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Robust Nuclei Segmentation using Statistical Level Set Method with Topology Preserving Constraint
Published in Ayman El-Baz, Jasjit S. Suri, Level Set Method in Medical Imaging Segmentation, 2019
Shaghayegh Taheri, Thomas Fevens, Tien D. Bui
To validate our new method, we have also tested our proposed method on a set of 47 fluorescent microscopy images (U2OS cells) [39]. In all experiments, fixed parameters, e.g., major and minor axes ( and ) and smoothness parameter (), for all the images have been used. Also, since this data set contains small nuclei patches on the boundary with small GFRS responses, a simple mean thresholding and nonlinear diffusion are added to the segmentation pipeline. In Figure 4.8 we have compared the proposed method with the CellProfiler previously mentioned nuclei segmentation unit: Maximum correlation thresholding (MCT) segmentation combined with shape-based separation (CellProfiler Automatic strategy).
Intestinal microbiota drives cholestasis-induced specific hepatic gene expression patterns
Published in Gut Microbes, 2021
Oriol Juanola, Mohsin Hassan, Pavitra Kumar, Bahtiyar Yilmaz, Irene Keller, Cédric Simillion, Cornelius Engelmann, Frank Tacke, Jean-François Dufour, Andrea De Gottardi, Sheida Moghadamrad
In-depth cell phenotyping was performed with multiplex immunostaining as described elsewhere.37 Briefly sequential immunostaining and antibody stripping was performed on 4 µm-thick formalin-fixed paraffin embedded liver tissue sections obtained from all experimental groups (n = 3/group). After deparaffinization with xylene, all tissue sections underwent repetitive cycles of antigen retrieval (Tris-EDTA, pH 9.0, Novus Biologicals, Centennial, CO, USA) for 30 minutes in water bath and antibody stripping using 2-mercaptoethanol/SDS as described previously.38 To avoid nonspecific antibody binding the sections were incubated with 2.5% horse serum (Vector BioLabs, Malvern, PA, USA), for 1 h at room temperature for the first round of staining. Afterward, the sections were incubated overnight at 4°C with primary antibodies (Supplementary table 1S). Whole scanning of sections was performed using Zeiss Axio Observer 7 followed by stitching and background subtraction. The scans were aligned, hyperstacks and concatenated using FIJI HyperStackReg V5.6 plugin. The image segmentation was performed on all binary images using Ilastik software (v 1.3.3). CellProfiler v3.1.9 was used for cell identification, counting and intensity measurement.
Harnessing the power of microscopy images to accelerate drug discovery: what are the possibilities?
Published in Expert Opinion on Drug Discovery, 2020
Justin Boyd, Myles Fennell, Anne Carpenter
The most common application of automated imaging and image analysis for drug discovery is high-content screening for small molecules. For over two decades, this approach has become a staple strategy for phenotypic drug discovery across diverse applications [2,3,12]. The vast majority of HCS relies on a few discrete features to define hits [5]. Conventional screens focus on single primary feature measurements – how bright, how many, what size, etc. – relating to the proximal biology being targeted for defining hits, which often requires identifying a cell-based biomarker associated with desired disease-related outcomes. Cell count is often used as a proxy for toxicity and for normalizing primary signals. The potential for more content to define the functional consequences of a small molecule is inherent to the technology (hence ‘high-content screening’), yet rarely leveraged. In a seminal study, Perlman et al. [13] presented the strategy of profiling compounds using automated imaging and analysis. The authors describe how 11 probes were combined to generate 93 image-based measurements for profiling the activity of compounds. Although the study was limited to 100 compounds, it demonstrated the power of image-based profiling to cluster compounds by mechanism. Developments in the 16 years since that study have increased the number of feature measurements and developed improved computational tools for dealing with high dimensional data to cluster compounds [14]. For example, the latest version of CellProfiler, a common image analysis tool for HCS and image-based profiling, typically measures over 2000 feature measurements per imaged cell, enabling a tremendous amount of content contributing to morphological profiles.
Identification of a new structural family of SGK1 inhibitors as potential neuroprotective agents
Published in Journal of Enzyme Inhibition and Medicinal Chemistry, 2023
Ines Maestro, Enrique Madruga, Patricia Boya, Ana Martínez
Image analysis was done with CellProfiler35. In order to identify red-only structures per cell, segmentation of nuclei, cells and mitochondrial network were performed. Mitochondrial structures were further filtered as “yellow” or “red-only” based on the ratio between their EGFP/GFP and mCherry integrated intensities. The final number of red-only structures per cell was used as a mitophagy rate readout. Later, this pipeline was modified in order to analyse mitophagy in cells seeded in 24-well plates. The final number of yellow structures per cell was used to measure mitochondrial mass.