RESUMO
Humans evolved an extraordinarily expanded and complex cerebral cortex, associated with developmental and gene regulatory modifications 1-3 . Human accelerated regions (HARs) are highly conserved genomic sequences with human-specific nucleotide substitutions. Although there are thousands of annotated HARs, their functional contribution to human-specific cortical development is largely unknown 4,5 . HARE5 is a HAR transcriptional enhancer of the WNT signaling receptor Frizzled8 (FZD8) active during brain development 6 . Here, using genome-edited mouse and primate models, we demonstrate that human (Hs) HARE5 fine-tunes cortical development and connectivity by controlling the proliferative and neurogenic capacity of neural progenitor cells (NPCs). Hs-HARE5 knock-in mice have significantly enlarged neocortices containing more neurons. By measuring neural dynamics in vivo we show these anatomical features correlate with increased functional independence between cortical regions. To understand the underlying developmental mechanisms, we assess progenitor fate using live imaging, lineage analysis, and single-cell RNA sequencing. This reveals Hs-HARE5 modifies radial glial progenitor behavior, with increased self-renewal at early developmental stages followed by expanded neurogenic potential. We use genome-edited human and chimpanzee (Pt) NPCs and cortical organoids to assess the relative enhancer activity and function of Hs-HARE5 and Pt-HARE5. Using these orthogonal strategies we show four human-specific variants in HARE5 drive increased enhancer activity which promotes progenitor proliferation. These findings illustrate how small changes in regulatory DNA can directly impact critical signaling pathways and brain development. Our study uncovers new functions for HARs as key regulatory elements crucial for the expansion and complexity of the human cerebral cortex.
RESUMO
BACKGROUND: Recent advances in tissue clearing techniques, combined with high-speed image acquisition through light sheet microscopy, enable rapid three-dimensional (3D) imaging of biological specimens, such as whole mouse brains, in a matter of hours. Quantitative analysis of such 3D images can help us understand how changes in brain structure lead to differences in behavior or cognition, but distinguishing densely packed features of interest, such as nuclei, from background can be challenging. Recent deep learning-based nuclear segmentation algorithms show great promise for automated segmentation, but require large numbers of accurate manually labeled nuclei as training data. RESULTS: We present Segmentor, an open-source tool for reliable, efficient, and user-friendly manual annotation and refinement of objects (e.g., nuclei) within 3D light sheet microscopy images. Segmentor employs a hybrid 2D-3D approach for visualizing and segmenting objects and contains features for automatic region splitting, designed specifically for streamlining the process of 3D segmentation of nuclei. We show that editing simultaneously in 2D and 3D using Segmentor significantly decreases time spent on manual annotations without affecting accuracy as compared to editing the same set of images with only 2D capabilities. CONCLUSIONS: Segmentor is a tool for increased efficiency of manual annotation and refinement of 3D objects that can be used to train deep learning segmentation algorithms, and is available at https://www.nucleininja.org/ and https://github.com/RENCI/Segmentor .
