Session 2: Foundations of Discovery Omics: Proteomics, Metabolomics & Biomarker Discovery Workshop

Summary

The session explained how mass spectrometry is used in discovery proteomics and metabolomics, with emphasis on peptide identification, quantification, acquisition strategies, and practical database-search settings.

1. How tandem mass spectrometry identifies peptides

• A mass spectrometer first measures a peptide’s precursor ion by its mass-to-charge ratio (m/z). This reveals its mass, but not necessarily its amino-acid sequence: different peptides can have the same or similar masses.
• In MS/MS, the precursor is fragmented, and the resulting product ions are measured. Their pattern helps determine the peptide sequence and link it to its source protein.
• b-ions are fragments associated with the peptide’s N-terminus, while y-ions are associated with its C-terminus. Differences between adjacent fragment masses can indicate the mass of individual amino acids.
• A typical tandem-MS setup uses two stages of analysis: one to select or measure the precursor and another to measure its fragments. Common configurations include quadrupole-based systems and hybrid instruments.
• Software compares measured spectra with theoretical spectra generated from a sequence database. Identification depends on how well the observed and theoretical spectra match.

2. General proteomics workflow and approaches

The workflow presented was:

1. Prepare the sample.
2. Digest proteins into peptides for a bottom-up analysis.
3. Separate peptides, commonly by liquid chromatography (LC).
4. Ionize them, for example by electrospray ionization (ESI) or MALDI.
5. Measure precursor and, where applicable, fragment ions in the mass spectrometer.
6. Analyze the data using software and a suitable database.

Two broad proteomics approaches were contrasted:

• Bottom-up proteomics: Proteins are digested into peptides, which are identified and used to infer the proteins they came from. This was the approach used in the hands-on example.
• Top-down proteomics: Intact proteins are measured and fragmented for identification, rather than first being digested into peptides.

3. Data-dependent and data-independent acquisition

The session distinguished two ways to choose ions for fragmentation:

• Data-dependent acquisition (DDA): After an MS1 survey scan, the instrument selects a set number of the most abundant precursor ions—such as the top 10 or 20—and fragments them. This prioritizes high-abundance ions.
• Data-independent acquisition (DIA): The instrument fragments ions across defined m/z windows rather than selecting only the most abundant precursors. This can include lower-abundance ions as well.

4. Quantification strategies

Two general quantification approaches were introduced:

• Label-free quantification: No label is added. The session described measuring either:
  • Spectral counts: the number of spectra assigned to a protein; or
  • Precursor peak area or intensity: the area or intensity of a precursor’s chromatographic peak.
• Label-based quantification: Samples are tagged with isobaric labels, such as iTRAQ or TMT, allowing multiple samples to be analyzed together. Labels must be compatible with mass-spectrometric measurement.

The instructor emphasized that peptide identification and protein quantification are related but distinct tasks. Identification relies on matching spectra, while quantification uses signal measurements or related algorithms.

5. Why metabolite identification needs more than MS1 mass

Unlike peptides, metabolites do not have amino-acid sequences to infer. An MS1 mass alone is often insufficient because many molecules can have the same nominal or similar molecular mass.

• MS/MS fragmentation provides a molecular fingerprint that can help distinguish compounds.
• Glucose and aspirin were used as examples of compounds with similar masses but different fragmentation patterns.
• Identification is supported by comparing observed spectra with reference libraries or databases, such as HMDB.
• Some isomers, especially positional isomers, may be difficult to distinguish from spectra alone. Authentic standards measured under comparable conditions may be needed.
• Collision energy affects fragmentation patterns, so reference and sample spectra should be compared with experimental conditions in mind.

6. Hands-on Mascot search: workflow and key settings

Participants were shown an MGF file (Mascot Generic Format) containing precursor information and fragment-ion data. The class used a web-based Mascot MS/MS ion search to identify peptides.

Key settings and considerations included:

• Sequence database: Choose an appropriate database. Swiss-Prot was used for the demonstration when a more specific organism database was not selected.
• Contaminant database: Include common contaminants, such as keratins from skin or hair, which can appear during sample preparation.
• Digestion enzyme: Trypsin was discussed as a common choice.
• Missed cleavages: Enzymatic digestion is not perfectly efficient. The instructor recommended allowing up to two missed cleavages as a common practice; allowing too many can increase false identifications.
• Modifications:
  • Fixed: Carbamidomethylation of cysteine, associated with the reduction and alkylation steps in sample preparation.
  • Variable: Oxidation of methionine and, when relevant to the experiment, phosphorylation. Phosphorylation can occur on serine, threonine, or tyrosine.
• Data format: Set the format to MGF for the provided file.
• False discovery rate (FDR): The instructor emphasized controlling the FDR at 1%.
• Decoy sequences: Reversed database sequences were described as decoys. They preserve precursor mass but are not expected to match the true peptide’s fragment pattern. Decoy matches help estimate false identifications.

The demonstration produced peptide and protein matches. The instructor showed how to inspect annotated fragment spectra, with y- and b-ion patterns supporting the proposed peptide sequence. The example included an alpha-tubulin-family protein from Arabidopsis thaliana; the remaining protein names in the transcript were unclear.

7. Software, databases, and quality control

• DIA-NN was demonstrated as a tool for DIA workflows. FASTA protein sequences can be used to generate or support spectral-library predictions; reviewed UniProt sequences were offered as a common source for FASTA databases.
• FragPipe/MSFragger was mentioned for DDA analysis, and Spectronaut as a DIA-oriented software option. Interfaces and algorithms differ, but the basic concepts—database searching, enzyme rules, modifications, and FDR control—remain similar.
• Metabolomics analysis options mentioned included MetaboAnalyst, XCMS, and MS-DIAL. Metabolite databases included HMDB and SMPDB.
• PRIDE was clarified as a data repository, not a protein-sequence database.
• Pooled quality-control (QC) samples were recommended, particularly in metabolomics. A pooled sample made from study samples can be run repeatedly to monitor variation in instrument or column performance over a batch.

8. Workshop logistics

The workshop representative said the session recording would be available for 30 days, while downloadable datasets and reading materials could be retained. The certificate would be issued after all four workshop sessions were completed.

Speakers and sources featured

Speakers

• Ankit — Instructor who led the technical explanations and software demonstrations.
• Workshop moderator/coordinator — Unnamed; provided participant instructions and closing information.
• Workshop participants — Unnamed; asked questions and contributed brief responses.

Tools and data sources discussed

• Software: Mascot, DIA-NN, FragPipe/MSFragger, Spectronaut, MetaboAnalyst, XCMS, and MS-DIAL.
• Databases and repositories: Swiss-Prot, UniProt, HMDB, SMPDB, and PRIDE.
• Example data: MGF-format data used in the hands-on demonstration.