Liquid biopsy is an emerging and promising biomedical tool that aims to the early cancer diagnosis and the definition of personalized therapies in non-invasive and cost-effective way, since it is based on the blood sample analysis. Several strategies have been tested to implement an effective liquid biopsy system. Among them, searching of circulating tumor cells (CTCs) released by the tumor into the bloodstream can be a valid solution. Within a blood sample, CTCs can be considered as rare cells due to their extremely low percentage with respect to white blood cells (WBCs). Therefore, a technology able to perform an advanced single-cell analysis is requested for implementing a CTCs-based liquid biopsy. Recently, tomographic phase imaging flow cytometry (TPIFC) has been developed as a technique for the reconstruction of the 3D volumetric distribution of the refractive indices (RIs) of single cells flowing along a microfluidic channel. Hence, TPIFC allows collecting large datasets of single cells thanks to the flow-cytometry high-throughput property in 3D and quantitative manner. Moreover, TPIFC works in label-free modality as no exogenous marker is employed, thus avoiding the limitations of marker-based techniques. For this reason, here we investigate the possibility of exploiting the 3D dataset of single cells recorded by TPIFC to feed a machine learning model, in order to recognize tumor cells with respect to a background of monocytes, which are the most similar cells among the WBCs in terms of morphology. Reported results aim to emulate a real scenario for the label-free liquid biopsy based on TPIFC.
In recent years, the dynamic role of Lipid Droplets (LDs) in many cellular activities has been increasingly brought to light. In fact, it has been discovered that LDs are involved in many pathologies (e.g., diabetes, atherosclerosis, pathogen infections, neurodegenerative diseases and cancer). Moreover, it has been demonstrated that their number and size increase during an inflammation or infectious inside the immune cells, also with the COVID-19. Therefore, detecting LDs within single cells could aid the diagnosis of several pathologies. Currently, the gold-standard technique in this field is Fluorescence Imaging Flow Cytometry (FIFC), in which the single-cell analysis of fluorescence microscopy is implemented in high-throughput modality thanks to the flow-cytometry module. However, to overcome the drawbacks related to the fluorescence staining, Holographic Imaging Flow Cytometry (HIFC) has gaining momentum as label-free alternative to the FIFC tool. Thanks to the interferometric principles at the basis of digital holography, it has been already demonstrated that a suspended cell acts as a biological lens with specific focusing features. Here we show that the presence of intracellular LDs inside the cell is able to change its focalization features, measured through a HIFC system. Therefore, based on this property, we demonstrate that a detection of single cells containing intracellular LDs is possible by means of a direct analysis of the digital holograms recorded in flow cytometry modality. The attained results open the route to the development of a fast, non-destructive, and high-throughput tool for the diagnosis of LDs-related pathologies by exploiting the biolens’ signature in HIFC.
Detection and quantification of intracellular structures is fundamental in biomedical sciences. New emerging inspection tools based on holographic microscopy and quantitative phase imaging can give answers to such critical demands. Holographic tomography (HT) systems are the best candidates for this challenge. Recently, HT has been demonstrated working in flow-cytometry (FC) modality. Results show that the novel HTFC tool is capable to furnish 3D visualization and quantifications of the different intracellular particles. In particular, here we report that exogenous nanographene oxide particles as well as endogenous lipid droplets can be detected, measured, and visualized in each flowing cell by label-free HTFC. This method opens the way for accurate and high-throughput measurements at the 3D single-cell level for different applications such as diagnosis of diseases, development of drug delivery applications, and examination of cell functionalities. Experiments and processing methods will be described, and several examples will be discussed.
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