Rumpler Éva, Göcz Balázs, Skrapits Katalin, Sárvári Miklós, Takács Szabolcs, Farkas Imre, Póliska Szilárd, Papp Márton, Solymosi Norbert, Hrabovszky Erik
Laboratory of Reproductive Neurobiology, Institute of Experimental Medicine, Budapest, Hungary.
Laboratory of Reproductive Neurobiology, Institute of Experimental Medicine, Budapest, Hungary; János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary.
J Biol Chem. 2023 Sep;299(9):105121. doi: 10.1016/j.jbc.2023.105121. Epub 2023 Aug 1.
Single-cell transcriptomics are powerful tools to define neuronal cell types based on co-expressed gene clusters. Limited RNA input in these technologies necessarily compromises transcriptome coverage and accuracy of differential expression analysis. We propose that bulk RNA-Seq of neuronal pools defined by spatial position offers an alternative strategy to overcome these technical limitations. We report a laser-capture microdissection (LCM)-Seq method which allows deep transcriptome profiling of fluorescently tagged neuron populations isolated with LCM from histological sections of transgenic mice. Mild formaldehyde fixation of ZsGreen marker protein, LCM sampling of ∼300 pooled neurons, followed by RNA isolation, library preparation and RNA-Seq with methods optimized for nanogram amounts of moderately degraded RNA enabled us to detect ∼15,000 different transcripts in fluorescently labeled cholinergic neuron populations. The LCM-Seq approach showed excellent accuracy in quantitative studies, allowing us to detect 2891 transcripts expressed differentially between the spatially defined and clinically relevant cholinergic neuron populations of the dorsal caudate-putamen and medial septum. In summary, the LCM-Seq method we report in this study is a versatile, sensitive, and accurate bulk sequencing approach to study the transcriptome profile and differential gene expression of fluorescently tagged neuronal populations isolated from transgenic mice with high spatial precision.
单细胞转录组学是基于共表达基因簇来定义神经元细胞类型的强大工具。这些技术中有限的RNA输入必然会影响转录组覆盖范围以及差异表达分析的准确性。我们提出,对由空间位置定义的神经元群体进行批量RNA测序提供了一种克服这些技术限制的替代策略。我们报告了一种激光捕获显微切割(LCM)-Seq方法,该方法能够对从转基因小鼠组织切片中通过LCM分离的荧光标记神经元群体进行深度转录组分析。对ZsGreen标记蛋白进行轻度甲醛固定,对约300个合并的神经元进行LCM采样,随后进行RNA分离、文库制备以及针对纳克量中度降解RNA优化的方法进行RNA测序,这使我们能够在荧光标记的胆碱能神经元群体中检测到约15,000种不同的转录本。LCM-Seq方法在定量研究中显示出出色的准确性,使我们能够检测到在空间定义的背侧尾状核-壳核和内侧隔的临床相关胆碱能神经元群体之间差异表达的2891种转录本。总之,我们在本研究中报告的LCM-Seq方法是一种通用、灵敏且准确的批量测序方法,用于以高空间精度研究从转基因小鼠中分离的荧光标记神经元群体的转录组图谱和差异基因表达。