SIGLEC-1 expression on monocytes as a diagnostic biomarker in pediatric type I interferon-mediated diseases.

BACKGROUND

Type I interferon-mediated diseases present diagnostic challenges due to heterogeneous clinical manifestations and limitations of current molecular diagnostics, such as the type I interferon score (IS).

OBJECTIVES

We sought to evaluate SIGLEC-1 expression on monocytes as a practical biomarker for type I interferon activation.

METHODS

We conducted a combined retrospective (n = 47) and prospective (n = 62) study of patients with suspected interferon-mediated diseases. SIGLEC-1 expression was quantified by flow cytometry as mean fluorescence intensity (MFI) and percentage of SIGLEC-1+ monocytes. Analytical robustness was assessed in 52 paired samples comparing whole blood versus PBMCs and 2 cytometers (BD Fortessa vs BD Lyric). Fold-change normalization was evaluated to reduce interinstrument variability.

RESULTS

In the retrospective cohort, SIGLEC-1 MFI and percentage of SIGLEC-1+ monocytes strongly correlated with the type I IS (R2 = 0.76; P < .0001), with excellent diagnostic accuracy (area under the curve [AUC] = 0.99 and 0.98). In 11 patients with paired samples (high and low IS), SIGLEC-1 decreased in parallel with the IS. In the prospective cohort, optimized MFI and percentage cutoffs (2307 and 40.65) perfectly discriminated patients with interferonopathies from those with alternative diagnoses (AUC = 1.0). Analytical validation showed that although absolute values differed between PBMCs and whole blood, correlations were strong (R2 > 0.97) and diagnostic classification was maintained. Similarly, Lyric values were lower than Fortessa but remained highly correlated (R2 > 0.93) with stable classification. Fold-change normalization (cutoffs: 4.7 for MFI; 12 for %) minimized platform variability while preserving 100% sensitivity and specificity.

CONCLUSIONS

Flow-cytometric SIGLEC-1 is a robust, cost-effective, and reproducible surrogate of type I IS, supporting its implementation for diagnosis, patient stratification, and longitudinal monitoring.