Sentinel cells enable genetic detection of SARS-CoV-2 Spike protein

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Abstract

The COVID-19 pandemic has demonstrated the need for exploring different diagnostic and therapeutic modalities to tackle future viral threats. In this vein, we propose the idea of sentinel cells, cellular biosensors capable of detecting viral antigens and responding to them with customizable responses. Using SARS-CoV-2 as a test case, we developed a live cell sensor (SARSNotch) using a de novo-designed protein binder against the SARS-CoV-2 Spike protein. SARSNotch is capable of driving custom genetically-encoded payloads in immortalized cell lines or in primary T lymphocytes in response to purified SARS-CoV-2 Spike or in the presence of Spike-expressing cells. Furthermore, SARSNotch is functional in a cellular system used in directed evolution platforms for development of better binders or therapeutics. In keeping with the rapid dissemination of scientific knowledge that has characterized the incredible scientific response to the ongoing pandemic, we extend an open invitation for others to make use of and improve SARSNotch sentinel cells in the hopes of unlocking the potential of the next generation of smart antiviral therapeutics.

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  1. SciScore for 10.1101/2021.04.20.440678: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: All cells were maintained at 37°C with 5% CO2 Source of Primary Human T cells: Blood was obtained from Blood Centers of the Pacific (San Francisco, CA) as approved by the University Institutional Review Board.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Antibodies: Surface expressed proteins were assayed for using Alexa Fluor 647 Anti-Myc tag antibody (Cell Signaling Technologies #2233S)
    Anti-Myc tag
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    HEK293T cells expressing ACE2 and TMPRSS2, a generous gift of Hannah S Sperber and Dr. Satish Pillai, were cultured in DMEM High Glucose (Gibco #10569-010) supplemented with 10% FBS, 1% of Antibiotic-Antimycotic,
    HEK293T
    suggested: None
    Generation of Stable Cell Lines: Lenti-X 293T cells (Takara Bio #632180) were seeded at approximately 7e5 cells/well in a 6-well plate to yield ~80% confluency the following day.
    293T
    suggested: None
    Media was changed after 24 hours, and at 48 hours the viral supernatant was filtered through a 0.45μm PVDF filter and added to Jurkat or K562 cells seeded at approximately 1e5 cells/well in a 12-well plate.
    Jurkat
    suggested: None
    K562
    suggested: None
    Prior to the flow cytometry, cells were seeded at densities described below in a 96 well plates, using flat-bottom plates (Falcon #353072) for experiments involving BHK-21 cells and U bottom plates for all other experiments (Falcon #877217) and incubated for 24-72 hours as specified by the experiment.
    BHK-21
    suggested: None
    Recombinant DNA
    SentencesResources
    The following day cells were transfected with 1.5μg of transfer vector containing the desired expression cassette, and the lentiviral packaging plasmids pMD2
    pMD2
    suggested: None
    G (170ng) and pCMV-dR8.91 (1.33μg) using 10μl of Lipofectamine 2000 (Invitrogen #11668-027) according to manufacturer protocols.
    pCMV-dR8.91
    suggested: None
    Briefly, 293T cells were transfected with plasmid DNA (340 ng of Spike vector, 1μg CMV-Gag-Pol (pCMV-dΔR8.91), 125 ng pAdvantage (Promega), 1 μg Luciferase reporter (per 6-well plate)) for 48 h.
    pCMV-dΔR8.91
    suggested: None
    For positive control, Spike vector was replaced with pMD2.G and for negative control this vector was omitted.
    pMD2.G
    suggested: RRID:Addgene_12259)
    Software and Algorithms
    SentencesResources
    For LCB1 and LCB3, protein sequences were translated to human optimized coding sequences using Benchling.
    Benchling
    suggested: (Benchling, RRID:SCR_013955)
    All data were collected using FACSDiva (BD Biosciences) Flow Cytometry: Flow cytometry was performed using a LSR-Fortessa (BD Biosciences).
    FACSDiva
    suggested: (BD FACSDiva Software, RRID:SCR_001456)
    The protein concentration was estimated based on the protein absorbance at 280nm with a spectrophotometer (Nanodrop One, Thermo), flash frozen, and stored in −80 °C.
    Thermo
    suggested: (Thermo Xcalibur, RRID:SCR_014593)
    All data analysis was conducted using custom Python scripts, available on github (https://github.com/weinberz/sarsnotch).
    Python
    suggested: (IPython, RRID:SCR_001658)
    Analysis was conducted in Jupyter59 and relied on numpy60, matplotlib, seaborn, pandas, SciPy61, scikit-learn62 and fcsparser.
    matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)

    Results from OddPub: Thank you for sharing your code and data.


    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.