To gain better insight into the dynamic interaction between cells and their environment, we developed the agonist-induced functional analysis and cell sorting (aiFACS) technique, which allows the simultaneous recording and sorting of cells in real-time according to their immediate and individual response to a stimulus. By modulating the aiFACS selection parameters, testing different developmental times, using various stimuli, and multiplying the analysis of readouts, it is possible to analyze cell populations of any normal or pathological tissue. The association of aiFACS with single-cell transcriptomics allows the construction of functional tissue cartography based on specific pharmacological responses of cells. As a proof of concept, we used aiFACS on the dissociated mouse brain, a highly heterogeneous tissue, enriching it in interneurons by stimulation with KCl or with AMPA, an agonist of the glutamate receptors, followed by sorting based on calcium levels. After AMPA stimulus, single-cell transcriptomics of these aiFACS-selected interneurons resulted in a nine-cluster classification. Furthermore, we used aiFACS on interneurons derived from the brain of the Fmr1-KO mouse, a rodent model of fragile X syndrome. We showed that these interneurons manifest a generalized defective response to AMPA compared with wild-type cells, affecting all the analyzed cell clusters at one specific postnatal developmental time.

中文翻译：

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与单细胞转录组学相关的激动剂诱导的功能分析和细胞分选表征正常和病理脑中的细胞亚型

为了更好地了解细胞与其环境之间的动态相互作用，我们开发了激动剂诱导的功能分析和细胞分选 (aiFACS) 技术，该技术允许根据细胞的即时反应和个体反应实时同时记录和分选细胞到刺激。通过调节 aiFACS 选择参数、测试不同的发育时间、使用各种刺激并乘以读数分析，可以分析任何正常或病理组织的细胞群。aiFACS 与单细胞转录组学的关联允许基于细胞的特定药理反应构建功能性组织图。作为概念证明，我们在分离的小鼠大脑（一种高度异质组织）上使用了 aiFACS，通过用 KCl 或 AMPA（一种谷氨酸受体激动剂）刺激在中间神经元中富集它，然后根据钙水平进行分类。在 AMPA 刺激后，这些 aiFACS 选择的中间神经元的单细胞转录组学产生了九个簇分类。此外，我们在来自大脑的中间神经元上使用了 aiFACS。Fmr1 -KO 小鼠，脆性 X 综合征的啮齿动物模型。我们表明，与野生型细胞相比，这些中间神经元表现出对 AMPA 的普遍缺陷反应，在特定的出生后发育时间影响所有分析的细胞簇。