Guo Jingtao and Wang Xiaoyan at the Institute of Zoology of the Chinese Academy of Sciences led a study that mapped how human urogenital organs develop together during embryogenesis. The work, published in Science, reveals that neighboring organs coordinate their development through cellular communication and molecular signaling.
The researchers combined spatial transcriptomics, single-cell multi-omics, tissue protein imaging and human embryonic stem cell models to analyze male and female embryos at three developmental stages. They identified 33 cell types and found that the gonadal primordium receives WNT, BMP and NCAM signals from nearby tissues including the mesonephros and adrenal primordium.
In experiments, the team treated human embryonic stem cell-derived intermediate mesoderm with three factors, BMP7, NCAM1 and CHIR99021. This combination increased early gonadal marker expression and shifted the transcriptome toward an in vivo early gonadal state, suggesting neighboring tissues shape early gonadal fate.
The study also identified cells with features of both rete cells and gonadal supporting cells. Results suggest a possible contribution from mesonephros-associated rete cells to a subset of gonadal supporting cells. PAX8 appears to help maintain rete cell identity and may restrain the transition toward a supporting-cell state.
The researchers identified three transcriptionally distinct steroidogenic populations during the first trimester. In males, male gonadal steroidogenic cells differentiate during embryogenesis and express androgen biosynthetic genes. In females, gonadal steroidogenic cells do not emerge until puberty. During sex differentiation, cortisol reduces androgen levels through negative-feedback regulation, protecting normal female development.
The findings describe human urogenital development as a coordinated multiorgan process involving chemical signaling, cellular communication and hormonal coordination. The researchers created an interactive platform for exploring the spatial transcriptomic and single-cell multi-omic datasets.