Mechanical stretch regulates inflammatory signaling in human smooth muscle cells

Mechanical stretch regulates inflammatory signaling in human smooth muscle cells | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Mechanical stretch regulates inflammatory signaling in human smooth muscle cells View ORCID Profile Lise Filt Jensen , View ORCID Profile Anton Markov , View ORCID Profile Laura Alonso-Herranz , View ORCID Profile Emil Aagaard Thomsen , View ORCID Profile Jacob Giehm Mikkelsen , View ORCID Profile Jacob Fog Bentzon , View ORCID Profile Julián Albarrán-Juárez doi: https://doi.org/10.1101/2025.10.29.685276 Lise Filt Jensen 1 Atherosclerosis Research Unit, Department of Clinical Medicine, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lise Filt Jensen Anton Markov 1 Atherosclerosis Research Unit, Department of Clinical Medicine, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anton Markov Laura Alonso-Herranz 1 Atherosclerosis Research Unit, Department of Clinical Medicine, Aarhus University , Aarhus, Denmark 2 Steno Diabetes Center Aarhus, Aarhus University Hospital , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Laura Alonso-Herranz Emil Aagaard Thomsen 3 Department of Biomedicine, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Emil Aagaard Thomsen Jacob Giehm Mikkelsen 3 Department of Biomedicine, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jacob Giehm Mikkelsen Jacob Fog Bentzon 1 Atherosclerosis Research Unit, Department of Clinical Medicine, Aarhus University , Aarhus, Denmark 2 Steno Diabetes Center Aarhus, Aarhus University Hospital , Aarhus, Denmark 4 Experimental Pathology of Atherosclerosis Laboratory, Spanish National Centre for Cardiovascular Research (CNIC) , Madrid, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jacob Fog Bentzon Julián Albarrán-Juárez 1 Atherosclerosis Research Unit, Department of Clinical Medicine, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Julián Albarrán-Juárez For correspondence: jalbarran{at}clin.au.dk Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Aims During atherosclerosis progression, vascular smooth muscle cells (SMCs) undergo phenotypic modulation from a contractile state to alternative modulated and proliferative states. Similar transitions occur in vitro , likely due to loss of physiological cues such as specific extracellular matrix (ECM) components and mechanical forces. Here, we investigated how defined ECM substrates and stretch conditions influence the phenotype of human aortic SMCs. Methods and results SMCs were cultured on collagen I-, fibronectin-, or laminin-coated plates under static conditions, physiological stretch (10% elongation), or pathological stretch (15% elongation), followed by bulk RNA sequencing. Mechanical stretch regulated genes involved in cell cycle regulation, contractile function, and inflammatory signaling. While functional effects on proliferation and contractility were modest, inflammatory pathways were strongly affected by stretch intensity. Physiological stretch suppressed basal and TNF-induced inflammatory gene expression, whereas pathological stretch enhanced it, with consistent effects across all ECM substrates. Physiological stretch downregulated multiple NF-κB target genes and reduced IKBKB expression. IKBKB knockdown lowered baseline inflammatory gene expression and abolished stretch-induced suppression of CCL2 , indicating an NF-κB-dependent mechanism, likely downstream of p65 translocation. Single-cell RNA sequencing revealed pronounced phenotypic heterogeneity in cultured SMCs. Integration with human atherosclerosis datasets showed that in vitro SMC states partially overlapped with plaque SMC phenotypes but displayed a globally enhanced pro-inflammatory phenotype. Importantly, stretch-induced suppression of inflammatory signaling was observed across the heterogeneous SMC population. Conclusions Physiological mechanical stretch induces strong anti-inflammatory effects in human SMCs. Single-cell transcriptomic analysis further revealed marked heterogeneity among cultured SMCs, recapitulating the phenotypic diversity seen in human atherosclerotic plaques. The stretch-induced anti-inflammatory response in SMCs was observed across all cell clusters, highlighting a conserved protective effect of physiological mechanical forces. 1. Introduction Cardiovascular disease is the leading cause of mortality worldwide, and atherosclerosis, a chronic inflammatory condition of the arteries, is responsible for over 80% of these deaths 1 . During atherosclerosis, vascular smooth muscle cells (SMCs) undergo phenotypic modulation from a contractile state to alternative states characterized by decreased expression of contractile proteins and increased production of extracellular matrix (ECM) components 2 – 5 . A similar phenotypic shift occurs when SMCs are removed from their native environment and cultured in vitro , likely due to the loss of physiological signals, such as mechanical forces and ECM specific components, that otherwise maintain the differentiated SMC phenotype in vivo 6 – 9 . In healthy elastic arteries, the vessel wall experiences approximately 5-10% cyclic stretch with each cardiac cycle 9 – 12 . ECM composition differs between arterial layers, contributing to distinct mechanical environments. The intima is enriched in proteoglycans and adhesive glycoproteins such as fibronectin, whereas the media contains elastin, laminin isoforms, type IV collagen, and glycosaminoglycans that support a differentiated contractile SMC phenotype 13 – 15 . During atherosclerosis, extensive ECM remodeling leads to vessel stiffening and altered mechanical stresses 16 , 17 , with tensile forces concentrating in thin fibrous caps and plaque shoulders, regions prone to rupture 18 – 20 . This remodeling, characterized by increased deposition of interstitial collagens, fibronectin, osteopontin, and proteoglycans, further promotes SMC phenotypic modulation 13 , 21 . Despite extensive evidence linking ECM remodeling and altered biomechanics to atherosclerosis, the combined effects of mechanical stretch and disease-associated ECM substrates on SMC modulation remain poorly understood. To address this knowledge gap, we investigated how physiological and pathological stretch conditions, in combination with ECM microenvironments characteristic of healthy or diseased arteries, influence the phenotype of human SMCs. Transcriptomic analysis revealed that mechanical stretch regulates SMC phenotype, in particular physiological stretch suppressed inflammatory gene expression compared to both pathological levels of stretch and static conditions. Complementary single-cell analysis showed that stretch-induced inflammatory modulation was consistently observed across the heterogeneous SMC population. Although in vitro SMC states partially overlapped with the spectrum of modulated SMC phenotypes present in atherosclerotic plaques, they exhibited a globally enhanced pro-inflammatory phenotype. 2. Methods and Materials Smooth muscle cell culture Human aortic SMCs (ATCC, PCS-100-012, male donor) were authenticated by ATCC and tested negative for mycoplasma contamination prior to experiments. Cells were cultured in smooth muscle cell growth medium (SMC-GM) containing 5% fetal bovine serum (FBS), bFGF (2 ng/mL), EGF (0.5 ng/mL), insulin (5 µg/mL), gentamicin (50 µg/mL), and amphotericin B (50 ng/mL) (Provitro, 211-0601) at 37 °C and 5% CO 2 . Only cells in passages 4-6 were used for the experiments. ECM protein coating For plastic labware, plates were coated with collagen type I (COL1) from rat tail (Corning, 354236) diluted in 0.1% acetic acid at a concentration of 20 µg/mL for >20 min at room temperature (RT) and washed once with Phosphate-Buffered Saline (PBS). Fibronectin (FIB) (Sigma, F0895-5MG) was diluted in Hank’s Balanced Salt Solution at 10 µg/mL and incubated at RT for 20 min and air-dried. Laminin (LAM) (Corning, 354232) was diluted in serum-free SMC-GM at a concentration of 20 µg/mL and incubated at 37 °C overnight (ON). For BioFlex plates, we used precoated COL1 plates (Flexcell, BF-3001C), and in-house FIB- or LAM-coated BioFlex plates (Flexcell, BF-3001U), following the same protocol as for plastic labware. Mechanical stretch SMCs were seeded onto BioFlex 6-well plates. At ∼80-90% confluency, they were subjected to either 10% or 15% cyclic equibiaxial stretch or left under static conditions (control), at 37 °C and 5% CO 2 . Mechanical stretch was applied using the Flexcell tension system (FX-5000 T) 22 , 23 with a sinusoidal waveform at a frequency of 1 Hz for 6 h. Immunostaining SMCs were washed with pre-warmed PBS and fixed with pre-warmed 4% formaldehyde for 15 min at room temperature (RT). SMCs were washed three times with PBS and permeabilized with 0.1% Triton X-100 and 3% bovine serum albumin (BSA) in PBS for 10 min at RT. Unspecific binding was blocked with 3% BSA in PBS for 1h. For staining of SMCs on BioFlex plates, the silicon membranes were cut out before continuing with antibody incubations in humid chambers. Primary antibodies were diluted in 0.3% Triton X-100 and 1% BSA in PBS and applied as follows: mouse monoclonal anti-α-tubulin (1:2000, Sigma-Aldrich, clone B512, T5168, RRID:AB_477579) for 1h at 37°C; phalloidin conjugated to Alexa Fluor 568 (1:1000, Invitrogen, A12380) for 30 min at RT; rabbit monoclonal anti-MKI67 (SP6) (1:1000, Abcam, ab16667, RRID:AB_302459) ON at 4°C; rabbit monoclonal anti-NF-κB p65 (D14E12) (1:1000, Cell Signaling Technology, 8242, RRID:AB10859369) ON at 4°C. Secondary antibodies, Alexa Fluor 488 Donkey-anti-Mouse IgG (H+L) (1:800, Invitrogen A21202) or Alexa Fluor 488 Donkey-anti-Rabbit IgG (H+L) (1:800, Invitrogen A21206), were incubated for 1 h, at RT. DNA was stained using DAPI (1:2000, from stock at 5 μg/mL; Invitrogen, D1306) in PBS for 10 min at RT. Coverslips or BioFlex membranes were mounted using Fluoromount aqueous mounting media (Sigma-Aldrich, F4680). Representative images (3-6 per replicate) were acquired using an inverted fluorescent microscope (Nikon eclipse Ti2) with a Nikon CFI S plan Flour ELWD ADM 20x objective, NA=0.45 and analysis was performed in ImageJ 24 . MKI67 was quantified as the percentage of MKI67-positive cells relative to the total number of cells. NF-κB p65 nuclear localization was assessed by the nuclear-to-cytoplasmic MFI ratio, with ratios >1 indicating nuclear localization and ratios <1 indicating cytoplasmic localization. RNA isolation and cDNA synthesis RNA was isolated using the NucleoSpin RNA Plus Kit (Macherey-Nagel, 790984). RNA quantity and quality were assessed using a Nanodrop One spectrophotometer. cDNA was synthesized from RNA using the RevertAid First Strand cDNA Synthesis kit (ThermoFisher Scientific, K1622). Bulk RNA sequencing and bioinformatics analysis The bulk RNA (transcriptome) was sequenced by BGI Genomics (Copenhagen, Denmark). All RNA samples passed quality control (Agilent 4200 electrophoresis system). Non-stranded DNA libraries were prepared with polyA-selected mRNA, and 100 bp paired-end sequencing was conducted with at least 20M pair reads per sample using DNBSEQ-G400. RNA sequencing data were processed with the Nextflow-based pipeline “nf-core/rnaseq” (v3.9). Sequenced reads were aligned to the GRCh38.p13 human reference genome using STAR aligner (v2.7.10a), and gene-level quantification was done with Salmon (v1.5.2). The reference genome sequence and gene annotation were downloaded from Gencode (release 41). Further analysis was performed in R (v4.4.2) with Bioconductor (v3.20) packages. Gene counts were imported using tximport. Gene filtering was performed, and only genes with ≥10 counts in at least 3 samples were analyzed. DESeq2 was used for data normalization, principal component analysis, and estimation of differential gene expression between compared groups. Results with p-values adjusted for false discovery rate 0.5 were considered statistically significant. Enrichment and pathway analysis Enrichment analysis of the RNA-seq data was performed using the WEB-based Gene SeT AnaLysis Toolkit (WebGestalt) 25 with the Kyoto Encyclopedia of Genes and Genomes (KEGG) database 25 . Enrichment analysis for genes regulated by stretch on all ECM proteins was performed by the Enrichr website using the KEGG database 26 . Real-time qPCR Relative gene expression was measured by real-time quantitative PCR (RT-qPCR). Intron-spanning primers amplifying 50-150 bp amplicons were designed (Supplementary Table 10). The Maxima SYBR Green qPCR master mix kit, including ROX (0.18 µM, Thermo Fisher Scientific, K0252), was mixed with forward and reverse primers (300 nM each) and cDNA (12.5 ng) in a final volume of 14 µL. Samples were run in duplicates. RT-qPCR programs was run on a CFX Opus Biorad machine as follows: 1 cycle of 95°C for 10 min; 40 cycles of 95°C for 30 sec, 60°C for 1 min, and 72°C for 1 min; 1 cycle of 95°C for 1 min, 60°C for 30 sec, and 95°C for 30 sec. The relative mRNA expression was calculated using the ΔΔCt method with HPRT1 as the housekeeping gene and static untreated samples as the reference samples. Primer sequences can be found in Supplementary Table 1 . Proliferation assay When cells reached ∼70%-80% confluency, they were incubated with 5-ethyl-2’-deoxyuridine (EdU) (Sigma-Aldrich BCK-EDU488) to label proliferating cells. After 6 h, the cells were fixed and stained according to the manufactureŕs instructions, and all nuclei were stained with DAPI (1:2000, from stock at 5 μg/mL) in PBS. BioFlex silicon membranes were cut out and mounted using FluoroMount aqueous mounting media (Sigma-Aldrich F4680). Representative images (4-8 per independent replicate) were acquired using an inverted fluorescent microscope (Nikon eclipse Ti2) with a Nikon CFI S plan Flour ELWD ADM 20x objective, NA=0.45. The percentage of proliferating cells (EdU %) was calculated as the number of EdU-positive cells divided by the total number of cells. Each data point represents the average EdU % per replicate. Collagen I gel contraction assay Human aortic SMCs were cultured on COL1-coated BioFlex plates and exposed to either static conditions or 10% stretch for 6 h. Then, gel contraction assay was performed as described 27 . SMCs were harvested and resuspended at 5x10 5 cells/mL in SMC-GM. COL1 gels were prepared by mixing rat tail collagen-I (Corning, 354236) with 0.1% acetic acid to a final concentration of 3 mg/mL and kept on ice. SMCs were mixed with the collagen-I solution at a 1:1 ratio, and the pH was neutralized by addition of 1 M NaOH (11.67 µL per mL solution). A volume of 0.5 mL of the cell–collagen mixture was added per well of a low-adherence 24-well plate (Falcon, 10396881), corresponding to 1.25x10 5 cells per gel. Collagen gels without SMCs were included as negative controls. The gels were polymerized at 37°C and 5% CO 2 for 30 min, after which 0.5 mL of medium with or without transforming growth factor β1 (TGFβ1, 20 ng/mL; PeproTech, 100-21) was added. Gels were detached from the well edges using a pipette tip and gently swirled to allow free-floating contraction. Gel areas were recorded at 24 h and 48 h using a stereomicroscope (Kern Optics, OZM903) coupled to a camera (Kern Optics, OCD832) and quantified in ImageJ 24 . Gel contraction was reported as the relative gel area normalized to the area of the negative control. TNF treatment Human recombinant tumor necrosis factor (TNF) protein (Abcam, ab259410) was diluted in SMC-GM at a final concentration of 10 ng/mL. SMCs were treated at a confluency of ∼80-90% for 24 h before subjecting them to either static conditions, 10% stretch, or 15% stretch as already described. Small interference RNA (siRNA) knockdown IKBKB knockdown (KD) was performed using a pool of two different siRNAs targeting human IKBKB : Hs_IKBKB_8 (S102626456) and Hs_IKBKB_14 (S105460693) (stock 20 μM, QIAGEN). Cells were transfected with lipofectamine RNAiMAX (Invitrogen, 13778150) on two consecutive days following the manufacturer’s guidelines. A scramble siRNA (stock 20 µM, AllStars Negative S103650318, QIAGEN) was used as a control. NF-κB-EGFP reporter assay An NF-κB reporter cassette in a lentiviral vector was kindly donated by Martin Schwartz and Brian Coon (Yale University, USA). This cassette consisted of three NF-κB response elements (GGGAATTTCC) controlling the activation of a minimal human cytomegalovirus (CMVmin) promoter to drive the expression of a destabilized enhanced green fluorescent protein gene (d2EGFP). Additionally, the cassette contained a constitutively expressed fluorescent protein (DsRed) as an internal control. To enable antibiotic selection, the DsRed gene was removed by Gibson assembly and substituted with a blasticidin (Blast) resistance gene. Lentiviral production was performed as previously described 28 . SMCs were transduced at a multiplicity of infection (MOI) of 0.2 by adding the viral supernatant to SMC-GM supplemented with polybrene (8 µg/mL). To assess NF-κB activity, SMCs carrying the modified reporter cassette were stimulated with TNF (10 ng/mL) for 24 h or left untreated, washed twice with PBS, detached using 0.25% trypsin-EDTA (Gibco), and resuspended in PBS. Cells were analysed on a NovoCyte 2100YB flow cytometer with two lasers (488 nm and 561 nm). To detect d2EGFP expression, the 488 nm laser and 530/30 filter were used (Agilent, Santa Clara, CA). The data was analysed in Novo Express v1.5.6. The plasmid sequence and annotation are provided in the Supplementary material file . Single-cell RNA sequencing analysis To investigate the heterogeneity of human aortic SMCs in culture, we performed scRNA-seq. Cells were cultured on BioFlex plates coated with collagen-I (COL1) under three conditions: static, 10% stretch (6 h), and TNF-stimulation (10 ng/mL, 24 h). SMCs for all three conditions were seeded on the same day, and the following day, TNF-stimulation for 24 h was initiated. To align endpoints, the 6 h 10% mechanical stretch intervention was started 18 h after the start of TNF stimulation. Thereby, the static condition could serve as a control for both conditions. SMCs from each condition were harvested, resuspended as single cell suspensions in PBS, and captured using the BD Rhapsody™ microwell-based cell capture platform. We followed the BD Rhapsody™ HT Single-Cell Analysis System (23-24257(01)) and Single-Cell Capture and cDNA Synthesis (23-24252(01)) protocols. Briefly, for each condition, 40,000 cells were loaded per lane, resulting in capture of ∼28,000 cells with ∼7–8% multiplet rate. Beads containing cell barcodes, unique molecular identifiers (UMIs), and oligo-dT sequences were loaded to each lane and used to capture polyadenylated mRNA after cell lysis directly in the microwells, followed by cDNA synthesis. Library preparation of ∼14,000 cells (half of each condition) was performed according to the BD Rhapsody™ Whole Transcriptome Analysis (WTA) protocol (23-24117(02)), and quality was confirmed using an Agilent Bioanalyzer. As forward primer for the index PCR, we used the TruSeq D501 primer, while the Nextera N709, N710, and N707 were used as reverse primers for the static, TNF, and 10% stretch libraries, respectively. Indexed libraries were pooled in equimolar amounts and sequenced on Illumina NovaSeq X Plus (2 × 150 bp, 10B flow cell, one lane) at OMIICS (Aarhus, Denmark), yielding ∼2–2.5 billion paired-end reads, corresponding to an expected read-pair per cell of ∼50,000. RNA sequencing data was processed and analyzed on the GenomeDK cluster. Raw data quality control and generation of a gene expression matrix were carried out following the BD Rhapsody™ Sequence Analysis Pipeline (23-24580(01)) protocol, with local pipeline software v2.3 setup and basic cell detection algorithm. Further analysis was performed in R v4.4.2 software ( https://www.r-project.org/ ) environment with Seurat v5.2.1 package 29 . Cells having outlying decimal logarithms of transcripts and genes per cell (beyond five median absolute deviations from the median) were discarded. We also filtered out the cells having more than 15% of mitochondrial RNA content or mito-ribo ratio (the proportion of mitochondrial RNA in the total sum of mitochondrial and ribosomal read counts) above 0.75 30 , 31 . Putative doublets were detected using scDblFinder 32 with 7% expected multiplet rate. Data from different conditions was normalized using the “LogNormalize” method (for each cell, gene counts are divided by the total counts, multiplied by 10000, followed by natural log-transformation), and integrated in Seurat using a reciprocal principal component analysis (RPCA) approach with 15 principal components based on the expression matrix of 2000 most variable genes 33 . Cell clusters were identified using a shared nearest neighbor modularity optimization-based algorithm at varying resolutions (0.1 to 0.4). At a clustering resolution of 0.3, nine cell clusters were identified and retained for downstream analysis, as they corresponded to proliferating cells in various cell-cycle phases and to distinct quiescent populations. Wilcoxon signed-rank test was used for both identification of marker genes and comparison between conditions. Cell cluster annotation was manually curated based on expressed marker genes. Integration with single-cell RNA sequencing studies from public datasets To map in vitro SMCs to SMCs identified in atherosclerosis, we collected scRNA-seq data of human atherosclerotic lesions and healthy arteries previously published 34 – 36 . External data preparation and processing steps are described elsewhere 37 . Then datasets were integrated using RPCA method with 30 principal components and preserving the original structure and annotation of cell clusters. Statistical analysis The statistical analysis was performed in GraphPad Prism, version 8.0.2 (GraphPad Software, Boston, Massachusetts USA). Samples were processed in parallel; randomization and blinding were not used. Unless stated otherwise, all experiments were performed with ≥3 independent replicates. Sample size was based on previous studies and reproducibility considerations, not formal power calculations. Data is shown as the mean + standard error of the mean (S.E.M.). Comparison between two groups was performed by unpaired t-test. Comparison for more than two groups, if the data passed the Shapiro-Wilk test for normality, was done using one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test. If the data did not pass the normality test, a non-parametric test, Kruskal-Wallis, was performed, followed by Dunn’s post hoc test. An adjusted p-value <0.05 was considered statistically significant. Data availability statement Raw and processed data after RNA sequencing (bulk and single-cell) will be available in the BioStudies database ( http://www.ebi.ac.uk/biostudies ) under accession numbers E-MTAB-15611 and E-MTAB-15614, respectively. Any additional information required to reanalyze the data reported in this study is available upon request. 3. Results ECM proteins differentially influence SMC gene expression linked to proliferation and inflammation ECM proteins such as collagen I (COL1), fibronectin (FIB) and laminin (LAM) shape vascular responses to mechanical stress and are commonly used to recreate physiologically relevant culture conditions 38 . To test whether ECM proteins also influence SMC behavior, we examined baseline phenotypic responses of human aortic SMCs cultured on uncoated plastic plates or plastic plates coated with COL1, FIB, or LAM. Cell morphology and cytoskeleton organization were similar across all substrates ( Figure 1A ). However, SMCs on COL1 and FIB exhibited higher proliferation rates than those on uncoated or LAM-coated plates ( Figure 1B-C ). Analysis of contractile markers showed no change in ACTA2 expression, but CNN1 was reduced in cells on COL1 and FIB compared with LAM or uncoated controls ( Figure 1D ) . Expression of the pro-inflammatory cytokines IL6 and CCL2 increased on all ECM substrates relative to uncoated plates, with the lowest induction observed on LAM-coated plates ( Figure 1E ). Together, these results indicate that COL1 and FIB promote a proliferative, pro-inflammatory SMC phenotype, whereas LAM supports a more contractile and quiescent state. Download figure Open in new tab Figure 1: Morphology, cell proliferation, and gene expression changes elicited by ECM proteins in static cultures of SMCs. A. Representative images of SMCs cultured on uncoated plastic plates or coated with COL1, FIB, LAM, immunostained with tubulin (green) and F-actin (phalloidin staining, red). Nuclei are stained with DAPI (blue). B. Representative images of SMCs cultured on uncoated, COL1-, FIB-, or LAM-coated plastic plates and labelled with EdU for 6 h. EdU incorporation was detected with Alexa Fluor 488 (green), and the nuclei were stained with DAPI (blue) C. Quantification of EdU-positive SMCs relative to the total nuclei count from nine independent replicates per condition. D. Relative mRNA expression levels of contractile marker genes ( ACTA2 and CNN1) and E. inflammatory marker genes ( IL6 and CCL2 ) on SMCs cultured on the different ECM proteins. D and E contained three independent replicates per condition. The groups were compared using one-way ANOVA followed by Tukey’s post hoc analysis. P-value: n.s., not significant; *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001. Scale bars in A and B:100 μm. Mechanical stretch intensity elicits distinct transcriptomic responses in human SMCs To investigate how mechanical stretch influences the SMC transcriptome, we cultured SMCs on soft silicon membranes coated with COL1, the main structural protein in the arterial wall 39 . SMCs were maintained under static conditions (no stretch) or subjected to 10% (physiological) or 15% (pathological) stretch for 6 hours, followed by bulk RNA sequencing (RNA-seq) ( Figure 2A ). These levels of stretch were chosen to model different stretch conditions, as previously discussed elsewhere 40 – 42 . No major morphological or cytoskeletal differences were observed ( Figure 2B ), but principal component analysis revealed marked transcriptomic shifts between experimental conditions ( Figure 2C ) . Differential gene expression analysis comparing 10% stretch vs. static and 15% vs. 10% stretch identified cell cycle and inflammatory signaling as the most significant enriched pathways ( Figure 2D-E ) . Cell cycle genes (e.g., MKI67, CCNB1 ) were upregulated by physiological stretch but downregulated under pathological stretch. Inflammatory genes, including adhesion molecules ( ICAM1, VCAM1 ), chemokines ( CCL2, CCL8 ), signaling mediators ( IRAK2 ), and related transcription factors ( CEBPD, RELB ), were suppressed by physiological stretch and induced at pathological levels of stretch, with CCL2, and CCL8 , showing the strongest responses. Only a few marker genes of contractile activity were regulated by physiological stretch ( CNN1, ACTG2 ). RT-qPCR validation confirmed stretch-dependent regulation of a subset of genes i.e., MKI67, CCL2 , and CNN1 ( Figure 2F–G ) , supporting the RNA-seq findings. Full RNA-seq datasets of SMCs under different stretch conditions can be found in Supplementary Tables 2-3. Together, these results show that mechanical stretch intensity drives distinct transcriptomic responses in human SMCs, modulating mainly proliferative gene programs and inflammatory signatures. Download figure Open in new tab Figure 2: Mechanical stretch intensity regulates the transcriptome of SMCs. A. Schematic of the experimental design. SMCs were cultured on collagen-I (COL1)-coated surfaces followed by exposure to different mechanical stretch conditions (0, 10, or 15% elongation for 6 h, 1Hz) and RNA-seq. B. Morphology and cytoskeleton (F-actin, grey color) of SMCs upon exposure to different stretch conditions. Nuclei are stained with DAPI (cyan). Scale bars: 100 μm. C. Principal component analysis (PCA) of transcriptome profiles of SMCs exposed to the different stretch conditions (based on 37483 genes). D-E. Volcano plots indicate differentially expressed genes (DEGs) with a false discovery rate adjusted p-value 0.5. Enrichment analysis of the RNA-seq data was performed using the WEB-based Gene SeT AnaLysis Toolkit (WebGestalt) with the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. D . Illustrates the analysis comparing 10% stretch and static conditions, and E. 15% and 10% stretch conditions, accordingly. F-G . Relative mRNA expression of gene markers in SMCs subjected to the different mechanical stretch conditions by RT-qPCR. Datapoints represent three independent replicates. The groups were compared by unpaired t-test. P-value: *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001. Proliferation and contractility of SMCs cultured on collagen I are unaffected by mechanical stretch To assess whether the stretch-induced cell cycle gene expression translated into functional changes, we applied two complementary assays. Immunostaining for MKI67, a marker of active cell cycle phases, revealed lower levels after 15% stretch compared to 10% stretch, with no significant difference between 10% stretch and static conditions ( Figure 3A ) . EdU incorporation assays showed no change in DNA synthesis at either stretch level relative to static cultures ( Figure 3B ) , nor after 24 h of 10% stretch (Supplementary Figure 1). Thus, despite transcriptional enrichment of cell cycle-associated genes, particularly under 10% stretch, no corresponding increase in MKI67- or EdU-positive cells was observed, suggesting the presence of regulatory mechanisms that counterbalance stretch-induced gene expression at the functional level. Download figure Open in new tab Figure 3: Mechanical stretch does not alter proliferation or contractile phenotype of SMCs cultured on collagen I. A. Representative images of SMCs immunostained with MKI67-Alexa Flour 488 (magenta) and DAPI (grey). Cells were fixed immediately after being exposed to the different stretch conditions and stained. The bar graph shows the percentage of MKI67-positive SMCs relative to the total nuclei count ( n =3-6 independent replicates per group). B. Representative images of SMCs labelled with EdU-Alexa Flour 488 (green) and DAPI (blue). Cells were cultured for 24 hours, then EdU was added to the culture and cells were subsequently exposed to the different stretch conditions (as indicated) for 6h or left under static conditions. After stretching, the cells were fixed and EdU-labelling detected. The bar graph shows the percentage of EdU-positive SMCs relative to the total nuclei count ( n =3 independent replicates per group). C. Schematic of the experimental design for the contractility assay. Cells were left static or subjected to 10% stretch and embedded into collagen-I gels. Cells were either non- or TGFβ1-stimulated. D. Shown are representative images of collagen-I gels, without cells (negative control), or with SMCs subjected to the different experimental conditions. E. The quantification of the gel area is shown as the FC compared to the negative control ( n =8 independent replicates per group). The groups were compared by one-way ANOVA followed by Tukey’s post hoc analysis, adjusted p-value: n.s., not significant; *p<0.05; **p<0.01. D: Days. Scale bars:100 µm (for A-B), 5 mm (for D). Physiological stretch led to a modest increase in contractile gene expression, (as observed in Figure 2D ). To assess whether this transcriptional upregulation translated into functional effects, we performed a collagen gel contraction assay ( Figure 3C ) . SMCs pre-exposed to 10% stretch or maintained under static conditions were embedded in collagen gels and monitored over 48 h ( Figure 3D ) . Gel contraction did not differ between stretched and static cells, and both groups responded similarly to TGFβ1 stimulation, used as a positive control ( Figure 3E ) . These findings indicate that while mechanical stretch induces modest contractile gene changes, these do not result in measurable alterations in contractile function. Physiological mechanical stretch suppresses NF-κB-dependent inflammatory signaling in SMCs The most striking finding from the RNA-seq analysis was that physiological stretch downregulates multiple inflammatory genes, several of which are NF-κB targets (e.g., CCL2, ICAM1, VCAM1 ). We therefore tested whether physiological stretch could also attenuate a stronger, NF-κB-induced inflammatory response. TNF stimulation markedly increased CCL2 and ICAM1 expression in static SMCs, whereas this response was significantly blunted under 10% stretch ( Figure 4A ). In contrast, 15% stretch did not alter TNF-induced gene expression ( Figure 4B ) , indicating that this anti-inflammatory effect depends on stretch intensity. Download figure Open in new tab Figure 4: Physiological stretch ameliorates inflammatory signaling in SMCs. A-B. Relative mRNA expression measured by RT-qPCR of inflammatory genes ( CCL2, ICAM1) in SMCs either untreated or TNF-stimulated for 24 h prior to being subjected to static, 10% stretch or 15% stretch, respectively ( n =3 independent replicates per condition). D: Days. C. SMCs were transfected with negative control siRNAs (siCtr) or against IKBKB (si IKBKB ), for two consecutive days. One day later, cells were treated with TNF for 24 h and then exposed to 10% stretch or left under static conditions (no stretch). Shown is the relative mRNA expression measured by RT-qPCR of IKBKB and CCL2 of three independent replicates. D. Representative images of p65 immunostaining of SMCs untreated or TNF-stimulated for 24 h and subjected to no stretch or 10% stretch conditions. The graph shows the percentage of cells positive for nuclear p65 relative to the total number of cells (n=3 independent replicates per group). Scale bars: 100 µm. The groups were compared by one-way ANOVA followed by Tukey’s post hoc analysis. P-value: n.s., not significant; **p<0.01; ***p<0.001; ****p<0.0001. To assess the role of NF-κB signaling, we combined TNF and physiological stretch with small interference RNA (siRNA)-mediated knockdown of IKBKB , a key component of the canonical NF-κB pathway 43 . Physiological stretch alone reduced IKBKB expression, suggesting a potential mechanism by which physiological stretch may regulate NF-κB signaling ( Figure 4C ) . Moreover, 10% stretch did not further decrease CCL2 expression in IKBKB -deficient cells, indicating that suppression of CCL2 is mediated through NF-κB signaling. We next examined NF-κB activation by assessing p65 nuclear translocation via immunostaining. Under static conditions, ∼10% of SMCs exhibited basal nuclear p65 localization. TNF increased this to ∼30%, but no differences were observed between static and stretched cells (6 h) with or without TNF ( Figure 4D ) . To further characterize basal NF-κB activity, we used a lentiviral EGFP NF-κB reporter and flow cytometry, confirming that approximately 10% of SMCs display baseline NF-κB activity in culture (Supplementary Figure 2). Together, these results indicate that physiological stretch suppresses NF-κB-dependent inflammatory gene expression in SMCs, likely through reduced IKBKB expression, while a subset of cells exhibits basal or TNF-inducible NF-κB activation. Physiological stretch suppresses inflammatory gene expression in SMCs independent of the ECM substrate We next examined whether the effects of mechanical stretch on SMC transcriptional responses were influenced by the ECM substrate. Using the same experimental design as for COL1, SMCs were cultured on FIB- or LAM-coated plates and exposed to the different stretch intensities ( Figure 5A-D ). Mechanical stretch regulated cell cycle, inflammatory and contractile gene programs across all substrates, and pathway enrichment analysis highlighted inflammatory/immune, cell cycle, Wnt, Rho GTPase, and Notch signaling among the most significantly affected pathways. Complete differential expression analysis is provided in Supplementary Tables 4-7 . Although SMC morphology remained similar across conditions ( Figure 5E ), marker gene expression (i.e., CCL2, MKI67, CNN1 or LMOD1 ) varied with stretch intensity, with only minor differences between ECM proteins ( Figure 5F ). EdU labeling revealed substrate-dependent differences in proliferation ( Figure 5G ): 10% stretch increased proliferation on FIB but not LAM, whereas 15% stretch reduced proliferation on LAM with no significant change on FIB. In contrast, the anti-inflammatory response to physiological stretch was consistent across substrates: 10% stretch significantly reduced CCL2 expression in TNF-stimulated SMCs compared to static TNF-treated controls ( Figure 5H ). Enrichment analysis of genes regulated across all ECM substrates (COL1, FIB and LAM) (Supplementary Figure 3 and Supplementary Tables 8-9) confirmed cell cycle (e.g., p53), inflammatory/immune, and contractile (e.g., actin cytoskeleton) pathways among the top regulated categories. Together, these results indicate that while the proliferative response to stretch is ECM-dependent, the anti-inflammatory effect of physiological stretch is conserved across different substrates. Download figure Open in new tab Figure 5: Mechanical stretch regulates similar genes and pathways in SMCs cultured on other ECM proteins. A - D. Differentially expressed genes (DEGs) in volcano plots with false discovery rate adjusted p-value 0.5, along with KEGG enrichment analysis for SMCs cultured on FIB- or LAM-coated plates and exposed to different stretch intensities. A & C . show comparisons between 10% stretch and static conditions, whereas B & D. illustrate the comparisons between 15% and 10% stretch conditions, accordingly. E. Representative brightfield images of SMC morphology under different stretch conditions. F . Relative mRNA expression of marker genes by RT-qPCR in SMCs subjected to the different mechanical stretch conditions. G. Representative images of SMCs labelled with EdU (green) and DAPI (blue). Graphs show the percentage of EdU-positive SMCs relative to the total nuclei count for FIB or LAM. H . Relative mRNA expression measured by RT-qPCR of CCL2 in SMCs that were either untreated or TNF-stimulated for 24 h prior to being subjected to no stretch or 10% stretch. Scale bars: 100 μm. D: Days. Datapoints represent three independent replicates in each experiment. The groups were compared by one-way ANOVA followed by Tukey’s post hoc analysis. P-value: n.s., not significant; *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001. Single-cell RNA sequencing reveals high phenotypic heterogeneity and attenuated inflammatory expression in stretched SMCs Our p65 nuclear translocation and NF-κB reporter analysis suggested a heterogeneous SMC response to TNF. Previous studies have shown that SMC heterogeneity, well recognized in vivo , is likewise evident under in vitro conditions 44 , 45 . To explore this further and assess how physiological stretch influences inflammatory regulation at the single-cell level, we performed single-cell RNA-seq (scRNA-seq) of SMCs cultured under static conditions, 10% stretch, or TNF stimulation. After rigorous quality control and filtering, we analyzed transcriptomes from 9656 static, 8569 stretched, and 9143 TNF-treated cells. Unsupervised clustering identified nine distinct phenotypic states (cluster C0-C8) with a 0.3 resolution ( Figure 6A-B and Supplementary Table 10 ). The relative distribution of cells across clusters was similar under all conditions ( Supplementary Table 11 ). SMCs of C0 and C1 displayed a fibromyocyte signature (e.g., FN1, COL1A1, DCN ), with C1 having higher expression of inflammatory genes (e.g., CCL2, PTGS2, CXCL1 ). C2, C4, and C5 had features of proliferating SMCs, with C2 being enriched for markers of the M-phase (e.g., MKI67, CDK1, CCNB1 ), and C4 being enriched in markers of the S-phase (e.g., CDK2, PCNA, MCM3 ). C5 displayed expression associated with both the M- and S-phase, though to a lesser extent, and additionally had an increased expression of cell adhesion molecules (e.g., EFEMP1, TINAGL1, MCAM ). C3 (intermediate SMCs) shared the high inflammatory gene expression characteristics of C1, but also displayed higher expression of cell adhesion and migration-associated genes (e.g., TGM2, IGFBP3, TGFB2 ), showing similarities with C5. SMCs of the small C6 showed the highest expression of contractile genes (e.g., ACTA2, TAGLN, MYL9 ), suggesting that this cluster represents the population more closely related to mature vascular SMCs found in healthy aorta. Clusters C7 and C8 were very small (≤1% of all cells). Cluster C7 likely represents apoptotic SMCs, whereas C8 expressed genes associated with vascular development and endothelial-like cells ( Figure 6B ). Representative gene markers for each cell cluster are shown in Supplementary Figure 4. Download figure Open in new tab Figure 6: Transcriptomic characterization of mechanical stretch and TNF in SMCs by scRNA-seq. A. Outline of the experiment. Human aortic SMCs were exposed to static conditions (24 h), mechanical stretch (10%, 6h) or TNF stimulation (10 ng/mL, 24h). Uniform Manifold Approximation and Projection (UMAP) visualization of the phenotypic diversity of SMCs after static culture, stretch and TNF stimulation. Each dot represents a single cell. Nine cell clusters were identified in human aortic SMCs (0.3 resolution) as follows: C0 (fibromyocyte SMCs), C1 (inflammatory SMCs), C2 (proliferative M-phase), C3 (intermediate SMCs), C4 (proliferative S-phase), C5 (proliferative M-S-phase), C6 (contractile SMCs), C7 (apoptotic SMCs), C8 (endothelial-like SMCs). B. Dot plot showing the expression of three genes defining each cell cluster shown in A. Color scale indicates log2 expression, and the size represents the percentage of cells. C. Integration of publicly available human coronary and carotid atherosclerotic plaques scRNA-seq datasets 34 – 36 with human aortic SMCs from this study. This analysis revealed 7 mesenchymal cell clusters (pC0 to pC6). The p stands for plaque. Analysis of human aortic SMCs in culture are shown in the background (light grey cluster). D. Human aortic SMCs (C0 to C8) are shown in front and human atherosclerotic cell populations are shown in the background (light grey clusters). Integrating in vitro and human plaque SMC transcriptomes reveals pro-inflammatory and proliferative states To investigate the relevance of the in vitro SMC phenotypes to human atherosclerosis, we integrated our scRNA-seq dataset with publicly available single-cell transcriptomes from coronary and carotid artery plaques 34 – 36 . After stringent quality control and filtering, the integrated plaque dataset revealed several major cell populations, including a large mesenchymal cell cluster (encompassing SMCs and fibroblast), an endothelial cell cluster, multiple immune cell clusters (proliferating cells, B cells, T cells, plasma cells, monocytes, dendritic cells, mast cells, and macrophages), and a small neuronal cluster. The plaque mesenchymal compartment was further subdivided into seven clusters based on marker gene expression, consistent with previous studies 37 . The clusters were labelled as follows, where the p stands for plaque: contractile SMCs (pC0), pericyte-like SMCs (pC1), intermediate SMCs (pC2), fibromyocyte SMCs (pC3), osteochondrogenic SMCs (pC4), fibroblasts (pC5) and undefined SMCs (pC6) ( Figure 6C ) . Integration analysis showed that most in vitro SMCs mapped with this mesenchymal supercluster, particularly with the modulated clusters (pC2, pC3, pC4, and pC6) ( Figure 6D ). Two distinct trajectories extending from the mesenchymal supercluster were observed. SMCs from in vitro clusters C3 and C5 skewed towards the plaque endothelial cell cluster, consistent with their high expression of cell adhesion molecules. Conversely, cells from clusters C0, C1, and C7 skewed towards the plaque monocyte cluster, reflecting high expression of inflammatory (C0, C1) or apoptotic (C7) genes. Proliferating in vitro clusters (C2, C4, and C5) mapped to the proliferative immune cell cluster in plaques, suggesting shared gene expression programs between in vitro and in vivo proliferative states. The contractile in vitro cluster C6, mapped across the contractile SMCs (pC0), osteochondrogenic SMCs (pC4), and the undefined SMCs (pC6) within the plaque mesenchymal compartment. Plots of integrated in vitro cell clusters are shown in Supplementary Figure 5 . Overall, this analysis reveals that SMC states in vitro are at least as heterogeneous as those in vivo . While they predominantly mapped to plaque fibromyocytes, they also display transcriptional programs associated with endothelial, immune, inflammatory, and proliferative phenotypes. Physiological stretch attenuates pro-inflammatory signaling across all clusters of cultured SMCs Differential expression analysis between 10% stretch and static conditions across all cells revealed a predominance of upregulated transcripts (331 up vs. 229 down). Upregulated genes (e.g., KIF20A, CDC20, CDKN3 ), were enriched in pathways related to cell cycle regulation, and proliferation, whereas downregulated genes (e.g., IL1RN, CSF3, CCL20, IL11, BIRC3 ), were associated with cytokine and chemokine signaling, as well as stress response pathways ( Supplementary Table 12) . These data indicate that physiological stretch attenuates inflammatory gene expression in SMCs. In contrast, TNF stimulation induced 866 upregulated and 598 downregulated genes compared with static conditions across all cells. TNF strongly activated inflammatory and stress response markers (e.g., CCL8, ADM2, CXCL2 ), while repressing contractile and homeostatic gene programs, (e.g., ACTC1, ANKRD1, DUSP2 ) ( Supplementary Table 13) , suggesting a switch from a contractile to a pro-inflammatory phenotype. Differentially expressed genes ( DEGs) for both comparisons (stretch vs. static and TNF vs. static) for each cluster are listed in Supplementary Table 14. To assess whether the anti-inflammatory effects of physiological stretch were cluster specific, we compared DEGs across both conditions for each cluster ( Figure 7A-F ). Several TNF-induced inflammatory genes (e.g., IL1B, CCL2, ICAM1 , CXCL1 , CXCL2, IL6) were significantly downregulated by 10% stretch, with the strongest suppression observed in clusters C0-C5. Clusters C6-C8 contained too few cells to assess ( Figure 7A-F ). Consistent with this, SMCs under physiological stretch expressed lower levels of classical NF-κB target genes ( IL1B, CCL2 , and IL6) compared to static or TNF-stimulated cells ( Figure 7G and Supplementary Figure 6) . Together, these findings demonstrate that physiological stretch broadly suppresses pro-inflammatory gene expression across major SMC clusters, counteracting TNF-driven activation of NF-κB signaling pathways. Download figure Open in new tab Figure 7. Physiological stretch attenuates pro-inflammatory signaling across clusters in cultured SMCs. A-F The log2 fold-change of shared differentially expressed genes (DEGs) with false discovery rate adjusted p-value 0.5 between stretch vs. static ( y -axis) and TNF vs. static conditions ( x -axis) in the different cell clusters C0 to C5 were plotted against each other. Genes significantly downregulated by 10% stretch and upregulated by TNF are highlighted in the yellow quadrant. Inflammatory genes are shown in red color. Only one shared DEG was found in C6 (LINC00486), and no shared DEGs were found in clusters C7 and C8. G. Normalized expression levels of IL1B, a pro-inflammatory gene, in each cell cluster during static conditions, 10% mechanical stretch and TNF stimulation. 4. Discussion Many studies investigating SMC function have been performed under static conditions on rigid and plastic culture surfaces. While these models have provided valuable mechanistic insights, they have a notable limitation: they often overlook critical factors that shape SMC behavior in vivo : mechanical forces and the vascular ECM composition 16 , 20 . Static cultures of human SMCs cultured on COL1 and FIB plastic-coated plates induced a more proliferative and inflammatory transcriptional profile compared with cells cultured on uncoated or LAM-coated surfaces. This aligns with previous findings showing that ECM composition modulates SMC phenotype 7,21,46 . Fibronectin and collagen I, typically enriched in atherosclerotic lesions, are associated with synthetic and inflammatory SMC states, whereas laminin is linked to a more quiescent contractile phenotype. Bulk RNA-seq revealed that SMCs respond to mechanical forces through coordinated regulation of cell cycle, contractile, and inflammatory pathways. Physiological stretch upregulated cell cycle genes ( MKI67 , cyclins) 47 , whereas pathological stretch reduced their expression. Despite these transcriptional changes, EdU incorporation and MKI67 staining showed no corresponding increase in proliferation, suggesting that gene induction may represent an early or transient response requiring sustained stimuli to yield functional effects. The observed differences in cell proliferation between SMCs cultured on static plastic labware and those on static silicon membranes may be attributed to the stiffness of the material used 48 . Similarly, contractile markers were modestly increased under physiological stretch, but this did not translate into enhanced contractile function in collagen gel contraction assays. These discrepancies between gene expression and function are consistent with previous observations 49 – 52 and likely reflect temporal or post-transcriptional regulation 49,53,54 . Interestingly, the proliferative response to stretch was ECM-dependent: physiological stretch increased proliferation on FIB but not LAM, whereas pathological stretch decreased proliferation on LAM with no effect on FIB. These findings highlight that ECM composition modulates how SMCs integrate mechanical cues. Physiological stretch attenuated basal and TNF-induced inflammatory gene expression, whereas pathological stretch enhanced it. This effect was observed across COL1, FIB, and LAM substrates, indicating that the anti-inflammatory response is robust and substrate-independent. Physiological stretch downregulated multiple NF-κB target genes (e.g., CCL2, ICAM1, VCAM1 ) and reduced IKBKB expression. Notably, siRNA-mediated knockdown of IKBKB lowered baseline inflammatory gene expression and abolished the suppressive effect of physiological stretch on CCL2 , demonstrating that NF-κB signaling is required for both baseline and stretch-mediated regulation of inflammation. These effects are likely mediated downstream of p65 nuclear translocation as indicated by immunofluorescence analysis of p65, although the exact molecular events remain to be defined. These findings align with previous studies implicating NF-κB as a central regulator of SMC inflammatory responses and atherosclerotic progression 55 – 58 . Furthermore, the loss of physiological mechanical forces within the plaque interior may favor NF-κB activation in modulated SMCs, contributing to their pro-inflammatory state in advance lesions 59 . Future studies will be required to elucidate how stretch influences NF-κB activity at the molecular level, including potential regulation of p65 translocation dynamics, IKKβ activity, or upstream mechanotransduction pathways. We observed that a subset of cells displayed basal NF-κB activity, and not all responded uniformly to TNF stimulation, pointing to intrinsic heterogeneity within SMC cultures. This prompted us to perform single-cell transcriptomic analysis. scRNA-seq revealed nine distinct clusters representing fibromyocyte-like, proliferative, intermediate, contractile, apoptotic, and endothelial-like states. Most cultured SMCs mapped to fibromyocyte and modulated phenotypes present in human atherosclerotic plaques, underscoring the relevance of in vitro models to in vivo disease states. Physiological stretch suppressed inflammatory gene expression across the major clusters (C0–C5), counteracting TNF-induced transcription of classical NF-κB targets. These results emphasize that static culture conditions promote an intrinsically modulated, pro-inflammatory SMC state, characterized by transcriptional overlap with plaque phenotypes. In contrast, applying physiological levels of stretch attenuates inflammatory signaling and may preserve features more representative of healthy vasculature. Incorporating physiological mechanical forces and relevant ECM environments could therefore yield more predictive and disease-relevant in vitro models of SMC function. Limitations of the study This study provides valuable insights into SMC phenotypic modulation, but the in vitro setup has inherent limitations. Recent studies have shown that SMCs can adapt to mechanical cues or respond differently depending on the duration of stretch 60 . While 10% stretch suppressed inflammatory gene expression and other markers of SMC modulation (e.g., FN1, DCN ), the stimulus was insufficient to induce substantial changes (e.g., proliferation, contractility) or to shift SMCs between clusters in the scRNA-seq analysis (e.g., from inflammatory cluster toward contractile cluster). Thus, although 10% stretch partially attenuates SMC modulation, the resulting phenotype does not fully recapitulate the contractile state of medial aortic SMCs observed in vivo . Funding This work was supported by grants from the Danish Ministry of Food, Agriculture and Fisheries (3R-Center Research Grant, 33010-NIFA-20-742) and from the Aarhus University Research Foundation (Starting Grant, AUFF-E-201 9-7-23) to J.A-J. and from the European Research Council under the European Union’s Horizon 2020 research and innovation program (grant no. 866240 to J.F.B.). Author contribution statement L.F.J. participated in the design of the experiments and performed most of the experiments, data analysis and interpretation, and manuscript writing. A.M. performed the bioinformatics analysis of bulk and single-cell RNA sequencing data. L.A.H. performed pilot experiments and contributed to manuscript editing and feedback. E.A.T. and J.G.M. contributed to the NF-κB reporter assays. J.F.B. participated in experimental design, manuscript editing, and feedback. J.A.-J. designed and performed experiments, analyzed and interpreted data, and wrote the manuscript. All authors read and approved the manuscript. Conflict of interest The authors declare that the research was conducted without commercial or financial relationships that could be interpreted as a potential conflict of interest. Download figure Open in new tab Supplementary Figure 1: Proliferation during prolonged stretch. A. Schematic of the experimental design. SMCs were cultured on COL1-coated plates, then EdU was added to the culture and cells were subsequently exposed to 10% stretch or left under static conditions for 24h. After stretching, the cells were fixed and EdU-labelling detected. B. Shown are representative images of SMCs labelled with EdU-Alexa Flour 488 (green) and DAPI (blue). C. Percentage of EdU-positive SMCs relative to the total nuclei count ( n =3 independent replicates per group). The groups were compared by unpaired t-test, P-value: n.s., no not significant. Download figure Open in new tab Supplementary Figure 2: NF-κB-EGFP reporter assay. A. Representative gating strategy in flow cytometry using non-transduced (Naïve) SMCs to define the EGFP + SMCs. 1) Time versus SSC-A was plotted to ensure stable flow. 2) SMCs were gated on SSC-H x FSC-H to exclude debris. 3) Singlets were identified by SSC-H x SSC-A. 4) EGFP + SMCs were gated on SSC-H x EGFP. SSC: side scatter, FSC: forward scatter, A: area, H: hight. B. Schematic of the NF-κB reporter, where three NF-κB-response elements control the activation of a CMV minimal (CMVmin) promoter controlling the expression of a destabilized EGFP gene (d2EGFP), and a phosphoglycerate kinase (PGK) promoter constitutively expressing a blasticidin resistance gene (Blast). Lentiviral transduction was used to deliver the reporter cassette. C. Representative plot of SMCs carrying the NF-κB-EGFP reporter cassette. The percentage of EGFP + SMCs represents SMCs with NF-κB activation. Download figure Open in new tab Supplementary Figure 3: Genes significantly regulated by stretch in all ECM substrates. A. Venn diagram showing the overlap of significantly differentially expressed genes (DEGs) (434 genes) regulated by 10% stretch vs. static conditions across all three ECM proteins: COL1, FIB, or LAM. The graph depicts the top regulated pathways among the overlapping genes based on enrichment analysis (KEGG). B. Venn diagram showing the overlap of significant DEGs (196 genes) regulated by 15% stretch vs. 10% stretch across all three ECM proteins, COL1, FIB, and LAM. The graph shows the top regulated pathways as in (A). In both (A) and (B), only protein-coding genes with p-values adjusted for false discovery rate 0.5 were considered statistically significant. Lists of the overlapping protein-coding genes in A and B are provided in Supplementary Tables 8-9. Download figure Open in new tab Supplementary Figure 4: Examples of marker genes for the different cell clusters. Violin plots show the distribution of normalized expression for the selected marker genes for each cluster obtained in the scRNA-seq analysis. Expression is shown as the mean of normalized expression of these genes in each cluster. Genes with a false discovery rate adjusted p-value 0.25. Download figure Open in new tab Supplementary Figure 5: Individual clusters (C0-C8) from cultured SMC scRNA-seq and their integration with human plaque scRNA-seq. Shown are UMAP plots for each cell cluster identified in human aortic SMCs (C0-C8, different colors). C0 (fibromyocyte SMCs), C1 (inflammatory SMCs), C2 (proliferative M-phase), C3 (intermediate SMCs), C4 (proliferative S-phase), C5 (proliferative M-S-phase), C6 (contractile SMCs), C7 (apoptotic SMCs), C8 (endothelial-like SMCs). Integration of human coronary and carotid atherosclerotic plaques scRNA-seq datasets 34 – 36 are shown in the background in a light grey color. Download figure Open in new tab Supplementary Figure 6: Expression of selected pro-inflammatory genes in scRNA-seq analysis. A. Mean of normalized expression across all cells of selected genes (as indicated) for each condition. Groups were compared by Wilcoxon’s test. P-value: ****p<0.0001. B. Single-cell expression (UMAP visualization) of selected proinflammatory markers genes ( IL6, and CCL2) , and normalized expression across SMCs clusters under static conditions, mechanical stretch (10%, 6 h) and TNF stimulation (10 ng/mL, 24 h). Color scale indicates log2 expression. C0 (fibromyocyte SMCs), C1 (inflammatory SMCs), C2 (proliferative M-phase), C3 (intermediate SMCs), C4 (proliferative S-phase), C5 (proliferative M-S-phase), C6 (contractile SMCs), C7 (apoptotic SMCs), C8 (endothelial-like SMCs). Acknowledgments We thank Lisa Maria Røge and Dorte Qualmann for their excellent technical assistance. Flow cytometry was performed at the FACS Core Facility, Aarhus University, Denmark. We are grateful to Martin Schwartz and Brian Coon (Yale University, USA) for generously providing the NF-κB reporter vector. We thank Miguel Angel del Pozo and Laura Sotodosos Alonso (CNIC, Spain) for valuable discussions and insights into mechanotransduction. 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Share Mechanical stretch regulates inflammatory signaling in human smooth muscle cells Lise Filt Jensen , Anton Markov , Laura Alonso-Herranz , Emil Aagaard Thomsen , Jacob Giehm Mikkelsen , Jacob Fog Bentzon , Julián Albarrán-Juárez bioRxiv 2025.10.29.685276; doi: https://doi.org/10.1101/2025.10.29.685276 Share This Article: Copy Citation Tools Mechanical stretch regulates inflammatory signaling in human smooth muscle cells Lise Filt Jensen , Anton Markov , Laura Alonso-Herranz , Emil Aagaard Thomsen , Jacob Giehm Mikkelsen , Jacob Fog Bentzon , Julián Albarrán-Juárez bioRxiv 2025.10.29.685276; doi: https://doi.org/10.1101/2025.10.29.685276 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Cell Biology Subject Areas All Articles Animal Behavior and Cognition (7622) Biochemistry (17648) Bioengineering (13870) Bioinformatics (41880) Biophysics (21423) Cancer Biology (18553) Cell Biology (25458) Clinical Trials (138) Developmental Biology (13364) Ecology (19866) Epidemiology (2067) Evolutionary Biology (24290) Genetics (15589) Genomics (22475) Immunology (17711) Microbiology (40327) Molecular Biology (17145) Neuroscience (88472) Paleontology (666) Pathology (2826) Pharmacology and Toxicology (4815) Physiology (7635) Plant Biology (15114) Scientific Communication and Education (2044) Synthetic Biology (4286) Systems Biology (9815) Zoology (2268)