Inclusion of temporal information in single cell transcriptomics

Abstract

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.

Keywords: High-throughput lineage tracing; Pseudo-temporal ordering; RNA metabolic labeling; Single cell transcriptomics.