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Flow Cytometry

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Updated: Nov 25, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Single-cell microfluidic impedance cytometry: from raw signals to cell phenotypes using data analytics.

Carlos Honrado¹, Paolo Bisegna, Nathan S Swami

¹Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904, USA. nswami@virginia.edu.

|December 17, 2020

Summary

This summary is machine-generated.

Microfluidic impedance cytometry offers label-free, high-throughput single-cell analysis for cellular heterogeneity stratification. Recent advancements enhance multiparametric characterization, crucial for diagnostics and precision medicine.

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Area of Science:

Biophysics
Cell Biology
Microfluidics

Background:

Microfluidic impedance cytometry (MIC) is an emerging label-free technique for analyzing single-cell electrophysiology.
It enables high-throughput stratification of cellular heterogeneity, with applications in life sciences, drug assessment, diagnostics, and precision medicine.

Purpose of the Study:

To provide a comparative survey of approaches for elucidating cellular and subcellular features using MIC data.
To highlight recent developments (2017-2020) and discuss future challenges and directions in the field.

Main Methods:

Review of device designs for microfluidic impedance cytometry.
Analysis of data processing techniques including signal processing, dielectric modeling, and population clustering.
Exploration of phenotyping applications and synergistic integration with other technologies.

Main Results:

Recent advancements in chip design and data analytics facilitate multiparametric cell characterization and subpopulation distinction.
MIC provides essential tools for understanding biological function, disease progression, and cell behavior in microsystems.

Conclusions:

MIC is a powerful tool for label-free quantification and isolation of cell subpopulations.
Synergistic application with microfluidic separation, sensor science, and machine learning will advance the stratification of heterogeneous biosystems.