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. 2022 Mar 11;12(1):4269.
doi: 10.1038/s41598-022-08156-6.

Micro-Fourier-transform infrared reflectance spectroscopy as tool for probing IgG glycosylation in COVID-19 patients

Carla Carolina Silva Bandeira  1 , Karen Cristina Rolim Madureira  2 , Meire Bocoli Rossi  2 , Juliana Failde Gallo  2 , Ana Paula Marques Aguirra da Silva  2 , Vilanilse Lopes Torres  2 , Vinicius Alves de Lima  3 , Norival Kesper Júnior  3 , Janete Dias Almeida  4 , Rodrigo Melim Zerbinati  3 , Paulo Henrique Braz-Silva  3   5 , José Angelo Lauletta Lindoso  2   3   6 , Herculano da Silva Martinho  7
Affiliations

Micro-Fourier-transform infrared reflectance spectroscopy as tool for probing IgG glycosylation in COVID-19 patients

Carla Carolina Silva Bandeira et al. Sci Rep. .

Abstract

It has been reported that patients diagnosed with COVID-19 become critically ill primarily around the time of activation of the adaptive immune response. However the role of antibodies in the worsening of disease is not obvious. Higher titers of anti-spike immunoglobulin IgG1 associated with low fucosylation of the antibody Fc tail have been associated to excessive inflammatory response. In contrast it has been also reported that NP-, S-, RBD- specific IgA, IgG, and IgM are not associated with SARS-CoV-2 viral load, indicating that there is no obvious correlation between antibody response and viral antigen detection. In the present work the micro-Fourier-transform infrared reflectance spectroscopy (micro-FTIR) was employed to investigate blood serum samples of healthy and COVID-19-ill (mild or oligosymptomatic) individuals (82 healthcare workers volunteers in "Instituto de Infectologia Emilio Ribas", São Paulo, Brazil). The molecular-level-sensitive, multiplexing quantitative and qualitative FTIR data probed on 1 µL of dried biofluid was compared to signal-to-cutoff index of chemiluminescent immunoassays CLIA and ELISA (IgG antibodies against SARS-CoV-2). Our main result indicated that 1702-1785 [Formula: see text] spectral window (carbonyl C=O vibration) is a spectral marker of the degree of IgG glycosylation, allowing to probe distinctive sub-populations of COVID-19 patients, depending on their degree of severity. The specificity was 87.5 % while the detection rate of true positive was 100%. The computed area under the receiver operating curve was equivalent to CLIA, ELISA and other ATR-FTIR methods ([Formula: see text]). In summary, overall discrimination of healthy and COVID-19 individuals and severity prediction as well could be potentially implemented using micro-FTIR reflectance spectroscopy on blood serum samples. Considering the minimal and reagent-free sample preparation procedures combined to fast (few minutes) outcome of FTIR we can state that this technology is suitable for fast screening of immune response of individuals with COVID-19. It would be an important tool in prospective studies, helping investigate the physiology of the asymptomatic, oligosymptomatic, or severe individuals and measure the extension of infection dissemination in patients.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Scheme for micro-FTIR reflectance measurements for human serum. One µL of diluted (1 : 3 in ultra-pure water) serum sample solution (1) was transferred to a circular platinum sample holder (2). Then the sample holder was installed in a desiccator with saturated solution of NaCl (3) which controls the relative moisture in 80%. The drying time was 10 min. After this period the sample holder was installed on the FTIR reflectance accessory of the micro-FTIR spectrometer. The IR beam (4) passing through the IR magnification Cassegrain lens (5) focuses the light on a given sample (6). The reflected light is collected by the same lens and analyzed by the spectrometer.
Figure 2
Figure 2
Average spectra and outliers. (a) Average micro-FTIR blood serum spectra for negative (black line) and positive (red line) groups. The vertical lines represents the main vibrational bands contributing to discrimination of groups (see band assignments on Table 2). (b) Outliers identification by inspection of Q2 residuals (reduced) versus T2 Hotelling’s. The outliers were indicated by “*”. Dashed horizontal lines and vertical lines represent confidence limits of 3% (Hotelling’s T2) and 97% (Q residuals), respectively.
Figure 3
Figure 3
Pairwise score plots for selected PLS-DA components. The explained variance of each component is shown in the corresponding diagonal cell.
Figure 4
Figure 4
Discrimination performance of micro-FTIR. PLS-DA classification performance using different number of components following accuracy, R2 and Q2 criteria for two (positive/negative, (a) and three (positive/mixture/negative, (c) groups. Regression coefficients and calculated response in PLS-DA for sample classes are shown in (b,d), respectively.
Figure 5
Figure 5
Heatmap for micro-FTIR data. Clustering result shown as a heatmap organized by samples (vertical axis) and wavenumber (horizontal axis). Negative, positive, mix classes grouped into distinct clusters.
Figure 6
Figure 6
micro-FTIR and CLIA comparison. (a)–(c) Histograms of signal-to-cutoff data of CLIA IgG antibodies against Sars-Cov-2 for positive (a), mix (b), and negative (c) classes as discriminated by micro-FTIR. (d) Important vibrational frequencies (VIP) identified by PLS-DA for three classes classification. (e)–(g) Histograms of signal-to-cutoff data of ELISA IgG antibodies against Sars-Cov-2 for positive (e), mix (f), and negative (g) classes as discriminated by micro-FTIR. (h) VIP for two classes discrimination. Color boxes on the right of (d),(h) indicate the relative intensity (high, intermediate and low) of the corresponding band in each group.
Figure 7
Figure 7
Diagnostic performance of micro-FTIR. Area under receiver operating characteristic (AUC) against wavenumber showing those bands with excellent discriminating power (AUC>0.80, dashed horizontal line) for positive/negative (a) and positive/mix (b) classes. Selected representative curves of receiver operating characteristic (ROC) and corresponding classification box-plot of the intensity of the left-handed helix DNA (Z form) (848cm-1, c), β-sheet structure of Amide I of proteins (1693cm-1, d), C=O in IgG carbonyl group (1772 and 1784cm-1 in (e,f), respectively) bands. The horizontal red line is the threshold for classification.
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