Study of CuO nanoparticle-induced cell death by high content cellular fluorescence imaging and analysis

To quantify cellular toxic responses to drug treatment or environmental stresses such as nanoparticles, a high throughput imaging modality with automated image analysis protocol is applied. Fluorescence images from human H4 neurogliomal cells exposed to different concentrations of CuO nanoparticles were collected by a high content fluorescence microscopy. A fully automated fluorescent cellular image analysis system has been developed for the consequential image analysis for cell viability. A data-driven background algorithm was used as adaptive multiple thresholding algorithm to categorize the cells into three classes: bright cells, dark cells, and background. Our image analysis approach includes: (1) the scale-space theory, namely Gaussian filtering with proper scale has been applied to the acquired images to generate local intensity maxima within each cell; (2) a novel method for defining local image intensity maxima based on the gradient vector field has been developed; and (3) a statistical model was proposed to overcome the problem of cell segmentation. Our data have shown that the automated image analysis protocol can achieve 90% success rate of cell detection compared to manual procedure. Cellular image analysis further indicated that H4 neuroglioma cells had a dose-dependent toxic response to the insult of CuO nanoparticles.