bioRxivBiochemistryPreprint
Defining Quality Control Standards for Single-Cell Proteomics by Inter-Laboratory Benchmarking
Sam van PuyenbroeckiD, Tine ClaeysiD, Anjali SethiD, Jeewan Babu RijaliD, Charline Keller, Lavender Lin, Rupert Laurenz MayeriD, Manuel MatzingeriD, Isaac Han, Pedro Aragon-FernandeziD, Valdemaras PetrosiusiD, Benjamin FurtwängleriD, Brian Boyle, Keith D. RiveraiD, Guilhem TourniaireiD, Florian A. RosenbergeriD, Lennart MartensiD, Steven A. CarriD, Zhen DongiD, Ákos VégváriiD, Christine CarapitoiD, Ryan KellyiD, Karl MechtleriD, Bogdan BudnikiD, Erwin M. SchoofiD, Claudia CtorteckaiD
Abstract
Single-cell proteomics can quantify thousands of proteins from individual mammalian cells, yet the absence of community-wide quality control limits biological interpretability. Here, the HUPO Single Cell Initiative presents the first inter-laboratory single-cell proteomics benchmarking study across seven laboratories using standardized 384-well plates acquired on Orbitrap Astral and timsTOF Ultra2 instruments. Centralized analysis across six DIA software tools revealed that software choice impacts identification depth and quantitative accuracy more than instrument vendor. Multi-layered quality control enabled the detection of cell-leakage during sorting, LC misconfiguration, column degradation and site-specific pipetting failures. Inter-lab quantitative correlations were strongest between instruments of the same vendor relative to cross-platform comparisons. Sequential correction for plate identity and well position recovered clean cell-type separation for confident downstream differential expression analysis. This study provides a data-driven quality control framework spanning plate design to batch correction for reproducible single-cell proteomics across laboratories and platforms.
- organism: human
- assay: CITE-seq
- assay: proteomics
- research focus: batch correction / integration
- research focus: differential expression
- research focus: cell type annotation
- approach: deep learning
- approach: statistical model
- Citations
- 0
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