Experimental Design and Reporting Guidelines for Proteomics Researchers

Although mass spectrometry is a well-established method for investigating proteomics, several strategies exist for conducting proteomic research and analyzing data. As a result, types of proteomics data can vary greatly. Experimental replication is also difficult. Therefore, there is potential for confusion when comparing results from different experiments.

Researchers from the Human Proteome Organization’s Proteomics Standards Initiative (HUPO-PSI) and Spain’s ProteoRed organization, among other proteomics working groups, have taken the initiative to summarize a set of guidelines to establish  consistency in reporting proteomics data.

Martínez-Bartolomé and colleagues have categorized this list of suggested protocols and conventions for experimental design and data reporting.¹ Their paper summarizes these guidelines—the Minimum Information about a Proteomics Experiment (MIAPE) Mass Spectrometry Quantification, or “MIAPE-Quant”—developed by the aforementioned working groups within the proteomics community.

The document comprises guidelines governing all aspects of experimental design, recording, data expression, reporting and storage, and software programs used in analysis:

1. General Features – The authors describe the basic experimental information, such as identifying the team of researchers and the quantitative method used that should be included in reports.
2. Experimental Design – Each report requires a description of experimental samples, the number of replicates and the inclusion of standards or internal references within the assay. Researchers should specify which labeling methods are used.
3. Input Data – Researchers should accurately identify type, format and availability of the data presented for quantitative analysis. This includes details on fractions pooling and the algorithms used.
4. Protocol – Reports need to include a description of software used for quantitative estimation and standards calibration. Researchers must also define any corrections used before analyzing data and list the statistical methods applied.
5. Resulting Data – Instead of simply reporting levels measured, researchers should accurately record actual values along with enough information to indicate confidence intervals. Researchers must also describe how each feature measured has been identified, again with suitable confidence intervals.

In summary, Martínez-Bartolomé et al. advise that researchers and publications need to consider widely adopting the guidelines to achieve consistency. This would enable valid comparisons among experiments. However, they also stress that the guidelines are not mandatory, but should function as a checklist when designing and reporting experiments. Finally, the authors note that these guidelines are a work-in-progress and will evolve as techniques develop.

Reference

1. Martínez-Bartolomé, S., et al. (2013) “Guidelines for reporting quantitative mass spectrometry based experiments in proteomics,” Journal of Proteomics, http://dx.doi.org/10.1016/j.jprot.2013.02.026 [Epub ahead of print].

Post Author: Amanda Maxwell. Mixed media artist; blogger and social media communicator; clinical scientist and writer; SAHM and expat trailing spouse.

A digital space explorer, engaging readers by translating complex theories and subjects creatively into everyday language.

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