Automated Confocal Microscope Bias Correction

A flow-cytometry assisted segregation of responding and non-responding population of endothelial cells for enhanced detection of intracellular nitric oxide production

Nitric oxide (NO) is an important paracrine substance released by the endothelium to regulate vasomotor tone. The constitutive levels of endothelium dependent NO production is low. However, it is induced significantly in response to certain environmental and biological stimuli. An accurate evaluation of such stimulus induced NO release is of pharmacological significance. We observed that the sensitivity of NO detection in endothelial cells is compromised by baseline fluorescence emanated from non-activated cells resulting in ambiguous detection. In order to measure NO levels in activated population independent of non-activated cells, we segregated DAF-FM loaded cells based on their fluorescence intensity using flow-cytometry. Specific agonists like bradykinin, VEGF and insulin enhanced the proportion of activated cells. This effect was partially blocked in presence of NO synthase inhibitor, N(G)-nitro-L-arginine-methyl ester (L-NAME). We demonstrate that the fluorescence yield of activated population serves as a sensitive measure to evaluate agonist induced nitric oxide production in endothelial cells. Such increase in NO production in activated cells was also associated with increased eNOS phosphorylation at Ser-1177. While the endothelial cells showed heterogeneity with respect to NO production, immuno-phenotyping for endothelial cell-surface markers revealed a homogenous population.

Contextual automated 3D analysis of subcellular organelles adapted to high-content screening

Advances in automated imaging microscopy allow fast acquisitions of multidimensional biological samples. Those microscopes open new possibilities for analyzing subcellular structures and spatial cellular arrangements. In this article, the authors describe a 3D image analysis framework adapted to medium-throughput screening. Upon adaptive and regularized segmentation, followed by precise 3D reconstruction, they achieve automatic quantification of numerous relevant 3D descriptors related to the shape, texture, and fluorescence intensity of multiple stained subcellular structures. A global analysis of the 3D reconstructed scene shows additional possibilities to quantify the relative position of organelles. Implementing this methodology, the authors analyzed the subcellular reorganization of the nucleus, the Golgi apparatus, and the centrioles occurring during the cell cycle. In addition, they quantified the effect of a genetic mutation associated with the early onset primary dystonia on the redistribution of torsinA from the bulk endoplasmic reticulum to the perinuclear space of the nuclear envelope. They show that their method enables the classification of various translocation levels of torsinA and opens the possibility for compound-based screening campaigns restoring the normal torsinA phenotype.

A fast, fully automated cell segmentation algorithm for high-throughput and high-content screening

High-throughput, high-content screening (HT-HCS) of large compound libraries for drug discovery imposes new constraints on image analysis algorithms. Time and robustness are paramount while accuracy is intrinsically statistical. In this article, a fast and fully automated algorithm for cell segmentation is proposed. The algorithm is based on a strong attachment to the data that provide robustness and have been validated on the HT-HCS of large compound libraries and different biological assays. We present the algorithm and its performance, a description of its advantages and limitations, and a discussion of its range of application.