High-Content Analysis of Single Cells Using a Wide-Field Imaging Sensor

Abstract

Introduction Single cell analysis can highlight the heterogeneity of individual cells that conventional bulk analysis cannot address, and thus has attracted much attention from a broad range of scientific fields. A number of techniques have emerged in order to separate, array, and detect single cells, however, conventional microscopy is an almost only choice to visualize biological phenomena in single cells. There is no doubt that microscopy is a powerful tool for the single cell analysis, while the inherent low throughput hampers the analysis of very rare cells or biological events. To address this issue, we have proposed a high-content analysis based on a wide-field imaging sensor that visualizes the area of several mm2 in one-shot. Since a complementary metal oxide semiconductor (CMOS) image sensor is employed as a detector, a space-saving and inexpensive platform can be designed. By taking these advantages, we have demonstrated a variety of cell analyses. In this presentation, after introducing the basic principle of our imaging sensor, detection of single cells for human immunodeficiency virus (HIV) testing and tumor evaluation, and microbial identification by lens-less imaging of microcolonies will be introduced. CD4 testing with a wide-field imaging sensor for detection of HIV infection Approximately 35 million people world-wide carry HIV, which causes acquired immune deficiency syndrome (AIDS). Because HIV infection causes decrease in T-cells expressing cluster of differentiation (CD) antigen, CD4, the number of CD4-positive T-cells in patient blood samples is a useful indicator of therapeutic effect of the antiviral treatment. Flow cytometry is used for this CD4 testing, however its high cost, lack of portability and difficulty in operation limit widespread use in resource-limited areas. To overcome this limitation, wide-field imaging sensor-based CD4 testing was developed with the aid of a cell arraying device (referred to as microcavity array) on which single T-cells can be trapped and arrayed. Immuno-stained CD4-positive T-cells on the microcavity array were visualized by the sensor, indicating the usefulness of this imaging system for medical applications. Detection of circulating tumor cells with a wide-field imaging sensor Circulating tumor cells (CTC) are tumor cells circulating in peripheral blood, and involved in metastasis. Since CTCs provide useful information such as malignancy and metastatic property of primary tumor, development of fundamental techniques for recovery, counting and analysis of CTCs is important for clinical purposes. We have developed a CTC-recovery technology using microcavity array by which very rare CTCs in blood can be captured. For subsequent analysis, we combined the microcavity array system with a wide-field imaging sensor, so that efficient recovery and detailed observation of rare CTCs can be performed in all-in-one system. Furthermore genome analysis of single cells captured on the microcavity array will be also discussed in the presentation. Microbial identification based on lens-less imaging of microcolonies with a wide-field imaging sensor Identification of microbial species is routinely performed in a wide range of industries, including production of beverages, foods, cosmetics and pharmaceuticals. It is also of great importance in clinical diagnosis. A number of methods based on phenotypic and genotypic analyses have been proposed for microbial identification, however such methods need expensive reagents and long assay time. Rather than analyzing chemical components (e.g., DNA, proteins and lipids) of the detected microbes, observation of colony morphology has long been employed to identify microbial species although it requires high expertise. We deskilled the colony morphology-based microbial identification by observing microcolonies with a wide-field imaging sensor which are free from optical lens systems (lens-less imaging). Microcolony growth was monitored with high time-resolution, and various parameters were extracted from the acquired images. Based on the cluster analysis, we succeeded in identification of microbial species with high accuracy.