Marco Eugenio, Karp Robert L, Guo Guoji, Robson Paul, Hart Adam H, Trippa Lorenzo, Yuan Guo-Cheng
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115;
Department of Systems Biology, Harvard Medical School, Boston, MA 02115;
Proc Natl Acad Sci U S A. 2014 Dec 30;111(52):E5643-50. doi: 10.1073/pnas.1408993111. Epub 2014 Dec 15.
We present single-cell clustering using bifurcation analysis (SCUBA), a novel computational method for extracting lineage relationships from single-cell gene expression data and modeling the dynamic changes associated with cell differentiation. SCUBA draws techniques from nonlinear dynamics and stochastic differential equation theories, providing a systematic framework for modeling complex processes involving multilineage specifications. By applying SCUBA to analyze two complementary, publicly available datasets we successfully reconstructed the cellular hierarchy during early development of mouse embryos, modeled the dynamic changes in gene expression patterns, and predicted the effects of perturbing key transcriptional regulators on inducing lineage biases. The results were robust with respect to experimental platform differences between RT-PCR and RNA sequencing. We selectively tested our predictions in Nanog mutants and found good agreement between SCUBA predictions and the experimental data. We further extended the utility of SCUBA by developing a method to reconstruct missing temporal-order information from a typical single-cell dataset. Analysis of a hematopoietic dataset suggests that our method is effective for reconstructing gene expression dynamics during human B-cell development. In summary, SCUBA provides a useful single-cell data analysis tool that is well-suited for the investigation of developmental processes.
我们展示了使用分叉分析的单细胞聚类（SCUBA），这是一种从单细胞基因表达数据中提取谱系关系并对与细胞分化相关的动态变化进行建模的新型计算方法。SCUBA借鉴了非线性动力学和随机微分方程理论中的技术，为涉及多谱系规范的复杂过程建模提供了一个系统框架。通过应用SCUBA分析两个互补的公开可用数据集，我们成功重建了小鼠胚胎早期发育过程中的细胞层次结构，对基因表达模式的动态变化进行了建模，并预测了干扰关键转录调节因子对诱导谱系偏差的影响。结果在逆转录聚合酶链反应（RT-PCR）和RNA测序之间的实验平台差异方面具有稳健性。我们在Nanog突变体中选择性地测试了我们的预测，发现SCUBA预测与实验数据之间有很好的一致性。我们通过开发一种从典型单细胞数据集中重建缺失时间顺序信息的方法，进一步扩展了SCUBA的实用性。对造血数据集的分析表明，我们的方法对于重建人类B细胞发育过程中的基因表达动态是有效的。总之，SCUBA提供了一个有用的单细胞数据分析工具，非常适合用于研究发育过程。
