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TMPL recommended best practices for data management plan – mass spectrometry

Disclaimer: The items mentioned below may not be applicable to all projects and each user should tailor their DMS plan to their specific proposal. Investigators should contact the TMPL core to discuss the nature of the experiments they plan to undertake.

Element 1: Data Type
1A. Types and amount of scientific data expected to be generated in the project:

  • Analytical methods
    • NanoLC-MS and MS/MS – untargeted proteomics
    • UPLC-MS and MSMS – untargeted metabolomics and lipidomics
    • LC-MS – targeted analysis of metabolites, lipids, peptides and other chemicals
    • Instrumentation details

1B. Scientific data that will be preserved and shared, and the rationale for doing so

  • Maximize sharing of raw data generated from TMPL with approval of the PI.
  • The TMPL lab records LC-MS data, along with instrument parameters, sample amounts, the methods of extraction/processing, and the volumes of processed samples subjected to analysis

1C. Metadata, other relevant data, and associated documentation:

  • Receipt of samples (metadata)
  • Storage of samples (metadata)
  • Processing of samples
    • Protein and proteomics samples (metadata)
    • Metabolomics and lipidomics samples (metadata)

 

Element 2: Related Tools, Software and/or Code:

  • Data analysis
    • Software and version for interpreting peptides/proteins SCIEX ProteinPilot version 5.0 (commercial).
    • Software and version for interpreting metabolites and lipids (open source)
      • Collected data (.wiff and .wiff.scan files) are analyzed and interpreted with MS-DIAL. The current version is 4.92.221218; however, implementation of version 5.1.230517 is ongoing. The MSMS data libraries currently used for ion feature annotation are version 17 of the public negative and positive ion data available at MS-DIAL (http://prime.psc.riken.jp/compms/msdial/main.html#MSP) and an in-house IROA Metabolite Standards library (IROA Technologies).
      • Visualization software used to inspect the data include SCIEX softwares Analyst 1.71, PeakView 2.2 (commercial) and MZmine open-source software (versions 2.53 and 3.3)
    • Software and version for targeted, quantitative analysis include SCIEX SWATH MicroApp version 2.0.1 for proteomics analyses and MultiQuant 3.03 for small molecule targeted analyses. (commercial)
  • Statistical and pathway analysis
    • Software and version (open source)
      • Data are analyzed using MetaboAnalyst version 5.0 (www.metaboanalyst.ca) using the available modules for statistical analysis (univariate and multivariate), hierarchal clustering, machine learning, and pathway analysis.
      • The latter involves additional analysis using KEGG mapper color pathway tools (https://www.genome.jp/kegg/mapper/color.html)

 

Element 3: Standards:

  • We have 600 compound IROA metabolite standards library.
  • The PI is responsible for obtaining authentic compounds that are either outside of the IROA metabolites standard library.

 

Element 4: Data Preservation, Access, and Associated Timelines
4A. Repository where scientific data and metadata will be archived:

4B. How scientific data will be findable and identifiable:

  • The data, as noted above, will be available at the Metabolomics Workbench, GNPS and ProteomeXchange. These websites are organized so particular experiment types can be recovered.

4C. When and how long the scientific data will be made available:

  • Data and availability and preservation
    • The MS data in the repositories will be made available publicly at the time of publication of manuscripts associated with these data and/or at the end of the grant funding period.
    • The chosen databases noted above have indicated that they plan to preserve the databases for the foreseeable future.

 

Element 5: Access, Distribution, or Reuse Considerations
5A. Factors affecting subsequent access, distribution, or reuse of scientific data:

  • Data at the sites noted above will be freely accessible by interested parties.
  • Parties contacting the PI or the TMPL will be directed to above websites.
  • When the parties ask for additional information, this would have to be approved by the PI.
  • Data at the websites noted above can be freely reused.

5B. Whether access to scientific data will be controlled:

  • For animal or cell culture experiments, there would be no controlled except that the PI should know in advance of the release of data from the TMPL lab.
  • For clinical studies, release of data will be controlled by the features of the IRB document(s) governing the studies.

5C. Protections for privacy, rights, and confidentiality of human research participants:

  • Data from clinical studies may require re-consenting by the patients for the inclusion of metadata. Release of de-identified data should be straightforward if approved by the IRB document.

 

Element 6: Oversight of Data Management and Sharing:
The responsibility for sharing data generated by the core facility (including TMPL) for a PI is the responsibility of the PI who requesting the service and should follow the same sharing rigor as data generated in their own lab.