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Flow Cytometry01:23

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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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Computational Hyperspectral Microflow Cytometry.

Hyo Geun Yun1, Yoel Alonso Cadierno2, Tae Won Kim1

  • 1Department of Electronic Engineering, Hanyang University, Seoul, 04763, Republic of Korea.

Small (Weinheim an Der Bergstrasse, Germany)
|May 21, 2024
PubMed
Summary

A new computational hyperspectral microflow cytometer (CHC) accurately distinguishes spectrally overlapping fluorophores in single cells. This breakthrough enhances portable cell analysis for diagnostics and immune cell monitoring.

Keywords:
T lymphocyte analysiscomputational hyperspectral fluorescence analysismicroflow cytometrysheathless focusingspectral reconstruction

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Area of Science:

  • Biomedical Engineering
  • Optical Engineering
  • Cell Biology

Background:

  • Miniaturized flow cytometry is crucial for portable diagnostics and cell manufacturing monitoring.
  • Current methods struggle to resolve spectrally overlapping fluorescence labels, limiting analytical precision.
  • Accurate cell analysis requires advanced techniques for distinguishing specific cellular markers.

Purpose of the Study:

  • To develop a computational hyperspectral microflow cytometer (CHC) for precise discrimination of spectrally overlapping fluorophores.
  • To enhance the accuracy and reliability of microflow cytometry through advanced spectral detection and microfluidic focusing.
  • To demonstrate the utility of CHC in analyzing T lymphocyte subpopulations and monitoring cell expansion.

Main Methods:

  • Utilized a dispersive optical element and optimization algorithm to capture full fluorescence emission spectra from single cells.
  • Achieved high spectral resolution (≈3 nm) in the visible range (450–650 nm).
  • Integrated a microfluidic device with viscoelastic sheathless focusing for in-focus cell imaging.

Main Results:

  • Successfully discriminated spectrally overlapping fluorophores labeling single cells with high accuracy.
  • Demonstrated the capability of CHC to analyze T lymphocyte subpopulations.
  • Showcased the monitoring of cellular composition changes during T cell expansion.

Conclusions:

  • CHC represents a significant advancement in microflow cytometry, overcoming limitations in spectral resolution.
  • The technology enables accurate analysis of complex cellular samples, including immune cells.
  • CHC facilitates the broader application of microflow cytometry in diagnostics and bioprocess monitoring.