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iCLOTS: open-source, artificial intelligence-enabled software for analyses of blood cells in microfluidic and microscopy-based assays

Author

Listed:
  • Meredith E. Fay

    (Georgia Institute of Technology & Emory University
    Emory University School of Medicine
    Winship Cancer Institute of Emory University
    Georgia Institute of Technology)

  • Oluwamayokun Oshinowo

    (Georgia Institute of Technology & Emory University
    Emory University School of Medicine
    Winship Cancer Institute of Emory University
    Georgia Institute of Technology)

  • Elizabeth Iffrig

    (Georgia Institute of Technology & Emory University
    Emory University)

  • Kirby S. Fibben

    (Georgia Institute of Technology & Emory University
    Emory University School of Medicine
    Winship Cancer Institute of Emory University
    Georgia Institute of Technology)

  • Christina Caruso

    (Emory University School of Medicine)

  • Scott Hansen

    (University of Minnesota)

  • Jamie O. Musick

    (Emory University School of Medicine)

  • José M. Valdez

    (University of Minnesota)

  • Sally S. Azer

    (Georgia Institute of Technology & Emory University
    Emory University School of Medicine
    Georgia Institute of Technology)

  • Robert G. Mannino

    (Georgia Institute of Technology & Emory University
    Emory University School of Medicine
    Winship Cancer Institute of Emory University
    Georgia Institute of Technology)

  • Hyoann Choi

    (Georgia Institute of Technology & Emory University
    Emory University School of Medicine
    Winship Cancer Institute of Emory University
    Georgia Institute of Technology)

  • Dan Y. Zhang

    (Georgia Institute of Technology
    The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology)

  • Evelyn K. Williams

    (Georgia Institute of Technology & Emory University
    Emory University School of Medicine
    Winship Cancer Institute of Emory University
    Georgia Institute of Technology)

  • Erica N. Evans

    (Emory University School of Medicine)

  • Celeste K. Kanne

    (Emory University School of Medicine)

  • Melissa L. Kemp

    (Georgia Institute of Technology & Emory University
    Winship Cancer Institute of Emory University
    Georgia Institute of Technology)

  • Vivien A. Sheehan

    (Emory University School of Medicine)

  • Marcus A. Carden

    (University of North Carolina)

  • Carolyn M. Bennett

    (Emory University School of Medicine)

  • David K. Wood

    (University of Minnesota)

  • Wilbur A. Lam

    (Georgia Institute of Technology & Emory University
    Emory University School of Medicine
    Winship Cancer Institute of Emory University
    Georgia Institute of Technology)

Abstract

While microscopy-based cellular assays, including microfluidics, have significantly advanced over the last several decades, there has not been concurrent development of widely-accessible techniques to analyze time-dependent microscopy data incorporating phenomena such as fluid flow and dynamic cell adhesion. As such, experimentalists typically rely on error-prone and time-consuming manual analysis, resulting in lost resolution and missed opportunities for innovative metrics. We present a user-adaptable toolkit packaged into the open-source, standalone Interactive Cellular assay Labeled Observation and Tracking Software (iCLOTS). We benchmark cell adhesion, single-cell tracking, velocity profile, and multiscale microfluidic-centric applications with blood samples, the prototypical biofluid specimen. Moreover, machine learning algorithms characterize previously imperceptible data groupings from numerical outputs. Free to download/use, iCLOTS addresses a need for a field stymied by a lack of analytical tools for innovative, physiologically-relevant assays of any design, democratizing use of well-validated algorithms for all end-user biomedical researchers who would benefit from advanced computational methods.

Suggested Citation

  • Meredith E. Fay & Oluwamayokun Oshinowo & Elizabeth Iffrig & Kirby S. Fibben & Christina Caruso & Scott Hansen & Jamie O. Musick & José M. Valdez & Sally S. Azer & Robert G. Mannino & Hyoann Choi & Da, 2023. "iCLOTS: open-source, artificial intelligence-enabled software for analyses of blood cells in microfluidic and microscopy-based assays," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40522-4
    DOI: 10.1038/s41467-023-40522-4
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    References listed on IDEAS

    as
    1. Abhishek Jain & Amanda Graveline & Anna Waterhouse & Andyna Vernet & Robert Flaumenhaft & Donald E. Ingber, 2016. "A shear gradient-activated microfluidic device for automated monitoring of whole blood haemostasis and platelet function," Nature Communications, Nature, vol. 7(1), pages 1-10, April.
    2. Anna Makles, 2012. "Stata tip 110: How to get the optimal k-means cluster solution," Stata Journal, StataCorp LP, vol. 12(2), pages 347-351, June.
    3. Onelia Gagliano & Camilla Luni & Yan Li & Silvia Angiolillo & Wei Qin & Francesco Panariello & Davide Cacchiarelli & Joseph S. Takahashi & Nicola Elvassore, 2021. "Synchronization between peripheral circadian clock and feeding-fasting cycles in microfluidic device sustains oscillatory pattern of transcriptome," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
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