March 6, 2006
New cell imaging method identifies aggressive cancer cells early
WEST LAFAYETTE, Ind. – Fluorescence that illuminates a specific protein within a cell's nucleus may be a key to identifying cancer virulence and to developing individualized treatment, according to researchers at Purdue University and Lawrence Berkeley National Laboratory.
The scientists created a technique that automatically locates and maps proteins involved in regulating cell behavior, said Sophie Lelièvre, Purdue assistant professor of basic medical sciences. The research results have for the first time made it possible to verify the distinction between multiplying cells that are harmless and those that are malignant.
Lelièvre and co-corresponding author on the study, David Knowles of the national lab, used human mammary cells to analyze nuclear protein distribution that shifted depending on whether a cell was malignant, had not yet developed a specific function or was a normally functioning mature mammary cell.
"When you look at cells that don't yet have a specific function – aren’t differentiated, compared to fully differentiated cells, which are now capable of functioning as breast cells – the organization of proteins in the nucleus varies tremendously," Lelièvre said. "Then looking at how the proteins in malignant cells are distributed, it's a totally different pattern compared to normal differentiated cells."
The research team's study on the imaging technique and its use in 3-D mapping and analysis of nuclear protein distribution is published this week online in Proceedings of the National Academy of Sciences. Ultimately, the scientists want to use the technique to determine not only if a lesion is malignant but also the exact kind of cancer, how likely it is to spread and the most appropriate treatment for a particular patient.
"The major problem exists in the pre-malignant stages of abnormal cells in determining whether cancer will develop, what type and how invasive it will be," Lelièvre said. "The decision then is whether to treat or not to treat and how to proceed in these preliminary stages because only a certain percentage of these patients will ultimately develop cancer.
"We want to use this technique to identify subtypes of cells within lesions that potentially could become more aggressive forms of cancer."
Lelièvre, Knowles and their team used an antibody attached to a fluorescent molecule that targeted and linked with a specific nuclear protein from mammary tissue. When malfunctioning, this protein, named nuclear mitotic apparatus protein (NuMA), has been linked to leukemia and breast cancer.
The imaging technique the researchers developed to identify NuMA location shifts is called an automated local bright feature image analysis. It recorded the average amount of luminescence throughout the nucleus and then located the brightest spots, which were the protein. The system then automatically measured the differences in the protein's distribution in each cell type and mapped it. This enabled the researchers to verify the changes exhibited by non-differentiated cells that were still multiplying, normal mammary cells and multiplying malignant cells.
The ability to see the protein patterns in the nucleus gives scientists one more tool in advancing the identification of types of cancer and appropriate treatment, Lelièvre said. The imaging tool should work for mapping and analyzing locations of any nuclear protein.
"We have genomics and proteomics that tell us about where genes are, whether they are functioning and interactions of genes with proteins, but no one had focused on the changing distribution of nuclear proteins," she said. "Looking at the location of the proteins is a third part of studying cancer.
"We call it architectural proteomics because the proteins are still there but the location changes."
These protein shifts in the nucleus also may change the protein function, Lelièvre said. The new technique to map protein location will help determine this as well. In the case of malignant cells, it may reveal what signaling process went awry causing abnormal cell growth.
"It's as if, instead of losing an arm, your arm was placed in another location. It's abnormal, but you have everything you need – just not in the right place," she said. "It's what happens in cancer, too; the needed proteins are still there but not in the right place anymore, so their function is altered."
The misplaced proteins in their new locations change how the cell behaves and participate in the promotion of cancer, she said. Being able to measure the protein location shifts to aid in determining their function in cancer cell development will allow scientists to use the proteins as treatment targets.
"With our new system, we now will be able to look at individual cells and nuclei and possibly identify some classes of cells that could be more dangerous than others," Lelièvre said.
The other researchers on the study were Carol Bator-Kelly, Purdue Department of Biological Science, and Damir Sudar and Mina Bissell, at the National Laboratory's Life Sciences Division Biophysics and Cancer Biology departments.
Lelièvre is also a member of Purdue’s National Cancer Institute-designated Cancer Center and Purdue’s Oncological Science Center at Discovery Park.
Funding for this research came from the Department of Defense Breast Cancer Research Program, the Department of Energy Office of Health and Environmental Research, the Walther Cancer Institute, "Friends you Can Count On" and the Purdue University Research Foundation.
Purdue University School of Veterinary Medicine, Department of Basic Medical Sciences
A publication-quality graphic is available at http://news.uns.purdue.edu/UNS/images/+2006/lelievre-flourescence.jpg
Automated local bright feature image analysis of nuclear protein distribution identifies changes in tissue phenotype
David W. Knowles*‡, Damir Sudar*, Carol Bator-Kelly†, Mina J. Bissell*, Sophie A. Leliè vre†‡. – * Biophysics and Cancer Biology Departments, Life Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California, 94720, USA; † Department of Basic Medical Sciences and Cancer Center, Purdue University, 625 Harrison Street, West Lafayette, Indiana 47907-2026, USA. – ‡Corresponding authors: David W Knowles and Sophie A Lelièvre.
The organization of nuclear proteins is linked to cell and tissue phenotypes. When cells arrest proliferation, undergo apoptosis, or differentiate, distribution of nuclear proteins changes. Conversely, forced alteration of the distribution of nuclear proteins modifies cell phenotype. Immunostaining and fluorescence microscopy have been critical for such findings. However, there is an increasing need for quantitative analysis of nuclear protein distribution to decipher epigenetic relationships between nuclear structure and cell phenotype, and to unravel the mechanisms linking nuclear structure and function. We have developed imaging methods to quantify the distribution of fluorescently-stained nuclear protein NuMA in different mammary phenotypes obtained using three-dimensional cell culture. Automated image segmentation of DAPI-stained nuclei was generated to isolate thousands of nuclei from three-dimensional confocal images. Prominent features of fluorescently-stained NuMA were detected by using a previously undescribed local bright feature analysis technique, and their normalized spatial density calculated as a function of the distance from the nuclear perimeter to its center. The results revealed marked changes in the distribution of the density of NuMA bright features when non-neoplastic cells underwent phenotypically normal acinar morphogenesis. Conversely, we did not detect any reorganization of NuMA during the formation of tumor nodules by malignant cells. Importantly, the analysis also discriminated proliferating nonneoplastic cells from proliferating malignant cells, suggesting that these imaging methods are capable of identifying alterations linked not only to the proliferation status but also to the malignant character of cells. We believe that this quantitative analysis will have additional applications for classifying normal and pathological tissues.
Abbreviations1 – 1 HMECs, human mammary epithelial cells; LBF, local bright feature; NuMA, nuclear mitotic apparatus protein; 3D, three-dimensional
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