(22g) AI-Assisted Immune Cell Phenotyping Via Biopolymer Functionalized Single-Walled Carbon Nanotubes: A Novel Approach to Disease Fingerprinting and Profiling

Spectral fingerprinting has emerged as a powerful tool for identifying chemical compounds and elucidating complex interactions within cells and engineered nanomaterials. Here, we leverage near-infrared (NIR) fluorescence spectral fingerprinting of Single Walled Carbon Nanotubes (SWCNTs) coupled with machine learning to reveal intricate interactions between DNA-functionalized-SWCNTs and live immune cells, enabling in situ phenotype discrimination. Through Raman microscopy, we observe statistically higher DNA-SWCNT uptake and a significantly lower defect ratio in M1 macrophages compared to M2 and naive phenotypes. Meanwhile, NIR fluorescence measurements indicate that the distinct intra-endosomal environments of these cell types produce substantial differences in optical features, including emission peak intensities, center wavelengths, and peak intensity ratios— thus enabling us to use these distinct NIR responses as a phenotype fingerprint, ultimately serving as reliable markers for accurate phenotype identification. A support vector machine (SVM) model trained on these SWCNT fluorescence data achieves >95% accuracy in discriminating M1 and M2 macrophages.

A key finding in this study highlights the importance of DNA sequence length in maintaining the stability of DNA-SWCNT complexes. Shorter sequences (e.g., GT₆) foster stronger interactions with proteins, lipids, metabolites and other biomolecules in the endosomal microenvironment, leading to improved classification accuracy (>87%). Collectively, these insights underscore the potential of SWCNT and NIR spectral fingerprinting, especially when integrated with machine learning, as a robust and noninvasive platform for real-time cell phenotype characterization. This innovative approach opens new avenues for phenotype fingerprinting, disease profiling, in vivo monitoring of dynamic cellular processes, disease progression, biomarker discovery and therapeutic response. By bridging the gap between spectral analysis and cellular dynamics, our methodology provides a significant advance in bio-nanotechnology-based diagnostics, offering heightened sensitivity and specificity for next-generation biomedicine and clinical interventions.