Probabilistic approaches for classifying highly variable anti-SARS-CoV-2 antibody responses
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Abstract
Antibody responses vary widely between individuals 1 , complicating the correct classification of low-titer measurements using conventional assay cut-offs. We found all participants in a clinically diverse cohort of SARS-CoV-2 PCR+ individuals ( n =105) – and n= 33 PCR+ hospital staff – to have detectable IgG specific for pre-fusion-stabilized spike (S) glycoprotein trimers, while 98% of persons had IgG specific for the receptor-binding domain (RBD). However, anti-viral IgG levels differed by several orders of magnitude between individuals and were associated with disease severity, with critically ill patients displaying the highest anti-viral antibody titers and strongest in vitro neutralizing responses. Parallel analysis of random healthy blood donors and pregnant women ( n= 1,000) of unknown serostatus, further demonstrated highly variable IgG titers amongst seroconverters, although these were generally lower than in hospitalized patients and included several measurements that scored between the classical 3 and 6SD assay cut-offs. Since the correct classification of seropositivity is critical for individual- and population-level metrics, we compared different probabilistic algorithms for their ability to assign likelihood of past infection. To do this, we used tandem anti-S and -RBD IgG responses from our PCR+ individuals ( n= 138) and a large cohort of historical negative controls ( n= 595) as training data, and generated an equal-weighted learner from the output of support vector machines and linear discriminant analysis. Applied to test samples, this approach provided a more quantitative way to interpret anti-viral titers over a large continuum, scrutinizing measurements overlapping the negative control background more closely and offering a probability-based diagnosis with potential clinical utility. Especially as most SARS-CoV-2 infections result in asymptomatic or mild disease, these platform-independent approaches improve individual and epidemiological estimates of seropositivity, critical for effective management of the pandemic and monitoring the response to vaccination.
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SciScore for 10.1101/2020.07.17.20155937: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Consent: All participants provided written informed consent. Randomization Anonymized samples from blood donors (n=100/week) and pregnant women (n=100/week) were randomly selected from their respective pools by the department of Clinical Microbiology, Karolinska University Hospital. Blinding All personal identifiers were pseudo-anonymized, and all clinical feature data were blinded to the researchers carrying out experiments until data generation was complete. Power Analysis not detected. Sex as a biological variable Anonymized samples from blood donors (n=100/week) and pregnant women (n=100/week) were randomly selected from their respective pools by the department of … SciScore for 10.1101/2020.07.17.20155937: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Consent: All participants provided written informed consent. Randomization Anonymized samples from blood donors (n=100/week) and pregnant women (n=100/week) were randomly selected from their respective pools by the department of Clinical Microbiology, Karolinska University Hospital. Blinding All personal identifiers were pseudo-anonymized, and all clinical feature data were blinded to the researchers carrying out experiments until data generation was complete. Power Analysis not detected. Sex as a biological variable Anonymized samples from blood donors (n=100/week) and pregnant women (n=100/week) were randomly selected from their respective pools by the department of Clinical Microbiology, Karolinska University Hospital. Cell Line Authentication not detected. Table 2: Resources
Antibodies Sentences Resources Secondary HRP-conjugated anti-human antibodies were diluted in blocking buffer and incubated with samples for 1 hour at room temperature. anti-humansuggested: NoneSecondary antibodies (all from Southern Biotech) and dilutions used: goat anti-human IgG (2014-05) at 1:10,000; goat anti-human IgM (2020-05) at 1:1000; goat anti-human IgA (2050-05) at 1:6,000. anti-human IgGsuggested: (SouthernBiotech Cat# 2014-05, RRID:AB_2795580)anti-human IgMsuggested: (SouthernBiotech Cat# 2020-05, RRID:AB_2795603)anti-human IgAsuggested: (SouthernBiotech Cat# 2050-05, RRID:AB_2687526)We trained the learners on all 733 training samples and used these to predict the probability of anti-SARS-CoV-2 antibodies in blood donors and pregnant volunteers sampled in 2020. anti-SARS-CoV-2suggested: NoneExperimental Models: Cell Lines Sentences Resources The RBD domain (RVQ – QFG) of SARS-CoV-2 was cloned upstream of a Sortase A recognition site (LPETG) and a 6xHIS tag, and expressed in 293F cells as described above. 293Fsuggested: RRID:CVCL_D615)In vitro virus neutralisation assay: Pseudotyped viruses were generated by the co-transfection of HEK293T cells with plasmids encoding the SARS-CoV-2 spike protein harboring an 18 amino acid truncation of the cytoplasmic tail14; a plasmid encoding firefly luciferase; a lentiviral packaging plasmid ( HEK293Tsuggested: NoneApproximately 15,000 HEK293T-ACE2 cells were then added to each well and the plates incubated at 37°C for 48 hours. HEK293T-ACE2suggested: NoneSoftware and Algorithms Sentences Resources This resulted in more similar distributions for 2019 blood donor samples with 2020 blood donors and pregnant volunteers, as well as smaller coefficients of variation amongst PCR+ COVID patients for both SPIKE and RBD. SPIKEsuggested: (SPIKE, RRID:SCR_010466)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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