Image Analysis
We have expertise in several cell analysis packages including open-source software ImageJ (NIH) and CellProfiler (Broad Institute) and proprietary software Columbus (Perkin Elmer) and Harmony (Perkin Elmer).
High Content Data Analysis and Storage
Columbus
The Columbus server is a high-performance computer designed for handling and storage of large-scale image data sets. It comprises a Dell PowerEdge R730xd server containing 2 Intel Xeon E5 processors, each with 8 cores. The system has 128 GB RAM and a total storage capacity of 240 TB. The Columbus system allows quantitative analysis of large-scale image sets coming from the screening microscopes, as well as image data coming from other microscope types. Access to Columbus is via a web interface from the UCD Cell Screening Laboratory.
Features:
- High volume storage and analysis via the internet
- Custom design of image analysis solutions using graphical ‘building blocks’
- Advanced segmentation of nuclei, cells, cytoplasm and other cellular structures
- Quantification of cell and subcellular morphology features
- Quantification of cell and subcellular intensity features
- Quantification of cell and subcellular texture features
- Machine learning and classification of complex phenotypes
- Automated graphical representation of output data
Harmony
The Harmony high-content image analysis software is an advanced image analysis software designed for detailed quantitative phenotyping of cells in a population. It utilises a series of ‘building blocks’ to allow segmentation of cellular features and their subsequent analysis. Harmony is capable of working with three-dimensional confocal data and making volumetric and surface area measurements from such data sets.
Features:
- Custom design of image analysis solutions using graphical ‘building blocks’
- Advanced segmentation of nuclei, cells, cytoplasm and other cellular structures
- Quantification of cell and subcellular morphology features
- Quantification of cell and subcellular intensity features
- Quantification of cell and subcellular texture features
- Quantification of 3D data (for example from spheroids or organoids)
- Creation of stitched images from multiple fields-of-view
- Machine learning and classification of complex phenotypes
- Automated graphical representation of output data