High throughput analysis of highly complex 3D cellular tissues from optical microscopy images

Automated protein distribution spatial analysis within cellular tissues becomes challenging when looking
at highly complex 3D tissues, due to difficulties with segmentation of low resolution optical microscopy images that contain densely packed cellular features.

Using a robust probabilistic tissue segmentation method, the proprietary University of Oxford platform SilentMark, enables high throughput analysis of complex 3D cellular tissues from noisy and difficult to segment optical microscopy images.

Protein fluorescence levels can be quantified in the different sub-cellular compartments, to facilitate the systematic analysis of protein localization under a diverse range of experimental conditions.

Tested in the analysis of 3D tissues of varying complexity

The proprietary University of Oxford platform has a graphical user interface and has been tested in the context of developing tissues where initially low cellular density geometrically simple structures transform into high cellular density geometrically complex structures.

The tissues on which the software has been tested include mouse stem cell embryoid bodies and mouse cardiomyocytes during heart development, tissues comprising from 2 to an estimated 2,000 cells

Applications in tissue engineering and drug screening

The platform is the first of a kind to provide quantitative three dimensional protein information for developing tissues across multiple organisms. This is particularly relevant where quantitative statistical analysis of protein organisation and gene expression lays the foundation for high throughput cancer research, tissue engineering, and drug screening.

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