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Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

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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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Wide-field Fluorescent Microscopy and Fluorescent Imaging Flow Cytometry on a Cell-phone
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Smartphone Imaging Flow Cytometry for High-Throughput Single-Cell Analysis.

Mahmut Kamil Aslan1, Yun Ding1, Stavros Stavrakis1

  • 1Institute for Chemical and Bioengineering, ETH Zurich, 8093 Zürich, Switzerland.

Analytical Chemistry
|September 21, 2023
PubMed
Summary
This summary is machine-generated.

We developed a portable smartphone imaging flow cytometer (sIFC) for particle analysis. This device enables accurate particle enumeration and cell classification with high-resolution imaging and minimal hardware.

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Area of Science:

  • Biomedical Engineering
  • Microfluidics
  • Optical Imaging

Background:

  • Traditional flow cytometry requires bulky and expensive equipment.
  • Portable and cost-effective solutions are needed for point-of-care diagnostics and field applications.

Purpose of the Study:

  • To develop a portable imaging flow cytometer using a smartphone.
  • To enable sheathless flow cytometric analysis with high throughput and real-time classification capabilities.

Main Methods:

  • Integration of a smartphone, optical framework, and PDMS microfluidic device.
  • Sheathless elasto-inertial focusing for particle manipulation.
  • Custom Android application with GUI for user control and machine learning integration.

Main Results:

  • Post-processing mode achieved particle sizing at up to 67,000 particles/s.
  • Real-time mode classified cells with 97% (Jurkat) and 93% (EL4) accuracy using machine learning.
  • High-resolution bright-field images with <700 nm spatial resolution were extracted.

Conclusions:

  • The smartphone imaging flow cytometer (sIFC) is a cost-effective and portable solution for particle enumeration and cell classification.
  • The system demonstrates high accuracy and resolution, suitable for diverse applications.
  • Minimal hardware requirements make this technology accessible for broader use.