Dr. Morteza Mahmoudi featured in Diagnostics World

By Deborah Borfitz

February 14, 2020 | The same technology that propels the world’s fastest electric train at speeds up to 267 miles per hour is now being used for the first time to levitate proteins in plasma for diagnostic purposes—and tease out differences between healthy and diseased plasma donors. Magnetic levitation (maglev) has been successfully deployed to identify opioid use disorder (OUD) and the method is now being extended to the search for clinical biomarkers that can discriminate between the four subtypes of multiple sclerosis (MS) and detect cancer earlier, according to Morteza Mahmoudi, Ph.D., assistant professor of the Radiology and Precision Health Program at Michigan State University. 

Maglev has been employed for more than a decade to measure the density of a broad range of biomaterials, including cells, which can’t be accurately measured by other techniques, says Mahmoudi. But the closest maglev has come to being deployed diagnostically is to discriminate cancer cells from healthy cells based on unique differences in their levitation and density blueprints.  

A critical first step to making the technology relevant to diagnostics by using simple plasma is ensuring the proteins won’t be denatured by the process, says Mahmoudi. To overcome that hurdle, Mahmoudi and his colleagues had the maglev system utilize a superparamagnetic solution comprised of iron oxide nanoparticles. Mahmoudi’s team has been working on the biomedical applications of superparamagnetic iron oxide nanoparticles since 2006. 

As was recently reported in Analytical Chemistry, the new maglev system can reliably and reproducibly levitate plasma proteins and define their densities. An accurate read on density can help elucidate many other physical properties of proteins, including their 3D structure, enabling the design of safer and more efficient therapeutic agents, such as nanomedicine, Mahmoudi says.  

Interestingly, Mahmoudi continues, the plasma proteins assembled themselves into “smiley-like bands” over a three-hour period. Other surprises were that the density and molecular weight of the protein bands had no correlation, and that the average density of the proteins was much lower than suggested by previous studies. 

The magnetic force in the solution effectively cancels out the weight of the proteins so their density can be more precisely defined, he explains.  

In a subsequent study, published in Advanced Healthcare Materials, the maglev method was used to compare the plasma of healthy individuals to that of people with OUD. The diagnostic technique involves little more than taking pictures of the levitated plasma protein bands, Mahmoudi says. Image analysis for the study was aided by machine learning, so it happened in a matter of minutes and with 95% OUD detection sensitivity. 

The spectrum of plasma proteins in the bands was distinctly different between the two groups, suggesting the system could be developed into a diagnostic tool and, perhaps more importantly, provide information of value in drug discovery, says Mahmoudi. 

Follow-up research is now underway in which plasma samples will be taken from patients before they’re prescribed pain medicine to see if the maglev method can discern between those who do and don’t subsequently develop OUD, based purely on protein patterns in their donated plasma. “If so, we may end up with particular proteins that may have critical roles in inducing addictive behaviors.” 

Global Chip 

Michigan State researchers are now working in collaboration with UMass Memorial Medical Center in Boston to see if the maglev technique can identify and discriminate between the four major subtypes of MS, for which there are currently no clinical biomarkers and therapeutic regimes differ significantly, Mahmoudi says. “If clinicians can detect them [precisely] in the first place, they can start with the right treatment approach.” 

In parallel, the researchers are also looking at how well the maglev diagnostic method performs on plasma samples from cancer patients, Mahmoudi adds. Almost 95% of patients survive cancer when it is detected at its earliest stages while a similar proportion do not survive at the latest stages, he notes.  

The maglev diagnostic approach is “very easy, portable and highly reproducible,” says Mahmoudi. But taken one step further, the maglev system could also help find new patterns of biomarkers that can be introduced into drug discovery research around chronic diseases. 

“We extracted each band and put it through proteomic analysis to see whether we could find any biological relevance to disease progress,” he says. In the case of OUD patients, “we found certain proteins such as hemoglobin subunit alpha, beta and delta as well as apolipoprotein C‐II. Our findings are aligned with current knowledge in the literature demonstrating hematological changes and increased levels of free hemoglobin in OUD patients.”  

Mahmoudi says the long-term goal is “a global chip you can put a drop of blood in to see [by naked eye] whether or not you need to see a doctor. It will give the risk factors for specific types of catastrophic disease… from a levitating proteins point of view.”  

Assessment of the complex plasma environment via machine learning will require “supervised classifiers” to define the specific proteins used for identification of different diseases, he adds. Positive results might prompt further, more time-consuming testing, including extraction of the levitated proteins for mass spectrometry-based detection of surrogate disease markers. 

If all goes well, Mahmoudi predicts that the maglev system five years from now will be used for the detection of multiple diseases, including cardiovascular conditions. It might also be deployed for the capture and analysis of lipids, metabolites, and other small molecules impacting protein-protein interactions and understanding of disease progression. 

The longer-term objective is to aid development of new therapeutics for individuals rather than subpopulations, he adds, which gets right at the heart of the precision health model.