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Related Concept Videos

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Identifying Immune-related Molecular Biomarkers in Autism Spectrum Disorder Using Data-independent Acquisition

Jun He1, Qingqing Hu2, Sifeng Wang1

  • 1College of Life Sciences, Hunan Normal University; Changsha Maternal and Child Health Hospital, Affiliated to Hunan Normal University.

Journal of Visualized Experiments : Jove
|October 13, 2025
PubMed
Summary
This summary is machine-generated.

Researchers developed a reproducible method using data-independent acquisition (DIA) mass spectrometry and machine learning (ML) to find autism spectrum disorder (ASD) biomarkers. This approach identified eight immune proteins with high accuracy, paving the way for new diagnostic tools.

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Area of Science:

  • Biochemistry
  • Proteomics
  • Computational Biology

Background:

  • Autism spectrum disorder (ASD) diagnosis relies on behavioral observation, lacking objective biological markers.
  • Serum proteomic analysis offers potential for identifying reliable biomarkers for ASD.
  • Previous proteomic studies faced challenges with reproducibility and identifying low-abundance proteins.

Purpose of the Study:

  • To establish a reproducible protocol for identifying serum protein biomarkers for ASD.
  • To leverage data-independent acquisition (DIA) mass spectrometry for comprehensive proteome profiling.
  • To apply machine learning (ML) for robust biomarker panel selection and diagnostic model development.

Main Methods:

  • Serum samples from 99 children with ASD and 70 controls were analyzed.
  • High-abundance proteins were depleted, followed by standardized peptide preparation and fractionation.
  • Data-independent acquisition (DIA) mass spectrometry was employed for high-resolution proteome analysis.
  • Machine learning algorithms were used for differential protein expression analysis and model building.

Main Results:

  • Eight immune-related proteins were identified as strong candidates for ASD biomarker development.
  • A logistic regression model achieved 95.27% accuracy, a Kappa of 0.9025, and an AUC of 1.000 in cross-validation.
  • The DIA-MS and ML approach demonstrated high reproducibility and robustness in biomarker discovery.

Conclusions:

  • DIA-based proteomics combined with ML provides a powerful framework for ASD biomarker discovery.
  • The identified immune protein panel shows significant potential for developing objective diagnostic tools for ASD.
  • This methodology can be adapted for biomarker discovery in other complex neurological and psychiatric disorders.