For example, we can take cells from patients with and without a disease and compare their morphology. This snapshot is called a profile, and once it has been extracted from a cell, we can compare and contrast that cell with other cells, which may have been treated with different drugs, for example.Įureka: Can you provide an example of how these tools have accelerated drug development?ĪC: This ability to quantitatively match cells based on their image-based profile is deceptively simple but there are so many applications in drug discovery. How does that work?ĪC: Images contain far more information about the state of cells than is typically measured by biologists-in fact, more than can even be detected by eye! We aim to capture that information by measuring a huge variety of properties of each cell in each image, producing a quantitative snapshot of the cell's state. A cancer researcher might use CellProfiler Analyst to detect cells that have a metastatic appearance by providing a few dozen example images of cells that are metastatic and non-metastatic.Įureka: You have also developed a unique way of mining and measuring the myriad features of a cell, called image-based profiling. For example, a neuroscientist might use CellProfiler to identify and count the number of synapses per cell in an image. CellProfiler Analyst allows biologists to use machine learning to train a classifier that can recognize cells that have a particular appearance, or phenotype, of interest. What is the difference between them?ĪC: CellProfiler automatically identifies cells in images and measures their properties. Here are her emailed responses.Įureka: Your lab developed the image software tools CellProfiler in 2005 and later CellProfiler Analyst. Carpenter as part of its series on AI in Drug Discovery. Today, the team’s open-source CellProfiler software is used by thousands of biologists worldwide. She decided to write her own software code to solve the imaging problems, and that code eventually became CellProfiler. When she was doing her post-doctoral work at MIT’s Whitehead Institute for Biomedical Research, she encountered a bottleneck in the processing of cell images while measuring the size of Drosophila fruit fly cells. Her research group develops algorithms and strategies for large-scale experiments involving images. Part four in our series on AI in Drug Discovery.Īnne Carpenter, an Institute Scientist at the Broad Institute of Harvard and MIT, is a pioneer in image-based profiling, the extraction of rich, unbiased information from images for a number of important applications in drug discovery and functional genomics. How a software tool for image analysis is helping to accelerate drug discovery.
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