AI-Based Digital Pathology-Enabled Spatial-Omics Data Analyses of the Human Kidney

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not quantified in abstract

• Primary Outcome: Tissue-region-specific N-glycan distribution patterns in kidney functional tissue units

• Nnt Or Sensitivity: Diagnostic potential for distinguishing DKD from AKI based on glycosylation patterns - sensitivity/specificity not reported

• Confidence Interval: Not reported

Clinical Application

Limited by requirement for specialised MALDI-MSI equipment and AI-pathology infrastructure, currently research-based Potential future application in major Australian pathology centres with advanced mass spectrometry capabilities; could complement current TGA-approved diagnostic methods for kidney disease Patients undergoing kidney biopsy for suspected diabetic kidney disease or acute kidney injury

Abstract

Identification of tissue-region-specific changes in glycosylation is crucial for understanding the pathogenesis of kidney diseases, yet it remains a great challenge. We developed a workflow that combines matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) data with AI-based digital pathology annotations of kidney functional tissue units (FTUs) to profile N-glycan distributions within biopsy tissues. This approach can generate molecular-level data relevant to diverse pathological outcomes, thereby aiding in the elucidation of disease mechanisms. As a proof-of-concept, we demonstrate that this AI-based digital pathology approach to MALDI data segmentation enables the detection and differentiation of N-glycosylation within FTUs of healthy kidney tissue. We then elucidated differences in N-glycosylation between the diseased kidney tissue samples from patients with different diagnoses. Sialic acid N-glycans, which have been linked to various kidney diseases, displayed enrichment in the glomeruli and tubules of tissues from patients diagnosed with diabetic kidney disease (DKD), whereas they were enriched in the tubules and arteries from patients with acute kidney injury (AKI), in comparison to healthy tissue. Furthermore, we found that polylactosamine N-glycans were enriched only in the AKI samples, indicating their potential roles in tubular injury and inflammation. This workflow has the potential to bridge the gap between region-specific glycosylation and its implications on FTUs in diseases, paving the way for targeted molecular imaging studies in the kidney and other tissues.

References

1. 1.Beishembieva, N., Gorman, B., Paul, A. S., Veličković, M., Veličković, D., Clair, G., Sharma, K., Sarder, P., & Anderton, C. R. (2026). AI-Based Digital Pathology-Enabled Spatial-Omics Data Analyses of the Human Kidney. Journal of Proteome Research. https://doi.org/10.1021/acs.jproteome.6c00147