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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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Differentiating Microplastics from Natural Particles in Aqueous Suspensions Using Flow Cytometry with Machine

Xinjie Wang1,2,3, Yang Li2, Alexandra Kroll4

  • 1Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland.

Environmental Science & Technology
|May 28, 2024
PubMed
Summary

This study introduces a rapid, stain-free flow cytometry method using machine learning to detect microplastics (MPs) in natural waters. The technique accurately quantifies MPs in complex environmental samples, aiding pollution monitoring.

Keywords:
algaemicroplasticsrapid screeningsedimentsemiautomated approach

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Biotechnology

Background:

  • Microplastics (MPs) are pervasive environmental contaminants, often mixed with natural particles in aquatic ecosystems.
  • Accurate and rapid detection methods are crucial for understanding MP distribution and impact.
  • Distinguishing MPs from natural colloids like algae and sediments presents analytical challenges.

Purpose of the Study:

  • To develop a fast, stain-free method for identifying and quantifying microplastics in natural waters.
  • To leverage flow cytometry and machine learning for analyzing MP scattering and fluorescence properties.
  • To assess the method's efficacy in complex environmental matrices containing natural particles.

Main Methods:

  • Established a database of microplastic scattering and fluorescence properties using stain-free flow cytometry.
  • Analyzed high-dimensional data with unsupervised (viSNE) and supervised (random forest) machine learning algorithms.
  • Tested the method on model MPs in suspensions with phototrophic microorganisms, biofilms, minerals, and sediments.

Main Results:

  • Achieved precise quantification of microplastics in microbial phototrophs and high organic carbon sediments (>93% accuracy).
  • Demonstrated the method's applicability as a rapid screening tool for microplastics in environmental samples.
  • Validated the workflow by spiking MPs into freshwater samples, confirming its practical utility.

Conclusions:

  • The developed workflow offers a time-efficient and easily applicable approach for assessing microplastic presence.
  • Machine learning analysis of flow cytometry data enables robust microplastic detection in complex natural waters.
  • This method significantly expedites analytical workflows for microplastic monitoring and environmental assessment.