As modern microscopes are able to generate images that magnify nano details, phenotypic analysis of sub-cellular structures requires modern technologies to keep up. Regardless of the challenges you encounter in your cell biology and cancer research, our powerful software brings you one step closer to obtaining reliable and reproducible results. Create automated AI-driven pipelines for a wide range of image analysis tasks to answer your research questions.

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  Cell tracking is one of the most challenging time-lapse image-analysis tasks. It is the basis for analyzing cells at the single-cell level and studying cell motility in various contexts, for example cancer cell invasion, immune cell migration, and embryo development.
  

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  Nuclei counting is one of the most common cell phenotype analysis tasks for biological research. Automating it is crucial for many applications and further downstream analyses. This workflow is based on a pre-trained Deep Learning model, automatically segmenting, separating, and counting nuclei based on any fluorescent nuclear marker, such as DAPI, in one or more microscopy images. It supports time-series images as well as multi-well measurements. The output is a matrix that shows the number of nuclei per image in each well per time point (if applicable). The respective Deep Learning model is trained on datasets from multiple microscopes with different resolutions and magnifications.
  

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  Analysis of DNA damage is key for cancer research. High-content screening allows testing the effect of different conditions on genotoxicity in an efficient manner.

  After acquiring the data, as a first step, the DAPI nuclei are segmented. Intensity measurements of another nuclear signal (red signal) determines the cell cycle stage. It is then possible to classify these nuclei based on intracellular DNA-damage foci (EdU, green signal) using a parent-child operation in the analysis pipeline. The solution allows for fast and flexible stratification of nuclei according to a diverse array of parameters for an in-depth mechanistic analysis of genotoxicity.

  Once set up in the ZEISS arivis Platform, analysis results can be viewed in 3D, and scaled up to hundreds of samples.
  

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  Phenotypic screening is a target agnostic approach to drug discovery that monitors for phenotypic changes in cells. This application allows for high-throughput quantitative analysis of multi-well plates providing outputs on various sub-cellular intensities and morphological measurements on complex cellular models. In this example, a nuclear maker is used to identify all cells and specific cell populations are identified based on nuclear and cytoplasmic marker combinations. Cytoplasmic markers are also used to characterize the shape and size of all individual cells expressing this marker.