Dataset Information

Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2.

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.

SUBMITTER: Pietiainen V

PROVIDER: S-EPMC10475844 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2.

Pietiäinen Vilja V Polso Minttu M Migh Ede E Guckelsberger Christian C Harmati Maria M Diosdi Akos A Turunen Laura L Hassinen Antti A Potdar Swapnil S Koponen Annika A Sebestyen Edina Gyukity EG Kovacs Ferenc F Kriston Andras A Hollandi Reka R Burian Katalin K Terhes Gabriella G Visnyovszki Adam A Fodor Eszter E Lacza Zsombor Z Kantele Anu A Kolehmainen Pekka P Kakkola Laura L Strandin Tomas T Levanov Lev L Kallioniemi Olli O Kemeny Lajos L Julkunen Ilkka I Vapalahti Olli O Buzas Krisztina K Paavolainen Lassi L Horvath Peter P Hepojoki Jussi J

Cell reports methods 20230822 8

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 ac ...[more]

PMID: 37671026

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Project description:The differentiation of induced pluripotent stem cells (iPSCs) into diverse functional cell types provides a promising solution to support drug discovery, disease modeling, and regenerative medicine. However, functional cell differentiation is currently limited by the substantial line-to-line and batch-to-batch variability, which severely impede the progress of scientific research and the manufacturing of cell products. For instance, iPSC-to-cardiomyocyte (CM) differentiation is vulnerable with great sensitivity to inappropriate doses of CHIR99021 (CHIR) that are applied in the initial stage of mesoderm differentiation. Here, by harnessing live-cell bright-field imaging and machine learning (ML), we realize real-time cell recognition in the entire differentiation process, e.g., CMs, cardiac progenitor cells (CPCs), iPSC clones, and even misdifferentiated cells. This enables non-invasive prediction of differentiation efficiency, purification of ML-recognized CMs and CPCs for reducing cell contamination, early assessment of the CHIR dose for correcting the misdifferentiation trajectory, and evaluation of initial iPSC colonies for controlling the start point of differentiation, all of which provide a more invulnerable differentiation method with resistance to variability. Moreover, with the established ML models as a readout for the chemical screen, we identify a CDK8 inhibitor to further improve the cell resistance to the overdose of CHIR. Together, this study indicates that artificial intelligence is able to guide and iteratively optimize iPSC differentiation to achieve consistently high efficiency across cell lines and batches, providing a better understanding and rational modulation of the differentiation process for functional cell manufacturing in biomedical applications. With the assistance of RNA sequencing, we transcriptomically characterize the image-recognized CPCs (IR-CPCs), the cells treated with different CHIR doses at stage I, iPSC-CM treated with or without BI-1347, and cells treated with or without BI-1347 at stage I.

Dataset Information

Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2.

Publications

Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2.

Similar Datasets

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