Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
Development. 2019 Jun 27;146(12):dev170506. doi: 10.1242/dev.170506.
Single cell genomics has become a popular approach to uncover the cellular heterogeneity of progenitor and terminally differentiated cell types with great precision. This approach can also delineate lineage hierarchies and identify molecular programmes of cell-fate acquisition and segregation. Nowadays, tens of thousands of cells are routinely sequenced in single cell-based methods and even more are expected to be analysed in the future. However, interpretation of the resulting data is challenging and requires computational models at multiple levels of abstraction. In contrast to other applications of single cell sequencing, where clustering approaches dominate, developmental systems are generally modelled using continuous structures, trajectories and trees. These trajectory models carry the promise of elucidating mechanisms of development, disease and stimulation response at very high molecular resolution. However, their reliable analysis and biological interpretation requires an understanding of their underlying assumptions and limitations. Here, we review the basic concepts of such computational approaches and discuss the characteristics of developmental processes that can be learnt from trajectory models.
单细胞基因组学已经成为一种流行的方法，可以非常精确地揭示祖细胞和终末分化细胞类型的细胞异质性。这种方法还可以描绘谱系层次结构，并确定细胞命运获得和分离的分子程序。如今，单细胞方法通常可以对成千上万的细胞进行测序，未来预计还会有更多的细胞进行分析。然而，解释由此产生的数据具有挑战性，需要在多个抽象层次上使用计算模型。与单细胞测序的其他应用不同，聚类方法占据主导地位，发育系统通常使用连续的结构、轨迹和树来建模。这些轨迹模型有望以非常高的分子分辨率阐明发育、疾病和刺激反应的机制。然而，要可靠地分析和生物学解释它们，就需要了解它们的基本假设和局限性。在这里，我们回顾了这些计算方法的基本概念，并讨论了可以从轨迹模型中学习到的发育过程的特征。
