Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel.
Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel.
Cell Rep Methods. 2022 Jul 25;2(8):100259. doi: 10.1016/j.crmeth.2022.100259. eCollection 2022 Aug 22.
Profiling of gene expression in sparse populations of genetically defined neurons is essential for dissecting the molecular mechanisms that control the development and plasticity of neural circuits. However, current transcriptomic approaches are ill suited for detailed mechanistic studies in sparse neuronal populations, as they either are technically complex and relatively expensive (e.g., single-cell RNA sequencing [RNA-seq]) or require large amounts of input material (e.g., traditional bulk RNA-seq). Thus, we established Meso-seq, a meso-scale protocol for identifying more than 10,000 robustly expressed genes in as little as 50 FACS-sorted neuronal nuclei. We demonstrate that Meso-seq works well for multiple neuroscience applications, including transcriptomics in antibody-labeled cortical neurons in mice and non-human primates, analyses of experience-regulated gene programs, and RNA-seq from visual cortex neurons labeled ultra-sparsely with viruses. Given its simplicity, robustness, and relatively low costs, Meso-seq is well suited for molecular-mechanistic studies in ultra-sparse neuronal populations in the brain.
对遗传定义神经元的稀疏群体中的基因表达进行分析对于剖析控制神经回路发育和可塑性的分子机制至关重要。然而,目前的转录组学方法不适合稀疏神经元群体的详细机制研究,因为它们要么技术复杂且相对昂贵(例如单细胞 RNA 测序(RNA-seq)),要么需要大量输入材料(例如,传统的批量 RNA-seq)。因此,我们建立了 Meso-seq,这是一种中尺度方案,可以在仅 50 个 FACS 分选的神经元核中鉴定出超过 10000 个稳健表达的基因。我们证明了 Meso-seq 非常适用于多种神经科学应用,包括在小鼠和非人类灵长类动物的抗体标记的皮质神经元中转录组学、经验调节基因程序的分析以及使用病毒超稀疏标记的视觉皮层神经元的 RNA-seq。鉴于其简单性、稳健性和相对较低的成本,Meso-seq 非常适合大脑中超稀疏神经元群体的分子机制研究。