The molecular landscape of neural differentiation in the developing Drosophila brain revealed by targeted scRNA-seq and a multi-informatic analysis paradigm

Abstract

To adopt terminal neural fates, neural progenitors respond to a combination of molecular factors, the interplay between which remains difficult to untangle. In this work, we interrogated the type-II neuroblast lineages in the developing Drosophila brain using targeted single-cell mRNA sequencing. We used pseudotime analysis to group cells by their differentiation state and subsequently used marker gene analysis to identify genes that may play a role in neural fate specification. To aid the candidate gene selection process, we created MiCV, a scRNA-seq data visualization web tool that integrates results from the multifaceted analysis, displays the co-expression pattern of multiple genes, and provides an automated gene function curating feature. In situ profiling of these mRNAs further recovered the spatial information lacking in the single-cell data. Combining prior knowledge, in silico analysis, and in situ evidence, we characterize the molecular landscape of the developing Drosophila type-II neuroblast lineages and provide a roadmap for navigating the more complex brains.