New quantitative microscopy method offers stain-free cancer detection

The century old process of staining biological tissue for examination under a microscope remains today as the standard assessment tool for pathologists looking for signs of cancer. Now a new stain-free imaging method for cancer diagnosis has been developed that provides images that rival or surpass those from histological staining. In addition, the technique is automated, and it provides quantitative data on structures at the cellular level.

Shown above are biopsy slides of a 5+4 Gleason grade, or high grade tumor, done with the SLIM stain-free method, bottom, and standard histological staining at top.

Researchers from the Beckman Institute, Christie Clinic in Urbana, and the University of Illinois at Chicago reported on the results of using the method for cancer detection in a paper for the Journal of Biomedical Optics titled Tissue refractive index as marker of disease. Gabriel Popescu, director of the Quantitative Light Imaging Laboratory (QLI) at Beckman and faculty member in the Department of Electrical and Computer Engineering, developed the technique, called Spatial Light Interference Microscopy (SLIM), and led the study.

SLIM employs phase contrast microscopy and holography that combine multiple light waves to enable visualization of nanoscale structures quantitatively and without staining.

In their paper the researchers wrote that an alternative to the current method in histopathology of evaluating slides of stained tissue biopsies under a microscope is required: “A sensitive and quantitative method for in situ tissue specimen inspection is highly desirable, as it would allow early disease diagnosis and automatic screening.” Using the SLIM method to examine more than 1,200 biopsies, the researchers were able to visualize unstained, or label-free, cells with high-resolution, high-contrast images. The results demonstrated, they wrote, “that quantitative phase imaging of entire unstained biopsies has the potential to fulfill this requirement.

“Our data indicates that the refractive index distribution of histopathology slides, which contains information about the molecular scale organization of tissue, reveals prostate tumors and breast calcifications. These optical maps report on subtle, nanoscale morphological properties of tissues and cells that cannot be recovered by common stains, including hematoxylin and eosin. We found that cancer progression significantly alters the tissue organization, as exhibited by consistently higher refractive index variance in prostate tumors versus normal regions.”

Popescu said that the SLIM’s interferometric (which measures phase differences in the light path) capability is what makes it work with a great sensitivity, down to the molecular scale. And that capability is why SLIM can be so valuable for greatly improving the chances of early detection and treatment of cancer.

What that means is that only a small number of molecules arranged in a certain way are enough to give us the optical signal that something is going to happen here,” he said. “Ideally, we would like to detect cancer at the single-cell level. So, can we find a cell that looks abnormal and do everything so much earlier where the process is still reversible. We know that the disease  starts at the nanoscale, at the molecular level, and we think we have the proper tool to catch these early events.”

Staining is commonly used for biological tissue to provide contrast in light microscopes and spotlight features such as tumors. The SLIM method is not only label-free but can gather empirical data on morphological, or structural, tissue information for automated screening at the nanoscale, providing objective evaluations of data on structures such as tumor margins that can be difficult for pathologists to assess.

“We think that the most important advantage of SLIM is that it provides quantitative, objective information,” Popescu said. “Right now, in the clinic, the diagnosis is subjective; it’s a human that does it. There are studies showing that two pathologists agree on a diagnosis only four out of five times.

“What we hope to achieve is information that will have a quantitative, objective measure of the degree of cancer, the stage that determines treatment; there is a lot of work ahead of us to get there.”

If adopted, a major benefit of the technology would be to curtail what Popescu and others say is the overtreatment of cancers, especially prostate cancer.

“The holy grail of the research will be to actually predict information about the outcome of the patient,” Popescu said. “So for those that undergo surgery, what are the chances that the cancer will reoccur. This, right now, is actually a 50/50 guess.

“What we think is that we will be able to catch the disease early at the single-cell level and with very high accuracy. For prostate cancer especially, remove the overtreatment, only treat those patients that really need it, and hopefully catch reversible states. If we’re able to do that then basically everyone will be treated and there will be no bad outcomes.”

Plans, the researchers write, are to add more datasets and, eventually, work toward including the SLIM method in a clinical setting: “The prospect of a highly automatic procedure, together with the low cost and high-speed associated with the absence of staining, may make a significant impact in pathology at a global scale.”

Popescu was joined in the project by collaborators Zhuo Wang of the QLI, Krishnarao Tangella of the Department of Pathology and Christie Clinic, and Andre Balla of the University of Illinois at Chicago.

Contact: Gabriel Popescu, Department of Electrical and Computer Engineering, 217/333-4840.

Rick Kubetz, Engineering Communications Office, University of Illinois at Urbana-Champaign, 217/244-7716.

Writer: Steve McGaughey, Beckman Institute, 217/244-5582.

Images acquired by Shamira Sridharan