CHARACTERIZING IMMUNE CELL SUBSETS AND INTERFERON IN THE KIDNEYS OF SLE PATIENTS
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».