Hypertensive nephropathy (HN) is a leading cause of chronic kidney disease (CKD), but molecular mechanisms underlying disease progression remain poorly understood. We aimed to identify transcriptomic signatures associated with disease progression and to localize these signatures within renal architectures. Patients with biopsy-proven HN were selected from the Norwegian Kidney Biopsy Registry and stratified after ≥5 years of follow-up into early stable (ES), early progressor (EP), late stable (LS) and late progressor (LP) based on baseline kidney function and annual eGFR decline. Bulk transcriptomic profiling was performed in a discovery cohort and validated in an independent cohort using nCounter profiling. Spatial transcriptomics was applied to map progression-associated signatures to nephron compartments. Bulk RNA sequencing identified 415 differentially expressed genes (DEGs) between ES and EP, and 692 between LS and LP samples. ES was enriched in circadian rhythm pathways, with reduced NR1D1 and NR1D2 expression in EP. LP was associated with metabolic reprogramming including glutathione, retinol and amino acid metabolism. Random forest modelling identified NR1D1 gene expression as a key classifier, validated in an independent cohort. Spatial transcriptomics revealed nephron segment-specific expression patterns, including a proximal tubule cluster enriched in ferroptosis in LP. Comparative nephron-segment analyses across groups revealed progression-associated biomarkers, including CHGA, CCN2 and SFRP2 in glomeruli, and APOE in proximal tubules. HN progression appears to involve stage-specific and compartmentalized molecular programs. Integration of bulk and spatial transcriptomics identifies candidate biomarkers and pathways, potentially supporting innovative prognostic stratification and therapeutic strategies.
import lamindb as ln
import spatialdata as sd
coll = ln.Collection.get(key="geo-gse339455")
coll.ordered_artifacts.df() # the 4 members, ordered
# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]
# …or load each straight into memory as a SpatialData object:
sdatas = {a.key: sd.read_zarr(a.cache()) for a in coll.ordered_artifacts.all()}
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