Determining Particle Size and Position in a Coplanar Electrode Setup Using Measured Opacity for Microfluidic Cytometry

Bruijn, Douwe S. de; Jorissen, Koen F.A.; Olthuis, Wouter; Berg, Albert van den

doi:10.3390/bios11100353

Cited by 16 publications

(18 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So far, all results were based on processing a 0.5 and 12 MHz signal for which the previous method worked best. 9 These frequencies were chosen rather far apart to enhance the opacity effect. For size determination in flow cytometry it is required to operate below the β-dispersion of cells (<several MHz 9,22 ).…”

Section: Resultsmentioning

confidence: 99%

“…9 These frequencies were chosen rather far apart to enhance the opacity effect. For size determination in flow cytometry it is required to operate below the β-dispersion of cells (<several MHz 9,22 ). Therefore, we also have tested the model with a 0.5 and 1 MHz signal response (Figure 5) and we have observed a significant improvement for all particles compared to the previous method.…”

Section: Resultsmentioning

confidence: 99%

“…The microfluidic device and experimental parameters that were used to acquire the multi-frequency impedance signals have been discussed elaborately in previous work. 9 In short, impedance data was recorded at 0.5, 1, and 12 MHz simultaneously using a lock-in amplifier and preamplifier (HF2LI and HF2TA, Zurich Instruments). The sample rate was 28.8 kSa/s and the signal was set at 1 V peak-to-peak .…”

Section: Methodsmentioning

confidence: 99%

“…Meanwhile signal processing solutions have been studied to assess particle sizes accurately in all sorts of flow cytometers without particle focusing. [5][6][7][8][9][10] Most studies focus on pre-defined template and compensation functions to extract and process features from impedance signals: for example, particle size and position, 9,10 particle size and velocity [5][6][7][8] and particle size, velocity and cross-sectional This paper is an extended version of a conference contribution. position.…”

Section: Introductionmentioning

confidence: 99%

“…The irregular signal response is the result of the particle position: an M-shaped signal for particles passing close the electrodes, 12 owing to the high field density near the electrodes and a single peak for particles further away from the electrodes (for more details see SI Reference [9]). We demonstrate a reduction in particle size variation and the ease of data processing, with respect to this earlier work, 9 using a multiple linear regression model in combination with a machine learning algorithm. 13 Recent advances in machine learning and (impedance) flow cytometry have already shown promising results regarding: quantification of algal lipid accumulation using feature selection and regression analysis, 14 real-time intrinsic characterization of cancer cells and lymphocyte cells using a neural network, 15 classification of pollen using a combination of impedance cytometry and optical image processing, 16 a recurrent neural network to find the size, velocity and crosssectional position of beads, red blood cells and yeast 17 and intrinsic characterization of red blood cells and detecting coinciding particles with a neural network.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Supervised machine learning in microfluidic impedance flow cytometry for improved particle size determination

et al. 2022

Self Cite

View full text Add to dashboard Cite

The assessment of particle and cell size in electrical microfluidic flow cytometers has become common practice. Nevertheless, in flow cytometers with coplanar electrodes accurate determination of particle size is difficult, owing to the inhomogeneous electric field. Pre-defined signal templates and compensation methods have been introduced to correct for this positional dependence, but are cumbersome when dealing with irregular signal shapes. We introduce a simple and accurate post-processing method without the use of pre-defined signal templates and compensation functions using supervised machine learning. We implemented a multiple linear regression model and show an average reduction of the particle diameter variation by 37% with respect to an earlier processing method based on a feature extraction algorithm and compensation function. Furthermore, we demonstrate its application in flow cytometry by determining the size distribution of a population of small (4.6 ± 0.9 μm) and large (5.9 ± 0.8 μm) yeast cells. The improved performance of this coplanar, two electrode chip enables precise cell size determination in easy to fabricate impedance flow cytometers.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Supervised machine learning in microfluidic impedance flow cytometry for improved particle size determination

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

Electrical micro flow cytometry with LSTM and its application in leukocyte differential

Tan,

Chen,

Huang

et al. 2023

Cytometry Pt A

View full text Add to dashboard Cite

This paper developed an electrical micro flow cytometry to realize leukocyte differentials leveraging a constrictional microchannel and a deep neural network. Firstly, purified granulocytes, lymphocytes or monocytes traveled through the constrictional microchannel with a cross‐sectional area marginally larger than individual cells and produced large impedance variations by blocking focused electric field lines. By optimizing key elements (e.g., normalization, learning rate, batch size and neuron number) of the recurrent neural network (RNN), electrical results of purified leukocytes were analyzed to establish a leukocyte differential system with a classification accuracy of 95.2%. Then the leukocyte mixtures were forced to travel through the same constrictional microchannel, producing mixed impedance profiles which were classified into granulocytes, lymphocytes and monocytes based on the aforementioned differential system. As to the classification results, two leukocyte mixtures from the same donor were processed, producing comparable classification results, which were 57% versus 59% of granulocytes, 37% versus 34% of lymphocytes and 6% versus 7% of monocytes. These results validated the established classification system based on the constrictional microchannel and the recurrent neural network, providing a new perspective of differentiating white blood cells by electrical flow cytometry.

show abstract

Particle detection and size recognition based on defocused particle images: a comparison of a deterministic algorithm and a deep neural network

et al. 2023

View full text Add to dashboard Cite

The systematic manipulation of components of multimodal particle solutions is a key for the design of modern industrial products and pharmaceuticals with highly customized properties. In order to optimize innovative particle separation devices on microfluidic scales, a particle size recognition with simultaneous volumetric position determination is essential. In the present study, the astigmatism particle tracking velocimetry is extended by a deterministic algorithm and a deep neural network (DNN) to include size classification of particles of multimodal size distribution. Without any adaptation of the existing measurement setup, a reliable classification of bimodal particle solutions in the size range of $$1.14~\upmu \hbox {m}$$ 1.14 μ m –$$5.03~\upmu \hbox {m}$$ 5.03 μ m is demonstrated with a precision of up to 99.9 %. Concurrently, the high detection rate of the particles, suspended in a laminar fluid flow, is quantified by a recall of 99.0 %. By extracting particle images from the experimentally acquired images and placing them on a synthetic background, semi-synthetic images with consistent ground truth are generated. These contain labeled overlapping particle images that are correctly detected and classified by the DNN. The study is complemented by employing the presented algorithms for simultaneous size recognition of up to four particle species with a particle diameter in between $$1.14~\upmu \hbox {m}$$ 1.14 μ m and $$5.03~\upmu \hbox {m}$$ 5.03 μ m . With the very high precision of up to 99.3 % at a recall of 94.8 %, the applicability to classify multimodal particle mixtures even in dense solutions is confirmed. The present contribution thus paves the way for quantitative evaluation of microfluidic separation and mixing processes.

show abstract

Determining Particle Size and Position in a Coplanar Electrode Setup Using Measured Opacity for Microfluidic Cytometry

Cited by 16 publications

References 36 publications

Supervised machine learning in microfluidic impedance flow cytometry for improved particle size determination

Supervised machine learning in microfluidic impedance flow cytometry for improved particle size determination

Electrical micro flow cytometry with LSTM and its application in leukocyte differential

Particle detection and size recognition based on defocused particle images: a comparison of a deterministic algorithm and a deep neural network

Contact Info

Product

Resources

About