Dr. Arinze Okafor is an accomplished scientist with a focus on genomics and biomedicine, known for his work on muscle stem cells, gene regulation, and the effects of environmental pollutants on cancer development. Trained in cell and computational biology, he has made significant strides in using single-cell genomics to address complex biomedical challenges. Through his research, Okafor aims to bridge the gap in African representation in cutting-edge science and inspire the next generation of African scientists. In this interview, he speaks about his journey in research and the transformative potential of precision medicine.

What inspired your research on muscle stem cells and lifelong regeneration, and how did your training in cell and computational biology help you balance wet-lab and computational approaches while overcoming key challenges?

I was first inspired by a desire to master single-cell technologies, which allow researchers to analyse individual cells rather than bulk populations. This enables the identification of unknown cell types and deeper insights into cellular dynamics—crucial in biomedicine. Single-cell technologies are relatively new, with few Nigerian experts in the field. At Duke, I had the opportunity to work in a lab specializing in single-cell genomics, applying it to complex biomedical questions. Our discovery of self-renewing muscle stem cells emerged from my exploratory analyses of single-cell data. While the discovery was exciting, my initial drive stemmed from learning single-cell genomics.

Balancing computational and experimental approaches is challenging but essential. Biologists should understand computational methods, just as computational biologists should grasp experimental design. Both require distinct mindsets: computational biology involves quantitative, programmatic thinking, while experimental biology demands logical, procedural design with rigorous control testing. Excelling in both is difficult, so I often dedicate separate time blocks to each planning and executing experiments before shifting to analytical tasks.

Modern biology is very data-driven, making computational skills vital for unbiased hypothesis generation. AI tools even transform literature reviews into computational tasks. However, experiments remain the foundation of biomedical research. Strong computational expertise aids experimental design, while biological knowledge guides relevant data analysis. Mastering both fosters interdisciplinary collaboration and accelerates scientific progress.

You’ve developed a new genomic technology to study gene regulation more accurately. Can you elaborate on the potential applications of this technology in precision medicine and how it could transform current practices in genomics?

Classically, the gene-central view of how our DNA functions emphasises the role of protein genes in determining our features or what we call ‘phenotypes’. However, since the sequencing of the human genome in 2001, we know that only about 2 per cent of our genome constitutes protein genes. We call this the coding genome. The other 98 per cent, which is non-coding, mostly function to regulate where (as in, what cells), when, and at what levels genes get expressed genes. The non-coding genome regulates gene expression by forming complex interactions between DNA regulatory elements and the genes they target. However, all these years, it has been unfeasible to measure the functional consequences of 3D-genome interactions between candidate regulatory elements and their target genes or other elements, at a genome-wide scale. As part of my research, I developed the technology, 3D-STARRseq, to enable this functional measurement.

Why is such a technology important? Firstly, since we know that gene regulatory elements largely function in partnership with other elements and gene promoters, 3D-STARRseq gives us a more accurate way to assess regulatory element function. Current practices do not take this 3D interaction context into account, and we know that this interaction context is important for DNA function. With 3D-STARRseq, we can now better answer questions such as: does a regulatory element promote or inhibit gene expression when it interacts with a gene promoter or another regulatory element? Secondly, when we detect a mutation in one’s DNA—especially mutations that are in non-coding genome regions—it is hard to say if the mutation will lead to increased or decreased gene expression or DNA function. 3D-STARRseq can also be applied to understand this. Such a tool can be applied in the future to better interpret genetic mutations and individual DNA sequences. Thirdly, using 3D-STARRseq we can learn the rules that underlie the cooperativity of DNA regions in a cell-type-specific manner. We can then use this knowledge to design effective genetic circuits for the precise expression of transgenes and plasmids in different cell types.

What surprising findings have emerged from your ongoing research, and how do you see this work influencing public health policies and advancing precision medicine for clinical applications?

We will disseminate findings from our ongoing research when it matures, and I can’t comment publicly about it at this stage. However, cancer is a genetic disease driven by malignant mutations, which may arise internally or from environmental factors. Part of my research explores how environmental pollutants damage DNA and contribute to cancer development, along with other critical questions. I’m excited about the insights our work will reveal.

Cancer is highly complex, with no two cases being exactly alike. Effective treatments shouldn’t follow a one-size-fits-all approach. Future strategies must first decode each cancer’s genetic complexity and key mutations. Understanding molecular profiles and treatment responses will help design better therapies, especially for hard-to-treat cancers. The challenge lies in making precision medicine widely accessible, requiring collaboration among scientists, clinicians, investors, and policymakers. Investors and governments are key—where funding and policies align with solving critical issues, attracting top talent is easier.

Additionally, studying how environmental pollutants trigger cancer-driving mutations could inform preventive measures, ensuring that public health policies can evolve to mitigate harmful exposure to identified environmental risks.

Precision medicine is a rapidly evolving field. Where do you see the most significant opportunities for innovation in the next 5–10 years, and how do you plan to contribute to these advancements?

Yes, precision medicine is rapidly evolving, and I see it transforming medicine as we know it in the next decade. The biggest opportunities for innovation in precision medicine will lie in three areas: drug targeting, patient-centered integrative genomics for diagnostics, and AI-assisted disease risk, disease prognosis, and exposure reaction prediction.

Targeting drugs to the precise cells where they are needed will revolutionize medicine and reduce adverse drug effects. I believe that the integrative analysis of genomic data, especially at the single-cell level, will enable the design of personalized and more effective therapies. Additionally, predicting disease risk and prognosis will be essential for disease prevention and improving responses for poorly prognostic cases. The ability to predict responses to external factors like drugs, vaccines, and allergens, driven by AI, will also be revolutionary.

These areas excite me because they can all be advanced by integrating genomics and computational biology. While it’s difficult to know exactly how I’ll contribute to these at the moment, I am excited to see where my scientific exploration in the broader field of precision medicine and genomics takes me in the next decade.

How do you approach interdisciplinary collaborations in projects like the Precision Genomics Collaboratory Pilot Grant which you received, and what key factors ensure successful teamwork in science? Additionally, how do you mentor young scientists, and what advice do you have for early-career researchers aiming to impact precision medicine?

The Precision Genomics Collaboratory (PGC) Pilot Grant, led by Duke University’s PGC and graduate education offices, funds student researchers across departments to advance genomics research. Working within the PGC and Duke’s Center for Advanced Genomic Technology was transformative, exposing me to diverse disciplinary perspectives that provided a holistic approach to problem-solving. Effective collaboration requires clear communication, aligned motivation, and acknowledgement of contributions.

My mentoring approach involves being approachable, professional, and supportive to encourage open communication. I actively listen to mentees, understand unspoken concerns, and guide them without hindering their problem-solving abilities. I offer nudges and explain key considerations rather than providing direct solutions.

For early-career researchers in precision medicine, I recommend interdisciplinary training across biomedical fields like cell biology, genetics, genomics, bioinformatics, and therapeutics. Precision medicine increasingly demands cross-disciplinary expertise, and integrating knowledge from multiple areas is essential for impactful research and innovation.

How do you navigate ethical challenges like data privacy and genetic discrimination in research, and how has overcoming your biggest career obstacle shaped your problem-solving approach?

Ensuring compliance with genetic data privacy and confidentiality requires adherence to global guidelines and regulations. While laws are established by governments, scientists and sponsors can uphold best practices, including clearly defining data usage before collection, obtaining informed consent, securing data storage with access controls, de-identifying data, and obtaining ethical approval from a legitimate and impartial review board.

One of my biggest challenges was adapting to the professional and social culture in a new environment outside Nigeria. Professionally, I had to learn workplace etiquette, effective communication, and how to navigate diverse professional relationships. Excelling academically is different from maintaining a professional image in a high-performance setting. Managing productivity and handling unfavourable outcomes also required adaptation.

Socially, integrating into a culturally diverse workplace and local community was another learning curve. Science is increasingly collaborative, requiring close interactions with individuals from varied backgrounds. Adapting meant focusing on the bigger picture, being open to different approaches, and respecting diverse perspectives, as long as they aligned with ethical and legal standards.

Mastering these skills has enhanced my ability to thrive, conduct impactful research, and enjoy the process.

You’ve received numerous prestigious awards, including the Paul and Lauren Ghaffari Award for Cancer Research. Which of these accolades has been the most meaningful to you, and why?

The Paul and Lauren Ghaffari award which I received at Duke was highly meaningful because it was one of the personal proofs I needed that told me that I was doing well, even amongst a relatively excellent group of Ph.D. candidates and trainee scientists at a distinguished institution like Duke. Although it is less than perfect to receive personal validation from such competitive awards since there will always be failures too, considering my simpler background, I needed to get that validation once in a while.

I was also super inspired by my selection to join the Global Biotech Revolution’s Gap Summit in 2020. For this summit, 100 people were selected from a pool of global applicants—it is quite competitive. It gave me my first opportunity to rub minds and shoulders with global biotech leaders and talented early career professionals from around the world.

What motivates you to continue pushing the boundaries of genomics and biomedicine? Are there any scientists, mentors, or personal experiences that have profoundly influenced your career path?

I believe the science that I do can truly transform medicine and make people live healthier and happier lives, I am uniquely talented at what I do, and no one can fill my gap the exact way I would. I have something unique to offer the world through my science, and I take it almost as a social responsibility to give my best to it. Thirdly, I enjoy what I do. I truly have fun when I delve deep into my field, either in conversations when working in the lab or when brainstorming. I enjoy doing it.

One of my biggest motivations besides these is the dearth of Nigerians and Africans in this field. I want to be a gateway through which the path to a scientific (or even a non-scientific) career that advances genomics and precision medicine can open up for Africans and Nigerians and many aspiring young scholars who may feel that such a field is out of their reach. I want to empower every aspiring scientist whom I have the opportunity to mentor directly or indirectly, to have the courage to forge their scientific path and solve problems that they are inspired to solve, irrespective of their backgrounds.