CHARACTERIZING IMMUNE CELL SUBSETS AND INTERFERON IN THE KIDNEYS OF SLE PATIENTS
Bibliographic record
Abstract
O020 / #593 Topic: AS16 - Lupus Nephritis-Pathogenesis ABSTRACT CONCURRENT SESSION 03: INNATE AND ADAPTIVE IMMUNITY IN SLE 22-05-2025 1:40 PM - 2:40 PM Background/Purpose 20-65% of patients with systemic lupus erythematosus (SLE) will develop lupus nephritis (LN), with up to 30% failing to respond to standard immunosuppressive therapy. These patients are at risk of kidney functional decline, highlighting the need for biomarkers that predict therapeutic response at flare onset. One potential biomarker is interferon-induced gene (IFI-G) expression. Higher levels of IFI-G expression in the peripheral blood have been associated with a more severe disease course and transcriptomic studies suggest that higher IFI-G expression in renal cells is associated with a poor response to conventional treatment. However further validation is required, and it remains unclear whether the poor outcomes in patients with high IFI-G expression in their kidneys are due to the direct effects of interferon on renal cells or indirectly through recruitment of inflammatory cells. In this study, we optimized a panel of antibodies for imaging mass cytometry (IMC) enabling examination of IFI-protein (P) expression and its association with immune cells infiltration and disruption of kidney architecture in archived renal biopsies for LN patients. Methods Paraffin embedded renal biopsies from patients with LN who were part of the Lupus Nephritis New Emerging Team and University of Toronto Lupus Clinic cohorts were available for testing. We have previously demonstrated that IFI-P levels in the peripheral blood strongly correlate with IFI-G expression, suggesting that antibodies against IFI-Ps (ISG15, MX1, PKR) are reliable surrogates for gene expression. To facilitate standardization, a pseudo-tissue was created from peripheral blood that was composed of a mixture of IFN-stimulated and unstimulated peripheral blood mononuclear cells from a healthy control. This was subsequently mixed with plasma, clotted, and embedded in paraffin. This pseudo-tissue served as both a positive and negative control for IFI-P expression. In addition to renal biopsies, archived lymph node and tonsil tissues were stained to facilitate titration of antibodies directed against immune subsets. Results A panel of 25 metal-conjugated antibodies was successfully created that enabled staining renal resident cells/structure, infiltrating immune cells (including T cells, B cells, plasma cells, monocytes, macrophages, and dendritic cells), and IFI-Ps (Table 1). Testing in the pseudo-tissue confirmed that the antibodies directed against IFI-Ps (MX1, ISG15, PKR) effectively discriminated between IFN-stimulated and unstimulated cells, with a low staining background and that this tissue could be used for batch standardization for subsequent staining of a larger number of LN biopsies over time. In our preliminary studies, staining of 3 kidney biopsies revealed distinct patterns: with the minimal change biopsy showing no cellular infiltration and minimal expression of IFI-Ps; the membranous LN biopsy showing increased IFNAR levels but similar IFI-P expression to the minimal change biopsy and few immune cells; and the proliferative LN biopsy showing high levels IFI-Ps and many immune cells in the glomerulus and proximal tubules (Figure 1). Table 1. Validated IMC Panel Figure 1. Comparative IMC staining of adult renal biopsies with minimal change disease (A), class V LN (B) and class IV LN (C). The 3 IMC images for each sample represent images obtained from the same region and time, with a selection of glomerular, tubular and stromal markers shown in the upper panels, immune markers shown in the middle panels and IFI-P and IFNAR1 in the lower panels. MEG, megalin; VIM, vimentin; aSMA, a-smooth muscle actin; COL IV, collagen IV. Conclusions This study validated an IMC approach for spatial analysis of interferon signatures and immune cell infiltration in LN kidney biopsies. Future analyses will apply this panel to a broader cohort of LN samples to identify biomarkers associated with treatment response. By enabling early stratification of LN patients, this approach could support the development of targeted therapies for individuals who are less likely to respond to standard treatments, ultimately improving long-term renal outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".