The hypothalamus is a ventral diencephalon region that is responsible for the regulation of homeostasis by a complex network of neurons. The impressive cellular diversity of this region and the mechanisms by which new neuroendocrine cells are born during adulthood are of broad interest due to its relation to a number of metabolic conditions - among them, obesity, a condition that greatly impacts life quality and predisposes individuals to many comorbidities, being the main risk factor for diabetes and cardiovascular disease. Obesity is caused by energy homeostasis unbalance, which is tightly regulated by the arcuate nucleus of the hypothalamus and its hunger regulating circuitry. Recently, single-cell RNA sequencing studies provided significant conceptual advance within systems biology and shed light on the hypothalamic cellular diversity upon a variety of stimuli, revealing dozens of cell types. However, there is a lack of comparative analysis of previous published studies that apply recent algorithmic advances in single-cell data analysis and that explore its full potential for the description of hypothalamic and arcuate cellular ontogeny. We herein propose the use of these state-of-the-art bioinformatics methods - including our novel dimensional reduction method - for the description of hypothalamic cellular ontogeny during development and adulthood. For that, we shall analyze the hypothalamic phenotypic manifold on the transcriptional level and with cellular resolution in in silico assays that integrate and correct technical effects from previous studies, approaching itsdynamics during homeostasis and its transcriptional response to high-fat diet, leptin, and fasting.
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