Tapestry: A Single-Round Smart Pooling Technique for COVID-19 Testing

  1. Sabyasachi Ghosh
  2. Ajit Rajwade
  3. Srikar Krishna
  4. Nikhil Gopalkrishnan
  5. Thomas E. Schaus
  6. Anirudh Chakravarthy
  7. Sriram Varahan
  8. Vidhya Appu
  9. Raunak Ramakrishnan
  10. Shashank Ch
  11. Mohit Jindal
  12. Vadhir Bhupathi
  13. Aditya Gupta
  14. Abhinav Jain
  15. Rishi Agarwal
  16. Shreya Pathak
  17. Mohammed Ali Rehan
  18. Sarthak Consul
  19. Yash Gupta
  20. Nimay Gupta
  21. Pratyush Agarwal
  22. Ritika Goyal
  23. Vinay Sagar
  24. Uma Ramakrishnan
  25. Sandeep Krishna
  26. Peng Yin
  27. Dasaradhi Palakodeti
  28. Manoj Gopalkrishnan

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Abstract

The COVID-19 pandemic has strained testing capabilities worldwide. There is an urgent need to find economical and scalable ways to test more people. We present Tapestry , a novel quantitative nonadaptive pooling scheme to test many samples using only a few tests. The underlying molecular diagnostic test is any real-time RT-PCR diagnostic panel approved for the detection of the SARS-CoV-2 virus. In cases where most samples are negative for the virus, Tapestry accurately identifies the status of each individual sample with a single round of testing in fewer tests than simple two-round pooling. We also present a companion Android application BYOM Smart Testing which guides users through the pipetting steps required to perform the combinatorial pooling. The results of the pooled tests can be fed into the application to recover the status and estimated viral load for each individual sample.

NOTE: This protocol has been validated with in vitro experiments that used synthetic RNA and DNA fragments and additionally, its expected behavior has been confirmed using computer simulations. Validation with clinical samples is ongoing. We are looking for clinical collaborators with access to patient samples. Please contact the corresponding author if you wish to validate this protocol on clinical samples.

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    SciScore for 10.1101/2020.04.23.20077727: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.04.23.20077727 on medRxiv