Phenotypic Profiling using Cell Painting
Phenotypic profiling of cells by high-throughput high-content microscopy allows for the identification of morphological changes in the cell that are indicative, for instance, of the mode of action (MoA) of a drug, novel compound or biological.
Morphological changes in the cell can be measured using two major approaches: 1) making use of image analysis software (e.g. Cell Profiler) to identify and segment the cells, and subsequently extracting selected cellular features, and 2) using Deep Learning (DL) to extract relevant cellular features in an unbiased manner.
Upon the extraction and selection of the morphological features, morphological profiles are generated that are unique per each cell type or perturbation. This is achieved through extensive analysis of the vast number of features extracted at a single cell level.
Figure: a) Overview of cell profiling using the Cell Painting methodology. Cells are exposed to perturbations in the form of chemical substances in multi well plates. Images of cells are acquired and used to generate morphological cell profiles describing a wide range of the cells’ properties. These cell profiles or the original images can then be used in predictive modeling with e.g. Deep Learning methods. b) Experiments are carried out in our automated cell profiling lab.
The phenotypic profiling unit at CBCS Uppsala
The CBCS Uppsala node has extensive experience in large scale data handling and analysis, and we have built a complete IT infrastructure capable of storing and analyzing the large quantities of images and subsequent data generated from Cell Painting experiments.
We offer morphological profiling using Cell Painting as a service, including:
Cell Painting protocol optimization for a given cell line.
Cell Painting experiments carried out using our automated lab.
The necessary cell culture work.
Automated imaging, image and data analysis, and visualization.
Personnel
Jordi Carreras-Puigvert, Head of Unit
Ola Spjuth, Platform Scientific Director, CBCS Uppsala
Malin Jarvius, Node Manager
Martin Johansson, Researcher
Visiting address
BMC, Husargatan 3, Uppsala