Biology subjects

Ripoll-Sanchez, L.

Publications and source records attributed to Ripoll-Sanchez, L..

3 recordsLinked to original sources

bioRxiv2025

A gene expression atlas of a juvenile nervous system

Although the fundamental architecture of metazoan nervous systems is typically established in the embryo, substantial numbers of neurons are added during post-natal development while existing neurons expand in size, refine connectivity, and undergo additional differentiation. To reveal the underlying molecular determinants of postembryonic neurogenesis and maturation, we have produced gene expression profiles of all neuron types and their progenitors in the first larval stage (L1) of C. elegans. Comparisons of the L1 profile to the embryo and to the later L4 larval stage identified thousands of differentially expressed genes across individual neurons throughout the nervous system. Key neuropeptide signaling networks, for example, are remodeled during larval development. Gene regulatory network analysis revealed potential transcription factors driving the temporal changes in gene expression across the nervous system, including a broad role for the heterochronic transcription factor lin-14. We utilized available connectomic data of juvenile animals in combination with our neuron-specific atlas to identify potential molecular determinants of membrane contact and synaptic connectivity. These expression data are available through a user-friendly interface at CeNGEN.org for independent investigations of the maturation, connectivity and function of a developing nervous system.

neuroscience↗

bioRxiv2024

Molecular patterns of evolutionary changes throughout the whole nervous system of multiple nematode species

One avenue to better understand brain evolution is to map molecular patterns of evolutionary changes in neuronal cell types across entire nervous systems of distantly related species. Generating whole-animal single-cell transcriptomes of three nematode species from the Caenorhabditis genus, we observed a remarkable stability of neuronal cell type identities over more than 45 million years of evolution. Conserved patterns of combinatorial expression of homeodomain transcription factors are among the best classifiers of homologous neuron classes. Unexpectedly, we discover an extensive divergence in neuronal signaling pathways. While identities of neurotransmitter-producing neurons (glutamate, acetylcholine, GABA and several monoamines) remain stable, ionotropic and metabotropic receptors for all these neurotransmitter systems show substantial divergence, resulting in more than half of all neuron classes changing their capacity to be receptive to specific neurotransmitters. Neuropeptidergic signaling is also remarkably divergent, both at the level of neuropeptide expression and receptor expression, yet the overall dense network topology of the wireless neuropeptidergic connectome remains stable. Novel neuronal signaling pathways are suggested by our discovery of small secreted proteins that show no obvious hallmarks of conventional neuropeptides, but show similar patterns of highly neuron-type-specific and highly evolvable expression profiles. In conclusion, by investigating the evolution of entire nervous systems at the resolution of single neuron classes, we uncover patterns that may reflect basic principles governing evolutionary novelty in neuronal circuits.

evolutionary biology↗

bioRxiv2018

Powerful and interpretable behavioural features for quantitative phenotyping of C. elegans

Behaviour is a sensitive and integrative readout of nervous system function and therefore an attractive measure for assessing the effects of mutation or drug treatment on animals. Video data provides a rich but high-dimensional representation of behaviour and so the first step of analysis is often some form of tracking and feature extraction to reduce dimensionality while maintaining relevant information. Modern machine learning methods are powerful but notoriously difficult to interpret, while handcrafted features are interpretable but do not always perform as well. Here we report a new set of handcrafted features to compactly quantify C. elegans behaviour. The features are designed to be interpretable but to capture as much of the phenotypic differences between worms as possible. We show that the full feature set is more powerful than a previously defined feature set in classifying mutant strains. We then use a combination of automated and manual feature selection to define a core set of interpretable features that still provides sufficient power to detect behavioural differences between mutant strains and the wild type. Finally, we apply the new features to detect time-resolved behavioural differences in a series of optogenetic experiments targeting different neural subsets.

animal behavior and cognition↗