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- Title
iCLOTS: open-source, artificial intelligence-enabled software for analyses of blood cells in microfluidic and microscopy-based assays.
- Authors
Fay, Meredith E.; Oshinowo, Oluwamayokun; Iffrig, Elizabeth; Fibben, Kirby S.; Caruso, Christina; Hansen, Scott; Musick, Jamie O.; Valdez, José M.; Azer, Sally S.; Mannino, Robert G.; Choi, Hyoann; Zhang, Dan Y.; Williams, Evelyn K.; Evans, Erica N.; Kanne, Celeste K.; Kemp, Melissa L.; Sheehan, Vivien A.; Carden, Marcus A.; Bennett, Carolyn M.; Wood, David K.
- 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. Microscopy has undoubtedly advanced biomedical research, but novel hypotheses are often lost to a lack of analytical tools. Here authors propose iCLOTS, a freely-available software that allows researchers to apply image processing and artificial intelligence algorithms to their own data.
- Subjects
MICROFLUIDICS; BLOOD testing; BLOOD cells; MACHINE learning; CELL analysis; CELL adhesion
- Publication
Nature Communications, 2023, Vol 14, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-023-40522-4