Optimizing scan parameters for antibody microarray experiments: accelerating robust systems diagnostics for life sciences

Gain-Scanning for Protein Microarray Assays

Protein microarrays consist of known proteins spotted onto solid substrates and are used to perform highly multivariate assessments of protein-binding interactions. Human protein arrays are routinely applied to pathogen detection, immune response biomarker profiling, and antibody specificity profiling. Here, we describe and demonstrate a new data processing procedure, gain-scan, in which data were acquired under multiple photomultiplier tube (PMT) settings, followed by data fitting with a power function model to estimate the incident light signals of the array spots. Data acquisition under multiple PMT settings solves the difficulty of determining the single optimal PMT gain setting and allows us to maximize the detection of low-intensity signals while avoiding the saturation of high-intensity ones at the same time. The gain-scan data acquisition and fitting also significantly lower the variances over the detectable range of signals and improve the linear data normalization. The performance of the proposed procedure was verified by analyzing the profiling data of both the human polyclonal serum samples and the monoclonal antibody samples with both technical replicates and biological replicates. We showed that the multigain power function was an appropriate model for describing data acquired under multiple PMT settings. The gain-scan fitting alone or in combination with the linear normalization could effectively reduce the technical variability of the array data and lead to better sample separability and more sensitive differential analysis.

Current applications of antibody microarrays

The concept of antibody microarrays is one of the most versatile approaches within multiplexed immunoassay technologies. These types of arrays have increasingly become an attractive tool for the exploratory detection and study of protein abundance, function, pathways, and potential drug targets. Due to the properties of the antibody microarrays and their potential use in basic research and clinical analytics, various types of antibody microarrays have already been developed. In spite of the growing number of studies utilizing this technique, few reviews about antibody microarray technology have been presented to reflect the quality and future uses of the generated data. In this review, we provide a summary of the recent applications of antibody microarray techniques in basic biology and clinical studies, providing insights into the current trends and future of protein analysis.

Materiomics for Oral Disease Diagnostics and Personal Health Monitoring: Designer Biomaterials for the Next Generation Biomarkers

We live in exciting times for a new generation of biomarkers being enabled by advances in the design and use of biomaterials for medical and clinical applications, from nano- to macro-materials, and protein to tissue. Key challenges arise, however, due to both scientific complexity and compatibility of the interface of biology and engineered materials. The linking of mechanisms across scales by using a materials science approach to provide structure-process-property relations characterizes the emerging field of 'materiomics,' which offers enormous promise to provide the hitherto missing tools for biomaterial development for clinical diagnostics and the next generation biomarker applications towards personal health monitoring. Put in other words, the emerging field of materiomics represents an essentially systematic approach to the investigation of biological material systems, integrating natural functions and processes with traditional materials science perspectives. Here we outline how materiomics provides a game-changing technology platform for disruptive innovation in biomaterial science to enable the design of tailored and functional biomaterials--particularly, the design and screening of DNA aptamers for targeting biomarkers related to oral diseases and oral health monitoring. Rigorous and complementary computational modeling and experimental techniques will provide an efficient means to develop new clinical technologies in silico, greatly accelerating the translation of materiomics-driven oral health diagnostics from concept to practice in the clinic.