Harnessing glycofluoroforms for impedimetric biosensing

Abstract

Glycans play a major role in biological cell-cell recognition and signal transduction but have found limited application in biosensors due to glycan/lectin promiscuity; multiple proteins are capable of binding to the same native glycan. Here, site-specific fluorination is used to introduce protein-glycan selectivity, and this is coupled with an electrochemical detection method to generate a novel biosensor platform. 3F-lacto-N-biose glycofluoroform is installed onto polymer tethers, which are subsequently immobilised onto gold screen printed electrodes, providing a non-fouling surface. The impedance biosensing platform is shown to selectively bind cancer-associated galectin-3 compared to control glycans and proteins. To improve the analytical capability, Bayesian statistical analysis was deployed in the equivalent circuit fitting of electrochemical impedance spectroscopy data. It is shown that Markov Chain Monte Carlo (MCMC) analysis is a helpful method for visualising experimental irreproducibility, and we apply this as a quality control step.

Fig. 1. An overview of the GFF-biosensor for galectin-3 detection. (a) The chemical structures and schematic representations of the core building blocks of the sensor: glycofluoroform Gal3F-β(1,3)-GlcNAc-N₃ and polymeric thiol linker DBCO-(PHEA)₂₅-SH. (b) Schematic diagram of the assembled biosensor and the proposed galectin-3 binding interaction. (c) Binding of an analogous native glycoform, LacNAc, on the surface of galectin-3 (PDB: 1KJL).

Fig. 2. Nyquist plots of experimental data (dots) and the computational fitting to a modified Randles circuit (line) for EIS measurements of a bare gold SPE (purple, E_dc = 0.160 V vs. Ref) and a P–GFF modified gold SPE (green, E_dc = 0.230 V vs. Ref). Inset: magnified view of the Nyquist plot for the bare gold SPE and the modified Randles circuit used in the data analysis. EIS measurements were performed in the presence of 10 mM K₃[Fe(CN)₆] in pH 7 aqueous buffer (100 mM sodium phosphate, 233 mM sodium chloride) with the following parameters: t_{(equilibration)} = 180 s, E_ac = 10 mV, f = 0.05 Hz–10 kHz.

Fig. 3. (a) Nyquist plots of experimental data (dots) and the computational fitting to a modified Randles circuit (line) for EIS measurements of eight P–GFF Au-SPEs modified simultaneously. (b) MCMC analysis of the data in (a). The same colour code is used to distinguish between the separate electrodes. The uppermost plots (along the top diagonal) comprise 1-D histograms (y-axis is frequency) for each parameter used in equivalent circuit fitting (see x-axis labels), showing the distribution of the best-fit values for multiple analysis runs for each individual SPE. The 2-D scatter plots show correlations between parameters. (c) Pictures of the tank used for simultaneous Au-SPE modification. (d) A box and whisker plot showing the variation in the extracted R_ct parameters for experiments on different ‘bare’, i.e. unmodified, Au-SPE (purple, n = 12) and different ‘P–GFF’ modified Au-SPEs (green, n = 63) following equivalent circuit fitting to the large datasets shown in Fig. S4 and S5. The limits of the solid box represent the 25th and 75th percentile (20.8 and 44.9 kΩ, respectively), the central horizontal line in the box shows the position of the median (31.7 kΩ) and the upper and lower horizontal “whisker” lines represent the 5th and 95th percentiles (5.5 and 79.1 kΩ, respectively). EIS measurements were performed in the presence of 10 mM K₃[Fe(CN)₆] in pH 7 aqueous buffer (100 mM sodium phosphate, 233 mM sodium chloride) with the following parameters: t_{(equilibration)} = 300 s, E_dc = 0.230 V vs. Ref, E_ac = 10 mV, f = 0.1 Hz–10 kHz.

Fig. 4. (a) Extracted R_ct parameters (black dots) for individual P–GFF modified Au-SPEs plotted against the age of the P–GFF conjugate solution showing no correlation in R_ct over time. The mean R_ct values (R̄_ct) for both bare (light purple dashed line) and polymer only, “P”, modified (orange dashed line) Au-SPEs are shown for comparison. (b) Distribution plots overlaying the datasets from Fig. 3(d) (purple and green), (a) (grey) and S6 (orange). Lines represent Kernel smooth distributions and a 50% gap is used to separate the vertical bars. The lower portion of the plot is a “rug” representing the point values of the individual measurements.

Fig. 5. 2D scatter plots showing correlations for circuit parameters used to infer parameter distributions from a representative galectin-3 incubation experiment. The diagonal represents the 1D histograms for each parameter showing the distribution of the fitted values. The MCMC used to generate these chains was run for 10 000 samples, with the first 3000 discarded as burn-in. Inset: resultant Nyquist plots (dots) and the computational fitting (line) and Bode plots for the galectin-3 incubation experiments. EIS measurements were performed in the presence of 10 mM K₃[Fe(CN)₆] in pH 7 aqueous buffer (100 mM sodium phosphate, 233 mM sodium chloride) with the following parameters: t_{(equilibration)} = 180 s, E_dc = 0.230 V vs. Ref, E_ac = 10 mV, f = 0.05 Hz–10 kHz.

Fig. 6. Combined datasets from three different P–GFF (green squares) and P-CS (pink dots) modified Au-SPEs titrated against galectin-3. The P–GFF dataset is fit to a straight line (black dashed line, eqn (1)) to enable determination of K_d, as described in the text. Inset: chemical structures of GFF (green) and CS (pink). The datapoints represent the average values from the combined datasets, while vertical error bars represent the standard deviation.

Fig. 7. Extracted R_ct parameters for BSA incubation of both bare and P–GFF modified Au-SPEs.