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bioRxiv · 10.64898/2026.01.14.699505

High-dimensional spatial proteomics and novel machine learning pipeline identifies disease specific renal damage states

Cao, T. P.·Durkee, M. S.·Ai, J.·Hara, S.·Andrade, M. S.·Chang, A.·Casella, G.·Chong, A. S.·Giger, M. L.·Clark, M. R.
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Abstract

Lupus nephritis (LuN) and renal allograft rejection (RAR) manifest inflammation and fibrosis that ultimately lead to kidney failure. To quantitatively assess spatial injury patterns, we collected high dimensional spatial proteomics data from 23 LuN, 33 RAR, and 8 kidney control (KC) biopsies. We developed a computational pipeline to segment and classify tubules, capillaries, and glomeruli in whole-slide images using three trained neural networks (Renal Damage diagnosis, RDDx). RDDx achieved high accuracy and generalizability, reliably identifying small capillaries and differentiating tubular and vascular inflammation in kidney tissues. Both LuN and RAR showed reduced tubular and capillary areas with expanded interstitial space. LuN displayed patchy clusters of stressed and inflamed tubules, whereas RAR exhibited diffuse injury. Within RAR, T cell-mediated rejection (TCMR) showed intense tubulitis while antibody-mediated rejection (ABMR) featured proliferating and inflamed capillaries near atrophic tubules. RDDx quantitative metric outputs correlated with histopathological scores, highlighting their reproducibility and clinical relevance. Stressed tubules in mildly inflamed LuN biopsies suggested they were a sensitive injury marker, while proliferating capillaries revealed microvascular remodeling in ABMR. These findings indicated RDDx can identify and quantify damage mechanisms specific to each renal disease thus facilitating future mechanistic studies and therapeutic target discovery.

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Cao, T. P., Durkee, M. S., Ai, J., Hara, S., Andrade, M. S., Chang, A., Casella, G., Chong, A. S., Giger, M. L., Clark, M. R.. 2026-01-15. High-dimensional spatial proteomics and novel machine learning pipeline identifies disease specific renal damage states. https://doi.org/10.64898/2026.01.14.699505

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