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Lion, A. T.

Publications and source records attributed to Lion, A. T..

2 recordsLinked to original sources

Ethanol Exposure Perturbs Sea Urchin Development and Disrupts Developmental Timing

Ethanol is a known vertebrate teratogen that causes craniofacial defects as a component of fetal alcohol syndrome (FAS). Our results show that sea urchin embryos treated with ethanol similarly show broad skeletal patterning defects, potentially analogous to the defects associated with FAS. The sea urchin larval skeleton is a simple patterning system that involves only two cell types: the primary mesenchymal cells (PMCs) that secrete the calcium carbonate skeleton and the ectodermal cells that provide migratory, positional, and differentiation cues for the PMCs. Perturbations in RA biosynthesis and Hh signaling pathways are thought to be causal for the FAS phenotype in vertebrates. Surprisingly, our results indicate that these pathways are not functionally relevant for the teratogenic effects of ethanol in developing sea urchins. We found that developmental morphology as well as the expression of ectodermal and PMC genes was delayed by ethanol exposure. Temporal transcriptome analysis revealed significant impacts of ethanol on signaling and metabolic gene expression, and a disruption in the timing of GRN gene expression that includes both delayed and precocious gene expression throughout the specification network. We conclude that the skeletal patterning perturbations in ethanol-treated embryos likely arise from a loss of temporal synchrony within and between the instructive and responsive tissues.

developmental biology↗

ICAT: A Novel Algorithm to Robustly Identify Cell States Following Perturbations in Single Cell Transcriptomes

MotivationThe detection of distinct cellular identities is central to the analysis of single-cell RNA sequencing 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 ICAT that employs self-supervised feature weighting and control-guided clustering to accurately resolve cell states across heterogeneous conditions. ResultsUsing simulated and real datasets, we show ICAT is superior in identifying and resolving cell states compared to 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 single-cell RNA sequencing data. Availability and implementationhttps://github.com/BradhamLab/icat Supplemental Methods, Tables and Figures are available online.

bioinformatics↗