1057. Development of a Flow Cytometry-Based Micro-Neutralisation Assay to Evaluate Humoral Immunity Against SARS-CoV-2 Variants of Concern in Vaccine Trials Article Swipe
Sophie O’Reilly
,
Grace Kenny
,
Tamara Alrawahneh
,
Nathan Francois
,
Matthew Angeliadis
,
Valentin de Masson d’Autume
,
A. Léon
,
Eoin R. Feeney
,
Obada Yousif
,
Aoife G. Cotter
,
Eoghan de Barra
,
Mary Horgan
,
Patrick Mallon
,
M B BCh
,
Virginie Gautier
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/ofid/ofac492.898
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/ofid/ofac492.898
Background Quantifying neutralising capacity of circulating SARS-COV-2 antibodies is critical in evaluating protective humoral immune responses generated post-infection/post-vaccination. Here we describe a novel medium-throughput flow cytometry based micro-neutralisation assay to evaluate Neutralising Antibody (NAb) responses against live SARS-CoV-2 Wild Type (D641G) and Variants of Concern (VoC) in convalescent/vaccinated populations. Methods Micro-Neutralisation assay (Micro-NT) was performed in 96-well plates using clinical isolate 2019-nCoV/Italy-INMI1, D641G (SARS-CoV-2/human/IRL/AIIDV1446/2020) and/or VOCs Beta (SARS-CoV-2/human/IRL/AIIDV1752/2021) and Omicron (SARS-Cov-2/human/IRL/AIIDV2326/2021). Plasma samples (All Ireland Infectious Diseases (AIID) Cohort) were serially diluted (8 points, half-log) from 1/20 and pre-incubated with SARS-CoV-2 (1h, 37°C). Virus-plasma mixture were added onto VERO E6/VERO-E6 TMPRSS2 cells for 18h. Percentage infected cells was analysed by automated flow cytometry following trypsinisation, fixation and SARS-CoV-2 Nucleoprotein intracellular staining. Half-maximal Neutralisation Titres (NT50) was determined using four-parameter logistic regression. Our assay was compared to Plaque Reduction Neutralisation Test (PRNT) and validated against WHO anti-SARS-CoV-2 Immunoglobulin Standards. Results Using WHO Standards with low, medium or high anti-SARS-CoV-2 IgG, both Micro-NT and PRNT achieved comparable NT50 values (Table 1). Micro-NT was found to be highly reproducible (inter-assay CV of 11.39%). Screening 190 convalescent samples and 11 COVID-19 naive controls (AIID cohort) we achieved an assay sensitivity of 90% and specificity of 81%. We demonstrated that Micro-NT has broad dynamic range differentiating NT50s < 1/20 to > 1/5000 (Figure 1). We could also characterise immune-escape VoC, observing up to 10-fold reduction in NT50 against Beta (Figure 2). Table 1:NT50s of Low, Medium and High Titre Anti-SARS-CoV-2 IgG Standards measured against Live SARS-CoV-2 using PRNT and Micro-NT Neutralising Capacity of low, medium and high-titre anti-SARS-CoV-2 IgG (WHO, International Standards) against live SARS-CoV-2 (2019-nCoV/Italy-INMI1) measured using PRNT and Micro-NT Assays on Vero E6 cells, as well as the potency of NAbs in each sample in International Units (IU/ml) as determined by the WHO. Figure 1:Dynamic Range of Micro-NT Micro-NT has a broad Dynamic Range, distinguishing low (A), medium (B) and high (C) neutralising plasma samples against live SARS-CoV-2 (2019-nCoV/Italy-INMI1) from a cohort of COVID-19 convalescent individuals (AIID cohort), as well as negative samples from COVID-19 naïve samples (D). Graphs show 3 representative samples of each NT50 range. (E) shows the population distribution of 190 Convalescent plasma samples as measured by Micro-NT on Vero E6 cells. Figure 2:Reduced Neutralisation Capacities measured against SARS-CoV-2 VoC using Micro-NT Low (A), Medium (B) and High (C) anti-SARS-CoV-2 IgG (WHO Standards) show different neutralising capacities against WT (D614G) SARS-CoV-2 and variants Beta and Omicron, measured using Micro-NT on Vero-E6-TMPRSS2 cells. Conclusion Our flow-cytometry-based Micro-NT is a robust and reliable assay to quantify NAb titres, an important evaluation endpoint in clinical trials. It has higher throughput (96 well format versus 12 well) and reduced infection time (18h vs 48-96h) compared to the gold standard PRNT. Disclosures All Authors: No reported disclosures.
Related Topics
Concepts
Neutralization
Virology
Medicine
Vero cell
Flow cytometry
Plaque reduction neutralization test
Antibody
Immunology
Virus
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/ofid/ofac492.898
- https://academic.oup.com/ofid/article-pdf/9/Supplement_2/ofac492.898/47892911/ofac492.898.pdf
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4311731934
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4311731934Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/ofid/ofac492.898Digital Object Identifier
- Title
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1057. Development of a Flow Cytometry-Based Micro-Neutralisation Assay to Evaluate Humoral Immunity Against SARS-CoV-2 Variants of Concern in Vaccine TrialsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-12-01Full publication date if available
- Authors
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Sophie O’Reilly, Grace Kenny, Tamara Alrawahneh, Nathan Francois, Matthew Angeliadis, Valentin de Masson d’Autume, A. Léon, Eoin R. Feeney, Obada Yousif, Aoife G. Cotter, Eoghan de Barra, Mary Horgan, Patrick Mallon, M B BCh, Virginie GautierList of authors in order
- Landing page
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https://doi.org/10.1093/ofid/ofac492.898Publisher landing page
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https://academic.oup.com/ofid/article-pdf/9/Supplement_2/ofac492.898/47892911/ofac492.898.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://academic.oup.com/ofid/article-pdf/9/Supplement_2/ofac492.898/47892911/ofac492.898.pdfDirect OA link when available
- Concepts
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Neutralization, Virology, Medicine, Vero cell, Flow cytometry, Plaque reduction neutralization test, Antibody, Immunology, VirusTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Dynamic | 310 |
| abstract_inverted_index.Ireland | 75 |
| abstract_inverted_index.Methods | 50 |
| abstract_inverted_index.Omicron | 70 |
| abstract_inverted_index.Results | 149 |
| abstract_inverted_index.TMPRSS2 | 101 |
| abstract_inverted_index.against | 36, 144, 233, 249, 268, 323, 378, 398 |
| abstract_inverted_index.cohort) | 191 |
| abstract_inverted_index.diluted | 82 |
| abstract_inverted_index.dynamic | 209 |
| abstract_inverted_index.humoral | 14 |
| abstract_inverted_index.isolate | 61 |
| abstract_inverted_index.mixture | 95 |
| abstract_inverted_index.points, | 84 |
| abstract_inverted_index.potency | 286 |
| abstract_inverted_index.reduced | 445 |
| abstract_inverted_index.samples | 73, 184, 322, 340, 344, 350, 364 |
| abstract_inverted_index.titres, | 426 |
| abstract_inverted_index.trials. | 433 |
| abstract_inverted_index.&gt; | 216 |
| abstract_inverted_index.&lt; | 213 |
| abstract_inverted_index.11.39%). | 180 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Antibody | 33 |
| abstract_inverted_index.Authors: | 459 |
| abstract_inverted_index.COVID-19 | 187, 331, 342 |
| abstract_inverted_index.Capacity | 257 |
| abstract_inverted_index.Diseases | 77 |
| abstract_inverted_index.Micro-NT | 161, 170, 206, 255, 276, 305, 306, 368, 382, 409, 416 |
| abstract_inverted_index.Omicron, | 406 |
| abstract_inverted_index.Variants | 43 |
| abstract_inverted_index.achieved | 164, 193 |
| abstract_inverted_index.analysed | 109 |
| abstract_inverted_index.capacity | 4 |
| abstract_inverted_index.clinical | 60, 432 |
| abstract_inverted_index.cohort), | 335 |
| abstract_inverted_index.compared | 135, 451 |
| abstract_inverted_index.controls | 189 |
| abstract_inverted_index.critical | 10 |
| abstract_inverted_index.describe | 21 |
| abstract_inverted_index.endpoint | 430 |
| abstract_inverted_index.evaluate | 31 |
| abstract_inverted_index.fixation | 116 |
| abstract_inverted_index.infected | 106 |
| abstract_inverted_index.logistic | 130 |
| abstract_inverted_index.measured | 248, 272, 366, 377, 407 |
| abstract_inverted_index.negative | 339 |
| abstract_inverted_index.quantify | 424 |
| abstract_inverted_index.reliable | 421 |
| abstract_inverted_index.reported | 461 |
| abstract_inverted_index.serially | 81 |
| abstract_inverted_index.standard | 455 |
| abstract_inverted_index.variants | 403 |
| abstract_inverted_index.1:Dynamic | 302 |
| abstract_inverted_index.2:Reduced | 374 |
| abstract_inverted_index.Reduction | 138 |
| abstract_inverted_index.Screening | 181 |
| abstract_inverted_index.Standards | 152, 247 |
| abstract_inverted_index.automated | 111 |
| abstract_inverted_index.cytometry | 26, 113 |
| abstract_inverted_index.different | 395 |
| abstract_inverted_index.following | 114 |
| abstract_inverted_index.generated | 17 |
| abstract_inverted_index.half-log) | 85 |
| abstract_inverted_index.important | 428 |
| abstract_inverted_index.infection | 446 |
| abstract_inverted_index.observing | 226 |
| abstract_inverted_index.performed | 55 |
| abstract_inverted_index.reduction | 230 |
| abstract_inverted_index.responses | 16, 35 |
| abstract_inverted_index.staining. | 121 |
| abstract_inverted_index.validated | 143 |
| abstract_inverted_index.(Micro-NT) | 53 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Capacities | 376 |
| abstract_inverted_index.Conclusion | 413 |
| abstract_inverted_index.E6/VERO-E6 | 100 |
| abstract_inverted_index.Infectious | 76 |
| abstract_inverted_index.Percentage | 105 |
| abstract_inverted_index.SARS-COV-2 | 7 |
| abstract_inverted_index.SARS-CoV-2 | 38, 91, 118, 251, 270, 325, 379, 401 |
| abstract_inverted_index.Standards) | 267, 393 |
| abstract_inverted_index.Standards. | 148 |
| abstract_inverted_index.antibodies | 8 |
| abstract_inverted_index.capacities | 397 |
| abstract_inverted_index.comparable | 165 |
| abstract_inverted_index.determined | 127, 297 |
| abstract_inverted_index.evaluating | 12 |
| abstract_inverted_index.evaluation | 429 |
| abstract_inverted_index.high-titre | 262 |
| abstract_inverted_index.population | 358 |
| abstract_inverted_index.protective | 13 |
| abstract_inverted_index.throughput | 437 |
| abstract_inverted_index.Disclosures | 457 |
| abstract_inverted_index.Quantifying | 2 |
| abstract_inverted_index.circulating | 6 |
| abstract_inverted_index.individuals | 333 |
| abstract_inverted_index.regression. | 131 |
| abstract_inverted_index.sensitivity | 196 |
| abstract_inverted_index.specificity | 200 |
| abstract_inverted_index.(inter-assay | 177 |
| abstract_inverted_index.Convalescent | 362 |
| abstract_inverted_index.Half-maximal | 122 |
| abstract_inverted_index.Neutralising | 32, 256 |
| abstract_inverted_index.Virus-plasma | 94 |
| abstract_inverted_index.characterise | 223 |
| abstract_inverted_index.convalescent | 183, 332 |
| abstract_inverted_index.demonstrated | 204 |
| abstract_inverted_index.disclosures. | 462 |
| abstract_inverted_index.distribution | 359 |
| abstract_inverted_index.neutralising | 3, 320, 396 |
| abstract_inverted_index.populations. | 49 |
| abstract_inverted_index.reproducible | 176 |
| abstract_inverted_index.International | 266, 293 |
| abstract_inverted_index.Nucleoprotein | 119 |
| abstract_inverted_index.immune-escape | 224 |
| abstract_inverted_index.intracellular | 120 |
| abstract_inverted_index.pre-incubated | 89 |
| abstract_inverted_index.Immunoglobulin | 147 |
| abstract_inverted_index.Neutralisation | 123, 139, 375 |
| abstract_inverted_index.distinguishing | 312 |
| abstract_inverted_index.four-parameter | 129 |
| abstract_inverted_index.representative | 349 |
| abstract_inverted_index.Anti-SARS-CoV-2 | 245 |
| abstract_inverted_index.Vero-E6-TMPRSS2 | 411 |
| abstract_inverted_index.anti-SARS-CoV-2 | 146, 158, 263, 390 |
| abstract_inverted_index.differentiating | 211 |
| abstract_inverted_index.trypsinisation, | 115 |
| abstract_inverted_index.medium-throughput | 24 |
| abstract_inverted_index.Micro-Neutralisation | 51 |
| abstract_inverted_index.flow-cytometry-based | 415 |
| abstract_inverted_index.micro-neutralisation | 28 |
| abstract_inverted_index.2019-nCoV/Italy-INMI1, | 62 |
| abstract_inverted_index.(2019-nCoV/Italy-INMI1) | 271, 326 |
| abstract_inverted_index.convalescent/vaccinated | 48 |
| abstract_inverted_index.post-infection/post-vaccination. | 18 |
| abstract_inverted_index.(SARS-CoV-2/human/IRL/AIIDV1446/2020) | 64 |
| abstract_inverted_index.(SARS-CoV-2/human/IRL/AIIDV1752/2021) | 68 |
| abstract_inverted_index.(SARS-Cov-2/human/IRL/AIIDV2326/2021). | 71 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 15 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7799999713897705 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.19395654 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |