Literature DB >> 32805524

Pooling of SARS-CoV-2 samples to increase molecular testing throughput.

Garrett A Perchetti1, Ka-Wing Sullivan1, Greg Pepper1, Meei-Li Huang1, Nathan Breit1, Patrick Mathias2, Keith R Jerome3, Alexander L Greninger4.   

Abstract

BACKGROUND: SARS-CoV-2 testing demand has outpaced its supply. Pooling samples for lower risk populations has the potential to accommodate increased demand for SARS-CoV-2 molecular testing.
OBJECTIVE: To evaluate the sensitivity, specificity, and reproducibility of 4-way pooling of SARS-CoV-2 specimens for high-throughput RT-PCR. STUDY
DESIGN: Individual samples were pooled 1:4 through automated liquid handling, extracted, and assayed by our emergency use authorized CDC-based RT-PCR laboratory developed test. Positive samples were serially diluted and theoretical and empirical PCR cycle thresholds were evaluated. Thirty-two distinct positive samples were pooled into negative specimens and individual CTs were compared to pooled CTs. Low positive samples were repeated for reproducibility and 32 four-way pools of negative specimens were assayed to determine specificity.
RESULTS: Four-way pooling was associated with a loss of sensitivity of 1.7 and 2.0 CTs for our N1 and N2 targets, respectively. Pooling correctly identified SARS-CoV-2 in 94 % (n = 30/32) of samples tested. The two low positive specimens (neat CT > 35) not detected by pooling were individually repeated and detected 75 % (n=6/8) and 37.5 % (n = 3/8) of the time, respectively. All specimens individually determined negative were also negative by pooling.
CONCLUSION: We report that 1:4 pooling of samples is specific and associated with an expected 2 CT loss in analytical sensitivity. Instead of running each sample individually, pooling of four samples will allow for a greater throughput and conserve scarce reagents.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  COVID-19; Diagnostics; Disease surveillance; Pooling; RT-PCR; SARS-CoV-2

Mesh:

Substances:

Year:  2020        PMID: 32805524      PMCID: PMC7396208          DOI: 10.1016/j.jcv.2020.104570

Source DB:  PubMed          Journal:  J Clin Virol        ISSN: 1386-6532            Impact factor:   3.168


Introduction

Widespread COVID-19 infections have placed extraordinary demand on molecular diagnostics. COVID-19 cases continue to rise and laboratory capacities to detect SARS-CoV-2 RNA are becoming increasingly strained, causing delays in testing turnaround times [[1], [2], [3]]. To accommodate demand for increased testing volumes, on July 18th, 2020, the FDA issued its first Emergency Use Authorization (EUA) for sample pooling in diagnostic testing [4]. Early testing during the COVID-19 pandemic focused primarily on symptomatic individuals, but as we expand the populations tested to asymptomatic patients, overall positivity rate declines and pooling methods become increasingly favorable [5]. Sample pooling provides improved benefits as SARS-CoV-2 incidence rates decline; higher incidence rates (i.e. > 10 %) provide little advantage, but pooling with incidence rates <5% can substantially increase testing capacity [6,7]. Modeling suggests that for asymptomatic or mild cases based on overall lower SARS-CoV-2 incidence, scaled pooling of up to 30 samples can provide substantial benefit in accommodating increased testing demands for low-risk populations [[8], [9], [10], [11], [12]]. Theoretical calculations of pooling are useful; however, here we describe the practical utility of 4-way pooling of our EUA laboratory developed test (LDT) and evaluate its clinical application.

Methods

We programmed a HAMILTON Microlab STARlet Automated Liquid Handler (Atlantic Lab Equipmant, Beverly, MA) to perform 4-way pooling on our CDC-based Washington state EUA SARS-CoV-2 RT-PCR assay targeting N1 and N2 as previously described [13,14]. Fifty μL of each specimen was pipetted into a 96-well deep well plate yielding 200 μL of total viral transport media (VTM) per well. The MagNA Pure 96 platform (Roche, Basel, Switzerland) was utilized for total nucleic acid extraction, eluting into 50 μL elution buffer. Initial water and VTM templates were used to confirm pipetting accuracy with 384 samples into a 96-well deep well plate. Artificial sample IDs were attributed to each respective pool and the eluted volume was manually confirmed for accuracy by pipette. Prior to pooling, neat samples were assayed by LDT and stored at 4 °C for <24 h. HeLa cells were included as a negative extraction control and water as a negative PCR template on every run. We created a web application for converting Hamilton output files into plate maps that can be imported into the Applied Biosystems 7500 software. It concatenates the container IDs that were mixed in each well into sample names for the 7500 application so that they can be tracked. The web application also provides a form where the experiment output from the Applied Biosystems 7500 software is uploaded and edited to produce extracts for other downstream systems and processes. These extracts include a. pdf print-out highlighting positive and inconclusive samples that need to be individually tested along with their rack location, a. json file with raw CT values that is loaded into our data warehouse, and a. csv file with the negative samples that can be imported into our Sunquest laboratory information system.

Results

To evaluate lower positivity levels of pooled samples, we first ran an initial 10-fold dilution series on a positive nasopharyngeal swab with an initial mean cycle threshold CT of 31.4 corresponding to 2,500–5,000 copies/mL by our LDT. The sample was serially diluted with phosphate-buffered saline 1:10, then 1:100, and then 1:1,000, corresponding to 1:40, 1:400, and 1:4,000 respective dilutions for pooling. Next, we verified that low positive samples with CTs ≥33 – indicative of a low viral load – were not missed by pooling of samples (Table 1 ). Our data indicate that even though individual samples are diluted by the process of pooling four separate specimens together, the effect is not substantial enough to push borderline positives beyond the limit of detection.
Table 1

CT comparison of low positives by pooling.

PoolOriginal N1 CTPooled N1 CTOriginal N2 CTPooled N2 CT
133.635.037.536.2
234.936.837.537.7
336.036.836.638.0
433.234.637.336.6

Abbreviations: CT, cycle threshold, NDET, not detected, N1 and N2 are SARS-CoV-2 nucleocapsid gene targets for PCR. Each pool contains 4 specimens: 1 unique positive sample pooled into 3 distinct negative samples. Low positive samples are defined as having a CT>33.0.

CT comparison of low positives by pooling. Abbreviations: CT, cycle threshold, NDET, not detected, N1 and N2 are SARS-CoV-2 nucleocapsid gene targets for PCR. Each pool contains 4 specimens: 1 unique positive sample pooled into 3 distinct negative samples. Low positive samples are defined as having a CT>33.0. We expanded this experiment to include 32 additional SARS-CoV-2 positive specimens by the CDC-based Washington state EUA assay pooled into negative samples. Our results confirm the empirical CTs of pooled samples are not dissimilar from the theoretically calculated values (Table 2 ). As expected, diluted samples with theoretical CTs beyond our limit of detection were not detected (NDET) by our LDT [13]. The average delay of N1 and N2 target CTs associated with 1:4 pooling was 1.7 (95 % CI:1.12−2.32) and 2.0 (95 % CI: 1.29–2.66), respectively. Thirty out of 32 (94 %) positive samples pooled into negative specimens were detected by pooling (Fig. 1 , Supplementary Table 1). The only two missed samples by pooling had CTs ≥35, corresponding to an absolute quantification of approximately 250–500 genomic copies/mL based on our quantitative RT-PCR, with RNA standard values quantified by droplet digital RT-PCR [14]. Two low positives (CT >33.5) were inconclusive by pooling (one target positive), and according to our EUA protocol would be considered positive, with individual specimens repeated. These inconclusive results were confirmed as low positives when repeated from neat sample.
Table 2

Positive sample serial dilution.

Sample DilutionExpected N1 CTActual N1 CTExpected N2 CTActual N2 CT
Neat30.831.930.330.9
1:10 (1:40)35.435.434.935.3
1:100 (1:400)38.7NDET38.2NDET
1:1,000 (1:4,000)42.0NDET41.5NDET

Abbreviations: CT, cycle threshold, NDET, not detected, N1 and N2 are SARS-CoV-2 nucleocapsid gene targets for PCR. Parentheticals denote respective pooling dilutions.

Fig. 1

CTs of 32 distinct positives pooled 1:4 into negative samples.

Abbreviations: CT, cycle threshold.

For the two samples that were not detected by pooling, CT values of 40 were artificially designated for pool 7 and pool 21. CT values are averaged from both N1 and N2 targets for the original neat samples and the pooled samples.

Positive sample serial dilution. Abbreviations: CT, cycle threshold, NDET, not detected, N1 and N2 are SARS-CoV-2 nucleocapsid gene targets for PCR. Parentheticals denote respective pooling dilutions. CTs of 32 distinct positives pooled 1:4 into negative samples. Abbreviations: CT, cycle threshold. For the two samples that were not detected by pooling, CT values of 40 were artificially designated for pool 7 and pool 21. CT values are averaged from both N1 and N2 targets for the original neat samples and the pooled samples. For reproducibility, the negative and inconclusive pools were repeated 8 additional times, and 3 additional times on neat positive specimens (Supplementary Table 2). Our data demonstrate we would have detected the first specimen (mean CT = 36.5) in 6 out of 8 pools (75 %). The second specimen (mean CT = 37.3) was detected in 3 out of 8 pools (37.5 %). To confirm specificity, we combined 128 unique patient specimens determined negative by the Washington state EUA assay into 32 pools. All 32 four-way pools were negative, demonstrating 100 % specificity.

Discussion

Here, we show the potential for four-way pooling to responsibly increase testing throughput with an expected 2 CT loss in sensitivity. We acknowledge that pooling multiple specimens together results in a mild - but expected - drop in PCR cycle thresholds, similar to other research [15]. According to Yelin et al. (2020), as pool size increases, each respective sample and potential SARS-CoV-2 RNA are diluted, corresponding with an observed linear CT increase of 1.24 for every twofold dilution [16]. Most theoretical calculations estimate pools between 4–5 samples optimize assay benefits by limiting the false negative rate and maintaining efficiency [[17], [18], [19]]. Our data indicate that the practical utility of pooling at this scale has applications for systematic community surveillance, testing of low-risk populations, and in resource-limited settings. The utilization of high-throughput and broadly available lab instruments such as Roche’s MP96 extraction platform and HAMILTON liquid handler allow concrete scalability of this EUA-authorized four-way pooling assay. Borderline patients with low viral loads may be missed by the pooling process [20]. Notably however, a low positive PCR test result may correspond with the detection of SARS-CoV-2 RNA, but not necessarily infectious virus [21,22]. Specimens with viral loads of CT > 35 have been shown to not be routinely culturable in vitro [23]. Here, four-way pooling correctly identified SARS-CoV-2 in 94 % of samples, only missing low viral load specimens with CT >35. We report that 1:4 pooling of samples is associated with an acceptable 2 CT loss in analytical sensitivity. Pooling samples for SARS-CoV-2 molecular detection can be performed efficiently without sacrificing substantial accuracy or specificity. Optimized for lower positivity ratios (i.e. < 8%) [20,24,25], four-way pooling has considerable potential to accommodate increased demand for diagnostic testing of low-risk populations.

Funding

This work was supported by the Department of Laboratory Medicine and Pathology at the University of Washington Medical Center.

Declaration of Competing Interest

The authors declare no conflict of interest.
  14 in total

1.  Sample Pooling as a Strategy to Detect Community Transmission of SARS-CoV-2.

Authors:  Catherine A Hogan; Malaya K Sahoo; Benjamin A Pinsky
Journal:  JAMA       Date:  2020-05-19       Impact factor: 56.272

2.  The mathematical strategy that could transform coronavirus testing.

Authors:  Smriti Mallapaty
Journal:  Nature       Date:  2020-07       Impact factor: 49.962

3.  Evaluation of COVID-19 RT-qPCR Test in Multi sample Pools.

Authors:  Idan Yelin; Noga Aharony; Einat Shaer Tamar; Amir Argoetti; Esther Messer; Dina Berenbaum; Einat Shafran; Areen Kuzli; Nagham Gandali; Omer Shkedi; Tamar Hashimshony; Yael Mandel-Gutfreund; Michael Halberthal; Yuval Geffen; Moran Szwarcwort-Cohen; Roy Kishony
Journal:  Clin Infect Dis       Date:  2020-11-19       Impact factor: 9.079

4.  Virological assessment of hospitalized patients with COVID-2019.

Authors:  Roman Wölfel; Victor M Corman; Wolfgang Guggemos; Michael Seilmaier; Sabine Zange; Marcel A Müller; Daniela Niemeyer; Terry C Jones; Patrick Vollmar; Camilla Rothe; Michael Hoelscher; Tobias Bleicker; Sebastian Brünink; Julia Schneider; Rosina Ehmann; Katrin Zwirglmaier; Christian Drosten; Clemens Wendtner
Journal:  Nature       Date:  2020-04-01       Impact factor: 49.962

5.  Simulation of Pool Testing to Identify Patients With Coronavirus Disease 2019 Under Conditions of Limited Test Availability.

Authors:  Alhaji Cherif; Nadja Grobe; Xiaoling Wang; Peter Kotanko
Journal:  JAMA Netw Open       Date:  2020-06-01

6.  Validation of SARS-CoV-2 detection across multiple specimen types.

Authors:  Garrett A Perchetti; Arun K Nalla; Meei-Li Huang; Haiying Zhu; Yulun Wei; Larry Stensland; Michelle A Loprieno; Keith R Jerome; Alexander L Greninger
Journal:  J Clin Virol       Date:  2020-05-13       Impact factor: 3.168

7.  Assessment of Specimen Pooling to Conserve SARS CoV-2 Testing Resources.

Authors:  Baha Abdalhamid; Christopher R Bilder; Emily L McCutchen; Steven H Hinrichs; Scott A Koepsell; Peter C Iwen
Journal:  Am J Clin Pathol       Date:  2020-05-05       Impact factor: 2.493

8.  Efficient high-throughput SARS-CoV-2 testing to detect asymptomatic carriers.

Authors:  Noam Shental; Shlomia Levy; Vered Wuvshet; Shosh Skorniakov; Bar Shalem; Aner Ottolenghi; Yariv Greenshpan; Rachel Steinberg; Avishay Edri; Roni Gillis; Michal Goldhirsh; Khen Moscovici; Sinai Sachren; Lilach M Friedman; Lior Nesher; Yonat Shemer-Avni; Angel Porgador; Tomer Hertz
Journal:  Sci Adv       Date:  2020-09-11       Impact factor: 14.136

9.  Comparative Performance of SARS-CoV-2 Detection Assays Using Seven Different Primer-Probe Sets and One Assay Kit.

Authors:  Arun K Nalla; Amanda M Casto; Meei-Li W Huang; Garrett A Perchetti; Reigran Sampoleo; Lasata Shrestha; Yulun Wei; Haiying Zhu; Keith R Jerome; Alexander L Greninger
Journal:  J Clin Microbiol       Date:  2020-05-26       Impact factor: 5.948

10.  Pooling of samples for testing for SARS-CoV-2 in asymptomatic people.

Authors:  Stefan Lohse; Thorsten Pfuhl; Barbara Berkó-Göttel; Jürgen Rissland; Tobias Geißler; Barbara Gärtner; Sören L Becker; Sophie Schneitler; Sigrun Smola
Journal:  Lancet Infect Dis       Date:  2020-04-28       Impact factor: 71.421

View more
  17 in total

1.  Singleplex, multiplex and pooled sample real-time RT-PCR assays for detection of SARS-CoV-2 in an occupational medicine setting.

Authors:  Kimberly S Butler; Bryan D Carson; Joshua D Podlevsky; Cathryn M Mayes; Jessica M Rowland; DeAnna Campbell; J Bryce Ricken; George Wudiri; Jerilyn A Timlin
Journal:  Sci Rep       Date:  2022-10-22       Impact factor: 4.996

2.  Analysis of current SARS-CoV-2 infection in a large population of blood donors evidenced that RNAemia is rare in plasma.

Authors:  Daniel Gonçalves Chaves; Maria Clara Fernandes da Silva Malta; Luciana de Souza Madeira Ferreira Boy; Aretuza Miranda Barbosa; Cinthia Neves Fonseca; Dayanne Ellen de Lima Torres; Janaína Patterson Nogueira; Hélinse Medeiros Moreira; Flávia Cristine Martineli Loureiro; Jaciane Vargas de Freitas Silva; Maísa Aparecida Ribeiro; Júnia Guimarães Mourão Cioffi; Marina Lobato Martins
Journal:  Transfusion       Date:  2021-06-10       Impact factor: 3.337

3.  SARS-CoV-2 transmission risk from asymptomatic carriers: Results from a mass screening programme in Luxembourg.

Authors:  Paul Wilmes; Jacques Zimmer; Jasmin Schulz; Frank Glod; Lisa Veiber; Laurent Mombaerts; Bruno Rodrigues; Atte Aalto; Jessica Pastore; Chantal J Snoeck; Markus Ollert; Guy Fagherazzi; Joël Mossong; Jorge Goncalves; Alexander Skupin; Ulf Nehrbass
Journal:  Lancet Reg Health Eur       Date:  2021-02-27

4.  Versatile and flexible microfluidic qPCR test for high-throughput SARS-CoV-2 and cellular response detection in nasopharyngeal swab samples.

Authors:  Julien Fassy; Caroline Lacoux; Sylvie Leroy; Latifa Noussair; Sylvain Hubac; Aurélien Degoutte; Georges Vassaux; Vianney Leclercq; David Rouquié; Charles-Hugo Marquette; Martin Rottman; Patrick Touron; Antoinette Lemoine; Jean-Louis Herrmann; Pascal Barbry; Jean-Louis Nahon; Laure-Emmanuelle Zaragosi; Bernard Mari
Journal:  PLoS One       Date:  2021-04-14       Impact factor: 3.240

5.  Enhanced throughput of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) real-time RT-PCR panel by assay multiplexing and specimen pooling.

Authors:  Xiaoyan Lu; Senthilkumar K Sakthivel; Lijuan Wang; Brian Lynch; Sheila M Dollard
Journal:  J Virol Methods       Date:  2021-04-08       Impact factor: 2.014

6.  Swab pooling: A new method for large-scale RT-qPCR screening of SARS-CoV-2 avoiding sample dilution.

Authors:  Ana Paula Christoff; Giuliano Netto Flores Cruz; Aline Fernanda Rodrigues Sereia; Dellyana Rodrigues Boberg; Daniela Carolina de Bastiani; Laís Eiko Yamanaka; Gislaine Fongaro; Patrícia Hermes Stoco; Maria Luiza Bazzo; Edmundo Carlos Grisard; Camila Hernandes; Luiz Felipe Valter de Oliveira
Journal:  PLoS One       Date:  2021-02-04       Impact factor: 3.240

7.  Implementation of a pooled surveillance testing program for asymptomatic SARS-CoV-2 infections in K-12 schools and universities.

Authors:  Rachelle P Mendoza; Chongfeng Bi; Hui-Ting Cheng; Elmer Gabutan; Guillerre Jan Pagaspas; Nadia Khan; Helen Hoxie; Stephen Hanna; Kelly Holmes; Nicholas Gao; Raychel Lewis; Huaien Wang; Daniel Neumann; Angela Chan; Meril Takizawa; James Lowe; Xiao Chen; Brianna Kelly; Aneeza Asif; Keena Barnes; Nusrat Khan; Brandon May; Tasnim Chowdhury; Gabriella Pollonini; Nourelhoda Gouda; Chante Guy; Candice Gordon; Nana Ayoluwa; Elvin Colon; Noah Miller-Medzon; Shanique Jones; Rauful Hossain; Arabia Dodson; Meimei Weng; Miranda McGaskey; Ana Vasileva; Andrew E Lincoln; Robby Sikka; Anne L Wyllie; Ethan M Berke; Jenny Libien; Matthew Pincus; Prem K Premsrirut
Journal:  EClinicalMedicine       Date:  2021-07-17

8.  Pathology Informatics and Robotics Strategies for Improving Efficiency of COVID-19 Pooled Testing.

Authors:  Balaji Balasubramani; Kimberly J Newsom; Katherine A Martinez; Petr Starostik; Michael Clare-Salzler; Srikar Chamala
Journal:  Acad Pathol       Date:  2021-06-15

9.  Can't Work From Home: Pooled Nucleic Acid Testing of Laboratory Workers During the COVID-19 Pandemic.

Authors:  Stephen A Rawlings; Brianna Scott; Laura Layman; Pramod Naranatt; Roy Heltsley; Caroline Ignacio; Magali Porrachia; Sara Gianella; Davey Smith; Antoine Chaillon
Journal:  Open Forum Infect Dis       Date:  2021-03-15       Impact factor: 3.835

10.  Evaluation of seven commercial RT-PCR kits for COVID-19 testing in pooled clinical specimens.

Authors:  Atul Garg; Ujjala Ghoshal; Sangram S Patel; D V Singh; Akshay K Arya; Shruthi Vasanth; Ankita Pandey; Nikki Srivastava
Journal:  J Med Virol       Date:  2020-12-17       Impact factor: 20.693

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.