The new tool, developed by Sriram Chandrasekaran, Associate Professor, Biomedical Engineering, and his PhD student Da-Wei Lin, is called SCOOTI (Single-cell Optimization Objective and Trade-off Inference).
U-M BME researchers have developed an exciting new tool to decipher the objectives of cells. The researchers tried to answer a simple but elusive scientific question: “What is a cell trying to produce?”
The new tool, developed by Sriram Chandrasekaran, Associate Professor, Biomedical Engineering, and his PhD student Da-Wei Lin, is called SCOOTI (Single-cell Optimization Objective and Trade-off Inference).
SCOOTI uses snapshots of omics data and predicts the ultimate metabolic “destination.” This is analogous to taking a picture of traffic and figuring out where most drivers are heading. SCOOTI can improve our understanding of cellular objectives, with implications for drug development, cellular reprogramming, and bioengineering. At its core, SCOOTI leverages machine learning algorithms to infer the objectives of individual cells by analyzing their metabolic and transcriptomic data.
“We’re trying to understand what cells want to do and what their objectives are,” Dr. Chandrasekaran explains. “By objectives, I mean that some cells, such as cancer cells or bacteria, want to proliferate and divide. But many other cells, such as stem cells or neurons, don’t rapidly proliferate—they perform other metabolic tasks.”
A fundamental challenge in computational biology drove the creation of SCOOTI. “One of the main goals in computational biology is to simulate entire cells and organisms,” Dr. Chandrasekaran notes. “For simulations, you need something that you’re trying to optimize. If it’s bacteria, they normally try to maximize their growth. But if you’re trying to simulate liver or brain cells, those don’t divide quickly. We don’t know what exactly they’re doing. We don’t know the simulation endpoints when creating a virtual model.”
To tackle this, his team at U-M BME developed SCOOTI to analyze the intricate “traffic patterns” of cellular metabolism. These patterns, much like the traffic flow in a city, reflect the pathways and interactions of various metabolites. “We use a metabolic network model, which is like the Google Maps of metabolism,” says Dr. Chandrasekaran. “Just like Google Maps gives you the connections between different landmarks or cities, this metabolic map has connections between different metabolites and what pathways connect those metabolites.”
In various test cases, SCOOTI correctly identified the objectives of cells that were either proliferating, quiescent (cell sleeping), or in specific phases of the cell cycle. It also correctly recalled genes that were essential for carrying out these objectives.
SCOOTI was then applied to study embryogenesis—the developmental process where a zygote forms a complete organism. In collaboration with stem cell biologists Ling Zhang and Jin Zhang at Zhejiang University, they analyzed metabolic and transcriptomic data from individual cells at various stages of embryonic development.
The authors discovered a crucial trade-off in embryonic cells. “Some cells are trying to produce more biomass and divide rapidly. While some cells surprisingly seek to reduce stress by producing antioxidant molecules,” Dr. Chandrasekaran explains. Antioxidants fight reactive oxygen species and also act as signaling molecules within cells.
“A striking observation we made was that cells cannot do both simultaneously. So, if a cell is trying to grow more, it has less metabolic energy to fight stress, but if it’s trying to produce redox molecules to fight stress, it can’t grow rapidly. These are exclusive—the cell can’t do both effectively.”
This discovery has significant implications for understanding cellular behavior in various contexts, from normal development to disease states. For instance, SCOOTI identified specific growth-related molecules (like cholesterol and phospholipids) and stress-related molecules (such as glutathione) that cells produce under different conditions. “SCOOTI can pinpoint which nutrients a cell needs and is trying to produce,” Dr. Chandrasekaran explains. “In some cells, we found glutathione was an important molecule the cells are trying to produce. In other cells, it was growth-related metabolites such as cholesterol.”
The insights gleaned from SCOOTI are not limited to academia—they hold real-world potential for therapeutic interventions and engineering applications. “Once we know the cellular objectives, we can investigate if we can find ways to change these objectives,” Dr. Chandrasekaran says. This could be incredibly impactful in cancer treatment, where understanding the growth objectives of cancer cells could lead to targeted therapies that exploit their specific metabolic weaknesses.
Moreover, SCOOTI’s applications extend to regenerative medicine and stem cell engineering. By identifying the distinct requirements of stem cells at various stages of development, researchers could tailor interventions to optimize cell growth and differentiation. “From a stem cell engineering point of view, if you can identify the distinct requirements for specific stem cells, then maybe we can help them grow and divide in a certain fashion,” Dr. Chandrasekaran notes.
Through SCOOTI, Dr. Sriram Chandrasekaran and his team are gaining a better understanding of the objectives of cells, opening opportunities to seek innovative therapeutic and engineering solutions.
Here is a link to the full journal article.