Kidney Week 2025 Annual Meeting-Team Science and Multi-Omics in the Quest to Cure Diabetic Kidney Disease

Video Transcription

Video Summary

The session “Team Science and Multi-omics and the Quest to Cure Diabetic Kidney Disease” highlighted how integrating digital pathology, computational image analysis, single-cell transcriptomics, and spatial mass spectrometry can better characterize DKD heterogeneity and guide precision therapy.<br /><br />Dr. Laura Barisoni argued that DKD assessment is overly glomerulocentric and misses key tubular, interstitial, vascular, and inflammatory changes. Digital pathology enables AI-driven “computational pathology” to quantify structures (glomeruli, tubules, vessels, lymphocytes, peritubular capillaries), discover features beyond human scoring (e.g., tubular injury spectra, immune “habitats,” capillary morphology), and integrate these “pathomics” with bulk and spatial omics as a “Rosetta Stone.” Discussion included modeling injury trajectories (pseudotime), using features to predict treatment response (e.g., IgA nephropathy), and the role of biopsies versus surrogate biomarkers.<br /><br />Dr. John He outlined the evolution from bulk RNA-seq to single-cell/nucleus and deconvolution approaches. Single-cell studies in DKD models and human cohorts reveal shifts in cell populations and states (notably macrophage subsets such as CHI3L1/CHM2-like), endothelial changes/angiogenesis in early DKD, and drug-specific pathways for SGLT2 inhibitors, ARBs, MRAs, and GLP-1 agonists. He emphasized limitations: small sample sizes, mouse–human differences, and the need for validation. Deconvolution of bulk RNA-seq can cheaply estimate cell-type changes and adjust differential expression for cell composition.<br /><br />Dr. Chris Anderton presented spatial metabolomics/lipidomics/glycomics via MALDI mass spectrometry imaging within KPMP, enabling molecular mapping on tissue and post-imaging histology. Examples included glomerular lipids linked to specific biosynthetic enzymes, altered adenine localization in DKD, and spatial N-glycan patterns tied to glycosylation enzymes and disease lesions. New pipelines align mass-spec images with AI-derived tissue unit annotations and multiplex immunostaining.<br /><br />Dr. Petter Bjornstad connected deep phenotyping (“phenomics”) with multiomics, focusing on youth-onset type 2 diabetes, which shows very high albuminuria incidence, worse biopsy lesions, and >2× kidney failure risk. Proteomics (SomaScan) linked early DKD outcomes to metabolic, inflammatory, and fibrotic pathways. Physiologic studies showed hyperfiltration with afferent dilation/efferent constriction and associations with renal oxygenation (BOLD MRI). Kidney biopsy single-cell and spatial proteomics suggested SGLT2 inhibitors reverse diabetic metabolic programs (e.g., mTOR/TCA cycle signals), corroborated by acetate PET evidence of abnormal renal oxidative metabolism that improves with SGLT2 inhibition.

Asset Subtitle

Moderator(s): Pinaki Sarder, Insa Schmidt

Presentation(s):

Role of Computational Image Analysis in DKD - Laura Barisoni
What Do Single-Cell Transcriptomics Tell Us About Kidney Diseases in Diabetes? - John He
Spatial Metabolomics in DKD - Christopher Anderton
Multi-Omics and the Path to Curing DKD - Petter Bjornstad

Meta Tag

Date 11/9/2025

Pathway 1 Diabetic Kidney Disease

Pathway 2 CKD Non-Dialysis

Session ID 507264

Keywords

diabetic kidney disease (DKD)

team science

multi-omics integration

digital pathology

computational pathology

AI-driven image analysis

pathomics

single-cell transcriptomics

bulk RNA-seq deconvolution

macrophage subsets (CHI3L1/CHM2-like)

spatial mass spectrometry imaging (MALDI MSI)

spatial metabolomics/lipidomics/glycomics

KPMP (Kidney Precision Medicine Project)

precision therapy and treatment response prediction

SGLT2 inhibitors