日本語
 
Help Privacy Policy ポリシー/免責事項
  詳細検索ブラウズ

アイテム詳細


公開

学術論文

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

MPS-Authors
/persons/resource/persons283135

Phlairaharn,  Teeradon
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

External Resource
There are no locators available
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
フルテキスト (公開)
公開されているフルテキストはありません
付随資料 (公開)
There is no public supplementary material available
引用

Petrosius, V., Aragon-Fernandez, P., Ueresin, N., Kovacs, G., Phlairaharn, T., Furtwaengler, B., op de Beeck, J., Skovbakke, S. L., Goletz, S., Thomsen, S. F., Keller, U. a. d., Natarajan, K. N., Porse, B. T., & Schoof, E. M. (2023). Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition. Nature Communications, 14(1):. doi:10.1038/s41467-023-41602-1.


引用: https://hdl.handle.net/21.11116/0000-000F-1E2F-D
要旨
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.
Single-cell proteomics by Mass Spectrometry (scpMS) provides unparalleled insights into cellular mechanisms from a proteome-centric standpoint. Here, the authors leverage sensitivity-tailored data acquisition methods to profile cell state heterogeneity in cultured model systems.