The ability to sequence DNA ushered in a new era of genetics, heralded by the completion of the human genome in 2003. However, nearly 20 years on, we still do not have the equivalent tools to study what DNA ultimately produces: proteins. Yet subtle differences in the way proteins are modified in our body can have profound effects on everything from disease and drug activity to susceptibility to infections.
One key protein modification—called glycosylation—involves joining a chain of sugars to a protein, thereby altering its structure and function. Currently, the most sensitive way to distinguish one glycosylated protein from another is a technique called mass spectrometry, but it is time consuming and does not always yield clear results.
“It can be very challenging to interpret these mass measurements into anything meaningful that can distinguish the subtle differences between the glycan building blocks to see how they are arranged in the overall 3D structure,” said Terry Nguyen-Khuong, a group leader at A*STAR’s Bioprocessing Technology Institute (BTI).
Instead, the researchers took advantage of a specific type of mass spectrometry known as ion-mobility mass spectrometry (IMS), which measures the resistance of a glycan molecules’ shape as they pass through a chamber of gas on its way to be detected by a mass detector. “As an analogy, consider what happens when you drop two pieces of paper, one crumpled and one open,” he explained. “While they have the same mass, their shape determines the resistance to the air and thus the speed at which it would fall to the floor.”
By integrating IMS into an existing workflow, the team was able to use information about the shape of both the glycan fragments and the intact molecule to piece together how the glycans were branched and linked. “While most scientists focus on the overall shape, we focused also on the way the fragments can be pieced together based on their shape and size,” said study first author Edward Palliser, then a PhD candidate jointly supervised by Nguyen-Khuong and co-corresponding author Sabine Flitch at the University of Manchester.
The team has also built a database of solutions to various glycan ‘puzzles’ so that others will be able to use the information to semi-automatically characterize the glycans in any sample. “This automatic process has the potential to speed up glycoanalytics, so that we can characterize, with high resolution, an entire glycome of a complex sample in an expedient time frame,” Nguyen-Khuong said.
The A*STAR-affiliated researchers contributing to this research are from the Bioprocessing Technology Institute (BTI).
