Evidence map›Paper›PMID 34591660›Full record

ArticleScience translational medicine2021

Early sample tagging and pooling enables simultaneous SARS-CoV-2 detection and variant sequencing.

Alon Chappleboim, Daphna Joseph-Strauss, Ayelet Rahat, Israa Sharkia, Miriam Adam, Daniel Kitsberg, Gavriel Fialkoff, Matan Lotem, Omer Gershon, Anna-Kristina Schmidtner and 7 more

Abstract read
In one paragraph

Article in Science translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
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

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Alon ChappleboimAlexander Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.ORCID 0000-0001-7676-956X
Daphna Joseph-StraussAlexander Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.ORCID 0000-0002-2742-9873
Ayelet RahatAlexander Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.
Israa SharkiaAlexander Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.
Miriam AdamEdmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.
Daniel KitsbergEdmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.
Gavriel FialkoffAlexander Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.
Matan LotemAlexander Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.
Omer GershonAlexander Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.ORCID 0000-0002-4662-3604
Anna-Kristina SchmidtnerEdmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.
Esther Oiknine-DjianHadassah Hebrew University Medical Center, Jerusalem 9112001, Israel.
Agnes KlochendlerDepartment of Developmental Biology and Cancer Research, IMRIC, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel.ORCID 0000-0002-7340-7464
Ronen SadehAlexander Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.ORCID 0000-0003-1920-3898
Yuval DorDepartment of Developmental Biology and Cancer Research, IMRIC, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel.ORCID 0000-0003-2456-2289
Dana WolfHadassah Hebrew University Medical Center, Jerusalem 9112001, Israel.ORCID 0000-0001-5507-4160
Naomi HabibEdmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.ORCID 0000-0002-6049-2487
Nir FriedmanAlexander Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.ORCID 0000-0002-9678-3550

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) diagnostic tests have relied on RNA extraction followed by reverse transcription quantitative polymerase chain reaction (RT-qPCR) assays. Whereas automation improved logistics and different pooling strategies increased testing capacity, highly multiplexed next-generation sequencing (NGS) diagnostics remain a largely untapped resource. NGS tests have the potential to markedly increase throughput while providing crucial SARS-CoV-2 variant information. Current NGS-based detection and genotyping assays for SARS-CoV-2 are costly, mostly due to parallel sample processing through multiple steps. Here, we have established ApharSeq, in which samples are barcoded in the lysis buffer and pooled before reverse transcription. We validated this assay by applying ApharSeq to more than 500 clinical samples from the Clinical Virology Laboratory at Hadassah hospital in a robotic workflow. The assay was linear across five orders of magnitude, and the limit of detection was Ct 33 (~1000 copies/ml, 95% sensitivity) with >99.5% specificity. ApharSeq provided targeted high-confidence genotype information due to unique molecular identifiers incorporated into this method. Because of early pooling, we were able to estimate a 10- to 100-fold reduction in labor, automated liquid handling, and reagent requirements in high-throughput settings compared to current testing methods. The protocol can be tailored to assay other host or pathogen RNA targets simultaneously. These results suggest that ApharSeq can be a promising tool for current and future mass diagnostic challenges.

Indexed as

COVID-19SARS-CoV-2COVID-19 Nucleic Acid TestingCOVID-19 TestingHumansSpecimen Handling

Identifiers

PMID34591660
PMCPMC9928115

What OpenQuestion holds

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Registered trials

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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.