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Single-walled carbon nanotubes (SWCNTs) are increasingly being investigated for biomedical imaging, sensing, and drug delivery. Cell types, cellular entry mechanisms, and SWCNT lengths dictate SWCNT uptake, subsequent intracellular trafficking, and retention. Specialized immune cells known as macrophages are capable of two size-dependent entry mechanisms: endocytosis of small particles (diameter < 200 nm) and phagocytosis of large particles (diameter > 500 nm). In comparison, fibroblasts uptake particles predominantly through endocytosis. We report dependence of cellular processing including uptake, subcellular distribution, and retention on the SWCNT length and immune cell-specific processes. We chose SWCNTs of three different average lengths: 50 nm (ultrashort, US), 150 nm (short) and 500 nm (long) to encompass two different entry mechanisms, and noncovalently dispersed them in water, cell culture media, and phosphate buffer (pH 5) with bovine serum albumin, which maintains the SWCNT optical properties and promotes their cellular uptake. Using confocal Raman imaging and spectroscopy, we quantified cellular uptake, tracked the intracellular dispersion state ( i.e. , individualized versus bundled), and monitored recovery as a function of SWCNT lengths in macrophages. Cellular uptake of SWCNTs increases with decreasing SWCNT length. Interestingly, short-SWCNTs become highly bundled in concentrated phase dense regions of macrophages after uptake and most of these SWCNTs are retained for at least 24 h. On the other hand, both US- and long-SWCNTs remain largely individualized after uptake into macrophages and are lost over a similar elapsed time. After uptake into fibroblasts, however, short-SWCNTs remain individualized and are exocytosed over 24 h. We hypothesize that aggregation of SWCNTs within macrophages but not fibroblasts may facilitate the retention of SWCNTs within the former cell type. Furthermore, the differential length-dependent cellular processing suggests potential applications of macrophages as live cell carriers of SWCNTs into tumors and regions of inflammation for therapy and imaging.
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Machine Learning-Assisted Near-Infrared Spectral Fingerprinting for Macrophage Phenotyping
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 features, such as emission peak intensities, center wavelengths, and peak intensity ratios. Such features serve as distinctive markers for identifying different macrophage phenotypes. We further use a support vector machine (SVM) model trained on SWCNT fluorescence data to identify M1 and M2 macrophages, achieving an impressive accuracy of >95%. Finally, we observe that the stability of DNA-SWCNT complexes, influenced by DNA sequence length, is a crucial consideration for applications, such as cell phenotyping or mapping intraendosomal microenvironments using AI techniques. Our findings suggest that shorter DNA-sequences like GT6 give rise to more improved model accuracy (>87%) due to increased active interactions of SWCNTs with biomolecules in the endosomal microenvironment. Implications of this research extend to the development of nanomaterial-based platforms for cellular identification, holding promise for potential applications in real time monitoring of in vivo cellular differentiation.
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- PAR ID:
- 10536783
- Publisher / Repository:
- American Chemical Society
- Date Published:
- Journal Name:
- ACS Nano
- Volume:
- 18
- Issue:
- 34
- ISSN:
- 1936-0851
- Page Range / eLocation ID:
- 22874 to 22887
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1021/acsnano.4c03387
