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.

Cells express distinct sets of genes in a precise spatio-temporal manner during embryonic development. There is a wealth of information on the deterministic embryonic development of Caenorhabditis elegans, but much less is known about embryonic development in nematodes from other taxa, especially at the molecular level. We are interested in insect pathogenic nematodes from the genus Steinernema as models of parasitism and symbiosis as well as a satellite model for evolution in comparison to C. elegans. To explore gene expression differences across taxa, we sequenced the transcriptomes of single embryos of two Steinernema species and two Caenorhabditis species at 11 stages during embryonic development and found several interesting features. Our findings show that zygotic transcription initiates at different developmental stages in each species, with the Steinernema species initiating transcription earlier than Caenorhabditis. We found that ortholog expression conservation during development is higher at the later embryonic stages than at the earlier ones. The surprisingly higher conservation of orthologous gene expression in later embryonic stages strongly suggests a funnel-shaped model of embryonic developmental gene expression divergence in nematodes. This work provides novel insight into embryonic development across distantly related nematode species and demonstrates that the mechanisms controlling early development are more diverse than previously thought at the transcriptional level.

Rapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using self-organizing maps (SOM) to link scATAC-seq regions with scRNA-seq genes that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of heterogeneous data.

Myoblasts are precursor skeletal muscle cells that differentiate into fused, multinucleated myotubes. Current single-cell microfluidic methods are not optimized for capturing very large, multinucleated cells such as myotubes. To circumvent the problem, we performed single-nucleus transcriptome analysis. Using immortalized human myoblasts, we performed RNA-seq analysis of single cells (scRNA-seq) and single nuclei (snRNA-seq) and found them comparable, with a distinct enrichment for long non-coding RNAs (lncRNAs) in snRNA-seq. We then compared snRNA-seq of myoblasts before and after differentiation. We observed the presence of mononucleated cells (MNCs) that remained unfused and analyzed separately from multi-nucleated myotubes. We found that while the transcriptome profiles of myoblast and myotube nuclei are relatively homogeneous, MNC nuclei exhibited significant heterogeneity, with the majority of them adopting a distinct mesenchymal state. Primary transcripts for microRNAs (miRNAs) that participate in skeletal muscle differentiation were among the most differentially expressed lncRNAs, which we validated using NanoString. Our study demonstrates that snRNA-seq provides reliable transcriptome quantification for cells that are otherwise not amenable to current single-cell platforms. Our results further indicate that snRNA-seq has unique advantage in capturing nucleus-enriched lncRNAs and miRNA precursors that are useful in mapping and monitoring differential miRNA expression during cellular differentiation.