UC Irvine

The reconstruction of gene regulatory networks underlying cell differentiation from high-throughput gene expression and chromatin data remains a challenge. Here, we derive dynamic gene regulatory networks for human myeloid differentiation using a 5-day time series of RNA-seq and ATAC-seq data. We profile HL-60 promyelocytes differentiating into macrophages, neutrophils, monocytes, and monocyte-derived macrophages. We find a rapid response in the expression of key transcription factors and lineage markers that only regulate a subset of their targets at a given time, which is followed by chromatin accessibility changes that occur later along with further gene expression changes. We observe differences between promyelocyte- and monocyte-derived macrophages at both the transcriptional and chromatin landscape level, despite using the same differentiation stimulus, which suggest that the path taken by cells in the differentiation landscape defines their end cell state. More generally, our approach of combining neighboring time points and replicates to achieve greater sequencing depth can efficiently infer footprint-based regulatory networks from long series data.