Determining changes in the physical properties of cells is crucial to diagnosing and treating some diseases, such as cancer. But a diagnosis requires the expertise of a pathologist. A team of scientists has developed a quick and simple method of analyzing tissue biopsies for cancer that might make pathologists a thing of the past.
When a cancer tumor has been biopsied, a specialist pathologist is required to analyze the sample and provide an assessment of the tissue’s health. It’s a process that needs to be fast and accurate as it usually happens while the patient is on the operating table.
Taking a biopsy of a solid tumor is the most common way of assessing cancer malignancy and guides surgeons in managing patients both during and after surgery. But what sounds like a simple process isn’t. A pathologist must slice and stain a sample and inspect it microscopically before providing their expert opinion; it’s very labor- and resource-intensive.
Waiting for a pathologist’s report may soon be a thing of the past, though, with a team of researchers developing a method of accurately analyzing solid cancer tumors in about 30 minutes without consulting a pathologist. They tested their method using mice tissue.
The new device uses a “tissue grinder” to reduce the sample down to single cells, a process which takes less than five minutes. The cells are then analyzed using real-time fluorescence and deformability cytometry (RT-FDC), which employs a syringe pump to push a stream of single cells through a microscopic constriction where the cells are exposed to hydrodynamic shear stress and pressure.
Cell deformability is a useful way of diagnosing diseases, especially the invasiveness of cancer cells. The ability of cancer cells to deform means they’re better able to invade other cells, leading to tumor metastasis.
RT-FDC can analyze up to 1,000 cells per second, 36,000 times faster than older, more traditional methods of analyzing cell deformability. An image is taken of each cell as it passes through, assessing its physical attributes and differentiating subtypes of tissue cells by image alone.
“With traditional methods to analyze a biopsy sample, a pathologist can only look at cells,” said Markéta Kubánková, co-lead author of the study. “We can do the physical examination of the individual cells, and that gives us much more information to work with.”
But observing a cell’s physical structure is not enough for diagnostic purposes. So, the researchers added AI in the form of a machine learning model that evaluates the data obtained by RT-FDC and quickly assesses whether a biopsy sample contains tumor cells.
The whole process, from sample processing to data analysis, took less than 30 minutes, meaning that it can be done while the patient is still on the operating table and the results provided to the surgeon without the need to engage a pathologist.
This is an advantage, say the researchers, because it’s not always possible to get hold of a pathologist while surgery is being performed, meaning that, in some cases, samples can’t be analyzed until after the surgery is over.
“Depending on the result this often means that days later, the patient has to return to the hospital for another surgery,” the researchers said.
In addition to testing for cancer tumors, the device was also used to detect tissue inflammation in a model of inflammatory bowel disease. The researchers hope that their new method of cellular analysis will one day be used in the clinical setting.
“This was a proof of concept study – the method could accurately determine the presence of tumor tissue in our samples very quickly,” said Despina Soteriou, co-lead author of the study. “The next step will be to continue to work very closely with clinicians to determine how this method can best be translated into the clinic.”
The study was published in the journal Nature Biomedical Engineering.
Source: Max-Planck-Zentrum für Physik und Medizin (PDF)
