System for unitary automation of library preparation

  • Funded by National Institutes of Health (NIH)
  • Total publications:0 publications

Grant number: 1R43GM145194-01

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Key facts

  • Disease

    Disease X
  • Start & end year

    2022
    2023
  • Known Financial Commitments (USD)

    $292,788
  • Funder

    National Institutes of Health (NIH)
  • Principal Investigator

    CHIEF SCIENTIFIC OFFICER Jay Fisher
  • Research Location

    United States of America
  • Lead Research Institution

    REDBUD LABS, INC.
  • Research Priority Alignment

    N/A
  • Research Category

    Health Systems Research

  • Research Subcategory

    Medicines, vaccines & other technologies

  • Special Interest Tags

    Innovation

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract

ABSTRACT Genomic epidemiology is crucial to outbreak surveillance and response. Sequencing data lets us track virus evolution in real time and thus characterize transmission chains, identify emerging variants, and predict future spread. This information, which cannot be deduced from diagnostic testing alone, informs the severity and speed of an outbreak before it becomes a pandemic. In order to inform corrective action from sequencing data, results must be obtained within hours, instead of days. Ideally, automated library preparation would be achieved with an easy-to-use, single button device that can be deployed in resource-limited settings as part of a point-of-need infectious disease surveillance/diagnostic system. However, given the diversity of sequencing applications, there cannot be a one-size-fits-all solution to library prep. Instead, we believe the correct strategy is to automate unitary library prep operations, with well-defined breakpoints in between, enabling the user to mix and match these operations as the application demands. This strategy will reduce hands-on time and operator burden while maintaining workflow flexibility. In this Phase I project, we focus on demonstrating the feasibility of automating three unitary library prep operations on our platform. The operations are: end repair/dA-tailing, barcoding, and adapter ligation. Each step involves an incubation followed by size selection. Our approach protects the customizability of manual workflows while eliminating up to 95% of pipette steps. The outcome of this project will be a solution for pre-analytical preparation in low-resource settings.