Neety Sahu is an expert orthopedic biotechnology and therapeutics postdoctoral researcher at the highly renowned Stanford University, and in just a few years, she has published numerous academic papers on her research in peer-reviewed medical journals.
Sahu’s advanced research focuses on osteoarthritis, a common and highly painful form of arthritis that affects millions of people in the United States alone.
Following its onset, osteoarthritis can affect a patient for their entire life, and as we will cover in a moment, treatment options largely aim to curb the painful symptoms of the disease.
There are no drugs that cure the disease, and that’s bad news for anyone currently experiencing the symptoms of osteoarthritis and those who may experience the disease in the future, which includes many millions of individuals.
Sahu is working to find better solutions for osteoarthritis treatment, and what we’d like to focus on in this article is how she and her colleagues are using cutting-edge technologies to conduct this research.
Testing treatments and identifying red flags
The first is an explanation of Sahu’s research goals. The overarching goal, of course, is to develop a more thorough understanding of osteoarthritis itself.
Yes, osteoarthritis is a known disease. Its common symptoms (joint pain, most often in the hands, neck, knees, and/or lower back) are well-documented, and for patients with sufficiently advanced osteoarthritis, these symptoms can lead to a lower quality of life.
However, as Sahu confirmed while speaking with us, no cure for osteoarthritis has been discovered. Meanwhile, treatment options for osteoarthritis focus on pain management and the alleviation of symptoms.
Sahu’s research efforts in this area involve testing treatment options and identifying red flags at the cellular level that indicate the presence of osteoarthritis.
Here, Sahu details the goals of her research and why this work is critical.
“This disease currently has no disease-modifying drug or early diagnosis marker. To date, potential drugs have had high rates of failure in clinical trials, despite showing successful results in pre-clinical studies in small animals. This is because of cellular and inter-patient heterogeneity in humans. I use single-cell methods to map the effects of candidate drugs on patient-specific osteoarthritic cells. I’m also using single-cell techniques to discover biomarkers for osteoarthritis”.
Research of these drugs and biomarkers involves advanced technologies, and we will explore these technologies and the accompanying research here today with Sahu serving as our guide and resident expert.
One cell at a time
A set of tools and methods that are vital to Sahu’s research is known as single-cell technologies. Single-cell technologies allow researchers and healthcare professionals to collect data at the cellular level, examining a multitude of individual cells to provide insights. The applications of this data are numerous, even within the specialization of osteoarthritis research.
Sahu: “Single-cell technologies provide deeper insight into cellular heterogeneity, identification of rare and/or unique subpopulations, and cellular events. This information is important for unlocking answers to diversity in disease propensity, response to drugs, etc. in human beings, in addition to biomarker and drug discovery”.
For the moment, we want to stress that single-cell technologies are extremely valuable and, further, that the collection and analysis of this data wouldn’t be possible without the use of tech-driven solutions, especially single-cell analysis software.
Single-cell technologies fall under the rather large umbrella of Big Data, and as anyone involved in Big Data work already knows, collecting data is one thing, but analyzing that data to turn it into something understandable and usable is a major undertaking.
Given the size of these datasets, manual analysis by humans would be impractical and, in certain cases, virtually impossible. Human analysis would also likely yield a greater number of errors.
In stark contrast, specialized software can analyze massive amounts of data quickly, giving professionals more time to extrapolate insights from this analysis.
Sahu: “Single-cell analysis generates hundreds of gigabytes of data, so software computing is essential for single-cell data analysis.”
Attempting manual analysis of this much data would be inadvisable at the very least. Next, a look at the impact of this type of analysis.
As is the case with many diseases, early detection is enormously beneficial for the patient. Early detection can mean that steps can be taken to slow or halt the progression of the disease. Sahu explained that osteoarthritis begins to develop long before there are measurable symptoms.
“The events leading to osteoarthritis happen years before the clinical symptoms, such as pain and cartilage degradation, are evident. Joint injuries like ACL tears, etc. increase the risk of developing osteoarthritis later in life. Early diagnosis of osteoarthritis will help identify at-risk individuals and possibly prevent the course of the disease.
In the past, it has been difficult or impossible to diagnose osteoarthritis before the symptoms have set in. But Sahu’s work has focused a great deal on finding ways to identify and diagnose osteoarthritis in its early stages, and the method in question involves what are known as biomarkers.
Biomarkers vs. Traditional diagnosis methods
The different means of identifying osteoarthritis each have their own unique advantages and disadvantages. The traditional diagnosis methods include X-ray or MRI imaging, looking at specific joints for telltale signs of tissue degradation.
“This imaging often occurs in the mid-to-late stages of osteoarthritis, when irreversible degradation of the tissues has already occurred.”
Biomarkers, however, offer a notable advantage: “Biomarkers offer a chance to see changes in the early stages of the disease when interventions for reversing the disease can be attempted.”
Referring to a definition from medical resource Cancer.gov, a biomarker is a “biological molecule found in blood, fluids, or other tissues that is a sign of a normal or abnormal process, or of a condition or disease.”
In simpler terms, biomarkers are signposts of what’s happening inside the body, at a level of specificity that simply can’t be rivaled by the imaging methods mentioned above.
By using single-cell technologies, top-level researchers like Sahu can identify any changes that might be indicative of the development of a disease.
Again, if osteoarthritis is identified during the early stages, there is a greater variety of treatment options for the patient. Single-cell technologies are also the driving force behind the testing of possible drug solutions. Rather than testing drugs on patients with osteoarthritis, drugs can be tested on tissues removed from a patient.
These tissues wouldn’t be useful for testing if researchers still relied exclusively on X-ray and MRI imaging, but with the ability to examine individual cells and cellular events, these samples can provide accurate information about how these drugs affect osteoarthritis at its core.
The work continues
Sahu has made a great deal of progress in her research, but, of course, there’s always more work to be done.
“Osteoarthritis is a complex and confounding disease, hence the absence of disease-modifying drugs or diagnostic markers.”
The complexity of this disease makes research that much more difficult, and it makes breakthroughs even harder to come by. Still, the progress thus far has been tangible, as Sahu details in this statement:
I have learned that finding common denominations in diverse and heterogeneous patient data is critical in identifying potential targets or biomarkers. These denominations can be found by stratification of patients by disease manifestation or phenotype.
This understanding helps narrow the search for solutions, and every step forward is heartening when working with such a difficult disease.
Sahu and her counterparts are using advanced technologies to great effect in their research, and it does seem likely that single-cell technologies will pave the way to further findings and treatments in the coming months and years.