Factors contributing to variability of glycan microarray binding profiles

Abstract

Protein-carbohydrate interactions play significant roles in a wide variety of biological systems. Glycan microarrays are commonly utilized to interrogate the selectivity, sensitivity, and breadth of these complex protein-carbohydrate interactions. During the past two decades, numerous distinct glycan microarray platforms have been developed, each assembled from a variety of slide-surface chemistries, glycan-attachment chemistries, glycan presentations, linkers, and glycan densities. Comparative analyses of glycan microarray data have shown that while many protein-carbohydrate interactions behave predictably across microarrays, there are instances when various array formats produce different results. For optimal construction and use of this technology, it is important to understand sources of variances across array platforms. In this study, we performed a systematic comparison of microarray data from 8 lectins across a range of concentrations on the CFG and neoglycoprotein array platforms. While there was good general agreement on the binding specificity of the lectins on the two arrays, there were some cases of large discrepancies. Differences in glycan density and linker composition contributed significantly to variability. The results provide insights for interpreting microarray data and designing future glycan microarrays.

Figure 1.. Representations of the two glycan microarray surfaces.

A) Depiction of the neoglycoprotein microarray with a high density of glycans. B) Depiction of the neoglycoprotein microarray with a low density of glycans. C) Depiction of the CFG microarray with glycans coupled to an NHS-hydrogel coated slide. For both formats, some glycans might not be accessible for lectin binding.

Figure 2.. Examples of discrepancies between the CFG and neoglycoprotein (NGP) array.

Data are shown for 6 of the 101 instances of large discrepancies between the CFG and NGP array. Data are graphed over all measured concentrations of lectin, with lectin concentration on the x-axis and RFU on the y-axis. Abbreviations are as follows: Man-a (CFG = #3, mannose alpha linked to Sp8; NGP = #275, low density mannose alpha linked to BSA, ~5/BSA), TF [CFG = #141, Galβ1–3GalNAcα-Sp14 (threonine); NGP = #109, Ac-Ser-(Galβ1–3GalNAcα)Ser-Ser-Gly-Hex-BSA, ~16/BSA], Fs (Forssman disaccharide; CFG = #92, GalNAcα1–3GalNAcβ-Sp8; NGP = #108, GalNAcα1–3GalNAcβ-BSA), LDN (LacDiNAc; #100, GalNAcβ1–4GlcNAcβ-Sp8; NGP = #234, GalNAcβ1–4GlcNAcβ-BSA, ~16/BSA), Chitotriose (CFG = #192, GlcNAcβ1–4GlcNAcβ1–4GlcNAcβ-Sp8; NGP = #129, GlcNAcβ1–4GlcNAcβ1–4GlcNAcβ-BSA, ~20/BSA), GNLacNAc (CFG = #183, GlcNAcβ1–3Galβ1–4GlcNAcβ-Sp0; NGP = #255, GlcNAcβ1–3Galβ1–4GlcNAcβ-BSA).

Figure 3.. Effects of linkers.

Data shown for all linker pairwise comparisons with RCA (A) and MAL-1 (B). RCA linker pairwise comparisons are broadly distributed when compared to linker pairwise comparisons with MAL-1. Data are graphed over all measured concentrations of lectin from each of the 69 pairwise comparisons. Data shown for all pairwise comparisons of the poorly correlated linker Sp14 (C; R=0.60) as compared to well correlated linker Sp0 (D; R=0.93). Data are graphed over all measured concentrations of all lectins.