Collections / A skin–kidney neutrophil axis links UV exposure to renal angiogenesis and tissue remodeling in healthy and lupus kidneys. [Spatial Transcriptomics]

A skin–kidney neutrophil axis links UV exposure to renal angiogenesis and tissue remodeling in healthy and lupus kidneys. [Spatial Transcriptomics]

Neutrophils have been implicated in mediating immune crosstalk between injured tissue and distal organs, either promoting local and distal inflammation or mediating tissue repair. Skin injury from ultraviolet (UV) light triggers neutrophil migration to the kidney, which may have implications for nephritis flares in systemic lupus erythematosus (SLE). Here, we demonstrate that after skin exposure to UV light, renal neutrophils upregulate pro-angiogenic transcriptional programs and activate renal endothelial angiogenesis and tissue remodeling processes in healthy kidneys. Ligand–receptor analysis identified Cd177–Pecam1 as a key neutrophil-endothelial interaction. In contrast to healthy controls, skin exposure to UV light stimulated the expression of Tgfβ and Wnt signaling pathways in lupus-prone MRL/lpr mice. This tissue remodeling response was neutrophil-dependent. Spatial transcriptomic analysis of lupus nephritis (LN) kidneys revealed that tissue remodeling and pro-fibrotic pathways are highly expressed in neutrophil-rich areas. Neutrophil intra-renal levels were higher in the kidneys of LN patients with active skin disease, compared to those without skin involvement at the time of the nephritis flare. Moreover, urinary neutrophils were increased in SLE patients with active skin disease, including sun-associated skin rash, even in the absence of lupus nephritis. These results support a model in which UV-triggered skin injury activates a neutrophil-mediated skin-kidney axis that promotes differential tissue remodeling pathways in healthy compared to lupus-prone kidneys.

Datasets4
Size~63 MB
AssayVisium
Samplehuman · kidney
KindOther
PMID:42679049 ↗
Python · lamindb
import lamindb as ln
import spatialdata as sd

coll = ln.Collection.get(key="geo-gse333529")
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()}
Open in Vitessce
Visium
Human kidney
GSM9767744
human kidney lupus nephritis 17 MB 830 obs bronze H&E
TechnologyVisium
Organismhuman
Tissuekidney
Conditionlupus nephritis
Size17 MB
Obs830
bronze H&E Open in Vitessce
Open in Vitessce
Visium
Human kidney
GSM9767741
human kidney lupus nephritis 15 MB 769 obs bronze H&E
TechnologyVisium
Organismhuman
Tissuekidney
Conditionlupus nephritis
Size15 MB
Obs769
bronze H&E Open in Vitessce
Open in Vitessce
Visium
Human kidney
GSM9767743
human kidney lupus nephritis 17 MB 900 obs bronze H&E
TechnologyVisium
Organismhuman
Tissuekidney
Conditionlupus nephritis
Size17 MB
Obs900
bronze H&E Open in Vitessce
Visium
Human kidney
GSM9767742
human kidney lupus nephritis 14 MB 955 obs bronze H&E
TechnologyVisium
Organismhuman
Tissuekidney
Conditionlupus nephritis
Size14 MB
Obs955
bronze H&E

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