Image analysis for high-content screens
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).
Algorithms developed in my group are made readily usable by the scientific community via our user-friendly software, CellProfiler (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 is launched around the world more than 90,000 times annually and cited in more than 600 publications. 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.
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 resulting from chemical or genetic perturbations to probe the causes and cures for various diseases. For example:
- Predicting how new chemical compounds act in cells
- Identifying and classifying toxicity of compounds destined for clinical trials
- Identifying differences in cell structure between patient cells affected by bipolar disorder or schizophrenia
- Discovering differences among histone deacetylase (HDAC) isoforms, and identifying specific inhibitors against them, which are likely useful against cancer and psychiatric disease
- Identifying gene function from large-scale genome sequencing studies
In co-cultured cell systems, two or more cell types are grown together in order to maintain more native physiological functions, enabling experiments that test genetic and chemical perturbations in a more realistic environment. We are developing image analysis approaches to extract information from fluorescence microscopy images of these cell systems, enabling experiments in liver regeneration and hepatotoxicity [more].
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.
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.