1. Immunology and Inflammation
  2. Microbiology and Infectious Disease
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Rapid and sensitive detection of SARS-CoV-2 infection using quantitative peptide enrichment LC-MS analysis

  1. Andreas Hober
  2. Khue Hua Tran-Minh
  3. Dominic Foley
  4. Thomas McDonald
  5. Johannes PC Vissers
  6. Rebecca Pattison
  7. Samantha Ferries
  8. Sigurd Hermansson
  9. Ingvar Betner
  10. Mathias Uhlén
  11. Morteza Razavi
  12. Richard Yip
  13. Matthew E Pope
  14. Terry W Pearson
  15. Leigh N Andersson
  16. Amy Bartlett
  17. Lisa Calton
  18. Jessica J Alm
  19. Lars Engstrand
  20. Fredrik Edfors  Is a corresponding author
  1. Science for Life Laboratory, Sweden
  2. The Royal Institute of Technology, Division of Systems Biology, Department of Protein Science, School of Chemistry, Biotechnology and Health, Sweden
  3. Waters Corporation, United Kingdom
  4. SISCAPA Assay Technologies, Inc, Canada
  5. Karolinska Institutet, Department of Microbiology, Tumor and Cell Biology & National Pandemic Center, Karolinska Institutet, Sweden
  6. Microbiology, Tumour and Cell Biology, Karolinska Institutet, Sweden
Research Article
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Cite this article as: eLife 2021;10:e70843 doi: 10.7554/eLife.70843

Abstract

Reliable, robust, large-scale molecular testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for monitoring the ongoing coronavirus disease 2019 (COVID-19) pandemic. We have developed a scalable analytical approach to detect viral proteins based on peptide immuno-affinity enrichment combined with liquid chromatography-mass spectrometry (LC-MS). This is a multiplexed strategy, based on targeted proteomics analysis and read-out by LC-MS, capable of precisely quantifying and confirming the presence of SARS-CoV-2 in phosphate-buffered saline (PBS) swab media from combined throat/nasopharynx/saliva samples. The results reveal that the levels of SARS-CoV-2 measured by LC-MS correlate well with their correspondingreal-time polymerase chain reaction (RT-PCR) read-out (r = 0.79). The analytical workflow shows similar turnaround times as regular RT-PCR instrumentation with a quantitative read-out of viral proteins corresponding to cycle thresholds (Ct) equivalents ranging from 21 to 34. Using RT-PCR as a reference, we demonstrate that the LC-MS-based method has 100% negative percent agreement (estimated specificity) and 95% positive percent agreement (estimated sensitivity) when analyzing clinical samples collected from asymptomatic individuals with a Ct within the limit of detection of the mass spectrometer (Ct ≤ 30). These results suggest that a scalable analytical method based on LC-MS has a place in future pandemic preparedness centers to complement current virus detection technologies.

Editor's evaluation

With the prospect of pandemic readiness, the data presented here shows that MS can and most probably will start to become an essential analytical contribution.

https://doi.org/10.7554/eLife.70843.sa0

Introduction

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Wu et al., 2020), leading to the coronavirus disease 2019 (COVID-19), has had a significant impact on human health globally, with more than 234 million confirmed cases (Dong et al., 2020), assessed October 4, 2021. The effects of the pandemic are devastating and have led to lockdowns of urban areas across the globe as a response to contain any potential outbreaks (Hale et al., 2021). To monitor the disease, huge investments have been directed toward infrastructure for large-scale testing for ongoing COVID-19 infection (Baker et al., 2020). Population-wide screening or cohort testing in the vicinity of an outbreak epicenter is an essential pillar in the global fight against COVID-19 and an indispensable contribution to currently ongoing vaccination programs that pave the way for re-opening societies when entering the endemic phase. Thus, specific molecular diagnostic tools suitable for efficient disease monitoring will play a key role when countries slowly lift their bans on public gatherings, events, and global travel.

The diagnostic method called real-time polymerase chain reaction (RT-PCR) (Freeman et al., 1999) is the most widely used technology for detecting SARS-CoV-2 and was established within days after the virus genome was released (Corman et al., 2020). The method is considered as the gold standard by WHO for diagnosing patients with COVID-19 in routine clinical practice. Large-scale laboratories dedicated to PCR-based diagnostics rapidly mobilized worldwide in the early phase of the pandemic, which led to a sudden global shortage of diagnostic reagents (Woolston, 2021). The PCR tests generally have high analytical sensitivity and specificity, even for self-collected samples, often in the range of 95–100% (Altamirano et al., 2020) when evaluated in clinical settings. The observed variance between tests can be partly explained by the inherent sensitivity of the PCR reaction itself or by pre-analytical biases (Lippi et al., 2020), which could lead to either false-positive or false-negative results. For example, the viral genes can be amplified to detect the virus within days of infection, but the high sensitivity has also been subjected to criticism since it can detect genetic material in circulation not only days after but also multiple weeks after the first day of symptom onset (Lan et al., 2020). The current level of the clinical false-positive rate associated with PCR tests is unknown but is dependent on what type of PCR kit and criteria have been used. Some studies report that it can be as much as 4% at certain test facilities (Surkova et al., 2020). This type of error has the potential to cause the most harm in a scenario entering post COVID-19 when large-volume screening is performed in communities with low prevalence (Healy et al., 2021).

As a response to the global shortage, rapid antigen tests have been deployed that directly detect viral antigens. These rapid tests show similar specificity to PCR-based assays (Weissleder et al., 2020), but several studies have shown that they lack sufficient sensitivity when compared to RT-PCR (Fitzpatrick et al., 2021; Perchetti et al., 2021). Antigen tests also require affinity reagents, an initial bottleneck and a significant hurdle to overcome in the initial phase of a pandemic, but can scale massively once they have been generated. Additionally, rapid tests only provide a binary read-out (positive/negative), which can be difficult to interpret and the antigen is rarely specified (Sethuraman et al., 2020). Due to their rapid turnaround and affordability, these tests can thus be deployed in millions to aid in large-scale screening efforts and by repeated testing over time, accuracy can be greatly improved (Mina et al., 2020; Ramdas et al., 2020).

In contrast to traditional PCR tests or antigen rapid tests, LC coupled to multiple reaction monitoring (MRM) tandem MS detection offers a straightforward assay toward pre-defined targets. Turning to MS measurements to detect SARS-CoV-2 in samples directly addresses the issue of specificity and the risk of returning false-positive results as the measurement benefits from the fundamental properties of MS detection of peptides through multiple specific product ions (Gillette and Carr, 2013), which results in sequence-based specificity through direct physical detection of analyte molecules. The instrumentation provides reliable quantification for absolute protein concentration determination and modern MS instrumentation offers unsurpassed specificity, high precision, excellent quantitative performance, and high analytical sensitivity.

When combining these features with affinity reagents, such as antibodies, assays can reach very high sensitivities and low levels of a protein can be detected even in complex matrices. The combination of immuno-based strategies with MS read-out can complement each other and provide target-specific protein quantification (Whiteaker and Paulovich, 2011). In fact, it is an ideal combination for rapid detection and reliable quantification of low abundance proteins. Stable isotope labeled (SIL) standards and capture by anti-peptide antibodies (SISCAPA) (Anderson et al., 2004) enables multiplexed analysis of pre-digested clinical samples using peptide-reactive antibodies, selective for SARS-CoV-2 peptides, immobilized onto magnetic beads. Additionally, spiked SIL peptide standards further improve precise protein measurements performed by MRM (Brun et al., 2009). The use of LC-MS for protein quantification of SARS-CoV-2 peptides eliminates the dependence on PCR reactions and any issues related to unspecific amplification, thanks to the selectivity achieved at three different levels: first by the antibody; second by the mass spectrometric read-out, and finally, the internal standard. As a proof of concept, we analyzed clinical samples collected from asymptomatic individuals screened for ongoing disease by RT-PCR. Samples were taken from the upper respiratory tract (combined triple-point collection strategy throat/nasopharynx/saliva) and a set of 48 PCR positive and 308 RT-PCR negative samples were selected for LC-MS analysis. All samples were analyzed using the SISCAPA immuno-affinity peptide enrichment protocol followed by LC-MS read-out outlined in Figure 1. The application of immuno-affinity peptide enrichment is typically associated with the detection of protein disease markers in body fluids, such as plasma or dried blood spot samples. Here, the novel application of the technology is demonstrated to detect and quantify infection by analyzing the protein complement of viruses at relevant levels, which are proven difficult to reach without enrichment (Van Puyvelde et al., 2021). This study thereby presents a precise and complementary approach to RT-PCR to reliably detect SARS-CoV-2 in a research or clinical setting and a possible route forward to support population-wide screening.

Experimental workflow for immuno-affinity peptide (stable isotope labeled [SIL] standards and capture by anti-peptide antibodies [SISCAPA]) enrichment liquid chromatography-mass spectrometry (LC-MS) of nucleocapsid protein (NCAP) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) peptides.

Swab sample extracts were subjected to tryptic digestion, SIL standards added to the tryptic digest solution, and magnetic beads coupled with specific anti-peptide antibodies incubated to allow binding of the peptides. Unbound peptides are removed and the target peptides eluted and measured using multiple reaction monitoring (MRM) analysis with LC-MS.

Results

The application of LC-MS to detect tryptic digest peptides of SARS-CoV-2 proteins has been successfully demonstrated (Cardozo et al., 2020; Cazares et al., 2020; Freire-Paspuel and Garcia-Bereguiain, 2021; Gouveia et al., 2020a; Gouveia et al., 2020b; Ihling et al., 2020; Saadi et al., 2021; Van Puyvelde et al., 2021). However, these studies also highlight that the technique can be hampered by matrix effects, that is, analysis interferences arising from the constituent components of swab (preservation) media or other matrices, as well as base sensitivity, to be able to reach clinically relevant detection levels, suggesting the need for clean-up, for example, solid phase-based extraction and/or affinity enrichment (Renuse et al., 2020; Van Puyvelde et al., 2021). Moreover, commonality can be observed within the results of these studies in terms of which tryptic digest peptides are typically detected by means of LC-MS. Nucleocapsid protein (NCAP) is the most abundant viral SARS-CoV-2 protein with an estimated ~300–1000 copies per virion particle (Bezstarosti et al., 2020; Phimister et al., 2020), making it, because of the relatively high number of NCAP copies per virion, an attractive target for LC-MS-based detection compared to other viral proteins. A number of NCAP candidate peptides were therefore evaluated in terms of enrichment efficiency and LC-MS behavior, that is, sensitivity and linear dynamic range, and peptide immunoassay suitability (Whiteaker et al., 2011). The LC-MS MRM responses of a number of candidate NCAP SIL peptides are shown in Figure 2—figure supplement 1, ranking the peptides in descending order of MRM sensitivity. From this set of peptides, primarily based on both MRM response and peptide immunoassay suitability, peptide AYNVTQAFGR was found to be one of the best surrogate peptide candidates, but, equally importantly, it is not significantly affected to date by known SARS-CoV-2 virus mutations (https://www.gisaid.org/). Other evaluated peptides, but not discussed in detail, included ADETQALPQR, DGIIWVATEGALNTPK, and NPANNAAIVLQLPQGTTLPK, of which the basic quantitative characterization results are summarized in Figure 2—figure supplements 24, respectively.

Method characterization

The LC-MS MRM data were processed using TargetLynx XS and with a cut-off threshold algorithm based on peptide peak height and area thresholds, as well as quantifier to qualifier ion ratio threshold (30%). In other words, using two different consistently measured peptide fragment ions, that is, MRM transitions, to confirm the presence of SARS-CoV-2 proteins. Typical detection examples for the quantifier, qualifier, and SIL MRM transitions are shown in the (A) panel of Figure 2. An internal standard SIL corrected LC-MS calibration curve for antibody enriched NCAP peptide AYNVTQAFGR detected in a spiked nasopharyngeal swab matrix solution is shown in the (B) panel of Figure 2, covering a linear dynamic range from 3 to 50,000 amol/µl, providing >4 orders of linear dynamic range, meanwhile affording an LLOQ amount of 3 amol/µl of AYNVTQAFGR peptide (with precision ≤20%, bias ±20 % and S/N > 10:1 [peak-to-peak]). Shown as well are example quantifier and qualifier MRM chromatograms of positive (Figure 2C) and negative (Figure 2D) SARS-CoV-2 phosphate-buffered saline (PBS) swab samples. The selectivity of the method is highlighted by the complete absence of signal in the MRM chromatogram of the negative SARS-CoV-2 sample (Figure 2D).

Figure 2 with 4 supplements see all
Multiple reaction monitoring (MRM) chromatograms of antibody enriched nucleocapsid protein (NCAP) AYNVTQAFGR peptide.

Quantifier, qualifier, and stable isotope labeled (SIL) internal standard peptide chromatograms spiked at the lower limit of quantification (3 amol/µl) (A). Calibration curve of the AYNVTQAFGR peptide based on enriched recombinant NCAP digest, spiked with a constant amount of SIL peptide (B). Two representative intensity-scaled MRM chromatograms of positive (mean cycle threshold [Ct] 31) (C) and negative (blank) (D) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) swab samples, respectively, normalized to the most abundant shared MRM transition. Intensity-scaled SIL internal standard peptide MRM chromatograms of positive (E) and negative (F) SARS-CoV-2 swab samples.

The precision of the method was evaluated at 3, 10, 400, and 25,000 amol/µl for NCAP AYNVTQAFGR peptide and NCAP spiked into PBS and viral transport medium (VTM, Liofilchem, Italy). Peptides were enriched by antibodies and samples were analyzed in replicates of 5-over-5 separate occasions. The inter- and intra-day precision values of the method, as summarized in Table 1, were shown to be ≤20 % CV.

Table 1
Intra- and inter-day method precision (n = 5) when monitoring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid protein (NCAP) peptide AYNVTQAFGR using immuno-affinity peptide enrichment liquid chromatography-mass spectrometry (LC-MS) (multiple reaction monitoring [MRM]).
Precision (% CV)
Intra (concentration [amol/μl])Inter (concentration [amol/lμl])
31040025,00031040025,000
Peptide-spiked PBS12.011.15.85.2
NCAP-spiked PBS18.93.94.86.4
Peptide-spiked VTM12.56.82.43.015.510.26.84.7
NCAP-spiked VTM13.210.22.42.911.617.618.511.1
  1. –, not tested.

Additionally, the AYNVTQAFGR peptide was shown to be stable in the autosampler at 10°C for over 48 hr following re-analysis and comparison to a stored calibration curve.

Sample analysis

The samples analyzed by LC-MS and RT-PCR were compared. The high and low pools were analyzed in triplicate with a precision of 3.0% CV and 12.2% CV, respectively, for each pool. Example quantifier and qualifier LC-MS MRM chromatograms of peptide AYNVTQAFGR are shown in the two bottom panes of Figure 2, respectively. The results shown in Figure 3A suggests good (inverse) correlation between the LC-MS (log2 transformed quantifier response, i.e., SIL corrected quantifier peak area) and the RT-PCR (Ct) data, which has also been noted in other so-called ‘non-enriched’ studies (Van Puyvelde et al., 2021). The results shown in Figure 3B represent the LC-MS data in an alternative, quartile distribution-based format, suggesting that differentiation between sample types is feasible and that the detected abundances are significantly different (p = 0.00018; Mann-Whitney U test).

Liquid chromatography-mass spectrometry (LC-MS) (log2 quantifier response) vs. real-time polymerase chain reaction (RT-PCR) (cycle threshold [Ct]) read-out correlation with linear regression (A) and quartiles distribution of the LC-MS results (B).

Color labeling is based on RT-PCR diagnoses; blue = positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); gray = not detected (no light signals) or inconclusively quantified (single transition) by LC-MS.

Following CLSI EP 12-A2 User Protocol for Evaluation of Qualitative Test Performance guidance, a summary of the sample analysis results is shown in a 2 × 2 contingency table format in Figure 4, using the RT-PCR results as a reference, estimated sensitivity and specificity values for LC-MS are 83.3% and 100%, respectively. The 95% score confidence interval (CI) limits for sensitivity calculations were 70.4–91.3% and for specificity were 98.8–100%. Accordingly, the agreement between RT-PCR and LC-MS was strong (kappa value of 0.9 [95% CI 0.83–0.97]). When analyzing samples above the estimated LLOQ (3 amol/μl, which approximates to Ct ≤30), the estimated sensitivity is improved to 94.7% with the corresponding 95% score CI limits for sensitivity 82.7–98.5%. Further work will look at adding a secondary confirmatory peptide to the cut-off algorithm. However, RT-PCR does not distinguish between infectious virus and non-infectious nucleic acids (Engelmann et al., 2021), whereas LC-MS will only detect one or multiple peptides from the protein complement of the virus. This has implications on the interpretation of RT-PCR Ct levels itself in terms of infectious vs. non-infectious classification of patient samples but also for determining the sensitivity and specificity of complementary and/or alternative methods. Peptide levels have not been evaluated in the context of infectiousness yet, but other conditions, such as sample storage before LC-MRM/MS, can also give rise to analytical variance due to the inherent difference in stability between RNA and proteins. Additionally, Ct values are not universally applicable as they differ between manufacturers and methods (Engelmann et al., 2021; van Kasteren et al., 2020), which enforces the need of methods that are capable of determining viral load more accurately.

Output class (liquid chromatography-mass spectrometry [LC-MS]) vs. target class (real-time polymerase chain reaction [RT-PCR]) contingency matrix, used to calculate the positive percent agreement (PPA) and negative percent agreement (NPA) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immuno-affinity peptide enrichment LC-MS method (A).

The LC-multiple reaction monitoring (MRM)/MS performance is based on RT-PCR results obtained from 48 positive and 308 negative samples. (B) The LC-MRM/MS performance based on all positive samples with an RT-PCR results below cycle threshold (Ct) 30 (limit of detection [LOD] for the LC-MRM/MS) and 308 negative samples.

Discussion

Any diagnostic test result should be interpreted in the context of the probability of disease, but also include proper internal controls to ensure a high level of clinical specificity when used as a tool for large-scale screening. In this conceptual study, we have established an assay capable of detecting SARS-CoV-2 in self-collected samples. Multiple peptide assays were generated toward the NCAP and the assay that gave the best response was used to profile SARS-CoV-2 in clinical samples. The unsurpassed specificity of mass spectrometers combined with antibodies is an attractive route forward for a future molecular pandemic surveillance system. This specificity can help grow the assay repertoire, which can be expanded to cover multiple peptides or proteins by rather simple means, if anti-peptide antibodies are available. The SISCAPA peptide enrichment method ensures both high sensitivity and low risk of reporting false positives due to the combination of specific binding of the antibody (Hoofnagle and Wener, 2009) with LC-MRM/MS read-out. This is achieved by multiple factors that greatly outperform RT-PCR and rapid antigen test in theory. Firstly, antibodies are used to selectively enrich for the target peptide in a complex mixture. This helps increase the overall analytical sensitivity while LC-MS readily can distinguish between peptides in the separation and MRM steps. Secondly, internal standards added to the sample enable accurate and robust quantification. This provides an internal standard reference trace for every analyte and can help distinguish between false-positive chromatographic peaks based on retention time and ion ratios, that in RT-PCR experiment would be reported as a positive due to the absence of internal standards and since each gene is detected by a single reporter dye.

The sensitivity can be further improved by increasing the sample load if needed. Additionally, the number of viral protein targets can also be scaled by introducing additional anti-peptide antibodies into the sample mixture. This would allow for an LC-MS-based viral protein panel analysis method where relevant peptides, also including relevant spike peptides for mutation surveillance, are monitored in an endemic scenario, either covering new emerging SARS-CoV-2 strains or other viruses, such as influenza or respiratory syncytial viruses.

We show that the SISCAPA technology is an attractive route forward for future molecular pandemic surveillance systems. The accuracy of the LC-MS-based method would tolerate low levels of positive samples without compromising the positive predictive value of large-scale screening efforts, and thereby providing a next-generation platform for disease surveillance and an attractive alternative to today’s RT-PCR-based technologies.

Materials and methods

Sample collection

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The study was performed in accordance with the declaration of Helsinki and the study protocol (Jämförande studier av Covid-19 smitta och antikroppssvar i olika grupper i samhället) was approved by the Ethical Review Board of Linköping, Sweden (Regionala etikprövningsnämnden, Linköping, DNR – 2020–06395). Informed consent and consent to publish, including consent to publish anonymized data, was obtained from all subjects. Briefly, asymptomatic individuals working at an elderly caregiver in Sweden were screened on a regular basis at their workplace. A three-point collection (throat, nasal, saliva) was performed by participants using a self-sampling collection kit (Sansure Biotech, Changsha, China) containing PBS (1× PBS, 137 mM NaCl; 2.7 mM KCl; 4.3 mM Na2HPO4; 1.47 mM KH2PO4). Clinical samples were collected by swabs dipped into the sample collection tube and transported to the laboratory within 8 hr. All samples were heat inactivated upon arrival to ensure that the core temperature of the vial reached at least 56°C for 30 min. The protocol used ensured that the core temperature did not reach above 60°C ± 0.5°C (1 sd), which has been shown to have no effect on the RT-PCR sensitivity.

RT-PCR

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Samples were analyzed using an RT-PCR test from Sansure Biotech (Changsha, China) according to FDA-EUA guidelines. The Novel Coronavirus (2019-nCoV) Nucleic Acid Diagnostic Kit was used for quantitative detection of the ORF-1ab and the N gene of novel coronavirus (2019-nCoV). Briefly, samples were lysed at room temperature for at least 10 min to allow for RNA release by chemical lysis using Sample Release Reagent (Sansure Biotech). The presence or absence of SARS-CoV-2 RNA was determined by RT-PCR combined with multiplexed fluorescent probing, which targets a SARS-CoV-2-specific region of ORF-1ab (FAM) and N gene (ROX) together with the human Rnase P internal control (Cy5). The RT-PCR analysis was performed using a CFX96 Real‐Time PCR Detection System (Bio-Rad, Hercules, CA) programmed with the following RT-PCR protocol according to the manufacturer’s instruction (50°C, 30 min; 95°C 1 min) followed by 45 cycles of (95°C 30 s, 60 °C 30 s). The RT-PCR results were interpreted according to instructions. Positive (FAM/ROX Amplification, Ct < 40). Negative (FAM/ROX No amplification; Cy5 Amplification, Ct < 40).

Immuno-affinity peptide enrichment LC-MS

Materials

Recombinant NCAP was from R&D Systems, Minneapolis, MN, trypsin from Worthington, Lakewood, NJ, and anti-peptide antibodies from SISCAPA Assay Technologies, Washington, DC. All other chemicals were from MilliporeSigma, St Louis, MI, unless stated otherwise.

Calibrator preparation

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NCAP digest, protocol described below, was used for calibration and quantitation of viral proteins. A serial dilution from 2.2 pmol/µl NCAP to 10,000, 2000, 400, 80, 16, and 3 amol/µl was performed consecutively in pooled negative sample background.

Samples

Clinical samples subjected to two freeze-thaw cycles prior were anonymized and two control pools were established by pooling randomly chosen samples based on their RT-PCR result (Ct < 30 [high pool], 30 ≤ Ct < 33 [low pool]). A total of 180 µl from each sample was used per enrichment experiment. A set of 48 positive and 308 negative samples was subjected to the LC-MS analysis.

Protein extraction and digestion

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A total of 20 µl of denaturant mixture (1 % (w/v) RapiGest [Waters Corporation, Milford, MA]) in 1 M triethylammonium bicarbonate, 50 mM dithiothreitol (Waters Corporation) were aliquoted into the collection plate (Waters Corporation). Next, 180 µl of the diluted NCAP and patient samples were carefully transferred from the collection tubes into the same plate. The plate was incubated on a heater-shaker at 500 rpm at 56°C for 15 min followed by the addition of 50 µl trypsin solution (7.3 mg/ml trypsin in 10 mM HCl). After mixing at 500 rpm for 30 s, the samples were digested at 37°C for 30 min and thereafter quenched by addition of trypsin stopping agent (0.22 mg/ml of Tosyl-L-lysyl-chloromethane hydrochloride in 10 mM HCl) at a final concentration of 37 µg/ml. The sample plate was mixed at 500 rpm for 30 s and incubated at room temperature for 5 min. The samples were spiked with 20 µl of SIL peptide mixture solution and mixed thoroughly on a shaker at 500 rpm for 30 s.

Peptide enrichment

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Anti-peptide antibodies, raised toward proteotypic peptides from the NCAP, were screened and validated as previously described (Pope et al., 2009). The antibody-coupled magnetic bead immune adsorbents corresponding to four SIL peptides (ADETQALPQR-13C615N4, AYNVTQAFGR-13C615N4, DGIIWVATEGALNTPK-13C615N2, and NPANNAAIVLQLPQGTTLPK-13C615N2) were resuspended fully by vortex mixing. The suspension of each anti-peptide antibody tube was mixed together in 1:1 ratio and 40 µl of the mixture was added to each digest. The plate was mixed at 1400 rpm to ensure that beads were resuspended and thereafter incubated for 1 hr at 800 rpm at room temperature. After 1 hr incubation, the plate was placed on a magnet array (SISCAPA Assay Technologies). As soon as the beads had settled on the sides of each well (typically 1 min), the supernatant was removed; 150 µl of wash buffer (0.03% CHAPS, 1× PBS) was added to each sample and the beads were fully resuspending at 1400 rpm for 30 s and 450 rpm for another 30 s. The plate was placed on the magnet array again and the supernatant was removed. This step was repeated three times. The beads were subsequently resuspended in 50 µl elution buffer (0.5% formic acid, 0.03% CHAPS) and incubated for 5 min at room temperature. The beads were discarded by transferring the eluent to a QuanRecovery plate (Waters Corporation) for LC-MS analysis.

LC-MS detection and quantification

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Chromatography was performed on an ACQUITY UPLC I-Class FTN system, with Binary Solvent Manager and column heater (Waters Corporation); 20 µl of the enriched sample was injected onto a ACQUITY Premier Peptide BEH C18, 2.1 mm × 50 mm, 1.7 µm, 300 Å column (Waters Corporation) and separated using a gradient elution of mobile phase A containing laboratory LC-MS grade de-ionized water with 0.1% (v/v) formic acid, and mobile phase B containing LC-MS grade acetonitrile with 0.1% (v/v) formic acid. The gradient elution was performed at 0.6 ml/min with initial inlet conditions at 5% B, increasing to 28% B over 4.5 min, followed by a column wash at 90% B for 0.6 min and a return to initial conditions at 5% B. The total run time was 5.7 min, with a 6.5 min injection-to-injection cycle time.

A Xevo TQ-XS tandem MS (Waters Corporation, Wilmslow, UK) operating in positive electrospray ionization (ESI+) was used for the detection and quantification of the peptides. The instrument conditions were as follows: capillary voltage 0.5 kV, source temperature 150°C, desolvation temperature 600°C, cone gas flow 150 l/h, and desolvation gas flow 1000 l/h. The MS was calibrated at unit mass resolution for MS1 and MS2. Light and heavy labeled peptides were detected using MRM mode of acquisition with experimental details overviewed in Table 2.

Table 2
Multiple reaction monitoring (MRM) transitions and mass spectrometry (MS) method details target nucleocapsid protein (NCAP) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) peptides.
PeptideMRMMRM transition typeCone voltage (V)Collision energy (V)Retention time (min)Scan window (min)
ADETQALPQR564.8 > 400.2Quantifier35191.090.6–1.4
564.8 > 584.4Qualifier3520
564.8 > 712.4Qualifier3524
569.8 > 410.2SIL3519
AYNVTQAFGR563.8 > 679.4Quantifier35192.492.0–3.0
563.8 > 578.3Qualifier3518
563.8 > 892.5Qualifier3519
568.8 > 689.4SIL3519
DGIIWVATEGALNTPK562.3 > 643.4Quantifier35144.123.6–4.8
562.3 > 572.3Qualifier3518
562.3 > 700.4Qualifier3514
565.2 > 708.4SIL3514
NPANNAAIVLQLPQGTTLPK687.4 > 841.5Quantifier35183.923.6–4.2
687.4 > 766.4Qualifier3523
687.4 > 865.5Qualifier3523
690.4 > 849.5SIL3518

TargetLynx XS (Waters Corporation) was used to process the raw LC-MS data, that is, signal processing (mean smoothing and background subtraction), peak detection (area and height), and quantification of the MRM chromatograms, including the calculation of the quantifier ion to qualifier ion ratio. The quantified data were exported as tables (Supplementary file 1) and additional analysis and visualization carried out using Python 3.

Data and materials availability

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The ProteomeXchange ID for this dataset is PXD026366. The proteomics data have been deposited to Panorama Public (Sharma et al., 2014) (https://panoramaweb.org/sars-cov-2_siscapa.url). This dataset includes raw files and integrated peak areas from TargetLynx XS, as well as visualization of all LC-MRM/MS chromatograms.

Data availability

The ProteomeXchange ID for this dataset is PXD026366. The proteomics data have been deposited to Panorama Public (https://panoramaweb.org/sars-cov-2_siscapa.url), allowing for access to raw files and integrated peak areas from TargetLynx XS, as well as visualization of all LC-MRM/MS chromatograms.

The following data sets were generated
    1. Edfors F
    (2021) ProteomeXchange
    ID PXD026366. Rapid and sensitive detection of SARS-CoV-2 infection using quantitative peptide enrichment LC-MS analysis.

References

Decision letter

  1. Maarten Dhaenens
    Reviewing Editor; ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, Ghent, Belgium
  2. Y M Dennis Lo
    Senior Editor; The Chinese University of Hong Kong, Hong Kong
  3. Maarten Dhaenens
    Reviewer; ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, Ghent, Belgium
  4. Marica Grossegesse
    Reviewer

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Rapid and Sensitive Detection of SARS-CoV-2 Infection Using Quantitative Peptide Enrichment LC-MS/MS Analysis Running title: Quantitative Peptide Affinity LC-MS/MS Analysis of SARS-CoV-2" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Maarten Dhaenens as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Y M Dennis Lo as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Jeroen Demmers (Reviewer #2); Marica Grossegesse (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

As a guest editor, I have tried to organize the main concerns as described in more detail below. Please make sure to address these where possible. The original revisions provide a broader context of the requested reviews.

1. Sample preparation

a. How were the antibodies used in the present study made and characterized? Can you specify this in the material and methods section? (MG, JD) If proprietary and legal issues forbid the sharing of this data, we are willing to trust the overall performance.

b. It would be reasonable show with own data or to cite a publication stating that heat inactivation does not compromise the real-time PCR readout. (MG)

c. "50 μL elution buffer (0.5 % 180 formic acid, 0.03% CHAPS, 1X PBS) and incubated for 5 min at room temperature." This minor sentence is placed under major remarks, because in our understanding the elution buffer needs to be acidic and adding PBS will reduce acidity. If this is a typo, please correct. If this is not, could the authors try and use H2O instead and see if their results improve? (MD)

2. Figure 2: Peptide detection

a. What is represented in panels A and B. Is this the pure SIL peptide of the endogenous peptide in a complex matrix? What does '3 amol/ul' in the middle chromatogram exactly mean? (JD, MD)

b. Calibration curves:

i. I assume that the input is pure SIL peptide? (JD)

ii. Throughout the manuscript, could the calibration curves be displayed with a Log2-transformation in the X-axis to increase the resolution of the low abundant signal? Alternatively, or additionally provide a zoom inset to make it even more clear that 60 amol is still more signal than the negative sample. (MD)

c. Figure 2B: Add description of y-axis and place the text outside the line, so that the reader can see if there are any data points hidden. (MG)

d. "on-column amount of 60 amol." Because of the enrichment procedure, could the authors specify what initial conditions they spiked into the dilution series prior to enrichment. This would allow recalculation and avoid confusion about the correctness of the 60 amol on column claim (which in our hands is still detectable). (MD)

3. Patient population:

a. I oppose to the representation of the results and the claim of 86% sensitivity. (MD) Please see detailed revisions for an argumentation and for an alternative way of reproting your results. MG additionally pointed out during the Review evaluation session that true negatives can only be obtained from pre-Sars-Cov-2 era patients (prior sampling).

b. "asymptomatic individuals screened for ongoing disease": How were these obtained? (MD)

c. The majority was tested positive for SARS-CoV-2. This is very different from the percentages observed in regular testing facilities. How was the study group composed? Were these individuals who were already admitted to the hospital? (JD)

d. Authors conclude at the end of the Results section that patient samples were collected at an infectious stage (MG). "patient-collected samples" is a little misleading. Do you mean self-collected samples? (MG)

e. P5L98: why saliva samples? These actually are the hardest matrix for SISCAPA in our hands… If the triple sampling is what underlies the mentioning of saliva, I would prefer not to mention it separately, because its weight in the final assay cannot be assessed and several groups have already reported having difficulties with this matrix. (MD)

4. Bubble plot:

a. What is the size of the bubbles? (JD, MD)

b. Why was the highest signal reported in Figure 3 as a green dot at log2 MRM response of -6 was not called "positive" in the LCMS assay? (JD, MD) If manual peak inspection was used for diagnosis, please elaborate.

c. When counting dots, this only adds up to 55 sample, not 86 as depicted in the confusion matrix. (MD) Specify the number of samples (MG)

d. In Figure 3 the grey data points represent "not detected" or "inconclusively identified". What is meant by this? (MD, JD)

e. Calculate the correlation of the MS response to the cT value and add to graphic. (MG)

5. The access to the raw data was denied (MD, MG)

6. "For the LC-MS results, the lowest response divided by three was used". What is meant here? (MD, JD)

7. Please minimize downplaying qPCR in the text. (MG)

8. All reviewers would really appreciate extending the data beyond only one peptide. If impossible, please elaborate very clearly on the dangers and emphasize that this is only a proof of principle.

Overall, there was a general agreement amongst the reviewers that we are at a pivotal point in time for mass spectrometry in the clinic. Only with the highest standard data and the best possible representation of the results will we reach the common goal of showing the potential of MS.

Reviewer #3 (Recommendations for the authors):

The manuscript adds interesting and important knowledge to the field of virus-targeted proteomics. Still, in my opinion, some points need to be clarified or changed:

1) Line 72: Could you specify what rapid test detect nucleic acids? Rapid tests for my understanding are antigen tests detecting mainly the NCAP protein of SARS-CoV-2.

2) Line 72-78: The authors write about the sensitivity as a drawback of antigen tests. What about the specificity?

3) Line 103: The selectivity of antibodies is still a widely discussed topic. If the antibody is well characterized it can definitely contribute to a high specificity what leads me to my question: How were the antibodies used in the present study made and characterized? Can you specify this in the material and methods section?

4) Line 169/170: During sample preparation four different SARS-CoV-2 peptides were enriched. Were these also detected by MRM in the patient samples? Or to ask the other way around: why do you only show results for a single peptide?

5) Line 263: What do you mean with QC samples? Are these the two pools?

6) Line 279: Do you mean highest instead of lowest cT values here? Why do negatively diagnosed samples even a have a cT value (shouldn't they be negative)?

7) Figure 3A: Calculate the correlation of the MS response to the cT value and add to graphic.

8) Figure 3B: Specify the number of samples (N=?).

9) Line 302: What do you mean with "true sensitivity"?

Further general remarks:

10) The data has been uploaded to respective data repositories (ProteomeXchange and Panorama Public). I was able to access the Panorama Public data, but not the ProteomeXchange data. Could you please provide the login data?

11) Page 3, line 57: The expression "patient-collected samples" is a little misleading. Do you mean self-collected samples?

12) Line 77: Repetition that antigen tests are less sensitive (already stated in line 74).

13) Line 87: Typo: remove "="

14) Line 125f: In the study heat-inactivated samples were used. Hence, it would be reasonable show with own data or to cite a publication that heat inactivation does not compromise the real-time PCR readout.

15) Line 215: Do you mean "Results and Discussion" here? Otherwise I am missing the "discussion" section.

16) Line 220-222: remove blank line.

17) Figure 2B: Add description of y-axis and place the text outside the line, so that the reader can see if there are any data points hidden.

18) Table 2: typo in caption, replace "="by "-"

19) Line 326: Typo: outperform.

https://doi.org/10.7554/eLife.70843.sa1

Author response

Essential revisions:

As a guest editor, I have tried to organize the main concerns as described in more detail below. Please make sure to address these where possible. The original revisions provide a broader context of the requested reviews.

1. Sample preparation

a. How were the antibodies used in the present study made and characterized? Can you specify this in the material and methods section? (MG, JD) If proprietary and legal issues forbid the sharing of this data, we are willing to trust the overall performance.

We have included relevant references describing how SISCAPA polyclonal antibodies are raised and evaluated, but cannot reveal any proprietary information other than previously published work in this field. The antibody performance has been evaluated our hands, by assessing their LOD and LOQ by serial dilution of recombinant NCAP protein.

b. It would be reasonable show with own data or to cite a publication stating that heat inactivation does not compromise the real-time PCR readout. (MG)

We have added two references supporting this claim and we have also stated that the temperature core was validated in-house by repeatedly heating sample tubes in our ovens. We measured their outside and inside temperature as well as the ambient temperature of the oven over the course of 40 minutes.

c. "50 μL elution buffer (0.5 % 180 formic acid, 0.03% CHAPS, 1X PBS) and incubated for 5 min at room temperature." This minor sentence is placed under major remarks, because in our understanding the elution buffer needs to be acidic and adding PBS will reduce acidity. If this is a typo, please correct. If this is not, could the authors try and use H2O instead and see if their results improve? (MD)

We apologize for the typo. This should say H2O and has been updated in the new version of the manuscript.

2. Figure 2: Peptide detection

a. what is represented in panels A and B. Is this the pure SIL peptide of the endogenous peptide in a complex matrix? What does '3 amol/ul' in the middle chromatogram exactly mean? (JD, MD)

The calibration curve represents the AYN-peptide, which is serially diluted in matrix background with a constant spike-in of full-length NCAP-protein. We have clarified this in the figure legend.

b. Calibration curves:

i. I assume that the input is pure SIL peptide? (JD)

The calibration curves represent digested NCAP protein spiked with SIL peptides. All samples are prepared in digestion buffer and thereafter enriched by the SISCAPA workflow prior LC-MS quantification. This has been clarified in the manuscript.

ii. Throughout the manuscript, could the calibration curves be displayed with a Log2-transformation in the X-axis to increase the resolution of the low abundant signal?

We have inserted a zoomed-in window resolving the lower concentration range in the curve.

Alternatively, or additionally provide a zoom inset to make it even more clear that 60 amol is still more signal than the negative sample. (MD)

This zoom has been provided in the updated version of the manuscript.

c. Figure 2B: Add description of y-axis and place the text outside the line, so that the reader can see if there are any data points hidden. (MG)

This has been done in the updated in the new version of the manuscript.

d. "on-column amount of 60 amol." Because of the enrichment procedure, could the authors specify what initial conditions they spiked into the dilution series prior to enrichment. This would allow recalculation and avoid confusion about the correctness of the 60 amol on column claim (which in our hands is still detectable). (MD)

We have removed the on-column amount from the results and discussion and we present the initial concentration as this is more relevant for clinical tests.

3. Patient population:

a. I oppose to the representation of the results and the claim of 86% sensitivity. (MD) Please see detailed revisions for an argumentation and for an alternative way of reproting your results. MG additionally pointed out during the Review evaluation session that true negatives can only be obtained from pre-Sars-Cov-2 era patients (prior sampling).

We have followed the suggestion and will refer to this as Positive Percent Agreement (PPA). We have therefore changed sensitivity to PPA and NPA accordingly: Positive percent agreement (estimated sensitivity) and negative percent agreement (estimated specificity).

b. "asymptomatic individuals screened for ongoing disease": How were these obtained? (MD)

We have described the enrolment procedure in more detail to clarify this further.

c. The majority was tested positive for SARS-CoV-2. This is very different from the percentages observed in regular testing facilities. How was the study group composed? Were these individuals who were already admitted to the hospital? (JD)

A number of positive samples were specifically selected based on PCR-based results from a larger study. This has been specified under Samples (Immuno-Affinity Peptide Enrichment LC-MS).

d. Authors conclude at the end of the Results section that patient samples were collected at an infectious stage (MG). "patient-collected samples" is a little misleading. Do you mean self-collected samples? (MG)

We have changed the wording in the new version of the manuscript.

e. P5L98: why saliva samples? These actually are the hardest matrix for SISCAPA in our hands… If the triple sampling is what underlies the mentioning of saliva, I would prefer not to mention it separately, because its weight in the final assay cannot be assessed and several groups have already reported having difficulties with this matrix. (MD)

This is the standard procedure in Sweden. We have clarified that all samples were collected using the same triple point strategy, and that we were not mixing nasal swabs with saliva swabs. The mentioning of saliva by itself was not intended and has been removed in the updated version of the manuscript.

4. Bubble plot:

a. What is the size of the bubbles? (JD, MD)

The size of the markers represents the (redundant) log2 MRM response of peptide AYNVTQAFGR. A fixed marker size has been used instead in the updated version.

b. Why was the highest signal reported in Figure 3 as a green dot at log2 MRM response of -6 was not called "positive" in the LCMS assay? (JD, MD) If manual peak inspection was used for diagnosis, please elaborate.

All green dots are missing the qualifier and thus do not pass the QC. This set of samples have been removed from the plot and are visualized in the boxplot instead. We have removed them from Figure 3A and are only visualizing them in Figure 3B.

c. When counting dots, this only adds up to 55 sample, not 86 as depicted in the confusion matrix. (MD) Specify the number of samples (MG)

Some of the markers overlay so attempting to count them may lead to a disconnect. The sample size has been added to the caption text and dots are only visualized in the boxplot (Figure 3B).

d. In Figure 3 the grey data points represent "not detected" or "inconclusively identified". What is meant by this? (MD, JD)

The grey dots represent PCR-positive samples where either the qualifier was missing (inconclusively) or nor the qualifier nor the quantifier were present (not detected). This has been added to the figure text.

e. Calculate the correlation of the MS response to the cT value and add to graphic. (MG)

The requested regression has been added to the figure.

5. The access to the raw data was denied (MD, MG)

We are really sorry for this misconception. The raw data is currently only available through Panorama with the provided login credentials as described above and in the Data Availability section. The PXD identifier is currently only reserved for the dataset. The data will though be available through ProteomeXchange after the review process.

6. "For the LC-MS results, the lowest response divided by three was used". What is meant here? (MD, JD)

We have excluded this visualization in the new version of the manuscript.

7. Please minimize downplaying qPCR in the text. (MG)

We have re-written the text to reflect a more neutral tone about the RT-PCR technology, which is truly amazing. We have removed the sentence listing all potential issues with the technology. The discussion is now centered around the sensitivity provided by the technology, which is subjected for criticism in relation to the clinical relevance (reference 11).

8. All reviewers would really appreciate extending the data beyond only one peptide. If impossible, please elaborate very clearly on the dangers and emphasize that this is only a proof of principle.

We have highlighted the limitations to this study in the conclusion-part of the paper. We have emphasized that this is a proof-of-principle study in the conclusion and discussed future applications.

Overall, there was a general agreement amongst the reviewers that we are at a pivotal point in time for mass spectrometry in the clinic. Only with the highest standard data and the best possible representation of the results will we reach the common goal of showing the potential of MS.

Reviewer #3 (Recommendations for the authors):

The manuscript adds interesting and important knowledge to the field of virus-targeted proteomics. Still, in my opinion, some points need to be clarified or changed:

1) Line 72: Could you specify what rapid test detect nucleic acids? Rapid tests for my understanding are antigen tests detecting mainly the NCAP protein of SARS-CoV-2.

This sentence has been re-written to be more accurate. We have removed nucleic acids from the text.

2) Line 72-78: The authors write about the sensitivity as a drawback of antigen tests. What about the specificity?

This has been addressed in terms of binary read-out.

3) Line 103: The selectivity of antibodies is still a widely discussed topic. If the antibody is well characterized it can definitely contribute to a high specificity what leads me to my question: How were the antibodies used in the present study made and characterized? Can you specify this in the material and methods section?

We have added one sentence to the discussion where we explain that the selectivity of the assay is further improved by the mass spectrometer. We cannot present exactly how these antibodies have been generated, but have included references to the general SISCAPA workflow. The polyclonal antibodies have thus been validated in many different background matrices, and their sensitivity has been evaluated using recombinant NCAP protein serially diluted down to 3 amol/µl.

4) Line 169/170: During sample preparation four different SARS-CoV-2 peptides were enriched. Were these also detected by MRM in the patient samples? Or to ask the other way around: why do you only show results for a single peptide?

Only one antibody-peptide pair was selected for the cohort study. Only one peptide was selected, partly due to availability of reagents and multiplexing capability (amount of beads when eluting).

5) Line 263: What do you mean with QC samples? Are these the two pools?

Yes, this has been clarified in the manuscript. This was the peptide stored in elution buffer over the time course of two days (48 hours).

6) Line 279: Do you mean highest instead of lowest cT values here? Why do negatively diagnosed samples even a have a cT value (shouldn't they be negative)?

This section has been removed since the negative samples did not have any Ct value. This was imputed for visual purpose only.

7) Figure 3A: Calculate the correlation of the MS response to the cT value and add to graphic.

This has been added to the figure.

8) Figure 3B: Specify the number of samples (N=?).

This has been included in the new version of the manuscript in all figures.

9) Line 302: What do you mean with "true sensitivity"?

This has been updated in the new version, separating PPA, NPA from sensitivity, specificity.

Further general remarks:

10) The data has been uploaded to respective data repositories (ProteomeXchange and Panorama Public). I was able to access the Panorama Public data, but not the ProteomeXchange data. Could you please provide the login data?

The ProteomeXchange data is hosted by Panorama and will be made available through the ProteomeXchange link after the review process. The current PXD-identifier is reserved for the dataset.

11) Page 3, line 57: The expression "patient-collected samples" is a little misleading. Do you mean self-collected samples?

This has been updated in the new version of the manuscript.

12) Line 77: Repetition that antigen tests are less sensitive (already stated in line 74).

This has been updated in the new version of the manuscript.

13) Line 87: Typo: remove "="

This has been removed.

14) Line 125f: In the study heat-inactivated samples were used. Hence, it would be reasonable show with own data or to cite a publication that heat inactivation does not compromise the real-time PCR readout.

This has been updated in the new version of the manuscript and we have provided references to relevant studies investigating this potential issue.

15) Line 215: Do you mean "Results and Discussion" here? Otherwise I am missing the "discussion" section.

This has been updated.

16) Line 220-222: remove blank line.

This has been removed.

17) Figure 2B: Add description of y-axis and place the text outside the line, so that the reader can see if there are any data points hidden.

This has been updated.

18) Table 2: typo in caption, replace "="by "-"

This has been updated.

19) Line 326: Typo: outperform.

This has been updated.

https://doi.org/10.7554/eLife.70843.sa2

Article and author information

Author details

  1. Andreas Hober

    Science for Life Laboratory, Solna, Sweden
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8947-2562
  2. Khue Hua Tran-Minh

    1. Science for Life Laboratory, Solna, Sweden
    2. The Royal Institute of Technology, Division of Systems Biology, Department of Protein Science, School of Chemistry, Biotechnology and Health, Stockholm, Sweden
    Contribution
    Investigation, Methodology, Validation
    Competing interests
    No competing interests declared
  3. Dominic Foley

    Waters Corporation, Milford, United Kingdom
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation
    Competing interests
    employed by Waters Corporation
  4. Thomas McDonald

    Waters Corporation, Milford, United Kingdom
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology
    Competing interests
    employed by Waters Corporation
  5. Johannes PC Vissers

    Waters Corporation, Milford, United Kingdom
    Contribution
    Data curation, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing - original draft, Writing - review and editing
    Competing interests
    employed by Waters Corporation
  6. Rebecca Pattison

    Waters Corporation, Milford, United Kingdom
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology
    Competing interests
    employed by Waters Corporation
  7. Samantha Ferries

    Waters Corporation, Milford, United Kingdom
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology
    Competing interests
    employed by Waters Corporation
  8. Sigurd Hermansson

    Waters Corporation, Milford, United Kingdom
    Contribution
    Data curation, Formal analysis
    Competing interests
    employed by Waters Corporation
  9. Ingvar Betner

    Waters Corporation, Milford, United Kingdom
    Contribution
    Funding acquisition, Investigation, Supervision
    Competing interests
    employed by Waters Corporation
  10. Mathias Uhlén

    1. Science for Life Laboratory, Solna, Sweden
    2. The Royal Institute of Technology, Division of Systems Biology, Department of Protein Science, School of Chemistry, Biotechnology and Health, Stockholm, Sweden
    Contribution
    Funding acquisition, Supervision, Writing - review and editing
    Competing interests
    No competing interests declared
  11. Morteza Razavi

    SISCAPA Assay Technologies, Inc, Victoria, Canada
    Contribution
    Conceptualization, Methodology, Resources
    Competing interests
    employed by SISCAPA Assay Technologies
  12. Richard Yip

    SISCAPA Assay Technologies, Inc, Victoria, Canada
    Contribution
    Conceptualization, Methodology, Resources
    Competing interests
    employed by SISCAPA Assay Technologies
  13. Matthew E Pope

    SISCAPA Assay Technologies, Inc, Victoria, Canada
    Contribution
    Conceptualization, Methodology, Resources
    Competing interests
    employed by SISCAPA Assay Technologies
  14. Terry W Pearson

    SISCAPA Assay Technologies, Inc, Victoria, Canada
    Contribution
    Conceptualization, Methodology, Resources
    Competing interests
    employed by SISCAPA Assay Technologies
  15. Leigh N Andersson

    SISCAPA Assay Technologies, Inc, Victoria, Canada
    Contribution
    Conceptualization, Methodology, Resources, Validation, Writing - review and editing
    Competing interests
    employed by SISCAPA Assay Technologies
  16. Amy Bartlett

    Waters Corporation, Milford, United Kingdom
    Contribution
    Conceptualization, Formal analysis, Investigation, Methodology
    Competing interests
    employed by Waters Corporation
  17. Lisa Calton

    Waters Corporation, Milford, United Kingdom
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Validation, Writing - original draft, Writing - review and editing
    Competing interests
    employed by Waters Corporation
  18. Jessica J Alm

    Karolinska Institutet, Department of Microbiology, Tumor and Cell Biology & National Pandemic Center, Karolinska Institutet, Solna, Sweden
    Contribution
    Project administration, Resources, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  19. Lars Engstrand

    Microbiology, Tumour and Cell Biology, Karolinska Institutet, Stockholm, Sweden
    Contribution
    Investigation, Project administration, Resources, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  20. Fredrik Edfors

    1. Science for Life Laboratory, Solna, Sweden
    2. The Royal Institute of Technology, Division of Systems Biology, Department of Protein Science, School of Chemistry, Biotechnology and Health, Stockholm, Sweden
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing - original draft, Writing - review and editing
    For correspondence
    fredrik.edfors@scilifelab.se
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0017-7987

Funding

No external funding was received for this work.

Ethics

Human subjects: The study was performed in accordance with the declaration of Helsinki and the study protocol ("Jämförande studier av Covid-19 smitta och antikroppssvar i olika grupper i samhället") was approved by the Ethical Review Board of Linköping, Sweden (Regionala etikprövningsnämnden, Linköping, DNR - 2020-06395). Informed consent and consent to publish, including consent to publish anonymized data, was obtained from all subjects.

Senior Editor

  1. Y M Dennis Lo, The Chinese University of Hong Kong, Hong Kong

Reviewing Editor

  1. Maarten Dhaenens, ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, Ghent, Belgium

Reviewers

  1. Maarten Dhaenens, ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, Ghent, Belgium
  2. Marica Grossegesse

Publication history

  1. Received: May 31, 2021
  2. Preprint posted: June 4, 2021 (view preprint)
  3. Accepted: November 4, 2021
  4. Accepted Manuscript published: November 8, 2021 (version 1)
  5. Version of Record published: November 26, 2021 (version 2)

Copyright

© 2021, Hober et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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