During Drosophila melanogaster embryogenesis, tight regulation of gene expression in time and space is required for the orderly emergence of specific cell types. While the general importance of microRNAs in regulating eukaryotic gene expression has been well-established, their role in early neurogenesis remains to be addressed. In this survey, we investigate the transcriptional dynamics of microRNAs and their target transcripts during neurogenesis of Drosophila melanogaster. To this end, we use the recently developed DIV-MARIS protocol, a method for enriching specific cell types from the Drosophila embryo in vivo, to sequence cell type-specific transcriptomes. We generate dedicated small and total RNA-seq libraries for neuroblasts, neurons and glia cells at early (6–8 h after egg laying (AEL)) and late (18–22 h AEL) stage. This allows us to directly compare these transcriptomes and investigate the potential functional roles of individual microRNAs with spatiotemporal resolution genome-wide, which is beyond the capabilities of existing in situ hybridization methods. Overall, we identify 74 microRNAs that are significantly differentially expressed between the three cell types and the two developmental stages. In all cell types, predicted target genes of down-regulated microRNAs show a significant enrichment of Gene Ontology terms related to neurogenesis. We also investigate how microRNAs regulate the transcriptome by targeting transcription factors and find many candidate microRNAs with putative roles in neurogenesis. Our survey highlights the roles of microRNAs as regulators of differentiation and glioneurognesis in the fruit fly and provides distinct starting points for dedicated functional follow-up studies.

Splicing is one key mechanism determining the state of any eukaryotic cell. Apart from linear splice variants, circular splice variants (circRNAs) can arise via non-canonical splicing involving a back-splice junction (BSJ). Most existing methods only identify circRNAs via the corresponding BSJ, but do not aim to estimate their full sequence identity or to identify different, alternatively spliced circular isoforms arising from the same BSJ. We here present CYCLeR, the first computational method for identifying the full sequence identity of new and alternatively spliced circRNAs and their abundances while simultaneously co-estimating the abundances of known linear splicing isoforms. We show that CYCLeR significantly outperforms existing methods in terms of F score and quantification of transcripts in simulated data. In a in a comparative study with long-read data, we also show the advantages of CYCLeR compared to existing methods. When analysing Drosophila melanogaster data, CYCLeR uncovers biological patterns of circRNA expression that other methods fail to observe.

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