TopNEXt: automatic DDA exclusion framework for multi-sample mass spectrometry experiments

Abstract

Motivation: Liquid Chromatography Tandem Mass Spectrometry experiments aim to produce high-quality fragmentation spectra, which can be used to annotate metabolites. However, current Data-Dependent Acquisition approaches may fail to collect spectra of sufficient quality and quantity for experimental outcomes, and extend poorly across multiple samples by failing to share information across samples or by requiring manual expert input.

Results: We present TopNEXt, a real-time scan prioritization framework that improves data acquisition in multi-sample Liquid Chromatography Tandem Mass Spectrometry metabolomics experiments. TopNEXt extends traditional Data-Dependent Acquisition exclusion methods across multiple samples by using a Region of Interest and intensity-based scoring system. Through both simulated and lab experiments, we show that methods incorporating these novel concepts acquire fragmentation spectra for an additional 10% of our set of target peaks and with an additional 20% of acquisition intensity. By increasing the quality and quantity of fragmentation spectra, TopNEXt can help improve metabolite identification with a potential impact across a variety of experimental contexts.

Availability and implementation: TopNEXt is implemented as part of the ViMMS framework and the latest version can be found at https://github.com/glasgowcompbio/vimms. A stable version used to produce our results can be found at 10.5281/zenodo.7468914.

Figure 1.

An illustration of RoI-tracking when using RoIs as exclusion windows, where from top to bottom each subplot represents a successive injection. The points are individual observations in MS1 scans. A cross represents the precursor of a fragmentation event. On the first injection, the RoI a is drawn. On the second injection, a persists as an exclusion window, while b is drawn around the new points, forming the overlapping area ab. Note that a and b are drawn here after all points were observed, but as RoIs would be dynamically extended to the right to cover the points as we observed them in real-time.

Figure 2.

(A) Simulated experiment with the same beer repeated for 20 injections. (B) Simulated experiment with six different beers each repeated four times.

Figure 3.

(A) Lab experiment with the same beer repeated for 10 injections. (B) Lab experiment with six different beers each repeated four times.