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Peeking at the molecular secrets of disease

12/31/2010
Suzanne E. Winter

A new technology improves histological identification precision and could radically influence the disease diagnosis process.

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Researchers at the University of Illinois at Urbana-Champaign (UIUC) have developed an imaging technology that identifies abnormal cells and normal cells on the molecular level. In a recent issue of Cancer Research, the team reports that their new diagnostic tool assesses molecule abundance and applies a color code to differentiate between healthy and diseased areas of tissue faster and more precisely than current, observation-based techniques.

Standard histological techniques section and stain a sample before it is observed by a trained specialist; these protocols tend to rely on the presence of structural changes within cell or tissue morphology to diagnose disease, such as cancers. These procedures, however, are limited by the experience and visual acuity of the technician. Furthermore, disease may be present for a considerable amount of time before structural changes can be visualized.

“Our technology scans tissue sections that are removed from the body, and without any stains or labels, it begins to tell us what molecules may be present,” said Stephen A. Boppart, a researcher at the Beckman Institute for Advanced Science and Technology at UIUC. “We know that disease has most of its origins from the molecular level, so we think that this is going to be more indicative of finding normal or abnormal areas of tissue or cells.”

A team of scientists in Boppart’s Biophotonics Imaging Laboratory at UIUC combined several existing imaging methods to develop a new technique—non-linear interferometric vibrational imaging (NIVI). NIVI integrates coherent anti-Stokes Raman scattering (CARS)—a chemistry tool often plagued by non-resonant background noise—with spectral interferometry, which extracts the desired signals in a faster, cleaner fashion. These crisp signals represent the ratios of targeted molecules present within cells.

Spectrally reconstructed NIVI captures the molecular vibrational spectrum of the sample and renders the signal into a color-coded image that can easily be used in diagnostics. Source: Cancer Research.


“What we’ve shown in this first study was an ability to discriminate between proteins and lipids, which are common in cells but whose ratio will change in different states of health and disease,” said Boppart. The extracted signals are then subjected to an algorithm to statistically analyze whether the cell is normal or abnormal; a false color spectrum is applied to the result to allow clinicians to quickly visualize the ratio of health to unhealthy cells in the sample. “We were very surprised that the uncertainty boundary between the two was so strong and distinct—about 100 microns—which is only a few cells wide; this is promising because it’s sharper than someone might be able to determine with the naked eye in standard histology.”

The team continues to fine-tune their technology and is working on a compact, easy-to-use, portable laser source. “That’s a key advancement to make this technology more clinically useful,” said Boppart. “What we hope is that we’ll have a portable system that we can use to start looking at resected or in vivo human tissue.”

Since publishing, the team has reduced the size of the laser system to that of a shoebox and anticipates that NIVI could become a standard diagnostic technology, able to be wheeled down the halls of hospitals and clinics around the world.

“Tissue diagnostics is moving to the molecular world, and it is going to be an imaging method that will give us this type of information,” said Boppart.

The paper, “Molecular histopathy by spectrally reconstructed nonlinear interferometric vibrational imaging,” was published online on 23 Nov. 2010 in Cancer Research.

Keywords:  imaging cancer NIVI