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

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, Dan Y. Zhang, Evelyn K. Williams, Erica N. Evans, Celeste K. Kanne, Melissa L. Kemp, Vivien A. Sheehan, Marcus A. Carden, Carolyn M. Bennett, David K. Wood and Wilbur A. Lam ()
Additional contact information
Meredith E. Fay: Georgia Institute of Technology & Emory University
Oluwamayokun Oshinowo: Georgia Institute of Technology & Emory University
Elizabeth Iffrig: Georgia Institute of Technology & Emory University
Kirby S. Fibben: Georgia Institute of Technology & Emory University
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
Robert G. Mannino: Georgia Institute of Technology & Emory University
Hyoann Choi: Georgia Institute of Technology & Emory University
Dan Y. Zhang: Georgia Institute of Technology
Evelyn K. Williams: Georgia Institute of Technology & Emory University
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
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

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-40522-4

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