Machine-learning-aided multiplexed nanobiosensor for COVID-19 population immunity profiling

Abstract

Serological population surveillance can elucidate immunity landscapes against SARS-CoV-2 variants and are critical in monitoring infectious disease spread, evolution, and outbreak risks. However, current serological tests fall short of capturing complex humoral immune responses from different communities. Here, we report a machine-learning (ML)-aided nanobiosensor that can simultaneously quantify antibodies against the ancestral strain and Omicron variants of SARS-CoV-2 with epitope resolution. Our approach is based on a multiplexed, rapid, and label-free nanoplasmonic biosensor, which can detect past infection and vaccination status and is sensitive to SARS-CoV-2 variants. After training an ML model with antigen-specific antibody datasets from four COVID-19 immunity groups (naive, convalescent, vaccinated, and convalescent-vaccinated), we tested our approach on 100 blind blood samples collected in Dane County, WI. Our results are consistent with public epidemiological data, demonstrating that our user-friendly and field-deployable nanobiosensor can capture community-representative public health trends and help manage COVID-19 and future outbreaks.