Siblings born to the same parents and raised in the same household often have different traits and abilities. Maybe it’s a different hair color or varied levels of athletic ability.

Cells can be a little bit like this, too.

Even within homogeneous cell populations, where cells of the same type are living in the same environment, these otherwise identical cells can still have — or develop — single-cell differences. This diversity can lead to disease, infection, even drug resistance.

Abhyudai Singh, professor of electrical and computer engineering at the University of Delaware, wants to know more about how this works at the cellular level. Armed with $2 million in funding from the National Institutes of Health’s Maximizing Investigators Research Award program (MIRA) for established investigators, Singh is developing computational tools for understanding the mechanisms behind specific cell states within cell populations.

Broadly, the idea is to advance methods to understand drug tolerance in bacterial, fungal and cancer cells in order to inform techniques for altering cell states as a therapeutic approach for disease.

As an engineer, computational scientist and modeler, Singh doesn’t do laboratory experiments. But he has several collaborators both locally and across the world who are generating experimental data for him to model as part of this study. Specifically, he wants to figure out how an identical population of cells goes from being identical in every way, to becoming resistant to antibiotics.

“For example, when cancer cells are hit with chemotherapy, we get these ‘lucky survivors’ — a small number of cells that always survive. We want to know how this diversity happens and why,” said Singh, who holds joint appointments in UD’s biomedical engineering and mathematical sciences departments and is affiliated with the Center for Bioinformatics and Computational Biology.

UDaily caught up with Singh to discuss how his computational work can inform future health care solutions.

Q: How do cells develop the ability to transform themselves?

Singh: We’ve found that initially there is a nongenetic plasticity in the system. So, there is diversity in the expression of various proteins inside the cell and that makes them different. These cells are not genetically different from others in the same cell population, but by randomly determined changes in gene expression, they can survive. Then, during exposure to stress, these cells can develop resistance through genetic and nongenetic mechanisms.

Q: How does this happen?

Singh: Some cells have already mastered how to flip back and forth between states to outlast different environmental conditions. They call this survival mechanism “cell-state switching.” This switching ability creates diversity in a cell population. We developed a model to estimate how fast or slow cells can flip back and forth between states. This is important because we know this switching capability is not genetic, so it is reversible. 

Q: How does this relate to human health?

Singh: In the context of disease, knowing when and how this cell-state switching occurs can help lead to the design of better therapy protocols to delay the emergence of resistance to treatment. For example, when patients undergo chemotherapy for cancer, typically tumor size shrinks as tumor cells die. But sometimes tumor cells become resistant or tolerant to the therapy and rebound instead. If we know the timing of this switch, maybe clinicians one day will be able to prevent this tolerance by scheduling therapy earlier or later or pulsing medications during optimal time periods to prevent tumor cells from becoming tolerant or resistant.

Q: How will your model help?

Singh: From preliminary data studies, we think this model is general enough to have broad applications for different kinds of cells, from bacteria to cancer cells to fungi. I’m a mathematical and computational modeler, so I’ve enlisted colleagues from various disciplines, including immunology, microbiology and oncology, to provide experimental data with which to do my work. Using single-cell measurements on the level of proteins and messenger RNA molecules that provide cells instructions to survive, combined with modeling of complex regulatory gene networks, we want to uncover mechanisms that drive interconversion between cell states. The hope is to better understand when these changes happen that lead to this type of state-switching.

Q: What inspired you to look at this?

Singh: The idea for this work goes back to 1943, when researchers called Luria and Delbrück developed a Nobel prize-winning test for determining why some cells grow and others don’t. By looking at the fluctuation of cell survivors between colonies, they could tell whether the mutation to persist was genetic or random. My work takes this fluctuation test idea and adapts it to include cell-state switching. By looking at the fluctuations in the surviving cells in colonies, I’m able to estimate the kinetics and timing that cause these changes to happen. In the context of melanoma, we have shown that individual cells can become tolerant to target drug therapy for roughly five generations, before switching back to being sensitive to therapy. This form of cell-state switching primes a rare subpopulation of cells to survive and transform in response to therapy. We have several ongoing projects applying these ideas to other cancer types and to investigate antimicrobial resistance.

Q: Where do you hope this work will lead?

Singh: The aim is to better understand how different timing or dosing of therapeutics can prevent cells from becoming resistant or tolerant to treatment. My model showed cells that survive are already primed for survival based on the cell-state switching that allows them to morph and avoid cell-fate death. Now, through gene sequencing studies, we are working to figure out what genes are upregulated or downregulated in these subpopulations of cells that are surviving. Quantifying these processes by modeling the complex control systems and gene networks responsible for them can help us better understand what’s happening. Once we understand the process, in the future, hopefully, it can inform the design of better therapeutics and smarter therapies. This is the bigger picture.