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Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation
Published on: June 4, 2015
Statistical dynamics of flowing red blood cells by morphological image processing.
John M Higgins1, David T Eddington, Sangeeta N Bhatia
1School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America.
Plos Computational Biology
|February 14, 2009
Summary
This study quantifies blood cell flow dynamics using advanced imaging and machine learning. It reveals how cell properties influence flow and suggests a new method to detect conditions like sickle cell disease and thrombosis.
Area of Science:
- Biophysics
- Computational Biology
- Rheology
Background:
- Blood flow involves complex cell interactions influenced by concentration, morphology, rheology, and confinement.
- Understanding these dynamics is crucial for diagnosing and treating blood disorders.
Purpose of the Study:
- To analyze blood cell interactions and flow dynamics in a microfluidic setting.
- To quantify non-equilibrium fluctuations in cellular velocities and measure hydrodynamic diffusivity.
- To differentiate normal and sickled red blood cells using effective suspension temperature.
Main Methods:
- Computational morphological image analysis.
- Machine learning algorithms for analyzing high-resolution spatio-temporal measurements.
- Microfluidic experiments in a quasi-two-dimensional setting.
Main Results:
- Quantified non-equilibrium fluctuations in cellular velocities.
- Measured effective hydrodynamic diffusivity and its relation to flow velocity and density.
- Successfully distinguished normal from sickled red blood cells using effective suspension temperature.
Conclusions:
- Effective suspension temperature can characterize blood flow and potentially predict thrombosis risk.
- This approach offers insights into blood rheology and disease states.
- Highlights the importance of cellular interactions in physiological blood flow.
