tetano
Editor, Senior Moderator
Cell Rep Methods
. 2023 Aug 22;3(8):100565.
doi: 10.1016/j.crmeth.2023.100565. eCollection 2023 Aug 28. Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2
Vilja Pietiäinen[SUP] 1 [/SUP], Minttu Polso[SUP] 1 [/SUP], Ede Migh[SUP] 2 [/SUP], Christian Guckelsberger[SUP] 1 3 4 5 [/SUP], Maria Harmati[SUP] 2 [/SUP], Akos Diosdi[SUP] 2 [/SUP], Laura Turunen[SUP] 1 [/SUP], Antti Hassinen[SUP] 1 [/SUP], Swapnil Potdar[SUP] 1 [/SUP], Annika Koponen[SUP] 6 7 [/SUP], Edina Gyukity Sebestyen[SUP] 2 [/SUP], Ferenc Kovacs[SUP] 2 8 [/SUP], Andras Kriston[SUP] 2 8 [/SUP], Reka Hollandi[SUP] 2 [/SUP], Katalin Burian[SUP] 9 [/SUP], Gabriella Terhes[SUP] 9 [/SUP], Adam Visnyovszki[SUP] 10 [/SUP], Eszter Fodor[SUP] 11 [/SUP], Zsombor Lacza[SUP] 11 [/SUP], Anu Kantele[SUP] 12 13 [/SUP], Pekka Kolehmainen[SUP] 14 [/SUP], Laura Kakkola[SUP] 14 [/SUP], Tomas Strandin[SUP] 15 [/SUP], Lev Levanov[SUP] 15 [/SUP], Olli Kallioniemi[SUP] 1 16 [/SUP], Lajos Kemeny[SUP] 17 [/SUP], Ilkka Julkunen[SUP] 14 18 [/SUP], Olli Vapalahti[SUP] 15 19 20 [/SUP], Krisztina Buzas[SUP] 2 21 [/SUP], Lassi Paavolainen[SUP] 1 [/SUP], Peter Horvath[SUP] 1 2 8 [/SUP], Jussi Hepojoki[SUP] 15 22 [/SUP]
Affiliations
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.
Keywords: COVID-19; SARS-CoV-2; antibody response; cell-based assay; high-content imaging; immunofluorescence assay; machine learning; mini-IFA; serology; virus.
. 2023 Aug 22;3(8):100565.
doi: 10.1016/j.crmeth.2023.100565. eCollection 2023 Aug 28. Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2
Vilja Pietiäinen[SUP] 1 [/SUP], Minttu Polso[SUP] 1 [/SUP], Ede Migh[SUP] 2 [/SUP], Christian Guckelsberger[SUP] 1 3 4 5 [/SUP], Maria Harmati[SUP] 2 [/SUP], Akos Diosdi[SUP] 2 [/SUP], Laura Turunen[SUP] 1 [/SUP], Antti Hassinen[SUP] 1 [/SUP], Swapnil Potdar[SUP] 1 [/SUP], Annika Koponen[SUP] 6 7 [/SUP], Edina Gyukity Sebestyen[SUP] 2 [/SUP], Ferenc Kovacs[SUP] 2 8 [/SUP], Andras Kriston[SUP] 2 8 [/SUP], Reka Hollandi[SUP] 2 [/SUP], Katalin Burian[SUP] 9 [/SUP], Gabriella Terhes[SUP] 9 [/SUP], Adam Visnyovszki[SUP] 10 [/SUP], Eszter Fodor[SUP] 11 [/SUP], Zsombor Lacza[SUP] 11 [/SUP], Anu Kantele[SUP] 12 13 [/SUP], Pekka Kolehmainen[SUP] 14 [/SUP], Laura Kakkola[SUP] 14 [/SUP], Tomas Strandin[SUP] 15 [/SUP], Lev Levanov[SUP] 15 [/SUP], Olli Kallioniemi[SUP] 1 16 [/SUP], Lajos Kemeny[SUP] 17 [/SUP], Ilkka Julkunen[SUP] 14 18 [/SUP], Olli Vapalahti[SUP] 15 19 20 [/SUP], Krisztina Buzas[SUP] 2 21 [/SUP], Lassi Paavolainen[SUP] 1 [/SUP], Peter Horvath[SUP] 1 2 8 [/SUP], Jussi Hepojoki[SUP] 15 22 [/SUP]
Affiliations
- PMID: 37671026
- PMCID: PMC10475844
- DOI: 10.1016/j.crmeth.2023.100565
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.
Keywords: COVID-19; SARS-CoV-2; antibody response; cell-based assay; high-content imaging; immunofluorescence assay; machine learning; mini-IFA; serology; virus.