Analysis of red blood cell deformability using parallel ladder electrodes in a microfluidic manipulation system

Li, Wanting; Zhu, Botao; Cai, Yifan; Wu, Zhengtian; Sun, Lining; Yang, Hao

doi:10.1007/s00170-019-04102-5

Cited by 8 publications

(11 citation statements)

References 30 publications

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“…Second, a major barrier to physical measurement of RBC deformability is that these methods are prohibitively difficult, time consuming, and require specialized equipment. 35,57,58 In contrast, microscope systems are ubiquitous in both research and clinical laboratories. Finally, while there are several methods available for physical measurement of RBC deformability, there are no accepted standards by which to compare studies.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Image Classification of Red Blood Cell Deformability

Lamoureux

Islamzada

Wiens

et al. 2021

Preprint

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Red blood cells (RBCs) must be highly deformable to transit through the microvasculature to deliver oxygen to tissues. The loss of RBC deformability resulting from pathology, natural aging, or storage in blood bags can impede the proper function of these cells. A variety of methods have been developed to measure RBC deformability, but these methods require specialized equipment, long measurement time, and highly skilled personnel. To address this challenge, we investigated whether a machine learning approach could be applied to determine donor RBC deformability using single cell microscope images. We used the microfluidic ratchet device to sort RBCs based on deformability. Sorted cells are then imaged and used to train a deep learning model to classify RBCs based on deformability. This model correctly predicted deformability of individual RBCs with 84 ± 11% accuracy averaged across ten donors. Using this model to score the deformability of RBC samples were accurate to within 4.4 ± 2.5% of the value obtained using the microfluidic ratchet device. While machine learning methods are frequently developed to automate human image analysis, our study is remarkable in showing that deep learning of single cell microscopy images could be used to measure RBC deformability, a property not normally measurable by imaging. Measuring RBC deformability by imaging is also desirable because it can be performed rapidly using a standard microscopy system, potentially enabling RBC deformability studies to be performed as part of routine clinical assessments.

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Section: Discussionmentioning

confidence: 99%

Deep Learning Image Classification of Red Blood Cell Deformability

Lamoureux

Islamzada

Wiens

et al. 2021

Preprint

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“…An approach using electrode-based microfluidic systems has studied RBC stretching using dielectrophoretic forces. This also elucidates the relationship between the dielectrophoretic forces, voltage, and electrode gap distances at the single-cell level [159]. A pressure gradient-based approach is also beneficial in the assessment of cell rigidity.…”

Section: Deformation Of Single Erythrocytes In Microchannelsmentioning

confidence: 93%

Section: Deformation Of Single Erythrocytes In Microchannelsmentioning

confidence: 99%

“…Figure 10B shows the critical pressure for driving a RBC through a contracting microfluidic channel [160]. and Figure 10C shows the system structure of RBC deformability measurements using parallel ITO electrodes in a microfluidic manipulation system [159]. [152]; (B) A microfluidic chip for studying the critical pressure to drive a RBC through a contracting microfluidic channel [160]; (C) The microfluidic system to measure the RBC deformability with parallel electrodes in a microfluidic manipulation system [159].…”

Section: Deformation Of Single Erythrocytes In Microchannelsmentioning

confidence: 99%

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Advances in Microfluidics for Single Red Blood Cell Analysis

et al. 2023

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The utilizations of microfluidic chips for single RBC (red blood cell) studies have attracted great interests in recent years to filter, trap, analyze, and release single erythrocytes for various applications. Researchers in this field have highlighted the vast potential in developing micro devices for industrial and academia usages, including lab-on-a-chip and organ-on-a-chip systems. This article critically reviews the current state-of-the-art and recent advances of microfluidics for single RBC analyses, including integrated sensors and microfluidic platforms for microscopic/tomographic/spectroscopic single RBC analyses, trapping arrays (including bifurcating channels), dielectrophoretic and agglutination/aggregation studies, as well as clinical implications covering cancer, sepsis, prenatal, and Sickle Cell diseases. Microfluidics based RBC microarrays, sorting/counting and trapping techniques (including acoustic, dielectrophoretic, hydrodynamic, magnetic, and optical techniques) are also reviewed. Lastly, organs on chips, multi-organ chips, and drug discovery involving single RBC are described. The limitations and drawbacks of each technology are addressed and future prospects are discussed.

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“…In contrast, the traditional DEP method requires a series of separate processes such as video recording, manual selection, manual measurement, etc. After accumulating the time of all the measurement processes, the measurement data per minute is less than 1 ( Li et al., 2019 ). Second, the automatic measurement method we proposed can obtain effective measurement data of more than 1000 cells in one experiment, compared to less than 100 for traditional DEP methods ( Du et al., 2014 ), which is an order of magnitude difference.…”

Section: Introductionmentioning

confidence: 99%