Understanding cellular metabolism—how a cell uses energy—could be key to treating a wide array of diseases, including vascular diseases and cancer.
While many techniques can measure these processes among tens of thousands of cells, researchers have been unable to measure them at the single-cell level.
Researchers at the University of Chicago’s Pritzker School of Molecular Engineering (PME) and Biological Sciences Division have developed a combined imaging and machine learning technique that can, for the first time, measure a metabolic process at both the cellular and sub-cellular levels.
Using a genetically encoded biosensor paired with artificial intelligence, the researchers were able to measure glycolysis, the process of turning glucose into energy, of single endothelial cells, the cells that line blood vessels.
They found that when these cells move and contract, they use more glucose, and they also found that cells uptake glucose through a previously unknown receptor. Understanding this process could lead to better treatments for cancer and vascular diseases, including COVID-19.
The research, published in Nature Metabolism, was led by Assoc. Prof. Yun Fang and co-led by Asst. Prof. Jun Huang, with former postdoctoral fellow and now Asst. Prof. David Wu and biophysical sciences graduate student Devin Harrison.
“Understanding cellular metabolism is universally important,” Huang said. “By measuring single-cell metabolism, we potentially have a new way of treating a wide range of diseases.”
“This is the first time that we can visualize cellular metabolism at different temporal and spatial scales, even at the subcellular level, which could fundamentally change the language and approach for researchers to study cellular metabolism,” Fang said.
Endothelial cells normally provide a tight layer inside blood vessels, but they can contract, leaving gaps within this layer, when they need help from the immune system. Abnormal contraction can cause leaky blood vessels, leading to heart attack or stroke. Such contraction in blood vessels around the lungs can also cause fluid to leak in, which happens in the case of acute respiratory distress syndrome. This often occurs in patients with severe cases of COVID-19.
To better understand how cells metabolize energy to fuel this contraction, the researchers turned to Förster resonance energy transfer (FRET) sensors, genetically encoded biosensors that can measure the amount of lactate inside cells. Lactate is the byproduct of glycolysis.
Though the researchers did not create the sensors, by pairing the sensors with machine learning algorithms, they created an even more powerful technique that allowed them to image cells, analyze the data, and parse out glycolysis reactions at the cellular and subcellular levels.