Initial recommendations for performing, benchmarking and reporting single-cell 
proteomics experiments

Gatto, Laurent; Aebersold, Ruedi; Cox, Jürgen; Demichev, Vadim; Derks, Jason; Emmott, Edward; Franks, Alexander M.; Ivanov, Alexander R.; Kelly, Ryan T.; Khoury, Luke; Leduc, Andrew; MacCoss, Michael J.; Nemes, Peter; Perlman, David H.; Petelski, Aleksandra A.; Rose, Christopher M.; Schoof, Erwin M.; Van Eyk, Jennifer; Vanderaa, Christophe; Yates III, John R. R.; Slavov, Nikolai

doi:10.1038/s41592-023-01785-3

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments

MPS-Authors

/persons/resource/persons77870

Cox, Jürgen
Cox, Jürgen / Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Max Planck Society;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Gatto, L., Aebersold, R., Cox, J., Demichev, V., Derks, J., Emmott, E., et al. (2023). Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments. Nature Methods, 20(3), 375-386. doi:10.1038/s41592-023-01785-3.

Cite as: https://hdl.handle.net/21.11116/0000-000C-F817-3

Abstract

Analyzing proteins from single cells by tandem mass spectrometry (MS) has recently become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of the results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition and data analysis. We expect that broadly accepted community guidelines and standardized metrics will enhance rigor, data quality and alignment between laboratories. Here we propose best practices, quality controls and data-reporting recommendations to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics.