Back

Proneural genes form a combinatorial code to diversify neocortical neural progenitor cells

Moffat, A.; Oproescu, A.-M.; Okawa, S.; Han, S.; Vasan, L.; Ghazale, H.; Dennis, D. J.; Zinyk, D.; Guillemot, F.; del Sol, A.; Schuurmans, C.

2023-07-31 developmental biology
10.1101/2023.07.29.551096 bioRxiv
Show abstract

Neocortical neural progenitor cells (NPCs) are molecularly heterogeneous, yet the genes that confer distinct neuronal morphologies and connectivities during development are poorly understood. Here, we determined that a proneural gene combinatorial code diversifies cortical NPCs. By mining scRNA-seq data from murine embryonic and early postnatal cortices and generating trajectory inference models, we found that Neurog2 is predominant, and is transiently co-expressed with Ascl1 and/or Neurog1 during an apical-to-basal NPC transition state in NPCs with early pseudotime identities. To assess whether proneural gene pairs confer distinct properties, we first used Neurog2/Ascl1 reporter mice expressing unique reporters, revealing that NPCs have distinct cell division modes and cell cycle dynamics dependent on their proneural gene profile. To assess Neurog2/Neurog1 interactions, we used double knock-out mice and novel split-Cre transgenics crossed to a Rosa-diptheria-toxin-A line to delete double+ cells, showing Neurog1/Neurog2 are specifically required to generate early-born neurons and to maintain NPCs. Finally, in silico mutation of a cortical Neurog2-gene regulatory network and validation using Neurog1/Neurog2 mutant and deleter mice, identified Bclllb and Nhlh2, expressed in early-born neurons, as dependent on Neurog1/Neurog2. Our data explains how proneural genes act combinatorically to diversify gene regulatory networks, thereby lineage restricting NPCs and creating cortical neuronal diversity.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.