Single-cell RNA sequencing has become a standard technique to characterize tissue development. Hereby, cross-sectional snapshots of the diversity of cell transcriptomes were transformed into (pseudo-) longitudinal trajectories of cell differentiation using computational methods, which are based on similarity measures distinguishing cell phenotypes. Cell development is driven by alterations of transcriptional programs e.g., by differentiation from stem cells into various tissues or by adapting to micro-environmental requirements. We here complement developmental trajectories in cell-state space by trajectories in gene-state space to more clearly address this latter aspect. Such trajectories can be generated using self-organizing maps machine learning. The method transforms multidimensional gene expression patterns into two dimensional data landscapes, which resemble the metaphoric Waddington epigenetic landscape. Trajectories in this landscape visualize transcriptional programs passed by cells along their developmental paths from stem cells to differentiated tissues. In addition, we generated developmental “vector fields” using RNA-velocities to forecast changes of RNA abundance in the expression landscapes. We applied the method to tissue development of planarian as an illustrative example. Gene-state space trajectories complement our data portrayal approach by (pseudo-)temporal information about changing transcriptional programs of the cells. Future applications can be seen in the fields of tissue and cell differentiation, ageing and tumor progression and also, using other data types such as genome, methylome, and also clinical and epidemiological phenotype data.

中文翻译：

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基因和细胞状态空间中的 scRNAseq 发育轨迹——扁形虫例子


单细胞 RNA 测序已成为表征组织发育的标准技术。因此，使用计算方法将细胞转录组多样性的横截面快照转化为细胞分化的（伪）纵向轨迹，该轨迹基于区分细胞表型的相似性测量。细胞发育是由转录程序的改变驱动的，例如，通过从干细胞分化成各种组织或通过适应微环境要求。我们在这里用基因状态空间中的轨迹补充细胞状态空间中的发育轨迹，以更清楚地解决后一个方面。这样的轨迹可以使用自组织地图机器学习来生成。该方法将多维基因表达模式转换为二维数据景观，类似于隐喻的沃丁顿表观遗传景观。该景观中的轨迹可视化细胞沿着从干细胞到分化组织的发育路径传递的转录程序。此外，我们使用 RNA 速度生成发育“矢量场”，以预测表达景观中 RNA 丰度的变化。我们将该方法应用于涡虫的组织发育作为说明性例子。基因状态空间轨迹通过有关细胞转录程序变化的（伪）时间信息补充了我们的数据描述方法。未来的应用可以在组织和细胞分化、衰老和肿瘤进展领域看到，也可以使用其他数据类型，如基因组、甲基化组以及临床和流行病学表型数据。