Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition

Valdemaras Petrosius, Pedro Aragon-Fernandez, Nil Üresin, Gergo Kovacs, Teeradon Phlairaharn, Benjamin Furtwängler, Jeff Op De Beeck, Sarah L Skovbakke, Steffen Goletz, Simon Francis Thomsen, Ulrich Auf dem Keller, Kedar N Natarajan, Bo T Porse, Erwin M Schoof*

*Corresponding author for this work


Single-cell resolution analysis of complex biological tissues is fundamental to capture cell-state heterogeneity and distinct cellular signaling patterns that remain obscured with population-based techniques. The limited amount of material encapsulated in a single cell however, raises significant technical challenges to molecular profiling. Due to extensive optimization efforts, single-cell proteomics by Mass Spectrometry (scp-MS) has emerged as a powerful tool to facilitate proteome profiling from ultra-low amounts of input, although further development is needed to realize its full potential. To this end, we carry out comprehensive analysis of orbitrap-based data-independent acquisition (DIA) for limited material proteomics. Notably, we find a fundamental difference between optimal DIA methods for high- and low-load samples. We further improve our low-input DIA method by relying on high-resolution MS1 quantification, thus enhancing sensitivity by more efficiently utilizing available mass analyzer time. With our ultra-low input tailored DIA method, we are able to accommodate long injection times and high resolution, while keeping the scan cycle time low enough to ensure robust quantification. Finally, we demonstrate the capability of our approach by profiling mouse embryonic stem cell culture conditions, showcasing heterogeneity in global proteomes and highlighting distinct differences in key metabolic enzyme expression in distinct cell subclusters.

Original languageEnglish
Article number5910
JournalNature Communications
Issue number1
Pages (from-to)5910
Publication statusPublished - 22 Sep 2023


  • Animals
  • Mice
  • Proteomics
  • Mass Spectrometry
  • Mouse Embryonic Stem Cells
  • Proteome
  • Single-Cell Analysis


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