Bottleneck Reported At the Intersection of Data Generation and Identification
Congestion used to be a nightmare for the Department of Biomolecular Medicine at Imperial College in London (UK). Not automobile congestion, but data congestion.
One of the school’s specialties is metabolite profiling. The bottleneck occurred due to the overwhelming array of data that was being generated. No problem finding discriminating metabolites, big problem identifying them at the same speed. That’s how you create a bottleneck.
But then the college scientists decided to trade up from HPLC to Waters UPLC. Their Waters Tof and QTof mass spectrometry instruments provided better separation, improved signal-to-noise ratio, and increased sensitivity. Suddenly they could see lower level metabolites better than ever before.
But the big bottleneck-breaker was throughput time: Half of HPLC.
A typical runtime for serum samples used to be one hour. With UPLC, they could now see twice as many metabolite features in about 20 minutes, urine samples in about 10 minutes. So they can run a much larger amount of samples in the same amount of time.
No more congestion. No more bottleneck.