved or when neurons are weakly coupled. This phenomenon occurs across different network structures, including random networks and balanced excitatory-inhibitory networks. We use a combination of simulations and a mean-field analysis with fluctuation corrections to elucidate the relationships between spike train covariances, inferred synaptic filters, and ground-truth connections in partially observed networks.

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).

Molecular control of temporal integration matches decision-making to motivational state

Motivations bias our responses to stimuli, producing behavioral outcomes that match our needs and goals. We describe a mechanism behind this phenomenon: adjusting the time over which stimulus-derived information is permitted to accumulate toward a decision. As a Drosophila copulation progresses, the male becomes less likely to continue mating through challenges. We show that a set of Copulation Decision Neurons (CDNs) flexibly integrates information about competing drives to mediate this decision. Early in mating, dopamine signaling restricts CDN integration time by potentiating CaMKII activation in response to stimulatory inputs, imposing a high threshold for changing behaviors. Later into mating, the timescale over which the CDNs integrate termination-promoting information expands, increasing the likelihood of switching behaviors. We suggest scalable windows of temporal integration at dedicated circuit nodes as a key but underappreciated variable in state-based decision-making.

Exploration-Exploitation Paradigm for Networked Biological Systems

The stochastic exploration of the configuration space and the exploitation of functional states underlie many biological processes. The evolutionary dynamics stands out as a remarkable example. Here, we introduce a novel formalism that mimics evolution and encodes a general exploration-exploitation dynamics for biological networks. We apply it to the brain wiring problem, focusing on the maturation of that of the nematode Caenorhabditis elegans. We demonstrate that a parsimonious maxent description of the adult brain combined with our framework is able to track down the entire developmental trajectory.

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.

Adult Single-nucleus Neuronal Transcriptomes of Insulin Signaling Mutants Reveal Regulators of Behavior and Learning

The insulin/insulin-like signaling (IIS) pathway regulates many of C. elegans' adult functions, including learning and memory 1 . While whole-worm and tissue-specific transcriptomic analyses have identified IIS targets 2,3 , a higher-resolution single-cell approach is required to identify changes that confer neuron-specific improvements in the long-lived insulin receptor mutant, daf-2 . To understand how behaviors that are controlled by a small number of neurons change in daf-2 mutants, we used the deep resolution of single-nucleus RNA sequencing to define each neuron type's transcriptome in adult wild-type and daf-2 mutants. First, we found surprising differences between wild-type L4 larval neurons and young adult neurons in chemoreceptor expression, synaptic genes, and learning and memory genes. These Day 1 adult neuron transcriptomes allowed us to identify adult AWC-specific regulators of chemosensory function and to predict neuron-to-neuron peptide/receptor pairs. We then identified gene expression changes that correlate with daf-2's improved cognitive functions, particularly in the AWC sensory neuron that controls learning and associative memory 4 , and used behavioral assays to test their roles in cognitive function. Combining deep single-neuron transcriptomics, genetic manipulation, and behavioral analyses enabled us to identify genes that may function in a single adult neuron to control behavior, including conserved genes that function in learning and memory.

One-Sentence Summary

Single-nucleus sequencing of adult wild-type and daf-2 C. elegans neurons reveals functionally relevant transcriptional changes, including regulators of chemosensation, learning, and memory.

Characterization of viscerofugal neurons in human colon by retrograde tracing and multi-layer immunohistochemistry

Background and Aims

Viscerofugal neurons (VFNs) have cell bodies in the myenteric plexus and axons that project to sympathetic prevertebral ganglia. In animals they activate sympathetic motility reflexes and may modulate glucose metabolism and feeding. We used rapid retrograde tracing from colonic nerves to identify VFNs in human colon for the first time, using ex vivo preparations with multi-layer immunohistochemistry.

Methods

Colonic nerves were identified in isolated preparations of human colon and set up for axonal tracing with biotinamide. After fixation, labeled VFN cell bodies were subjected to multiplexed immunohistochemistry for 12 established nerve cell body markers.

Results

Biotinamide tracing filled 903 viscerofugal nerve cell bodies (n = 23), most of which (85%) had axons projecting orally before entering colonic nerves. Morphologically, 97% of VFNs were uni-axonal. Of 215 VFNs studied in detail, 89% expressed ChAT, 13% NOS, 13% calbindin, 9% enkephalin, 7% substance P and 0 of 123 VFNs expressed CART. Few VFNs contained calretinin, VIP, 5HT, CGRP, or NPY. VFNs were often surrounded by dense baskets of axonal varicosities, probably reflecting patterns of connectivity; VAChT+ (cholinergic), SP+ and ENK+ varicosities were most abundant around them. Human VFNs were diverse; showing 27 combinations of immunohistochemical markers, 4 morphological types and a wide range of cell body sizes. However, 69% showed chemical coding, axonal projections, soma-dendritic morphology and connectivity similar to enteric excitatory motor neurons.

Conclusion

Viscerofugal neurons are present in human colon and show very diverse combinations of features. High proportions express ChAT, consistent with cholinergic synaptic outputs onto postganglionic sympathetic neurons in prevertebral ganglia.

Light evokes stereotyped global brain dynamics in Caenorhabditis elegans

Stereotyped oscillations in population neural activity recordings from immobilized Caenorhabditis elegans have garnered interest for their striking low dimensionality and their evocative state-space trajectories or manifolds. Previously these oscillations have been interpreted as intrinsically driven global motor commands. Here we test whether these oscillations are intrinsic. We show that similar oscillations are evoked by high-intensity blue light commonly used for calcium imaging. Oscillations are reduced or absent and have a lower frequency when a longer imaging wavelength is used. Under the original blue light illumination, oscillations are reduced or have a lower frequency in animals that lack GUR-3, an endogenous light- and hydrogen-peroxide-sensitive gustatory receptor. Additional experiments with hydrogen peroxide are consistent with GUR-3's involvement. We therefore propose that blue light evokes global oscillations in part through the creation of reactive oxygen species that activate the hydrogen-peroxide-sensing receptor GUR-3.