The recent development of tomographic phase imaging flow cytometry has unlocked the possibility to achieve data throughput comparable to the state-of-the-art imaging flow cytometry systems, but with the great advantages to be fully label-free and 3D. On the other hand, the huge amount of data to manage becomes one of the main computational problems to face with. Here we show that by using the 3D version of Zernike polynomials it is possible to efficiently encode single-cell phase-contrast tomograms, demonstrating high data compression capability with negligible information loss. A full simulative analysis is reported also quantifying the trade-off between compression factor and representation accuracy.
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.
Cellular populations are often heterogeneous, thus the intraspecies variability is usually lost in the average measurements accessed by conventional measuring devices. For this reason, the single-cell analysis provided by optical microscopy is opening new promising perspectives in biomedicine. The gold standard technique in this context is Fluorescence Imaging Flow Cytometry (FIFC), as big datasets of single cells flowing in suspension through a measuring device can be collected in short times and multiple parameters can be measured at the single-cell level. However, to overcome the limitations related to the staining process, in the recent years Holographic Imaging Flow Cytometry (HIFC) has been proved to be a viable label-free alternative to FIFC, able to give access also to the cell biophysical features. Very recently, the latest evolution of HIFC has been demonstrated, i.e. Tomographic Phase Imaging Flow Cytometry (TPIFC), in which the 3D spatial distribution of the refractive indices of single cells can be reconstructed thanks to their roto-translation along a microfluidic channel. However, to retrieve one single tomogram, hundreds of digital holograms must be converted into the corresponding phase-contrast maps. Currently, this is the actual bottleneck for the high-throughput TPIFC, which aims to a fast 3D analysis of the cellular populations. Therefore, here we show that a fully convolutional end-to-end context aggregation neural network can greatly speed up the phase retrieval process, thus reducing the computational time for the tomographic reconstruction from tens of minutes to few seconds, while providing at the same time high fidelity and small memory footprint.
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.
Tomographic phase microscopy in cytometry environment is feasible at single cell level and without the a-priori knowledge of the cell orientation. In the present paper we demonstrate different strategies for recovery the rotation angles of single cells and clusters when rotating into microfluidic channels, thus realistically opening to the implementation of marker-free cytofluorimeter for three-dimensional imaging of biological fluids. The pioneering developed strategies allows to measure quantitatively the inner distribution of the refractive indexes inside the cell volume avoiding the use of chemical and fluorescent tags. The imaging apparatus is based on label-free Digital Holography in microscopy setup designed in transmission geometry to image 700x700μm Field of View with lateral resolution of 0.5μm. Digital Holography is perfectly suited for imaging in microchannels as it allows the numerical refocusing of sample into a three-dimensional volume. In the present paper, such imaging arrangement is combined with a high-precision pumping system connected to a microfluidic channel that allow the complete rotation of the flowing cells into the Field of View. High-speed 25Megapixel camera acquires holographic set measurements of all rotating cells that are numerically processed to obtain quantitative two-dimensional phase-contrast maps at different view angles. Accurate numerical algorithms allow to tag each phasecontrast maps with the rotation angle in the microchannel. The couples made of phase-contrast map and measured angle are given as input at tomography algorithms to obtain the refractive index distribution into the cell volume. The approach in principles works properly for any kind of biological matter subjected to rotation as already demonstrated in case of nuclei of plant cells during dehydration. Furthermore, the same approach allows to show the three-dimensional distribution of internalized nano-particles as in case of nano-graphene oxide. The most important achievement and innovation of such strategy is the high-throughput phase-contrast tomography at single cell level that opens to new diagnostic tool thanks to the possibility to have statistically relevant measurement on cell population and also for the possibility to use artificial intelligence architecture for cell identification and classification.
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