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Single-Cell Spatial Mapping of Human Kidney Development Reveals Cellular Niches and Lineage Dynamics

GSE277893 Homo sapiens Expression profiling by high throughput sequencing 5 samples Submitted 2026/04/22 Platform GPL24676
Summary
Organ development is shaped by both cell-autonomous changes and critical environmental cues. While single-cell genomic tools have enabled the characterization of gene expression and regulatory transitions during development, they have not fully captured the influence of neighboring cells. In this study, we combined single-cell spatial transcriptomics with gene expression data to investigate how cellular interactions influence human kidney development. By analyzing a large collection of fetal human kidney samples, we defined cellular differentiation patterns and uncovered two novel differentiation trajectories for parietal epithelial cells, suggesting greater developmental plasticity than previously recognized. Furthermore, using spatial gene expression data we identify novel spatially plausible ligand-receptor interactions that likely mediate differentiation processes. Among these, we highlight the role of WNT, FGF, and RET signaling, which have been classically described in mouse nephrogenesis but are now observed in human kidney development. In examining ligand expression, we estimate and quantify the microenvironment for each cell, enabling us to identify ligands associated with driving differentiation trajectories, and identify microenvironments which predict gene expression within cell types and differentiation status. Using this approach, we identified IGF2-mediated signaling as enriched within the blastema and associated with renewal signals suggesting its role as a niche renewal factor. This finding may explain the alterations in nephrogenesis associated with uncontrolled maternal diabetes, providing new insights how the local cellular environment shapes cellular differentiation in the kidney and potential therapeutic interventions.
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Direct links to NCBI, no account and no request form: the whole study as GSE277893_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 5 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1164719. Searching any of these in the dataset finder brings you back here.

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