The challenge to understand differentiation and cell lineages in development has resulted in many bioinformatics software tools, notably those working with gene expression data obtained via single-cell RNA sequencing obtained at snapshots in time. Reconstruction methods for trajectories often proceed by dimension reduction, data clustering and then computation of a tree graph in which edges indicate closely related clusters. Cell lineages can then be deduced by following paths through the tree. In the case of multi-potent cells undergoing differentiation, this trajectory reconstruction involves the reconstruction of multiple distinct lineages corresponding to commitment to each of a set of distinct fates. Recent work suggests that there may be cases in which the cell differentiation process involves trajectories that explore, in a dynamic and oscillatory fashion, propensity to differentiate into a number of possible cell fates before commitment finally occurs. Here, we show theoretically that the presence of such oscillations provides intrinsic constraints on the quality and resolution of the trajectory reconstruction process, even for idealized noise-free data. These constraints point to inherent common limitations of current methodologies and serve both to provide additional challenge in the development of software tools and also may help to understand features observed in recent experiments.

理解发育过程中的分化和细胞谱系的挑战催生了许多生物信息学软件工具，特别是那些处理通过及时获得快照的单细胞 RNA 测序获得的基因表达数据的软件工具。轨迹的重建方法通常通过降维、数据聚类，然后计算树形图来进行，其中边缘表示密切相关的簇。然后可以通过沿着树中的路径来推断细胞谱系。在多能细胞经历分化的情况下，这种轨迹重建涉及重建多个不同的谱系，对应于对一组不同命运中的每一个的承诺。最近的研究表明，在某些情况下，细胞分化过程可能涉及以动态和振荡的方式探索在最终发生之前分化成多种可能的细胞命运的倾向的轨迹。在这里，我们从理论上证明，即使对于理想化的无噪声数据，这种振荡的存在也对轨迹重建过程的质量和分辨率提供了内在的限制。这些限制指出了当前方法论固有的共同局限性，既给软件工具的开发带来了额外的挑战，也可能有助于理解最近实验中观察到的特征。