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High-throughput screening system for immunogenic cell death inducers using artificial intelligence-based real-time

Eunseo Kim1, Donghoon Jang2, Minji Kim1

  • 1Department of Biomedical Science, Program in Biomedical Science and Engineering, Graduate school, Inha University, Incheon, 22212, Republic of Korea.

Computers in Biology and Medicine
|July 23, 2025
PubMed
Summary

An artificial intelligence (AI) system screens for immunogenic cell death (ICD) inducers by analyzing cell morphology. This AI-based approach efficiently identifies potential cancer immunotherapy agents, improving drug discovery.

Keywords:
Cell detectionDamage-associated molecular patternsHigh-throughput screeningImmunogenic cell death inducerReal-time image analysisTransfer learningdeep learning

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Area of Science:

  • Immunology
  • Biotechnology
  • Computational Biology

Background:

  • Immunogenic cell death (ICD) is crucial for converting immunologically 'cold' tumors into 'hot' tumors, thereby enhancing cancer immunotherapy efficacy.
  • Effective screening of ICD inducers is challenging due to the need for rapid, large-scale assessment of cellular morphology and damage-associated molecular pattern (DAMP) dynamics.
  • Advanced image-processing capabilities are essential for developing efficient ICD screening systems.

Purpose of the Study:

  • To develop an artificial intelligence (AI)-based detector for high-throughput screening (HTS) of ICD inducers.
  • To identify typical cellular morphologies associated with ICD using AI.
  • To improve the efficiency and accuracy of ICD inducer screening.

Main Methods:

  • Developed an AI detector leveraging transfer learning from fluorescent markers and fine-tuning with differential interference contrast (DIC) images.
  • Utilized model-assisted labeling (MAL) to enhance annotation efficiency and reduce manual labeling efforts.
  • Validated AI-identified candidates through analyses of cell death type, DAMP release, and immune activation.

Main Results:

  • The AI system successfully identified three ICD-inducing agents from eight candidates in a blind test.
  • The AI-based HTS system efficiently screened ICD candidates using only real-time optical images, significantly reducing time and resources.
  • The system demonstrated the ability to detect subtle morphological differences often missed by manual analysis.

Conclusions:

  • The developed AI-based HTS system offers an efficient method for identifying ICD inducers.
  • This approach has the potential to accelerate the discovery of novel cancer immunotherapies.
  • The AI system shows promise for ICD prediction, foundational research, and broader screening applications in drug discovery.