Exploring Intensity Distributions and Sampling in SERS-Based Immunoassays

Abstract

Surface-enhanced Raman scattering (SERS) is a sensitive, widely used spectroscopic technique. However, SERS is perceived as poorly reproducible and insufficiently robust for standard applications in analytical chemistry. Here, we demonstrated that reliable SERS immunoassay quantification at low concentrations (pM range) can be achieved by careful experimental design and appropriate data analysis statistics. A SERS-based immunoassay for IgG in human serum (3.1–50.0 ng mL–1 or 20.6–333 pM) was developed as a proof of concept. Calibration curves were created using the population median of the band area, centered at 592 cm–1, of a SERS reporter (Nile Blue A). Histograms of 7200 SERS spectra show lognormal distributions. SEM images of the sensor platform confirm a correlation between the number of SERS probes (ERLs) at the surface and the SERS intensity response. The IgG immunosensor reported here presented a limit of detection of 1.11 ng mL–1 or 7.39 pM and a limit of quantification of 9.04 ng mL–1 or 60.30 pM, within a 95% confidence level. The % error of the predicted versus the actual response of a quality control (QC) sample was 0.13%. The percent error of the QC sample decreases exponentially with the number of measurements. Randomly selected spatially separated measurements provided lower QC % error than a larger number of measurements that were closely spaced. We propose that it is necessary to describe the measured populations using an appropriate sample size for good statistics and consider the interrogation of sufficiently large and well-separated areas of the sensor surface to achieve a reliable sampling.

Digital SERS Protocol Using Au Nanoparticle-Based Extrinsic Raman Labels for the Determination of SARS-CoV-2 Spike Protein in Saliva Samples

“…The median of the intensities was chosen for the plot in Figure 5 instead of the mean due to the nature of the lognormal distributed population for both assays (see Figure S10). 19 The linearity in this case was not adequate for a reliable quantitative assay, whereas, for the digital protocol, relating counts to the S1−S2 spike protein concentration, the R 2 was 0.93, as seen in the black curve in Figure 5. For the digital protocol, the standard deviation is calculated considering counting statistics.…”

“…The nanoparticles now are referenced as ERLs and consist of AuNP + NB + PEG + antibody (Figure S1C). The binding capacity of the ERLs to the antigen was investigated by a lateral flow assay (LFA) 19,35 developed in parallel. The results for the LFA are presented in Figure S2.…”

“…37 Next, the slides were mounted into a 3D-printed assay platform with a PDMS mask demarking the areas for reactions described elsewhere. 19 Details on this platform are in the Supporting Information (Figure S3). Using the PDMS mask A (Figure S3A), 600 μL of an EDC/sulfo-NHS solution (0.2 M EDC and 0.05 M sulfo-NHS) was added to the platform.…”

“…A box plot shows the variability of the datasets and is a better representation of lognormally distributed populations. 19 The coefficient of determination (R 2 ) for the linear regression model was compared between the conventional data analysis and the digital protocol for the same dataset. For the conventional data analysis, relating the median of the SERS intensities and the S1−S2 spike protein concentration, the R 2 was 0.67, as seen in the blue curve in Figure 5.…”

“…SERS-based sandwich immunoassays have been used quantitatively. ,, They usually rely on a linear relationship between the average Raman intensity of a particular spectral feature of an SERS reporter and the concentration of the target analyte. ,, However, this approach becomes challenging as the concentration of the analyte decreases and the probability of a statistically significant number of SERS probes to be illuminated by the laser excitation also decreases. , Additionally, there is a large variation of signal intensities attributed to the non-homogeneous distribution of hotspots around the Raman reporters present at the surface of the ERLs. − There are several examples of SERS-based COVID-19 assays, , including immunoassays. − Here, we used the detection of COVID-19 antigens as a platform to prove the efficacy of the digital protocol. In this study, we constructed an SERS-based sandwich assay for detection of SARS-CoV-2 (2019-nCoV) spike S1 + S2 ECD-His recombinant protein, dubbed S1–S2 spike protein, in saliva in the pM range.…”