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Related Experiment Video

Updated: Jul 14, 2025

Enrichment of Astrocyte-Derived Extracellular Vesicles from Human Plasma
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Extracellular vesicle biomarkers for complement dysfunction in schizophrenia.

Ting Xue1,2, Wenxin Liu3, Lijun Wang1,2

  • 1Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 201108, China.

Brain : a Journal of Neurology
|October 10, 2023
PubMed
Summary

Objective diagnostic tools for schizophrenia are needed. Extracellular vesicle (EV) proteome profiling and machine learning identified novel EV biomarkers for accurate schizophrenia diagnosis and predicting treatment response.

Keywords:
antipsychotic responseextracellular vesiclemachine learningproteomicsschizophrenia

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Area of Science:

  • Neuroscience
  • Biomarker Discovery
  • Computational Biology

Background:

  • Schizophrenia diagnosis relies on subjective assessments, leading to frequent misdiagnosis.
  • Objective diagnostic tools are crucial for improving patient outcomes.
  • Extracellular vesicles (EVs) offer a promising source for developing novel biomarkers.

Purpose of the Study:

  • To develop objective diagnostic markers for schizophrenia using EV proteome profiling.
  • To create personalized discrimination scores for schizophrenia diagnosis and treatment response prediction.
  • To compare the diagnostic performance of EV-based biomarkers against plasma-based markers.

Main Methods:

  • Analysis of plasma and plasma-derived EVs from 343 participants (schizophrenia, bipolar disorder, major depressive disorder, healthy controls).
  • Utilized extracellular vesicle (EV) proteome profiling.
  • Employed XGBoost-based machine learning for biomarker discovery and score development.

Main Results:

  • Identified EVs-based complement changes specific to schizophrenia subtypes.
  • EV-based biomarkers achieved high accuracy in distinguishing schizophrenia from healthy controls (AUC=0.895).
  • Successfully differentiated schizophrenia from bipolar disorder (AUC=0.966) and major depressive disorder (AUC=0.893).
  • Personalized scores correlated with antipsychotic treatment response.

Conclusions:

  • EV proteome profiling combined with machine learning provides objective biomarkers for schizophrenia.
  • EV-based biomarkers demonstrate superior diagnostic performance compared to plasma markers.
  • This approach holds potential for personalized diagnosis and treatment guidance in schizophrenia.