Summary Cerebral cortical development is controlled by key transcription factors that specify the neuronal identities in the different layers. The mechanisms controlling their expression in distinct cells are only partially known. We investigated the expression and stability of Tbr1 , Bcl11b , Fezf2 , Satb2 , and Cux1 mRNAs in single developing mouse cortical cells. We observe that Satb2 mRNA appears much earlier than its protein and in a set of cells broader than expected, suggesting an initial inhibition of its translation, subsequently released during development. Mechanistically, Satb2 3′UTR modulates protein translation of GFP reporters during mouse corticogenesis. We select miR-541, a eutherian-specific miRNA, and miR-92a/b as the best candidates responsible for SATB2 inhibition, being strongly expressed in early and reduced in late progenitor cells. Their inactivation triggers robust and premature SATB2 translation in both mouse and human cortical cells. Our findings indicate RNA interference as a major mechanism in timing cortical cell identities.

Estimating the co-expression of cell identity factors in single-cell is crucial. Due to the low efficiency of scRNA-seq methodologies, sensitive computational approaches are critical to accurately infer transcription profiles in a cell population. We introduce COTAN, a statistical and computational method, to analyze the co-expression of gene pairs at single cell level, providing the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts’ distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can assess the correlated or anti-correlated expression of gene pairs, providing a new correlation index with an approximate p-value for the associated test of independence. COTAN can evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Similarly to correlation network analysis, it provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions, becoming a new tool to identify cell-identity markers. We assayed COTAN on two neural development datasets with very promising results. COTAN is an R package that complements the traditional single cell RNA-seq analysis and it is available at https://github.com/seriph78/COTAN.

The transition from dividing progenitors to postmitotic motor neurons (MNs) is orchestrated by a series of events, which are mainly studied at the transcriptional level by analyzing the activity of specific programming transcription factors. Here, we identify a posttranscriptional role of a MN-specific transcriptional unit (MN2) harboring a lncRNA (lncMN2-203) and two miRNAs (miR-325-3p and miR-384-5p) in this transition. Through the use of in vitro mESC differentiation and single-cell sequencing of CRISPR/Cas9 mutants, we demonstrate that lncMN2-203 affects MN differentiation by sponging miR-466i-5p and upregulating its targets, including several factors involved in neuronal differentiation and function. In parallel, miR-325-3p and miR-384-5p, co-transcribed with lncMN2-203, act by repressing proliferation-related factors. These findings indicate the functional relevance of the MN2 locus and exemplify additional layers of specificity regulation in MN differentiation.

Estimating co-expression of cell identity factors in single-cell transcriptomes is crucial to decode new mechanisms of cell state transition. Due to the intrinsic low efficiency of single-cell mRNA profiling, novel computational approaches are required to accurately infer gene co-expression in a cell population. We introduce COTAN, a statistical and computational method to analyze the co-expression of gene pairs at single cell level, providing the foundation for single-cell gene interactome analysis.