The Utility of Multiplex Assays for Identification of Proteomic Signatures in Psychiatry

Challenges of Multiplex Assays for COVID-19 Research: A Machine Learning Perspective

Multiplex assays that provide simultaneous measurement of multiple analytes in biological samples have now developed into widely used technologies in the study of diseases, drug discovery, and other medical areas. These approaches span multiple assay systems and can provide readouts of specific assay components with similar accuracy as the respective single assay measurements. Multiplexing allows the consumption of lower sample volumes, lower costs, and higher throughput compared with carrying out single assays. A number of recent studies have demonstrated the impact of multiplex assays in the study of the SARS-CoV-2 virus, the infectious agent responsible for the current COVID-19 pandemic. In this respect, machine learning techniques have proven to be highly valuable in capturing complex disease phenotypes and converting these insights into models which can be applied in real-world settings. This chapter gives an overview of opportunities and challenges of multiplexed biomarker analysis, with a focus on the use of machine learning aimed at identification of biological signatures for increasing our understanding of COVID-19 disease, and for improved diagnostics and prediction of disease outcomes.

Comparative Analysis of Multiplex Platforms for Detecting Vitreous Biomarkers in Diabetic Retinopathy

Purpose

To evaluate the feasibility of using the Proximity Extension Assay (PEA) platform to detect biomarkers in vitreous and to compare the findings with results obtained with an electrochemiluminescent (ECL) sandwich immunoassay.

Methods

Vitreous samples from patients with proliferative diabetic retinopathy (PDR) and non-diabetic controls were tested using two different proteomics platforms. Forty-one assays were completed with the ECL platform and 459 with the PEA platform. Spearman's rank correlation coefficient (r_s) was used to determine the direction and strength of the relationship between protein levels detected by both platforms.

Results

Three hundred sixty-six PEA assays detected the tested protein in at least 25% of samples, and the difference in protein abundance between PDR and controls was statistically significant for 262 assays. Seventeen ECL assays yielded a detection rate ≥ 25%, and the difference in protein concentration between PDR and controls was statistically significant for 13 proteins. There was a subset of proteins that were detected by both platforms, and for those the Spearman's correlation coefficient was higher than 0.8.

Conclusions

PEA is suitable for the analysis of vitreous samples, showing a strong correlation with the ECL platform. The detection rate of PEA panels was higher than the panels tested with ECL. The levels of several proinflammatory and angiogenic cytokines were significantly higher in PDR vitreous compared to controls.

Translational Relevance

This study provides new information on the yields of small-volume assays that can detect proteins of interest in ocular specimens, and it identifies patterns of cytokine dysregulation in PDR.

Protein biomarkers for early detection of diseases: The decisive contribution of combinatorial peptide ligand libraries

The present review deals with biomarker discovery, especially in regard to sample treatment via combinatorial peptide ligand libraries, perhaps the only technique at present allowing deep exploration of biological fluids and tissue extracts in search for low- to very-low-abundance proteins, which could possibly mark the onset of most pathologies. Early-stage biomarkers, in fact, might be the only way to detect the beginning of most diseases thus permitting proper intervention and care. The following cancers are reviewed, with lists of potential biomarkers suggested in various reports: hepatocellular carcinoma, ovarian cancer, breast cancer and pancreatic cancer, together with some other interesting applications. Although panels of proteins have been presented, with robust evidence, as potential early-stage biomarkers in these different pathologies, their approval by FDA as novel biomarkers in routine clinical chemistry settings would require plenty of additional work and efforts from the pharma industry. The science environment in universities could simply not afford such heavy monetary investments.

SIGNIFICANCE

After more than 16years of search for novel biomarkers, to be used in a clinical chemistry set-up, via proteomic analysis (mostly in biological fluids) it was felt a critical review was due. In the present report, though, only papers reporting biomarker discovery via combinatorial peptide ligand libraries are listed and assessed, since this methodology seems to be the most advanced one for digging in depth into low-to very-low-abundance proteins, which might represent important biomarkers for the onset of pathologies. In particular, a large survey has been made for the following diseases, since they appear to have a large incidence on human population and/or represent fatal diseases: ovarian cancer, breast cancer, pancreatic cancer and hepatocellular carcinoma.