Thanks to a new technology that can track the glucose metabolism in a single cell, high-resolution images of the human metabolism were recorded with the camera for the first time. Tracking glucose could provide insights and even new therapies for treating cancer – a disease that often disrupts cell metabolism.
The new technology combines fluorescence resonance energy transfer (Fret) between two light-sensitive molecules with machine learning – a form of analysis based on the idea that computer systems can “learn” from data, which makes the analysis more accurate. The technology made it possible for Yun Fang and his team at the University of Chicago to visualize glucose metabolism – glycolysis. The team genetically engineered human cells to express specific fret biosensor molecules that glow when glucose is broken down, so the scientists can capture the process with a camera with a fluorescence microscope.
Glucose is a vital source of energy for almost all cell types, but the glycolysis process in cancer often goes wrong. This can contribute to a malignant cell’s disease state and aid its ability to move, grow, and divide. By combining Fret technology with a new machine learning algorithm, Fang’s team was able to obtain images of glycolysis with unprecedented resolution, showing in real time exactly which parts of the cell are consuming glucose. “Now we can look at and understand details within the cells, such as certain areas of the cells where glycolysis is increasing,” says Fang. “This is a key technological innovation.”
The new approach of machine learning to the existing Fret technology has opened up a number of new experimental possibilities. Glucose metabolism can now be observed alongside other visible cell processes, so the team was able to show that some human cells consume more glucose when moving and twitching – something that previously could not have been detected in a single experiment.
Improperly regulated metabolism can cause some cancer cells to metastasize and invade new tissue. The new technology should help scientists to better understand the connection between these disease processes and could help to discover new therapeutic approaches. The new technology also led the researchers to discover a previously unknown cell surface receptor that is capable of glucose uptake.
‘Coupled with existing technology, this [machine learning approach] will help expand our understanding of how glucose and other key energy molecules affect metabolic rewiring in carcinogenesis and could be used to develop new anticancer drug targets, “says Gemma Beasy of the Quadram Institute, the effects of glucose metabolism in the prostate is studying cancer. “This is an amazing advance in technology that could be used in cancer research.”
In addition to cancer research, wider applications of Fret-based machine learning technology are already being explored, including helping patients whose immune systems are overreacting to Covid-19.