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Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2.
- Source :
-
Cell reports methods [Cell Rep Methods] 2023 Aug 22; Vol. 3 (8), pp. 100565. Date of Electronic Publication: 2023 Aug 22 (Print Publication: 2023). - Publication Year :
- 2023
-
Abstract
- We present a miniaturized immunofluorescence assay (mini-IFA) for measuring antibody response in patient blood samples. The method utilizes machine learning-guided image analysis and enables simultaneous measurement of immunoglobulin M (IgM), IgA, and IgG responses against different viral antigens in an automated and high-throughput manner. The assay relies on antigens expressed through transfection, enabling use at a low biosafety level and fast adaptation to emerging pathogens. Using severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the model pathogen, we demonstrate that this method allows differentiation between vaccine-induced and infection-induced antibody responses. Additionally, we established a dedicated web page for quantitative visualization of sample-specific results and their distribution, comparing them with controls and other samples. Our results provide a proof of concept for the approach, demonstrating fast and accurate measurement of antibody responses in a research setup with prospects for clinical diagnostics.<br />Competing Interests: P.H. is the founder and shareholder and A. Kriston and F.K. are employees of Single-Cell Technologies Ltd. This study has been protected with invention disclosures (ID965/2020 and ID115/2021 University of Helsinki, Finland), and the patent application has been filed (Hungary, no. P2100295).<br /> (© 2023 The Authors.)
- Subjects :
- Humans
COVID-19 Testing
Acclimatization
Machine Learning
SARS-CoV-2
COVID-19
Subjects
Details
- Language :
- English
- ISSN :
- 2667-2375
- Volume :
- 3
- Issue :
- 8
- Database :
- MEDLINE
- Journal :
- Cell reports methods
- Publication Type :
- Academic Journal
- Accession number :
- 37671026
- Full Text :
- https://doi.org/10.1016/j.crmeth.2023.100565