Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments.

Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy's utility. Recently, it has been shown that artificial intelligence (AI) can transform one form of contrast into another. We present phase imaging with computational specificity (PICS), a combination of quantitative phase imaging and AI, which provides information about unlabeled live cells with high specificity. Our imaging system allows for automatic training, while inference is built into the acquisition software and runs in real-time. Applying the computed fluorescence maps back to the quantitative phase imaging (QPI) data, we measured the growth of both nuclei and cytoplasm independently, over many days, without loss of viability. Using a QPI method that suppresses multiple scattering, we measured the dry mass content of individual cell nuclei within spheroids. In its current implementation, PICS offers a versatile quantitative technique for continuous simultaneous monitoring of individual cellular components in biological applications where long-term label-free imaging is desirable.

Epi-illumination gradient light interference microscopy for imaging opaque structures.

Multiple scattering and absorption limit the depth at which biological tissues can be imaged with light. In thick unlabeled specimens, multiple scattering randomizes the phase of the field and absorption attenuates light that travels long optical paths. These obstacles limit the performance of transmission imaging. To mitigate these challenges, we developed an epi-illumination gradient light interference microscope (epi-GLIM) as a label-free phase imaging modality applicable to bulk or opaque samples. Epi-GLIM enables studying turbid structures that are hundreds of microns thick and otherwise opaque to transmitted light. We demonstrate this approach with a variety of man-made and biological samples that are incompatible with imaging in a transmission geometry: semiconductors wafers, specimens on opaque and birefringent substrates, cells in microplates, and bulk tissues. We demonstrate that the epi-GLIM data can be used to solve the inverse scattering problem and reconstruct the tomography of single cells and model organisms.

Decellularized Matrix Produced by Mesenchymal Stem Cells Modulates Growth and Metabolic Activity of Hepatic Cell Cluster.

Miniature organlike three-dimensional cell clusters often called organoids have emerged as a useful tool for both fundamental and applied bioscience studies. However, there is still a great need to improve the quality of organoids to a level where they exhibit similar biological functionality to an organ. To this end, we hypothesized that a decellularized matrix derived from mesenchymal stem cell (MSC) could regulate the phenotypic and metabolic activity of organoids. This hypothesis was examined by culturing cells of interest in the decellularized matrix of MSCs cultured on a 2D substrate at confluency or in the form of spheroids. The decellularized matrix prepared with MSC spheroids showed a 3D porous structure with a higher content of extracellular matrix molecules than the decellularized matrix derived from MSCs cultured on a 2D substrate. HepG2 hepatocarcinoma cells, which retain the metabolic activity of hepatocytes, were cultured in these decellularized matrices. Interestingly, the decellularized matrix from the MSC spheroids served to develop the hepatic cell clusters with higher levels of E-cadherin-mediated cell-cell adhesion and detoxification activity than the decellularized matrix from the MSCs cultured on a 2D substrate. Overall, the results of this study are useful in improving biological functionality of a wide array of organoids.