Molecularly stratified hypothalamic astrocytes are cellular foci for obesity

Tibor Harkany, Evgenii Tretiakov, Luis Varela, Jasna Jarc, Patrick Rebernik, Sylvia Newbold, Erik Keimpema, Alexei Verkhratsky, Tamas Horvath, Roman Romanov
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Abstract

Abstract Astrocytes safeguard the homeostasis of the central nervous system 1,2 . Despite their prominent morphological plasticity under conditions that challenge the brain’s adaptive capacity 3–5 , the classification of astrocytes, and relating their molecular make-up to spatially devolved neuronal operations that specify behavior or metabolism, remained mostly futile 6,7 . Although it seems unexpected in the era of single-cell biology, the lack of a major advance in stratifying astrocytes under physiological conditions rests on the incompatibility of ‘neurocentric’ algorithms that rely on stable developmental endpoints, lifelong transcriptional, neurotransmitter, and neuropeptide signatures for classification 6–8 with the dynamic functional states, anatomic allocation, and allostatic plasticity of astrocytes 1 . Simplistically, therefore, astrocytes are still grouped as ‘resting’ vs. ‘reactive’, the latter referring to pathological states marked by various inducible genes 3,9,10 . Here, we introduced a machine learning-based feature recognition algorithm that benefits from the cumulative power of published single-cell RNA-seq data on astrocytes as a reference map to stepwise eliminate pleiotropic and inducible cellular features. For the healthy hypothalamus, this walk-back approach revealed gene regulatory networks (GRNs) that specified subsets of astrocytes, and could be used as landmarking tools for their anatomical assignment. The core molecular censuses retained by astrocyte subsets were sufficient to stratify them by allostatic competence, chiefly their signaling and metabolic interplay with neurons. Particularly, we found differentially expressed mitochondrial genes in insulin-sensing astrocytes and demonstrated their reciprocal signaling with neurons that work antagonistically within the food intake circuitry. As a proof-of-concept, we showed that disrupting Mfn2 expression in astrocytes reduced their ability to support dynamic circuit reorganization, a time-locked feature of satiety in the hypothalamus, thus leading to obesity in mice. Overall, our results suggest that astrocytes in the healthy brain are fundamentally more heterogeneous than previously thought and topologically mirror the specificity of local neurocircuits.
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分子分层的下丘脑星形胶质细胞是肥胖症的细胞病灶
摘要 星形胶质细胞维护着中枢神经系统的平衡1,2 。尽管星形胶质细胞在挑战大脑适应能力的条件下具有突出的形态可塑性 3-5 ,但对星形胶质细胞进行分类,并将其分子构成与指定行为或新陈代谢的神经元空间偏离操作联系起来,大多仍是徒劳无功 6,7 。虽然在单细胞生物学时代这似乎出乎意料,但在生理条件下对星形胶质细胞进行分层却缺乏重大进展,这是因为 "以神经为中心 "的算法依赖于稳定的发育终点、终身转录、神经递质和神经肽特征来进行分类 6-8 ,而星形胶质细胞的动态功能状态、解剖分配和异位可塑性与之不相容 1 。因此,简单地说,星形胶质细胞仍被分为 "静息 "与 "反应 "两类,后者指的是以各种诱导基因为标志的病理状态3,9,10。在这里,我们引入了一种基于机器学习的特征识别算法,该算法利用已发表的星形胶质细胞单细胞 RNA-seq 数据的累积能力作为参考图,逐步消除多效性和诱导性细胞特征。对于健康的下丘脑,这种回溯方法揭示了指定星形胶质细胞子集的基因调控网络(GRNs),并可用作其解剖学分配的地标工具。星形胶质细胞亚群保留的核心分子普查足以根据异位能力对它们进行分层,主要是它们与神经元之间的信号转导和新陈代谢相互作用。特别是,我们在胰岛素感应星形胶质细胞中发现了不同表达的线粒体基因,并证明了它们与神经元之间的相互信号传递,而神经元在食物摄入回路中起着拮抗作用。作为概念验证,我们证明了破坏星形胶质细胞中 Mfn2 的表达会降低它们支持动态回路重组的能力,而动态回路重组是下丘脑饱腹感的一个时间锁定特征,因此会导致小鼠肥胖。总之,我们的研究结果表明,健康大脑中的星形胶质细胞从根本上说比以前认为的更具异质性,并在拓扑学上反映了局部神经回路的特异性。
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