Machine Learning-Assisted Near-Infrared Spectral Fingerprinting for Macrophage Phenotyping.

ABSTRACT: Spectral fingerprinting has emerged as a powerful tool that is adept at identifying chemical compounds and deciphering complex interactions within cells and engineered nanomaterials. Using near-infrared (NIR) fluorescence spectral fingerprinting coupled with machine learning techniques, we uncover complex interactions between DNA-functionalized single-walled carbon nanotubes (DNA-SWCNTs) and live macrophage cells, enabling in situ phenotype discrimination. Utilizing Raman microscopy, we showcase statistically higher DNA-SWCNT uptake and a significantly lower defect ratio in M1 macrophages compared to M2 and naive phenotypes. NIR fluorescence data also indicate that distinctive intraendosomal environments of these cell types give rise to significant differences in many optical featur

SUBMITTER: Nadeem A 

PROVIDER: S-EPMC12020776 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature