Bone marrow aspirate concentrate (BMAC) is a common treatment for joint injuries.
The invasive therapy involves extracting bone marrow—often from the hip—and concentrating it to preserve stem cells and growth factors, which help promote wound healing and tissue regeneration. The concentrated mixture is then injected into the injury site to speed up tissue repair and reduce inflammation. BMAC can be used on its own or in conjunction with surgery to repair ACL, MCL, and meniscus tears and other injuries.
"We know that BMAC has therapeutic properties that seem to be effective," says Colin Herna, a fifth-year doctoral student in bioengineering who is advised by Sabrina Jedlicka , an associate professor of bioengineering and materials science and engineering and Lehigh's deputy provost of graduate education. "But exactly what is being injected is not always understood. We're trying to uncover what's in the black box."
Herna is the lead author of a recent paper published in ACS Omega detailing how he and his team identified measurable differences in the composition of bone marrow extracted from the hip versus the shoulder.
Bone marrow is typically harvested from the iliac crest of the hip (the curved upper ridge of the pelvic bone), which provides large volumes of marrow. In theory, says Herna, this means it contains more stem cells and results in anecdotally better outcomes. For certain injuries—such as rotator cuff tears—surgeons can draw marrow from the humeral head of the shoulder (the ball-like end of the upper arm bone forming part of the shoulder joint), avoiding a second invasive procedure. Herna set out to determine if marrow from the shoulder was comparable to hip marrow.
Using samples from both sites, Herna applied two machine learning models to screen 109 unique proteins for relevance. He then compared where the models' results overlapped.
"We identified six proteins that may help distinguish between the two extraction sites," he says. "In other words, the marrow in the hip and in the shoulder share many of the same ingredients, but not in the same ratios."
The difference may stem from the unique microenvironments of individual bones. Such variability matters because proteins and growth factors can significantly influence healing, even in tiny amounts.
Herna believes the process he and his team developed will help guide other researchers.
"Right now, BMAC extraction is not standardized—different kits follow different protocols," says Herna. "That variability leads to differences in stem cell and protein concentrations. With this machine learning approach, we created a framework medical professionals can use to study BMAC or other biological tissues."
Further down the road, this research could deepen understanding of BMAC and allow physicians to tailor treatments, choosing extraction sites based on the proteins most likely to benefit each patient. Combined with future research into demographic factors, such as age, sex, and lifestyle, it could bring regenerative medicine closer to personalized care.
For Herna, who expects to complete his PhD in December and pursue quantitative research, the data science aspect of the project was especially motivating.
"There is so much information out there," he says. "Having the skills to make informed decisions based on that data is valuable across many fields of research."
