Open Access Research article

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

Karl Egerer1*, Dirk Roggenbuck2, Rico Hiemann3, Max-Georg Weyer4, Thomas Büttner2, Boris Radau2, Rosemarie Krause1, Barbara Lehmann1, Eugen Feist1 and Gerd-Rüdiger Burmester1

Author Affiliations

1 Department of Rheumatology and Clinical Immunology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany

2 GA Generic Assays GmbH, Ludwig-Erhard-Ring 3, 15287 Dahlewitz/Berlin, Germany

3 University of Applied Science Lausitz, Großenhainer Str. 57, 01968 Senftenberg, Germany

4 Medizinisches Versorgungszentrum für Laboratoriumsmedizin, Mikrobiologie, Virologie und Infektionsepidemiologie, Hygiene und Umweltmedizin, Dr. Löer - Dr. Treder und Kollegen, Hafenweg 11, 48155 Münster, Germany

For all author emails, please log on.

Arthritis Research & Therapy 2010, 12:R40  doi:10.1186/ar2949

Published: 9 March 2010

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.