Your browser doesn't support javascript.
loading
ApharSeq: An Extraction-free Early-Pooling Protocol for Massively Multiplexed SARS-CoV-2 Detection
Alon Chappleboim; Daphna Joseph-Strauss; Ayelet Rahat; Israa Sharkia; 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.
Affiliation
  • Alon Chappleboim; Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel; Rachel and Selim Benin School of Computer Science, Hebrew Univer
  • Daphna Joseph-Strauss; Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel; Rachel and Selim Benin School of Computer Science, Hebrew Univer
  • Ayelet Rahat; Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel; Rachel and Selim Benin School of Computer Science, Hebrew Univer
  • Israa Sharkia; Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel; Rachel and Selim Benin School of Computer Science, Hebrew Univer
  • Miriam Adam; Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel; Edmond and Lily Safra Center for Brain Sciences, H
  • Daniel Kitsberg; Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel; Edmond and Lily Safra Center for Brain Sciences, H
  • Gavriel Fialkoff; Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel; Rachel and Selim Benin School of Computer Science, Hebrew Univer
  • Matan Lotem; Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel; Rachel and Selim Benin School of Computer Science, Hebrew Univer
  • Omer Gershon; Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel; Rachel and Selim Benin School of Computer Science, Hebrew Univer
  • Anna-Kristina Schmidtner; Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel; Edmond and Lily Safra Center for Brain Sciences, H
  • Esther Oiknine-Djian; Hadassah - Hebrew University Medical Centre, Jerusalem 9112001, Israel; The Lautenberg Centre for Immunology and Cancer Research, IMRIC, Faculty of Medicine, Th
  • Agnes Klochendler; Department of Developmental Biology and Cancer Research, IMRIC, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112001, Israel
  • Ronen Sadeh; Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel; Rachel and Selim Benin School of Computer Science, Hebrew Univer
  • Yuval Dor; Department of Developmental Biology and Cancer Research, IMRIC, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112001, Israel
  • Dana Wolf; Hadassah - Hebrew University Medical Centre, Jerusalem 9112001, Israel; The Lautenberg Centre for Immunology and Cancer Research, IMRIC, Faculty of Medicine, Th
  • Naomi Habib; Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel; Edmond and Lily Safra Center for Brain Sciences, H
  • Nir Friedman; Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel; Rachel and Selim Benin School of Computer Science, Hebrew Univer
Preprint in En | PREPRINT-MEDRXIV | ID: ppmedrxiv-20170746
ABSTRACT
The global SARS-CoV-2 pandemic created a dire 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. While automation and improved logistics increased the capacity of these tests, they cannot exceed the lower bound dictated by one extraction and one RT-PCR reaction per sample. Multiplexed next generation sequencing (NGS) assays provide a dramatic increase in throughput, and hold the promise of richer information including viral strains, host immune response, and multiple pathogens. Here, we establish a significant improvement of existing RNA-seq detection protocols. Our workflow, ApharSeq, 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 non-enzymatic step is performed before pooling hundreds of barcoded samples for subsequent steps and further analysis. We characterize the quantitative aspects of the assay by applying ApharSeq to more than 500 clinical samples in a robotic workflow. The assay results are linear, and the empirical limit of detection is found to be Ct 33 (roughly 1000 copies/ml). A single ApharSeq test currently costs under 1.2$, and we estimate costs can further go down 3-10 fold. Similarly, we estimate a labor reduction of 10-100 fold, automated liquid handling of 5-10 fold, and reagent requirement reduction of 20-1000 fold compared to existing testing methods.
License
cc_by_nc_nd
Full text: 1 Collection: 09-preprints Database: PREPRINT-MEDRXIV Type of study: Prognostic_studies Language: En Year: 2020 Document type: Preprint
Full text: 1 Collection: 09-preprints Database: PREPRINT-MEDRXIV Type of study: Prognostic_studies Language: En Year: 2020 Document type: Preprint