Ontogenetic rules for the molecular diversification of hypothalamic neurons.
Marco BeneventoTomas G M HökfeltTibor HarkanyPublished in: Nature reviews. Neuroscience (2022)
The hypothalamus is an evolutionarily conserved endocrine interface that, among other roles, links central homeostatic control to adaptive bodily responses by releasing hormones and neuropeptides from its many neuronal subtypes. In its preoptic, anterior, tuberal and mammillary subdivisions, a kaleidoscope of magnocellular and parvocellular neuroendocrine command neurons, local-circuit neurons, and neurons that project to extrahypothalamic areas are intermingled in partially overlapping patches of nuclei. Molecular fingerprinting has produced data of unprecedented mass and depth to distinguish and even to predict the synaptic and endocrine competences, connectivity and stimulus selectivity of many neuronal modalities. These new insights support eminent studies from the past century but challenge others on the molecular rules that shape the developmental segregation of hypothalamic neuronal subtypes and their use of morphogenic cues for terminal differentiation. Here, we integrate single-cell RNA sequencing studies with those of mouse genetics and endocrinology to describe key stages of hypothalamus development, including local neurogenesis, the direct terminal differentiation of glutamatergic neurons, transition cascades for GABAergic and GABAergic cell-derived dopamine cells, waves of local neuronal migration, and sequential enrichment in neuropeptides and hormones. We particularly emphasize how transcription factors determine neuronal identity and, consequently, circuit architecture, and whether their deviations triggered by environmental factors and hormones provoke neuroendocrine illnesses.
Keyphrases
- spinal cord
- cerebral ischemia
- single cell
- transcription factor
- induced apoptosis
- rna seq
- single molecule
- subarachnoid hemorrhage
- blood brain barrier
- case control
- high throughput
- optical coherence tomography
- machine learning
- big data
- oxidative stress
- multiple sclerosis
- deep learning
- cell cycle arrest
- artificial intelligence