Voltage-gated sodium channel activity mediates sea urchin larval skeletal patterning through spatial regulation of Wnt5 expression.

Defining pattern formation mechanisms during embryonic development is important for understanding the etiology of birth defects and to inform tissue engineering approaches. In this study, we used tricaine, a voltage-gated sodium channel (VGSC) inhibitor, to show that VGSC activity is required for normal skeletal patterning in Lytechinus variegatus sea urchin larvae. We demonstrate that tricaine-mediated patterning defects are rescued by an anesthetic-insensitive version of the VGSC LvScn5a. Expression of this channel is enriched in the ventrolateral ectoderm, where it spatially overlaps with posterolaterally expressed Wnt5. We show that VGSC activity is required to spatially restrict Wnt5 expression to this ectodermal region that is adjacent and instructive to clusters of primary mesenchymal cells that initiate secretion of the larval skeleton as triradiates. Tricaine-mediated Wnt5 spatial expansion correlates with the formation of ectopic PMC clusters and triradiates. These defects are rescued by Wnt5 knockdown, indicating that the spatial expansion of Wnt5 is responsible for the patterning defects induced by VGSC inhibition. These results demonstrate a previously unreported connection between bioelectrical status and the spatial control of patterning cue expression during embryonic pattern formation.

ICAT: a novel algorithm to robustly identify cell states following perturbations in single-cell transcriptomes.

MOTIVATION: The detection of distinct cellular identities is central to the analysis of single-cell RNA sequencing (scRNA-seq) experiments. However, in perturbation experiments, current methods typically fail to correctly match cell states between conditions or erroneously remove population substructure. Here, we present the novel, unsupervised algorithm Identify Cell states Across Treatments (ICAT) that employs self-supervised feature weighting and control-guided clustering to accurately resolve cell states across heterogeneous conditions. RESULTS: Using simulated and real datasets, we show ICAT is superior in identifying and resolving cell states compared with current integration workflows. While requiring no a priori knowledge of extant cell states or discriminatory marker genes, ICAT is robust to low signal strength, high perturbation severity, and disparate cell type proportions. We empirically validate ICAT in a developmental model and find that only ICAT identifies a perturbation-unique cellular response. Taken together, our results demonstrate that ICAT offers a significant improvement in defining cellular responses to perturbation in scRNA-seq data. AVAILABILITY AND IMPLEMENTATION: https://github.com/BradhamLab/icat.