Cross-site reproducibility of human cortical organoids reveals consistent cell type composition and architecture.

While guided human cortical organoid (hCO) protocols reproducibly generate cortical cell types at one site, variability in hCO phenotypes across sites using a harmonized protocol has not yet been evaluated. To determine the cross-site reproducibility of hCO differentiation, three independent research groups assayed hCOs in multiple differentiation replicates from one induced pluripotent stem cell (iPSC) line using a harmonized miniaturized spinning bioreactor protocol across 3 months. hCOs were mostly cortical progenitor and neuronal cell types in reproducible proportions that were consistently organized in cortical wall-like buds.

Cross-site reproducibility of human cortical organoids reveals consistent cell type composition and architecture.

Background: Reproducibility of human cortical organoid (hCO) phenotypes remains a concern for modeling neurodevelopmental disorders. While guided hCO protocols reproducibly generate cortical cell types in multiple cell lines at one site, variability across sites using a harmonized protocol has not yet been evaluated. We present an hCO cross-site reproducibility study examining multiple phenotypes.

MicroRNA-eQTLs in the developing human neocortex link miR-4707-3p expression to brain size.

Expression quantitative trait loci (eQTL) data have proven important for linking non-coding loci to protein-coding genes. But eQTL studies rarely measure microRNAs (miRNAs), small non-coding RNAs known to play a role in human brain development and neurogenesis. Here, we performed small-RNA sequencing across 212 mid-gestation human neocortical tissue samples, measured 907 expressed miRNAs, discovering 111 of which were novel, and identified 85 local-miRNA-eQTLs.

Segmentor: a tool for manual refinement of 3D microscopy annotations.

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.