Introduction

Recently, microscale technologies have emerged as a powerful tool for high-throughput cellular and biological studies by enabling miniaturized experiments (1,2,3). These miniaturized well arrays can be used to segregate cells for single-cell analysis (4,5,6) and multiple-cell studies, such as to form neurospheres and embryoid bodies (7,8). Typically, these arrays contain hundreds or thousands of microwells, such that the throughput is significantly higher than those conducted using traditional multi-well plates. For example, a hydrogel microwell array was used to clonally analyze neural stem cell (NSC) fates to demonstrate that neurosphere formation can be attributed to a single cell. Using time-lapse microscopy and immunostaining, the fate of several hundred single NSCs was tracked simultaneously (9).

The increase in the number of high-throughput cellular and biological studies have generated a need for high-throughput analysis and screening tools capable of processing the raw data and presenting it in a meaningful and efficient way. New advancement in digital imaging has made it possible to image biological events on micro- and nanoscale levels. To solve combinatorial problems related to cell biology and medical science, such as optimization of materials or screening of chemical libraries, images are taken from systems such as microwell arrays in which a large number of experiments are investigated in parallel. High-throughput image processing techniques are desired in order to analyze such images, but image processing and pattern recognition techniques are not adequately developed to analyze such biological and medical images. To investigate large numbers of images and to address new questions in biological and medical research, further design and development of digital cytometry techniques is required.

An important problem in many high-throughput experiments—specifically in stem cell engineering and drug discovery—is the analysis of cell viability in thousands of parallel experiments spotted in micro-and nanoliter volumes. To quantify cell viability in microwells, the number of cells in each microwell of a microarray system must be counted. Manual cell counting as a practical approach has been widely used; however, these results may be subjective and vary from person to person. Moreover, this is a tedious and laborious task that is prone to error due to the large number of measurement and analysis steps. Therefore, advanced techniques in digital image processing and pattern recognition must be employed for emerging biological applications.

Cell recognition and counting in microwell systems are challenging tasks due to the presence of debris, high noise, and the difficulties of adapting available image segmentation approaches. It is imperative that cells in these substrates be imaged and automatically analyzed in a high-throughput manner. A variety of semi-automatic or automatic methods have been proposed for applications of image processing techniques to medical and biomedical research (10,11,12,13). However, the lack of software complementing such techniques makes it difficult for high-throughput screening. Currently available software—for example, ImageJ (http://rsbweb.nih.gov/ij) and Cellprofiler (www.cellprofiler.com)—can only count the total number of cells in a defined region, which is not suitable for analyzing microwell array cell data in large quantities. Cellpro-filer provides ‘Grid Analysis’ emulating hemocytometer format, but it is limited to grid annotation or cell colony intensity measurement (14). For ImageJ, the region of interest could be defined by using macros to segregate individual spots but this may not be suitable to process many images in high-throughput. Proprietary microarray software, such as Quantarray (Packard BioChip Technologies, Billerica, MA, USA) and Genepix (Molecular Devices, Sunnyvale, CA, USA), while allowing user to define individual arrays on one image, cannot recognize the arrays automatically. Moreover, the readout from such microarray software is generally limited to fluorescent intensity and gives no direct cell counting. MetaMorph (Molecular Devices) has a wide range of application modules with intuitive setting selections for biology analysis. The software has an application module for Count Nuclei but it does not seem to give the number of cells in individual spots in a microarray system in a high-throughput manner. Cellomics is a complete system for high-content screening (HCS) and high-content analysis (HCA) (Pittsburgh, PA, USA). The Thermo Scientific Cellomics is a powerful platform including automated imaging, image analysis software, and high-content informatics which is coupled with reagents, and laboratory automation. As a total platform, it is equipped with hardware and software to perform both imaging and image analysis. It seems that in order to use the software, the user needs the entire platform. This software has an application named “Spot Detector” to count receptors, nuclei, and cells, but it does not appear to give the number of cells in each microwell in high-throughput. Most systems are focused on overall cell or cell colony counting. For example, ScanCount and circular Hough image transform algorithm (CHiTA) can only give the number of cell colonies automatically, but not the individual cell numbers in the culture plate (15,16). Although many studies of bioimage informatics have been done over the past few years, software for cell counting in microarray format still remains largely underdeveloped (17).