Data from: Nascent extracellular matrix converts biomaterial cues into cell fate decisions
Data files
Sep 10, 2026 version files 298.12 KB
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README.md
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Supplementary_table_1.xlsx
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Abstract
This dataset contains proteomics data generated to characterize protein deposition at the cell–hydrogel interface in hydrogels with low and high levels of chemical modification. The dataset includes protein abundance measurements from three biological replicates for each hydrogel condition (low and high modification). One table contains all identified proteins (3,479 protein entries), with gene names, matrisome classification, and abundance values for each sample. A second, matrisome-focused table contains 182 extracellular matrix (ECM) and matrisome-associated protein entries classified by matrisome division and category. For these proteins, the dataset additionally provides normalized abundance values, z-scores, fold changes, log2 fold changes, p-values, multiple-testing-adjusted p-values, and corresponding –log10-transformed values comparing high- and low-modification conditions.
These data enable analysis of how hydrogel chemical modification is associated with changes in the composition and abundance of deposited proteins, with particular emphasis on the nascent ECM. The dataset may be reused to examine individual ECM proteins or protein classes, perform alternative differential-abundance or pathway analyses, compare matrisome composition across biomaterial environments, or integrate these proteomic measurements with other datasets investigating cell–matrix and cell–biomaterial interactions. The dataset contains proteomic measurements and associated annotations and does not contain personally identifiable information. Reuse should follow the license specified for the Dryad dataset. Citation of the dataset and associated publication is encouraged.
Dataset DOI: 10.5061/dryad.0k6djhbfj
This dataset contains proteomics data characterizing nascent extracellular matrix (ECM) deposited by juvenile bovine chondrocytes cultured for 7 days within norbornene-modified hyaluronic acid hydrogels. Cells were cultured in hydrogels with low or high degrees of norbornene modification, representing two hydrogel environments with distinct levels of chemical modification.
Proteomic analysis was performed to compare the composition and abundance of deposited proteins between these conditions and to investigate how hydrogel chemical modification influences nascent ECM composition at the cell–hydrogel interface. Protein abundance measurements are provided for three biological replicates per condition.
The dataset contains 3,479 identified protein entries and a subset of 182 extracellular matrix (ECM) and matrisome-associated protein entries. The matrisome dataset additionally includes normalized abundance, z-scores, fold changes, log2 fold changes, p-values, and multiple-testing-adjusted p-values comparing the high- and low-modification conditions.
Description of the data and file structure
The Excel workbook Supplementary_table_1.xlsx contains two worksheets:
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All identified proteins: 3,479 protein entries with gene names, matrisome classification, and protein abundance measurements for three high-modification ('High_1'-'High_3') and three low-modification ('Low_1'-'Low_3') biological replicates. This worksheet includes both matrisome and non-matrisome proteins; proteins not classified as part of the matrisome are labeled Non-matrisome.
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Matrisome-sorted: 182 matrisome protein entries classified into two major divisions: Core matrisome and Matrisome-associated. Core matrisome categories include Collagens, ECM Glycoproteins, and Proteoglycans, while matrisome-associated categories include ECM Regulators, ECM-affiliated Proteins, and Secreted Factors. This worksheet additionally contains mean-normalized abundance values, z-scores, fold changes, log2 fold changes, p-values, adjusted p-values, and corresponding −log10-transformed values.
Matrisome proteins were annotated using Matrisome AnalyzeR and the bovine Matrisome Project classification. Protein abundances were normalized within each sample to the mean abundance of all detected matrisome proteins.
Variables:
Protein annotation
- Gene names: Official gene symbol for each protein
- Division: Matrisome division (e.g., Core matrisome, Matrisome-associated)
- Category: ECM functional category (e.g., Collagens, ECM glycoproteins)
Raw abundance values
- High_1_abundance, High_2_abundance, High_3_abundance: Raw abundance values for High-condition replicates
- Low_1_abundance, Low_2_abundance, Low_3_abundance: Raw abundance values for Low-condition replicates
Normalized values
- High_1_norm, High_2_norm, High_3_norm: Normalized abundance values for High-condition replicates
- Low_1_norm, Low_2_norm, Low_3_norm: Normalized abundance values for Low-condition replicates
Z-score–normalized values
- High_1_norm_zscore, High_2_norm_zscore, High_3_norm_zscore
- Low_1_norm_zscore, Low_2_norm_zscore, Low_3_norm_zscore
Differential analysis and statistics
- fold_change: Mean normalized abundance ratio (High / Low)
- log2_fold_change: Log2-transformed fold change
- p_value: P-value from statistical comparison between conditions
- neg_log10_p: −log10-transformed p-value
- p_adjust: Multiple-testing–adjusted p-value
- neg_log10_p_adjust: −log10-transformed adjusted p-value
All abundance values are unitless and represent relative protein quantities.
'NA' indicates that the protein was identified but no quantitative protein abundance was available across the six samples. Numerical zeros indicate that the protein was quantified in the dataset, but no abundance was reported for the corresponding sample. Missing values were not imputed.
Sharing/Access information
Matrisome annotations were derived from the following sources:
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Bovine Matrisome Project annotations: https://sites.google.com/uic.edu/matrisome/matrisome-annotations/bos-taurus
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Listrat A, Boby C, Tournayre J, Jousse C. Bovine extracellular matrix proteins and potential role in meat quality: First in silico Bos taurus compendium. Journal of Proteomics. 2023;279:104891. doi:10.1016/j.jprot.2023.104891
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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. Journal of Cell Science. 2023;136(17). doi:10.1242/jcs.261255
Code/Software
No analysis code is included in this dataset. Protein identification and quantification were performed using Proteome Discoverer 3.0. Subsequent data processing and statistical analyses were performed in R, and matrisome proteins were annotated using Matrisome AnalyzeR and the bovine Matrisome Project classification.
Protein isolation and nascent extracellular matrix (nECM) enrichment were performed using a previously described protocol. Hydrogels were snap-frozen in liquid nitrogen and decellularized in 1.5 M KCl and 0.1% Triton X-100 in 50 mM Tris-HCl, pH 8.0, for 6 h on ice in the presence of protease inhibitors. Residual hydrogel and DNA were digested overnight at 37 °C using hyaluronidase and DNase. Protein pellets were solubilized in 8 M urea in 50 mM ammonium bicarbonate, followed by acetone precipitation. Protein concentration was determined by BCA assay, and 40 µg protein per sample was used for proteomic analysis.
Samples were labeled using TMTsixplex isobaric tags and analyzed by tandem mass spectrometry at the University of Michigan Proteomics Core. Protein identification and quantification were performed using Proteome Discoverer 3.0. Spectra were searched against the Bos taurus UniProt database (TaxID 9913; version 2023-06-28). Variable modifications included methionine oxidation (+15.995 Da), asparagine/glutamine deamidation (+0.984 Da), and methionine-to-azidohomoalanine substitution (−4.986 Da). Trypsin was specified as the digestion enzyme, and peptide and protein identifications were filtered at a 1% false discovery rate.
Matrisome proteins were annotated using Matrisome AnalyzeR and the bovine Matrisome Project classification, including collagens, glycoproteins, proteoglycans, ECM-affiliated proteins, ECM regulators, and secreted factors. Within each sample, matrisome protein abundances were normalized to the mean abundance of all detected matrisome proteins. Z-scores were calculated across samples for differential-abundance visualization and clustering. Hierarchical clustering was performed on normalized matrisome abundances, and principal component analysis and volcano plots were generated from the processed proteomics data. For Gene Ontology over-representation analysis, differentially abundant matrisome proteins defined by fold change >2 and adjusted p-value <0.1 were analyzed using WebGestalt with the bovine matrisome as the reference background. Statistical enrichment was assessed by Fisher’s exact test with Benjamini-Hochberg correction for multiple testing.