Data on characterization of glass biochips and validation of the label-free biosensor for detection of autoantibodies in human serum

• Main Authors: A.V. Pushkarev, A.V. Orlov, S.L. Znoyko, D.O. Novichikhin, V.A. Bragina, A.A. Sizikov, E. Alipour, H. Ghourchian, A.I. Nikitin, G.M. Sorokin, B.G. Gorshkov, P.I. Nikitin
• Format: Article
• Language: English
• Published: Elsevier 2020-06-01
• Series: Data in Brief
• Subjects: autoantibodies; detection in human serum; native kinetics; label-free detection; multiplex sensing; diagnostics of autoimmune disorders
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352340920305424

The data represent in-depth characterization of a novel method for highly sensitive simultaneous measuring in human serum of both critical parameters of autoantibodies: concentration and native kinetics. The latter refers to autoantibody interaction with free, not immobilized, antigen. The method and related biosensors are based on the spectral-correlation and spectral-phase interferometry. The data cover: multi-factor optimization and quantitative characterization of the developed affordable single-used biochips, including X-ray photoelectron spectroscopy (XPS) control of chemical modifications of the surface during fabrication; antibody screening; optimization and verification of protocols for label-free biosensing in human serum; mathematical model for fitting experimental data and calculation of kinetic constants of interaction of autoantibodies with free antigen; comprehensive verification of the method specificity; correlation between the data obtained with the developed biosensor and with enzyme linked immunosorbent assay (ELISA); comparison of analytical characteristics of the developed biosensor with the most advanced label-based methods. The data importance is confirmed by a companion paper (DOI 10.1016/j.bios.2020.112187), which shows that the combination of mentioned autoantibody parameters is promising for more accurate criteria for early diagnostics and efficient therapy of autoimmune disorders. The obtained data can be used in development of a wide range of biosensors, both label-free and based on various labels.