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Robust and Easy-to-Use One-Pot Workflow for Label-Free Single-Cell Proteomics.

Manuel MatzingerElisabeth MüllerGerhard DürnbergerPeter PichlerKarl Mechtler
Published in: Analytical chemistry (2023)
The analysis of ultralow input samples or even individual cells is essential to answering a multitude of biomedical questions, but current proteomic workflows are limited in their sensitivity and reproducibility. Here, we report a comprehensive workflow that includes improved strategies for all steps, from cell lysis to data analysis. Thanks to convenient-to-handle 1 μL sample volume and standardized 384-well plates, the workflow is easy for even novice users to implement. At the same time, it can be performed semi-automatized using CellenONE, which allows for the highest reproducibility. To achieve high throughput, ultrashort gradient lengths down to 5 min were tested using advanced μ-pillar columns. Data-dependent acquisition (DDA), wide-window acquisition (WWA), data-independent acquisition (DIA), and commonly used advanced data analysis algorithms were benchmarked. Using DDA, 1790 proteins covering a dynamic range of four orders of magnitude were identified in a single cell. Using DIA, proteome coverage increased to more than 2200 proteins identified from single-cell level input in a 20 min active gradient. The workflow enabled differentiation of two cell lines, demonstrating its suitability to cellular heterogeneity determination.
Keyphrases
  • single cell
  • data analysis
  • label free
  • high throughput
  • electronic health record
  • rna seq
  • induced apoptosis
  • machine learning
  • mass spectrometry
  • cell cycle arrest
  • deep learning
  • signaling pathway
  • artificial intelligence