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Current liquid chromatography–mass spectrometry (LC-MS) proteomics methods are limited by low throughput, which restricts their application in large-scale biological studies. While approaches such as isobaric tagging or plexDIA enable sample multiplexing in the mass domain to increase throughput, this one-dimensional strategy yields only linear gains, with throughput scaling proportionally to the level of plex. Derks et al. developed timePlex, a novel time-domain multiplexing approach designed to complement existing mass-domain multiplexing (plexDIA), with the goal of achieving multiplicative gains in sample throughput.
The experimental approach involved splitting the gradient flow from a single LC system into multiple separate columns, with time offsets controlled by varying the volumes of the capillary transfer lines. Two implementations were tested: “single-emitter”, in which flows were recombined into a single emitter after column separation for electrospray ionization (ESI), and “separate-emitters”, in which each column had its own dedicated emitter for ESI. For the single-emitter approach, the researchers employed µPAC Neo 50 cm columns, while the separate-emitters approach used IonOpticks Aurora Ultimate™ 25 cm×75 µm C18 UHPLC columns. Both setups were operated on an UltiMate 3000 system and coupled to Orbitrap Exploris 240 and 480 mass spectrometers equipped with Nanospray Flex ion sources.
The timePlex approach demonstrated significant improvements in proteomics throughput. The 3-timePlex platform achieved a 2.4- to 2.6-fold increase in protein identifications per run compared to non-multiplexed methods. When combined with plexDIA, the 3×3 timePlex–plexDIA configuration yielded a 5.4- to 5.6-fold increase in protein IDs per run, while the 3×9 setup enabled a 27-plex multiplexing capacity, allowing for the analysis of over 500 samples per day.
The researchers demonstrated that temporal encoding provides an orthogonal dimension to mass-based multiplexing, maintaining quantitative accuracy whilst dramatically increasing sample throughput. This advancement addresses critical bottlenecks in proteomics workflows and enables the generation of larger-scale datasets essential for systems biology investigations, with the potential to transform high-throughput applications including single-cell analysis.
Publication
bioRxiv
Authors
Jason Derks, Kevin McDonnell, Nathan Wamsley, Peyton Stewart, Maddy Yeh, Harrison Specht, & Nikolai Slavov;
Title
Increasing mass spectrometry throughput using time-encoded sample multiplexing
