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Proteomic data of endometrial and myometrial hydrogels

Citation

López-Martínez, Sara et al. (2021), Proteomic data of endometrial and myometrial hydrogels, Dryad, Dataset, https://doi.org/10.5061/dryad.vdncjsxsv

Abstract

Decellularization techniques support the creation of biocompatible extracellular matrix hydrogels, providing tissue-specific environments for both in vitro cell culture and in vivo tissue regeneration. We obtained endometrium derived from porcine decellularized uteri to create endometrial extracellular matrix (EndoECM) hydrogels. After decellularization and detergent removal, we investigated the physicochemical features of the EndoECM, including gelation kinetics, ultrastructure, and proteomic profile. The matrisome showed conservation of structural and tissue-specific components with low amounts of immunoreactive molecules. This was corroborated by hypoimmunogenic reaction when injected subcutaneously in immunocompetent mice. EndoECM supported in vitro culture of human endometrial cells in two- and three-dimensional conditions and improved proliferation of endometrial stem cells with respect to collagen and Matrigel. Further, we developed a three-dimensional endometrium-like co-culture system of epithelial and stromal cells from different origins. Endometrial co-cultures remained viable and showed significant remodeling. Biomimetic endometrial milieus offer new strategies in reproductive techniques and endometrial repair and our findings demonstrate that EndoECM has potential for in vitro endometrial culture and as treatment for endometrial pathologies.

Methods

The proteomic analysis was performed in the SCSIE proteomics facility of University of Valencia. 50 µg of EndoECM, MyoECM, and No-DC Endo (8 mg/mL) were loaded and resolved in a 1D SDS-PAGE gel. Every sample lane was sliced into seven fragments. Gel slides were digested using sequencing grade trypsin (Promega) at 37ºC. 200 ng of trypsin were used for samples and digestion was performed at 37 ºC. Trypsin digestion was stopped with 10% trifluoroacetic acid (TFA) and the supernatant (SN) was removed, then the library gel slides were dehydrated with pure acetonitrile (ACN). The new peptide solutions were combined with the corresponding SN. The peptide mixtures were dried in a speed vacuum and resuspended in 2% ACN; 0.1% TFA. The final volume was between 6 and 25 mL.

Liquid chromatography and tandem mass spectrometry (LC–MS/MS) were performed. 5 µL of sample was loaded onto a trap column (NanoLC Column, 3µ C18-CL, 350 mmx0.5mm; Eksigen) and desalted with 0.1% TFA at 2 µL/min for 10 min. Peptides were then loaded onto an analytical column (LC Column, 3 µ C18-CL, 75 umx12cm, Nikkyo) equilibrated in 5% acetonitrile 0.1% FA (formic acid). Elution was carried out with a linear gradient of 5a40% B in A for 60 min. (A: 0.1% FA; B: ACN, 0.1% FA) at a flow rate of 300nL/min. Peptides were analyzed in a mass spectrometer nanoESI qQTOF (5600 TripleTOF, ABSCIEX). Sample was ionized applying 2.8 kV to the spray emitter. Analysis was carried out in a data-dependent mode. Survey MS1 scans were acquired from 350–1250 m/z for 250 ms. The quadrupole resolution was set to ‘UNIT’ for MS2 experiments, which were acquired 100–1500 m/z for 50 ms in ‘high sensitivity’ mode. Following switch criteria were used: charge: 2+ to 5+; minimum intensity; 70 counts per second (cps). Up to 50 ions were selected for fragmentation after each survey scan. Dynamic exclusion was set to 15s. The system sensitivity was controlled with 2 fmol of 6 proteins (LC Packings).

ProteinPilot default parameters were used to generate peak list directly from 5600 TripleTof wiff files. The Paragon algorithm (Shilov et al., 2007) of ProteinPilot v 5.0 was used to search the UniprotMammals database (version 03-2018) with the following parameters: Trypsin specificity, (iodoacetamide) cys-alkylation, taxonomy not restricted, and the search effort set to through. The protein grouping was done by Pro group algorithm. The formation of protein groups was guided entirely by observed peptides only, which originated from the experimentally acquired spectra. Because of this, the grouping was guided by spectra. Unobserved regions of protein sequence played no role in explaining the data. Proteins showing unused score >1.3 were identified with confidence ≥95%. Mass spectrometry information of all the fragments were combined for protein identification using the UniprotMammals database (SCSIE University of Valencia).

Filtered output files for each peptide were grouped according to the protein from which they were derived and their individual coverage (% cov) was determined as an indicator of protein abundance of relative quantitation analysis. Common contaminants were excluded.

Funding

the Carlos III Health Institute (ISCIII) [CPI19/00149 and PI17/01039]; the Spanish Ministry of Science and Innovation by Torres Quevedo Program [PTQ2018-009918] and the Regional Valencian Ministry of Education [PROMETEO/2018/137, ACIF/2017/117].