Biology subjects

Yemini, E.

Publications and source records attributed to Yemini, E..

14 recordsLinked to original sources

bioRxiv2025

Whole-brain chemosensory responses of both C. elegans sexes

Sexually-dimorphic neural circuits play a critical role in shaping sex-specific animal behaviors. Maps of the structural dimorphisms in these circuits have been explored by analyzing "synaptic connectomes", electron micrograph reconstructions of synaptic connectivity. Nevertheless, recent studies in the model organism C. elegans have shown little to no correlation between the synaptic connectome and dynamic neural activity. Therefore, the extent of sexual dimorphism in functional neural activity remains unknown. To determine the extent of functional sexual-dimorphisms in C. elegans we compared activity, neuron-by-neuron, across all neurons in the heads of both sexes. To sample a broad view of responses to different sensory modalities, we tested a diverse panel of ethologically-relevant olfactory, gustatory, and chemical stimuli, representing both attractive and aversive cues. We found that nearly every sensory neuron responded dimorphically to at least one cue and monomorphically to other cues, indicating that sexually-dimorphic circuits are pervasive and stimulus dependent. This dimorphic and monomorphic activity was present to a lesser extent in downstream interneurons and even less so in motoneurons, implicating sensory neurons as the primary source and location of sexually-dimorphic activity. Comparing the functional activity we measured to the published synaptic connectomes of both sexes revealed that sexual dimorphism in functional connectivity was distinct from and complementary to sexual dimorphism in synaptic connectivity. Our results provide a first-of-its-kind comparison of whole-brain dynamics between sexes at the level of single neurons, serving as an extensive resource for further investigations of functional sex differences.

neuroscience↗

bioRxiv2025

Integrating bulk and single cell RNA-seq refines transcriptomic profiles of individual C. elegans neurons

Neuron-specific morphology and function are fundamentally tied to differences in gene expression across the nervous system. We previously generated a single cell RNA-seq (scRNA-Seq) dataset for every anatomical neuron class in the C. elegans hermaphrodite. Here we present a complementary set of bulk RNA-seq samples for 52 of the 118 canonical neuron classes in C. elegans. We show that the bulk RNA-seq dataset captures both lowly expressed and noncoding RNAs that are not detected in the scRNA-Seq profile, but also includes false positives due to contamination by other cell types. We present an analytical strategy that integrates the two datasets, preserving both the specificity of scRNA-Seq data and the sensitivity of bulk RNA-Seq. We show that this integrated dataset enhances the sensitivity and accuracy of transcript detection and differential gene analysis. In addition, we show that the bulk RNA-Seq data set detects differentially expressed non-coding RNAs across neuron types, including multiple families of non-polyadenylated transcripts. We propose that our approach provides a new strategy for interrogating gene expression by bridging the gap between bulk and single cell methodologies for transcriptomic studies. We suggest that these datasets advance the goal of delineating the mechanisms that define morphology and connectivity in the nervous system.

genomics↗

bioRxiv2025

WormID-Benchmark: Extracting Whole-Brain Neural Dynamics of C. elegans At the Neuron Resolution

The nematode C. elegans is a premier model organism for studying neural circuit function due to its fully mapped connectome and genetically identifiable neurons. Recent advances in 3D light microscopy and fluorescent protein tagging have enabled whole-brain imaging at single-neuron resolution. However, extracting meaningful neural dynamics from these high-resolution recordings requires addressing three fundamental challenges: (i) accurate detection of individual neurons in fluorescence images, (ii) precise identification of neuron classes based on anatomical and colorimetric cues, and (iii) robust tracking of neurons over time in calcium imaging videos. To systematically evaluate these challenges, we introduce WormID-Bench, a large-scale, multi-laboratory dataset comprising 118 worms from five distinct research groups, along with standardized evaluation metrics for detection, identification, and tracking. Our benchmark reveals that existing computational approaches show substantial room for improvement in sensitivity, specificity, and generalization across diverse experimental conditions. By providing an open and reproducible benchmarking framework1, WormID-Bench aims to accelerate the development of high-throughput and scalable computational tools for whole-brain neural dynamics extraction in C. elegans, setting the stage for broader advancements in functional connectomics.

neuroscience↗

bioRxiv2023

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.

bioengineering↗

bioRxiv2022

Restricted effects of the sole C. elegans Daughterless/E homolog, HLH-2, on nervous system development

Are there common mechanisms of neurogenesis used throughout an entire nervous system? Making use of the well-defined and relatively small size of the nervous system of the nematode C. elegans, we explored to what extent canonical proneural class I/II bHLH complexes are responsible for neurogenesis throughout the entire C. elegans nervous system. Distinct, lineage-specific proneural "class II" bHLH factors are generally thought to operate via interaction with a common, "class I" bHLH subunit, encoded by Daugtherless in flies, the E (E2A, E2-2, HEB) proteins in vertebrates, and hlh-2 in C. elegans. To eliminate function of all proneuronal class I/II bHLH complexes, we therefore genetically removed maternal and zygotic hlh-2 gene activity. We observed broad effects on neurogenesis, but still detected normal neurogenesis in many distinct neuron-producing lineages of the central and peripheral nervous system. Moreover, we find that hlh-2 selectively affects some aspects of neuron differentiation while leaving others unaffected. While our studies confirm the function of proneuronal class I/II bHLH complexes in many different lineages throughout a nervous system, we conclude that their function is not universal, but rather restricted by lineage, cell type and components of differentiation programs affected.

neuroscience↗

bioRxiv2022

Integrating bulk and single cell RNA-seq refines transcriptomic profiles of specific C. elegans neurons.

genetics↗

bioRxiv2021

Toward a More Accurate 3D Atlas of C. elegans Neurons

Determining cell identity in volumetric images of tagged neuronal nuclei is an ongoing challenge in contemporary neuroscience. Frequently, cell identity is determined by aligning and matching tags to an "atlas" of labeled neuronal positions and other identifying characteristics. Previous analyses of such C. elegans datasets have been hampered by the limited accuracy of such atlases, especially for neurons present in the ventral nerve cord, and also by time-consuming manual elements of the alignment process. We present a novel automated alignment method for sparse and incomplete point clouds of the sort resulting from typical C. elegans fluorescence microscopy datasets. This method involves a tunable learning parameter and a kernel that enforces biologically realistic deformation. We also present a pipeline for creating alignment atlases from datasets of the recently developed NeuroPAL transgene. In combination, these advances allow us to label neurons in volumetric images with confidence much higher than previous methods. We release, to the best of our knowledge, the most complete C. elegans 3D positional neuron atlas, encapsulating positional variability derived from 7 animals, for the purposes of cell-type identity prediction for myriad applications (e.g., imaging neuronal activity, gene expression, and cell-fate).

neuroscience↗

bioRxiv2021

State dependence of neural networks on past history and stimulus presentation in Caenorhabditis elegans

Neurons represent changes in external and internal environments by altering their activity patterns. While coherent brain-wide patterns of neural activity have been observed in neuronal populations, very little is known about their dimensionality, geometry, and how they are correlated with sensory inputs. Here, we recorded the activity of most head neurons in Caenorhabditis elegans experiencing changes in bacterial or control buffer stimuli around their nose. We first classified active neurons into six functional clusters: two sensory neuron clusters (ON and OFF responding to addition and removal of stimuli, respectively) and four motor/command neuron clusters (AVA, RME, SMDD and SMDV). Next, we estimated stimulus selectivity for each cluster and found that while sensory neurons exhibit their maximal responses within 15 seconds, changes in bacterial stimuli drive maximal responses in command and motor neuron clusters after tens of seconds. Furthermore, we show that bacterial stimuli induce neural dynamics that are best described by a hyperbolic, not Euclidean, space, of dimensionality eight. The hyperbolic space provided a better description of neural activity than the standard Euclidean space. This space can be separated into three components - one sensory, and two motor directions (forward-backward and dorsal-ventral). Collectively, we show that C. elegans neural activity can be effectively represented in low-dimensional hyperbolic space to describe a sensorimotor transformation. Significance statementA major function of a nervous system is to transform sensory information into behavioral outputs. As the first receiver of sensory input, sensory neuronal activity is often most correlated with stimulus features. However, this sensory activity is modified as it travels to other neurons, where it integrates with network activity before altering motor neurons and driving corresponding behavior. Activity in non-sensory neurons is driven by ongoing network activity and sensory input, but distinguishing between their relative contributions is often difficult. Here, we identify two sensory and four command/motor neuron clusters in the C. elegans neural network responding to bacterial stimuli and define their receptive fields. We then use a hyperbolic embedding to identify how these clusters interact with each other and identify the relevant dimensions that might alter behavior. Our method is fully scalable to other systems, including those without neuronal identities, and allows us to attribute neural activity to network states and behavioral outputs.

neuroscience↗

bioRxiv2021

Visualizing the organization and differentiation of the male-specific nervous system of C. elegans

Sex differences in the brain are prevalent throughout the animal kingdom and particularly well appreciated in the nematode C. elegans. While 294 neurons are shared between the two sexes, the nervous system of the male contains an additional 93 malespecific neurons, most of which have received very little attention so far. To make these neurons amenable for future study, we describe here how a multicolor, multipromoter reporter transgene, NeuroPAL, is capable of visualizing the distinct identities of all male specific neurons. We used this tool to visualize and characterize a number of features of the male-specific nervous system. We provide several proofs of concept for using NeuroPAL to identify the sites of expression of gfp-tagged reporter genes. We demonstrate the usage of NeuroPAL for cellular fate analysis by analyzing the effect of removal of developmental patterning genes, including a HOX cluster gene (egl-5), a miRNA (lin-4) and a proneural gene (lin-32/Ato), on neuronal identity acquisition within the male-specific nervous system. We use NeuroPAL and its intrinsic cohort of more than 40 distinct differentiation markers to show that, even though male-specific neurons are generated throughout all four larval stages, they execute their terminal differentiation program in a coordinated manner in the fourth larval stage that is concomitant with male tale retraction. This wave of differentiation couples neuronal maturation programs with the appearance of sexual organs. We call this wave "just-in-time" differentiation by its analogy to the mechanism of "just-in-time" transcription of metabolic pathway genes.

neuroscience↗

bioRxiv2020

Molecular topography of an entire nervous system

Nervous 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.

neuroscience↗

bioRxiv2020

Extracting neural signals from semi-immobilized animals with deformable non-negative matrix factorization

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

neuroscience↗

bioRxiv2019

The connectome of the Caenorhabditis elegans pharynx

Detailed anatomical maps of individual organs and entire animals have served as invaluable entry points for ensuing dissection of their evolution, development, and function. The pharynx of the nematode Caenorhabditis elegans is a simple neuromuscular organ with a self-contained, autonomously acting nervous system, composed of 20 neurons that fall into 14 anatomically distinct types. Using serial EM reconstruction, we re-evaluate here the connectome of the pharyngeal nervous system, providing a novel and more detailed view of its structure and predicted function. Contrasting the previous classification of pharyngeal neurons into distinct inter- and motorneuron classes, we provide evidence that most pharyngeal neurons are also likely sensory neurons and most, if not all, pharyngeal neurons also classify as motorneurons. Together with the extensive cross-connectivity among pharyngeal neurons, which is more widespread than previously realized, the sensory-motor characteristics of most neurons define a shallow network architecture of the pharyngeal connectome. Network analysis reveals that the patterns of neuronal connections are organized into putative computational modules that reflect the known functional domains of the pharynx. Compared to the somatic nervous system, pharyngeal neurons both physically associate with a larger fraction of their neighbors and create synapses with a greater proportion of their neighbors. We speculate that the overall architecture of the pharyngeal nervous system may be reminiscent of the architecture of ancestral, primitive nervous systems.

neuroscience↗

bioRxiv2019

NeuroPAL: A Neuronal Polychromatic Atlas of Landmarks for Whole-Brain Imaging in C. elegans

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

neuroscience↗

bioRxiv2018

An open source platform for analyzing and sharing worm behavior data

Animal behavior is increasingly being recorded in systematic imaging studies that generate large data sets. To maximize the usefulness of these data there is a need for improved resources for analyzing and sharing behavior data that will encourage re-analysis and method development by computational scientists1. However, unlike genomic or protein structural data, there are no widely used standards for behavior data. It is therefore desirable to make the data available in a relatively raw form so that different investigators can use their own representations and derive their own features. For computational ethology to approach the level of maturity of other areas of bioinformatics, we need to address at least three challenges: storing and accessing video files, defining flexible data formats to facilitate data sharing, and making software to read, write, browse, and analyze the data. We have developed an open resource to begin addressing these challenges using worm tracking as a model.

animal behavior and cognition↗