Evaluation of Cellular Heterogeneity Using Patchclamp and RNA-Seq of Single Cells

描述(由申请人提供)：我们的总体目标是评估在一些人类组织类型中测量的单细胞RNA水平中的技术和生物噪声，并开发分析工具来解决在单细胞水平上观察到的复杂性。了解技术和生物噪声的来源和相对大小变得至关重要，因为RNA-Seq的检测下限现在在总RNA的10皮克范围内--即单个细胞中的RNA量。技术噪音可能来自几个不同的来源，我们将尝试分别进行评估。这些包括：1)样品获取和RNA检索，2)测序库制备，3)测序方法学，4)测序实验中的批次效应，5)数据分析的生物信息学方法，6)基因-基因变异性。评估来自不同来源的技术噪声的相对大小将有助于在今后的实验中如何减少这种噪声，从而减少对有意义的生物变异或噪声研究的干扰。生物噪声或细胞间差异源于细胞历史或命运、细胞周期阶段、与邻近细胞的连接、表面上相同的细胞(例如，不同的嗅觉感受器和嗅神经元)的真实功能差异。我们建议研究三种不同的细胞系统，我们预计它们具有不同程度的细胞间差异(生物噪声)：第一，来自胎盘的合体滋养层细胞，预计细胞间差异相对较小；第二，来自鼻神经上皮的嗅觉神经元，预计每个神经元表达不同的嗅觉受体，为RNA-SEQ数据中的差异提供积极的控制；第三，来自小脑的单个浦肯野神经元，可能具有较大的细胞间差异。从单个细胞中提取细胞质的方法--膜片钳移液管提取--不需要完全 破坏组织或分散细胞。我们已经使用膜片钳技术确定了小鼠大脑中多个神经元的转录，使用了从单个细胞中提取的细胞质，我们已经对单个细胞进行了膜片钳电生理记录，然后进行了RNA-Seq。对于选定的每种细胞类型--合体滋养层细胞、嗅觉神经元、浦肯野神经元、皮质神经元，我们将生成单细胞转录组数据集，以评估表面上相似的细胞之间的异质性，使用膜片钳提取细胞内容物和RNA-Seq；调查技术噪声的来源，并应用系统化的方法来减少技术噪声。我们将测试神经元的可塑性是否反映为 转录组。在此开发的所有分析工具和转录组数据库将在我们的网站上公开共享，所有项目数据将在数据质量控制6个月后存入DBGaP和短读档案(或其替代档案)。 与公共健康相关：既然今天的工具已经变得足够强大，可以让我们研究编码细胞功能和身份的分子，我们将解决一个基本问题：表面上相同的细胞有多相似或不同？还有，由于其他细胞的影响，或者由于衰老和疾病，细胞发生了多大的变化。

DESCRIPTION (provided by applicant): Our overall aim is to assess the technical and biological noise in measured RNA levels in single cells in a number of human tissue types, and to develop analytical tools to address the complexity observed at the single-cell level. Understanding the sources and relative sizes of technical and biological noise has become essential, as the lower detection limit of RNA-Seq is now in the range of 10 picograms of total RNA -- i.e. the amount of RNA in single cells. Technical noise can come from several different sources that we will attempt to evaluate separately. These include: 1) sample procurement and RNA retrieval, 2) sequencing library preparation, 3) sequencing methodology, 4) batch effects in sequencing experiments, 5) bioinformatics approaches for data analysis, 6) gene-gene variability. Assessing the relative magnitude of technical noise from different sources will infor how to reduce that noise in future experiments, and thereby reduce interference with studies of meaningful biological variations or noise. Biological noise, or inter-cell differences arise from differences in cellular history or fate, stages of cell cycle, connections to neighboring cells, an true functional differences of ostensibly identical cells (e.g., different olfactory receptors amon olfactory neurons). We propose to study three different cellular systems that we expect to have different levels of inter-cell variation (biological noise): first, syncytiotrophoblast cells from placenta, which are expected to have relatively low inter-cell variation; second, olfactory neurons from nasal neuroepithelium, each of which is expected to express a different olfactory receptor, providing a positive control for differences in the RNA-Seq data; and third, individual Purkinje neurons from the cerebellum, which may have larger inter-cell variation. The method to extract cytoplasm from individual cells -- patch clamp pipette extraction -- does not require fully disrupting the tissue or dispersing the cells. We have already used patch clamp to determine the transcriptomes of multiple individual neurons in the mouse brain, using the cytoplasm extracted from single cells on which we had already performed patch-clamp electrophysiology recordings, followed by RNA-Seq. For each of the cell types chosen - syncytiotrophoblasts, olfactory neurons, Purkinje neurons, cortical neurons we will generate single-cell transcriptome datasets to evaluate heterogeneity among ostensibly similar cells, using patch clamp to extract cell contents and RNA-Seq; investigate sources of technical noise and apply a systematic approach to reduce technical noise. We will test whether neuronal plasticity is reflected as a change in the transcriptome. All analytical tools and the transcriptome database developed here will be shared openly on our website and all project data will be deposited into dbGAP and the Short Read Archive (or its replacement) 6 months after data QC. PUBLIC HEALTH RELEVANCE: Now that today's tools have become powerful enough to allow us to look into the molecules that code for cell function and identity, we will address a fundamental question: How similar or different are ostensibly identical cells? And, how much do cells change due to influences of other cells or due to aging and disease.