PT  - JOURNAL ARTICLE
AU  - Dequiedt, Lucie
AU  - Forjaz, André
AU  - Lo, Jamie O.
AU  - McCarty, Owen
AU  - Wu, Pei-Hsun.
AU  - Rosenberg, Avi
AU  - Wirtz, Denis
AU  - Kiemen, Ashley
TI  - Three-dimensional reconstruction of fetal rhesus macaque kidneys at single-cell resolution reveals complex inter-relation of structures
AID  - 10.1101/2023.12.07.570622
DP  - 2023 Jan 01
TA  - bioRxiv
PG  - 2023.12.07.570622
4099  - http://biorxiv.org/content/early/2023/12/08/2023.12.07.570622.short
4100  - http://biorxiv.org/content/early/2023/12/08/2023.12.07.570622.full
AB  - Kidneys are among the most structurally complex organs in the body. Their architecture is critical to ensure proper function and is often impacted by diseases such as diabetes and hypertension. Understanding the spatial interplay between the different structures of the nephron and renal vasculature is crucial. Recent efforts have demonstrated the value of three-dimensional (3D) imaging in revealing new insights into the various components of the kidney; however, these studies used antibodies or autofluorescence to detect structures and so were limited in their ability to compare the many subtle structures of the kidney at once. Here, through 3D reconstruction of fetal rhesus macaque kidneys at cellular resolution, we demonstrate the power of deep learning in exhaustively labelling seventeen microstructures of the kidney. Using these tissue maps, we interrogate the spatial distribution and spatial correlation of the glomeruli, renal arteries, and the nephron. This work demonstrates the power of deep learning applied to 3D tissue images to improve our ability to compare many microanatomical structures at once, paving the way for further works investigating renal pathologies.Competing Interest StatementThe authors have declared no competing interest.