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Machine Learning-Assisted Near-Infrared Spectral Fingerprinting for Macrophage Phenotyping
Aceer Nadeem1, Sarah Lyons1, Aidan Kindopp1
1Department of Chemical Engineering, University of Rhode Island, Kingston, Rhode Island 02881, United States.
ACS Nano
|August 16, 2024
Summary
Near-infrared spectral fingerprinting of DNA-single-walled carbon nanotubes (DNA-SWCNTs) in macrophages reveals distinct cellular phenotypes. Machine learning accurately identifies M1 and M2 macrophages based on their intraendosomal environments and DNA-SWCNT interactions.
Area of Science:
- Nanomaterials Science
- Cellular Biology
- Spectroscopy
Background:
- Spectral fingerprinting identifies compounds and cellular interactions.
- Engineered nanomaterials offer novel tools for biological studies.
Purpose of the Study:
- To investigate interactions between DNA-functionalized single-walled carbon nanotubes (DNA-SWCNTs) and live macrophage cells.
- To enable in situ phenotype discrimination of macrophages using spectral fingerprinting and machine learning.
Main Methods:
- Near-infrared (NIR) fluorescence spectral fingerprinting.
- Raman microscopy for uptake and defect ratio analysis.
- Support vector machine (SVM) model for phenotype classification.
Main Results:
- Statistically higher DNA-SWCNT uptake and lower defect ratio in M1 macrophages.
- Distinct intraendosomal environments yield significant differences in optical features.
- SVM model achieved >95% accuracy in identifying M1 and M2 macrophages.
Conclusions:
- Spectral fingerprinting of DNA-SWCNTs can serve as a marker for macrophage phenotypes.
- DNA sequence length influences DNA-SWCNT complex stability and model accuracy.
- Nanosensor platforms hold promise for real-time in vivo cellular differentiation monitoring.
