|The BioSig system: finding new meaning in microscopy|
|Contact: Paul Preuss, [email protected]|
"Cell biologists love a good microscope image," according to Mary Helen Barcellos-Hoff of Berkeley Lab's Life Sciences Division, "but a radiation biologist is likely to say, 'Well that's pretty, but what does it mean?'"
Barcellos-Hoff, trained as a microscopist, studies the effects of low-dose radiation and other factors on cells and their environment. "Radiation biology is classically a quantitative field. It has been difficult to put new information gained from microscopy into quantitative form."
Bahram Parvin of the National Energy Research Scientific Computing Center (NERSC) has a particular interest in feature-based representation of scientific images. "How do you represent images so that such a representation reduces the data volume, and at the same time it is information preserving? What quantifiable insights can you obtain from an image collection?"
Now Barcellos-Hoff and Parvin have given quantitative meaning to microscope images in a way that promises far-reaching consequences for the field they have dubbed "phenomics," which deals with the proteins a genome codes for -- how proteins are regulated and expressed in the cell and how they interact with each other to condition the cell's responses to outside stimuli and a changing microenvironment.
Called BioSig, the system Parvin and his NERSC colleagues Qing Yang and Gerald Fontenay have created can process and annotate numerous images of multicellular systems. While Yang focused on developing unique algorithms to compute meaningful features from images, Fontenay concentrated on annotating experimental images with computed features, accommodating user input, and making information available over the web. BioSig has already proved its worth in a variety of image-based studies; it can be used by researchers working alone or together on a wide range of problems.
BioSig's specialty lies in keeping track of the myriad proteins that cells use to send and receive signals, necessary for understanding the forms of cells and cell structures and their organization in tissues. The first step in any program that wants to link quantitative information to images of cells is transforming them so that they can clearly and consistently show where proteins are at work, e.g., which cell in the tissue? Which compartment in the cell? And how much protein is expressed in each cell type?
Any biological system exhibits significant variations to begin with. Coupled with technical variability in sample preparations, underlying image representation becomes quite heterogeneous: staining is nonuniform, images are noisy, and subcellular compartments can overlap each other. Each compartment within the cell must be clearly separated from its neighbors in a process called segmentation.
Yang and Parvin developed means of segmentation that remove noise and clearly outline each cell or nucleus, in two-dimensional images as readily as in 3-D ones. Their method is completely automatic and does not require human interaction, as other segmentation programs do. The routine recognizes curved sections of the envelopes or membranes of nuclei; by calculating individual "centroids" from this information, adjacent objects can be distinguished even when they overlap. With further calculation, the location and magnitude of expressed proteins -- identified by specific antibodies -- can be resolved with great precision.