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.

Figure 1

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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 2–4, 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

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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

	Precision (% CV)
	Intra (concentration [amol/μl])				Inter (concentration [amol/lμl])
	3	10	400	25,000	3	10	400	25,000
Peptide-spiked PBS	12.0	11.1	5.8	5.2	–	–	–	–
NCAP-spiked PBS	18.9	3.9	4.8	6.4	–	–	–	–
Peptide-spiked VTM	12.5	6.8	2.4	3.0	15.5	10.2	6.8	4.7
NCAP-spiked VTM	13.2	10.2	2.4	2.9	11.6	17.6	18.5	11.1

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).

Figure 3

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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.

Figure 4

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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.

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.

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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

   "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

    "This ORCID iD identifies the author of this article:" 0000-0002-0017-7987

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