Measurements of cell viability (percentage live versus dead cells) may be used to evaluate: cellular damage due to toxins, the effect of infection, response to drugs, toxicity during early stages of drug development, the life of cancerous cells, the rejection of implanted organs, etc. There is a need to focus on label-free approaches to drug discovery, especially regarding hit identification (focused screening and primary screening/HTS) and lead optimization. Identifying hits generally requires analysis of cell population viability under drug treatment. Early stages of drug discovery rely on screening large numbers of compounds to analyze their toxic effects on cells. These results are based on biochemical assays performed in an ultra high throughput format. First and foremost is the cell viability analysis, i.e., to recognize the live and dead cells in a population of cells treated by various compounds at different concentration levels. Cell death can occur in many ways: apoptosis, autophagy, cornification and necrosis, to name a few. The underlying biochemical causes responsible for events resulting in different kinds of cell deaths are beginning to be better understood. Although cell morphology and significant ultrastructural changes occur during the initiation and progress of cell death, most of the current protocols to identify dead cells and the death mechanism depend on some form of labeling, such as fluorescence. Disadvantages in using labels for viability analysis include difficulty in delivering the dye to the target, keepingthe dye passive, unknown response to high frequency light, unknown response to dye perturbation over a long period, etc. Also, conducting high throughput studies using fluorescent dyes is expensive and requires complex robotics to prepare specimens. The proposed Cell Analytics for Viability Experiments (CAVETM) system would be a significant step forward in achieving label-free, accurate experiments in life sciences and drug discovery for label free viability analysis and hit-analysis. Label-free techniques work in the absence of fluorescence markers;hence, they do not possess the disadvantages of using fluorescent labels. This is also amenable to the study of cells using video microscopy and time-lapse microscopy. The ability to directly measure the morphological and ultra-structural changes of the cell components and relate the changes to cell death phenomena could open up the drug discovery world to the label-free, live primary cell monitoring experiments. In Phase 1, we plan to accomplish complete software development for detecting apoptosis induced cell death using a label-free process. This includes novel mechanisms to segment label-free cells in a dense cell population, measure a large number of features, automatic assay dependent feature selection, and classification of live and dead cells. In Phase 2, viability analysis will be expanded to other forms of cell death induction such as necrosis, autophagy, cornification, etc. and will develop informatics support to predict cell death based on initial cell features. The whole cell viability analysis will also be extended to 2D+T (time) data in the form of time-lapse or video data of live cells. We plan to market the software through our tie ups with microscopy vendors, UC drug discovery center and by directly providing customized services.
Transitioning cell-based assays to label-free systems has significant potential to streamline, improve, and reduce cost for drug discovery. Two drug discovery steps where the label-free approach will be significant are hit identification (focused screening and primary screening/HTS) and lead compound optimization. Identifying hits requires analysis of cell population viability under drug treatment, including screening a large number of compounds to analyze toxic effects on cells using defined biochemical assays in an ultra-high throughput format. Foremost is cell viability, i.e., recognition of live vs. dead cells treated by various compounds at different concentrations. Cell death occurs in many ways: apoptosis, autophagy, cornification and necrosis to name a few. Biochemical causes for events resulting in different kinds of cell death are beginning to be deciphered. Although cell morphology and significant ultra-structural changes occur during initiation and progress of cell death, most current protocols to identify dead cells and death mechanism depend on some form of labeling, fluorescent or otherwise. There are several disadvantages in using labeled viability analysis. First, difficulty in delivering dyes to targets or requirement to deliver it via DMSO or other solvent, which may have additional effects on cells. Second, ensuring dyes are passive to cells or subcellular locations, in that the dyes do not cause an effect, especially over long incubation times. Third, repeated exposure of cells to high frequency light to monitor progression of cell death and/or cellular assay being monitored. In addition to concerns regarding effect of dyes within cells, conducting high throughput studies using fluorescent dyes is expensive and requires complex robotics to prepare specimens. The proposed Cell Analytics for Viability Experiments (CAVETM) system would be a significant step forward in achieving label-free, accurate experiments in life sciences and drug discovery. The ability to directly measure morphological and ultra-structural changes of cell components, and relate changes to cell death phenomena, could open up the drug discovery world to label-free, live primary cell monitoring experiments, thus significantly reducing both costs and complexities of experiments. Cell studies with video and time-lapse microscopy could also benefit from this approach. In Phase 1, software development for label-free detection of apoptosis induced cell death will be completed. This includes novel mechanisms to segment label-free cells in a dense cell population: 1) measuring large numbers of features, 2) automatic assay-dependent feature selection, and 3) classification of live and dead cells. In Phase 2, viability analysis will be expanded to other forms of cell death induction (necrosis, autophagy, cornification, etc.). Informatics support will be developed to predict cell death based on cell features in initial stage of assays. Whole cell viability analysis will be extended to 2D+T (time) data as live cell time-lapse or video data. Software will be marketed through licenses with microscopy vendors, collaborators at the UC drug discovery center who have significant contacts in the drug discovery field, and by directly providing customized services to customers.