1
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Booeshaghi AS, Min KH(J, Gehring J, Pachter L. Quantifying orthogonal barcodes for sequence census assays. BIOINFORMATICS ADVANCES 2023; 4:vbad181. [PMID: 38213823 PMCID: PMC10783946 DOI: 10.1093/bioadv/vbad181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 10/02/2023] [Accepted: 12/19/2023] [Indexed: 01/13/2024]
Abstract
Summary Barcode-based sequence census assays utilize custom or random oligonucloetide sequences to label various biological features, such as cell-surface proteins or CRISPR perturbations. These assays all rely on barcode quantification, a task that is complicated by barcode design and technical noise. We introduce a modular approach to quantifying barcodes that achieves speed and memory improvements over existing tools. We also introduce a set of quality control metrics, and accompanying tool, for validating barcode designs. Availability and implementation https://github.com/pachterlab/kb_python, https://github.com/pachterlab/qcbc.
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Affiliation(s)
- A Sina Booeshaghi
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, United States
| | - Kyung Hoi (Joseph) Min
- Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
| | - Jase Gehring
- Arcadia Science, Berkeley, CA 94702, United States
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, United States
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, United States
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2
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Choi JH, Jang W, Lim YJ, Mun SJ, Bong KW. Highly Flexible Deep-Learning-Based Automatic Analysis for Graphically Encoded Hydrogel Microparticles. ACS Sens 2023; 8:3158-3166. [PMID: 37489756 DOI: 10.1021/acssensors.3c00857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
Graphically encoded hydrogel microparticle (HMP)-based bioassay is a diagnostic tool characterized by exceptional multiplex detectability and robust sensitivity and specificity. Specifically, deep learning enables highly fast and accurate analyses of HMPs with diverse graphical codes. However, previous related studies have found the use of plain particles as data to be disadvantageous for accurate analyses of HMPs loaded with functional nanomaterials. Furthermore, the manual data annotation method used in existing approaches is highly labor-intensive and time-consuming. In this study, we present an efficient deep-learning-based analysis of encoded HMPs with diverse graphical codes and functional nanomaterials, utilizing the auto-annotation and synthetic data mixing methods for model training. The auto-annotation enhanced the throughput of dataset preparation up to 0.11 s/image. Using synthetic data mixing, a mean average precision of 0.88 was achieved in the analysis of encoded HMPs with magnetic nanoparticles, representing an approximately twofold improvement over the standard method. To evaluate the practical applicability of the proposed automatic analysis strategy, a single-image analysis was performed after the triplex immunoassay for the preeclampsia-related protein biomarkers. Finally, we accomplished a processing throughput of 0.353 s per sample for analyzing the result image.
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Affiliation(s)
- Jun Hee Choi
- Department of Chemical and Biological Engineering, Korea University, Seoul 02841, South Korea
| | - Wookyoung Jang
- Department of Chemical and Biological Engineering, Korea University, Seoul 02841, South Korea
| | - Yong Jun Lim
- Department of Chemical and Biological Engineering, Korea University, Seoul 02841, South Korea
| | - Seok Joon Mun
- Department of Chemical and Biological Engineering, Korea University, Seoul 02841, South Korea
| | - Ki Wan Bong
- Department of Chemical and Biological Engineering, Korea University, Seoul 02841, South Korea
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3
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Kang H, Allison S, Spangenberg A, Carr T, Sprissler R, Halonen M, Cusanovich DA. Evaluation of Swab-Seq as a scalable, sensitive assay for community surveillance of SARS-CoV-2 infection. Sci Rep 2022; 12:3047. [PMID: 35197492 PMCID: PMC8866503 DOI: 10.1038/s41598-022-06901-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 02/08/2022] [Indexed: 11/25/2022] Open
Abstract
The ongoing SARS-CoV-2 pandemic and subsequent demand for viral testing has led to issues in scaling diagnostic lab efforts and in securing basic supplies for collection and processing of samples. This has motivated efforts by the scientific community to establish improved protocols that are more scalable, less resource intensive, and less expensive. One such developmental effort has resulted in an assay called "Swab-Seq", so named because it was originally developed to work with dry nasal swab samples. The existing gold standard test consists of RNA extracted from a nasopharyngeal (NP) swab that is subjected to quantitative reverse transcription polymerase chain reaction (qRT-PCR). Swab-Seq adapts this method to a next-generation sequencing readout. By pairing this modification with extraction-free sampling techniques, Swab-Seq achieves high scalability, low cost per sample, and a reasonable turnaround time. We evaluated the effectiveness of this assay in a community surveillance setting by testing samples collected from both symptomatic and asymptomatic individuals using the traditional NP swab. In addition, we evaluated extraction-free sampling techniques (both saliva and saline mouth gargle samples). We found the assay to be as clinically sensitive as the qRT-PCR assay, adaptable to multiple sample types, and able to easily accommodate hundreds of samples at a time. We thus provide independent validation of Swab-Seq and extend its utility regarding sample type and sample stability. Assays of this type greatly expand the possibility of routine, noninvasive, repeated testing of asymptomatic individuals suitable for current and potential future needs.
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Affiliation(s)
- HyunJin Kang
- Asthma and Airway Disease Research Center (A2DRC), University of Arizona, Tucson, AZ, USA
| | - Sheilah Allison
- Asthma and Airway Disease Research Center (A2DRC), University of Arizona, Tucson, AZ, USA
| | - Amber Spangenberg
- Asthma and Airway Disease Research Center (A2DRC), University of Arizona, Tucson, AZ, USA
| | - Tara Carr
- Asthma and Airway Disease Research Center (A2DRC), University of Arizona, Tucson, AZ, USA
| | - Ryan Sprissler
- Center for Applied Genetics and Genomic Medicine, University of Arizona, Tucson, AZ, USA
- University of Arizona Genetics Core, University of Arizona, Tucson, AZ, USA
| | - Marilyn Halonen
- Asthma and Airway Disease Research Center (A2DRC), University of Arizona, Tucson, AZ, USA
| | - Darren A Cusanovich
- Asthma and Airway Disease Research Center (A2DRC), University of Arizona, Tucson, AZ, USA.
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA.
- BIO5 Institute, University of Arizona, Tucson, AZ, USA.
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4
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Ludwig KU, Schmithausen RM, Li D, Jacobs ML, Hollstein R, Blumenstock K, Liebing J, Słabicki M, Ben-Shmuel A, Israeli O, Weiss S, Ebert TS, Paran N, Rüdiger W, Wilbring G, Feldman D, Lippke B, Ishorst N, Hochfeld LM, Beins EC, Kaltheuner IH, Schmitz M, Wöhler A, Döhla M, Sib E, Jentzsch M, Borrajo JD, Strecker J, Reinhardt J, Cleary B, Geyer M, Hölzel M, Macrae R, Nöthen MM, Hoffmann P, Exner M, Regev A, Zhang F, Schmid-Burgk JL. LAMP-Seq enables sensitive, multiplexed COVID-19 diagnostics using molecular barcoding. Nat Biotechnol 2021; 39:1556-1562. [PMID: 34188222 PMCID: PMC8678193 DOI: 10.1038/s41587-021-00966-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 05/26/2021] [Indexed: 02/06/2023]
Abstract
Frequent testing of large population groups combined with contact tracing and isolation measures will be crucial for containing Coronavirus Disease 2019 outbreaks. Here we present LAMP-Seq, a modified, highly scalable reverse transcription loop-mediated isothermal amplification (RT-LAMP) method. Unpurified biosamples are barcoded and amplified in a single heat step, and pooled products are analyzed en masse by sequencing. Using commercial reagents, LAMP-Seq has a limit of detection of ~2.2 molecules per µl at 95% confidence and near-perfect specificity for severe acute respiratory syndrome coronavirus 2 given its sequence readout. Clinical validation of an open-source protocol with 676 swab samples, 98 of which were deemed positive by standard RT-qPCR, demonstrated 100% sensitivity in individuals with cycle threshold values of up to 33 and a specificity of 99.7%, at a very low material cost. With a time-to-result of fewer than 24 h, low cost and little new infrastructure requirement, LAMP-Seq can be readily deployed for frequent testing as part of an integrated public health surveillance program.
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Affiliation(s)
- Kerstin U. Ludwig
- Institute of Human Genetics, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - Ricarda M. Schmithausen
- Institute of Hygiene and Public Health, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - David Li
- Broad Institute of MIT and Harvard, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Max L. Jacobs
- Institute of Clinical Chemistry and Clinical Pharmacology, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany,Center for Molecular Biology of Heidelberg University (ZMBH), Heidelberg, Germany
| | - Ronja Hollstein
- Institute of Human Genetics, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - Katja Blumenstock
- Institute of Clinical Chemistry and Clinical Pharmacology, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - Jana Liebing
- Institute of Experimental Oncology, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - Mikołaj Słabicki
- Broad Institute of MIT and Harvard, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.,Division of Translational Medical Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany
| | - Amir Ben-Shmuel
- Department of Infectious Diseases, Israel Institute for Biological Research, Ness Ziona, Israel
| | - Ofir Israeli
- Department of Biochemistry and Molecular Genetics, Israel Institute for Biological Research, Ness Ziona, Israel
| | - Shay Weiss
- Department of Infectious Diseases, Israel Institute for Biological Research, Ness Ziona, Israel
| | - Thomas S. Ebert
- Institute of Clinical Chemistry and Clinical Pharmacology, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - Nir Paran
- Department of Infectious Diseases, Israel Institute for Biological Research, Ness Ziona, Israel
| | - Wibke Rüdiger
- Institute of Clinical Chemistry and Clinical Pharmacology, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - Gero Wilbring
- Institute of Hygiene and Public Health, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - David Feldman
- Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Bärbel Lippke
- Institute of Human Genetics, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - Nina Ishorst
- Institute of Human Genetics, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany.,Institute of Anatomy, Division of Neuroanatomy, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - Lara M. Hochfeld
- Institute of Human Genetics, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - Eva C. Beins
- Institute of Human Genetics, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - Ines H. Kaltheuner
- Institute of Structural Biology, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - Maximilian Schmitz
- Institute of Structural Biology, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - Aliona Wöhler
- Department of General, Visceral and Thoracic Surgery, Bundeswehr Central Hospital Koblenz, Koblenz, Germany
| | - Manuel Döhla
- Institute of Hygiene and Public Health, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany.,Department of Microbiology and Hospital Hygiene, Bundeswehr Central Hospital Koblenz, Koblenz, Germany
| | - Esther Sib
- Institute of Hygiene and Public Health, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - Marius Jentzsch
- Institute of Clinical Chemistry and Clinical Pharmacology, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - Jacob D. Borrajo
- Broad Institute of MIT and Harvard, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jonathan Strecker
- Broad Institute of MIT and Harvard, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Julia Reinhardt
- Institute of Experimental Oncology, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - Brian Cleary
- Broad Institute of MIT and Harvard, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Matthias Geyer
- Institute of Structural Biology, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - Michael Hölzel
- Institute of Experimental Oncology, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - Rhiannon Macrae
- Broad Institute of MIT and Harvard, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Markus M. Nöthen
- Institute of Human Genetics, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - Per Hoffmann
- Institute of Human Genetics, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany.,Genomics Research Group, Department of Biomedicine, University of Basel, Switzerland
| | - Martin Exner
- Institute of Hygiene and Public Health, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Klarman Cell Observatory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Howard Hughes Medical Institute, Cambridge, MA 02139, USA.,Current address: Genentech, 1 DNA Way, South San Francisco, CA, USA
| | - Feng Zhang
- Broad Institute of MIT and Harvard, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Howard Hughes Medical Institute, Cambridge, MA 02139, USA
| | - Jonathan L. Schmid-Burgk
- Broad Institute of MIT and Harvard, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Institute of Clinical Chemistry and Clinical Pharmacology, University of Bonn and University Hospital Bonn, 53127 Bonn, Germany,Correspondence to:
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5
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Chappleboim A, Joseph-Strauss D, Rahat A, Sharkia I, Adam M, Kitsberg D, Fialkoff G, Lotem M, Gershon O, Schmidtner AK, Oiknine-Djian E, Klochendler A, Sadeh R, Dor Y, Wolf D, Habib N, Friedman N. Early sample tagging and pooling enables simultaneous SARS-CoV-2 detection and variant sequencing. Sci Transl Med 2021; 13:eabj2266. [PMID: 34591660 PMCID: PMC9928115 DOI: 10.1126/scitranslmed.abj2266] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Most severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) diagnostic tests have relied on RNA extraction followed by reverse transcription quantitative polymerase chain reaction (RT-qPCR) assays. Whereas automation improved logistics and different pooling strategies increased testing capacity, highly multiplexed next-generation sequencing (NGS) diagnostics remain a largely untapped resource. NGS tests have the potential to markedly increase throughput while providing crucial SARS-CoV-2 variant information. Current NGS-based detection and genotyping assays for SARS-CoV-2 are costly, mostly due to parallel sample processing through multiple steps. Here, we have established ApharSeq, in which samples are barcoded in the lysis buffer and pooled before reverse transcription. We validated this assay by applying ApharSeq to more than 500 clinical samples from the Clinical Virology Laboratory at Hadassah hospital in a robotic workflow. The assay was linear across five orders of magnitude, and the limit of detection was Ct 33 (~1000 copies/ml, 95% sensitivity) with >99.5% specificity. ApharSeq provided targeted high-confidence genotype information due to unique molecular identifiers incorporated into this method. Because of early pooling, we were able to estimate a 10- to 100-fold reduction in labor, automated liquid handling, and reagent requirements in high-throughput settings compared to current testing methods. The protocol can be tailored to assay other host or pathogen RNA targets simultaneously. These results suggest that ApharSeq can be a promising tool for current and future mass diagnostic challenges.
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Affiliation(s)
- Alon Chappleboim
- Alexander 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
| | - Daphna Joseph-Strauss
- Alexander 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
| | - Ayelet Rahat
- Alexander 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
| | - Israa Sharkia
- Alexander 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
| | - Miriam Adam
- Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Daniel Kitsberg
- Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Gavriel Fialkoff
- Alexander 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
| | - Matan Lotem
- Alexander 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
| | - Omer Gershon
- Alexander 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
| | - Anna-Kristina Schmidtner
- Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Esther Oiknine-Djian
- Hadassah Hebrew University Medical Center, Jerusalem 9112001, Israel.,Lautenberg Centre for Immunology and Cancer Research, IMRIC, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel
| | - Agnes Klochendler
- Department of Developmental Biology and Cancer Research, IMRIC, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel
| | - Ronen Sadeh
- Alexander 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
| | - Yuval Dor
- Department of Developmental Biology and Cancer Research, IMRIC, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel
| | - Dana Wolf
- Hadassah Hebrew University Medical Center, Jerusalem 9112001, Israel.,Lautenberg Centre for Immunology and Cancer Research, IMRIC, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel
| | - Naomi Habib
- Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Nir Friedman
- Alexander 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
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6
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Bloom JS, Sathe L, Munugala C, Jones EM, Gasperini M, Lubock NB, Yarza F, Thompson EM, Kovary KM, Park J, Marquette D, Kay S, Lucas M, Love T, Sina Booeshaghi A, Brandenberg OF, Guo L, Boocock J, Hochman M, Simpkins SW, Lin I, LaPierre N, Hong D, Zhang Y, Oland G, Choe BJ, Chandrasekaran S, Hilt EE, Butte MJ, Damoiseaux R, Kravit C, Cooper AR, Yin Y, Pachter L, Garner OB, Flint J, Eskin E, Luo C, Kosuri S, Kruglyak L, Arboleda VA. Massively scaled-up testing for SARS-CoV-2 RNA via next-generation sequencing of pooled and barcoded nasal and saliva samples. Nat Biomed Eng 2021; 5:657-665. [PMID: 34211145 PMCID: PMC10810734 DOI: 10.1038/s41551-021-00754-5] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 05/20/2021] [Indexed: 02/02/2023]
Abstract
Frequent and widespread testing of members of the population who are asymptomatic for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for the mitigation of the transmission of the virus. Despite the recent increases in testing capacity, tests based on quantitative polymerase chain reaction (qPCR) assays cannot be easily deployed at the scale required for population-wide screening. Here, we show that next-generation sequencing of pooled samples tagged with sample-specific molecular barcodes enables the testing of thousands of nasal or saliva samples for SARS-CoV-2 RNA in a single run without the need for RNA extraction. The assay, which we named SwabSeq, incorporates a synthetic RNA standard that facilitates end-point quantification and the calling of true negatives, and that reduces the requirements for automation, purification and sample-to-sample normalization. We used SwabSeq to perform 80,000 tests, with an analytical sensitivity and specificity comparable to or better than traditional qPCR tests, in less than two months with turnaround times of less than 24 h. SwabSeq could be rapidly adapted for the detection of other pathogens.
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Affiliation(s)
- Joshua S Bloom
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
- Howard Hughes Medical Institute, Chevy Chase, MD, USA.
- Octant Inc., Emeryville, CA, USA.
| | - Laila Sathe
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Chetan Munugala
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | | | | | | | | | | | | | | | - Dawn Marquette
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Stephania Kay
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Mark Lucas
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - TreQuan Love
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | | | - Oliver F Brandenberg
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Department of Biological Chemistry, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Longhua Guo
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Department of Biological Chemistry, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - James Boocock
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Department of Biological Chemistry, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | | | | | - Isabella Lin
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Nathan LaPierre
- Department of Computer Science, Samueli School of Engineering, UCLA, Los Angeles, CA, USA
| | - Duke Hong
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Yi Zhang
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Gabriel Oland
- Department of Surgery, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Bianca Judy Choe
- Department of Emergency Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Sukantha Chandrasekaran
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Evann E Hilt
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Manish J Butte
- Department of Pediatrics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Department of Microbiology, Immunology & Molecular Genetics, UCLA, Los Angeles, CA, USA
| | - Robert Damoiseaux
- California NanoSystems Institute, UCLA, Los Angeles, CA, USA
- Department of Bioengineering, Samueli School of Engineering, UCLA, Los Angeles, CA, USA
- Department of Medical and Molecular Pharmacology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Clifford Kravit
- Department of Digital Technology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | | | - Yi Yin
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Lior Pachter
- Division of Biology and Bioengineering, Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA, USA
| | - Omai B Garner
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Jonathan Flint
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Eleazar Eskin
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Department of Computer Science, Samueli School of Engineering, UCLA, Los Angeles, CA, USA
| | - Chongyuan Luo
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Sriram Kosuri
- Octant Inc., Emeryville, CA, USA.
- Department of Chemistry and Biochemistry, UCLA, Los Angeles, CA, USA.
| | - Leonid Kruglyak
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
- Howard Hughes Medical Institute, Chevy Chase, MD, USA.
- Department of Biological Chemistry, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
| | - Valerie A Arboleda
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
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Bloom JS, Sathe L, Munugala C, Jones EM, Gasperini M, Lubock NB, Yarza F, Thompson EM, Kovary KM, Park J, Marquette D, Kay S, Lucas M, Love T, Booeshaghi AS, Brandenberg OF, Guo L, Boocock J, Hochman M, Simpkins SW, Lin I, LaPierre N, Hong D, Zhang Y, Oland G, Choe BJ, Chandrasekaran S, Hilt EE, Butte MJ, Damoiseaux R, Kravit C, Cooper AR, Yin Y, Pachter L, Garner OB, Flint J, Eskin E, Luo C, Kosuri S, Kruglyak L, Arboleda VA. Swab-Seq: A high-throughput platform for massively scaled up SARS-CoV-2 testing. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021. [PMID: 32909008 PMCID: PMC7480060 DOI: 10.1101/2020.08.04.20167874] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is due to the high rates of transmission by individuals who are asymptomatic at the time of transmission1,2. Frequent, widespread testing of the asymptomatic population for SARS-CoV-2 is essential to suppress viral transmission. Despite increases in testing capacity, multiple challenges remain in deploying traditional reverse transcription and quantitative PCR (RT-qPCR) tests at the scale required for population screening of asymptomatic individuals. We have developed SwabSeq, a high-throughput testing platform for SARS-CoV-2 that uses next-generation sequencing as a readout. SwabSeq employs sample-specific molecular barcodes to enable thousands of samples to be combined and simultaneously analyzed for the presence or absence of SARS-CoV-2 in a single run. Importantly, SwabSeq incorporates an in vitro RNA standard that mimics the viral amplicon, but can be distinguished by sequencing. This standard allows for end-point rather than quantitative PCR, improves quantitation, reduces requirements for automation and sample-to-sample normalization, enables purification-free detection, and gives better ability to call true negatives. After setting up SwabSeq in a high-complexity CLIA laboratory, we performed more than 80,000 tests for COVID-19 in less than two months, confirming in a real world setting that SwabSeq inexpensively delivers highly sensitive and specific results at scale, with a turn-around of less than 24 hours. Our clinical laboratory uses SwabSeq to test both nasal and saliva samples without RNA extraction, while maintaining analytical sensitivity comparable to or better than traditional RT-qPCR tests. Moving forward, SwabSeq can rapidly scale up testing to mitigate devastating spread of novel pathogens.
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Affiliation(s)
- Joshua S Bloom
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Howard Hughes Medical Institute, HHMI.,Octant, Inc
| | - Laila Sathe
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA
| | - Chetan Munugala
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Howard Hughes Medical Institute, HHMI
| | | | | | | | | | | | | | | | - Dawn Marquette
- Department of Computational Medicine, David Geffen School of Medicine, UCLA
| | - Stephania Kay
- Department of Computational Medicine, David Geffen School of Medicine, UCLA
| | - Mark Lucas
- Department of Computational Medicine, David Geffen School of Medicine, UCLA
| | - TreQuan Love
- Department of Computational Medicine, David Geffen School of Medicine, UCLA
| | | | - Oliver F Brandenberg
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Howard Hughes Medical Institute, HHMI.,Department of Biological Chemistry, David Geffen School of Medicine, UCLA
| | - Longhua Guo
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Howard Hughes Medical Institute, HHMI.,Department of Biological Chemistry, David Geffen School of Medicine, UCLA
| | - James Boocock
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Howard Hughes Medical Institute, HHMI.,Department of Biological Chemistry, David Geffen School of Medicine, UCLA
| | | | | | - Isabella Lin
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA
| | - Nathan LaPierre
- Department of Computer Science, Samueli School of Engineering, UCLA
| | - Duke Hong
- Department of Computational Medicine, David Geffen School of Medicine, UCLA
| | - Yi Zhang
- Department of Human Genetics, David Geffen School of Medicine, UCLA
| | - Gabriel Oland
- Department of Surgery, David Geffen School of Medicine, UCLA
| | - Bianca Judy Choe
- Department of Emergency Medicine, David Geffen School of Medicine, UCLA
| | | | - Evann E Hilt
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA
| | - Manish J Butte
- Department of Pediatrics, David Geffen School of Medicine, UCLA.,Department of Microbiology, Immunology & Molecular Genetics, David Geffen School of Medicine, UCLA
| | - Robert Damoiseaux
- California NanoSystems Institute, UCLA.,Department of Bioengineering, Samueli School of Engineering, UCLA.,David Geffen School of Medicine, Research Information Technology
| | - Clifford Kravit
- David Geffen School of Medicine, Research Information Technology
| | | | - Yi Yin
- Department of Human Genetics, David Geffen School of Medicine, UCLA
| | - Lior Pachter
- Division of Biology and Bioengineering & Department of Computing and Mathematical Sciences, Caltech
| | - Omai B Garner
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA
| | - Jonathan Flint
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, UCLA
| | - Eleazar Eskin
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Department of Computer Science, Samueli School of Engineering, UCLA.,Department of Computational Medicine, David Geffen School of Medicine, UCLA
| | - Chongyuan Luo
- Department of Human Genetics, David Geffen School of Medicine, UCLA
| | - Sriram Kosuri
- Octant, Inc.,Department of Chemistry and Biochemistry, UCLA
| | - Leonid Kruglyak
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Howard Hughes Medical Institute, HHMI.,Department of Biological Chemistry, David Geffen School of Medicine, UCLA
| | - Valerie A Arboleda
- Department of Human Genetics, David Geffen School of Medicine, UCLA.,Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA
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