Slice-PASEF: Maximising Ion Utilisation in LC-MS Proteomics

  • bioRxiv. 2025 Sep 2:2022.10.31.514544. doi: 10.1101/2022.10.31.514544.
Ludwig R Sinn  1 ,  Lukasz Szyrwiel  1 ,  Justus Grossmann  1 ,  Kate Lau  1 ,  Katharina Faisst  1 ,  Di Qin  2  3 ,  Florian Mutschler  4  5 ,  Luke Khoury  6 ,  Andrew Leduc  6 ,  Markus Ralser  1 ,  Fabian Coscia  2 ,  Matthias Selbach  4 ,  Nikolai Slavov  6 ,  Nagarjuna Nagaraj  7 ,  Martin Steger  8 ,  Vadim Demichev  1
Affiliations
  • 1. Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany.
  • 2. Spatial Proteomics Group, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
  • 3. Charité - Universitätsmedizin Berlin.
  • 4. Proteome Dynamics, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany.
  • 5. Faculty of Life Sciences, Humboldt-Universität zu Berlin, Berlin Germany.
  • 6. Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA; Parallel Squared Technology Institute, Watertown, MA, USA.
  • 7. Evotec International GmbH, Neuried, Germany.
  • 8. NEOsphere Biotechnologies GmbH, Planegg, Germany.
PMID: 40950106 DOI: 10.1101/2022.10.31.514544
Abstract

Quantitative mass spectrometry (MS)-based proteomics has become a streamlined technology with a wide range of usage. Many emerging applications, such as single-cell proteomics, spatial proteomics of tissue sections and the profiling of low-abundant posttranslational modifications, require the analysis of minimal sample amounts and are thus constrained by the sensitivity of the workflow. Here, we present Slice-PASEF, a mass spectrometry technology that leverages trapped ion mobility separation of ions to attain the theoretical maximum of tandem MS sensitivity. We implement Slice-PASEF using a new module in our DIA-NN software and show that Slice-PASEF uniquely enables precise quantitative proteomics of low sample amounts. We further demonstrate its utility towards a range of applications, including single cell proteomics and degrader drug screens via ubiquitinomics.

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