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  News in Brief

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This issue...

  News in Brief

   11 Physics Questions for the New Century

   View from the Inside

  Better Airport Security

  A New Way to Visualize Cells

  Astrophysicists Explore Supernovae

  Long-Life Rechargeable Batteries

  People

  About

  Subscribe Free

A New Way to Visualize Cells and Nuclei

by Paul Preuss

The U.S. Department of Energy's Lawrence Berkeley National Laboratory's Life Sciences and Computing Sciences divisions have joined forces to apply new biological and computational methods to mark and detect the boundaries of closely packed cells and nuclei crowded together under the microscope.

nuclei seeds
A computer agenda automatically plants a "seed" in the image of a nucleus. The program then grows the seed until the envelope of the nucleus is outlined.

To learn how tissues develop and maintain their organization—and especially to learn what goes wrong when cancer strikes—it's essential to study individual cells and their nuclei within tissues. The problem is that in real tissues, and in many cell cultures grown in the laboratory, cells are often tightly clustered; their boundaries and the borders of their nuclei are hard to distinguish.

Now Carlos Ortiz de Solorzano of Berkeley Lab's Life Sciences Division and Ravi Malladi of the Lab's Computing Sciences Division, with their colleagues Sophie Lelièvre and Stephen Lockett, have joined forces to apply new biological and computational methods to mark and detect the boundaries of closely packed cells and nuclei crowded together under the microscope. Their methods work not only with the envelopes of nuclei but with the membranes of tightly clustered cells as well.

To label nuclei the researchers first chose a set of stains that specifically bind to the protein lining of the inner and outer layers of the nuclear membrane, called lamina.

Next they developed programs that could find and outline the stained lamina in the microscopic images after they were stored on the computer.

"Typically, researchers use fluorescent stains that bind to the DNA inside the nucleus, not to the nuclear envelope," says Ortiz de Solorzano. "But when nuclei are clumped together"—typical in tumor tissue—"there's no contrasting background area between them, and there's no easy way to distinguish one from the next."

Seen through the microscope, even stained nuclear membranes may present a mixed bag of dim and cluttered outlines, and while the human eye can pick through these one by one, the new program identifies each nucleus and marks it with a tiny internal graphical "seed," which grows until its periphery corresponds to the outline of the lamina.

"Planting just one seed in each nucleus is important, " says Ortiz de Solorzano. "If no seed is planted, the algorithm will be unable to find the boundaries, but if more than one seed is planted, they will mistakenly divide the nucleus into more than one object."

Malladi explains that the process begins by automatically choosing seed points to lie inside the nuclei. This is done by computing a crude estimate of the gradient magnitude and direction—a measure of change in the intensity of the image's pixels—in the entire image and translating the gradient points along the "inward" direction. As a result, the gradient points corresponding to the nuclei boundaries reinforce each other in a small region inside nuclei.

These ensembles of reinforced gradient points define the initial seed points. "The gradients won't clump exactly in the center," Malladi says, "but that doesn't matter as long as they are inside the nucleus."

Next the program moves the periphery of each seed (called the "front") outward until it conforms to the shape defined by the stained lamina. The movement proceeds in stages that are highly sensitive to changes in pixel intensity; the front moves outward freely where changes are minor, but slows almost to a stop when pixel intensity changes markedly, alerting the program to a boundary.

labeled mammary cells
In this image of mammary tissue from a mouse, some cells are so densely clustered their boundaries are ambiguous.
(Click for larger view)

Having marked the inner membrane surface, the front is now allowed to move beyond it, coming to a halt only when it encounters other boundaries expanding from other nuclei. Then the front backtracks, seeking maximum intensity values that clearly identify the stained outer lamina.

The algorithm works in similar ways to unambiguously outline whole cells using, for example, fluorescently labeled integrins, which are proteins specific to cell membranes.

In both cases "the program makes use of edge-finding algorithms earlier developed for medical imaging," Malladi explains. "We have also developed features that enable the program to find boundaries accurately in very noisy images and to 'see' the right shape even where there are discontinuities." These robust algorithms produce few errors unless the original images are so bad that they are not worth using to begin with.

mammary insets
After seeds are planted "interactively" (by human eye), the new program outlines the cell membranes clearly. (Click for larger view)

Computer scientist Malladi is excited about widening the frontiers of software that can abstract visual information from a variety of disparate image sources, ranging from medical images of bones and organs to problems in combustion and fluid mechanics to the underground structures of oil reservoirs. Ortiz de Solorzano, whose focus is biological image analysis, welcomes the nuclei and cell detection techniques as a new means of "quantifying information to understand biological processes."

The new techniques are described in the article "Segmentation of nuclei and cells using membrane related protein makers," which appeared in the March 2001 issue of the Journal of Microscopy (see Related Web Links below).

Media contact: Paul Preuss, LBNL Public Information Department, (510) 486-6249, paul_preuss@lbl.gov
Research contacts: Carlos Ortiz de Solorzano, LBNL Life Sciences, (510) 486-4923, CODeSolorzano@lbl.gov
Ravi Malladi, LBNL Computing Sciences, (510) 486-6020, R_Malladi@lbl.gov


Related Web Links

"Segmentation of nuclei and cells using membrane related protein makers," by C. Ortiz de Solorzano, R. Malladi, S.A. Lelièvre, and S.J. Lockett, Journal of Microscopy, March 2001 (398K pdf).

"Subjective Surfaces: A Geometric Model for Boundary Completion," A. Sarti, R. Malladi, and J.A. Sethian, to appear in the International Journal of Computer Vision, 2002 (1959K pdf).

"Seeing the Cell Nucleus in 3-D," Paul Preuss, Berkeley Lab Review, Summer 2000. (Earlier work by Ortiz de Solorzano with programs that visualize cell nuclei using confocal microscopy)

"A Faster, Better Way to Compute Medical Models from Noisy Images," by Paul Preuss, Berkeley Lab Science Articles Archive, March 30, 1998. (Ravi Malladi's earlier work with medical imaging.)

Ravi Malladi's Shape Modeling and Medical Visualization Research and Publications


Funding: LBNL Computing Sciences Division is supported by the U.S. Department of Energy's Office of Science through its Advanced Computational Sciences Research office.

LBNL Life Science Division's research funds result from peer-reviewed grants from the DOE's Office of Biological and Environmental Research (OBER) within the Office of Science, the National Institutes of Health, NASA, the University of California, as well as other sources including industrial partnerships.

Lawrence Berkeley National Laboratory was founded by Ernest Orlando Lawrence founded this Lab in 1931, making it the oldest of the national laboratories. Lawrence invented the cyclotron, which led to a Golden Age of particle physics and revolutionary discoveries about the nature of the universe. Of our nine Nobel Prizes awarded to LBNL scientists, five are in physics and four in chemistry. Today, LBNL is a multiprogram lab where research in advanced materials, life sciences, energy efficiency, detectors and accelerators serves America's needs in technology and the environment. LBNL is managed by the University of California for the DOE.

Author: Paul Preuss has been a science writer at Lawrence Berkeley National Laboratory for the last four years, covering the broad range of research conducted by the Lab, from astrophysics to earth sciences to chemistry and materials sciences to genetics to cell biology, etc. Before joining LBNL, Preuss spent 20 years as a novelist, writing mostly science fiction, even collaborating with Arthur C. Clarke on his "Venus Prime" series, which is still prominent on Amazon.com. Preuss said, "I've stopped writing science fiction. The truth is that real science is far more fascinating. The fictional stuff is a pale reflection." For more science news, see Berkeley Lab's Science Beat.

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