A Dynamic, Mathematical Model for Quantitative Glycoprofiling Using Label-Free Lectin Microarrays

The emergence of label-free lectin microarrays promises rapid and efficient glycoprofiling of complex analyte mixtures. Lectins have limited selectivity for different carbohydrate motifs necessitating relatively large array sizes to discriminate between glycoforms. Microarray technologies able to transduce the dynamics, instead of only the extent of binding, can introduce additional orthogonality in the array and therefore reduce its size. In this work, we develop a mathematical model of glycan binding dynamics to a label-free lectin sensor array, linking the matrix of observed dissociation constants, kinetics of binding, and occupancy to distinct glycoforms for identification. We introduce a matrix algebra approach that formulates the observed array dynamics in terms of a glycosylation matrix containing identifiers for each glycan chain on each protein isoform in the mixture. This formulation allows for straightforward calculation of the minimum array size necessary to distinguish a given set of glycans. As examples, we evaluate the binding of human IgG to two lectins, peanut agglutinin (PNA) and <i>Erythrina cristagalli</i> lectin (ECL), attached to near-infrared fluorescent single-walled carbon nanotube sensor array elements, both of which have affinities for terminal galactose residues. We demonstrate the application of both the steady state and transient model solutions to the glycan-lectin binding data, and we validate that linking microarray dynamics to glycan structure promises to significantly reduce requisite array size and complexity for rapid and efficient glycoprofiling

Noncovalent Monolayer Modification of Graphene Using Pyrene and Cyclodextrin Receptors for Chemical Sensing

Surprisingly few details have been reported in the literature that help the experimentalist to determine the conditions necessary for the preparation of self-assembled monolayers on graphene with a high surface coverage. With a view to graphene-based sensing arrays and devices and, in particular, in view of graphene-based varactors for gas sensing, graphene was modified in this work by the π–π interaction-driven self-assembly of 10 pyrene and cyclodextrin derivatives from solution. The receptor compounds were pyrene, pyrene derivatives with hydroxyl, carboxyl, ester, ammonium, amino, diethylamino, and boronic acid groups, and perbenzylated α-, β-, and γ-cyclodextrins. Adsorption of these compounds onto graphene was quantified by contact-angle measurements and X-ray photoelectron spectroscopy. Data thus obtained were fitted with the Langmuir adsorption model to determine the equilibrium constants for surface adsorption and the concentrations of self-assembly solutions needed to form dense monolayers on graphene. The equilibrium constants of all pyrene derivatives fell into the range from 10^3.4 to 10^4.6 M^–1. For the perbenzylated α-, β-, and γ-cyclodextrins, the equilibrium constants are 10^3.24, 10^2.97, and 10^2.95 M^–1, respectively. Monolayers of 1-pyrenemethylammonium chloride on graphene were confirmed to be stable under heating to 100 °C in a high vacuum (2 × 10^–5 Torr)