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Identification of cell types in a mouse brain single-cell atlas using low sampling coverage

机译:使用低采样覆盖率鉴定小鼠脑单细胞阿特拉斯中的细胞类型

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摘要

Abstract Background High throughput methods for profiling the transcriptomes of single cells have recently emerged as transformative approaches for large-scale population surveys of cellular diversity in heterogeneous primary tissues. However, the efficient generation of such atlases will depend on sufficient sampling of diverse cell types while remaining cost-effective to enable a comprehensive examination of organs, developmental stages, and individuals. Results To examine the relationship between sampled cell numbers and transcriptional heterogeneity in the context of unbiased cell type classification, we explored the population structure of a publicly available 1.3 million cell dataset from E18.5 mouse brain and validated our findings in published data from adult mice. We propose a computational framework for inferring the saturation point of cluster discovery in a single-cell mRNA-seq experiment, centered around cluster preservation in downsampled datasets. In addition, we introduce a “complexity index,” which characterizes the heterogeneity of cells in a given dataset. Using Cajal-Retzius cells as an example of a limited complexity dataset, we explored whether the detected biological distinctions relate to technical clustering. Surprisingly, we found that clustering distinctions carrying biologically interpretable meaning are achieved with far fewer cells than the originally sampled, though technical saturation of rare populations such as Cajal-Retzius cells is not achieved. We additionally validated these findings with a recently published atlas of cell types across mouse organs and again find using subsampling that a much smaller number of cells recapitulates the cluster distinctions of the complete dataset. Conclusions Together, these findings suggest that most of the biologically interpretable cell types from the 1.3 million cell database can be recapitulated by analyzing 50,000 randomly selected cells, indicating that instead of profiling few individuals at high “cellular coverage,” cell atlas studies may instead benefit from profiling more individuals, or many time points at lower cellular coverage and then further enriching for populations of interest. This strategy is ideal for scenarios where cost and time are limited, though extremely rare populations of interest (< 1%) may be identifiable only with much higher cell numbers.
机译:摘要背景技术高吞吐量用于分析单细胞的转录组,最近被出现为异构原代组织中细胞多样性大规模群体调查的转化性方法。然而,这些拟筑酶的有效生成将取决于多种细胞类型的充分采样,同时保持成本效益,可以全面检查器官,发展阶段和个人。结果检查采样细胞数与转录异质性之间的关系,在无偏心细胞类型分类的背景下,我们探讨了来自E18.5小鼠大脑的公共可用的130万个细胞数据集的人口结构,并验证了来自成人小鼠的公布数据的研究结果。我们提出了一种计算框架,用于推断在单个小区MRNA-SEQ实验中的群集发现的饱和点,以下采样的数据集中的群集保存为中心。此外,我们介绍了“复杂性指数”,其特征在于给定数据集中的细胞的异质性。使用CAJAL-Retzius单元作为有限复杂度数据集的示例,我们探讨了检测到的生物学区别是否涉及技术聚类。令人惊讶的是,我们发现携带生物学上可解释的含义的聚类区分是比最初采样的更少的细胞,尽管没有达到诸如Cajal-Retzius细胞的稀有群体的技术饱和度。我们另外通过鼠标器官验证了最近发布的细胞类型的图表,并再次使用了较少数量的单元格重新发布了较少数量的数据集。结论在一起,这些研究结果表明,来自130万个细胞数据库的大多数生物可解释的细胞类型可以通过分析50,000个随机选择的细胞来重新携带,表明,而不是在高“细胞覆盖下的少数个体,”细胞阿特拉斯研究可以效益从分析更多的人,或者在较低的细胞覆盖下的时间点,然后进一步丰富群体的感兴趣。该策略非常适合成本和时间有限的情况,尽管极其罕见的群体(<1%)可以识别,但只能识别更高的细胞数。

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