Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments.
Laurent GattoRuedi AebersoldJuergen CoxVadim DemichevJason DerksEdward EmmottAlexander M FranksAlexander R IvanovRyan T KellyLuke KhouryAndrew LeducMichael J MacCossPeter NemesDavid H PerlmanAleksandra A PetelskiChristopher M RoseErwin M SchoofJennifer E Van EykChristophe VanderaaJohn Yates IiiNikolai SlavovPublished in: Nature methods (2023)
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. Resources and discussion forums are available at https://single-cell.net/guidelines .
Keyphrases
- single cell
- data analysis
- electronic health record
- rna seq
- induced apoptosis
- mass spectrometry
- tandem mass spectrometry
- clinical practice
- cell cycle arrest
- liquid chromatography
- big data
- healthcare
- ultra high performance liquid chromatography
- high performance liquid chromatography
- high throughput
- adverse drug
- high resolution
- gas chromatography
- mental health
- multiple sclerosis
- primary care
- ms ms
- simultaneous determination
- cell death
- signaling pathway
- solid phase extraction
- emergency department
- label free
- machine learning
- cell proliferation
- artificial intelligence
- deep learning