Spatial Proteomics Pipeline Enabling Single Glomerulus Study of Crescentic Glomerulonephritis
Bibliographic record
Abstract
Background: Glomerular crescent formation is characteristic of rapidly progressive glomerulonephritis (RPGN). Current non-specific anti-inflammatory treatments are based on incomplete understanding of the molecular mechanisms governing crescent formation. We hypothesize that spatial proteomics comparing crescentic (CR) to non-crescentic (NCR) glomeruli can reveal differential protein expression patterns valuable to understanding CR disease. Methods: Stained pediatric renal biopsy sections (2 IgAN, 1 Pauci-immune) were digitally annotated by renal pathologists for directed laser capture microdissection of CR and NCR glomeruli across serial sections. Single CR (n=13) and NCR (n=11) glomerulus samples were analyzed using a directDIA workflow and quantitatively compared by 2-way ANOVA to identify changes related to crescents. Proteomic differences were functionally annotated and integrated with KPMP scRNA-seq data using deep learning (DL) methods to assign cellular sourcing and guide pilot spatial interpretation studies using imaging mass cytometry (IMC). Results: 3,000+ protein groups were detected, 20% were differentially distributed between CR and NCR proteomes. Functional annotation suggested CR-enhanced proteins were associated with increased accumulation of extracellular matrix (ECM) and remodeling, increased ribosomal proteins for protein translation, and increased ER-chaperones and oxidative stress. DL analysis of integrated (proteome:sc-RNAseq) data attributed ECM changes to mesangial and fibroblastic remodeling, while ribosomal presence was attributed to immune cells. IMC analysis for periostin abundance and localization suggested likely mesangial role in cellular/fibrocellular crescents and fibroblastic role in fibrous crescents. Conclusion: We developed a spatial proteomics research pipeline using DL to study single glomeruli that integrates scRNAseq data and culminates in IMC, to confirm protein abundance and incorporate spatial understanding of stromal cell composition. Our data suggest that crescent formation may proceed along common pathways across GN etiologies. Proteomic differences represent candidate biomarkers for more targeted future treatments of RPGN. Funding: NIDDK Support - NIDDK Support, NIDDK Support
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".