Modeling of Blood-Brain Barrier (BBB) Dysfunction and Immune Cell Migration Using Human BBB-on-a-Chip for Drug Discovery Research.

Blood-brain barrier (BBB) dysfunction is a key feature in neuroimmunological and neurodegenerative diseases. In this study, we developed a microfluidic human BBB-on-a-chip to model barrier dysfunction and immune cell migration using immortalized TY10 brain endothelial cells, pericytes, and astrocytes. It was found that immortalized TY10 brain endothelial cells developed a microvascular structure under flow. Pericytes were localized on the basal side surrounding the TY10 microvascular structure, showing an in vivo-like structure. Barrier integrity increased under co-culture with pericytes. In addition, both ethylenediaminetetraacetic acid (EDTA) and anti-Claudin-5 (CLDN5) neutralizing antibody caused a decrease in the transendothelial electrical resistance (TEER). EDTA caused the leakage of 20 kDa dextran, suggesting different effects on the BBB based on the mechanism of action, whereas anti-CLDN5 antibody did not cause leakage. In the tri-culture model, human T cells migrated through endothelial vessels towards basal C-X-C motif chemokine ligand 12 (CXCL12). The live-imaging analysis confirmed the extravasation of fluorescence-labelled T cells in a CXCL12-concentration- and time-dependent manner. Our BBB model had an in vivo-like structure and successfully represented barrier dysfunction and transendothelial T cell migration. In addition, our study suggests that the inhibition of CLDN5 attenuates the BBB in humans. This platform has various potential uses in relation to the BBB in both drug discovery research and in elucidating the mechanisms of central nervous system diseases.

Automated cell isolation from photodegradable hydrogel based on fluorescence image analysis.

We report an automated cell-isolation system based on fluorescence image analysis of cell aggregates cultured in a photodegradable hydrogel. The system incorporates cell culture in a humidified atmosphere with controlled CO2 concentration and temperature, image acquisition and analysis, micropatterned light exposure, and cell collection by pipetting. Cell aggregates were cultured on hydrogels, and target cells were selected by phase contrast and fluorescence image analysis. After degradation of the hydrogel by exposure to micropatterned UV light, cell aggregates were transferred to a collection vessel by robotic pipetting. We assessed the system for hydrogel degradation, recovery of target cells, and contamination by off-target cells. We demonstrated two practical applications of our method: (i) in cell aggregates from MCF-7-RFP strains in which 18.8% of cells produced red fluorescent protein (RFP), we successfully obtained 14 proliferative fluorescence-positive cell aggregates from 31-wells, and all of the isolated strains produced a higher proportion of RFP production than the original populations; (ii) after fluorescent immunostaining of human epidermal growth factor receptor 2 (HER2) in cancer cells, we successfully isolated HER2-positive cells from a mixed population of HER2-positive and -negative cells, and gene sequence analysis confirmed that the isolated cells mainly contained the target cells.

The importance of scoring recognition fitness in spheroid morphological analysis for robust label-free quality evaluation.

Because of the growing demand for human cell spheroids as functional cellular components for both drug development and regenerative therapy, the technology to non-invasively evaluate their quality has emerged. Image-based morphology analysis of spheroids enables high-throughput screening of their quality. However, since spheroids are three-dimensional, their images can have poor contrast in their surface area, and therefore the total spheroid recognition by image processing is greatly dependent on human who design the filter-set to fit for their own definition of spheroid outline. As a result, the reproducibility of morphology measurement is critically affected by the performance of filter-set, and its fluctuation can disrupt the subsequent morphology-based analysis. Although the unexpected failure derived from the inconsistency of image processing result is a critical issue for analyzing large image data for quality screening, it has been tackled rarely. To achieve robust analysis performances using morphological features, we investigated the influence of filter-set's reproducibility for various types of spheroid data. We propose a new scoring index, the "recognition fitness deviation (RFD)," as a measure to quantitatively and comprehensively evaluate how reproductively a designed filter-set can work with data variations, such as the variations in replicate samples, in time-course samples, and in different types of cells (a total of six normal or cancer cell types). Our result shows that RFD scoring from 5000 images can automatically rank the best robust filter-set for obtaining the best 6-cell type classification model (94% accuracy). Moreover, the RFD score reflected the differences between the worst and the best classification models for morphologically similar spheroids, 60% and 89% accuracy respectively. In addition to RFD scoring, we found that using the time-course of morphological features can augment the fluctuations in spheroid recognitions leading to robust morphological analysis.

Imaging cell picker: A morphology-based automated cell separation system on a photodegradable hydrogel culture platform.

Cellular morphology on and in a scaffold composed of extracellular matrix generally represents the cellular phenotype. Therefore, morphology-based cell separation should be interesting method that is applicable to cell separation without staining surface markers in contrast to conventional cell separation methods (e.g., fluorescence activated cell sorting and magnetic activated cell sorting). In our previous study, we have proposed a cloning technology using a photodegradable gelatin hydrogel to separate the individual cells on and in hydrogels. To further expand the applicability of this photodegradable hydrogel culture platform, we here report an image-based cell separation system imaging cell picker for the morphology-based cell separation on a photodegradable hydrogel. We have developed the platform which enables the automated workflow of image acquisition, image processing and morphology analysis, and collection of a target cells. We have shown the performance of the morphology-based cell separation through the optimization of the critical parameters that determine the system's performance, such as (i) culture conditions, (ii) imaging conditions, and (iii) the image analysis scheme, to actually clone the cells of interest. Furthermore, we demonstrated the morphology-based cloning performance of cancer cells in the mixture of cells by automated hydrogel degradation by light irradiation and pipetting.