Single cell transcriptional profiling to identify novel neurocircuit targets for reproductive mood disorders
It’s known that reproductive mood disorders—like postpartum depression, premenstrual dysphoric disorder, and perimenopausal depression—are largely caused by fluctuations of natural hormones like estradiol and progesterone. What is less well understood is how, or why, particular types of neurons in the brain respond to these hormones. Researchers will perform a detailed study of individual neurons reacting to a variety of hormonal conditions, in the hopes that we can design more targeted therapies for reproductive mood disorders.
Garret Stuber, Ph.D.
Need/Problem: Rationale strategies for therapeutic targeting of neuronal cell types in the brain for the treatment of neuropsychiatric disease are largely absent. We propose to utilize state of the art high throughput sequencing technology establish an experimental and computational pipeline to quantitatively assess the number of cell types on the mammalian hypothalamus and how these are regulated by various reproductive hormonal manipulations.
Grant Summary: This grant proposes experiments to classify the number of phenotype of neurons in the mammalian brain.
Goals & Projected Outcomes: The goal of the study is to develop a high throughput experimental and computational pipeline for accurately classifying and describing changes neuronal cell gene expression in response to environmental or hormonal manipulations. This approach will identify many novel therapeutic targets for the treatment of neuropsychiatric illnesses.
Grant Details: Reproductive mood disorders, such as postpartum depression, premenstrual dysphoric disorder, and perimenopausal depression exact a tremendous toll on individuals afflicted with these conditions as well as on society as a whole. Women are more than twice as likely to be afflicted by psychiatric mood disorders, compared to men. Further, changes in reproductive steroids, such as those that occur during the puerperium, premenstrual period, and menopause transition are associated with increased vulnerability for affective dysregulation. The symptomology of hormonally related mood disorders, including anhedonia and anxiousness implies a dysregulation of neurocircuitry that regulates anxiety and motivation. However, the underlying neurobiological etiology remains unclear, in part due to the complexity of hormonal contributors that influence cellular signaling and the previous lack of tools to genetically and molecularly identify and disentangle the functional elements involved. Circulating hormones, such as estradiol and progesterone, are thought to be critical regulators of mood in women, and also serve as an important signaling link between the endocrine and nervous systems. While optimal signaling of these hormones likely engages brain circuitry to promote behavioral states to optimize mood and ensure reproductive success, little is known about how hormonal fluctuations impact neural circuits that are ultimately the critical modulators of mood. While hormones are hypothesized to directly affect the function of neurons to thus control affective state, it is completely unknown how hormonal signaling alters the gene expression and function of molecularly distinct neuronal subtypes. This represents a major gap in our understanding of how gonadal hormones impact neural circuits implicated in affective disorders.
Here we propose to study how experimental alterations in reproductive hormones affects gene expression in individual molecularly defined neurons. We propose to utilize a recently developed high throughput single cell transcriptional profiling approach, DropSeq, in which we can quantitatively assess the transcriptional landscape of 10,000s – 100,000s of neurons individually from a given brain region taken from animals with distinct hormonal states. This is a transformative approach for many reasons. First, within any given brain region there is an intense degree of cellular heterogeneity. Because many distinct neuronal cell types exist, bulk profiling of gene expression from tissue punches is highly non-specific and limited in its capacity to detect changes that may be occurring in subsets of neurons. Second, because our previous work has demonstrated that multiple genetically and anatomically distinct neuronal subpopulations likely have unique roles in orchestrating affective behavior, knowledge of the gene expression changes in individual neurons will identify new and precise circuit and gene targets, which could be leveraged in the future to develop therapies aimed at normalizing maladaptive activity dynamics that are observed during hormonal fluctuations in susceptible men and women.