Automated evaluation of autoantibodies on human epithelial-2 cells as an approach to standardize cell-based immunofluorescence tests

Abstract

Introduction: Analysis of autoantibodies (AAB) by indirect immunofluorescence (IIF) is a basic tool for the serological diagnosis of systemic rheumatic disorders. Automation of autoantibody IIF reading including pattern recognition may improve intra- and inter-laboratory variability and meet the demand for cost-effective assessment of large numbers of samples. Comparing automated and visual interpretation, the usefulness for routine laboratory diagnostics was investigated.

Methods: Autoantibody detection by IIF on human epithelial-2 (HEp-2) cells was conducted in a total of 1222 consecutive sera of patients with suspected systemic rheumatic diseases from a university routine laboratory (n = 924) and a private referral laboratory (n = 298). IIF results from routine diagnostics were compared with a novel automated interpretation system.

Results: Both diagnostic procedures showed a very good agreement in detecting AAB (kappa = 0.828) and differentiating respective immunofluorescence patterns. Only 98 (8.0%) of 1222 sera demonstrated discrepant results in the differentiation of positive from negative samples. The contingency coefficients of chi-square statistics were 0.646 for the university laboratory cohort with an agreement of 93.0% and 0.695 for the private laboratory cohort with an agreement of 90.6%, P < 0.0001, respectively. Comparing immunofluorescence patterns, 111 (15.3%) sera yielded differing results.

Conclusions: Automated assessment of AAB by IIF on HEp-2 cells using an automated interpretation system is a reliable and robust method for positive/negative differentiation. Employing novel mathematical algorithms, automated interpretation provides reproducible detection of specific immunofluorescence patterns on HEp-2 cells. Automated interpretation can reduce drawbacks of IIF for AAB detection in routine diagnostics providing more reliable data for clinicians.

Figure 1

Flowchart of automated human epithelial (HEp-2) cell assay interpretation by the automated reading system [18]. The fundamental analysis chain of the image processing by the automated system is divided into acquisition, quality control, segmenting, object description, and object classification. Segmented objects were described by boundary, regional, topological, and texture/surface descriptors. Digital features were combined to rules, analogous to rules defined by experts.

Figure 2

Immunofluorescence patterns of two sera (a, b) which were both scored as negative by visual examination but demonstrated positive cytoplasmic staining by AKLIDES^® system. Green color: fluorescein isothiocyanate staining of autoantibody; blue color: 4',6-diamidino-2-phenylindol staining of chromatin.

Figure 3

Immunofluorescence patterns of two sera (a, b) which were both assessed as positive with speckled pattern by AKLIDES^® system but revealed staining of the nuclear membrane by visual examination. Green color: fluorescein isothiocyanate staining of autoantibody; blue color: 4',6-diamidino-2-phenylindol staining of chromatin.

Figure 4

Immunofluorescence pattern with staining of the Golgi complex, which was identified by AKLIDES^® system as cytoplasmic speckled pattern. Green color: fluorescein isothiocyanate staining of autoantibody; blue color: 4',6-diamidino-2-phenylindol staining of chromatin.

Figure 5

Comparison of positive and negative findings of 1,222 patient sera referred to a routine university laboratory (white bars) and a private laboratory (black bars). Negative samples demonstrated titers below 1 in 80, weak positive samples 1 in 80 or 1 in 160, and positive samples 1 in 320 or above.