SLICE: determining cell differentiation and lineage based on single cell entropy.
SLICE: determining cell differentiation and lineage based on single cell entropy.
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SLICE：基于单细胞熵确定细胞分化与谱系
DOI：
10.1093/nar/gkw1278
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发表时间：
2017-04-20
影响因子：
14.9
通讯作者：
Xu Y
中科院分区：
文献类型：
--
作者：
Guo M;Bao EL;Wagner M;Whitsett JA;Xu Y
一个复杂的器官包含多种细胞类型，每种细胞都有自己独特的谱系和功能。了解每个细胞的谱系和分化状态对于器官形成和功能的最终描述至关重要。我们开发了SLICE，这是一种新的算法，它利用单细胞RNA-seq（scRNA-seq）基于单细胞熵定量测量细胞分化状态，并通过构建熵指导的细胞轨迹来预测细胞分化谱系。我们验证了我们的方法，使用三个独立的数据集与已知的谱系和发育时间信息，从智人和小家鼠。SLICE成功地测量了单个细胞的分化状态，并重建了先前已被实验验证的细胞分化轨迹。然后，我们将SLICE应用于E16.5的胚胎小鼠肺的scRNA-seq，以鉴定目前仍不清楚的肺间充质细胞谱系关系。使用SLICE预测了五种成纤维细胞亚型的两分支分化途径。本研究证明了SLICE在scRNA-seq分析中确定细胞分化状态和重建细胞分化谱系的普遍适用性和高预测准确性。
A complex organ contains a variety of cell types, each with its own distinct lineage and function. Understanding the lineage and differentiation state of each cell is fundamentally important for the ultimate delineation of organ formation and function. We developed SLICE, a novel algorithm that utilizes single-cell RNA-seq (scRNA-seq) to quantitatively measure cellular differentiation states based on single cell entropy and predict cell differentiation lineages via the construction of entropy directed cell trajectories. We validated our approach using three independent data sets with known lineage and developmental time information from both Homo sapiens and Mus musculus. SLICE successfully measured the differentiation states of single cells and reconstructed cell differentiation trajectories that have been previously experimentally validated. We then applied SLICE to scRNA-seq of embryonic mouse lung at E16.5 to identify lung mesenchymal cell lineage relationships that currently remain poorly defined. A two-branched differentiation pathway of five fibroblastic subtypes was predicted using SLICE. The present study demonstrated the general applicability and high predictive accuracy of SLICE in determining cellular differentiation states and reconstructing cell differentiation lineages in scRNA-seq analysis.