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<p>The early and precise diagnosis of gynecological malignancies, such as cervical cancer, is critical for improving patient treatments. Extracellular vesicles (EVs), such as exosomes, which carry molecular signals from their parental cells, offer a promising method for non-invasive liquid biopsy, however, conventional detection methods are often complex, high in reagent consumption, and susceptible to environmental fluctuations. To address this, we present an integrated, self-validated microfluidic system for the rapid, on-chip isolation and multiplexed identification of the gynecological EV markers PD-L1 and ERBB3. The chip achieved simultaneous on-chip processing of test and positive samples for parallel analysis within 1&#xa0;h, enabling synchronous detection under the same conditions and thereby significantly enhancing the reliability of the assay. Additionally, a deep learning YOLOv8-based self-validated detection strategy facilitates automated and precise fluorescence identification. Validation with four cell lines (SiHa, C33A, HeLa, and H8) revealed remarkable EV protein signatures, achieving a limit of detection (LOD) of 15.56 particles/μL. This platform provides an integrated tool for sensitive and precise EV marker analysis, holding prospective potential for the early screening and personalized therapy guidance of gynecological tumor detection.</p> Graphical abstract <p></p> <p>Integrated analytical system for one-stop and self-validated exosome complex formation and multiplex tumor fingerprint analysis by deep learning. Exosome samples were immuno-isolated and labeled with probes, followed by monodispersed among the particular arrays for deep learning model YOLOv8-based positional migration and identification automatically. Four kinds of samples were measured and remarkable differences were acquired, the two tumor progressions: immune evasion and proliferative signaling were revealed. The integrated system is pospective for sensitive, easy-handing and automatic exosome markers analysis in POCT field.</p>

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An integrated microfluidic system for automatic and self-validated analysis of cervical extracellular vesicle markers PD-L1 and ERBB3

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月16日

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  • Yunxing Lu,
  • Han Qin,
  • Wenjing Zhang,
  • Qiang Shi,
  • Jianan Hui,
  • Zhenhua Wu,
  • Yiman Song,
  • Xiaoyue Yang

摘要

The early and precise diagnosis of gynecological malignancies, such as cervical cancer, is critical for improving patient treatments. Extracellular vesicles (EVs), such as exosomes, which carry molecular signals from their parental cells, offer a promising method for non-invasive liquid biopsy, however, conventional detection methods are often complex, high in reagent consumption, and susceptible to environmental fluctuations. To address this, we present an integrated, self-validated microfluidic system for the rapid, on-chip isolation and multiplexed identification of the gynecological EV markers PD-L1 and ERBB3. The chip achieved simultaneous on-chip processing of test and positive samples for parallel analysis within 1 h, enabling synchronous detection under the same conditions and thereby significantly enhancing the reliability of the assay. Additionally, a deep learning YOLOv8-based self-validated detection strategy facilitates automated and precise fluorescence identification. Validation with four cell lines (SiHa, C33A, HeLa, and H8) revealed remarkable EV protein signatures, achieving a limit of detection (LOD) of 15.56 particles/μL. This platform provides an integrated tool for sensitive and precise EV marker analysis, holding prospective potential for the early screening and personalized therapy guidance of gynecological tumor detection.

Graphical abstract

Integrated analytical system for one-stop and self-validated exosome complex formation and multiplex tumor fingerprint analysis by deep learning. Exosome samples were immuno-isolated and labeled with probes, followed by monodispersed among the particular arrays for deep learning model YOLOv8-based positional migration and identification automatically. Four kinds of samples were measured and remarkable differences were acquired, the two tumor progressions: immune evasion and proliferative signaling were revealed. The integrated system is pospective for sensitive, easy-handing and automatic exosome markers analysis in POCT field.