Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany.
Int J Biochem Cell Biol. 2020 May;122:105745. doi: 10.1016/j.biocel.2020.105745. Epub 2020 Apr 10.
Single cell transcriptomics has emerged as a powerful method for dissecting cell type diversity and for understanding mechanisms of cell fate decisions. However, inclusion of temporal information remains challenging, since each cell can be measured only once by sequencing analysis. Here, we discuss recent progress and current efforts towards inclusion of temporal information in single cell transcriptomics. Even from snapshot data, temporal dynamics can be computationally inferred via pseudo-temporal ordering of single cell transcriptomes. Temporal information can also come from analysis of intronic reads or from RNA metabolic labeling, which can provide additional evidence for pseudo-time trajectories and enable more fine-grained analysis of gene regulatory interactions. These approaches measure dynamics on short timescales of hours. Emerging methods for high-throughput lineage tracing now enable information storage over long timescales by using CRISPR/Cas9 to record information in the genome, which can later be read out by sequencing.
单细胞转录组学已经成为剖析细胞类型多样性和理解细胞命运决定机制的强大方法。然而，纳入时间信息仍然具有挑战性，因为通过测序分析，每个细胞只能被测量一次。在这里，我们讨论了在单细胞转录组学中纳入时间信息的最新进展和当前努力。即使是从静态数据中，也可以通过对单细胞转录组进行伪时间排序来计算推断出时间动态。时间信息也可以来自内含子读取的分析或来自 RNA 代谢标记，这可以为伪时间轨迹提供额外的证据，并能够更精细地分析基因调控相互作用。这些方法可以在数小时的短时间尺度上测量动态。新兴的高通量谱系追踪方法现在可以通过使用 CRISPR/Cas9 在基因组中记录信息来实现长时间尺度上的信息存储，随后可以通过测序读取这些信息。
