Cross-Species Analysis of Single-Cell Transcriptomic Data
Cross-Species Analysis of Single-Cell Transcriptomic Data
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DOI：
10.3389/fcell.2019.00175
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发表时间：
2019-09-02
影响因子：
5.5
通讯作者：
Shafer, Maxwell E. R.
中科院分区：
文献类型：
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作者：
Shafer, Maxwell E. R.
使用scRNA测序分析数十万到数百万个单细胞的能力彻底改变了细胞和发育生物学领域，为许多物种中细胞类型的形式和功能多样性提供了令人难以置信的见解。这些技术有望开发详细的细胞类型遗传学，可以描述跨物种细胞类型之间的进化和发育关系。这将需要使用单细胞转录组学对许多物种和分类群进行采样，并对细胞类型同源性和多样性进行分类。目前存在许多用于分析单细胞数据和识别细胞类型的工具。然而，跨物种比较由于许多生物和技术因素而变得复杂。这些因素包括深度测序方法中常见的批次效应，直系同源基因和旁系同源基因之间众所周知的进化关系，以及对物种之间转录组变异的进化力量知之甚少。在这篇综述中，我讨论了最近的发展，在计算方法比较单细胞组学数据跨物种。这些方法有可能提供宝贵的洞察力如何进化的力量在细胞水平上的行为，并将进一步我们的动物和细胞多样性的进化起源的理解。
The ability to profile hundreds of thousands to millions of single cells using scRNA-sequencing has revolutionized the fields of cell and developmental biology, providing incredible insights into the diversity of forms and functions of cell types across many species. These technologies hold the promise of developing detailed cell type phylogenies which can describe the evolutionary and developmental relationships between cell types across species. This will require sampling of many species and taxa using single-cell transcriptomics, and methods to classify cell type homologies and diversifications. Many tools currently exist for analyzing single cell data and identifying cell types. However, cross-species comparisons are complicated by many biological and technical factors. These factors include batch effects common to deep-sequencing approaches, well known evolutionary relationships between orthologous and paralogous genes, and less well-understood evolutionary forces shaping transcriptome variation between species. In this review, I discuss recent developments in computational methods for the comparison of single-cell-omic data across species. These approaches have the potential to provide invaluable insight into how evolutionary forces act at the level of the cell and will further our understanding of the evolutionary origins of animal and cellular diversity.