Denninger Jiyeon K, Walker Logan A, Chen Xi, Turkoglu Altan, Pan Alex, Tapp Zoe, Senthilvelan Sakthi, Rindani Raina, Kokiko-Cochran Olga N, Bundschuh Ralf, Yan Pearlly, Kirby Elizabeth D
Department of Psychology, College of Arts and Sciences, The Ohio State University, Columbus, OH, United States.
Department of Physics, College of Arts and Sciences, The Ohio State University, Columbus, OH, United States.
Front Mol Neurosci. 2022 Jan 31;15:810722. doi: 10.3389/fnmol.2022.810722. eCollection 2022.
Multipotent neural stem cells (NSCs) are found in several isolated niches of the adult mammalian brain where they have unique potential to assist in tissue repair. Modern transcriptomics offer high-throughput methods for identifying disease or injury associated gene expression signatures in endogenous adult NSCs, but they require adaptation to accommodate the rarity of NSCs. Bulk RNA sequencing (RNAseq) of NSCs requires pooling several mice, which impedes application to labor-intensive injury models. Alternatively, single cell RNAseq can profile hundreds to thousands of cells from a single mouse and is increasingly used to study NSCs. The consequences of the low RNA input from a single NSC on downstream identification of differentially expressed genes (DEGs) remains insufficiently explored. Here, to clarify the role that low RNA input plays in NSC DEG identification, we directly compared DEGs in an oxidative stress model of cultured NSCs by bulk and single cell sequencing. While both methods yielded DEGs that were replicable, single cell sequencing using the 10X Chromium platform yielded DEGs derived from genes with higher relative transcript counts compared to non-DEGs and exhibited smaller fold changes than DEGs identified by bulk RNAseq. The loss of high fold-change DEGs in the single cell platform presents an important limitation for identifying disease-relevant genes. To facilitate identification of such genes, we determined an RNA-input threshold that enables transcriptional profiling of NSCs comparable to standard bulk sequencing and used it to establish a workflow for profiling of endogenous NSCs. We then applied this workflow to identify DEGs after lateral fluid percussion injury, a labor-intensive animal model of traumatic brain injury. Our work joins an emerging body of evidence suggesting that single cell RNA sequencing may underestimate the diversity of pathologic DEGs. However, our data also suggest that population level transcriptomic analysis can be adapted to capture more of these DEGs with similar efficacy and diversity as standard bulk sequencing. Together, our data and workflow will be useful for investigators interested in understanding and manipulating adult hippocampal NSC responses to various stimuli.
多能神经干细胞（NSCs）存在于成年哺乳动物大脑的几个孤立微环境中，在这些微环境中它们具有协助组织修复的独特潜力。现代转录组学提供了高通量方法来识别内源性成年神经干细胞中与疾病或损伤相关的基因表达特征，但需要进行调整以适应神经干细胞的稀缺性。对神经干细胞进行批量RNA测序（RNAseq）需要汇集几只小鼠，这阻碍了其在劳动密集型损伤模型中的应用。另外，单细胞RNAseq可以对来自一只小鼠的数百到数千个细胞进行分析，并且越来越多地用于研究神经干细胞。单个神经干细胞的低RNA输入量对下游差异表达基因（DEGs）鉴定的影响仍未得到充分探索。在这里，为了阐明低RNA输入在神经干细胞DEG鉴定中的作用，我们通过批量测序和单细胞测序直接比较了培养的神经干细胞氧化应激模型中的DEGs。虽然两种方法都产生了可重复的DEGs，但使用10X Chromium平台的单细胞测序产生的DEGs来自相对转录本计数高于非DEGs的基因，并且与批量RNAseq鉴定的DEGs相比，其倍数变化更小。单细胞平台中高倍数变化DEGs的缺失对识别疾病相关基因提出了一个重要限制。为了便于识别此类基因，我们确定了一个RNA输入阈值，该阈值能够实现与标准批量测序相当的神经干细胞转录谱分析，并使用它建立了一个内源性神经干细胞分析的工作流程。然后，我们将此工作流程应用于在侧方流体冲击伤（一种劳动密集型创伤性脑损伤动物模型）后鉴定DEGs。我们的工作加入了越来越多的证据，表明单细胞RNA测序可能低估了病理性DEGs的多样性。然而，我们的数据也表明，群体水平的转录组分析可以进行调整，以与标准批量测序类似的功效和多样性捕获更多这些DEGs。总之，我们的数据和工作流程将对有兴趣了解和操纵成年海马神经干细胞对各种刺激反应的研究人员有用。
