[Paper Review] Initial recommendations for performing, benchmarking, and reporting single-cell proteomics experiments
The paper provides best practices, quality controls, and reporting guidelines to enable rigorous, reproducible single-cell proteomics workflows.
Analyzing proteins from single cells by tandem mass spectrometry (MS) has 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. 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.
Motivation & Objective
- Motivate the need for community guidelines to improve accuracy and reproducibility in single-cell proteomics.
- Propose best practices for experimental design, sample preparation, data acquisition, and data analysis.
- Recommend quality control measures and standardized data reporting to enable cross-lab comparability.
Proposed method
- Propose a set of best practices for experimental design and workflow choices in single-cell proteomics.
- Suggest quality control checks and benchmarks to assess performance across steps from sample prep to data analysis.
- Provide recommendations for data reporting to standardize outputs and facilitate comparison between laboratories.
Experimental results
Research questions
- RQ1What are the essential best practices for performing single-cell proteomics experiments?
- RQ2How should benchmarking and quality controls be implemented to ensure data reliability?
- RQ3What reporting standards are needed to enable cross-lab data comparability in single-cell proteomics?
Key findings
- A set of recommended best practices to improve rigor and reproducibility in single-cell proteomics workflows.
- Quality control frameworks are proposed to monitor accuracy and reproducibility across experimental steps.
- Reporting recommendations are provided to enhance data transparency and cross-lab comparability.
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This review was created by AI and reviewed by human editors.