PRM Proteomics Data Processing Workflow
Parallel reaction monitoring (PRM) is a core targeted proteomics approach characterized by high specificity, high sensitivity, and strong quantitative reproducibility. It has been widely applied to biomarker verification, mechanistic studies, and quantitative analysis of clinical specimens. Importantly, generating high-quality PRM results depends not only on an optimized mass spectrometry platform and experimental workflow, but also on a standardized, rigorous, and well-documented data-processing strategy. Here, we systematically outline a complete PRM data-processing workflow, from raw data import to quantitative report generation, to support efficient and accurate interpretation of PRM datasets.
PRM Proteomics Raw Data Acquisition and File Formats
PRM data are typically acquired on high-resolution mass spectrometers (e.g., the Orbitrap Exploris series). The resulting vendor-specific raw files are commonly generated in .raw (Thermo) or .wiff (SCIEX) formats. These files contain MS/MS fragment-ion information collected for each targeted precursor ion and serve as the foundation for downstream quantitative analysis.
Selection of Data Processing Software
Skyline Skyline is currently one of the most widely used open-source platforms for PRM data analysis and provides:
1. Cross-vendor compatibility (supporting data formats from Thermo, SCIEX, Agilent, and others).
2. Visualization of fragment-ion chromatograms and related spectral evidence.
3. Calibration using stable isotope internal standards.
4. Flexible quantification parameter configuration and batch export capabilities.
Standard Workflow for PRM Data Processing
1. Build a Target List (Peptide Targets)
(1) Import peptide sequences corresponding to the proteins of interest. Targets can be obtained from public resources (e.g., UniProt, ProteomeTools) or selected based on prior DDA/DIA experiments.
(2) Define filtering parameters, including digestion rules, peptide length constraints, modification types, and precursor charge states.
2. Import Raw Data Files
(1) Import .raw files into Skyline.
(2) Skyline automatically associates peptide precursors with their fragment ions and extracts XICs (extracted ion chromatograms) within the specified mass window.
3. Manually Review Peak Quality and Integration Boundaries
(1) Inspect fragment-ion XICs for each peptide.
(2) Adjust integration start and end points to ensure alignment with the internal-standard peptide signal and to minimize interference from co-eluting ions.
4. Select and Evaluate Quantifier Ions
(1) Typically, select 2-4 y-ions that show strong responses, stable signals, and symmetric peak shapes for quantification.
(2) Use the “dotp” metric (spectral similarity) to assess the consistency between observed fragment-ion patterns and the expected/reference pattern, thereby supporting signal confidence.
5. Internal-Standard Calibration and Construction of Standard Curves
(1) For absolute quantification, use stable isotope-labeled (SIL) peptides and compute the peak-area ratio of the target to the internal standard.
(2) For multi-point quantification designs, generate calibration curves to evaluate the linear dynamic range and determine the lower limit of quantification (LLOQ).
6. Data Normalization and Inter-Batch Adjustment
(1) Use QC samples (quality-control samples) to track instrument stability.
(2) Apply appropriate normalization strategies (e.g., total peak-area normalization or internal-standard ratio normalization) to reduce systematic variation.
7. Export Results and Perform Statistical Analysis
(1) Export quantitative matrices at the peptide level or protein level (in .csv format).
(2) Import the output into R, Python, or Perseus for downstream analyses, including differential testing, clustering, and visualization.
Key Considerations for PRM Data Quality Control
1. Replicate RSD Should Be <15%: To evaluate reproducibility across replicate measurements.
2. Peaks Should Be Symmetric and Free of Pronounced Tailing: To assess chromatographic performance.
3. A Dotp Value >0.8 Is Generally Desirable: Indicating strong agreement between measured and reference fragment-ion patterns.
4. The Signal-to-Noise Ratio at the Lloq Should Be >10: Supporting reliable quantification at low abundance.
MtoZ Biolabs has established a stringent PRM data-analysis framework, including:
1. Skyline + Python Automated Pipelines: Improving high-throughput processing efficiency while reducing operator-dependent variability.
2. Internal-Standard Peptide Quality Review: Ensuring stable SIL peptide responses for each batch and maintaining controllable calibration linearity.
3. QC Standards and Instrument Monitoring: Implementing periodic QC profiling and tracking key MS parameters (e.g., RT and mass accuracy).
4. Cross-Batch Consistency Control: Using anchor peptides for inter-batch normalization to support scientifically robust longitudinal comparisons.
PRM data processing is not merely a set of procedural steps; it represents a highly standardized analytical workflow in which quality control is integrated throughout. Ensuring accuracy and traceability at each stage, from raw data handling to quantitative reporting, maximizes the utility of PRM for biomarker discovery and precise targeted quantification. MtoZ Biolabs will continue to refine PRM data-analysis workflows to support high-quality targeted protein quantification studies for research users.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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