Mathias Girault

Development of On-Chip Multi-Imaging Flow Cytometry for Identification of Imaging Biomarkers of Clustered Circulating Tumor Cells

An on-chip multi-imaging flow cytometry system has been developed to obtain morphometric parameters of cell clusters such as cell number, perimeter, total cross-sectional area, number of nuclei and size of clusters as “imaging biomarkers”, with simultaneous acquisition and analysis of both bright-field (BF) and fluorescent (FL) images at 200 frames per second (fps); by using this system, we examined the effectiveness of using imaging biomarkers for the identification of clustered circulating tumor cells (CTCs). Sample blood of rats in which a prostate cancer cell line (MAT-LyLu) had been pre-implanted was applied to a microchannel on a disposable microchip after staining the nuclei using fluorescent dye for their visualization, and the acquired images were measured and compared with those of healthy rats. In terms of the results, clustered cells having (1) cell area larger than 200 µm2 and (2) nucleus area larger than 90 µm2 were specifically observed in cancer cell-implanted blood, but were not observed in healthy rats. In addition, (3) clusters having more than 3 nuclei were specific for cancer-implanted blood and (4) a ratio between the actual perimeter and the perimeter calculated from the obtained area, which reflects a shape distorted from ideal roundness, of less than 0.90 was specific for all clusters having more than 3 nuclei and was also specific for cancer-implanted blood. The collected clusters larger than 300 µm2 were examined by quantitative gene copy number assay, and were identified as being CTCs. These results indicate the usefulness of the imaging biomarkers for characterizing clusters, and all of the four examined imaging biomarkers—cluster area, nuclei area, nuclei number, and ratio of perimeter—can identify clustered CTCs in blood with the same level of preciseness using multi-imaging cytometry.

Identification of cells using morphological information of bright field/fluorescent multi-imaging flow cytometer images

We have examined the ability of real-time simultaneous measurement of bright field/fluorescent images of cells in an on-chip bright field/fluorescent multi-imaging flow cytometer system. The system consists of (1) a disposable microfluidic hydrofocusing flow cytometry chip, (2) an optical microscopy module with splittable bright field/fluorescent multi-imaging optics, and (3) a real-time image-processing module with a 200 images/s high-speed digital camera. In the double “Y” shape three-way-inlet microfluidic pathways fabricated in the poly(dimethylsiloxane) (PDMS) microchip, we applied fluorescent polystyrene standard beads and HeLa cells stained with fluorescent dye, Hoechst 33258, and measured the z-axis (depth) dependence of the morphological index; the intensity profile of cells and nuclei. Then, we measured the tendency of the blur of bright field/fluorescent images in the simultaneous measurement of bright field/fluorescent images on a single light-receiving surface, and found that their blurs were similar within the same range of the depth of the microfluidic pathway for small cell cluster measurement, 25 µm. Hence, the fluorescent images were applied as supporting information of the bright field images of cell clusters at the focal plane for the cell number counting. The result indicates the potential of precise identification of various types of cells by simultaneous morphological analysis of bright field and fluorescent images distributed with a single camera in a wider depth of microfluidic chip as a substitute for conventional biomarker detection.

Algorithm for the precise detection of single and cluster cells in microfluidic applications

Recent advances in imaging flow cytometry and microfluidic applications have led to the development of suitable mathematical algorithms capable of detecting and identifying targeted cells in images. In contrast to currently existing algorithms, we herein proposed the identification and reconstruction of cell edges based on original approaches that overcome frequent detection limitations such as halos, noise, and droplet boundaries in microfluidic applications. Reconstructed cells are then discriminated between single cells and clusters of round‐shaped cells, and cell information such as the area and location of a cell in an image is output. Using this method, 76% of cells detected in an image had an error <5% of the cell area size and 41% of the image had an error <1% of the cell area size (n = 1,000). The method developed in the present study is the first image processing algorithm designed to be flexible in use (i.e. independent of the size of an image, using a microfluidic droplet system or not, and able to recognize cell clusters in an image) and provides the scientific community with a very accurate imaging algorithm in the field of microfluidic applications.

Microfluidic technology for plankton research

Plankton produces numerous chemical compounds used in cosmetics and functional foods. They also play a key role in the carbon budget on the Earth. In a context of global change, it becomes important to understand the physiological response of these microorganisms to changing environmental conditions. Their adaptations and the response to specific environmental conditions are often restricted to a few active cells or individuals in large populations. Using analytical capabilities at the subnanoliter scale, microfluidic technology has also demonstrated a high potential in biological assays. Here, we review recent advances in microfluidic technologies to overcome the current challenges in high content analysis both at population and the single cell level.

High-Content Screening of Plankton Alkaline Phosphatase Activity in Microfluidics

One way for phytoplankton to survive orthophosphate depletion is to utilize dissolved organic phosphorus by expressing alkaline phosphatase. The actual methods to assay alkaline phosphate activity-either in bulk or as a presence/absence of enzyme activity-fail to provide information on individual living cells. In this context, we develop a new microfluidic method to compartmentalize cells in 0.5 nL water-in-oil droplets and measure alkaline phosphatase activity at the single-cell level. We use enzyme-labeled fluorescence (ELF), which is based on the hydrolysis of ELF-P substrate, to monitor in real time and at the single-cell level both qualitative and quantitative information on cell physiology (i.e., localization and number of active enzyme sites and alkaline phosphatase kinetics). We assay the alkaline phosphatase activity of Tetraselmis sp. as a function of the dissolved inorganic phosphorus concentration and show that the time scale of the kinetics spans 1 order of magnitude. The advantages of subnanoliter-scale compartmentalization in droplet-based microfluidics provide a precise characterization of a population with single-cell resolution. Our results highlight the key role of cell physiology to efficiently access dissolved organic phosphorus.

Particle recognition in microfluidic applications using a template matching algorithm

We herein examined the ability of a template matching algorithm to recognize particles with diameters ranging from 1 to 20 µm in a microfluidic channel. The algorithm consisted of measurements of the distance between the templates and the images captured with a high-speed camera in order to search for the presence of the desired particle. The results obtained indicated that the effects of blur and diffraction rings observed around the particle are important phenomena that limit the recognition of a target. Owing to the effects of diffraction rings, the distance between a template and an image is not exclusively linked to the position of the focus plane; it is also linked to the size of the particle being searched for. By using a set of three templates captured at different Z focuses and an 800× magnification, the template matching algorithm has the ability to recognize beads ranging in diameter from 1.7 to 20 µm with a resolution between 0.3 and 1 µm.