A Circular RNA Expressed from the FAT3 Locus Regulates Neural Development.

Circular RNAs (circRNAs) are key regulators of cellular processes, are abundant in the nervous system, and have putative regulatory roles during neural differentiation. However, the knowledge about circRNA functions in brain development is limited. Here, using RNA-sequencing, we show that circRNA levels increased substantially over the course of differentiation of human embryonic stem cells into rostral and caudal neural progenitor cells (NPCs), including three of the most abundant circRNAs, ciRS-7, circRMST, and circFAT3. Knockdown of circFAT3 during early neural differentiation resulted in minor transcriptional alterations in bulk RNA analysis. However, single-cell transcriptomics of 30 and 90 days differentiated cerebral organoids deficient in circFAT3 showed a loss of telencephalic radial glial cells and mature cortical neurons, respectively. Furthermore, non-telencephalic NPCs in cerebral organoids showed changes in the expression of genes involved in neural differentiation and migration, including FAT4, ERBB4, UNC5C, and DCC. In vivo depletion of circFat3 in mouse prefrontal cortex using in utero electroporation led to alterations in the positioning of the electroporated cells within the neocortex. Overall, these findings suggest a conserved role for circFAT3 in neural development involving the formation of anterior cell types, neuronal differentiation, or migration.

Spacemake: processing and analysis of large-scale spatial transcriptomics data.

BACKGROUND: Spatial sequencing methods increasingly gain popularity within RNA biology studies. State-of-the-art techniques quantify messenger RNA expression levels from tissue sections and at the same time register information about the original locations of the molecules in the tissue. The resulting data sets are processed and analyzed by accompanying software that, however, is incompatible across inputs from different technologies. FINDINGS: Here, we present spacemake, a modular, robust, and scalable spatial transcriptomics pipeline built in Snakemake and Python. Spacemake is designed to handle all major spatial transcriptomics data sets and can be readily configured for other technologies. It can process and analyze several samples in parallel, even if they stem from different experimental methods. Spacemake's unified framework enables reproducible data processing from raw sequencing data to automatically generated downstream analysis reports. Spacemake is built with a modular design and offers additional functionality such as sample merging, saturation analysis, and analysis of long reads as separate modules. Moreover, spacemake employs novoSpaRc to integrate spatial and single-cell transcriptomics data, resulting in increased gene counts for the spatial data set. Spacemake is open source and extendable, and it can be seamlessly integrated with existing computational workflows.