Developmental Ethanol Exposure Impacts Purkinje Cells but Not Microglia in the Young Adult Cerebellum.

Fetal alcohol spectrum disorders (FASD) caused by developmental ethanol exposure lead to cerebellar impairments, including motor problems, decreased cerebellar weight, and cell death. Alterations in the sole output of the cerebellar cortex, Purkinje cells, and central nervous system immune cells, microglia, have been reported in animal models of FASD. To determine how developmental ethanol exposure affects adult cerebellar microglia and Purkinje cells, we used a human third-trimester binge exposure model in which mice received ethanol or saline from postnatal (P) days 4-9. In adolescence, cerebellar cranial windows were implanted and mice were aged to young adulthood for examination of microglia and Purkinje cells in vivo with two-photon imaging or in fixed tissue. Ethanol had no effect on microglia density, morphology, dynamics, or injury response. However, Purkinje cell linear frequency was reduced by ethanol. Microglia-Purkinje cell interactions in the Purkinje Cell Layer were altered in females compared to males. Overall, developmental ethanol exposure had few effects on cerebellar microglia in young adulthood and Purkinje cells appeared to be more susceptible to its effects.

A model-based hierarchical Bayesian approach to Sholl analysis.

MOTIVATION: Due to the link between microglial morphology and function, morphological changes in microglia are frequently used to identify pathological immune responses in the central nervous system. In the absence of pathology, microglia are responsible for maintaining homeostasis, and their morphology can be indicative of how the healthy brain behaves in the presence of external stimuli and genetic differences. Despite recent interest in high throughput methods for morphological analysis, Sholl analysis is still widely used for quantifying microglia morphology via imaging data. Often, the raw data are naturally hierarchical, minimally including many cells per image and many images per animal. However, existing methods for performing downstream inference on Sholl data rely on truncating this hierarchy so rudimentary statistical testing procedures can be used. RESULTS: To fill this longstanding gap, we introduce a parametric hierarchical Bayesian model-based approach for analyzing Sholl data, so that inference can be performed without aggressive reduction of otherwise very rich data. We apply our model to real data and perform simulation studies comparing the proposed method with a popular alternative. AVAILABILITY AND IMPLEMENTATION: Software to reproduce the results presented in this article is available at: https://github.com/vonkaenelerik/hierarchical_sholl. An R package implementing the proposed models is available at: https://github.com/vonkaenelerik/ShollBayes.

Developmental ethanol exposure has minimal impact on cerebellar microglial dynamics, morphology, and interactions with Purkinje cells during adolescence.

Introduction: Fetal alcohol spectrum disorders (FASD) are the most common cause of non-heritable, preventable mental disability, occurring in almost 5% of births in the United States. FASD lead to physical, behavioral, and cognitive impairments, including deficits related to the cerebellum. There is no known cure for FASD and their mechanisms remain poorly understood. To better understand these mechanisms, we examined the cerebellum on a cellular level by studying microglia, the principal immune cells of the central nervous system, and Purkinje cells, the sole output of the cerebellum. Both cell types have been shown to be affected in models of FASD, with increased cell death, immune activation of microglia, and altered firing in Purkinje cells. While ethanol administered in adulthood can acutely depress the dynamics of the microglial process arbor, it is unknown how developmental ethanol exposure impacts microglia dynamics and their interactions with Purkinje cells in the long term. Methods: To address this question, we used a mouse model of human 3rd trimester exposure, whereby L7cre/Ai9+/-/Cx3cr1G/+ mice (with fluorescently labeled microglia and Purkinje cells) of both sexes were subcutaneously treated with a binge-level dose of ethanol (5.0 g/kg/day) or saline from postnatal days 4-9. Cranial windows were implanted in adolescent mice above the cerebellum to examine the long-term effects of developmental ethanol exposure on cerebellar microglia and Purkinje cell interactions using in vivo two-photon imaging. Results: We found that cerebellar microglia dynamics and morphology were not affected after developmental ethanol exposure. Microglia dynamics were also largely unaltered with respect to how they interact with Purkinje cells, although subtle changes in these interactions were observed in females in the molecular layer of the cerebellum. Discussion: This work suggests that there are limited in vivo long-term effects of ethanol exposure on microglia morphology, dynamics, and neuronal interactions, so other avenues of research may be important in elucidating the mechanisms of FASD.

A Model-Based Hierarchical Bayesian Approach to Sholl Analysis.

Due to the link between microglial morphology and function, morphological changes in microglia are frequently used to identify pathological immune responses in the central nervous system. In the absence of pathology, microglia are responsible for maintaining homeostasis, and their morphology can be indicative of how the healthy brain behaves in the presence of external stimuli and genetic differences. Despite recent interest in high throughput methods for morphological analysis, Sholl analysis is still the gold standard for quantifying microglia morphology via imaging data. Often, the raw data are naturally hierarchical, minimally including many cells per image and many images per animal. However, existing methods for performing downstream inference on Sholl data rely on truncating this hierarchy so rudimentary statistical testing procedures can be used. To fill this longstanding gap, we introduce a fully parametric model-based approach for analyzing Sholl data. We generalize our model to a hierarchical Bayesian framework so that inference can be performed without aggressive reduction of otherwise very rich data. We apply our model to three real data examples and perform simulation studies comparing the proposed method with a popular alternative.