Robust and Easy-to-Use One-Pot Workflow for Label-Free Single-Cell Proteomics.
Manuel MatzingerElisabeth MüllerGerhard DürnbergerPeter PichlerKarl MechtlerPublished 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
- machine learning
- induced apoptosis
- healthcare
- mass spectrometry
- stem cells
- cell cycle arrest
- mesenchymal stem cells
- deep learning
- signaling pathway
- affordable care act
- molecularly imprinted
- high resolution
- solid phase extraction
- tandem mass spectrometry
- artificial intelligence
- finite element analysis