Most laboratories studying biological processes and human disease use microscopes to image samples. Whether in small or largescale microscopy experiments, biologists increasingly need software to identify and measure cells and other biological entities in images, to improve speed, objectivity, and/or statistical power. The principal investigator envisions bringing transformative image analysis and machine learning algorithms and software to a wide swath of biomedical researchers. In a decade, researchers will tackle fundamentally new problems with quantitative image analysis, using seamless imaging workflows that have dramatic new capabilities going beyond the constraints of human vision. To this end, the PI will collaborate with biologists on important quantitative imaging projects that also yield major advancements to their opensource image analysis software, CellProfiler. This versatile, userfriendly software is indispensable for biomedical research. Launched 125,000+ times/year worldwide, it is cited in 3,400+ papers from 1,000+ laboratories, impacting a huge variety of biomedical fields via assays from counting cells to scoring complex phenotypes by machine learning. CellProfiler evolves in an intensely collaborative and interdisciplinary research environment that has yielded dozens of discoveries and several potential drugs. Still, many biologists are missing out on the quantitative bioimaging revolution due to lack of effective algorithms and usable software for their needs. In addition to maintaining and supporting CellProfiler, the team will implement biologistrequested features, algorithms, and interoperability to cope with the changing land scape of microscopy experiments. Challenges include increases in scale (sometimes millions of images), size (20+ GB images), and dimensionality (timelapse, threedimensional, multispectral). Researchers also need to accommodate a variety of modalities (superresolution, singlemolecule, and others) and integrate image analysis into complex workflows with other software for microscope control, cloud computing, and data mining. The PI will also pioneer novel algorithms and approaches changing the way images are used in biology, including: (1) a fundamental redesign of the image processing workflow for biologists, leveraging revolutionary advancements in deep learning, (2) image analysis for more physiologically relevant systems, such as model organisms, human tissue samples, and patientderived cultures, and (3) data visualization and interpretation software for highdimensional singlecell morphological profiling. In profiling, subtle patterns of morphological changes in cells are detected to identify causes and treatments for various diseases. We will also (4) integrate multiple profiling data types: morphology with gene expression, epigenetics, and proteomics. Ultimately, we aim to make perturbations in cell morphology as computable as other largescale functional genomics data. Overall, the laboratory?s research will yield highimpact discoveries from microscopy images, and its software will enable hundreds of other NIHfunded laboratories to do the same, across all biological disciplines.
Modern microscopy experiments are increasing in scale and scope? the research will result in pioneering computational techniques and software that will change the way microscopy images are used in biology. Biologists will use the resulting software to tackle fundamentally new problems using quantitative image analysis, including detecting changes in the appearance of cells that are overlooked by human vision and studying intact organisms and human tissue rather than isolated cells. The methods will be developed in the context of dozens of projects addressing important fundamental biological questions and world health problems, and the resulting new functionality will be added to the team?s popular, userfriendly, opensource image analysis software, CellProfiler.
| Simm, Jaak; Klambauer, Günter; Arany, Adam et al. (2018) Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery. Cell Chem Biol 25:611-618.e3 |
| Vasilevich, Aliaksei S; Mourcin, Frédéric; Mentink, Anouk et al. (2018) Designed Surface Topographies Control ICAM-1 Expression in Tonsil-Derived Human Stromal Cells. Front Bioeng Biotechnol 6:87 |
| Bray, Mark-Anthony; Gustafsdottir, Sigrun M; Rohban, Mohammad H et al. (2017) A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay. Gigascience 6:1-5 |
