Data Science Institute and School of Computer Science, University of Technology Sydney, Ultimo, NSW 2007, Australia.
School of Biomedical Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia.
Nucleic Acids Res. 2022 Nov 28;50(21):e122. doi: 10.1093/nar/gkac785.
Tree- and linear-shaped cell differentiation trajectories have been widely observed in developmental biologies and can be also inferred through computational methods from single-cell RNA-sequencing datasets. However, trajectories with complicated topologies such as loops, disparate lineages and bifurcating hierarchy remain difficult to infer accurately. Here, we introduce a density-based trajectory inference method capable of constructing diverse shapes of topological patterns including the most intriguing bifurcations. The novelty of our method is a step to exploit overlapping probability distributions to identify transition states of cells for determining connectability between cell clusters, and another step to infer a stable trajectory through a base-topology guided iterative fitting. Our method precisely re-constructed various benchmark reference trajectories. As a case study to demonstrate practical usefulness, our method was tested on single-cell RNA sequencing profiles of blood cells of SARS-CoV-2-infected patients. We not only re-discovered the linear trajectory bridging the transition from IgM plasmablast cells to developing neutrophils, and also found a previously-undiscovered lineage which can be rigorously supported by differentially expressed gene analysis.
树状和线状的细胞分化轨迹在发育生物学中被广泛观察到，也可以通过计算方法从单细胞 RNA 测序数据集中推断出来。然而，具有复杂拓扑结构的轨迹，如循环、不同的谱系和分支层次结构，仍然难以准确推断。在这里，我们介绍了一种基于密度的轨迹推断方法，能够构建包括最有趣的分支在内的各种拓扑模式的形状。我们方法的新颖之处在于，它利用重叠概率分布来识别细胞的过渡状态，以确定细胞簇之间的连接性，以及通过基于基础拓扑的迭代拟合来推断稳定的轨迹。我们的方法精确地重建了各种基准参考轨迹。作为一个案例研究来展示实际用途，我们的方法在 SARS-CoV-2 感染患者的血细胞单细胞 RNA 测序图谱上进行了测试。我们不仅重新发现了连接 IgM 浆母细胞向发育中的中性粒细胞过渡的线性轨迹，还发现了一个以前未被发现的谱系，这可以通过差异表达基因分析得到严格支持。
