Application of Single Cell Gene Expression Technologies to Neurotoxicology.

Neurotoxicological research faces the challenge of linking biological changes resulting from exposures to neuronal function. An additional challenge is understanding cell-type specific differences and selective vulnerabilities of distinct neuronal populations to toxic insults. Single cell RNA-sequencing (scRNA-seq) allows for measurement of the transcriptome of individual cells. This makes it a valuable tool for validating and characterizing cell types present in multicell type samples in complex tissue or cell culture models, but also for understanding how different cell types respond to toxic insults. Pathway analysis of differentially expressed genes can provide in depth insights into underlying cell type-specific mechanisms of neurotoxicity. Toxicological data often has to be translated to outcomes for human health which requires an understanding of inter-species differences. Transcriptomic data aids in understanding these differences, including understanding developmental timelines of different species. We believe that scRNA-seq holds exciting promises for future neurotoxicological research.

Statistical inference of genetic pathway analysis in high dimensions.

Genetic pathway analysis has become an important tool for investigating the association between a group of genetic variants and traits. With dense genotyping and extensive imputation, the number of genetic variants in biological pathways has increased considerably and sometimes exceeds the sample size [Formula: see text]. Conducting genetic pathway analysis and statistical inference in such settings is challenging. We introduce an approach that can handle pathways whose dimension [Formula: see text] could be greater than [Formula: see text]. Our method can be used to detect pathways that have nonsparse weak signals, as well as pathways that have sparse but stronger signals. We establish the asymptotic distribution for the proposed statistic and conduct theoretical analysis on its power. Simulation studies show that our test has correct Type I error control and is more powerful than existing approaches. An application to a genome-wide association study of high-density lipoproteins demonstrates the proposed approach.

The genome-wide expression effects of escitalopram and its relationship to neurogenesis, hippocampal volume, and antidepressant response.

Antidepressant-induced hippocampal neurogenesis (AHN) is hypothesized to contribute to increases in hippocampal volume among major depressive disorder patients after long-term treatment. Furthermore, rodent studies suggest AHN may be the cellular mechanism mediating the therapeutic benefits of antidepressants. Here, we perform the first investigation of genome-wide expression changes associated with AHN in human cells. We identify gene expression networks significantly activated during AHN, and we perform gene set analyses to probe the molecular relationship between AHN, hippocampal volume, and antidepressant response. The latter were achieved using genome-wide association summary data collected from 30,717 individuals as part of the ENIGMA Consortium (genetic predictors of hippocampal volume dataset), and data collected from 1,222 major depressed patients as part of the NEWMEDS Project (genetic predictors of response to antidepressants dataset). Our results showed that the selective serotonin reuptake inhibitor, escitalopram evoked AHN in human cells; dose-dependently increasing the differentiation of cells into neuroblasts, as well as increasing gliogenesis. Activated genome-wide expression networks relate to axon and microtubule formation, and ribosomal biogenesis. Gene set analysis revealed that gene expression changes associated with AHN were nominally enriched for genes predictive of hippocampal volume, but not for genes predictive of therapeutic response.