BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has revealed the global public health importance of robust diagnostic testing. To overcome the challenge of nucleic acid (NA) extraction and testing kit availability, an efficient method is urgently needed. OBJECTIVES: To establish an efficient, time and resource-saving and cost-effective methods, and to propose an ad hoc pooling approach for mass screening of SARS-CoV-2. METHODS: We evaluated pooling approach on both direct clinical and NA samples. The standard reverse transcriptase polymerase chain reaction (RT-PCR) test of the SARS CoV-2 was employed targeting the nucleocapsid (N) and open reading frame (ORF1ab) genomic region of the virus. The experimental pools were created using SARS CoV-2 positive clinical samples and extracted RNA spiked with up to 9 negative samples. For the direct clinical samples viral NA was extracted from each pool to a final extraction volume of 200μL, and subsequently both samples tested using the SARS CoV-2 RT-PCR assay. RESULTS: We found that a single positive sample can be amplified and detected in pools of up to 7 samples depending on the cycle threshold (Ct) value of the original sample, corresponding to high, and low SARS CoV-2 viral copies per reaction. However, to minimize false negativity of the assay with pooling strategies and with unknown false negativity rate of the assay under validation, we recommend pooling of 4/5 in 1 using the standard protocols of the assay, reagents and equipment. The predictive algorithm indicated a pooling ratio of 5 in 1 was expected to retain accuracy of the test irrespective of the Ct value samples spiked, and result in a 137% increase in testing efficiency. CONCLUSIONS: The approaches showed its concept in easily customized and resource-saving manner and would allow expanding of current screening capacities and enable the expansion of detection in the community. We recommend clinical sample pooling of 4 or 5 in 1. However, we don't advise pooling of clinical samples when disease prevalence is greater than 7%; particularly when sample size is large.
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has revealed the global public health importance of robust diagnostic testing. To overcome the challenge of nucleic acid (NA) extraction and testing kit availability, an efficient method is urgently needed. OBJECTIVES: To establish an efficient, time and resource-saving and cost-effective methods, and to propose an ad hoc pooling approach for mass screening of SARS-CoV-2. METHODS: We evaluated pooling approach on both direct clinical and NA samples. The standard reverse transcriptase polymerase chain reaction (RT-PCR) test of the SARS CoV-2 was employed targeting the nucleocapsid (N) and open reading frame (ORF1ab) genomic region of the virus. The experimental pools were created using SARS CoV-2 positive clinical samples and extracted RNA spiked with up to 9 negative samples. For the direct clinical samples viral NA was extracted from each pool to a final extraction volume of 200μL, and subsequently both samples tested using the SARS CoV-2 RT-PCR assay. RESULTS: We found that a single positive sample can be amplified and detected in pools of up to 7 samples depending on the cycle threshold (Ct) value of the original sample, corresponding to high, and low SARS CoV-2 viral copies per reaction. However, to minimize false negativity of the assay with pooling strategies and with unknown false negativity rate of the assay under validation, we recommend pooling of 4/5 in 1 using the standard protocols of the assay, reagents and equipment. The predictive algorithm indicated a pooling ratio of 5 in 1 was expected to retain accuracy of the test irrespective of the Ct value samples spiked, and result in a 137% increase in testing efficiency. CONCLUSIONS: The approaches showed its concept in easily customized and resource-saving manner and would allow expanding of current screening capacities and enable the expansion of detection in the community. We recommend clinical sample pooling of 4 or 5 in 1. However, we don't advise pooling of clinical samples when disease prevalence is greater than 7%; particularly when sample size is large.
Authors: Marie Wunsch; Dominik Aschemeier; Eva Heger; Denise Ehrentraut; Jan Krüger; Martin Hufbauer; Adnan S Syed; Gibran Horemheb-Rubio; Felix Dewald; Irina Fish; Maike Schlotz; Henning Gruell; Max Augustin; Clara Lehmann; Rolf Kaiser; Elena Knops; Steffi Silling; Florian Klein Journal: J Clin Virol Date: 2021-10-28 Impact factor: 3.168
Authors: Terren Chang; Jolene M Draper; Anouk Van den Bout; Ellen Kephart; Hannah Maul-Newby; Yvonne Vasquez; Jason Woodbury; Savanna Randi; Martina Pedersen; Maeve Nave; Scott La; Natalie Gallagher; Molly M McCabe; Namrita Dhillon; Isabel Bjork; Michael Luttrell; Frank Dang; John B MacMillan; Ralph Green; Elizabeth Miller; Auston M Kilpatrick; Olena Vaske; Michael D Stone; Jeremy R Sanford Journal: PLoS One Date: 2021-12-17 Impact factor: 3.752
Authors: Eva Rajh; Tina Šket; Arne Praznik; Petra Sušjan; Alenka Šmid; Dunja Urbančič; Irena Mlinarič-Raščan; Polona Kogovšek; Tina Demšar; Mojca Milavec; Katarina Prosenc Trilar; Žiga Jensterle; Mihaela Zidarn; Viktorija Tomič; Gabriele Turel; Tatjana Lejko-Zupanc; Roman Jerala; Mojca Benčina Journal: Molecules Date: 2021-10-31 Impact factor: 4.411
Authors: Anika Kästner; Petra Lücker; Martina Sombetzki; Manja Ehmke; Nicole Koslowski; Swantje Mittmann; Arne Hannich; Antje Schwarz; Kristian Meinck; Lena Schmeyers; Katrin Schmidt; Emil C Reisinger; Wolfgang Hoffmann Journal: PLoS One Date: 2022-09-13 Impact factor: 3.752
Authors: Vibol Iem; Phonepadith Xangsayarath; Phonenaly Chittamany; Sakhone Suthepmany; Souvimone Siphanthong; Phimpha Paboriboune; Silaphet Somphavong; Kontogianni Konstantina; Jahangir A M Khan; Thomas Edwards; Tom Wingfield; Jacob Creswell; Jose Dominguez; Luis E Cuevas Journal: PLoS One Date: 2022-09-29 Impact factor: 3.752