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了解细胞命运决定机制的有力方法。然而，包含时间信息仍然具有挑战性，因为每个细胞只能通过测序分析测量一次。在这里，我们讨论在单细胞转录组学中纳入时间信息的最新进展和当前的努力。即使从快照数据，也可以通过以下方式从时间上推断时间动态单细胞转录组的伪时间排序。时间信息也可以来自内含子读数分析或RNA代谢标记，这可以为伪时间轨迹提供更多证据，并可以对基因调节相互作用进行更细粒度的分析。这些方法在小时的短时间内测量动态。现在，新兴的高通量谱系追踪方法可以通过使用CRISPR / Cas9在基因组中记录信息来实现长时间存储信息，随后可以通过测序将其读出。