May 12, 2026
Proteomic Data Processing and Matrisome Annotation of Human Placenta LC-MS/MS Data
- Amanpreet K. Bains1,
- James M. Considine1,
- Alexandra Naba1
- 1Department of Physiology and Biophysics, University of Illinois Chicago
- Human BioMolecular Atlas Program (HuBMAP) Method Development CommunityTech. support email: [email protected]
Protocol Citation: Amanpreet K. Bains, James M. Considine, Alexandra Naba 2026. Proteomic Data Processing and Matrisome Annotation of Human Placenta LC-MS/MS Data. protocols.io https://dx.doi.org/10.17504/protocols.io.n92ld4437l5b/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: May 11, 2026
Last Modified: May 12, 2026
Protocol Integer ID: 316835
Keywords: LC-MS/MS proteomics, placenta matrisome, MaxQuant LFQ, Matrisome AnalyzeR, Scaffold, extracellular matrix, ECM, matrisome annotation of human placenta lc, ms proteomic data, proteomic data, proteomic data processing, human placenta sample, human placenta lc, extracellular matrix component, extracellular matrix, human uniprot database, matrisome annotation, ms data, raw spectra
Funders Acknowledgements:
National Institutes of Health - Thinking outside the cell: Leveraging HuBMAP data to build the human ECM atlas
Grant ID: U01 HG012680
National Institutes Of Health - Female Reproductive Tissue Mapping Center
Grant ID: U54 HD104393
National Institutes Of Health - Pregnant Female Reproductive Tissue Mapping Center
Grant ID: U54 HD110347
Abstract
This protocol describes the computational processing and analysis of LC-MS/MS proteomic data generated from extracellular matrix (ECM)-enriched human placenta samples. Raw spectra are searched against the human UniProt database using MaxQuant, validated using Scaffold, and annotated for extracellular matrix components using Matrisome AnalyzeR. Label-free quantification (LFQ) is performed using precursor ion intensities.
Materials
Software
- MaxQuant
- Scaffold
- Matrisome AnalyzeR
Databases
- UniProt human reviewed protein database: uniprotkb_Human_reviewed_2025_03_10
Database Searching Using MaxQuant
Import of Raw Mass Spectrometry Data
Import raw LC-MS/MS files into MaxQuant (version 2.0.3.1).
Configure database searching using the uniprotkb_Human_reviewed_2025_03_10 human protein database.
MaxQuant Search Parameters
Set the parent ion mass tolerance to 10 ppm.
Set the fragment ion mass tolerance to 20 ppm.
Specify the digestion enzyme as strict trypsin.
Allow a maximum of two missed cleavage sites.
Fixed and Variable Modifications
Specify carbamidomethylation of cysteine residues as a fixed modification.
Specify the following variable modifications:
- N-terminal glutamine to pyroglutamate conversion (Gln→pyro-Glu)
- Deamidation of asparagine
- Deamidation of glutamine
- Oxidation of methionine
- Oxidation of proline
- Oxidation of lysine
The latter two variable modifications are included to facilitate detection of extracellular matrix proteins, including collagens and collagen-domain-containing proteins, described in:
Citation
LINK
MaxQuant LFQ Configuration
Enable MaxQuant LFQ for label-free quantification using default software settings.
Validation of Peptide and Protein Identifications
Import into Scaffold
Import MaxQuant search results into Scaffold (version 5.2.2, Proteome Software Inc., Portland, OR) for validation of peptide and protein identifications.
Peptide Validation Criteria
Accept peptide identifications only if they achieve a false discovery rate (FDR) below 1.0% by the Percolator posterior error probability calculation.
Citation
LINK
Protein Validation Criteria
Accept protein identifications only if:
- Protein FDR is below 1.0%
- At least two unique peptides are identified per protein
Assign protein probabilities using the Protein Prophet algorithm.
Citation
LINK
Group proteins sharing similar peptides according to the principles of parsimony when proteins cannot be distinguished solely by MS/MS evidence.
Matrisome Annotation and Classification
Annotation of ECM Components
Import validated protein identification data into Matrisome AnalyzeR, and use this tool to annotate proteins as ECM or non-ECM components.
Citation
LINK
Citation
LINK
Classification of Matrisome Proteins
Classify matrisome proteins into:
- Core matrisome components
- Matrisome-associated components
Further classify core matrisome proteins into:
- ECM glycoproteins
- Collagens
- Proteoglycans
Further classify matrisome-associated proteins into:
- ECM-affiliated proteins
- ECM regulators
- Secreted factors
Quantitative Data Processing and Normalization
Total Precursor Ion Intensity Quantification
Use total precursor ion intensity (TPI) values for label-free inter-group quantification and estimation of protein abundance.
Sample-Level Normalization
Calculate a scaling factor for each sample as:
Normalize protein TPI values within each sample using the corresponding sample-specific scaling factor to bring all samples to a common intensity level.
Use normalized TPI values for downstream comparative analyses.
Protocol references
Käll L, Krogh A, Sonnhammer ELL. 2005. An HMM Posterior Decoder for Sequence Feature Prediction That Includes Homology Information. Bioinformatics 21 Suppl 1: i251–i257. DOI: 10.1093/bioinformatics/bti1014
Naba A, Pearce OMT, Del Rosario A, et al. 2017. Characterization of the Extracellular Matrix of Normal and Diseased Tissues Using Proteomics. Journal of Proteome Research 16(8): 3083–3091. DOI: 10.1021/acs.jproteome.7b00191
Nesvizhskii AI, Keller A, Kolker E, Aebersold R. 2003. A Statistical Model for Identifying Proteins by Tandem Mass Spectrometry. Analytical Chemistry 75(17): 4646–4658. DOI: 10.1021/ac0341261
Petrov PB, Considine JM, Izzi V, Naba A. 2023. Matrisome AnalyzeR – a Suite of Tools to Annotate and Quantify ECM Molecules in Big Datasets across Organisms. Journal of Cell Science 136(17): jcs261255. DOI: 10.1242/jcs.261255
Citations
Step 3.2
Naba A, Pearce OMT, Del Rosario A, Ma D, Ding H, Rajeeve V, Cutillas PR, Balkwill FR, Hynes RO. Characterization of the Extracellular Matrix of Normal and Diseased Tissues Using Proteomics.
https://doi.org/10.1021/acs.jproteome.7b00191Step 6.1
Käll L, Krogh A, Sonnhammer EL. An HMM posterior decoder for sequence feature prediction that includes homology information.
https://doi.org/Step 7.2
Nesvizhskii AI, Keller A, Kolker E, Aebersold R. A statistical model for identifying proteins by tandem mass spectrometry.
https://doi.org/Step 8.1
Naba A, Clauser KR, Hoersch S, Liu H, Carr SA, Hynes RO. The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices.
https://doi.org/10.1074/mcp.M111.014647Step 8.1
Petrov PB, Considine JM, Izzi V, Naba A. Matrisome AnalyzeR - a suite of tools to annotate and quantify ECM molecules in big datasets across organisms.
https://doi.org/10.1242/jcs.261255Acknowledgements
The authors would like to thank Dr. Hui Chen and Lasanthi Jayathilaka from the Mass Spectrometry Core facility at UIC.