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Protocol Name
SARS-CoV-2 detection with ApharSeq
DOI:10.17504/protocols.io.bjgukjww RRID Copied  
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Daphna Strauss, Ayelet Rahat, Israa Sharkia, Alon Chappleboim, Miriam Adam, Daniel Kitsberg, Gavriel Fialkoff, Matan Lotem, Omer Gershon, Anna-Kristina Schmidtner, Esther Oiknine-Djian, Agnes Klochendler, Ronen Sadeh, Yuval Dor, Dana Wolf, Naomi Habib, Nir Friedman 2020. SARS-CoV-2 detection with ApharSeq. protocols.io dx.doi.org/10.17504/protocols.io.bjgukjww
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Protocol Information

URL: https://dx.doi.org/10.17504/protocols.io.bjgukjww

Authors: Daphna Strauss, Ayelet Rahat, Israa Sharkia, Alon Chappleboim, Miriam Adam, Daniel Kitsberg, Gavriel Fialkoff, Matan Lotem, Omer Gershon, Anna-Kristina Schmidtner, Esther Oiknine-Djian, Agnes Klochendler, Ronen Sadeh, Yuval Dor, Dana Wolf, Naomi Habib, Nir Friedman

Summary: The global SARS-CoV-2 pandemic led to a steep increase in the need for viral detection tests worldwide. Most current tests for SARS-CoV-2 are based on RNA extraction followed by quantitative reverse-transcription PCR assays that involve a separate RNA extraction and qPCR reaction for each sample with a fixed cost and reaction time. While automation and improved logistics can increase the capacity of these tests, they cannot exceed this lower bound dictated by one extraction and reaction per sample. Multiplexed next generation sequencing (NGS) assays provide a dramatic increase in throughput, and hold the promise of richer information on viral strains and host immune response.Here, we establish a significant improvement of existing RNA-seq detection protocols. Our workflow, ApharSeq (Amplicon Pooling by Hybridization And RNA-Seq), includes a fast and cheap RNA capture step, that is coupled to barcoding of individual samples, followed by sample-pooling prior to the reverse transcription, PCR and massively parallel sequencing. Thus, only one step is performed before pooling hundreds of barcoded samples for subsequent steps and further analysis. Considering these improvements, our proposed workflow is estimated to reduce costs by 10-50 fold, labor by 5-100 fold, automated liquid handling by 5-10 fold, and reagent requirements by 100-1000 fold compared to existing methods.

Affiliations: Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel;Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel, Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel;Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel, Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel;Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel, Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel;Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel, Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel;Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190401, Israel, Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel;Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190401, Israel, Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel;Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel, Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel;Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel, Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel;Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel, Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel;Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190401, Israel, The Lautenberg Centre for Immunology and Cancer Research, IMRIC, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112001, Israel; Hadassah - Hebrew University Medical Centre, Jerusalem 9112001, Israel, Department of Developmental Biology and Cancer Research, IMRIC, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112001, Israel, Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel;Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel, Department of Developmental Biology and Cancer Research, IMRIC, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112001, Israel, The Lautenberg Centre for Immunology and Cancer Research, IMRIC, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112001, Israel; Hadassah - Hebrew University Medical Centre, Jerusalem 9112001, Israel, Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel;Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190401, Israel, Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel;Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel

Version: 2

Publication Date: 2020

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Source: Protocols.io