A Holistic 4D Approach to Optimize Intrinsic and Extrinsic Factors Contributing to Variability in Microarray Biosensing in Glycomics

Abstract

Protein–carbohydrate interactions happen to be a crucial facet of biology, discharging a myriad of functions. Microarrays have become a premier choice to discern the selectivity, sensitivity and breadth of these interactions in a high-throughput manner. The precise recognition of target glycan ligands among the plethora of others is central for any glycan-targeting probe being tested by microarray analyses. Ever since the introduction of the microarray as an elemental tool for high-throughput glycoprofiling, numerous distinct array platforms possessing different customizations and assemblies have been developed. Accompanying these customizations are various factors ushering variances across array platforms. In this primer, we investigate the influence of various extrinsic factors, namely printing parameters, incubation procedures, analyses and array storage conditions on the protein–carbohydrate interactions and evaluate these factors for the optimal performance of microarray glycomics analysis. We hereby propose a 4D approach (Design–Dispense–Detect–Deduce) to minimize the effect of these extrinsic factors on glycomics microarray analyses and thereby streamline cross-platform analyses and comparisons. This work will aid in optimizing microarray analyses for glycomics, minimize cross-platform disparities and bolster the further development of this technology.

Surface-Controlled Sialoside-Based Biosensing of Viral and Bacterial Neuraminidases

“…Screening methodologies that utilize PCA often rely on large databases. 10,22 The above results show that a combination of a biorelevant interaction, e.g., sialoside structure, with an orthogonal surface interaction mode provides a way to rationally increase the database, which can lead to enhanced selectivity. ■ CONCLUSIONS Pathogenic neuraminidases bind many types of sialosides, which makes discrimination based on a single glycan analysis impossible.…”

“…In the future, further adjustment of these conditions will allow the evaluation of activity and inhibition in a physiological environment. Screening methodologies that utilize PCA often rely on large databases. , The above results show that a combination of a biorelevant interaction, e.g., sialoside structure, with an orthogonal surface interaction mode provides a way to rationally increase the database, which can lead to enhanced selectivity.…”

“…IV targets sialic acid via two protein families, namely, hemagglutinins (HAs) and neuraminidases (NAs). − HA binds sialosides, while NA cleaves sialic acid from the cell surface (Figure a) . IV strains differ in their HA and NA components, which dictates their preferred sialoside binding and catalysis . Therefore, influenza strains can be characterized by sialoside profiling .…”

“…Since IV NAs interact with many different sialosides preferentially, they cannot be accurately analyzed by a single moiety biosensing strategy. Using biosensor array and principle component analyses proved to be useful in discriminating between undistinguishable analytes. ,,, Employing the sensitive EIS methodology for the detection of NAs using a sialoside array grafted on variable surfaces can enable selective determination of IV NA type and set the ground for the IV NA inhibitor efficacy evaluation strategy. ,, …”

“…Using biosensor array and principle component analyses proved to be useful in discriminating between undistinguishable analytes. 10,22,24,25 Employing the sensitive EIS methodology for the detection of NAs using a sialoside array grafted on variable surfaces can enable selective determination of IV NA type and set the ground for the IV NA inhibitor efficacy evaluation strategy. 23,26,27 Previously, we showed that by using the same sialosides on different electrodes, we can discriminate between binding and catalysis of bacterial NAs.…”