Abstract
With the exception of a few master transcription factors, regulators of neutrophil maturation are poorly annotated in the intermediate phenotypes between the granulocyte-macrophage progenitor (GMP) and the mature neutrophil phenotype. Additional challenges in identifying gene expression regulators in differentiation pathways relate to challenges wherein starting cell populations are heterogeneous in lineage potential and development, are spread across various states of quiescence, as well as sample quality and input limitations. These factors contribute to data variability make it difficult to draw simple regulatory inferences. In response we have applied a multi-omics approach using primary blood progenitor cells primed for homogeneous proliferation and granulocyte differentiation states which combines whole transcriptome resequencing (Ampliseq RNA) supported by droplet digital PCR (ddPCR) validation and mass spectrometry-based proteomics in a hypothesis-generation study of neutrophil differentiation pathways. Primary CD34+ cells isolated from human cord blood were first precultured in non-lineage driving medium to achieve an active, proliferating phenotype from which a neutrophil primed progenitor was isolated and cultured in neutrophil lineage supportive medium. Samples were then taken at 24-hour intervals over 9 days and analysed by Ampliseq RNA and mass spectrometry. The Ampliseq dataset depth, breadth and quality allowed for several unexplored transcriptional regulators and ncRNAs to be identified using a combinatorial approach of hierarchical clustering, enriched transcription factor binding motifs, and network mapping. Network mapping in particular increased comprehension of neutrophil differentiation regulatory relationships by implicating ARNT, NHLH1, PLAG1, and 6 non-coding RNAs associated with PU.1 regulation as cell-engineering targets with the potential to increase total neutrophil culture output. Overall, this study develops and demonstrates an effective new hypothesis generation methodology for transcriptome profiling during differentiation, thereby enabling identification of novel gene targets for editing interventions.