Project Summary
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.