Evidence map›Paper›PMID 34747696›Full record

ArticleeLife2021

Rapid and sensitive detection of SARS-CoV-2 infection using quantitative peptide enrichment LC-MS analysis.

Andreas Hober, Khue Hua Tran-Minh, Dominic Foley, Thomas McDonald, Johannes Pc Vissers, Rebecca Pattison, Samantha Ferries, Sigurd Hermansson, Ingvar Betner, Mathias Uhlén and 10 more

Open access · goldAbstract read
In one paragraph

Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. A Targeted LC-MRMMolecular & cellular proteomics : MCP · 2024
    Article
  8. Article
  9. Review
  10. The Edge Effect in High-Throughput Proteomics: A Cautionary Tale.Journal of the American Society for Mass Spectrometry · 2023
    Article
  11. CovAnalytical chemistry · 2022
    Article
  12. Article
  13. Article
  14. Review
  15. Article
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  18. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

20 authors at 3 institutions in 2 countries.

Andreas HoberScience for Life Laboratory, Solna, Sweden.ORCID 0000-0001-8947-2562
Khue Hua Tran-MinhScience for Life Laboratory, Solna, Sweden.
Dominic FoleyWaters Corporation, Milford, United Kingdom.
Thomas McDonaldWaters Corporation, Milford, United Kingdom.
Johannes Pc VissersWaters Corporation, Milford, United Kingdom.
Rebecca PattisonWaters Corporation, Milford, United Kingdom.
Samantha FerriesWaters Corporation, Milford, United Kingdom.
Sigurd HermanssonWaters Corporation, Milford, United Kingdom.
Ingvar BetnerWaters Corporation, Milford, United Kingdom.
Mathias UhlénScience for Life Laboratory, Solna, Sweden.
Morteza RazaviSISCAPA Assay Technologies, Inc, Victoria, Canada.
Richard YipSISCAPA Assay Technologies, Inc, Victoria, Canada.
Matthew E PopeSISCAPA Assay Technologies, Inc, Victoria, Canada.
Terry W PearsonSISCAPA Assay Technologies, Inc, Victoria, Canada.
Leigh N AnderssonSISCAPA Assay Technologies, Inc, Victoria, Canada.
Amy BartlettWaters Corporation, Milford, United Kingdom.
Lisa CaltonWaters Corporation, Milford, United Kingdom.
Jessica J AlmKarolinska Institutet, Department of Microbiology, Tumor and Cell Biology & National Pandemic Center, Karolinska Institutet, Solna, Sweden.
Lars EngstrandMicrobiology, Tumour and Cell Biology, Karolinska Institutet, Stockholm, Sweden.
Fredrik EdforsScience for Life Laboratory, Solna, Sweden.ORCID 0000-0002-0017-7987
Waters (United States) · USScience for Life Laboratory · SEKarolinska Institutet · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Chromatography, LiquidCOVID-19HumansLinear ModelsMass SpectrometryMolecular Diagnostic TechniquesNasopharynxPeptide FragmentsProteomicsReproducibility of ResultsSARS-CoV-2Sensitivity and SpecificityViral ProteinsPeptide FragmentsViral ProteinsCOVID-19diagnosticshumanimmunologyinfectious diseaseinflammationmass spectrometrymicrobiologyproteomicsSARS CoV-2SISCAPA

Identifiers

PMID34747696
PMCPMC8626084
OpenAlexW3211420494

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.