Guo Minzhe, Wang Hui, Potter S Steven, Whitsett Jeffrey A, Xu Yan
The Perinatal Institute, Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States of America.
Department of Electrical Engineering and Computing Systems, College of Engineering and Applied Science, University of Cincinnati, Cincinnati, Ohio, United States of America.
PLoS Comput Biol. 2015 Nov 24;11(11):e1004575. doi: 10.1371/journal.pcbi.1004575. eCollection 2015 Nov.
A major challenge in developmental biology is to understand the genetic and cellular processes/programs driving organ formation and differentiation of the diverse cell types that comprise the embryo. While recent studies using single cell transcriptome analysis illustrate the power to measure and understand cellular heterogeneity in complex biological systems, processing large amounts of RNA-seq data from heterogeneous cell populations creates the need for readily accessible tools for the analysis of single-cell RNA-seq (scRNA-seq) profiles. The present study presents a generally applicable analytic pipeline (SINCERA: a computational pipeline for SINgle CEll RNA-seq profiling Analysis) for processing scRNA-seq data from a whole organ or sorted cells. The pipeline supports the analysis for: 1) the distinction and identification of major cell types; 2) the identification of cell type specific gene signatures; and 3) the determination of driving forces of given cell types. We applied this pipeline to the RNA-seq analysis of single cells isolated from embryonic mouse lung at E16.5. Through the pipeline analysis, we distinguished major cell types of fetal mouse lung, including epithelial, endothelial, smooth muscle, pericyte, and fibroblast-like cell types, and identified cell type specific gene signatures, bioprocesses, and key regulators. SINCERA is implemented in R, licensed under the GNU General Public License v3, and freely available from CCHMC PBGE website, https://research.cchmc.org/pbge/sincera.html.
发育生物学中的一个主要挑战是了解驱动器官形成以及构成胚胎的各种细胞类型分化的遗传和细胞过程/程序。虽然最近使用单细胞转录组分析的研究展示了测量和理解复杂生物系统中细胞异质性的能力，但处理来自异质细胞群体的大量RNA测序数据催生了对易于获取的单细胞RNA测序（scRNA-seq）分析工具的需求。本研究提出了一种普遍适用的分析流程（SINCERA：用于单细胞RNA测序分析的计算流程），用于处理来自整个器官或分选细胞的scRNA-seq数据。该流程支持对以下内容的分析：1）主要细胞类型的区分和鉴定；2）细胞类型特异性基因特征的鉴定；3）特定细胞类型驱动力的确定。我们将此流程应用于对从E16.5胚胎小鼠肺中分离的单细胞进行RNA测序分析。通过流程分析，我们区分了胎鼠肺的主要细胞类型，包括上皮细胞、内皮细胞、平滑肌细胞、周细胞和成纤维细胞样细胞类型，并鉴定了细胞类型特异性基因特征、生物过程和关键调节因子。SINCERA用R语言实现，根据GNU通用公共许可证v3授权，可从辛辛那提儿童医院医学中心（CCHMC）PBGE网站https://research.cchmc.org/pbge/sincera.html免费获取。
