Laser induced breakdown spectroscopy for the rapid detection of SARS-CoV-2 immune response in plasma

• Published in: Scientific reports Vol. 12; no. 1; pp. 1614 - 10
• Main Authors: Berlo, K., Xia, W., Zwillich, F., Gibbons, E., Gaudiuso, R., Ewusi-Annan, E., Chiklis, G. R., Melikechi, N.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 31.01.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/92/321; 631/92/56; 639/624; 639/638/11/296; 692/53/2421; 692/699/255/2514; Barium - analysis; COVID-19; COVID-19 - blood; COVID-19 - diagnosis; COVID-19 - epidemiology; COVID-19 - virology; Data Accuracy; Discriminant Analysis; False Negative Reactions; False Positive Reactions; Humanities and Social Sciences; Humans; Immune response; Immune status; Immunity; Lasers; Learning algorithms; Machine Learning; multidisciplinary; Pandemics; Polymerase chain reaction; Reverse Transcriptase Polymerase Chain Reaction - methods; SARS-CoV-2 - genetics; SARS-CoV-2 - immunology; Science; Science (multidisciplinary); Sensitivity and Specificity; Severe acute respiratory syndrome coronavirus 2; Spectrophotometry, Atomic - methods; Spectroscopy; Spectrum analysis; Zinc - analysis
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-05509-z

As the SARS-CoV-2 pandemic persists, methods that can quickly and reliably confirm infection and immune status is extremely urgently and critically needed. In this contribution we show that combining laser induced breakdown spectroscopy (LIBS) with machine learning can distinguish plasma of donors who previously tested positive for SARS-CoV-2 by RT-PCR from those who did not, with up to 95% accuracy. The samples were also analyzed by LIBS-ICP-MS in tandem mode, implicating a depletion of Zn and Ba in samples of SARS-CoV-2 positive subjects that inversely correlate with CN lines in the LIBS spectra.