Image analysis for high-throughput image-based cell experiments

Our main research theme is quantifying and mining the rich information present in cellular images to yield biological discoveries. We work on high-throughput projects (100,000–1,000,000 images) probing a variety of biological processes and diseases of interest, with a special interest in psychiatric research, infectious disease, and cancer.

Recent projects include the identification of genetic regulators (glioblastoma differentiation, breast cancer cells' response to heregulin, meiosis) and chemical regulators (leukemic differentiation, mitochondrial function, tuberculosis infection).

CellProfiler

Algorithms developed in my group are made readily usable by the scientific community via our user-friendly software, CellProfiler (http://www.cellprofiler.org). CellProfiler is versatile, open-source software for quantifying a variety of phenotypes in biological images. Since its release in 2005, it has become well established and widely used. CellProfiler has been downloaded more than 15,000 times around the world and cited in more than 250 papers. The software evolves within an active research environment involving dozens of diverse image-based assays, resulting in rich functionality as we continue to improve its capabilities, interface, and support.

Quantifying C. elegans

The worm C. elegans can be robotically prepared and imaged and is an effective model to probe a variety of biological questions that require whole animals rather than isolated cells. We are developing sorely needed C. elegans analysis algorithms and validating them in specific large-scale experiments to identify regulators of fat metabolism and pathogen infection.

Image-based systems biology

High-throughput imaging experiments generate extremely large, multidimensional data sets with quantifiable phenotypic information for every individual cell. We use this rich, latent information to identify patterns in chemical or genetic perturbations in order to distinguish genes and chemicals with related cellular effects and to discover chemical targets and side effects.

Quantifying dynamic phenotypes

Many biological questions can only be investigated by collecting time-lapse movies. We are analyzing these images to identify, for example, novel cell cycle landmarks and motor protein regulators. We are also integrating this data with flow cytometry data to quantify unusual cell cycle outcomes.