Evidence map›Paper›PMID 32908995›Full record

ArticlemedRxiv : the preprint server for health sciences2020

Digital droplet PCR accurately quantifies SARS-CoV-2 viral load from crude lysate without nucleic acid purification.

Harish Vasudevan, Peng Xu, Venice Servellita, Steve Miller, Leqian Liu, Allan Gopez, Charles Y Chiu, Adam R Abate

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed, 8 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Harish Vasudevan
Peng Xu
Venice Servellita
Steve Miller
Leqian Liu
Allan Gopez
Charles Y Chiu
Adam R Abate
University of California, San Francisco · US

Funding

Gigapixel digital PCR in Giant Unilamellar VesiclesR01EB019453 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ABATE, ADAM R., CHIU, CHARLES YEN · 2014 to 2022
$3.9M
NIBIB NIH HHS R01 EB019453
6 · The paper itself

Abstract

The COVID-19 pandemic caused by the SARS-CoV-2 virus motivates diverse diagnostic approaches due to the novel causative pathogen, incompletely understood clinical sequelae, and limited availability of testing resources. Given the variability in viral load across and within patients, absolute viral load quantification directly from crude lysate is important for diagnosis and surveillance. Here, we investigate the use of digital droplet PCR (ddPCR) for SARS-CoV-2 viral load measurement directly from crude lysate without nucleic acid purification. We demonstrate ddPCR accurately quantifies SARS-CoV-2 standards from purified RNA and multiple sample matrices, including commonly utilized universal transport medium (UTM). In addition, we find ddPCR functions robustly at low input viral copy numbers on nasopharyngeal swab specimens stored in UTM without upfront RNA extraction. We also show ddPCR, but not qPCR, from crude lysate shows high concordance with viral load measurements from purified RNA. Our data suggest ddPCR offers advantages to qPCR for SARS-CoV-2 detection with higher sensitivity and robustness when using crude lysate rather than purified RNA as input. More broadly, digital droplet assays provide a potential method for nucleic acid measurement and infectious disease diagnosis with limited sample processing, underscoring the utility of such techniques in laboratory medicine.

Identifiers

PMID32908995
PMCPMC7480047
OpenAlexW3082828065

What OpenQuestion holds

Textmetadata
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.