High-throughput electrical position detection of single flowing particles/cells with non-spherical shape

Reale, Riccardo; Ninno, Adele De; Businaro, Luca; Bisegna, Paolo; Caselli, Federica

doi:10.1039/c9lc00071b

Cited by 40 publications

(24 citation statements)

References 59 publications

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“…Finally, the peak-to-peak time is used to compute particle velocity V. The procedure to obtain electrical estimates of particle diameter, position, and velocity from the features of the template is reported in Section 1 of the Electronic Supplementary Material (ESM). Compared with our previous chip for multiparametric cell characterization [7], the current design has two improvements in data acquisition and extraction. First, the length of the sensing zone is significantly shorter, thereby reducing the fraction of particle coincidence.…”

Section: Microfluidic Impedance Chip Designmentioning

confidence: 99%

“…beads and RBCs. The latter could be distinguished from beads by means of electrical opacity (e.g., [7]). The histograms of diameter, velocity, and vertical position are reported in the first, second, and third columns, respectively.…”

Section: Training Validation and Testing On Experimental Data Streamsmentioning

confidence: 99%

“…In this work, the features (diameter, velocity, and position) computed by means of least-square template fitting on impedance signals have been used as labels (i.e., target values). Their accuracy has been assessed in our previous works [7,37,44]. Alternatively, features computed by means of image processing could be used [33].…”

Section: Training Validation and Testing On Experimental Data Streamsmentioning

confidence: 99%

“…For instance, depending on the stimulation frequency, signal peak amplitude provides information on particle size, membrane capacitance, or intracellular conductivity [5]. Moreover, electrical fingerprints conveying inf7ormation on both cell intrinsic properties (size, electrical opacity, shape indicators) and cell motion (velocity, cross-sectional position) can be obtained, using particular electrode designs and associated wiring schemes [6][7][8]. Signal processing of the raw impedance data streams typically involves four steps: preliminary detrending/ denoising, data stream segmentation (event detection), extraction of features (like peak amplitude and transit time), and cell classification.…”

Section: Introductionmentioning

confidence: 99%

“…Signal processing of the raw impedance data streams typically involves four steps: preliminary detrending/ denoising, data stream segmentation (event detection), extraction of features (like peak amplitude and transit time), and cell classification. Several signal processing techniques have been implemented in impedance cytometry, like Savitzky-Golay filter for denoising [9,10], wavelet-based or correlation-based approaches for event detection [10,11], least-squares template fitting for feature extraction [7,9], and support vector machines or neural networks for classification [12][13][14][15]. However, other than protocols based on threshold-based particle counting [16], signal processing of the raw impedance data streams is usually performed post-experiment (offline).…”

Section: Introductionmentioning

confidence: 99%

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A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry

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Microfluidic applications such as active particle sorting or selective enrichment require particle classification techniques that are capable of working in real time. In this paper, we explore the use of neural networks for fast label-free particle characterization during microfluidic impedance cytometry. A recurrent neural network is designed to process data from a novel impedance chip layout for enabling real-time multiparametric analysis of the measured impedance data streams. As demonstrated with both synthetic and experimental datasets, the trained network is able to characterize with good accuracy size, velocity, and crosssectional position of beads, red blood cells, and yeasts, with a unitary prediction time of 0.4 ms. The proposed approach can be extended to other device designs and cell types for electrical parameter extraction. This combination of microfluidic impedance cytometry and machine learning can serve as a stepping stone to real-time single-cell analysis and sorting.

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Section: Microfluidic Impedance Chip Designmentioning

confidence: 99%

Section: Training Validation and Testing On Experimental Data Streamsmentioning

confidence: 99%

Section: Training Validation and Testing On Experimental Data Streamsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry

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

et al. 2022

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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.

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Microfluidic characterization of single‐cell biophysical properties and the applications in cancer diagnosis

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Xue,

et al. 2023

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Single‐cell biophysical properties play a crucial role in regulating cellular physiological states and functions, demonstrating significant potential in the fields of life sciences and clinical diagnostics. Therefore, over the last few decades, researchers have developed various detection tools to explore the relationship between the biophysical changes of biological cells and human diseases. With the rapid advancement of modern microfabrication technology, microfluidic devices have quickly emerged as a promising platform for single‐cell analysis offering advantages including high‐throughput, exceptional precision, and ease of manipulation. Consequently, this paper provides an overview of the recent advances in microfluidic analysis and detection systems for single‐cell biophysical properties and their applications in the field of cancer. The working principles and latest research progress of single‐cell biophysical property detection are first analyzed, highlighting the significance of electrical and mechanical properties. The development of data acquisition and processing methods for real‐time, high‐throughput, and practical applications are then discussed. Furthermore, the differences in biophysical properties between tumor and normal cells are outlined, illustrating the potential for utilizing single‐cell biophysical properties for tumor cell identification, classification, and drug response assessment. Lastly, we summarize the limitations of existing microfluidic analysis and detection systems in single‐cell biophysical properties, while also pointing out the prospects and future directions of their applications in cancer diagnosis and treatment.

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High-throughput electrical position detection of single flowing particles/cells with non-spherical shape

Abstract: A novel all-electrical approach for cross-sectional position detection of flowing particles and cells.

Cited by 40 publications

References 59 publications

A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry

A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry

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

Microfluidic characterization of single‐cell biophysical properties and the applications in cancer diagnosis

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