Combining Digital Holography and Flow Cytometry, a powerful imaging technique which offers an effective approach in biomedicine investigations is obtained. Indeed, the extension of phase-contrast tomography cells in microfluidic environments allows for high-throughput property addressing the rising need for realistic and comprehensive datasets. This is achieved through the rapid collection of numerous flowing and rotating cell populations by refraining from using dyes or fluorescent markers. Cells’ roto-translation motion produces holographic interference patterns which are recorded to allow implementation of the computational procedure that provides reconstructed two-dimensional phase quantitative maps which contain the whole information from the biological specimen, such as morphological biophysical properties. The capacity to examine the same biological sample from multiple perspectives enables complete label-free analysis, an accomplishment made possible through the application of holographic tomography technology. This advanced technology stands at the forefront of single-cell biological investigation. Here, we show the potential of this technique in different biomedical applications. Indeed, effectiveness of this imaging method is highlighted in identifying and analyzing cancer cells, e.g circulating tumor cells, diagnosing blood-related disorders, classifying distinct cell lines within diverse populations, and identifying intricate internal cellular microstructures. Moreover, this innovative approach hints at promising future applications across different domains, including liquid biopsy, addressing drug resistance, and understanding genetic anomalies.
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.
An emerging and developing paradigm in BioPhotonics concerns the Bio-lensing effect, that is, living organism as biological cells or their inner structures can be modeled as pure-phase objects with optical and photonics properties typical of micro-lenses. Nowadays micro-lenses are ubiquitous and a lot of effort is put into developing low-cost and low-impact fabrication processes and for searching smart and biocompatible materials. The lensing effect of different types of cells and sub-cellular compartments has been recently demonstrated generating a great impact. Indeed, the dimensions of biolenses allows to surpass all the issues related to the scaling down of the fabrication processes and, also, their natural origin solves the problem of material compatibility necessary for biomedical applications. Here, we will show the recent achievements based on the bio-lensing properties of different cells and organelles ranging from imaging, super-resolution, cell clustering and lithography.
The most used imaging method for biological samples is based on the use of markers or fluorescent colorants to label the cells micro-structures of interest. However, labeling the cell may cause alteration of its internal constituents, its natural behavior and its life cycle in the case of living cells. Digital Holography (DH) in microscopy is a powerful imaging technique which permits to obtain a posteriori multiple refocusing and quantitative phase contrast images. The main advantage of the DH is the ability to provide the cell morphological features in label-free mode. DH has been proved successfully in different biomedical applications, such as characterization and identification of cancer cells, diagnosis of blood diseases and marker-free detection of lipid droplets. We implemented a Mach-Zehnder interferometer in off-axis configuration which allows recording the resultant digital holograms. Therefore, we performed the 2D numerical reconstruction to achieve the quantitative phase maps through several computational steps, namely Fourier spectrum filtering, numerical refocusing, aberrations suppression and phase unwrapping. Here, we show a detailed study of two different classes of biological samples: HeLa cells and mouse embryonic fibroblasts. Specially, through the proposed method, we investigate the morphological variations induced by lysosomal aggregations to distinguish the difference between lysosomal storage diseases and wild type populations of both cell lines. This work demonstrates the validity and effectiveness of the presented method, revealing its potential to discriminate between healthy and unhealthy cells at subcellular level.
Holo-Tomographic Flow Cytometry is a new technology for single-cell analysis that combine Phase-Contrast Tomography and Flow Cytometry opening to a new approach in biomedical field by high-throughput, tri-dimensional imaging of unstained cell populations. Tomographic Phase Microscopy is a label-free phase contrast imaging method able to supply quantitative and volumetric refractive index distribution at single cell level in adherent or fixed populations. Here, we demonstrate that phase-contrast tomography can be achieved also for cells into a microfluidic environment obtaining accurate 3D tomographic imaging of thousands of flowing and rotating cells thanks to a robust and reliable computational strategy. Recording setups are based on Digital Holography in microscopy configurations integrated with microfluidic apparatus to record interference fringes (hologram) of rotating cells. Computational pipeline includes 3D cell tracking into the microfluidic channel, quantitative 2D phase-contrast maps retrieval for each acquired hologram, robust angle recovery code, tomographic processing to measure the inner refractive index distribution. Holo-Tomographic Flow Cytometry surpasses the limits of conventional Imaging flow cytometers because make available the recording of hundreds of informative images for each flowing cells avoiding the employment of fluorescent tags. Holo-Tomographic Flow Cytometry allows to retrieve the unique all-optical 3D fingerprint for each cell flowing into the field-of-view opening to a wide range of applications such as: (i) identification of inner subcellular compartments; (ii) recognition of nanoparticle uptake and (iii) phenotyping of different subclasses in heterogeneous populations. Future perspectives are presented in the fields of liquid biopsy, drug resistance and genetic disfunctions.
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