Abstract
In the quest for deciphering disease-associated biomarkers, high-performing tools for multiplexed protein expression profiling of crude clinical samples will be crucial. Affinity proteomics, mainly represented by antibody-based microarrays, have during recent years been established as a proteomic tool providing unique opportunities for parallelized protein expression profiling. But despite the progress, several main technical features and assay procedures remains to be (fully) resolved. Among these issues, the handling of protein microarray data, i.e. the biostatistics parts, is one of the key features to solve. In this study, we have therefore further optimized, validated, and standardized our in-house designed recombinant antibody microarray technology platform. To this end, we addressed the main remaining technical issues (e.g. antibody quality, array production, sample labelling, and selected assay conditions) and most importantly key biostatistics subjects (e.g. array data pre-processing and biomarker panel condensation). This represents one of the first antibody array studies in which these key biostatistics subjects have been studied in detail. Here, we thus present the next generation of the recombinant antibody microarray technology platform designed for clinical immunoproteomics.
Highlights
High-performing tools for multiplexed protein expression profiling of minimal amounts of crude clinical samples will be essential in the quest for deciphering disease-associated biomarkers for e.g. diagnosis and prognosis [1,2,3]
The number of high-performing antibody microarray set-ups at hand is still very low, which is explained by the fact that systematic cross-disciplinary efforts, addressing all of the key methodological areas involved in an array set-up (Table 1), must be pursued in such a developmental work [2, 8,9,10]
We have presented the generation of our recombinant antibody microarray technology platform for clinical immunoproteomics
Summary
High-performing tools for multiplexed protein expression profiling of minimal amounts of crude clinical samples will be essential in the quest for deciphering disease-associated biomarkers for e.g. diagnosis and prognosis [1,2,3]. A useful technology platform should be able to decode complex biological samples into detailed protein maps, as well as to filter and interpret these big data sets in terms of candidate biomarkers. The latter should result in both a full list of markers, reflecting the disease biology, and a condensed panel of biomarkers, displaying the best discriminatory power for e.g. diagnosis. This will, place high demands on the performance of the selected technology.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
