Ethical policy
The study received approval from the Bioethics Committee of the Poznan University of Medical Sciences (# 2022 − 569) for the use of discarded omental tissue for research purposes and all patients gave their informed consent. This study was conducted in accordance with relevant guidelines and regulations.
Cell culture
HPMCs were obtained from pieces of omentum removed during elective abdominal surgery from consenting patients. HPMCs were isolated, identified, and cultured essentially as previously described.42 HPMCs senescence occurred as a result of successive passages over time.57 Cells were regarded as senescent when proliferation ceased and > 70% cells stained for senescence-associated β-galactosidase (SA-β-Gal), as determined using the SA-β-Gal Staining Kit (Cell Signaling Technology, Danvers, MA, USA). For TGF-β stimulation, paired cultures of young and senescent HPMCs were first deprived of foetal calf serum (FCS) for 24 hours and then treated for 72 hours with 1 ng/mL of human recombinant TGF-β1 (R&D Systems, Minneapolis, MN, USA) in medium containing 0.1% FCS to maintain basic cell viability. As previously shown, under these conditions, TGF-β1 can effectively induce MMT in HPMCs.9,27
Immunofluorescence
Cells were fixed with 3.7% paraformaldehyde, permeabilized with 0.1% Triton X-100, blocked with 1% bovine serum albumin (BSA), and incubated for 15 minutes with 330 pM Alexa Fluor®488 Phalloidin (Cell Signaling Technology, Danvers, MA, USA) in PBS containing 0.1% BSA and 0.05% Tween 20. After that cells were counterstained with 4’,6-diamidino-2-phenylindole (DAPI) (Invitrogen/ThermoFisher Scientific, Waltham, USA) and visualized on Cellinsight CX5 platform (ThermoFisher).
Electron microscopy
Cells were harvested with a 0.05% trypsin/0.02% EDTA solution, washed with PBS and fixed with 2.5% glutaraldehyde in PBS for 1 hour at 4°C. After that, cells were post-fixed with 1% osmium tetroxide for 30 minutes at room temperature, washed, dehydrated in a graded series of ethanol (40–100%), and infiltrated and embedded in Epon epoxy resin (Plano, Marburg, Germany). Ultrathin (40–60 nm) sections were mounted on copper grids, contrasted with 5% uranyl acetate in 50% ethanol (30 minutes) and 1% lead citrate in water (30 minutes), and analysed with a Jeol transmission electron microscope at 80 kV (Jeol, Tokyo, Japan).
Microarray expression study
The microarray study was conducted according to previously described procedures.58–60 Total RNA from HPMCs was extracted with TRI Reagent (SigmaAlrdich, Merck Life Science, Darmstadt, Germany) purified with the Quick-RNA MiniPrep kit (Zymo Research Corp., Irvine, CA, USA) and quantified by spectrophotometry. 100 ng of total RNA was subjected to transcription in vitro, biotin labelling, and cDNA fragmentation using Affymetrix GeneChip® WT Plus Reagent Kit (Affymetrix, Santa Clara, CA, USA). The biotin-labelled cDNA fragments (5.5 µg) were hybridised with the Affymetrix® Human Gene 2.1 ST Array Strip (Affymetrix, Santa Clara, CA, USA) together with control cDNA and oligo B2. The hybridisation process was conducted with the AccuBlockTM Digital Dry Bath hybridisation oven (Labnet International, Inc., Edison, NJ, USA) at 45°C for 20 h. After hybridisation, the microarrays were washed and stained by the Imaging Station from a Gene Atlas System (Affymetrix, Santa Clara, CA, USA). Preliminary assessment of the scanned strips was conducted using Affymetrix Gene Atlas TM Operating Software (Affymetrix, Santa Clara, CA, USA). The quality of gene expression data was verified according to software-specific quality control criteria.
Microarray data analysis
The CEL files obtained from microarray scanning were used in further analyses using a BioConductor repository with the relevant Bioconductor libraries of the statistical R programming language (v4.1.2; R Core Team 2021). A robust multiarray average (RMA) algorithm integrated within the "Affy" library was applied to normalise and compute the expression values of examined genes61. The gene data table was formed by merging the annotated data table from the BioConductor "oligo" package with the normalised expression dataset.62 Genes exhibiting low variance were removed using a variance-based filtering function from the "genefilter" library.63 Expression data of the top 500 genes with the highest variance were utilised for principal component analysis (PCA) using the "factoextra" library.64 Differential expression analysis and statistical assessment were performed with the "limma" library, employing linear models designed for microarray data.65 Differentially expressed genes were determined using criteria involving an absolute fold difference exceeding 2 and a p-value of less than 0.05 with a 25% false discovery rate (FDR) correction. The general profile of transcriptome regulation was illustrated as a volcano plot, showing the total number of up-and downregulated genes. Obtained results were visualised using “ggplot2” and “ggprism” libraries. 66,67
Assignment of differentially expressed genes to relevant Gene Ontology (GO) Terms
For each analysed comparison, up and down-regulated genes were subjected separately to functional annotation and clusterisation using the DAVID (Database for Annotation, Visualisation, and Integrated Discovery) bioinformatics tool.68 Entrez IDs of differentially expressed genes were uploaded to DAVID by the “RDAVIDWebService” BioConductor library,69 where DEGs were assigned to relevant GO terms, with subsequent selection of significantly enriched GO terms from GO biological process (BP), cellular component (CC) and molecular function (MF) databases. Using the same approach, an enrichment analysis was carried out for differentially expressed genes in relation to Kyoto Encyclopedia of Genes and Genomes KEGG signalling pathways. The p-values of selected GO terms were corrected using Benjamini-Hochberg correction described as adjusted p-values. Relevant GO ontological groups with adjusted p-values below 0.05 were visualised using a bubble plot. Detailed analysis of genes belonging to selected ontological groups, with their expression fold changes, are presented as heatmap using “ComplexHeatmap” library.70
Gene Set Enrichment Analysis (GSEA)
GSEA was used to determine potential enrichment or depletion in gene expression between two distinct biological cohorts. This approach employed predefined gene sets encompassing Gene Ontology (GO) terms and pathways. The method uses the Kolmogorov–Smirnov (K-S) statistical test to identify significantly enriched or depleted groups of genes. GSEA analysis was performed using the FGSEA library).71 Normalised fold change values of all genes were log2 transformed and ordered. The enrichment of gene sets was examined in relation to the Reactome database (Molecular Signatures Database).72 Genes belonging to the selected set were ranked according to the difference in their expression level using a signal-to-noise ratio with 10,000-time permutation. By walking down, the ranked list of genes, the enrichment score (ES) was calculated for each selected gene set.71 These scores (ES) were normalised by their gene set size, and FDR corrected false positives. The top twenty significantly enriched and depleted ontological terms (with the highest and lowest normalised enrichment score - NES) were visualised as bar plots.
Protein extraction
To isolate cell proteins, HPMCs were washed extensively with ice-cold PBS, lysed with RIPA buffer with supplements (50 mM Tris, pH 7.4, 420 mM NaCl, 1% NP-40 (IGEPAL CA-630), 0.25% sodium deoxycholate, protease inhibitors (cOmplete, EDTA-free Protease Inhibitor Cocktail, Roche Diagnostics, Basel, Switzerland), 250 mM sodium fluoride, 50 mM sodium orthovanadate) and cleared by centrifugation. Total protein concentration was determined with the Bradford protein assay (Bio-Rad Protein Assay Dye Reagent, Bio-Rad Laboratories, Hercules, CA, USA) and the samples were stored at -80°C until assayed.
Proteomics analysis
The whole proteomics analysis was conducted as previously described.73 In brief, 70 µg total protein of each sample and an internal pooled standard (IPS), consisting of equal parts of all samples, were used. Digestion was performed using single-pot, solid-phase enhanced sample preparation (SP3). All samples were reduced (10 mM dithiothreitol for 1h at 56°C), alkylated (55 mM 2-iodoacetamide, 30 min at RT), and proteins were bound to SP3 beads (10:1 beads:protein ratio, GE Healthcare, Chicago, IL, USA), washed with 80% ethanol and acetonitrile, and subjected to on-bead digestion with trypsin/LysC (1:25 protease:protein ratio, Promega, Madison, WI, USA) overnight at 37°C in 50 mM ammonium bicarbonate, pH 8.5 (SigmaAlrdich, Merck Life Science, Darmstadt, Germany). After elution peptides were desalted (Pierce Peptide Desalting Columns, ThermoFisher Scientific); dried in a vacuum concentrator, and reconstituted in 100 mM tetraethylammonium bromide (TEAB), pH 8.5 (SigmaAlrdich, Merck Life Science). Peptide concentration was determined according to the manufacturers’ protocol (Colorimetric Peptide Assay, ThermoFisher Scientific).
For multiplexing, peptide labelling with isobaric tandem mass tags (TMTpro, ThermoFisher Scientific) was performed according the manufacturers’ protocol. TMTpro reagents were reconstituted with acetonitrile and 25 µg per sample were labelled with TMTpro. After incubation for 1h at RT the reaction was quenched by addition of 5% hydroxylamine (SigmaAlrdich, Merck Life Science) in TEAB and incubation for 15 min at RT. Labelling efficiency was determined via LC-MS.
Pooled samples (2x 12 samples + IPS) were concentrated and desalted (Pierce Peptide Desalting Columns, ThermoFisher). Eluates were dried in a vacuum concentrator and reconstituted in 20 mM ammonia formate buffer, pH 10 before fractionation at basic pH. Two-dimensional liquid chromatography (LC) was performed by reverse-phase chromatography at high and low pH. In the first dimension peptides were separated on a Gemini-NX C18 (150 x 2mm, 3 µm, 110 A, Phenomenex, Torrance, USA) in 20 mM ammonia formate buffer, pH 10 and eluted over a 48 min gradient from 0–60% solvent B followed by 5 min at 100% solvent B at 50 µl/min using an Ultimate 3000 RSLC micro system (ThermoFisher Scientific) equipped with a fraction collector. Thirty-six fractions were collected in a time-based manner (every 30s from min 11.5 to 57). Organic solvent was removed in a vacuum concentrator and samples were reconstituted in 0.1% trifluoroacetic acid.
Fractions were analysed on an Ultimate 3000 RSLC nano coupled directly to an Exploris 480 with FAIMSpro (all Thermo Fisher Scientific). Samples were injected onto a reversed-phase C18 column (50 cm x 75 µm i.d., packed in-house) and eluted with a gradient of 4–38% mobile phase B over 94 min by applying a flow rate of 230 nl/min. MS scans were performed in the range from m/z 375–1650 at a resolution of 120,000 (at m/z = 200). MS/MS scans were performed choosing a resolution of 30,000 with the turboTMT mode for TMTpro Reagent; normalized collision energy of 33%; isolation width of 0.7 m/z and dynamic exclusion of 90s. Two different FAIMS voltages were applied (-40V and − 60V) with a cycle time of 1.5 sec per voltage. FAIMS was operated in standard resolution mode with a static carrier gas flow of 4.6 L/min.
The acquired raw MS data files were processed and analysed using Proteome Discoverer (v2.4.0.305, Thermo Fisher). SequestHT was used as search engine and following parameters were chosen: database: Homo sapiens (SwissProt, downloaded on 2021-09-24); enzyme: trypsin; max. missed cleavage sites: 2; static modifications: TMTpro (K and peptide N-terminus) and carbamidomethyl (C); dynamic modifications: oxidation (M), deamidation (N, Q), acetyl (protein N-terminus), Met-loss (M) and Met-loss + Acetyl (M); precursor mass tolerance: 10 ppm; fragment mass tolerance: 0.02 Da. For reporter ion quantification the most intense m/z in a 20 ppm window around the theoretical m/z was used. Correction of isotopic impurities for reporter ion intensities was applied. Only unique peptides were used for quantification, which was based on S/N values with an average S/N threshold of 10. Normalization was based on total peptide amount and scaling mode on controls average (internal standard). Only peptides and proteins with FDR < 0.01 are reported and single peptide identifications were excluded from the dataset. The two multiplex runs were scaled and normalized via the IPS and combined.
Statistical analysis and graphical representation
Biological pathway enrichment analysis and functional analysis of differentially abundant proteins was performed using the Protein Analysis Through Evolutionary Relationships (PANTHER) database version 17.0. The PANTHER analysis tool was used to performed enrichment analysis for the identification of over-represented biological pathways by a gene list. Annotation databases included in the analysis were Gene Ontology (GO) cellular component and molecular function. Data and statistical analyses and graphical representations of results were performed using R (v4.0.3; http://www.r-project.org/). Pathway identification by Ingenuity Pathway Analysis (IPA 7.0, Qiagen, http://www.ingenuity.com), their respective predicted up/down regulation patterns and their affections by differentially abundant proteins were calculated for each functional pathway by a one-tailed Fisher’s exact test at an alpha level of 0.05. The IPA calculated z-score assessed the match of observed and predicted up/down regulation patterns and served as a predictor for the activation state. Differential protein abundances between conditions were analysed with linear models for microarray data (LIMMA) using the R package “limma”.65 Limma is a combinatory statistical approach for large-scale expression studies fitting linear models for each gene/protein and utilizing Empirical Bayes and other shrinkage methods to borrow information across genes/proteins to stabilize the analysis and correct variance by shrinking it towards a pooled variance.65 As mass spectrometry acquired proteomic data can be noisy, large, hierarchical in nature, and imbalanced due to acquisition and pre-processing methods, LIMMA, although being initially developed for microarray data, displayed superiority over conventional statistical modelling approaches (e.g. generalized linear models.74 Correction of multiple testing was performed by using the Benjamini-Hochberg procedure.