# Dark-Field Hyperspectral Microscopy for Carbon Nanotubes Bioimaging
## 5 Conclusions and Future Prospects  
Although dark-field imaging, as presented in the literature, allows direct observations of carbon nanotubes, the technique per se is insufficient for cell-nanoparticle identification. Modifications of DFM, such as enhanced dark-field optics coupled with fluorescence modules and hyperspectral sensors, have significantly increased the applicability of the method for cell biology, permitting spectral identification of particles, quantitative and qualitative analysis of cellular and nuclear uptake, and assessment of drug delivery efficiency. Comprehensive in vivo distribution studies of carbon nanotubes have demonstrated benefits from the EDFM-his technique over conventional methods due to fast sampling and robust detection in tissue sections. However, the method is not without the limitations arising when accurate subcellular imaging is needed. The hyperspectral analysis is also complicated in the case of histological staining, leading to the possibility of a false-positive outcome. At the same time, the increase in the amount of heterogenous spatio-spectral information obtained using correlation microscopy, as well as its still-developing stage,  
Figure 4: (**A**) Dark-field image (400\(\times\)) using Particle Fitting Function to locate maximally carboxylated MWCNT in lung tissue of a BALB/c mouse following three-day particle exposure. Bright grey areas designate regions with nanotubes. (**B**) Enhanced dark-field image of the transition region between a terminal bronchiole and first alveolar duct bifurcation of C57 BL/6J mouse at 168 days after exposure to MWCNT. The large arrow indicates a cluster of MWCNTs (white fibres) in the ridge of the first alveolar duct bifurcation. Smaller arrows indicate some of the numerous singlets and small MWCNT structures distributed throughout the alveolar septa of this critical transition region between conducting airways and gas exchange regions of the lungs. (**C**) Enhanced dark-field image of MWCNT fibres in the brain of C57 BL/6J mouse at 336 days after inhalation exposure. MWCNT fibre is bright white, cell nuclei are brownish red, and other tissue elements are green. Figure 4A adapted from Ref. [89] under the terms of the Creative Commons Attribution License (CC-BY 4.0), scale not specified. Figure 4B adapted from Ref. [94] under the terms of the Creative Commons Attribution License (CC-BY 2.0). Figure 4C adapted from Ref. [96] under the terms of the Creative Commons Attribution License (CC-BY 2.0).  
leads to another challenge of efficient data processing. Recent studies have shown the power of machine learning algorithms, including neural networks in environmental studies and bacterial identification. This approach automates spectral feature selection, excluding the necessity for data pre-processing, and may help improve the performance of EDFM-HSI for nanomaterial systematic investigation in the future.  
Writing--original draft preparation, I.I.; writing--review and editing, R.F. All authors have read and agreed to the published version of the manuscript.  
The work was funded by Russian Science Foundation grant 20-13-00247.  
Not applicable.  
The authors declare no conflict of interest.