Understanding the aging hypothalamus, one cell at a time

Abstract

The hypothalamus is a brain region that integrates signals from the periphery and the environment to maintain organismal homeostasis. To do so, specialized hypothalamic neuropeptidergic neurons control a range of processes, such as sleep, feeding, the stress response, and hormone release. These processes are altered with age, which can affect longevity and contribute to disease status. Technological advances, such as single-cell RNA sequencing, are upending assumptions about the transcriptional identity of cell types in the hypothalamus and revealing how distinct cell types change with age. In this review, we summarize current knowledge about the contribution of hypothalamic functions to aging. We highlight recent single-cell studies interrogating distinct cell types of the mouse hypothalamus and suggest ways in which single-cell 'omics technologies can be used to further understand the aging hypothalamus and its role in longevity.

Keywords: homeostasis; longevity; metabolism; single-cell RNA-seq.

Figure 1.. Schematic of hypothalamic nuclei and their functions.

In the following, the figure’s nuclei are described counterclockwise, starting with the preoptic area. Many of the specific behaviors described relate to studies in rodents, although some of the functions are relevant to other species as well. POA. Preoptic area neurons are involved in sleep and temperature regulation[25]. SON. The supraoptic nucleus contains two major populations: vasopressin (encoded by Avp) expressing neurons control osmoregulation, while oxytocin neurons (encoded by Oxt) play an essential role in parturition, lactation, and other social behaviors[30,31]. ANH. The anterior hypothalamic nucleus is understudied, but has a known role in defensive attack behaviors[32]. SCN. The suprachiasmatic nucleus is the core of the internal circadian clock, and entrains body processes and behaviors to the light cycle. TU. TU^Sst+ (encoding somatostatin) neurons are involved in feeding[33]. ARC. The arcuate nucleus of the hypothalamus is a critical region for energy homeostasis and reproduction[34,35]. ARC^Kiss1 (kisspeptin neurons) are essential for proper pulsatile release of reproductive hormones [34]. DMH. Leptin sensitive neurons in the dorsomedial hypothalamus are essential for energy expenditure via brown adipose tissue thermogenesis, as well as the circadian timing of feeding and activity[36]. DMH^Brs3 neurons are involved in heart rate and maintenance of body temperature[24]. VMH. Leptin sensitive neurons on the ventromedial hypothalamus protect against diet-induced obesity, and regulate the interaction between estrogen and body composition[37,38]. Glucose-excited and glucose-inhibited neurons in the VMH control whole-body glucose homeostasis[39]. VMH^Esr1 neurons coordinate social behaviors such as attack and mounting[40]. PMH. The ventral premamillary nucleus links conspecific odorant cues and energy balance signals to reproductive function[41,42]. The dorsal premammillary nucleus controls escape behavior in response to threat[43]. MB. The mammillary bodies encode information about head direction, and projections from the mammillary bodies to anterior thalamic nuclei are necessary for spatial memory[44]. SUM. The supramamillary nucleus projects to the dentate gyrus and is involved in spatial memory tasks and can promote hippocampal neurogenesis[45,46]. PH. The posterior hypothalamus is activated under chronic unpredictable stress[47,48]. Together with the supramamillary nucleus, stimulation of the posterior hypothalamus can induce theta oscillations in the hippocampus[49]. LH. Lateral hypothalamus neurons are implicated in arousal and feeding, especially motivation to eat[26,50]. PVN. The paraventricular nucleus relays information from the hypothalamus back to the body. PVN^Trh neurons release thyrotropin-releasing hormone to the pituitary to control the hypothalamic-pituitary-thyroid axis. PVN^Crh expressing neurons are the central regulators of the hypothalamic-pituitary-adrenal (HPA) axis [51,52]. PVN^MC4R neurons receive inputs from hypothalamic nuclei such as ARC and promote feeding[53,54].

Figure 2.. Analysis pipeline for single cell RNA-seq experiments.

A) The output of most alignment software is a barcode by gene matrix. Due to variability among cells, and the technical limitations of single cell RNA-seq sampling, datasets are sparse[82]. B) Quality control and filtering steps are essential and impact downstream analysis. Threshold values should be carefully considered and can vary depending on the tissue analyzed and the sample preparation[83]. The number of counts and features per cell can indicate whether a cell is low quality (few counts or features), or a doublet (very high features compared to other cells). However, some cell types may have more features than others, so strict cut-offs may remove sources of legitimate biological variability. Similarly, the percentage of mitochondrial reads can indicate whether a cell is dead or dying, but some cells do have naturally occurring higher mitochondrial counts. C) Data preprocessing steps include data normalization, the identification of highly variable genes, and data scaling. The subset of genes which are highly variable can be used for downstream analysis. D) Single-cell datasets have high dimensionality, therefore dimensional reduction is used for clustering and data visualization. The results of clustering can vary based on upstream quality control[83]. E) Testing for differential gene expression between groups is a major goal of many studies. Because of the sparse nature of single cell data distributions, single cell RNA-seq can require different statistical approaches from traditional bulk approaches. Pseudoreplication, in which cells from the same animal are treated as statistically independent replicates, can be avoided through the use of mixed models[84,85].