What is it about?
At present, most IFC works utilized phenomenological parameters (e.g., impedance amplitude, phase and opacity) to characterize single cells instead of intrinsic biophysical metrics (e.g., radius r, cytoplasm conductivity σi and specific membrane capacitance C¬sm). Intrinsic parameters are normally calculated off-line by time-consuming model fitting methods. Here, we propose to employ neural networks (NNs) enhanced IFC to achieve both real-time single-cell intrinsic characterization and intrinsic parameters-based cell classification at high throughput.
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Why is it important?
Three intrinsic parameters (r, σi and C¬sm) can be obtained online and in real-time via a trained NN at 0.3 ms per single-cell event, achieving significant improvement in calculation speed (10000x). Experiments involving four cancer cells and one lymphocyte cell demonstrated 91.5% classification accuracy in the cell type for a test group of 9751 cell samples. By performing a viability assay, we provide evidence that IFC test per se would not substantially affect the cell property.
Perspectives
We envision that the NN-enhanced real-time IFC provides a new platform for high-throughput, real-time and online cell intrinsic electrical characterization.
Professor Wenhui Wang
Tsinghua University
Read the Original
This page is a summary of: Neural network-enhanced real-time impedance flow cytometry for single-cell intrinsic characterization, Lab on a Chip, January 2022, Royal Society of Chemistry,
DOI: 10.1039/d1lc00755f.
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