Molecular topography of an entire nervous system

SummaryNervous systems are constructed from a deep repertoire of neuron types but the underlying gene expression programs that specify individual neuron identities are poorly understood. To address this deficit, we have produced an expression profile of all 302 neurons of the C. elegans nervous system that matches the single cell resolution of its anatomy and wiring diagram. Our results suggest that individual neuron classes can be solely identified by combinatorial expression of specific gene families. For example, each neuron class expresses unique codes of ∼23 neuropeptide-encoding genes and ∼36 neuropeptide receptors thus pointing to an expansive “wireless” signaling network. To demonstrate the utility of this uniquely comprehensive gene expression catalog, we used computational approaches to (1) identify cis-regulatory elements for neuron-specific gene expression across the nervous system and (2) reveal adhesion proteins with potential roles in synaptic specificity and process placement. These data are available at cengen.org and can be interrogated at the web application CengenApp. We expect that this neuron-specific directory of gene expression will spur investigations of underlying mechanisms that define anatomy, connectivity and function throughout the C. elegans nervous system.

Expression profiling of the mature C. elegans nervous system by single-cell RNA-Sequencing

A single neuron and its synapses define the fundamental structural motif of the brain but the underlying gene expression programs that specify individual neuron types are poorly understood. To address this question in a model organism, we have produced a gene expression profile of >90% of the individual neuron classes in the C. elegans nervous system, an ensemble of neurons for which both the anatomy and connectivity are uniquely defined at single cell resolution. We generated single cell transcriptomes for 52,412 neurons that resolve as clusters corresponding to 109 of the canonical 118 neuron classes in the mature hermaphrodite nervous system. Detailed analysis revealed molecular signatures that further subdivide identified classes into specific neuronal subtypes. Notably, neuropeptide-related genes are often differentially expressed between subtypes of the given neuron class which points to distinct functional characteristics. All of these data are publicly available at our website (http://www.cengen.org) and can be interrogated at the web application SCeNGEA (https://cengen.shinyapps.io/SCeNGEA). We expect that this gene expression catalog will spur the goal of delineating the underlying mechanisms that define the developmental lineage, detailed anatomy, synaptic connectivity and function of each type of C. elegans neuron.

Breakdown of Neural Function under Isoflurane Anesthesia

Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background Previous work on the action of volatile anesthetics has focused at either the molecular level or bulk neuronal measurement such as electroencephalography or functional magnetic resonance imaging. There is a distinct gulf in resolution at the level of cellular signaling within neuronal systems. The authors hypothesize that anesthesia is caused by induced dyssynchrony in cellular signaling rather than suppression of individual neuron activity. Methods Employing confocal microscopy and Caenorhabditis elegans expressing the calcium-sensitive fluorophore GCaMP6s in specific command neurons, the authors measure neuronal activity noninvasively and in parallel within the behavioral circuit controlling forward and reverse crawling. The authors compare neuronal dynamics and coordination in a total of 31 animals under atmospheres of 0, 4, and 8% isoflurane. Results When not anesthetized, the interneurons controlling forward or reverse crawling occupy two possible states, with the activity of the “reversal” neurons AVA, AVD, AVE, and RIM strongly intercorrelated, and the “forward” neuron AVB anticorrelated. With exposure to 4% isoflurane and onset of physical quiescence, neuron activity wanders rapidly and erratically through indeterminate states. Neuron dynamics shift toward higher frequencies, and neuron pair correlations within the system are reduced. At 8% isoflurane, physical quiescence continues as neuronal signals show diminished amplitude with little correlation between neurons. Neuronal activity was further studied using statistical tools from information theory to quantify the type of disruption caused by isoflurane. Neuronal signals become noisier and more disordered, as measured by an increase in the randomness of their activity (Shannon entropy). The coordination of the system, measured by whether information exhibited in one neuron is also exhibited in other neurons (multiinformation), decreases significantly at 4% isoflurane (P = 0.00015) and 8% isoflurane (P = 0.0028). Conclusions The onset of anesthesia corresponds with high-frequency randomization of individual neuron activity coupled with induced dyssynchrony and loss of coordination between neurons that disrupts functional signaling.

An ensemble learning approach to auto-annotation for whole-brain C. elegans imaging

Shifting from individual neuron analysis to whole-brain neural network analysis opens up new research opportunities for Caenorhabditis elegans (C. elegans). An automated data processing pipeline, including neuron detection, segmentation, tracking and annotation, will significantly improve the efficiency of analyzing whole-brain C. elegans imaging. The resulting large data sets may motivate new scientific discovery by exploiting many promising analysis tools for big data. In this study, we focus on the development of an automated annotation procedure. With only around 180 neurons in the central nervous system of a C. elegans, the annotation of each individual neuron still remains a major challenge because of the high density in space, similarity in neuron shape, unpredictable distortion of the worm’s head during motion, intrinsic variations during worm development, etc. We use an ensemble learning approach to achieve around 25% error for a test based on real experimental data. Also, we demonstrate the importance of exploring extra source of information for annotation other than the neuron positions.