Advances and pitfalls in anti-double-stranded DNA autoantibody detection and interpretation

Abstract

Anti-double-stranded DNA (anti-dsDNA) autoantibodies are detected in 20%-90% of patients with systemic lupus erythematosus (SLE), with prevalence varying according to disease activity, patient population, and assay methodology. Traditionally, four categories of anti-DNA autoantibodies have been described. The first recognizes only native dsDNA; the second recognizes both dsDNA and single-stranded DNA (ssDNA); the third reacts predominantly with denatured or ssDNA; and the fourth comprises anti-nucleosome autoantibodies, which recognize DNA-histone complexes and may yield results that differ from those obtained with assays specific for native dsDNA. Combining the Crithidia luciliae indirect immunofluorescence test with solid-phase immunoassays improves the diagnostic accuracy for anti-dsDNA antibody screening in patients with SLE. However, several limitations remain in the performance and interpretation of anti-dsDNA immassays, potentially leading to diagnostic errors. These pitfalls may arise mainly from analytical variability, assay design constraints, and a lack of standardized interpretation. Recent advances indicate a growing shift toward solid-phase multi-analyte arrays coupled with algorithm-based analysis, which may help reduce the seronegative gap in SLE while improving the harmonization of anti-dsDNA testing. This minireview critically evaluates current immunoassays for anti-dsDNA antibody testing, with particular emphasis on their analytical limitations, diagnostic pitfalls, and emerging advances.

Keywords: Anti-double-stranded DNA autoantibodies; Systemic lupus erythematosus; Crithidia luciliae indirect immunofluorescence; Solid-phase immunoassays; Pitfalls; Limitations

Core Tip: Crithidia luciliae indirect immunofluorescence testing provides excellent specificity by preferentially detecting high-avidity anti-double-stranded DNA autoantibodies, whereas solid-phase immunoassays offer greater sensitivity at the expense of specificity. Major analytical sources of variability include differences in substrate preparation, serum dilution protocols, conjugate performance, subjective interpretation of fluorescence microscopy, and the lack of standardized calibration and harmonized reference materials. Artificial intelligence-assisted image analysis may further enable automated interpretation of indirect immunofluorescence patterns, reducing inter-observer variability and improving analytical consistency.