A Paper-Based Multiplexed Serological Test to Monitor Immunity against SARS-COV‑2 Using Machine Learning

• Published in: ACS nano Vol. 18; no. 26; pp. 16819 - 16831
• Main Authors: Eryilmaz, Merve, Goncharov, Artem, Han, Gyeo-Re, Joung, Hyou-Arm, Ballard, Zachary S., Ghosh, Rajesh, Zhang, Yijie, Di Carlo, Dino, Ozcan, Aydogan
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 02.07.2024
• Subjects: Antibodies, Viral - blood; Antibodies, Viral - immunology; COVID-19 - diagnosis; COVID-19 - immunology; COVID-19 - virology; COVID-19 Serological Testing - methods; Humans; Immunoglobulin G - blood; Immunoglobulin G - immunology; Immunoglobulin M - blood; Immunoglobulin M - immunology; Machine Learning; SARS-CoV-2 - immunology; Serologic Tests - methods; COVID-19; paper-based assays; vertical flow assays; machine learning; serology
• ISSN: 1936-0851; 1936-086X; 1936-086X
• DOI: 10.1021/acsnano.4c02434

The rapid spread of SARS-CoV-2 caused the COVID-19 pandemic and accelerated vaccine development to prevent the spread of the virus and control the disease. Given the sustained high infectivity and evolution of SARS-CoV-2, there is an ongoing interest in developing COVID-19 serology tests to monitor population-level immunity. To address this critical need, we designed a paper-based multiplexed vertical flow assay (xVFA) using five structural proteins of SARS-CoV-2, detecting IgG and IgM antibodies to monitor changes in COVID-19 immunity levels. Our platform not only tracked longitudinal immunity levels but also categorized COVID-19 immunity into three groups: protected, unprotected, and infected, based on the levels of IgG and IgM antibodies. We operated two xVFAs in parallel to detect IgG and IgM antibodies using a total of 40 μL of human serum sample in <20 min per test. After the assay, images of the paper-based sensor panel were captured using a mobile phone-based custom-designed optical reader and then processed by a neural network-based serodiagnostic algorithm. The serodiagnostic algorithm was trained with 120 measurements/tests and 30 serum samples from 7 randomly selected individuals and was blindly tested with 31 serum samples from 8 different individuals, collected before vaccination as well as after vaccination or infection, achieving an accuracy of 89.5%. The competitive performance of the xVFA, along with its portability, cost-effectiveness, and rapid operation, makes it a promising computational point-of-care (POC) serology test for monitoring COVID-19 immunity, aiding in timely decisions on the administration of booster vaccines and general public health policies to protect vulnerable populations.