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 T Hansen, Jamie O. Musick, Jose 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. WoodWilbur A. Lam

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish (US)
Article number5022
JournalNature communications
Volume14
Issue number1
DOIs
StatePublished - Dec 2023

Bibliographical note

Publisher Copyright:
© 2023, Springer Nature Limited.

PubMed: MeSH publication types

  • Journal Article
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, N.I.H., Extramural

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