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Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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FloCyT: A Flow-Aware Centroid Tracker for Cell Analysis in High-Speed Capillary-Driven Microfluidic Flow.

Suraj K Maurya1,2, Matt Stark2, Cédric Bessire2

  • 1Bio/CMOS Interfaces Laboratory, Ecole Polytechnique Federale de Lausanne (EPFL), Rue de la Maladiere 71, 2000 Neuchatel, Switzerland.

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|November 27, 2025
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Summary

FloCyT accurately tracks cells in capillary-driven microfluidic devices, overcoming challenges like flow instability. This new tool enhances diagnostic capabilities for point-of-care applications.

Keywords:
anisotropic gatingcapillary-driven flowcentroid-based trackingcytological analysisimage flow cytometrymicrofluidic cell trackingmulti-object trackingpoint-of-care diagnostics

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Area of Science:

  • Microfluidics
  • Biomedical Engineering
  • Computational Biology

Background:

  • Capillary-driven microfluidic chips are valuable for point-of-care diagnostics due to their portability and low cost.
  • Accurate cell tracking is essential for quantitative analysis in microfluidic systems, but faces challenges like flow instabilities and similar cell appearances.

Purpose of the Study:

  • To develop a robust, high-speed centroid tracking tool, FloCyT, specifically for capillary-driven microfluidic flow.
  • To improve the accuracy and completeness of cell trajectories in challenging microfluidic environments.

Main Methods:

  • FloCyT utilizes microchannel geometry, anisotropic gating, global flow-aware track initialization, and channel-specific association for cell tracking.
  • The tool was evaluated on simulated and real patient datasets using metrics like IDF1, MOTA, ID switches, and percentage of mostly tracked objects.

Main Results:

  • FloCyT demonstrated superior performance compared to conventional algorithms like TrackPy and SORT, including flow-aware modifications.
  • Achieved higher accuracy, more complete trajectories, and significantly fewer identity switches in cell tracking.

Conclusions:

  • FloCyT enables precise and automated cell tracking in capillary-driven microfluidic devices.
  • Enhances quantitative sensing capabilities for image-based microfluidic diagnostics, supporting low-cost, portable cytometry applications.