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<p>Microplastics are pervasive environmental contaminants of increasing ecological concern. Despite substantial advances in analytical techniques, reliable microplastic detection remains challenging due to their small size, physicochemical heterogeneity, and close resemblance to natural particles. Additionally, many conventional detection techniques are inherently slow, low-throughput, or limited in their ability to detect the smallest and most environmentally relevant microplastics. Standard flow cytometry is emerging as a complementary approach for microplastic detection, offering rapid, high-throughput, multiparametric analysis with high sensitivity for identifying small particles. Imaging flow cytometry offers these capabilities while incorporating image acquisition to enable morphological characterisation and visual confirmation of microplastics. Despite this potential, the application of both techniques to environmental microplastic analysis remains relatively underexplored. This review provides a comprehensive evaluation of standard and imaging flow cytometry for environmental microplastic detection, examining stain-based and stain-free workflows and highlighting the complementary strengths of each approach. We critically compare the performance, advantages, limitations, and applications of both techniques alongside microscopy, spectroscopy, imaging, and thermo-analytical methods. Particular emphasis is placed on the environmental and methodological factors influencing detection and the remaining challenges associated with applying these techniques to environmental samples. Finally, emerging technologies, including machine learning, artificial intelligence, spectral flow cytometry, and hybrid analytical approaches, are discussed as future directions for improving the sensitivity, robustness, and broader environmental applicability of both techniques. Together, these developments have the potential to support the integration of standard and imaging flow cytometry into future environmental microplastic monitoring workflows.</p>

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Standard and imaging flow cytometry for environmental microplastic detection: progress, challenges, and future directions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月30日

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  • Georgina H. Tilly-Scholes,
  • Abbey L. K. Hutton,
  • Anita Tarasenko,
  • Jessica A. P. Carlson-Jones,
  • James S. Paterson,
  • James G. Mitchell,
  • Oliver G. Best

摘要

Microplastics are pervasive environmental contaminants of increasing ecological concern. Despite substantial advances in analytical techniques, reliable microplastic detection remains challenging due to their small size, physicochemical heterogeneity, and close resemblance to natural particles. Additionally, many conventional detection techniques are inherently slow, low-throughput, or limited in their ability to detect the smallest and most environmentally relevant microplastics. Standard flow cytometry is emerging as a complementary approach for microplastic detection, offering rapid, high-throughput, multiparametric analysis with high sensitivity for identifying small particles. Imaging flow cytometry offers these capabilities while incorporating image acquisition to enable morphological characterisation and visual confirmation of microplastics. Despite this potential, the application of both techniques to environmental microplastic analysis remains relatively underexplored. This review provides a comprehensive evaluation of standard and imaging flow cytometry for environmental microplastic detection, examining stain-based and stain-free workflows and highlighting the complementary strengths of each approach. We critically compare the performance, advantages, limitations, and applications of both techniques alongside microscopy, spectroscopy, imaging, and thermo-analytical methods. Particular emphasis is placed on the environmental and methodological factors influencing detection and the remaining challenges associated with applying these techniques to environmental samples. Finally, emerging technologies, including machine learning, artificial intelligence, spectral flow cytometry, and hybrid analytical approaches, are discussed as future directions for improving the sensitivity, robustness, and broader environmental applicability of both techniques. Together, these developments have the potential to support the integration of standard and imaging flow cytometry into future environmental microplastic monitoring workflows.