Abstract
A clever approach for biosensing is to leverage the concept of the proximity effect, where analyte binding to probes can be coupled to a second, controlled binding event such as short DNA strands. This analyte-dependent effect has been exploited in various sensors with optical or electrochemical readouts. Electrochemical proximity assays (ECPA) are more amenable to miniaturization and adaptation to the point-of-care, yet ECPA has been generally targeted toward protein sensing with antibody-oligonucleotide probes. Antibodies themselves are also important as biomarkers, since they are produced in bodily fluids at the onset of a disease, often in low amounts. In this work, by using antigen-DNA conjugates, we targeted an ECPA method for antibody sensing and showed that the assay performance can be greatly enhanced using flexible spacers in the DNA conjugates. After adding flexible polyethylene glycol (PEG) spacers at two distinct positions, the spacers ultimately increased the antibody-dependent current by a factor of 4.0 without significant background increases, similar to our recent work using thermofluorimetric analysis (TFA). The optimized ECPA was applied to anti-digoxigenin antibody quantification at concentrations ranging over two orders of magnitude, from the limit-of-detection of 300 pM up to 50 nM. The assay was functional in 90% human serum, where increased ionic strength was used to counteract double-layer repulsion effects at the electrode. This flexible-probe ECPA methodology should be useful for sensing other antibodies in the future with high sensitivity, and the mechanism for signal improvement with probe flexibility may be applicable to other DNA-based electrochemical sensor platforms.
Supplementary materials
Title
Supporting Information for: An Electrochemical Proximity Assay (ECPA) for Antibody Detection Incorporating Flexible Spacers for Improved Performance
Description
The uploaded documents contain the following supporting information contents-
Page S-2: Sequences of DNA strands used in this study, Table S-1.
Page S-3 & S-4: Gold electrode and electrochemical cell fabrication, Figure S-1.
Page S-5: Data analysis (MATLAB) with minimal nonfaradaic background, Figure S-2.
Page S-6: Data analysis (MATLAB) with significant nonfaradaic background, Figure S-3.
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