Zoga, K. | Search bioRxiv

bioRxiv2025

Multiple autism genes influence GABA neuron remodeling via distinct developmental trajectories

Variation in over 100 genes are now associated with increased risk for autism and related neurodevelopmental condition, but how this variation results in distinct and overlapping behavioral changes is still not well understood. Recent efforts have focused on screening many autism genes at once for functional and phenotypic convergence, and identified subsets that are crucial for many early steps of neurodevelopment. Few studies have screened later steps of neurodevelopment, circuit function, circuit plasticity, or behaviors. We screened twenty conserved autism-associated genes for impact on experience-dependent neuron remodeling in C. elegans. Loss of unc-44/ANK2, set-4/KMT5B, daf-18/PTEN, gap-2/SYNGAP1, and chd-1/CHD8 increased, while CACNA2D3/unc-36 decreased, neurite outgrowth of the GABAergic DVB neuron in adults. Although daf-18/PTEN, set-4/KMD5B, and unc-44/ANK2 had convergent phenotypes, they arise from distinct temporal trajectories with differential impact on DVB pre-synaptic morphology. Screening for the DVB regulated spicule protraction behavior identified multiple autism genes involved, but only unc-44/ANK2 and CACNA2D3/unc-36 were shared between screens. Application of a metric geometry computational framework (CAJAL) to the DVB morphology dataset identified 5 additional genes that impact DVB morphology, including unc-2/CACNA1A and unc-10/RIMS1, which also significantly impacted behavior. This work defines new regulators and molecular mechanisms of experience-dependent neuron remodeling and circuit plasticity, and further links these processes with conserved autism genes. It also demonstrates the utility of using intact, behavior generating circuits in C. elegans, to screen for novel roles for conserved autism genes.

neuroscience↗

bioRxiv2022

A general framework for the combined morphometric, transcriptomic, and physiological analysis of cells using metric geometry

High-resolution imaging-based single-cell profiling has transformed the study of cells in their spatial context. However, the lack of quantitative methods that can summarize the great diversity of complex cell shapes found in tissues and infer associations with other single-cell data modalities limits current analyses. Here, we report a general computational framework for the multi-modal analysis and integration of single-cell morphological data. We build upon metric geometry to construct cell morphology latent spaces, where distances in these spaces indicate the amount of physical deformation needed to change the morphology of one cell into that of another. Using these spaces, we integrate morphological data across technologies and leverage associated single-cell RNA-seq data to infer relations between morphological and transcriptomic cellular processes. We apply this framework to imaging and multi-modal data of neurons and glia to uncover genes related to neuronal plasticity. Our approach represents a strategy for incorporating cell morphological data into single-cell omics analyses.

bioinformatics↗