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Whole-cell MALDI-TOF Mass Spectrometry is an Accurate and Rapid Method to Analyze Different Modes of Macrophage Activation

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Machine learning-based label-free macrophage phenotyping in immune-material interactions.

Chawalwat Martkamjan^1,2, Kornlavit Lerdsudkanung^1,2, Paul Sean Tipay^2,3

¹International School of Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand.

Journal of Materials Chemistry. B

|April 28, 2025

Summary

This summary is machine-generated.

This study introduces an AI-powered imaging technique for non-invasive macrophage phenotyping, crucial for improving implant biocompatibility. The method accurately distinguishes macrophage subtypes, aiding in the development of safer biomedical implants.

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Area of Science:

Biomedical Engineering
Immunology
Materials Science

Background:

Implantable biomedical materials require understanding macrophage interactions for optimal immunocompatibility.
Macrophages are key immune regulators with plastic phenotypes (M1/M2), but conventional methods have limitations.
Developing advanced techniques for label-free macrophage phenotyping is essential.

Purpose of the Study:

To develop and validate a high-throughput, label-free macrophage phenotyping approach using AI and quantitative phase imaging (QPI).
To assess macrophage responses to different collagen coatings (types I, III, IV) for biomaterial applications.

Main Methods:

Differentiated THP-1 macrophages into M0, M1, M2a, and M2c phenotypes.
Utilized quantitative phase imaging (QPI) to analyze morphological and refractive index properties.
Employed deep learning models (GoogLeNet, ShuffleNet, VGG-16, ResNet-18) for AI-driven image classification, with ResNet-18 achieving >90% accuracy.
Assessed macrophage responses to collagen coatings using machine learning and cytokine profiling.

Main Results:

QPI combined with deep learning (ResNet-18) enabled accurate (>90%) macrophage phenotyping.
Collagen I coating induced a pro-inflammatory (M1) response.
Collagen III supported a balanced M1/M2 profile, while collagen IV promoted a controlled immune environment.

Conclusions:

AI-driven QPI offers a non-invasive, high-throughput method for macrophage characterization.
This approach provides valuable insights into biomaterial immunocompatibility.
Findings can inform the design of next-generation biomedical implants with enhanced biocompatibility.