Hypoplastic left heart syndrome cardiomyocytes exhibit intrinsic stress vulnerabilities and augmented stress responses in vitro

Hypoplastic left heart syndrome cardiomyocytes exhibit intrinsic stress vulnerabilities and augmented stress responses in vitro | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hypoplastic left heart syndrome cardiomyocytes exhibit intrinsic stress vulnerabilities and augmented stress responses in vitro Margarida Varela, Minna Ampuja, Martin Broberg, Amanda Ramste, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8250379/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Hypoplastic left heart syndrome (HLHS) is a severe congenital heart defect characterised by underdevelopment of left-sided cardiac structures. While genetic predisposition contributes to HLHS, the relevance of environmental stressors is increasingly recognised, yet the cellular mechanisms linking genetic susceptibility to environmental vulnerability remain unclear. We aimed to identify molecular and functional differences between cardiomyocytes derived from HLHS patients and healthy controls to uncover potential susceptibilities contributing to the HLHS phenotype. Methods Human induced pluripotent stem cell–derived cardiomyocytes (hiPSC-CMs) from HLHS patients and healthy controls were used to examine intrinsic cellular differences. Single-cell RNA sequencing compared baseline transcriptional profiles. Functional assays assessed responses to endothelin-1 (ET-1)–induced stress, cyclic mechanical stretch, and basal or mitogen-stimulated proliferation. These approaches were used to identify intrinsic functional impairments and altered stress responses in HLHS cardiomyocytes. Results Single-cell transcriptomics revealed downregulation of gene networks associated with cardiac stress responses, metabolic resilience, and rhythm regulation in HLHS cardiomyocytes. Regulon analysis revealed broad reductions in transcription factor activity across key cardiac regulatory networks. Functionally, HLHS cells showed heightened vulnerability to ET-1, with exaggerated proBNP induction compared with controls. No significant differences were observed following cyclic mechanical stretch. Basal proliferation varied across HLHS lines, while mitogen-induced proliferation remained comparable to controls. Conclusions These findings support a model in which intrinsic molecular and functional vulnerabilities in HLHS cardiomyocytes might reduce resilience to developmental stressors. Such gene–environment interactions may contribute to HLHS pathogenesis, underscoring the interplay between genetic predisposition and environmental influences in congenital heart disease. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Congenital heart diseases (CHDs) are structural malformations of the cardio-circulatory system that arise during embryonic development. Encompassing a broad spectrum of subtypes, CHDs range from relatively common minor lesions to complex and life-threatening anomalies such as hypoplastic left heart syndrome (HLHS). HLHS represents one of the most severe forms of CHD, characterised by the profound underdevelopment of left-sided cardiac structures, including the aortic and mitral valves, the left ventricle, and the aorta [ 1 ]. This defect critically impairs the ability of the left ventricle to support systemic circulation, rendering the condition fatal without surgical intervention [ 2 , 3 ]. The aetiology of CHDs is complex, and the precise pathogenic mechanisms underlying these disorders remain incompletely understood. Both genetic predisposition and environmental stressors contribute to the defects. In the context of HLHS, substantial efforts have been devoted to elucidating its molecular and genetic underpinnings. While HLHS occasionally co-occurs with syndromic disorders[ 4 – 6 ] it predominantly arises as an isolated defect. Genetic investigations have implicated a range of critical genes, including transcription factors (TF) such as NKX2-5 [ 7 – 9 ], TBX5 [ 10 ], HAND1 [ 11 , 12 ], as well as receptor and structural protein genes such as NOTCH1 [ 13 – 16 ] and MYH6 [ 17 , 18 ]. These findings underscore the diversity of genetic disruptions capable of derailing cardiac development. Nevertheless, genetic susceptibility alone does not fully account for the phenotypic heterogeneity and variable phenotype, suggesting that additional factors contribute significantly to disease pathogenesis. Increasing attention has been directed towards the role of environmental influences, which may act synergistically with genetic defects [ 19 ]. Maternal diabetes and obesity [ 20 – 25 ], hyperglycaemia [ 24 , 26 – 28 ], oxidative stress [ 22 , 23 ], and aberrant haemodynamic forces in the developing foetus [ 29 – 33 ] have emerged as pivotal external factors capable of disrupting foetal cardiac development. These perturbations, often mediated through alterations at the maternal-foetal interface, may exert profound effects during critical windows of gestation. The advent of human induced pluripotent stem cells (hiPSCs) [ 34 , 35 ] has ushered in a new paradigm in cardiovascular disease modelling. hiPSCs, reprogrammed from patient-specific cells, retain the full complement of the individual’s genetic information. This technology enables the generation of cardiomyocytes (CMs) that faithfully recapitulate patient-specific genetic backgrounds, providing an unprecedented platform for dissecting the cellular and molecular mechanisms underlying congenital heart defects such as HLHS. In this study, we examined both healthy and HLHS-derived cardiomyocytes to investigate their inherent differences as well as their capacity to respond to environmental cues. Acknowledging that heart development results from a dynamic interplay between genetic programming and extrinsic signals, we sought to model key developmental stressors in vitro. We performed single-cell RNA sequencing (scRNA-seq) to profile transcriptomic changes and identify disease-associated molecular signatures. Following this, we applied three distinct stimuli to model critical aspects of cardiac development: Endothelin-1 (ET-1), a known pro-hypertrophic hormone elevated in adverse maternal environments, was used to assess hypertrophic responses. Cyclic mechanical stretch simulated the biomechanical forces experienced by the foetal heart, and pro-proliferative compounds were used to evaluate the cells’ proliferative potential. These stimuli were chosen to represent essential developmental processes relevant to both normal cardiac formation and the pathogenesis of HLHS. Together, these complementary approaches allowed us to interrogate intrinsic deficits and impaired adaptability that may contribute to the abnormal cardiac morphogenesis characteristic of HLHS. Materials and Methods hiPS lines culture Eight induced pluripotent stem cell (iPSC) lines were used in this study, including four from healthy controls (HEL24.3 [ 36 ], HEL47.2 [ 37 ], HEL46.11 [ 38 ], and K1), three from individuals with HLHS (HEL149.2, HEL218.6, HEL169.4), and one from an individual with left ventricular outflow tract obstruction (LVOTO; HEL216.6). All lines, except for K1, which was was kindly gifted by Anu-Suomalainen-Wartiovaara, were obtained from Biomedicum Stem Cell Center Core Facility. To improve readability throughout this study, these lines are hereafter referred to as follows: HEL24.3 as Healthy 1, HEL47.2 as Healthy 2, HEL46.11 as Healthy 3, K1 as Healthy 4, HEL149.2 as HLHS 1, HEL218.6 as HLHS 2, HEL169.4 as HLHS 3, and HEL216.6 as HLHS 4. Detailed information on donor sex, genetic variants, and cardiac phenotypes is provided in (Additional file 1: Table S1 ). The cell lines were created using retroviral or Sendai virus-mediated transduction with Yamanaka reprogramming factors OCT3/4 , SOX2 , KLF4 , and MYC [ 34 , 35 ] as previously described by Trokovic et al . 2015 [ 36 , 37 ]. All hiPSC lines used in this study were evaluated for pluripotency and regularly tested for genomic stability through karyotyping. The hiPSCs were seeded on plates thin-coated with Matrigel™ (Corning, #354277; diluted at 1:200) and cultured in Essential 8™ medium (Thermo Fisher Scientific, #A1517001). Cells were passaged twice weekly using PBS with 0.5 mM EDTA until at least passage 20 before differentiation. The absence of mycoplasma contamination was routinely confirmed using the MycoAlert Mycoplasma Detection Kit (Lonza, #LT07-218). Differentiation of hiPSCs into CMs Cardiomyocyte (CM) differentiation was induced in a monolayer culture as previously described by Helle et al. (2021) [ 39 ]. Briefly, human induced pluripotent stem cells (hiPSCs) were seeded at a 1:10–1:15 ratio on 12-well plates coated with Matrigel™ (Corning, #354277). Upon reaching 80–90% confluency (Day 0), cardiac mesoderm induction was initiated by replacing Essential 8™ with RPMI 1640 medium containing L-glutamine and glucose (Corning, #10-040-CV), supplemented with B-27™ Supplement minus insulin (Thermo Fisher Scientific, #A1895601) and 4–5 µM GSK-3α/β inhibitor CHIR-99021 (CHIR, Selleck Chemicals, #S2924). On Day 1, fresh medium containing CHIR was added. After 48 hours (Day 2), the culture medium was replaced with fresh RPMI 1640, replacing CHIR with 5 µM IWR-1 (Sigma, #I0161) and replenished on Day 3. From Day 4 to Day 5, cells were maintained in fresh RPMI 1640 supplemented with B-27™ minus insulin. On Day 6 and Day 7, the medium was changed to RPMI 1640 with B-27™ containing insulin (Thermo Fisher Scientific, #17504044). Feeding on Day 8 depended on the appearance of the cells: if significant cell death was observed, feeding was performed to help reduce the dead cells. From Day 9 to Day 11, to enrich for cardiomyocytes, the medium was replaced with RPMI 1640 without glucose (Thermo Fisher Scientific, #11560406), supplemented with B-27™ containing insulin and 5 mM sodium L-lactate (Sigma Aldrich, #71718). No medium change was performed on Day 12. On Day 13, robust beating of the monolayer was typically observed, and the differentiated hiPSC-CMs were passaged. Cells were detached using Accutase (0.4 mL for 12-well plates, 0.6 mL for 6-well plates), quenched with RPMI 1640 culture medium with L-glutamine with glucose supplemented with B-27™ (Thermo Fisher Scientific, #17504044), and centrifuged for 4 minutes at 200 × g. Cells were resuspended in same medium with 10 µM Rock inhibitor (StemCell Technologies, #Y-27632) and replated onto a new plate. From the following day, cells were maintained in RPMI without glucose, supplemented with B-27 containing insulin and 5mM sodium L-lactate and fed every other day until day 25–30, when they were moved to RPMI with glucose, supplemented with B-27 containing insulin until they were used in experiments - scRNAseq (Day 38, 39 or 27), Cell Cycle (Day 20 and Day 33), Endothelin-1 stimulation (Day 33) and cyclic mechanical stretch (Day 30–40). To ensure experimental consistency, only differentiations achieving a cardiomyocyte purity of ≥ 80%, as determined by cardiac troponin T immunostaining, were included in this study. CM processing for RNA-seq hiPSC-CMs were harvested for scRNA-seq at differentiation day 38 or 39, with the exception of HEL47.2, which was collected at day 27. Following established protocols [ 39 , 40 ], the cells were counted and washed with 0.04% BSA in PBS. For scRNA-seq preparation, patient-derived and control-derived cells were pooled separately by combining equal contributions from each respective cell line. The pooled samples were then sent to the Institute for Molecular Medicine Finland (FIMM) for processing and sequencing using the 10x Genomics Single Cell Protocol. RNA extraction, cDNA synthesis and qPCR RNA extraction, cDNA synthesis and qPCR For qPCR, total RNA was purified using the NucleoSpin RNA Kit (Macherey-Nagel, #740961) according to the manufacturer’s protocol. The cells were lysed in 350 µl of RA1 lysis buffer supplemented with 1% β-mercaptoethanol and stored at − 80°C until RNA isolation. Analysis of the RNA concentration and quality was performed with a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific). Total RNA (100–500 ng) was reverse transcribed in 10 µl reactions by using the Transcriptor First Strand cDNA Synthesis Kit (Roche, #04897030001) using random hexamer primers and an MJ Mini Personal Thermal Cycler (Bio-Rad). The cDNA was diluted 1:10 in PCR grade H2O and stored at − 20°C. Commercial TaqMan® Gene Expression Assays (Thermo Fisher Scientific), detailed in Additional file 1: Table S2, were used in conjunction with the LightCycler® 480 Probes Master reagent (Roche) following the manufacturer's protocols. Gene expression was analysed using the LightCycler® 480 Real-Time PCR System (Roche) with 4.5 µl of cDNA in a 10 µl reaction volume on a white LightCycler® 480 Multiwell Plate 384 (Roche). No-template controls were included to confirm the absence of PCR contamination. Each reaction was performed in triplicate, with the mean of technical replicates representing a single biological replicate (n = 1). Outliers within technical replicates were identified using Grubbs' test at a significance level of 0.05 and subsequently excluded from the analysis. Relative gene expression levels were quantified using the 2 − ΔΔCt method, referenced to the average of ACTB and 18S rRNA housekeeping genes and normalised to the average of untreated biological replicates of Healthy 2. RNA-seq bioinformatics In the scRNA-seq analysis, four HLHS lines and four healthy control lines were used. The healthy control lines were pooled and analyzed as a single control sample. To distinguish the sequencing data corresponding to each HLHS line within the pooled sample, we employed FreeBayes v1.3.1 [ 41 ] to call genetic variants from the exome data of the four HLHS individuals. These variant calls were subsequently used as input for Demuxlet [ 42 ], enabling the assignment of individual cells to their respective cell lines within the combined single-cell RNA-sequencing dataset. Downstream analysis was performed in R (2024.09.1 + 394) using the Seurat (version 5.1.0) package. A total of 17,980 high-quality cells were isolated to identify distinct cell populations and enable subsequent downstream analyses. The following quality control criteria were applied to the data: (1) Genes expressed in fewer than 200 cells or in more than 8,000 cells were excluded; (2) Cells with fewer than 200 or more than 8,000 detected genes were removed, as these may indicate low-complexity cells or doublets; (3) Cells exhibiting more than 30% mitochondrial gene expression were excluded to eliminate potentially damaged or stressed cells. Data normalization was performed using the “NormaliseData” function in Seurat, and the top 2,000 most variable genes were identified with the “vst” method via the “FindVariableFeatures” function. Integration anchors were computed using “FindIntegrationAnchors” to align shared features across datasets, and the data were integrated using “IntegrateData” . These genes were subsequently analysed using principal component analysis (PCA) for linear dimensionality reduction. Cell type identification and cluster analysis Following data integration and normalization, we performed unsupervised clustering to identify distinct cellular populations within the cardiac dataset and assess compositional differences between HLHS and healthy samples. The two-dimensional Uniform Manifold Approximation and Projection (UMAP) was performed using the RunUMAP function in Seurat on the first 30 principal components. Graph-based clustering was applied to identify cell populations based on gene expression profiles using the FindClusters function, and a resolution of 0.4 was selected for downstream analysis, resulting in 14 distinct clusters. The resulting UMAP projection was used to visualize the clusters, and cell types were annotated based on known marker genes. Cluster-specific markers were identified using the FindAllMarkers function and annotated using published literature. Cell populations were classified as follows: immature cardiomyocytes (clusters 0, 4, 5, 6, 7, 8, 10, 12, 13) expressing cardiac transcription factors ( NKX2-5 , TBX5 , GATA4 , ISL1 ) and sarcomeric genes; mature cardiomyocytes (clusters 1, 2, 3, 11) with robust expression of structural genes ( MYH6 , MYL7 , TNNT2 , TTN ); and cardiac fibroblasts/smooth muscle cells (cluster 9) expressing POSTN , COL1A1 , TAGLN , and ACTA2 . To assess differences in cluster composition between healthy and HLHS samples, the proportion of cells in each cluster was calculated relative to the total number of cells per sample or group. Statistical significance was evaluated using chi-square tests with Benjamini-Hochberg FDR correction. Comparisons were performed between Healthy versus all HLHS samples combined, and between Healthy versus each individual HLHS line separately. Differential gene expression analysis Differential gene expression analysis was performed on cardiomyocyte populations (clusters 0–8, 10–13) using the FindMarkers function in Seurat with the default statistical test, Wilcoxon Rank Sum test. Two analytical approaches were employed: (1) overall comparison of Healthy versus all HLHS samples combined, and (2) line-specific comparisons of Healthy versus each individual HLHS line (HLHS 1–4). Differentially expressed genes (DEGs) were selected only if they met the following criteria: average log₂ fold change > 0.5 or < -0.5 and adjusted p-value < 0.05. To identify core transcriptional changes, we examined genes consistently dysregulated in the same direction across multiple HLHS samples. For visualization purposes in volcano plots, genes with adjusted p-values of 0 ( S100A10 , NPPB , NPPA, RPS27 , RPS29 , RPS26 ) were assigned an adjusted p-value of 1⁻³²⁰ to enable numerical calculation of -log₁₀(adjusted p-value). Key differentially expressed genes were visualized using volcano plots and heatmaps. Gene ontology enrichment analysis was performed to identify biological processes associated with differentially expressed genes, with results displayed as bubble plots showing fold enrichment and statistical significance. Gene Ontology Enrichment Analysis of Differentially Expressed Genes The set of differentially expressed genes identified across all four HLHS-CM lines was used as input for Gene Ontology (GO) enrichment analysis. This was conducted using the Functional Annotation Tool within the Database for Annotation, Visualization and Integrated Discovery (DAVID) and the DAVID Knowledgebase (v2023q4, updated quarterly; https://davidbioinformatics.nih.gov/tools.jsp ) [ 43 , 44 ]. The gene list was uploaded using official gene symbols as identifiers, and Homo sapiens was selected as the background species. From the resulting Annotation Summary, the Gene Ontology Biological Processes category was selected for further analysis. GO terms with a false discovery rate (FDR)-adjusted p value < 0.05 were considered significantly overrepresented. Transcription factor regulon analysis To systematically evaluate transcriptional regulatory changes in HLHS, we performed regulon activity analysis across all cardiac cell populations. Transcription factors were identified from the Lambert et al. (2018) validated human TF database [ 45 ], filtering for those expressed in ≥ 5% of cells. For each TF, target genes were predicted by calculating Spearman correlation coefficients (threshold > 0.1), retaining only regulons with ≥ 10 target genes. Regulon activity in individual cells was quantified using AUCell (v1.24.0), with the AUC maximum rank parameter set to 5% of total genes. Differential regulon activity between HLHS and healthy samples was assessed using Wilcoxon rank-sum tests with Benjamini-Hochberg FDR correction (FDR < 0.05). For each regulon, we calculated log₂ fold change and percent change. Cardiac-specific TF regulons were identified by cross-referencing TFs with cardiac development Gene Ontology terms. For the 90 cardiac TF regulons, GO enrichment analysis was performed on target genes using clusterProfiler (v4.10.0) with FDR correction (FDR < 0.05). For each regulon, the top 3 most significantly enriched pathways were retained and manually grouped into four functional categories for downstream interpretation. Cell proliferation assay To induce and assess cell proliferation, hiPSC-CMs at Days 20 and 33 of differentiation were treated with a combination of CHIR99021 and SB203580, which work synergistically to promote cardiomyocyte proliferation [ 46 ]. CHIR99021, a GSK-3 inhibitor, activates Wnt/β-catenin signalling [ 47 ], while SB203580 inhibits p38 MAPK-mediated cell cycle suppression [ 48 ], together producing more robust mitogenic effects than either compound alone. hiPSC-CMs were seeded onto Matrigel-coated 96-well PhenoPlates (PerkinElmer, #6055300) at a density of 4.5×10 4 cells per well and allowed to adhere overnight. The hiPSC-CMs were then divided into two treatment groups: the experimental group was treated with a combination of 5 µM CHIR99021 (CHIR, Selleck Chemicals, #S2924) and 10 µM SB203580 (SB, Selleck Chemicals, #S1076) alongside 10 µM Bromodeoxyuridine (BrdU, Abcam, #ab142567) for 24 hours, while the control group received vehicle (DMSO) with BrdU for the same period. Induction of cardiomyocyte hypertrophy Endothelin-1 (ET-1) treatment To induce cell hypertrophy, Day 33 hiPSC-CMs were seeded onto 96-well matrigel-coated PhenoPlates (PerkinElmer, #6055300) at 4.5×10 4 cells per well and allowed to attach overnight. The cells were then exposed to either ET-1 or a vehicle control consisting of 1% bovine serum albumin (BSA; Sigma-Aldrich, #A9418) in Dulbecco’s Modified Eagle Medium (DMEM; Sigma D-7777) for 24 hours. Brefeldin A (1000X Solution; Invitrogen, #B7450) was added to the last 3 hours to inhibit the exocytosis of pro-B-type natriuretic peptide (proBNP)-containing vesicles as described previously [ 49 ]. Cyclic mechanical stretch To assess the effect of cyclic mechanical loading on the expression of hypertrophy-related genes, hiPSC-CMs aged between Day 30–40 were cultured on BioFlex® plates at a cell density of 8.5×10 5 per well. These cells were then subjected to 24 or 48 hours of cyclic mechanical strain using an FX-5000 Tension System (Flexcell International Corporation). Equibiaxial cyclic stretch was applied in two-second cycles (0.5 Hz) at a level sufficient to promote cyclic 10 to 21% elongation, corresponding to 42–80 kPa at the point of maximal distension of the culture surface [ 50 ]. Unstretched control cells from the same differentiation were maintained in BioFlex® plates in the same environmental conditions, but no stretch was applied. Immunofluorescence staining For immunofluorescence staining, all procedures were carried out at room temperature (RT) unless otherwise specified. hiPSC-CMs were washed twice with phosphate-buffered saline (PBS) and fixed with 4% paraformaldehyde for 15 minutes. Cells were then washed 3x5 min with PBS. Permeabilization was performed using 0.1% Triton X-100 (AppliChem, #A4975) in PBS for 10 minutes, followed by 2x5 min washes with PBS. For BrdU staining, DNA was hydrolysed with 2 M hydrochloric acid for 30 minutes, neutralized with 0.1 M sodium borate (pH 8.5) for 30 minutes, and washed 3x5 min with PBS. To prevent nonspecific binding, the cells were blocked with 4% foetal bovine serum (FBS, Thermo Fisher Scientific, #10500064) in PBS for 45 minutes. The cells were then incubated for 60 minutes with the following primary antibodies diluted in 4% FBS in PBS: cardiac troponin T (cTnT) antibody (Abcam, #ab45932, 1:800), BrdU antibody (Abcam, #ab6326, 1:250), or proBNP antibody (Abcam, #ab13115, 1:250), followed by 3x5 min washes with PBS. Cells were subsequently incubated for 45 minutes with Alexa Fluor®-conjugated secondary antibodies: Alexa Fluor™ 546 (Invitrogen, #A-11035, 1:200), Alexa Fluor™ 647 (Invitrogen, #A-21247, 1:200), Alexa Fluor™ 647 (Invitrogen, #A-21236, 1:200), and 4′,6-diamidino-2-phenylindole (DAPI) (Sigma-Aldrich, #D9542, 1 µg/ml), followed by 3x5 min washes with PBS. Finally, cells were stored in PBS at 4°C until imaging. Imaging and analysis Automated fluorescence microscopy was performed using the ImageXpress Micro Confocal imaging system (Molecular Devices). Representative images were acquired with a Nikon 20× Plan Apo 0.5 NA air objective, while images for downstream analysis were captured using a Nikon 10× Plan Apo objective. The acquired images were analysed using MetaXpress software (Molecular Devices). First, the nuclei were identified based on DAPI staining, and cardiomyocytes were distinguished by the presence of cTnT staining in the cytoplasm, with non-myocytes excluded based on the absence of cTnT. To identify BrdU-positive CMs, nuclei confirmed as CMs by DAPI and cTnT staining were cross-referenced with BrdU staining, and the overlap of these signals was defined as BrdU-positive CMs. The threshold for BrdU positivity was manually adjusted in each experiment to account for variations in staining intensity. For proBNP quantification, the average intensity of proBNP staining was measured within the perinuclear region, defined as a 10-pixel ring surrounding each CM nucleus. Similarly to the BrdU analysis, cells were categorized as proBNP-positive or proBNP-negative based on staining intensity, with thresholds manually adjusted for each experiment to account for staining variations. Data analysis Statistical analyses were carried out using GraphPad Prism 8 software. Data are presented as mean ± standard error of the mean (SEM), unless otherwise stated. Statistical significance was determined using two-way ANOVA followed by Tukey’s post hoc multiple-comparison test. A p-value of less than 0.05 was considered statistically significant. All experiments were conducted with a minimum of three biological replicates (n), unless otherwise stated. Statistical analysis of the scRNAseq was performed as a pairwise comparisons between each HLHS sample and healthy controls were performed using the Wilcoxon Rank Sum test, implemented in the “FindAllMarkers” function of the Seurat package. In addition, a combined analysis comparing all HLHS samples to healthy controls was conducted using the same statistical method. Genes were considered differentially expressed if they exhibited an absolute log₂fold change greater than 0.5 and a Bonferroni-adjusted p-value of less than 0.05. The Bonferroni correction was applied to adjust for multiple testing across all genes in the dataset. Results Identification of cell types Unsupervised graph-based clustering of the single cell RNA sequencing data identified 14 distinct cell clusters (clusters 0–13) across both conditions (Fig. 1 a). Transcriptional profiling revealed largely overlapping distributions between healthy and HLHS cardiomyocytes, suggesting considerable similarity in overall cellular heterogeneity. While the general clustering pattern was comparable across samples, differences in cluster distribution were observed (Fig. 1 b and Fig. 1 c) Cluster 1 showed the largest enrichment in HLHS samples, while cluster 3 exhibited the lowest number of HLHS cells. Individual HLHS line analysis is shown in (Additional file 1: Fig. S1 a-c) To characterise the molecular identity of each cluster, we identified differentially expressed marker genes, with the top 3 markers per cluster displayed in (Fig. 1 d). Based on the canonical marker expression patterns, we classified the 14 clusters into three major cell populations (Fig. 1 e and Fig. 1 f). Clusters 0, 4, 5, 6, 7, 8, 10, 12, and 13 robustly expressed both cardiac-specific transcription factors ( NKX2-5 , TBX5 , GATA4 , and ISL1 ) and structural genes essential for cardiomyocyte function ( MYH6 , MYL7 , TNNT2 , and TTN ), indicating immature cardiomyocytes at an earlier stage of differentiation where developmental regulatory networks remain active. In contrast, clusters 1, 2, 3, and 11 displayed a gene expression profile consistent with more mature cardiomyocytes. These clusters showed minimal expression of developmental transcription factors while maintaining robust expression of functional sarcomeric components Cluster 9 displayed a distinct expression profile, with relatively low expression of cardiac markers but high levels of fibroblast markers ( POSTN , COL1A1 , DDR2 , THY1 ) and smooth muscle cell markers ( TAGLN , ACTA2 , CNN1 , MYH11 ), identifying this cluster as a non-myocyte population. Pluripotency markers ( SOX2 , NANOG , POU5F1 , LIN28A ) and primitive streak markers ( FOXA2 , TBXT , EOMES , GSC ) were undetectable across all clusters (Additional file 1: Fig. S1 d), confirming the absence of undifferentiated cells or early mesodermal progenitors. Differential gene expression and functional enrichment analysis To identify the molecular alterations underlying the compositional differences between healthy and HLHS hiPSC-CMs, we performed differential gene expression analysis. This analysis revealed substantial transcriptomic differences, identifying 1,211 differentially expressed genes, of which 292 were upregulated and 919 downregulated in HLHS compared to healthy cardiomyocytes (Fig. 2 a, Additional file 2). Individual HLHS line analysis is shown in (Additional file 1: Fig. S2a-d). Gene Ontology (GO) enrichment analysis of differentially expressed genes (Fig. 2 b; Additional file 3) revealed significant involvement in a range of developmental and functional pathways. Notably, many of the enriched biological processes were observed among the downregulated genes, including processes related to circulatory system, vasculature development, and muscle cell development. To explore these in more detail, we generated heatmaps of representative genes from the downregulated categories (Fig. 2 c). Among the downregulated genes in circulatory system processes, key genes included EDN1 , SOD3 , and ACE2 . Genes involved in vasculature development showed reduced expression, including APOE , EDN1 , and JAG1 . Muscle cell development genes such as MYH6 , BMP10 , and TNNT1 were also downregulated in HLHS samples. Additional downregulated genes and their associated terms included genes involved in regulation of heart contraction ( MYH6 , HCN2 , HCN4 , KCNA5 , KCNE1 ), regulation of response to stress ( ENO1 , SESN2 , APOA1 , NPPA ), and transport ( SLC7A5 , ATP2B4 ). These findings indicate that HLHS hiPSC-CMs exhibit widespread downregulation of genes involved in cardiac development, contractile function, circulatory system processes, and cellular stress responses. TF Regulon Activity in HLHS Cardiomyocytes To understand regulatory mechanisms underlying transcriptional changes in HLHS, we performed transcription factor regulon analysis. This approach reveals whether TFs are actively regulating their downstream targets, which can be disrupted in disease even without changes in TF expression itself. We constructed and analyzed 844 TF regulons, of which 645 showed statistically significant differential activity between HLHS and healthy samples (FDR < 0.05; Additional file 4). The vast majority (626 regulons, 97.1%) exhibited decreased activity in HLHS compared to healthy controls, whereas 19 regulons (2.9%) showed increased activity (Fig. 3 a and b). A total of 90 of the differentially active regulons were driven by cardiac-specific transcription factors, with 86 (95.6%) showing reduced activity and 4 (4.4%) showing increased activity in HLHS (Fig. 3 a). The most increased regulons included CAMTA1, NKX2-5, and YBX1, while the most decreased included MEIS3, RXRG, and ETV5. To explore the functional implications of these changes, we performed GO enrichment analysis on the target genes of each of the 90 cardiac-specific TF regulons (Additional file 5). Enrichment in four major functional categories was observed: cardiac conduction and signalling, cardiac morphogenesis and septation, extracellular matrix and tissue organization, and cardiac muscle development and differentiation (Fig. 3 c; Additional file 5). Pathways related to cardiac muscle development and differentiation showed the most extensive connections. Characterisation of hiPSC-CMs proliferation To explore basal proliferation and the response to mitogenic stimulation, cardiomyocytes were treated with CHIR and SB. Representative immunofluorescence images from Healthy 2 and HLHS 1 at Day 22 illustrate increased BrdU incorporation following CHIR + SB treatment, along with elevated baseline levels in HLHS 1 (Fig. 4 a). This pattern was supported by quantification, which showed that under control conditions, HLHS 1 exhibited a significantly higher percentage of BrdU + cardiomyocytes compared to Healthy 2 (1.9-fold increase; p < 0.05), indicating an intrinsically elevated proliferative capacity (Fig. 3 b). Upon CHIR + SB stimulation at Day 22, all cell lines exhibited significant increases in BrdU + cardiomyocytes compared to their respective controls: Healthy 1 (1.6-fold, p < 0.001), Healthy 2 (1.9-fold, p < 0.01), HLHS 1 (1.5-fold, p < 0.01), and HLHS 2 (1.8-fold, p < 0.0001) (Fig. 4 b). In contrast, baseline BrdU incorporation at Day 35 was comparable across all cell lines (Fig. 4 c). CHIR + SB stimulation at this later time point elicited robust proliferative responses in all lines: Healthy 1 (3.6-fold, p < 0.001), Healthy 2 (4.8-fold, p < 0.01), HLHS 1 (3.5-fold, p < 0.01), and HLHS 2 (2.8-fold, p < 0.05) (Fig. 4 c). Quantification of average BrdU intensity (Additional file 1: Fig. S3a and S3b) and average cTnT intensity (Additional file 1: Fig. S3c and S3d) remained consistent across all groups and time points. Characterisation of hypertrophic responses Basal and ET-1-induced stress responses in hiPSC-CMs To investigate potential intrinsic differences in hypertrophic stress response between healthy and HLHS cardiomyocytes, we assessed the expression of proBNP, a key marker of cardiac stress, under both basal and ET-1-stimulated conditions. Representative immunofluorescence images demonstrated an increased number of proBNP + cardiomyocytes in HLHS 1 following ET-1 treatment, while Healthy 2 cardiomyocytes exhibited a detectable but non-significant increase (Fig. 5 a). This observation was supported by quantitative analysis, which revealed significant increases in the number of proBNP + cardiomyocytes in HLHS 1 and HLHS 2 (7.1-fold, p < 0.01 and 5-fold, p < 0.05 over control, respectively), whereas Healthy 1 and Healthy 2 showed non-significant increases (2-fold and 2.4-fold, respectively; Fig. 5 b). A similar trend was observed in the quantification of perinuclear proBNP intensity (Fig. 5 c), and cTnT expression remained consistent across all conditions (Additional file 1: Fig. S4). Cyclic mechanical stretching Cardiomyocytes are constantly exposed to mechanical forces in vivo , and their ability to adapt is critical for maintaining heart function. To investigate mechanotransduction differences between healthy and HLHS cardiomyocytes, we applied cyclic stretch for 24 and 48 hours and assessed gene expression profiles associated with hypertrophic remodelling, contractility, and metabolism. We first validated our stretch model by assessing two canonical hypertrophy markers, NPPA and NPPB . Under static conditions, their expression levels were comparable between HLHS and healthy cardiomyocytes. Following 24 hours of cyclic stretch, both groups showed a non-significant trend toward increased NPPA and NPPB expression (Fig. 6 a, 6 b). Given the importance of contractile protein remodelling in response to stress, we also measured the expression of MYH6 and MYH7 . Under static conditions, MYH6 expression in HLHS 1 was ~ 20% of Healthy 1 (p < 0.001), ~ 21% of Healthy 2 (p < 0.001), and ~ 25% of HLHS 2 (p < 0.01; Fig. 6 c), suggesting intrinsic differences in contractile gene expression. After 24 hours of stretch, MYH6 showed a downward trend in both healthy and HLHS cardiomyocytes. MYH7 expression (Fig. 6 d) and MYH6/MYH7 ratio (Additional file 1: Fig. S5) remained stable across all conditions, indicating that short-term mechanical stimulation did not alter myosin isoform mRNA expression. To assess metabolic adaptations, we evaluated expression of LDHA and SDHA, key enzymes in glycolysis and oxidative phosphorylation, respectively. LDHA expression did not differ among groups and remained unchanged following 24-h stretch (Fig. 6 e). By contrast, SDHA expression was lower in HLHS 1 under static conditions (~ 40% of Healthy 1, p < 0.05; and ~ 42% of Healthy 2, p < 0.05) and was not affected by mechanical stimulation (Fig. 6 f). We also examined cardiac transcription factors NKX2.5 [ 7 ], MEF2C [ 51 , 52 ], HES1 [ 53 ] and CSRP3 [ 50 ], which regulate cardiomyocyte development and stress responses. No notable changes were observed under static or stretched conditions (Additional file 1: Fig. S5). The results of the 48-hour stretching experiments mirrored those observed at 24 hours, suggesting that gene expression differences between HLHS and healthy cardiomyocytes are stable and not substantially altered by prolonged mechanical stimulation (Additional file 1: Fig. S6). Discussion The results of this study indicate that patient-derived HLHS cardiomyocytes exhibit heightened vulnerability to stress, as demonstrated through both transcriptomic profiling of unstimulated cells and functional stress response assays. These findings suggest impaired adaptive mechanisms, potentially contributing to reduced cardiac resilience in HLHS. Maternal metabolic disease [ 20 – 25 ] and maternal hypertension [ 54 ] are well known risk factors for CHD in the offspring, and both conditions are likely to result in exposure to increased metabolic and oxidative stress in the developing embryo and foetus. Thus, genetic predisposition for reduced tolerance to environmental stressors in the developing heart may contribute to the multifactorial aetiology of CHD. Transcriptomic profiling of HLHS cardiomyocytes indicated downregulation of genes involved in metabolic resilience and antioxidant defence, such as ENO1 and SESN2 , wherein ENO1 plays a crucial role in maintaining glycolytic flux under hypoxic conditions, facilitating ATP production when oxidative phosphorylation is compromised, thus having a protective role during cardiac stress [ 55 ]. Interestingly, downregulation of ENO1 has also been observed in a left ventricle cardioid model harbouring the transcription factor FOXF1 knockout recapitulating CHD [ 56 ]. SESN2 is integral to regulating oxidative stress responses and maintaining metabolic homeostasis; its deficiency has been linked to impaired cardiac protection and increased susceptibility to oxidative damage [ 57 , 58 ]. SESN2 polymorphism has also been associated with CHD [ 59 ]. Additional reductions in APOA1 and SOD3 , which are involved in lipid regulation and reactive oxygen species detoxification respectively, further suggest compromised oxidative stress defence mechanisms. These signatures are consistent with prior reports of mitochondrial dysfunction and apoptosis in HLHS iPSC-CMs under metabolic challenge[ 60 , 61 ], and may reflect pathophysiologic mechanisms behind maternal metabolic disease as a risk factor for CHD in the offspring [ 20 – 25 ]. While differential gene expression analysis identified specific genes with altered expression, we sought to determine whether these changes reflected coordinated disruption of transcriptional regulatory networks. Regulon analysis revealed widespread disruption of transcriptional regulatory networks in HLHS cardiomyocytes. Of 645 significantly differentially active regulons, the vast majority showed decreased activity in HLHS compared to healthy controls, with a similar pattern among cardiac-specific transcription factors. While the magnitude of these changes was modest, even small alterations in transcription factor activity can have amplified downstream effects on target gene expression networks [ 62 ]. The affected regulons span multiple aspects of cardiac function, including cardiac muscle development, morphogenesis, conduction, and extracellular matrix organization. This pattern of coordinated transcriptional downregulation may suggest impairments in differentiation, function, and interaction with other cell types, potentially leading to systemic regulatory dysfunction rather than isolated pathway defects. This may affect proper cardiac development and stress adaptation. To further probe cardiomyocyte adaptability, we examined their responses to external stressors, including endothelin-1 (ET-1) and cyclic mechanical stretch. The HLHS lines showed significantly greater increases in proBNP expression during ET-1 stimulation compared to controls, indicating a heightened sensitivity to pro-hypertrophic hormonal challenge. Indeed, ET-1 stimulation may recapitulate increased stress in the developing heart well, as several lines of evidence suggest that elevated plasma ET-1 is one of the mediators of vascular complications in individuals with metabolic disease and it is an important mediator in uteroplacental circulation and foetal vascular function [ 63 ] and [ 64 ]. ET-1 is also known to play an important role in regulating cardiomyocyte differentiation during heart development [ 64 ]. The heightened responses to ET-1 stimulation in HLHS hiPS-CMs may thus reflect disease-specific vulnerability to external stressors such as maternal metabolic disease. In contrast, responses to mechanical stretch were more heterogeneous. Cyclic stretch did not substantially alter gene expression profiles in a manner that consistently separated HLHS from controls. A previous study applying stretch to HLHS-CMs observed downregulation of cell cycle genes and upregulation of structural genes [ 61 ], partially similar to our findings, although the overall transcriptional response in our dataset was modest. Overall, these results point to transcriptional heterogeneity among HLHS lines in their response to biomechanical cues, underscoring the complexity of modelling this condition and the value of patient-specific approaches. Previous studies have consistently reported reduced basal proliferation in HLHS cardiomyocytes compared to controls, including in HLHS tissue [ 65 , 66 ], iPSC models [ 67 ], and animal models [ 67 – 69 ]. To assess both basal and inducible proliferative capacity, we used CHIR99021 and SB203580, which act synergistically to enhance cardiomyocyte proliferation through complementary mechanisms [ 46 ]. In our study, HLHS cardiomyocytes showed either similar or increased proliferation at baseline compared to controls, challenging the idea of a uniformly reduced proliferative capacity. A recent study using CHIR99021 to stimulate proliferation reported lower proliferation in HLHS cells relative to controls at both baseline and after treatment [ 61 ]. Importantly, their data, while focused on intergroup comparison, also showed that HLHS cardiomyocytes were capable of increasing proliferation in response to CHIR. This is consistent with our findings, which demonstrate that HLHS cardiomyocytes retain mitogenic responsiveness despite baseline variability. In addition to metabolic and proliferative abnormalities, HLHS cardiomyocytes exhibited changes in the expression of several ion channels. Transcriptomic signatures showed reduced expression of key pacemaker channel genes, including HCN2 and HCN4 , which are essential for maintaining sinoatrial node function and regulating cardiac automaticity. Notably, genetic variants in HCN4 have been implicated in atrial arrhythmias, atrioventricular nodal disease, and left ventricular noncompaction [ 70 ]. In parallel, expression of potassium channel genes KCNA5 and KCNE1 , associated with atrial fibrillation and cardiac repolarization anomalies, respectively [ 71 – 73 ], were also reduced in HLHS cardiomyocytes. These changes were accompanied by decreased expression of SHOX2 , a transcription factor essential for sinoatrial node development and atrial fibrillation [ 74 – 76 ]. While the direct impact of these electrophysiological abnormalities on cardiac morphogenesis remains uncertain, such alterations may contribute to the intrinsic component of the increased arrhythmic susceptibility in congenital heart disease patients. These findings provide insight into how HLHS cardiomyocytes integrate, or fail to integrate, key developmental cues. While maternal conditions such as diabetes and hypertension are known to elevate circulating ET-1, our data show that HLHS cardiomyocytes exhibit a markedly heightened response to ET-1. This suggests that HLHS cells may be primed for pathological activation when challenged by hormonal cues during development. Taken together, these results emphasize the importance of gene–environment interactions and support a model in which inappropriate responses to developmental stressors contribute to disease progression. This insight may help explain the clinical variability observed in HLHS and highlights the value of patient-specific models for therapeutic development. Study Limitations and Future Directions While our study provides valuable insights into the cellular and molecular basis of HLHS, the small number of patient-derived cell lines limits our ability to fully capture the full spectrum of HLHS heterogeneity. The variable responses observed among our HLHS lines reflect the complex, patient-specific nature of this disorder and suggest that multiple pathogenic mechanisms may exist. Nevertheless, despite the limited sample size, our findings identify common maladaptive processes in HLHS cardiomyocytes, supporting the validity and broader relevance of our conclusions. Another limitation is that our hiPSC-CMs were studied in 2D monoculture systems, lacking the native cardiac microenvironment and heterotypic cell-cell interactions that could influence cellular behaviour, indicating that additional non-myocyte-derived pathways and mechanisms not identified here are likely to contribute to the disease development. While this simplification may exclude important contributions from non-myocyte-derived pathways, it allowed us to focus specifically on intrinsic cardiomyocyte defects. Moreover, the relative immaturity of hiPSC-CMs, often considered a limitation [ 77 ], may actually serve as a strength in this context, as it mirrors the developmental stage during which congenital heart defects such as HLHS originate. Future studies should expand the cohort of patient-derived lines to more robustly characterise cellular heterogeneity and patient-specific variability. Increasing the number of lines would help validate and refine the molecular and cellular mechanisms identified here. In addition, employing advanced culture systems, such as cardiac co-culture models or engineered heart tissues, could better replicate the in vivo cardiac microenvironment and provide further insights into the multicellular and biomechanical interactions underlying HLHS. Despite the inherent complexity and heterogeneity of HLHS, these advanced patient-derived in vitro models are expected to unravel cellular and molecular mechanisms underlying HLHS, which may lead to improved diagnostic and preventative and/or therapeutic strategies in the future. Conclusion Our results indicate that patient-derived HLHS cardiomyocytes display impaired function and heightened sensitivity to oxidative and metabolic challenges. Despite the heterogeneity among patient lines, these common features suggest shared vulnerability mechanisms in HLHS. Collectively, these findings support the view that congenital heart defects, including HLHS, may arise from a complex interplay between genetic predisposition and adverse developmental conditions during cardiac development. Declarations Ethics approval and consent to participate This study, titled “The development origins of congenital cardiac malformations with a special focus on left ventricular outflow tract obstruction defects” was conducted in accordance with the principles of the Declaration of Helsinki and the Council of Europe Convention on Human Rights and Biomedicine. The study protocol was approved by the Ethics Committee of the Helsinki University Hospital District (reference number: HUS/2054/2016, approval granted on November 17 th ,2016). Written informed consent was obtained from all participants aged six years and older, and from parents or legal guardians for participants who were minors. Clinical trial number Not applicable Consent for publication Not applicable Availability of data and materials The RNAseq data are freely available in the Gene Expression Omnibus (GEO) repository (accession numbers GSE194103). All other datasets generated during and analysed during the current study are available from the corresponding authors on reasonable request. Competing interests The authors declare no competing interests. Funding This work was supported by the Academy of Finland (grants 321564 and 353109 to VT and grant 331405 to EH), Sigrid Jusélius Foundation (EH, VT), the Finnish Foundation for Cardiovascular Research (EH, VT), Finnish Foundation for Pediatric Research (EH), the Finnish Medical Foundation (EH), the Finnish Cultural Foundation (EH), the Helsinki and Uusimaa Hospital district (EH), the Stiftelsen Frimurare Barnhuset i Stockholm (EH) and Ida Montin’s Foundation (MV). Authors' contributions The authors contributed to the article as follows: Conceptualization was led by (EH and VT). Investigation was carried out by (MV and MA) while data curation, formal analysis, and software were handled by (MV, MB, AR). Visualization was done by (MV) Writing of the original draft was performed by (MV) followed by review and editing by (VT, EH, MV, MA). Funding acquisition by (EH, VT and MV) and supervision were managed by (VT and EH) and project administration was overseen by (VT and EH). All authors contributed to writing the article and approved the submitted version. Acknowledgements We thank Ilse Paetau for her assistance with cell culture and administrative support. We also thank Professor Timo Otonkoski and Docent Ras Trokovic at the Biomedicum Stem Cell Center for providing three control hiPSC lines, and Professor Anu Suomalainen-Wartiovaara for the gift of the hiPSC line K1. Additionally, we acknowledge the Institute of Molecular Medicine Finland (FIMM) for the scRNA-seq service. AI Authorship The authors declare that they have not use AI-generated work in this manuscript. Authors' information 1 Drug Research Program, Division of Pharmacology and Pharmacotherapy, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland 2 Stem Cells and Metabolism Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland 3 Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland 4 Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, Faculty of Medicine, University of Turku, Turku, Finland 5 New Children’s Hospital, Paediatric Research Centre, Helsinki University Hospital, Helsinki, Finland ORCiD: Margarida Varela: 0009-0005-6899-1976 Minna Ampuja: 0000-0003-1407-5399 Martin Broberg: 0000-0002-5419-9479 Amanda Ramste: 0000-0002-2612-5165 Virpi Talman: 0000-0002-2702-6505 Emmi Helle: 0000-0001-8993-2194 References Tchervenkov, C. I., Jacobs, J. P., Weinberg, P. M., Aiello, V. D., Béland, M. J., Colan, S. D., et al. (2006). The nomenclature, definition and classification of hypoplastic left heart syndrome. Cardiology In The Young , 16 , 339. https://doi.org/10.1017/S1047951106000291 Barron, D. J., Kilby, M. D., Davies, B., Wright, J. G., Jones, T. J., & Brawn, W. J. (2009). Hypoplastic left heart syndrome. The Lancet , 374 , 551–564. https://doi.org/10.1016/S0140-6736(09)60563-8 Bondy, C. A. (2010). Hypoplastic Left Heart Syndrome. New England Journal of Medicine , 362 , 2026–2028. https://doi.org/10.1056/NEJMe1002923 Bondy, C. A. (2008). Congenital Cardiovascular Disease in Turner Syndrome. Congenital Heart Disease , 3 , 2–15. https://doi.org/10.1111/j.1747-0803.2007.00163.x Grossfeld, P. D., Mattina, T., Lai, Z., Favier, R., Jones, K. L., Cotter, F., et al. (2004). The 11q terminal deletion disorder: A prospective study of 110 cases. Am J Med Genet A , 129A , 51–61. https://doi.org/10.1002/ajmg.a.30090 Reis, P. (1999). Outcome of infants with hypoplastic left heart and turner syndromes. Obstetrics & Gynecology , 93 , 532–535. https://doi.org/10.1016/S0029-7844(98)00462-1 Kobayashi, J., Yoshida, M., Tarui, S., Hirata, M., Nagai, Y., Kasahara, S., et al. (2014). Directed Differentiation of Patient-Specific Induced Pluripotent Stem Cells Identifies the Transcriptional Repression and Epigenetic Modification of NKX2-5, HAND1, and NOTCH1 in Hypoplastic Left Heart Syndrome. PLoS One , 9 , e102796. https://doi.org/10.1371/journal.pone.0102796 Elliott, D. A., Kirk, E. P., Yeoh, T., Chandar, S., McKenzie, F., Taylor, P., et al. (2003). Cardiac homeobox gene NKX2-5mutations and congenital heart disease. Journal Of The American College Of Cardiology , 41 , 2072–2076. https://doi.org/10.1016/S0735-1097(03)00420-0 Stallmeyer, B., Fenge, H., Nowak-Göttl, U., & Schulze‐Bahr, E. (2010). Mutational spectrum in the cardiac transcription factor gene NKX2.5 associated with congenital heart disease. Clinical Genetics , 78 , 533–540. https://doi.org/10.1111/j.1399-0004.2010.01422.x Miyao, N., Hirono, K., Hata, Y., Yoshimura, N., & Ichida, F. (2019). Novel compound heterozygous TBX5 variants may induce hypoplastic left heart syndrome. Pediatrics International , 61 , 607–609. https://doi.org/10.1111/ped.13854 Reamon-Buettner, S. M., Ciribilli, Y., Inga, A., & Borlak, J. (2008). A loss-of-function mutation in the binding domain of HAND1 predicts hypoplasia of the human hearts. Human Molecular Genetics , 17 , 1397–1405. https://doi.org/10.1093/hmg/ddn027 Reamon-Buettner, S. M., Ciribilli, Y., Traverso, I., Kuhls, B., Inga, A., & Borlak, J. (2009). A functional genetic study identifies HAND1 mutations in septation defects of the human heart. Human Molecular Genetics , 18 , 3567–3578. https://doi.org/10.1093/hmg/ddp305 Theis, J. L., Hrstka, S. C. L., Evans, J. M., O’Byrne, M. M., de Andrade, M., O’Leary, P. W., et al. (2015). Compound heterozygous NOTCH1 mutations underlie impaired cardiogenesis in a patient with hypoplastic left heart syndrome. Human Genetics , 134 , 1003–1011. https://doi.org/10.1007/s00439-015-1582-1 Durbin, M. D., Cadar, A. G., Williams, C. H., Guo, Y., Bichell, D. P., Su, Y. R., et al. (2017). Hypoplastic Left Heart Syndrome Sequencing Reveals a Novel NOTCH1 Mutation in a Family with Single Ventricle Defects. Pediatric Cardiology , 38 , 1232–1240. https://doi.org/10.1007/s00246-017-1650-5 Helle, E., Córdova-Palomera, A., Ojala, T., Saha, P., Potiny, P., Gustafsson, S., et al. (2019). Loss of function, missense, and intronic variants in NOTCH1 confer different risks for left ventricular outflow tract obstructive heart defects in two European cohorts. Genetic Epidemiology , 43 , 215–226. https://doi.org/10.1002/gepi.22176 Ye, S., Wang, C., Xu, Z., Lin, H., Wan, X., Yu, Y., et al. (2023). Impaired Human Cardiac Cell Development due to NOTCH1 Deficiency. Circ Res , 132 , 187–204. https://doi.org/10.1161/CIRCRESAHA.122.321398 Theis, J. L., Zimmermann, M. T., Evans, J. M., Eckloff, B. W., Wieben, E. D., Qureshi, M. Y., et al. (2015). Recessive MYH6 Mutations in Hypoplastic Left Heart With Reduced Ejection Fraction. Circulation. Cardiovascular Genetics , 8 , 564–571. https://doi.org/10.1161/CIRCGENETICS.115.001070 Tomita-Mitchell, A., Stamm, K. D., Mahnke, D. K., Kim, M-S., Hidestrand, P. M., Liang, H. L., et al. (2016). Impact of MYH6 variants in hypoplastic left heart syndrome. Physiol Genomics , 48 , 912–921. https://doi.org/10.1152/physiolgenomics.00091.2016 Jenkins, K. J., Correa, A., Feinstein, J. A., Botto, L., Britt, A. E., Daniels, S. R., et al. (2007). Noninherited Risk Factors and Congenital Cardiovascular Defects: Current Knowledge. Circulation , 115 , 2995–3014. https://doi.org/10.1161/CIRCULATIONAHA.106.183216 Savla, J. J., Putt, M. E., Huang, J., Parry, S., Moldenhauer, J. S., Reilly, S., et al. (2022). Impact of Maternal– Fetal Environment on Mortality in Children With Single Ventricle Heart Disease. J Am Heart Assoc , 11. https://doi.org/10.1161/JAHA.120.020299 Chaikhouni, F., & Salih, H. G. H. (2024). Cardiac Evaluation of Children Born to Mothers with Diabetes Mellitus. Manual of Pediatric Cardiac Care: Volume 1, Second Edition. https://doi.org/10.1007/978-981-19-8357-3_10 Engineer, A., Saiyin, T., Greco, E. R., & Feng, Q. (2019). Say NO to ROS: Their Roles in Embryonic Heart Development and Pathogenesis of Congenital Heart Defects in Maternal Diabetes. Antioxidants , 8 , 436. https://doi.org/10.3390/antiox8100436 Wu, Y., Reece, E. A., Zhong, J., Dong, D., Shen, W-B., Harman, C. R., et al. (2016). Type 2 diabetes mellitus induces congenital heart defects in murine embryos by increasing oxidative stress, endoplasmic reticulum stress, and apoptosis. American Journal Of Obstetrics And Gynecology , 215 , 366e1. https://doi.org/10.1016/j.ajog.2016.03.036 Turunen, R., Pulakka, A., Metsälä, J., Vahlberg, T., Ojala, T., Gissler, M., et al. (2024). Maternal Diabetes and Overweight and Congenital Heart Defects in Offspring. JAMA Netw Open , 7 , e2350579. https://doi.org/10.1001/jamanetworkopen.2023.50579 Helle, E., & Priest, J. R. (2020). Maternal Obesity and Diabetes Mellitus as Risk Factors for Congenital Heart Disease in the Offspring. J Am Heart Assoc , 9. https://doi.org/10.1161/JAHA.119.011541 Zhang, S., Regnault, T. R. H., Barker, P. L., Botting, K. J., McMillen, I. C., McMillan, C. M., et al. (2015). Placental adaptations in growth restriction. Nutrients , 7 , 360–389. https://doi.org/10.3390/nu7010360 Hay, W. W. (2006). Placental-fetal glucose exchange and fetal glucose metabolism. Transactions Of The American Clinical And Climatological Association , 117 , 321–339. discussion 339 – 40. Helle, E. I. T., Biegley, P., Knowles, J. W., Leader, J. B., Pendergrass, S., Yang, W., et al. (2018). First Trimester Plasma Glucose Values in Women without Diabetes are Associated with Risk for Congenital Heart Disease in Offspring. Journal Of Pediatrics , 195 , 275–278. https://doi.org/10.1016/j.jpeds.2017.10.046 Salman, H. E., Alser, M., Shekhar, A., Gould, R. A., Benslimane, F. M., Butcher, J. T., et al. (2021). Effect of left atrial ligation-driven altered inflow hemodynamics on embryonic heart development: clues for prenatal progression of hypoplastic left heart syndrome. Biomechanics And Modeling In Mechanobiology , 20 , 733–750. https://doi.org/10.1007/s10237-020-01413-5 Goenezen, S., Rennie, M. Y., & Rugonyi, S. (2012). Biomechanics of early cardiac development. Biomechanics And Modeling In Mechanobiology , 11 , 1187–1204. https://doi.org/10.1007/s10237-012-0414-7 Salman, H. E., Kamal, R. Y., Hijazi, Z. M., & Yalcin, H. C. (2022). Hemodynamic and Structural Comparison of Human Fetal Heart Development Between Normally Growing and Hypoplastic Left Heart Syndrome-Diagnosed Hearts. Frontiers In Physiology , 13. https://doi.org/10.3389/fphys.2022.856879 Vallelonga, F., Airale, L., Tonti, G., Argulian, E., Milan, A., Narula, J., et al. (2021). Introduction to Hemodynamic Forces Analysis: Moving Into the New Frontier of Cardiac Deformation Analysis. J Am Heart Assoc , 10. https://doi.org/10.1161/JAHA.121.023417 Granados-Riveron, J. T., & Brook, J. D. (2012). The Impact of Mechanical Forces in Heart Morphogenesis. Circulation. Cardiovascular Genetics , 5 , 132–142. https://doi.org/10.1161/CIRCGENETICS.111.961086 Takahashi, K., & Yamanaka, S. (2006). Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors. Cell , 126 , 663–676. https://doi.org/10.1016/j.cell.2006.07.024 Takahashi, K., Tanabe, K., Ohnuki, M., Narita, M., Ichisaka, T., Tomoda, K., et al. (2007). Induction of Pluripotent Stem Cells from Adult Human Fibroblasts by Defined Factors. Cell , 131 , 861–872. https://doi.org/10.1016/j.cell.2007.11.019 Trokovic, R., Weltner, J., & Otonkoski, T. (2015). Generation of iPSC line HEL24.3 from human neonatal foreskin fibroblasts. Stem Cell Res , 15 , 266–268. https://doi.org/10.1016/j.scr.2015.05.012 Trokovic, R., Weltner, J., & Otonkoski, T. (2015). Generation of iPSC line HEL47.2 from healthy human adult fibroblasts. Stem Cell Res , 15 , 263–265. https://doi.org/10.1016/j.scr.2015.05.013 Weltner, J., Balboa, D., Katayama, S., Bespalov, M., Krjutškov, K., Jouhilahti, E-M., et al. (2018). Human pluripotent reprogramming with CRISPR activators. Nature Communications , 9 , 2643. https://doi.org/10.1038/s41467-018-05067-x Helle, E., Ampuja, M., Dainis, A., Antola, L., Temmes, E., Tolvanen, E., et al. (2021). HiPS-Endothelial Cells Acquire Cardiac Endothelial Phenotype in Co-culture With hiPS-Cardiomyocytes. Front Cell Dev Biol , 9. https://doi.org/10.3389/fcell.2021.715093 Helle, E., Ampuja, M., Antola, L., & Kivelä, R. (2020). Flow-Induced Transcriptomic Remodeling of Endothelial Cells Derived From Human Induced Pluripotent Stem Cells. Frontiers In Physiology , 11. https://doi.org/10.3389/fphys.2020.591450 Garrison, E., & Marth, G. (2012). Haplotype-based variant detection from short-read sequencing. . Kang, H. M., Subramaniam, M., Targ, S., Nguyen, M., Maliskova, L., McCarthy, E., et al. (2018). Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nature Biotechnology , 36 , 89–94. https://doi.org/10.1038/nbt.4042 Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols , 4 , 44–57. https://doi.org/10.1038/nprot.2008.211 Sherman, B. T., Hao, M., Qiu, J., Jiao, X., Baseler, M. W., Lane, H. C., et al. (2022). DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Research , 50 , W216–W221. https://doi.org/10.1093/nar/gkac194 Lambert, S. A., Jolma, A., Campitelli, L. F., Das, P. K., Yin, Y., Albu, M. (2018). The Human Transcription Factors Cell . ;172:650–665. https://doi.org/10.1016/j.cell.2018.01.029 . Torrieri, G., Ferreira, M. P. A., Shahbazi, M., Talman, V., Karhu, S. T., Pohjolainen, L., et al. (2022). In Vitro Evaluation of the Therapeutic Effects of Dual-Drug Loaded Spermine‐Acetalated Dextran Nanoparticles Coated with Tannic Acid for Cardiac Applications. Advanced Functional Materials , 32. https://doi.org/10.1002/adfm.202109032 Ying, Q-L., Wray, J., Nichols, J., Batlle-Morera, L., Doble, B., Woodgett, J., et al. (2008). The ground state of embryonic stem cell self-renewal. Nature , 453 , 519–523. https://doi.org/10.1038/nature06968 Engel, F. B., Schebesta, M., Duong, M. T., Lu, G., Ren, S., Madwed, J. B., et al. (2005). p38 MAP kinase inhibition enables proliferation of adult mammalian cardiomyocytes. Genes & Development , 19 , 1175–1187. https://doi.org/10.1101/gad.1306705 Karhu, S. T., Kinnunen, S. M., Tölli, M., Välimäki, M. J., Szabó, Z., Talman, V., et al. (2020). GATA4-targeted compound exhibits cardioprotective actions against doxorubicin-induced toxicity in vitro and in vivo: establishment of a chronic cardiotoxicity model using human iPSC-derived cardiomyocytes. Archives Of Toxicology , 94 , 2113–2130. https://doi.org/10.1007/s00204-020-02711-8 Pohjolainen, L., Ruskoaho, H., & Talman, V. (2022). Transcriptomics reveal stretched human pluripotent stem cell-derived cardiomyocytes as an advantageous hypertrophy model. Journal of Molecular and Cellular Cardiology Plus [Internet] , 2 , 100020. https://doi.org/https://doi.org/10.1016/j.jmccpl.2022.100020 Lin, Q., Schwarz, J., Bucana, C., Olson, E. N.. Control of mouse cardiac morphogenesis and myogenesis by transcription factor MEF2C., & Science (1979). 1997;276:1404–7. https://doi.org/10.1126/science.276.5317.1404 Lu, C-X., Wang, W., Wang, Q., Liu, X-Y., & Yang, Y-Q. (2018). A Novel MEF2C Loss-of-Function Mutation Associated with Congenital Double Outlet Right Ventricle. Pediatric Cardiology , 39 , 794–804. https://doi.org/10.1007/s00246-018-1822-y Rochais, F., Dandonneau, M., Mesbah, K., Jarry, T., Mattei, M-G., & Kelly, R. G. (2009). Hes1 Is Expressed in the Second Heart Field and Is Required for Outflow Tract Development. PLoS One , 4 , e6267. https://doi.org/10.1371/journal.pone.0006267 Ramakrishnan, A., Lee, L. J., Mitchell, L. E., & Agopian, A. J. (2015). Maternal Hypertension During Pregnancy and the Risk of Congenital Heart Defects in Offspring: A Systematic Review and Meta-analysis. Pediatric Cardiology , 36 , 1442–1451. https://doi.org/10.1007/s00246-015-1182-9 Semenza, G. L., Jiang, B-H., Leung, S. W., Passantino, R., Concordet, J-P., Maire, P., et al. (1996). Hypoxia Response Elements in the Aldolase A, Enolase 1, and Lactate Dehydrogenase A Gene Promoters Contain Essential Binding Sites for Hypoxia-inducible Factor 1. Journal of Biological Chemistry , 271 , 32529–32537. https://doi.org/10.1074/jbc.271.51.32529 Schmidt, C., Deyett, A., Ilmer, T., Haendeler, S., Torres Caballero, A., Novatchkova, M., et al. (2023). Multi-chamber cardioids unravel human heart development and cardiac defects. Cell , 186 , 5587–5605e27. https://doi.org/10.1016/j.cell.2023.10.030 Li, X., Wang, Z., Mouton, A. J., Omoto, A. C. M., da Silva, A. A., do, Carmo, J. M. (2024). Sestrin2 Attenuates Myocardial Endoplasmic Reticulum Stress and Cardiac Dysfunction During Ischemia/Reperfusion Injury. J Am Heart Assoc. ; https://doi.org/10.1161/JAHA.124.035193 Zahid, M. A., Abdelsalam, S. S., Raïq, H., Parray, A., Korashy, H. M., Zeidan, A., et al. (2023). Sestrin2 as a Protective Shield against Cardiovascular Disease. International Journal Of Molecular Sciences , 24. https://doi.org/10.3390/ijms24054880 Yang, W., Li, Y., Bai, J., You, T., Yi, K., Xie, D., et al. (2021). A Functional Variant Rs492554 Associated With Congenital Heart Defects Modulates SESN2 Expression Through POU2F1. Front Cell Dev Biol , 9. https://doi.org/10.3389/fcell.2021.668474 Xu, X., Jin, K., Bais, A. S., Zhu, W., Yagi, H., Feinstein, T. N., et al. (2022). Uncompensated mitochondrial oxidative stress underlies heart failure in an iPSC-derived model of congenital heart disease. Cell Stem Cell , 29 , 840–855e7. https://doi.org/10.1016/j.stem.2022.03.003 Yu, Y., Wang, C., Ye, S., Qin, H., Alonzo, M., Onorato, A., et al. (2025). Common and divergent cellular aetiologies underlying hypoplastic left heart syndrome and hypoplastic right heart syndrome. European Heart Journal . https://doi.org/10.1093/eurheartj/ehaf121 Boyle, E. A., Li, Y. I., & Pritchard, J. K. (2017). An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell , 169 , 1177–1186. https://doi.org/10.1016/j.cell.2017.05.038 Ergul, A. (2011). Endothelin-1 and diabetic complications: focus on the vasculature. Pharmacological Research , 63 , 477–482. https://doi.org/10.1016/j.phrs.2011.01.012 Paradis, A., & Zhang, L. (2013). Role of endothelin in uteroplacental circulation and fetal vascular function. Current Vascular Pharmacology , 11 , 594–605. https://doi.org/10.2174/1570161111311050004 Miao, Y., Tian, L., Martin, M., Paige, S. L., Galdos, F. X., Li, J., et al. (2020). Intrinsic Endocardial Defects Contribute to Hypoplastic Left Heart Syndrome. Cell Stem Cell , 27 , 574–589e8. https://doi.org/10.1016/j.stem.2020.07.015 Bohlmeyer, T. J., Helmke, S., Ge, S., Lynch, J., Brodsky, G., Sederberg, J. H., et al. (2003). Hypoplastic left heart syndrome myocytes are differentiated but possess a unique phenotype. Cardiovascular Pathology , 12 , 23–31. https://doi.org/10.1016/S1054-8807(02)00127-8 Nielsen, T., Kervadec, A., Theis, J. L., Missinato, M. A., Marchant, J., Lynott, M. (2022). Functional analysis across model systems implicates ribosomal proteins in growth and proliferation defects associated with hypoplastic left heart syndrome. https://doi.org/10.1101/2022.07.01.22277112 Sedmera, D., Hu, N., Weiss, K. M., Keller, B. B., Denslow, S., & Thompson, R. P. (2002). Cellular changes in experimental left heart hypoplasia. Anatomical Record , 267 , 137–145. https://doi.org/10.1002/ar.10098 Yagi, H., Liu, X., Gabriel, G. C., Wu, Y., Peterson, K., Murray, S. A., et al. (2018). The Genetic Landscape of Hypoplastic Left Heart Syndrome. Pediatric Cardiology , 39 , 1069–1081. https://doi.org/10.1007/s00246-018-1861-4 Servatius, H., Porro, A., Pless, S. A., Schaller, A., Asatryan, B., Tanner, H., et al. (2018). Phenotypic Spectrum of HCN4 Mutations. Circ Genom Precis Med , 11. https://doi.org/10.1161/CIRCGEN.117.002033 Yang, Y., Li, J., Lin, X., Yang, Y., Hong, K., Wang, L., et al. (2009). Novel KCNA5 loss-of-function mutations responsible for atrial fibrillation. Journal Of Human Genetics , 54 , 277–283. https://doi.org/10.1038/jhg.2009.26 Olson, T. M., Alekseev, A. E., Liu, X. K., Park, S., Zingman, L. V., Bienengraeber, M., et al. (2006). Kv1.5 channelopathy due to KCNA5 loss-of-function mutation causes human atrial fibrillation. Human Molecular Genetics , 15 , 2185–2191. https://doi.org/10.1093/hmg/ddl143 Christophersen, I. E., Olesen, M. S., Liang, B., Andersen, M. N., Larsen, A. P., Nielsen, J. B., et al. (2013). Genetic variation in KCNA5: impact on the atrial-specific potassium current IKur in patients with lone atrial fibrillation. European Heart Journal , 34 , 1517–1525. https://doi.org/10.1093/eurheartj/ehs442 Blaschke, R. J., Monaghan, A. P., Schiller, S., Schechinger, B., Rao, E., Padilla-Nash, H., et al. (1998). SHOT, a SHOX-related homeobox gene, is implicated in craniofacial, brain, heart, and limb development. Proc Natl Acad Sci U S A , 95 , 2406–2411. https://doi.org/10.1073/pnas.95.5.2406 Li, N., Wang, Z-S., Wang, X-H., Xu, Y-J., Qiao, Q., Li, X-M., et al. (2018). A SHOX2 loss-of-function mutation underlying familial atrial fibrillation. International Journal Of Medical Sciences , 15 , 1564–1572. https://doi.org/10.7150/ijms.27424 Blaschke, R. J., Hahurij, N. D., Kuijper, S., Just, S., Wisse, L. J., Deissler, K., et al. (2007). Targeted Mutation Reveals Essential Functions of the Homeodomain Transcription Factor Shox2 in Sinoatrial and Pacemaking Development. Circulation , 115 , 1830–1838. https://doi.org/10.1161/CIRCULATIONAHA.106.637819 Karbassi, E., Fenix, A., Marchiano, S., Muraoka, N., Nakamura, K., Yang, X., et al. (2020). Cardiomyocyte maturation: advances in knowledge and implications for regenerative medicine. Nature Reviews. Cardiology , 17 , 341–359. https://doi.org/10.1038/s41569-019-0331-x Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.pdf Additional files File name: Additional file 1.pdf File format: pdf Title of data: Supplementary information Description of data: Supplementary tables and figures File name: Additional file 2.xlsx File format: xlsx Title of data: Differential Expression HLHS vs Healthy Description of data: Excel file with separate sheets for each HLHS line vs. healthy controls, a combined analysis, top commonly regulated genes, and a summary. File name: Additional file 3.xlsx File format: xlsx Title of data: GO Enrichment Analysis Description of data: Excel file with GO term enrichment results for upregulated and downregulated genes, along with curated sets of selected terms. File name: Additional file 4.xlsx File format: xlsx Title of data: TF Regulon Activity Description of data: Excel file containing transcription factor regulon activity scores and statistical comparisons between HLHS and control samples. File name: Additional file 5.xlsx File format: xlsx Title of data: GO Enrichment Analysis of Cardiac TF Regulons Description of data: Excel file with Gene Ontology enrichment results for target genes of cardiac-specific transcription factor regulons. Includes all enriched GO terms and a filtered set of cardiac-specific terms. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Jan, 2026 Reviews received at journal 23 Jan, 2026 Reviewers agreed at journal 23 Jan, 2026 Reviews received at journal 26 Dec, 2025 Reviewers agreed at journal 17 Dec, 2025 Reviewers invited by journal 02 Dec, 2025 Editor assigned by journal 01 Dec, 2025 Submission checks completed at journal 01 Dec, 2025 First submitted to journal 01 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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10:56:50","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":240434,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8250379/v1/1d6d73b07c2e5ec9b84c858a.html"},{"id":97434916,"identity":"762b2f9b-a09b-402b-8964-bddf5e7d9fec","added_by":"auto","created_at":"2025-12-04 10:56:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1190206,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-cell transcriptional profiling of healthy and HLHS cardiomyocytes. Analysis compares HLHS patient lines (n=4) to a pooled healthy control (4 samples combined) (a) UMAP visualization of integrated single-cell RNA sequencing data from healthy and HLHS samples, split by group. Cells are colored by cluster identity (0-13) using unsupervised graph-based clustering (resolution = 0.4). Each dot represents a single cell (n = [total cells]). (b) Stacked bar chart showing the relative proportion of each cluster within healthy and HLHS samples. Clusters are ordered 0-13 from bottom to top, with colors corresponding to panel (a). (c) Bar chart depicting the percentage point difference in cluster proportions between HLHS and healthy samples (HLHS - Healthy). Bars are colored by statistical significance after FDR correction (red: FDR \u0026lt; 0.05; beige: not significant). Asterisks indicate significance levels: *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001. (d) Dot plot showing expression of the top 3 differentially expressed marker genes for each cluster. Dot size represents the percentage of cells expressing each gene within a cluster; dot color indicates average scaled expression level (blue: low, white: intermediate, red: high). (e) Dot plot showing expression of canonical cardiac cell type markers across clusters. Markers include cardiomyocyte markers (TTN, TNNT2, MYL7, MYH6), cardiac progenitor markers (NKX2-5, TBX5, GATA4, ISL1), smooth muscle cell markers (MYH11, CNN1, ACTA2, TAGLN), and cardiac fibroblast markers (THY1, DDR2, COL1A1, POSTN). Dot size and color as in panel (d). (f) UMAP visualization showing annotated cell populations based on marker gene expression. Three major cell types were identified: Mature CMs (red, expressing TTN, TNNT2, MYL7, MYH6), Immature CMs (beige/yellow, expressing ISL1, GATA4, TBX5, NKX2-5, TTN, TNNT2, MYL7, MYH6), and SMCs/CFs (blue, expressing CNN1, ACTA2, TAGLN, POSTN, THY1, DDR2, COL1A1).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8250379/v1/ed1f866eb80316ae610ad204.png"},{"id":97434910,"identity":"578b2b87-0200-4657-941b-48e7c895d2f0","added_by":"auto","created_at":"2025-12-04 10:56:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1140159,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential gene expression and functional enrichment in HLHS. \u003c/strong\u003eAnalysis compares HLHS patient lines (n=4) to a pooled healthy control (4 samples combined). \u003cstrong\u003e(a)\u003c/strong\u003e Volcano plot showing differentially expressed genes between HLHS and healthy samples. Blue dots represent downregulated genes (919), red dots represent upregulated genes (292), and yellow dots represent genes that were not differentially expressed (9,318). Selected key genes are labeled. Vertical dashed lines indicate log2(fold change) thresholds of -0.5 and 0.5; horizontal dashed line indicates adjusted p-value threshold of 0.05. \u003cstrong\u003e(b)\u003c/strong\u003e Gene Ontology (GO) Biological Processes enrichment analysis of differentially expressed genes in HLHS. Circle size represents the number of genes in each GO term (range: 9-220 genes). Color intensity indicates statistical significance (-log10 p-value), with darker colors representing more significant enrichment. X-axis indicates fold enrichment. Top panel shows enriched processes in upregulated genes; bottom panel shows enriched processes in downregulated genes. \u003cstrong\u003e(c)\u003c/strong\u003e Heatmaps showing expression patterns of key genes within six selected enriched biological processes across individual samples. Color scale represents log2(fold change) values from -2 (blue, downregulated) to 2 (red, upregulated).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8250379/v1/77c3d446b4f6e1f48155e99b.png"},{"id":97667914,"identity":"ee0df7c2-e5b0-40f6-b4b6-f93775a8c90f","added_by":"auto","created_at":"2025-12-08 09:24:28","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":901165,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscription factor regulon analysis in healthy and HLHS cardiomyocytes. \u0026nbsp;(a)\u003c/strong\u003e Stacked bar chart comparing differential regulon activity between HLHS and healthy cardiomyocytes. Of 645 significantly differentially active regulons, 626 showed decreased activity (dark blue: 86 cardiac-specific regulons; light blue: 540 non-cardiac regulons) and 19 showed increased activity (dark red: 4 cardiac-specific regulons; light pink: 15 non-cardiac regulons) in HLHS compared to healthy controls. \u003cstrong\u003e(b)\u003c/strong\u003eVolcano plot showing activity changes across all 844 analyzed regulons. X-axis represents percent change in regulon activity; y-axis shows statistical significance (-log10 adjusted p-value). Blue points indicate decreased activity in HLHS (n=626 significant), red points indicate increased activity in HLHS (n=19 significant), and yellow points represent non-significant regulons (n=199). Horizontal dashed line marks FDR \u0026lt; 0.05 significance threshold; vertical dashed line indicates no change. Top 10 most changed regulons in each direction are labeled. \u003cstrong\u003e(c)\u003c/strong\u003e Circus plot showing connections between 90 cardiac-specific TF regulons (outer ring: red = increased activity in HLHS, blue = decreased activity in HLHS) and four functional pathway categories derived from GO enrichment analysis (inner segments, colored by pathway type). Ribbon width is proportional to GO enrichment strength.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8250379/v1/9d00725fecd2c91545d6c52e.jpeg"},{"id":97434912,"identity":"913b9163-5ebc-47d5-9264-a361ea8d259e","added_by":"auto","created_at":"2025-12-04 10:56:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":843691,"visible":true,"origin":"","legend":"\u003cp\u003eProliferation of healthy and HLHS cardiomyocytes in basal and pro-mitotic conditions. Proliferation of human induced pluripotent stem cell-derived cardiomyocytes wasassessed by bromodeoxyuridine (BrdU) pulse labelling at Day 22 (a-b) and Day 35 (c). The CMs were treated with either DMSO (control) or a combination of CHIR99021 [5 µM] and SB203580 [10 µM] (CHIR + SB) in the presence of 10 µM BrdU for 24 hours. Cells were then fixed and stained for DNA (DAPI, blue), cardiac troponin T (cTnT, red) and BrdU (white). (a) Representative immunofluorescence images of control (Healthy 2) and HLHS 1 cardiomyocytes, with or without CHIR + SB treatment. (b) The percentage of BrdU-positive cells based on the average intensity of BrdU staining in the nuclei of cTnT+ cardiomyocytes at Day 22. (c) The percentage of BrdU-positive cells based on the average intensity of BrdU staining in the nuclei of cTnT-positive cardiomyocytes at Day 35. Data are presented as mean ± SEM, with individual points representing biological replicates from distinct differentiations (n = 4-6). Statistical significance was determined using two-way ANOVA followed by Tukey’s post hoc multiple-comparison test. Results are expressed as mean ± SEM. Significance levels are indicated as *p \u0026lt; 0.05, **p \u0026lt; 0.01,***p \u0026lt; 0.001 and ,****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8250379/v1/60bfffc5adfd01723a77bf0d.png"},{"id":97667378,"identity":"78ba3f68-eeae-4117-a4bc-dfe1b346488b","added_by":"auto","created_at":"2025-12-08 09:23:20","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1132653,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of endothelin-1 (ET-1) on pro-B-type natriuretic peptide (proBNP) expression in healthy and HLHS cardiomyocytes. Human induced pluripotent stem cell-derived cardiomyocytes were treated with either DMSO + BSA (control) or 100 nM ET-1 for 24 hours, then fixed and stained for DNA (DAPI, blue), cardiac troponin T (cTnT, red), and proBNP (yellow). (a) Representative immunofluorescence images of Healthy 2 and HLHS 1 cardiomyocytes, with or without ET-1 treatment, acquired using a 20× S Plan Fluor ELWD objective. (b) The percentage of proBNP-positive cardiomyocytes based on the average intensity of proBNP staining in the perinuclear region of cTnT+ cardiomyocytes. (c) Average perinuclear proBNP intensity. (b, c) Data are presented as mean ± SEM, with individual points representing biological replicates from distinct differentiations (n = 4). Statistical significance was determined using two-way ANOVA followed by Tukey’s post hoc multiple-comparison test. Results are expressed as mean ± SEM. Significance levels are indicated as *p \u0026lt; 0.05, **p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8250379/v1/56b1f78bc4791cc1537baaa3.jpeg"},{"id":97434914,"identity":"d4ca2d8a-15bc-4ca3-b92a-b4975635b29b","added_by":"auto","created_at":"2025-12-04 10:56:50","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":311280,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of cyclic mechanical stretching on hypertrophic gene expression in healthy and HLHS cardiomyocytes. \u003c/strong\u003eHuman pluripotent stem cell-derived cardiomyocytes were subjected to 24h of cyclic mechanical stretch, whereafter mRNA expression was measured by qPCR. \u003cstrong\u003e(a) \u003c/strong\u003eRelative mRNA expression of \u003cem\u003eNPPA\u003c/em\u003e (natriuretic peptide A). \u003cstrong\u003e(b) \u003c/strong\u003eRelative mRNA expression of \u003cem\u003eNPPB\u003c/em\u003e (natriuretic peptide B). \u003cstrong\u003e(c) \u003c/strong\u003eRelative mRNA expression of \u003cem\u003eMYH6\u003c/em\u003e (Myosin Heavy Chain 6). \u003cstrong\u003e(d) \u003c/strong\u003eRelative mRNA expression of \u003cem\u003eMYH7\u003c/em\u003e (Myosin Heavy Chain 7).\u003cstrong\u003e (e) \u003c/strong\u003eRelative mRNA expression of \u003cem\u003eLDHA\u003c/em\u003e. \u003cstrong\u003e(f) \u003c/strong\u003eRelative mRNA expression of \u003cem\u003eSDHA\u003c/em\u003e(Succinate dehydrogenase complex subunit A). Data are normalised to the average of Healthy 2 control and presented as mean ± SEM, with individual points representing biological replicates from distinct differentiations (n = 3-5). Statistical significance was determined using two-way ANOVA followed by Tukey’s post hoc multiple-comparison test. Results are expressed as mean ± SEM. Significance levels are indicated as *p \u0026lt; 0.05, **p \u0026lt; 0.01, and ***p \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8250379/v1/c66e7d7bb7ef4344fbf150d1.jpeg"},{"id":97677661,"identity":"ab6b0959-b2ad-45de-becb-41ffa719694f","added_by":"auto","created_at":"2025-12-08 09:53:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6851612,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8250379/v1/47daf55e-c3ee-4e6a-8786-99488ce5902f.pdf"},{"id":97434932,"identity":"3a56c3b6-f576-448e-b618-b38030f0ae08","added_by":"auto","created_at":"2025-12-04 10:56:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2826909,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional files\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFile name:\u003c/strong\u003e Additional file 1.pdf\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFile format:\u003c/strong\u003e pdf\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTitle of data:\u003c/strong\u003e Supplementary information\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescription of data:\u003c/strong\u003e Supplementary tables and figures\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFile name:\u003c/strong\u003e Additional file 2.xlsx\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFile format:\u003c/strong\u003e xlsx\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTitle of data:\u003c/strong\u003e Differential Expression HLHS vs Healthy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescription of data:\u003c/strong\u003e Excel file with separate sheets for each HLHS line vs. healthy controls, a combined analysis, top commonly regulated genes, and a summary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFile name:\u003c/strong\u003e Additional file 3.xlsx\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFile format:\u003c/strong\u003e xlsx\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTitle of data:\u003c/strong\u003e GO Enrichment Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescription of data:\u003c/strong\u003e Excel file with GO term enrichment results for upregulated and downregulated genes, along with curated sets of selected terms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFile name:\u003c/strong\u003e Additional file 4.xlsx\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFile format:\u003c/strong\u003e xlsx\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTitle of data:\u003c/strong\u003e TF Regulon Activity\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescription of data:\u003c/strong\u003e Excel file containing transcription factor regulon activity scores and statistical comparisons between HLHS and control samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFile name:\u003c/strong\u003e Additional file 5.xlsx\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFile format:\u003c/strong\u003e xlsx\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTitle of data:\u003c/strong\u003e GO Enrichment Analysis of Cardiac TF Regulons\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescription of data:\u003c/strong\u003e Excel file with Gene Ontology enrichment results for target genes of cardiac-specific transcription factor regulons. Includes all enriched GO terms and a filtered set of cardiac-specific terms.\u003c/p\u003e","description":"","filename":"Additionalfile1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8250379/v1/8c80a61953cafcc3e7959b9c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hypoplastic left heart syndrome cardiomyocytes exhibit intrinsic stress vulnerabilities and augmented stress responses in vitro","fulltext":[{"header":"Background","content":"\u003cp\u003eCongenital heart diseases (CHDs) are structural malformations of the cardio-circulatory system that arise during embryonic development. Encompassing a broad spectrum of subtypes, CHDs range from relatively common minor lesions to complex and life-threatening anomalies such as hypoplastic left heart syndrome (HLHS). HLHS represents one of the most severe forms of CHD, characterised by the profound underdevelopment of left-sided cardiac structures, including the aortic and mitral valves, the left ventricle, and the aorta [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This defect critically impairs the ability of the left ventricle to support systemic circulation, rendering the condition fatal without surgical intervention [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe aetiology of CHDs is complex, and the precise pathogenic mechanisms underlying these disorders remain incompletely understood. Both genetic predisposition and environmental stressors contribute to the defects. In the context of HLHS, substantial efforts have been devoted to elucidating its molecular and genetic underpinnings. While HLHS occasionally co-occurs with syndromic disorders[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] it predominantly arises as an isolated defect. Genetic investigations have implicated a range of critical genes, including transcription factors (TF) such as \u003cem\u003eNKX2-5\u003c/em\u003e [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], \u003cem\u003eTBX5\u003c/em\u003e [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], \u003cem\u003eHAND1\u003c/em\u003e [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], as well as receptor and structural protein genes such as \u003cem\u003eNOTCH1\u003c/em\u003e[\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and \u003cem\u003eMYH6\u003c/em\u003e [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These findings underscore the diversity of genetic disruptions capable of derailing cardiac development. Nevertheless, genetic susceptibility alone does not fully account for the phenotypic heterogeneity and variable phenotype, suggesting that additional factors contribute significantly to disease pathogenesis.\u003c/p\u003e\u003cp\u003eIncreasing attention has been directed towards the role of environmental influences, which may act synergistically with genetic defects [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Maternal diabetes and obesity [\u003cspan additionalcitationids=\"CR21 CR22 CR23 CR24\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], hyperglycaemia [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], oxidative stress [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and aberrant haemodynamic forces in the developing foetus [\u003cspan additionalcitationids=\"CR30 CR31 CR32\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] have emerged as pivotal external factors capable of disrupting foetal cardiac development. These perturbations, often mediated through alterations at the maternal-foetal interface, may exert profound effects during critical windows of gestation.\u003c/p\u003e\u003cp\u003eThe advent of human induced pluripotent stem cells (hiPSCs) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] has ushered in a new paradigm in cardiovascular disease modelling. hiPSCs, reprogrammed from patient-specific cells, retain the full complement of the individual\u0026rsquo;s genetic information. This technology enables the generation of cardiomyocytes (CMs) that faithfully recapitulate patient-specific genetic backgrounds, providing an unprecedented platform for dissecting the cellular and molecular mechanisms underlying congenital heart defects such as HLHS.\u003c/p\u003e\u003cp\u003eIn this study, we examined both healthy and HLHS-derived cardiomyocytes to investigate their inherent differences as well as their capacity to respond to environmental cues. Acknowledging that heart development results from a dynamic interplay between genetic programming and extrinsic signals, we sought to model key developmental stressors in vitro. We performed single-cell RNA sequencing (scRNA-seq) to profile transcriptomic changes and identify disease-associated molecular signatures. Following this, we applied three distinct stimuli to model critical aspects of cardiac development: Endothelin-1 (ET-1), a known pro-hypertrophic hormone elevated in adverse maternal environments, was used to assess hypertrophic responses. Cyclic mechanical stretch simulated the biomechanical forces experienced by the foetal heart, and pro-proliferative compounds were used to evaluate the cells\u0026rsquo; proliferative potential. These stimuli were chosen to represent essential developmental processes relevant to both normal cardiac formation and the pathogenesis of HLHS. Together, these complementary approaches allowed us to interrogate intrinsic deficits and impaired adaptability that may contribute to the abnormal cardiac morphogenesis characteristic of HLHS.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ehiPS lines culture\u003c/h2\u003e\u003cp\u003eEight induced pluripotent stem cell (iPSC) lines were used in this study, including four from healthy controls (HEL24.3 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], HEL47.2 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], HEL46.11 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and K1), three from individuals with HLHS (HEL149.2, HEL218.6, HEL169.4), and one from an individual with left ventricular outflow tract obstruction (LVOTO; HEL216.6). All lines, except for K1, which was was kindly gifted by Anu-Suomalainen-Wartiovaara, were obtained from Biomedicum Stem Cell Center Core Facility. To improve readability throughout this study, these lines are hereafter referred to as follows: HEL24.3 as Healthy 1, HEL47.2 as Healthy 2, HEL46.11 as Healthy 3, K1 as Healthy 4, HEL149.2 as HLHS 1, HEL218.6 as HLHS 2, HEL169.4 as HLHS 3, and HEL216.6 as HLHS 4. Detailed information on donor sex, genetic variants, and cardiac phenotypes is provided in (Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The cell lines were created using retroviral or Sendai virus-mediated transduction with Yamanaka reprogramming factors \u003cem\u003eOCT3/4\u003c/em\u003e, \u003cem\u003eSOX2\u003c/em\u003e, \u003cem\u003eKLF4\u003c/em\u003e, and \u003cem\u003eMYC\u003c/em\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] as previously described by Trokovic \u003cem\u003eet al\u003c/em\u003e. 2015 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. All hiPSC lines used in this study were evaluated for pluripotency and regularly tested for genomic stability through karyotyping. The hiPSCs were seeded on plates thin-coated with Matrigel\u0026trade; (Corning, #354277; diluted at 1:200) and cultured in Essential 8\u0026trade; medium (Thermo Fisher Scientific, #A1517001). Cells were passaged twice weekly using PBS with 0.5 mM EDTA until at least passage 20 before differentiation. The absence of mycoplasma contamination was routinely confirmed using the MycoAlert Mycoplasma Detection Kit (Lonza, #LT07-218).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDifferentiation of hiPSCs into CMs\u003c/h3\u003e\n\u003cp\u003eCardiomyocyte (CM) differentiation was induced in a monolayer culture as previously described by Helle et al. (2021) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Briefly, human induced pluripotent stem cells (hiPSCs) were seeded at a 1:10\u0026ndash;1:15 ratio on 12-well plates coated with Matrigel\u0026trade; (Corning, #354277). Upon reaching 80\u0026ndash;90% confluency (Day 0), cardiac mesoderm induction was initiated by replacing Essential 8\u0026trade; with RPMI 1640 medium containing L-glutamine and glucose (Corning, #10-040-CV), supplemented with B-27\u0026trade; Supplement minus insulin (Thermo Fisher Scientific, #A1895601) and 4\u0026ndash;5 \u0026micro;M GSK-3α/β inhibitor CHIR-99021 (CHIR, Selleck Chemicals, #S2924). On Day 1, fresh medium containing CHIR was added. After 48 hours (Day 2), the culture medium was replaced with fresh RPMI 1640, replacing CHIR with 5 \u0026micro;M IWR-1 (Sigma, #I0161) and replenished on Day 3. From Day 4 to Day 5, cells were maintained in fresh RPMI 1640 supplemented with B-27\u0026trade; minus insulin. On Day 6 and Day 7, the medium was changed to RPMI 1640 with B-27\u0026trade; containing insulin (Thermo Fisher Scientific, #17504044). Feeding on Day 8 depended on the appearance of the cells: if significant cell death was observed, feeding was performed to help reduce the dead cells. From Day 9 to Day 11, to enrich for cardiomyocytes, the medium was replaced with RPMI 1640 without glucose (Thermo Fisher Scientific, #11560406), supplemented with B-27\u0026trade; containing insulin and 5 mM sodium L-lactate (Sigma Aldrich, #71718). No medium change was performed on Day 12. On Day 13, robust beating of the monolayer was typically observed, and the differentiated hiPSC-CMs were passaged. Cells were detached using Accutase (0.4 mL for 12-well plates, 0.6 mL for 6-well plates), quenched with RPMI 1640 culture medium with L-glutamine with glucose supplemented with B-27\u0026trade; (Thermo Fisher Scientific, #17504044), and centrifuged for 4 minutes at 200 \u0026times; g. Cells were resuspended in same medium with 10 \u0026micro;M Rock inhibitor (StemCell Technologies, #Y-27632) and replated onto a new plate. From the following day, cells were maintained in RPMI without glucose, supplemented with B-27 containing insulin and 5mM sodium L-lactate and fed every other day until day 25\u0026ndash;30, when they were moved to RPMI with glucose, supplemented with B-27 containing insulin until they were used in experiments - scRNAseq (Day 38, 39 or 27), Cell Cycle (Day 20 and Day 33), Endothelin-1 stimulation (Day 33) and cyclic mechanical stretch (Day 30\u0026ndash;40). To ensure experimental consistency, only differentiations achieving a cardiomyocyte purity of \u0026ge;\u0026thinsp;80%, as determined by cardiac troponin T immunostaining, were included in this study.\u003c/p\u003e\n\u003ch3\u003eCM processing for RNA-seq\u003c/h3\u003e\n\u003cp\u003ehiPSC-CMs were harvested for scRNA-seq at differentiation day 38 or 39, with the exception of HEL47.2, which was collected at day 27. Following established protocols [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], the cells were counted and washed with 0.04% BSA in PBS. For scRNA-seq preparation, patient-derived and control-derived cells were pooled separately by combining equal contributions from each respective cell line. The pooled samples were then sent to the Institute for Molecular Medicine Finland (FIMM) for processing and sequencing using the 10x Genomics Single Cell Protocol.\u003c/p\u003e\n\u003ch3\u003eRNA extraction, cDNA synthesis and qPCR\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eRNA extraction, cDNA synthesis and qPCR\u003c/div\u003e\u003cp\u003eFor qPCR, total RNA was purified using the NucleoSpin RNA Kit (Macherey-Nagel, #740961) according to the manufacturer\u0026rsquo;s protocol. The cells were lysed in 350 \u0026micro;l of RA1 lysis buffer supplemented with 1% β-mercaptoethanol and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until RNA isolation. Analysis of the RNA concentration and quality was performed with a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific). Total RNA (100\u0026ndash;500 ng) was reverse transcribed in 10 \u0026micro;l reactions by using the Transcriptor First Strand cDNA Synthesis Kit (Roche, #04897030001) using random hexamer primers and an MJ Mini Personal Thermal Cycler (Bio-Rad). The cDNA was diluted 1:10 in PCR grade H2O and stored at \u0026minus;\u0026thinsp;20\u0026deg;C. Commercial TaqMan\u0026reg; Gene Expression Assays (Thermo Fisher Scientific), detailed in Additional file 1: Table S2, were used in conjunction with the LightCycler\u0026reg; 480 Probes Master reagent (Roche) following the manufacturer's protocols. Gene expression was analysed using the LightCycler\u0026reg; 480 Real-Time PCR System (Roche) with 4.5 \u0026micro;l of cDNA in a 10 \u0026micro;l reaction volume on a white LightCycler\u0026reg; 480 Multiwell Plate 384 (Roche). No-template controls were included to confirm the absence of PCR contamination. Each reaction was performed in triplicate, with the mean of technical replicates representing a single biological replicate (n\u0026thinsp;=\u0026thinsp;1). Outliers within technical replicates were identified using Grubbs' test at a significance level of 0.05 and subsequently excluded from the analysis. Relative gene expression levels were quantified using the 2\u0026thinsp;\u0026minus;\u0026thinsp;ΔΔCt method, referenced to the average of \u003cem\u003eACTB\u003c/em\u003e and \u003cem\u003e18S\u003c/em\u003e rRNA housekeeping genes and normalised to the average of untreated biological replicates of Healthy 2.\u003c/p\u003e\n\u003ch3\u003eRNA-seq bioinformatics\u003c/h3\u003e\n\u003cp\u003eIn the scRNA-seq analysis, four HLHS lines and four healthy control lines were used. The healthy control lines were pooled and analyzed as a single control sample. To distinguish the sequencing data corresponding to each HLHS line within the pooled sample, we employed FreeBayes v1.3.1 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] to call genetic variants from the exome data of the four HLHS individuals. These variant calls were subsequently used as input for Demuxlet [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], enabling the assignment of individual cells to their respective cell lines within the combined single-cell RNA-sequencing dataset. Downstream analysis was performed in R (2024.09.1\u0026thinsp;+\u0026thinsp;394) using the Seurat (version 5.1.0) package. A total of 17,980 high-quality cells were isolated to identify distinct cell populations and enable subsequent downstream analyses. The following quality control criteria were applied to the data: (1) Genes expressed in fewer than 200 cells or in more than 8,000 cells were excluded; (2) Cells with fewer than 200 or more than 8,000 detected genes were removed, as these may indicate low-complexity cells or doublets; (3) Cells exhibiting more than 30% mitochondrial gene expression were excluded to eliminate potentially damaged or stressed cells. Data normalization was performed using the \u003cem\u003e\u0026ldquo;NormaliseData\u0026rdquo;\u003c/em\u003e function in Seurat, and the top 2,000 most variable genes were identified with the \u003cem\u003e\u0026ldquo;vst\u0026rdquo;\u003c/em\u003e method via the \u003cem\u003e\u0026ldquo;FindVariableFeatures\u0026rdquo;\u003c/em\u003e function. Integration anchors were computed using \u003cem\u003e\u0026ldquo;FindIntegrationAnchors\u0026rdquo;\u003c/em\u003e to align shared features across datasets, and the data were integrated using \u003cem\u003e\u0026ldquo;IntegrateData\u0026rdquo;\u003c/em\u003e. These genes were subsequently analysed using principal component analysis (PCA) for linear dimensionality reduction.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCell type identification and cluster analysis\u003c/h2\u003e\u003cp\u003eFollowing data integration and normalization, we performed unsupervised clustering to identify distinct cellular populations within the cardiac dataset and assess compositional differences between HLHS and healthy samples. The two-dimensional Uniform Manifold Approximation and Projection (UMAP) was performed using the RunUMAP function in Seurat on the first 30 principal components. Graph-based clustering was applied to identify cell populations based on gene expression profiles using the FindClusters function, and a resolution of 0.4 was selected for downstream analysis, resulting in 14 distinct clusters. The resulting UMAP projection was used to visualize the clusters, and cell types were annotated based on known marker genes. Cluster-specific markers were identified using the FindAllMarkers function and annotated using published literature.\u003c/p\u003e\u003cp\u003eCell populations were classified as follows: immature cardiomyocytes (clusters 0, 4, 5, 6, 7, 8, 10, 12, 13) expressing cardiac transcription factors (\u003cem\u003eNKX2-5\u003c/em\u003e, \u003cem\u003eTBX5\u003c/em\u003e, \u003cem\u003eGATA4\u003c/em\u003e, \u003cem\u003eISL1\u003c/em\u003e) and sarcomeric genes; mature cardiomyocytes (clusters 1, 2, 3, 11) with robust expression of structural genes (\u003cem\u003eMYH6\u003c/em\u003e, \u003cem\u003eMYL7\u003c/em\u003e, \u003cem\u003eTNNT2\u003c/em\u003e, \u003cem\u003eTTN\u003c/em\u003e); and cardiac fibroblasts/smooth muscle cells (cluster 9) expressing \u003cem\u003ePOSTN\u003c/em\u003e, \u003cem\u003eCOL1A1\u003c/em\u003e, \u003cem\u003eTAGLN\u003c/em\u003e, and \u003cem\u003eACTA2\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eTo assess differences in cluster composition between healthy and HLHS samples, the proportion of cells in each cluster was calculated relative to the total number of cells per sample or group. Statistical significance was evaluated using chi-square tests with Benjamini-Hochberg FDR correction. Comparisons were performed between Healthy versus all HLHS samples combined, and between Healthy versus each individual HLHS line separately.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDifferential gene expression analysis\u003c/h3\u003e\n\u003cp\u003eDifferential gene expression analysis was performed on cardiomyocyte populations (clusters 0\u0026ndash;8, 10\u0026ndash;13) using the FindMarkers function in Seurat with the default statistical test, Wilcoxon Rank Sum test. Two analytical approaches were employed: (1) overall comparison of Healthy versus all HLHS samples combined, and (2) line-specific comparisons of Healthy versus each individual HLHS line (HLHS 1\u0026ndash;4). Differentially expressed genes (DEGs) were selected only if they met the following criteria: average log₂ fold change\u0026thinsp;\u0026gt;\u0026thinsp;0.5 or \u0026lt; -0.5 and adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. To identify core transcriptional changes, we examined genes consistently dysregulated in the same direction across multiple HLHS samples.\u003c/p\u003e\u003cp\u003eFor visualization purposes in volcano plots, genes with adjusted p-values of 0 (\u003cem\u003eS100A10\u003c/em\u003e, \u003cem\u003eNPPB\u003c/em\u003e, \u003cem\u003eNPPA, RPS27\u003c/em\u003e, \u003cem\u003eRPS29\u003c/em\u003e, \u003cem\u003eRPS26\u003c/em\u003e) were assigned an adjusted p-value of 1⁻\u0026sup3;\u0026sup2;⁰ to enable numerical calculation of -log₁₀(adjusted p-value). Key differentially expressed genes were visualized using volcano plots and heatmaps. Gene ontology enrichment analysis was performed to identify biological processes associated with differentially expressed genes, with results displayed as bubble plots showing fold enrichment and statistical significance.\u003c/p\u003e\n\u003ch3\u003eGene Ontology Enrichment Analysis of Differentially Expressed Genes\u003c/h3\u003e\n\u003cp\u003eThe set of differentially expressed genes identified across all four HLHS-CM lines was used as input for Gene Ontology (GO) enrichment analysis. This was conducted using the Functional Annotation Tool within the Database for Annotation, Visualization and Integrated Discovery (DAVID) and the DAVID Knowledgebase (v2023q4, updated quarterly; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://davidbioinformatics.nih.gov/tools.jsp\u003c/span\u003e\u003cspan address=\"https://davidbioinformatics.nih.gov/tools.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The gene list was uploaded using official gene symbols as identifiers, and Homo sapiens was selected as the background species. From the resulting Annotation Summary, the Gene Ontology Biological Processes category was selected for further analysis. GO terms with a false discovery rate (FDR)-adjusted p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly overrepresented.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eTranscription factor regulon analysis\u003c/h2\u003e\u003cp\u003eTo systematically evaluate transcriptional regulatory changes in HLHS, we performed regulon activity analysis across all cardiac cell populations. Transcription factors were identified from the Lambert et al. (2018) validated human TF database [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], filtering for those expressed in \u0026ge;\u0026thinsp;5% of cells. For each TF, target genes were predicted by calculating Spearman correlation coefficients (threshold\u0026thinsp;\u0026gt;\u0026thinsp;0.1), retaining only regulons with \u0026ge;\u0026thinsp;10 target genes.\u003c/p\u003e\u003cp\u003eRegulon activity in individual cells was quantified using AUCell (v1.24.0), with the AUC maximum rank parameter set to 5% of total genes. Differential regulon activity between HLHS and healthy samples was assessed using Wilcoxon rank-sum tests with Benjamini-Hochberg FDR correction (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For each regulon, we calculated log₂ fold change and percent change. Cardiac-specific TF regulons were identified by cross-referencing TFs with cardiac development Gene Ontology terms.\u003c/p\u003e\u003cp\u003eFor the 90 cardiac TF regulons, GO enrichment analysis was performed on target genes using clusterProfiler (v4.10.0) with FDR correction (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For each regulon, the top 3 most significantly enriched pathways were retained and manually grouped into four functional categories for downstream interpretation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCell proliferation assay\u003c/h2\u003e\u003cp\u003eTo induce and assess cell proliferation, hiPSC-CMs at Days 20 and 33 of differentiation were treated with a combination of CHIR99021 and SB203580, which work synergistically to promote cardiomyocyte proliferation [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. CHIR99021, a GSK-3 inhibitor, activates Wnt/β-catenin signalling [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], while SB203580 inhibits p38 MAPK-mediated cell cycle suppression [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], together producing more robust mitogenic effects than either compound alone. hiPSC-CMs were seeded onto Matrigel-coated 96-well PhenoPlates (PerkinElmer, #6055300) at a density of 4.5\u0026times;10\u003csup\u003e4\u003c/sup\u003e cells per well and allowed to adhere overnight. The hiPSC-CMs were then divided into two treatment groups: the experimental group was treated with a combination of 5 \u0026micro;M CHIR99021 (CHIR, Selleck Chemicals, #S2924) and 10 \u0026micro;M SB203580 (SB, Selleck Chemicals, #S1076) alongside 10 \u0026micro;M Bromodeoxyuridine (BrdU, Abcam, #ab142567) for 24 hours, while the control group received vehicle (DMSO) with BrdU for the same period.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eInduction of cardiomyocyte hypertrophy\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eEndothelin-1 (ET-1) treatment\u003c/strong\u003e\u003cp\u003eTo induce cell hypertrophy, Day 33 hiPSC-CMs were seeded onto 96-well matrigel-coated PhenoPlates (PerkinElmer, #6055300) at 4.5\u0026times;10\u003csup\u003e4\u003c/sup\u003e cells per well and allowed to attach overnight. The cells were then exposed to either ET-1 or a vehicle control consisting of 1% bovine serum albumin (BSA; Sigma-Aldrich, #A9418) in Dulbecco\u0026rsquo;s Modified Eagle Medium (DMEM; Sigma D-7777) for 24 hours. Brefeldin A (1000X Solution; Invitrogen, #B7450) was added to the last 3 hours to inhibit the exocytosis of pro-B-type natriuretic peptide (proBNP)-containing vesicles as described previously [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCyclic mechanical stretch\u003c/strong\u003e\u003cp\u003eTo assess the effect of cyclic mechanical loading on the expression of hypertrophy-related genes, hiPSC-CMs aged between Day 30\u0026ndash;40 were cultured on BioFlex\u0026reg; plates at a cell density of 8.5\u0026times;10\u003csup\u003e5\u003c/sup\u003e per well. These cells were then subjected to 24 or 48 hours of cyclic mechanical strain using an FX-5000 Tension System (Flexcell International Corporation). Equibiaxial cyclic stretch was applied in two-second cycles (0.5 Hz) at a level sufficient to promote cyclic 10 to 21% elongation, corresponding to 42\u0026ndash;80 kPa at the point of maximal distension of the culture surface [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Unstretched control cells from the same differentiation were maintained in BioFlex\u0026reg; plates in the same environmental conditions, but no stretch was applied.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eImmunofluorescence staining\u003c/h2\u003e\u003cp\u003eFor immunofluorescence staining, all procedures were carried out at room temperature (RT) unless otherwise specified. hiPSC-CMs were washed twice with phosphate-buffered saline (PBS) and fixed with 4% paraformaldehyde for 15 minutes. Cells were then washed 3x5 min with PBS. Permeabilization was performed using 0.1% Triton X-100 (AppliChem, #A4975) in PBS for 10 minutes, followed by 2x5 min washes with PBS. For BrdU staining, DNA was hydrolysed with 2 M hydrochloric acid for 30 minutes, neutralized with 0.1 M sodium borate (pH 8.5) for 30 minutes, and washed 3x5 min with PBS. To prevent nonspecific binding, the cells were blocked with 4% foetal bovine serum (FBS, Thermo Fisher Scientific, #10500064) in PBS for 45 minutes. The cells were then incubated for 60 minutes with the following primary antibodies diluted in 4% FBS in PBS: cardiac troponin T (cTnT) antibody (Abcam, #ab45932, 1:800), BrdU antibody (Abcam, #ab6326, 1:250), or proBNP antibody (Abcam, #ab13115, 1:250), followed by 3x5 min washes with PBS. Cells were subsequently incubated for 45 minutes with Alexa Fluor\u0026reg;-conjugated secondary antibodies: Alexa Fluor\u0026trade; 546 (Invitrogen, #A-11035, 1:200), Alexa Fluor\u0026trade; 647 (Invitrogen, #A-21247, 1:200), Alexa Fluor\u0026trade; 647 (Invitrogen, #A-21236, 1:200), and 4\u0026prime;,6-diamidino-2-phenylindole (DAPI) (Sigma-Aldrich, #D9542, 1 \u0026micro;g/ml), followed by 3x5 min washes with PBS. Finally, cells were stored in PBS at 4\u0026deg;C until imaging.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eImaging and analysis\u003c/h2\u003e\u003cp\u003eAutomated fluorescence microscopy was performed using the ImageXpress Micro Confocal imaging system (Molecular Devices). Representative images were acquired with a Nikon 20\u0026times; Plan Apo 0.5 NA air objective, while images for downstream analysis were captured using a Nikon 10\u0026times; Plan Apo objective. The acquired images were analysed using MetaXpress software (Molecular Devices). First, the nuclei were identified based on DAPI staining, and cardiomyocytes were distinguished by the presence of cTnT staining in the cytoplasm, with non-myocytes excluded based on the absence of cTnT. To identify BrdU-positive CMs, nuclei confirmed as CMs by DAPI and cTnT staining were cross-referenced with BrdU staining, and the overlap of these signals was defined as BrdU-positive CMs. The threshold for BrdU positivity was manually adjusted in each experiment to account for variations in staining intensity. For proBNP quantification, the average intensity of proBNP staining was measured within the perinuclear region, defined as a 10-pixel ring surrounding each CM nucleus. Similarly to the BrdU analysis, cells were categorized as proBNP-positive or proBNP-negative based on staining intensity, with thresholds manually adjusted for each experiment to account for staining variations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were carried out using GraphPad Prism 8 software. Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM), unless otherwise stated. Statistical significance was determined using two-way ANOVA followed by Tukey\u0026rsquo;s post hoc multiple-comparison test. A p-value of less than 0.05 was considered statistically significant. All experiments were conducted with a minimum of three biological replicates (n), unless otherwise stated. Statistical analysis of the scRNAseq was performed as a pairwise comparisons between each HLHS sample and healthy controls were performed using the Wilcoxon Rank Sum test, implemented in the \u003cem\u003e\u0026ldquo;FindAllMarkers\u0026rdquo;\u003c/em\u003e function of the Seurat package. In addition, a combined analysis comparing all HLHS samples to healthy controls was conducted using the same statistical method. Genes were considered differentially expressed if they exhibited an absolute log₂fold change greater than 0.5 and a Bonferroni-adjusted p-value of less than 0.05. The Bonferroni correction was applied to adjust for multiple testing across all genes in the dataset.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of cell types\u003c/h2\u003e\u003cp\u003eUnsupervised graph-based clustering of the single cell RNA sequencing data identified 14 distinct cell clusters (clusters 0\u0026ndash;13) across both conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Transcriptional profiling revealed largely overlapping distributions between healthy and HLHS cardiomyocytes, suggesting considerable similarity in overall cellular heterogeneity.\u003c/p\u003e\u003cp\u003eWhile the general clustering pattern was comparable across samples, differences in cluster distribution were observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec) Cluster 1 showed the largest enrichment in HLHS samples, while cluster 3 exhibited the lowest number of HLHS cells. Individual HLHS line analysis is shown in (Additional file 1: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea-c)\u003c/p\u003e\u003cp\u003eTo characterise the molecular identity of each cluster, we identified differentially expressed marker genes, with the top 3 markers per cluster displayed in (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003eBased on the canonical marker expression patterns, we classified the 14 clusters into three major cell populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). Clusters 0, 4, 5, 6, 7, 8, 10, 12, and 13 robustly expressed both cardiac-specific transcription factors (\u003cem\u003eNKX2-5\u003c/em\u003e, \u003cem\u003eTBX5\u003c/em\u003e, \u003cem\u003eGATA4\u003c/em\u003e, and \u003cem\u003eISL1\u003c/em\u003e) and structural genes essential for cardiomyocyte function (\u003cem\u003eMYH6\u003c/em\u003e, \u003cem\u003eMYL7\u003c/em\u003e, \u003cem\u003eTNNT2\u003c/em\u003e, and \u003cem\u003eTTN\u003c/em\u003e), indicating immature cardiomyocytes at an earlier stage of differentiation where developmental regulatory networks remain active. In contrast, clusters 1, 2, 3, and 11 displayed a gene expression profile consistent with more mature cardiomyocytes. These clusters showed minimal expression of developmental transcription factors while maintaining robust expression of functional sarcomeric components Cluster 9 displayed a distinct expression profile, with relatively low expression of cardiac markers but high levels of fibroblast markers (\u003cem\u003ePOSTN\u003c/em\u003e, \u003cem\u003eCOL1A1\u003c/em\u003e, \u003cem\u003eDDR2\u003c/em\u003e, \u003cem\u003eTHY1\u003c/em\u003e) and smooth muscle cell markers (\u003cem\u003eTAGLN\u003c/em\u003e, \u003cem\u003eACTA2\u003c/em\u003e, \u003cem\u003eCNN1\u003c/em\u003e, \u003cem\u003eMYH11\u003c/em\u003e), identifying this cluster as a non-myocyte population. Pluripotency markers (\u003cem\u003eSOX2\u003c/em\u003e, \u003cem\u003eNANOG\u003c/em\u003e, \u003cem\u003ePOU5F1\u003c/em\u003e, \u003cem\u003eLIN28A\u003c/em\u003e) and primitive streak markers (\u003cem\u003eFOXA2\u003c/em\u003e, \u003cem\u003eTBXT\u003c/em\u003e, \u003cem\u003eEOMES\u003c/em\u003e, \u003cem\u003eGSC\u003c/em\u003e) were undetectable across all clusters (Additional file 1: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ed), confirming the absence of undifferentiated cells or early mesodermal progenitors.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eDifferential gene expression and functional enrichment analysis\u003c/h2\u003e\u003cp\u003eTo identify the molecular alterations underlying the compositional differences between healthy and HLHS hiPSC-CMs, we performed differential gene expression analysis. This analysis revealed substantial transcriptomic differences, identifying 1,211 differentially expressed genes, of which 292 were upregulated and 919 downregulated in HLHS compared to healthy cardiomyocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Additional file 2). Individual HLHS line analysis is shown in (Additional file 1: Fig. S2a-d).\u003c/p\u003e\u003cp\u003eGene Ontology (GO) enrichment analysis of differentially expressed genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb; Additional file 3) revealed significant involvement in a range of developmental and functional pathways.\u003c/p\u003e\u003cp\u003eNotably, many of the enriched biological processes were observed among the downregulated genes, including processes related to circulatory system, vasculature development, and muscle cell development. To explore these in more detail, we generated heatmaps of representative genes from the downregulated categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Among the downregulated genes in circulatory system processes, key genes included \u003cem\u003eEDN1\u003c/em\u003e, \u003cem\u003eSOD3\u003c/em\u003e, and \u003cem\u003eACE2\u003c/em\u003e. Genes involved in vasculature development showed reduced expression, including \u003cem\u003eAPOE\u003c/em\u003e, \u003cem\u003eEDN1\u003c/em\u003e, and \u003cem\u003eJAG1\u003c/em\u003e. Muscle cell development genes such as \u003cem\u003eMYH6\u003c/em\u003e, \u003cem\u003eBMP10\u003c/em\u003e, and \u003cem\u003eTNNT1\u003c/em\u003e were also downregulated in HLHS samples. Additional downregulated genes and their associated terms included genes involved in regulation of heart contraction (\u003cem\u003eMYH6\u003c/em\u003e, \u003cem\u003eHCN2\u003c/em\u003e, \u003cem\u003eHCN4\u003c/em\u003e, \u003cem\u003eKCNA5\u003c/em\u003e, \u003cem\u003eKCNE1\u003c/em\u003e), regulation of response to stress (\u003cem\u003eENO1\u003c/em\u003e, \u003cem\u003eSESN2\u003c/em\u003e, \u003cem\u003eAPOA1\u003c/em\u003e, \u003cem\u003eNPPA\u003c/em\u003e), and transport (\u003cem\u003eSLC7A5\u003c/em\u003e, \u003cem\u003eATP2B4\u003c/em\u003e). These findings indicate that HLHS hiPSC-CMs exhibit widespread downregulation of genes involved in cardiac development, contractile function, circulatory system processes, and cellular stress responses.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eTF Regulon Activity in HLHS Cardiomyocytes\u003c/h2\u003e\u003cp\u003eTo understand regulatory mechanisms underlying transcriptional changes in HLHS, we performed transcription factor regulon analysis. This approach reveals whether TFs are actively regulating their downstream targets, which can be disrupted in disease even without changes in TF expression itself.\u003c/p\u003e\u003cp\u003eWe constructed and analyzed 844 TF regulons, of which 645 showed statistically significant differential activity between HLHS and healthy samples (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Additional file 4). The vast majority (626 regulons, 97.1%) exhibited decreased activity in HLHS compared to healthy controls, whereas 19 regulons (2.9%) showed increased activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and b). A total of 90 of the differentially active regulons were driven by cardiac-specific transcription factors, with 86 (95.6%) showing reduced activity and 4 (4.4%) showing increased activity in HLHS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eThe most increased regulons included CAMTA1, NKX2-5, and YBX1, while the most decreased included MEIS3, RXRG, and ETV5.\u003c/p\u003e\u003cp\u003eTo explore the functional implications of these changes, we performed GO enrichment analysis on the target genes of each of the 90 cardiac-specific TF regulons (Additional file 5). Enrichment in four major functional categories was observed: cardiac conduction and signalling, cardiac morphogenesis and septation, extracellular matrix and tissue organization, and cardiac muscle development and differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec; Additional file 5). Pathways related to cardiac muscle development and differentiation showed the most extensive connections.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eCharacterisation of hiPSC-CMs proliferation\u003c/h2\u003e\u003cp\u003eTo explore basal proliferation and the response to mitogenic stimulation, cardiomyocytes were treated with CHIR and SB. Representative immunofluorescence images from Healthy 2 and HLHS 1 at Day 22 illustrate increased BrdU incorporation following CHIR\u0026thinsp;+\u0026thinsp;SB treatment, along with elevated baseline levels in HLHS 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). This pattern was supported by quantification, which showed that under control conditions, HLHS 1 exhibited a significantly higher percentage of BrdU\u003csup\u003e+\u003c/sup\u003e cardiomyocytes compared to Healthy 2 (1.9-fold increase; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating an intrinsically elevated proliferative capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Upon CHIR\u0026thinsp;+\u0026thinsp;SB stimulation at Day 22, all cell lines exhibited significant increases in BrdU\u003csup\u003e+\u003c/sup\u003e cardiomyocytes compared to their respective controls: Healthy 1 (1.6-fold, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Healthy 2 (1.9-fold, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), HLHS 1 (1.5-fold, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and HLHS 2 (1.8-fold, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). In contrast, baseline BrdU incorporation at Day 35 was comparable across all cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). CHIR\u0026thinsp;+\u0026thinsp;SB stimulation at this later time point elicited robust proliferative responses in all lines: Healthy 1 (3.6-fold, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Healthy 2 (4.8-fold, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), HLHS 1 (3.5-fold, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and HLHS 2 (2.8-fold, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Quantification of average BrdU intensity (Additional file 1: Fig. S3a and S3b) and average cTnT intensity (Additional file 1: Fig. S3c and S3d) remained consistent across all groups and time points.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eCharacterisation of hypertrophic responses\u003c/h2\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eBasal and ET-1-induced stress responses in hiPSC-CMs\u003c/h2\u003e\u003cp\u003eTo investigate potential intrinsic differences in hypertrophic stress response between healthy and HLHS cardiomyocytes, we assessed the expression of proBNP, a key marker of cardiac stress, under both basal and ET-1-stimulated conditions. Representative immunofluorescence images demonstrated an increased number of proBNP\u003csup\u003e+\u003c/sup\u003e cardiomyocytes in HLHS 1 following ET-1 treatment, while Healthy 2 cardiomyocytes exhibited a detectable but non-significant increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). This observation was supported by quantitative analysis, which revealed significant increases in the number of proBNP\u003csup\u003e+\u003c/sup\u003e cardiomyocytes in HLHS 1 and HLHS 2 (7.1-fold, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and 5-fold, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 over control, respectively), whereas Healthy 1 and Healthy 2 showed non-significant increases (2-fold and 2.4-fold, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). A similar trend was observed in the quantification of perinuclear proBNP intensity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec), and cTnT expression remained consistent across all conditions (Additional file 1: Fig. S4).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eCyclic mechanical stretching\u003c/h2\u003e\u003cp\u003eCardiomyocytes are constantly exposed to mechanical forces \u003cem\u003ein vivo\u003c/em\u003e, and their ability to adapt is critical for maintaining heart function. To investigate mechanotransduction differences between healthy and HLHS cardiomyocytes, we applied cyclic stretch for 24 and 48 hours and assessed gene expression profiles associated with hypertrophic remodelling, contractility, and metabolism. We first validated our stretch model by assessing two canonical hypertrophy markers, \u003cem\u003eNPPA\u003c/em\u003e and \u003cem\u003eNPPB\u003c/em\u003e. Under static conditions, their expression levels were comparable between HLHS and healthy cardiomyocytes. Following 24 hours of cyclic stretch, both groups showed a non-significant trend toward increased \u003cem\u003eNPPA\u003c/em\u003e and \u003cem\u003eNPPB\u003c/em\u003e expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Given the importance of contractile protein remodelling in response to stress, we also measured the expression of \u003cem\u003eMYH6\u003c/em\u003e and \u003cem\u003eMYH7\u003c/em\u003e. Under static conditions, MYH6 expression in HLHS 1 was ~\u0026thinsp;20% of Healthy 1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ~\u0026thinsp;21% of Healthy 2 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and ~\u0026thinsp;25% of HLHS 2 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec), suggesting intrinsic differences in contractile gene expression. After 24 hours of stretch, \u003cem\u003eMYH6\u003c/em\u003e showed a downward trend in both healthy and HLHS cardiomyocytes. \u003cem\u003eMYH7\u003c/em\u003e expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed) and \u003cem\u003eMYH6/MYH7\u003c/em\u003e ratio (Additional file 1: Fig. S5) remained stable across all conditions, indicating that short-term mechanical stimulation did not alter myosin isoform mRNA expression. To assess metabolic adaptations, we evaluated expression of LDHA and SDHA, key enzymes in glycolysis and oxidative phosphorylation, respectively. LDHA expression did not differ among groups and remained unchanged following 24-h stretch (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee). By contrast, SDHA expression was lower in HLHS 1 under static conditions (~\u0026thinsp;40% of Healthy 1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; and ~\u0026thinsp;42% of Healthy 2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and was not affected by mechanical stimulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef). We also examined cardiac transcription factors \u003cem\u003eNKX2.5\u003c/em\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], \u003cem\u003eMEF2C\u003c/em\u003e [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], \u003cem\u003eHES1\u003c/em\u003e [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] and \u003cem\u003eCSRP3\u003c/em\u003e [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], which regulate cardiomyocyte development and stress responses. No notable changes were observed under static or stretched conditions (Additional file 1: Fig. S5). The results of the 48-hour stretching experiments mirrored those observed at 24 hours, suggesting that gene expression differences between HLHS and healthy cardiomyocytes are stable and not substantially altered by prolonged mechanical stimulation (Additional file 1: Fig. S6).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of this study indicate that patient-derived HLHS cardiomyocytes exhibit heightened vulnerability to stress, as demonstrated through both transcriptomic profiling of unstimulated cells and functional stress response assays. These findings suggest impaired adaptive mechanisms, potentially contributing to reduced cardiac resilience in HLHS. Maternal metabolic disease [\u003cspan additionalcitationids=\"CR21 CR22 CR23 CR24\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and maternal hypertension [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] are well known risk factors for CHD in the offspring, and both conditions are likely to result in exposure to increased metabolic and oxidative stress in the developing embryo and foetus. Thus, genetic predisposition for reduced tolerance to environmental stressors in the developing heart may contribute to the multifactorial aetiology of CHD.\u003c/p\u003e\u003cp\u003eTranscriptomic profiling of HLHS cardiomyocytes indicated downregulation of genes involved in metabolic resilience and antioxidant defence, such as \u003cem\u003eENO1\u003c/em\u003e and \u003cem\u003eSESN2\u003c/em\u003e, wherein \u003cem\u003eENO1\u003c/em\u003e plays a crucial role in maintaining glycolytic flux under hypoxic conditions, facilitating ATP production when oxidative phosphorylation is compromised, thus having a protective role during cardiac stress [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Interestingly, downregulation of \u003cem\u003eENO1\u003c/em\u003e has also been observed in a left ventricle cardioid model harbouring the transcription factor \u003cem\u003eFOXF1\u003c/em\u003e knockout recapitulating CHD [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. \u003cem\u003eSESN2\u003c/em\u003e is integral to regulating oxidative stress responses and maintaining metabolic homeostasis; its deficiency has been linked to impaired cardiac protection and increased susceptibility to oxidative damage [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. \u003cem\u003eSESN2\u003c/em\u003e polymorphism has also been associated with CHD [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Additional reductions in \u003cem\u003eAPOA1\u003c/em\u003e and \u003cem\u003eSOD3\u003c/em\u003e, which are involved in lipid regulation and reactive oxygen species detoxification respectively, further suggest compromised oxidative stress defence mechanisms. These signatures are consistent with prior reports of mitochondrial dysfunction and apoptosis in HLHS iPSC-CMs under metabolic challenge[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], and may reflect pathophysiologic mechanisms behind maternal metabolic disease as a risk factor for CHD in the offspring [\u003cspan additionalcitationids=\"CR21 CR22 CR23 CR24\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile differential gene expression analysis identified specific genes with altered expression, we sought to determine whether these changes reflected coordinated disruption of transcriptional regulatory networks. Regulon analysis revealed widespread disruption of transcriptional regulatory networks in HLHS cardiomyocytes. Of 645 significantly differentially active regulons, the vast majority showed decreased activity in HLHS compared to healthy controls, with a similar pattern among cardiac-specific transcription factors. While the magnitude of these changes was modest, even small alterations in transcription factor activity can have amplified downstream effects on target gene expression networks [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The affected regulons span multiple aspects of cardiac function, including cardiac muscle development, morphogenesis, conduction, and extracellular matrix organization. This pattern of coordinated transcriptional downregulation may suggest impairments in differentiation, function, and interaction with other cell types, potentially leading to systemic regulatory dysfunction rather than isolated pathway defects. This may affect proper cardiac development and stress adaptation.\u003c/p\u003e\u003cp\u003eTo further probe cardiomyocyte adaptability, we examined their responses to external stressors, including endothelin-1 (ET-1) and cyclic mechanical stretch. The HLHS lines showed significantly greater increases in proBNP expression during ET-1 stimulation compared to controls, indicating a heightened sensitivity to pro-hypertrophic hormonal challenge. Indeed, ET-1 stimulation may recapitulate increased stress in the developing heart well, as several lines of evidence suggest that elevated plasma ET-1 is one of the mediators of vascular complications in individuals with metabolic disease and it is an important mediator in uteroplacental circulation and foetal vascular function [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] and [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. ET-1 is also known to play an important role in regulating cardiomyocyte differentiation during heart development [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. The heightened responses to ET-1 stimulation in HLHS hiPS-CMs may thus reflect disease-specific vulnerability to external stressors such as maternal metabolic disease.\u003c/p\u003e\u003cp\u003eIn contrast, responses to mechanical stretch were more heterogeneous. Cyclic stretch did not substantially alter gene expression profiles in a manner that consistently separated HLHS from controls. A previous study applying stretch to HLHS-CMs observed downregulation of cell cycle genes and upregulation of structural genes [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], partially similar to our findings, although the overall transcriptional response in our dataset was modest. Overall, these results point to transcriptional heterogeneity among HLHS lines in their response to biomechanical cues, underscoring the complexity of modelling this condition and the value of patient-specific approaches.\u003c/p\u003e\u003cp\u003ePrevious studies have consistently reported reduced basal proliferation in HLHS cardiomyocytes compared to controls, including in HLHS tissue [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], iPSC models [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], and animal models [\u003cspan additionalcitationids=\"CR68\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. To assess both basal and inducible proliferative capacity, we used CHIR99021 and SB203580, which act synergistically to enhance cardiomyocyte proliferation through complementary mechanisms [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In our study, HLHS cardiomyocytes showed either similar or increased proliferation at baseline compared to controls, challenging the idea of a uniformly reduced proliferative capacity. A recent study using CHIR99021 to stimulate proliferation reported lower proliferation in HLHS cells relative to controls at both baseline and after treatment [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Importantly, their data, while focused on intergroup comparison, also showed that HLHS cardiomyocytes were capable of increasing proliferation in response to CHIR. This is consistent with our findings, which demonstrate that HLHS cardiomyocytes retain mitogenic responsiveness despite baseline variability.\u003c/p\u003e\u003cp\u003eIn addition to metabolic and proliferative abnormalities, HLHS cardiomyocytes exhibited changes in the expression of several ion channels. Transcriptomic signatures showed reduced expression of key pacemaker channel genes, including \u003cem\u003eHCN2\u003c/em\u003e and \u003cem\u003eHCN4\u003c/em\u003e, which are essential for maintaining sinoatrial node function and regulating cardiac automaticity. Notably, genetic variants in \u003cem\u003eHCN4\u003c/em\u003e have been implicated in atrial arrhythmias, atrioventricular nodal disease, and left ventricular noncompaction [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. In parallel, expression of potassium channel genes \u003cem\u003eKCNA5\u003c/em\u003e and \u003cem\u003eKCNE1\u003c/em\u003e, associated with atrial fibrillation and cardiac repolarization anomalies, respectively [\u003cspan additionalcitationids=\"CR72\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], were also reduced in HLHS cardiomyocytes. These changes were accompanied by decreased expression of \u003cem\u003eSHOX2\u003c/em\u003e, a transcription factor essential for sinoatrial node development and atrial fibrillation [\u003cspan additionalcitationids=\"CR75\" citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. While the direct impact of these electrophysiological abnormalities on cardiac morphogenesis remains uncertain, such alterations may contribute to the intrinsic component of the increased arrhythmic susceptibility in congenital heart disease patients.\u003c/p\u003e\u003cp\u003eThese findings provide insight into how HLHS cardiomyocytes integrate, or fail to integrate, key developmental cues. While maternal conditions such as diabetes and hypertension are known to elevate circulating ET-1, our data show that HLHS cardiomyocytes exhibit a markedly heightened response to ET-1. This suggests that HLHS cells may be primed for pathological activation when challenged by hormonal cues during development. Taken together, these results emphasize the importance of gene\u0026ndash;environment interactions and support a model in which inappropriate responses to developmental stressors contribute to disease progression. This insight may help explain the clinical variability observed in HLHS and highlights the value of patient-specific models for therapeutic development.\u003c/p\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003eStudy Limitations and Future Directions\u003c/h2\u003e\u003cp\u003eWhile our study provides valuable insights into the cellular and molecular basis of HLHS, the small number of patient-derived cell lines limits our ability to fully capture the full spectrum of HLHS heterogeneity. The variable responses observed among our HLHS lines reflect the complex, patient-specific nature of this disorder and suggest that multiple pathogenic mechanisms may exist. Nevertheless, despite the limited sample size, our findings identify common maladaptive processes in HLHS cardiomyocytes, supporting the validity and broader relevance of our conclusions.\u003c/p\u003e\u003cp\u003eAnother limitation is that our hiPSC-CMs were studied in 2D monoculture systems, lacking the native cardiac microenvironment and heterotypic cell-cell interactions that could influence cellular behaviour, indicating that additional non-myocyte-derived pathways and mechanisms not identified here are likely to contribute to the disease development. While this simplification may exclude important contributions from non-myocyte-derived pathways, it allowed us to focus specifically on intrinsic cardiomyocyte defects. Moreover, the relative immaturity of hiPSC-CMs, often considered a limitation [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e], may actually serve as a strength in this context, as it mirrors the developmental stage during which congenital heart defects such as HLHS originate.\u003c/p\u003e\u003cp\u003eFuture studies should expand the cohort of patient-derived lines to more robustly characterise cellular heterogeneity and patient-specific variability. Increasing the number of lines would help validate and refine the molecular and cellular mechanisms identified here. In addition, employing advanced culture systems, such as cardiac co-culture models or engineered heart tissues, could better replicate the in vivo cardiac microenvironment and provide further insights into the multicellular and biomechanical interactions underlying HLHS. Despite the inherent complexity and heterogeneity of HLHS, these advanced patient-derived in vitro models are expected to unravel cellular and molecular mechanisms underlying HLHS, which may lead to improved diagnostic and preventative and/or therapeutic strategies in the future.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur results indicate that patient-derived HLHS cardiomyocytes display impaired function and heightened sensitivity to oxidative and metabolic challenges. Despite the heterogeneity among patient lines, these common features suggest shared vulnerability mechanisms in HLHS.\u003c/p\u003e\u003cp\u003eCollectively, these findings support the view that congenital heart defects, including HLHS, may arise from a complex interplay between genetic predisposition and adverse developmental conditions during cardiac development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study, titled “The development origins of congenital cardiac malformations with a special focus on left ventricular outflow tract obstruction defects” was conducted in accordance with the principles of the Declaration of Helsinki and the Council of Europe Convention on Human Rights and Biomedicine. The study protocol was approved by the Ethics Committee of the Helsinki University Hospital District (reference number: HUS/2054/2016, approval granted on November 17\u003csup\u003eth\u003c/sup\u003e,2016). Written informed consent was obtained from all participants aged six years and older, and from parents or legal guardians for participants who were minors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNAseq data are freely available in the Gene Expression Omnibus (GEO) repository (accession numbers GSE194103). All other datasets generated during and analysed during the current study are available from the corresponding authors on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Academy of Finland (grants 321564 and 353109 to VT and grant\u0026nbsp;331405 to EH), Sigrid Jusélius Foundation (EH, VT), the Finnish Foundation for Cardiovascular Research (EH, VT), Finnish Foundation for Pediatric Research (EH), the Finnish Medical Foundation (EH), the Finnish Cultural Foundation (EH), the Helsinki and Uusimaa Hospital district (EH), the Stiftelsen Frimurare Barnhuset i Stockholm (EH) and Ida Montin’s Foundation (MV).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors contributed to the article as follows: Conceptualization was led by (EH and VT). Investigation was carried out by (MV and MA) while data curation, formal analysis, and software were handled by (MV, MB, AR). Visualization was done by (MV) Writing of the original draft was performed by (MV) followed by review and editing by (VT, EH, MV, MA). Funding acquisition by (EH, VT and MV) and supervision were managed by (VT and EH) and project administration was overseen by (VT and EH). All authors contributed to writing the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Ilse Paetau for her assistance with cell culture and administrative support. We also thank Professor Timo Otonkoski and Docent Ras Trokovic at the Biomedicum Stem Cell Center for providing three control hiPSC lines, and Professor Anu Suomalainen-Wartiovaara for the gift of the hiPSC line K1. Additionally, we acknowledge the Institute of Molecular Medicine Finland (FIMM) for the scRNA-seq service.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI Authorship\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have not use AI-generated work in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDrug Research Program, Division of Pharmacology and Pharmacotherapy, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eStem Cells and Metabolism Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eInstitute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eResearch Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, Faculty of Medicine, University of Turku, Turku, Finland\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003eNew Children’s Hospital, Paediatric Research Centre, Helsinki University Hospital, Helsinki, Finland\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCiD:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMargarida Varela:\u003c/strong\u003e 0009-0005-6899-1976\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMinna Ampuja:\u003c/strong\u003e 0000-0003-1407-5399\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMartin Broberg:\u003c/strong\u003e 0000-0002-5419-9479\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAmanda Ramste:\u003c/strong\u003e 0000-0002-2612-5165\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVirpi Talman:\u003c/strong\u003e 0000-0002-2702-6505\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmmi Helle:\u003c/strong\u003e 0000-0001-8993-2194\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTchervenkov, C. I., Jacobs, J. P., Weinberg, P. M., Aiello, V. D., B\u0026eacute;land, M. J., Colan, S. D., et al. (2006). The nomenclature, definition and classification of hypoplastic left heart syndrome. \u003cem\u003eCardiology In The Young\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e, 339. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S1047951106000291\u003c/span\u003e\u003cspan address=\"10.1017/S1047951106000291\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarron, D. J., Kilby, M. D., Davies, B., Wright, J. G., Jones, T. J., \u0026amp; Brawn, W. J. (2009). Hypoplastic left heart syndrome. \u003cem\u003eThe Lancet\u003c/em\u003e, \u003cem\u003e374\u003c/em\u003e, 551\u0026ndash;564. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(09)60563-8\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(09)60563-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBondy, C. A. (2010). Hypoplastic Left Heart Syndrome. \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e, \u003cem\u003e362\u003c/em\u003e, 2026\u0026ndash;2028. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMe1002923\u003c/span\u003e\u003cspan address=\"10.1056/NEJMe1002923\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBondy, C. A. (2008). Congenital Cardiovascular Disease in Turner Syndrome. \u003cem\u003eCongenital Heart Disease\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e, 2\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1747-0803.2007.00163.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1747-0803.2007.00163.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrossfeld, P. D., Mattina, T., Lai, Z., Favier, R., Jones, K. L., Cotter, F., et al. (2004). The 11q terminal deletion disorder: A prospective study of 110 cases. \u003cem\u003eAm J Med Genet A\u003c/em\u003e, \u003cem\u003e129A\u003c/em\u003e, 51\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ajmg.a.30090\u003c/span\u003e\u003cspan address=\"10.1002/ajmg.a.30090\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReis, P. (1999). Outcome of infants with hypoplastic left heart and turner syndromes. \u003cem\u003eObstetrics \u0026amp; Gynecology\u003c/em\u003e, \u003cem\u003e93\u003c/em\u003e, 532\u0026ndash;535. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0029-7844(98)00462-1\u003c/span\u003e\u003cspan address=\"10.1016/S0029-7844(98)00462-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKobayashi, J., Yoshida, M., Tarui, S., Hirata, M., Nagai, Y., Kasahara, S., et al. (2014). Directed Differentiation of Patient-Specific Induced Pluripotent Stem Cells Identifies the Transcriptional Repression and Epigenetic Modification of NKX2-5, HAND1, and NOTCH1 in Hypoplastic Left Heart Syndrome. \u003cem\u003ePLoS One\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, e102796. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0102796\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0102796\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElliott, D. A., Kirk, E. P., Yeoh, T., Chandar, S., McKenzie, F., Taylor, P., et al. (2003). Cardiac homeobox gene NKX2-5mutations and congenital heart disease. \u003cem\u003eJournal Of The American College Of Cardiology\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e, 2072\u0026ndash;2076. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0735-1097(03)00420-0\u003c/span\u003e\u003cspan address=\"10.1016/S0735-1097(03)00420-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStallmeyer, B., Fenge, H., Nowak-G\u0026ouml;ttl, U., \u0026amp; Schulze‐Bahr, E. (2010). Mutational spectrum in the cardiac transcription factor gene NKX2.5 associated with congenital heart disease. \u003cem\u003eClinical Genetics\u003c/em\u003e, \u003cem\u003e78\u003c/em\u003e, 533\u0026ndash;540. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1399-0004.2010.01422.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1399-0004.2010.01422.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiyao, N., Hirono, K., Hata, Y., Yoshimura, N., \u0026amp; Ichida, F. (2019). Novel compound heterozygous TBX5 variants may induce hypoplastic left heart syndrome. \u003cem\u003ePediatrics International\u003c/em\u003e, \u003cem\u003e61\u003c/em\u003e, 607\u0026ndash;609. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ped.13854\u003c/span\u003e\u003cspan address=\"10.1111/ped.13854\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReamon-Buettner, S. M., Ciribilli, Y., Inga, A., \u0026amp; Borlak, J. (2008). A loss-of-function mutation in the binding domain of HAND1 predicts hypoplasia of the human hearts. \u003cem\u003eHuman Molecular Genetics\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e, 1397\u0026ndash;1405. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/hmg/ddn027\u003c/span\u003e\u003cspan address=\"10.1093/hmg/ddn027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReamon-Buettner, S. M., Ciribilli, Y., Traverso, I., Kuhls, B., Inga, A., \u0026amp; Borlak, J. (2009). A functional genetic study identifies HAND1 mutations in septation defects of the human heart. \u003cem\u003eHuman Molecular Genetics\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e, 3567\u0026ndash;3578. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/hmg/ddp305\u003c/span\u003e\u003cspan address=\"10.1093/hmg/ddp305\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTheis, J. L., Hrstka, S. C. L., Evans, J. M., O\u0026rsquo;Byrne, M. M., de Andrade, M., O\u0026rsquo;Leary, P. W., et al. (2015). Compound heterozygous NOTCH1 mutations underlie impaired cardiogenesis in a patient with hypoplastic left heart syndrome. \u003cem\u003eHuman Genetics\u003c/em\u003e, \u003cem\u003e134\u003c/em\u003e, 1003\u0026ndash;1011. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00439-015-1582-1\u003c/span\u003e\u003cspan address=\"10.1007/s00439-015-1582-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDurbin, M. D., Cadar, A. G., Williams, C. H., Guo, Y., Bichell, D. P., Su, Y. R., et al. (2017). Hypoplastic Left Heart Syndrome Sequencing Reveals a Novel NOTCH1 Mutation in a Family with Single Ventricle Defects. \u003cem\u003ePediatric Cardiology\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e, 1232\u0026ndash;1240. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00246-017-1650-5\u003c/span\u003e\u003cspan address=\"10.1007/s00246-017-1650-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHelle, E., C\u0026oacute;rdova-Palomera, A., Ojala, T., Saha, P., Potiny, P., Gustafsson, S., et al. (2019). Loss of function, missense, and intronic variants in NOTCH1 confer different risks for left ventricular outflow tract obstructive heart defects in two European cohorts. \u003cem\u003eGenetic Epidemiology\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e, 215\u0026ndash;226. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/gepi.22176\u003c/span\u003e\u003cspan address=\"10.1002/gepi.22176\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYe, S., Wang, C., Xu, Z., Lin, H., Wan, X., Yu, Y., et al. (2023). Impaired Human Cardiac Cell Development due to NOTCH1 Deficiency. \u003cem\u003eCirc Res\u003c/em\u003e, \u003cem\u003e132\u003c/em\u003e, 187\u0026ndash;204. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCRESAHA.122.321398\u003c/span\u003e\u003cspan address=\"10.1161/CIRCRESAHA.122.321398\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTheis, J. L., Zimmermann, M. T., Evans, J. M., Eckloff, B. W., Wieben, E. D., Qureshi, M. Y., et al. (2015). Recessive MYH6 Mutations in Hypoplastic Left Heart With Reduced Ejection Fraction. \u003cem\u003eCirculation. Cardiovascular Genetics\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 564\u0026ndash;571. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCGENETICS.115.001070\u003c/span\u003e\u003cspan address=\"10.1161/CIRCGENETICS.115.001070\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTomita-Mitchell, A., Stamm, K. D., Mahnke, D. K., Kim, M-S., Hidestrand, P. M., Liang, H. L., et al. (2016). Impact of MYH6 variants in hypoplastic left heart syndrome. \u003cem\u003ePhysiol Genomics\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e, 912\u0026ndash;921. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/physiolgenomics.00091.2016\u003c/span\u003e\u003cspan address=\"10.1152/physiolgenomics.00091.2016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJenkins, K. J., Correa, A., Feinstein, J. A., Botto, L., Britt, A. E., Daniels, S. R., et al. (2007). Noninherited Risk Factors and Congenital Cardiovascular Defects: Current Knowledge. \u003cem\u003eCirculation\u003c/em\u003e, \u003cem\u003e115\u003c/em\u003e, 2995\u0026ndash;3014. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCULATIONAHA.106.183216\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.106.183216\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSavla, J. J., Putt, M. E., Huang, J., Parry, S., Moldenhauer, J. S., Reilly, S., et al. (2022). Impact of Maternal\u0026ndash; Fetal Environment on Mortality in Children With Single Ventricle Heart Disease. \u003cem\u003eJ Am Heart Assoc\u003c/em\u003e, 11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/JAHA.120.020299\u003c/span\u003e\u003cspan address=\"10.1161/JAHA.120.020299\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChaikhouni, F., \u0026amp; Salih, H. G. H. (2024). Cardiac Evaluation of Children Born to Mothers with Diabetes Mellitus. Manual of Pediatric Cardiac Care: Volume 1, Second Edition. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-981-19-8357-3_10\u003c/span\u003e\u003cspan address=\"10.1007/978-981-19-8357-3_10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEngineer, A., Saiyin, T., Greco, E. R., \u0026amp; Feng, Q. (2019). Say NO to ROS: Their Roles in Embryonic Heart Development and Pathogenesis of Congenital Heart Defects in Maternal Diabetes. \u003cem\u003eAntioxidants\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 436. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/antiox8100436\u003c/span\u003e\u003cspan address=\"10.3390/antiox8100436\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu, Y., Reece, E. A., Zhong, J., Dong, D., Shen, W-B., Harman, C. R., et al. (2016). Type 2 diabetes mellitus induces congenital heart defects in murine embryos by increasing oxidative stress, endoplasmic reticulum stress, and apoptosis. \u003cem\u003eAmerican Journal Of Obstetrics And Gynecology\u003c/em\u003e, \u003cem\u003e215\u003c/em\u003e, 366e1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ajog.2016.03.036\u003c/span\u003e\u003cspan address=\"10.1016/j.ajog.2016.03.036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTurunen, R., Pulakka, A., Mets\u0026auml;l\u0026auml;, J., Vahlberg, T., Ojala, T., Gissler, M., et al. (2024). Maternal Diabetes and Overweight and Congenital Heart Defects in Offspring. \u003cem\u003eJAMA Netw Open\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, e2350579. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jamanetworkopen.2023.50579\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2023.50579\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHelle, E., \u0026amp; Priest, J. R. (2020). Maternal Obesity and Diabetes Mellitus as Risk Factors for Congenital Heart Disease in the Offspring. \u003cem\u003eJ Am Heart Assoc\u003c/em\u003e, 9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/JAHA.119.011541\u003c/span\u003e\u003cspan address=\"10.1161/JAHA.119.011541\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, S., Regnault, T. R. H., Barker, P. L., Botting, K. J., McMillen, I. C., McMillan, C. M., et al. (2015). Placental adaptations in growth restriction. \u003cem\u003eNutrients\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 360\u0026ndash;389. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu7010360\u003c/span\u003e\u003cspan address=\"10.3390/nu7010360\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHay, W. W. (2006). Placental-fetal glucose exchange and fetal glucose metabolism. \u003cem\u003eTransactions Of The American Clinical And Climatological Association\u003c/em\u003e, \u003cem\u003e117\u003c/em\u003e, 321\u0026ndash;339. discussion 339\u0026thinsp;\u0026ndash;\u0026thinsp;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHelle, E. I. T., Biegley, P., Knowles, J. W., Leader, J. B., Pendergrass, S., Yang, W., et al. (2018). First Trimester Plasma Glucose Values in Women without Diabetes are Associated with Risk for Congenital Heart Disease in Offspring. \u003cem\u003eJournal Of Pediatrics\u003c/em\u003e, \u003cem\u003e195\u003c/em\u003e, 275\u0026ndash;278. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jpeds.2017.10.046\u003c/span\u003e\u003cspan address=\"10.1016/j.jpeds.2017.10.046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalman, H. E., Alser, M., Shekhar, A., Gould, R. A., Benslimane, F. M., Butcher, J. T., et al. (2021). Effect of left atrial ligation-driven altered inflow hemodynamics on embryonic heart development: clues for prenatal progression of hypoplastic left heart syndrome. \u003cem\u003eBiomechanics And Modeling In Mechanobiology\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e, 733\u0026ndash;750. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10237-020-01413-5\u003c/span\u003e\u003cspan address=\"10.1007/s10237-020-01413-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoenezen, S., Rennie, M. Y., \u0026amp; Rugonyi, S. (2012). Biomechanics of early cardiac development. \u003cem\u003eBiomechanics And Modeling In Mechanobiology\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 1187\u0026ndash;1204. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10237-012-0414-7\u003c/span\u003e\u003cspan address=\"10.1007/s10237-012-0414-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalman, H. E., Kamal, R. Y., Hijazi, Z. M., \u0026amp; Yalcin, H. C. (2022). Hemodynamic and Structural Comparison of Human Fetal Heart Development Between Normally Growing and Hypoplastic Left Heart Syndrome-Diagnosed Hearts. \u003cem\u003eFrontiers In Physiology\u003c/em\u003e, 13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphys.2022.856879\u003c/span\u003e\u003cspan address=\"10.3389/fphys.2022.856879\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVallelonga, F., Airale, L., Tonti, G., Argulian, E., Milan, A., Narula, J., et al. (2021). Introduction to Hemodynamic Forces Analysis: Moving Into the New Frontier of Cardiac Deformation Analysis. \u003cem\u003eJ Am Heart Assoc\u003c/em\u003e, 10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/JAHA.121.023417\u003c/span\u003e\u003cspan address=\"10.1161/JAHA.121.023417\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGranados-Riveron, J. T., \u0026amp; Brook, J. D. (2012). The Impact of Mechanical Forces in Heart Morphogenesis. \u003cem\u003eCirculation. Cardiovascular Genetics\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e, 132\u0026ndash;142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCGENETICS.111.961086\u003c/span\u003e\u003cspan address=\"10.1161/CIRCGENETICS.111.961086\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakahashi, K., \u0026amp; Yamanaka, S. (2006). Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors. \u003cem\u003eCell\u003c/em\u003e, \u003cem\u003e126\u003c/em\u003e, 663\u0026ndash;676. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2006.07.024\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2006.07.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakahashi, K., Tanabe, K., Ohnuki, M., Narita, M., Ichisaka, T., Tomoda, K., et al. (2007). Induction of Pluripotent Stem Cells from Adult Human Fibroblasts by Defined Factors. \u003cem\u003eCell\u003c/em\u003e, \u003cem\u003e131\u003c/em\u003e, 861\u0026ndash;872. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2007.11.019\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2007.11.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTrokovic, R., Weltner, J., \u0026amp; Otonkoski, T. (2015). Generation of iPSC line HEL24.3 from human neonatal foreskin fibroblasts. \u003cem\u003eStem Cell Res\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e, 266\u0026ndash;268. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scr.2015.05.012\u003c/span\u003e\u003cspan address=\"10.1016/j.scr.2015.05.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTrokovic, R., Weltner, J., \u0026amp; Otonkoski, T. (2015). Generation of iPSC line HEL47.2 from healthy human adult fibroblasts. \u003cem\u003eStem Cell Res\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e, 263\u0026ndash;265. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scr.2015.05.013\u003c/span\u003e\u003cspan address=\"10.1016/j.scr.2015.05.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeltner, J., Balboa, D., Katayama, S., Bespalov, M., Krjutškov, K., Jouhilahti, E-M., et al. (2018). Human pluripotent reprogramming with CRISPR activators. \u003cem\u003eNature Communications\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, 2643. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-018-05067-x\u003c/span\u003e\u003cspan address=\"10.1038/s41467-018-05067-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHelle, E., Ampuja, M., Dainis, A., Antola, L., Temmes, E., Tolvanen, E., et al. (2021). HiPS-Endothelial Cells Acquire Cardiac Endothelial Phenotype in Co-culture With hiPS-Cardiomyocytes. \u003cem\u003eFront Cell Dev Biol\u003c/em\u003e, 9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcell.2021.715093\u003c/span\u003e\u003cspan address=\"10.3389/fcell.2021.715093\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHelle, E., Ampuja, M., Antola, L., \u0026amp; Kivel\u0026auml;, R. (2020). Flow-Induced Transcriptomic Remodeling of Endothelial Cells Derived From Human Induced Pluripotent Stem Cells. \u003cem\u003eFrontiers In Physiology\u003c/em\u003e, 11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphys.2020.591450\u003c/span\u003e\u003cspan address=\"10.3389/fphys.2020.591450\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarrison, E., \u0026amp; Marth, G. (2012). Haplotype-based variant detection from short-read sequencing. .\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKang, H. M., Subramaniam, M., Targ, S., Nguyen, M., Maliskova, L., McCarthy, E., et al. (2018). Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. \u003cem\u003eNature Biotechnology\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e, 89\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nbt.4042\u003c/span\u003e\u003cspan address=\"10.1038/nbt.4042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang, D. W., Sherman, B. T., \u0026amp; Lempicki, R. A. (2009). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. \u003cem\u003eNature Protocols\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, 44\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nprot.2008.211\u003c/span\u003e\u003cspan address=\"10.1038/nprot.2008.211\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSherman, B. T., Hao, M., Qiu, J., Jiao, X., Baseler, M. W., Lane, H. C., et al. (2022). DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e, W216\u0026ndash;W221. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkac194\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkac194\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLambert, S. A., Jolma, A., Campitelli, L. F., Das, P. K., Yin, Y., Albu, M. (2018). \u003cem\u003eThe Human Transcription Factors Cell\u003c/em\u003e. ;172:650\u0026ndash;665. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2018.01.029\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2018.01.029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTorrieri, G., Ferreira, M. P. A., Shahbazi, M., Talman, V., Karhu, S. T., Pohjolainen, L., et al. (2022). In Vitro Evaluation of the Therapeutic Effects of Dual-Drug Loaded Spermine‐Acetalated Dextran Nanoparticles Coated with Tannic Acid for Cardiac Applications. \u003cem\u003eAdvanced Functional Materials\u003c/em\u003e, 32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/adfm.202109032\u003c/span\u003e\u003cspan address=\"10.1002/adfm.202109032\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYing, Q-L., Wray, J., Nichols, J., Batlle-Morera, L., Doble, B., Woodgett, J., et al. (2008). The ground state of embryonic stem cell self-renewal. \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e453\u003c/em\u003e, 519\u0026ndash;523. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature06968\u003c/span\u003e\u003cspan address=\"10.1038/nature06968\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEngel, F. B., Schebesta, M., Duong, M. T., Lu, G., Ren, S., Madwed, J. B., et al. (2005). p38 MAP kinase inhibition enables proliferation of adult mammalian cardiomyocytes. \u003cem\u003eGenes \u0026amp; Development\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e, 1175\u0026ndash;1187. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/gad.1306705\u003c/span\u003e\u003cspan address=\"10.1101/gad.1306705\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarhu, S. T., Kinnunen, S. M., T\u0026ouml;lli, M., V\u0026auml;lim\u0026auml;ki, M. J., Szab\u0026oacute;, Z., Talman, V., et al. (2020). GATA4-targeted compound exhibits cardioprotective actions against doxorubicin-induced toxicity in vitro and in vivo: establishment of a chronic cardiotoxicity model using human iPSC-derived cardiomyocytes. \u003cem\u003eArchives Of Toxicology\u003c/em\u003e, \u003cem\u003e94\u003c/em\u003e, 2113\u0026ndash;2130. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00204-020-02711-8\u003c/span\u003e\u003cspan address=\"10.1007/s00204-020-02711-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePohjolainen, L., Ruskoaho, H., \u0026amp; Talman, V. (2022). Transcriptomics reveal stretched human pluripotent stem cell-derived cardiomyocytes as an advantageous hypertrophy model. \u003cem\u003eJournal of Molecular and Cellular Cardiology Plus [Internet]\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e, 100020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.jmccpl.2022.100020\u003c/span\u003e\u003cspan address=\"10.1016/j.jmccpl.2022.100020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin, Q., Schwarz, J., Bucana, C., Olson, E. N.. Control of mouse cardiac morphogenesis and myogenesis by transcription factor MEF2C., \u0026amp; Science (1979). 1997;276:1404\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.276.5317.1404\u003c/span\u003e\u003cspan address=\"10.1126/science.276.5317.1404\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu, C-X., Wang, W., Wang, Q., Liu, X-Y., \u0026amp; Yang, Y-Q. (2018). A Novel MEF2C Loss-of-Function Mutation Associated with Congenital Double Outlet Right Ventricle. \u003cem\u003ePediatric Cardiology\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e, 794\u0026ndash;804. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00246-018-1822-y\u003c/span\u003e\u003cspan address=\"10.1007/s00246-018-1822-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRochais, F., Dandonneau, M., Mesbah, K., Jarry, T., Mattei, M-G., \u0026amp; Kelly, R. G. (2009). Hes1 Is Expressed in the Second Heart Field and Is Required for Outflow Tract Development. \u003cem\u003ePLoS One\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, e6267. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0006267\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0006267\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamakrishnan, A., Lee, L. J., Mitchell, L. E., \u0026amp; Agopian, A. J. (2015). Maternal Hypertension During Pregnancy and the Risk of Congenital Heart Defects in Offspring: A Systematic Review and Meta-analysis. \u003cem\u003ePediatric Cardiology\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e, 1442\u0026ndash;1451. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00246-015-1182-9\u003c/span\u003e\u003cspan address=\"10.1007/s00246-015-1182-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSemenza, G. L., Jiang, B-H., Leung, S. W., Passantino, R., Concordet, J-P., Maire, P., et al. (1996). Hypoxia Response Elements in the Aldolase A, Enolase 1, and Lactate Dehydrogenase A Gene Promoters Contain Essential Binding Sites for Hypoxia-inducible Factor 1. \u003cem\u003eJournal of Biological Chemistry\u003c/em\u003e, \u003cem\u003e271\u003c/em\u003e, 32529\u0026ndash;32537. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1074/jbc.271.51.32529\u003c/span\u003e\u003cspan address=\"10.1074/jbc.271.51.32529\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchmidt, C., Deyett, A., Ilmer, T., Haendeler, S., Torres Caballero, A., Novatchkova, M., et al. (2023). Multi-chamber cardioids unravel human heart development and cardiac defects. \u003cem\u003eCell\u003c/em\u003e, \u003cem\u003e186\u003c/em\u003e, 5587\u0026ndash;5605e27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2023.10.030\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2023.10.030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, X., Wang, Z., Mouton, A. J., Omoto, A. C. M., da Silva, A. A., do, Carmo, J. M. (2024). Sestrin2 Attenuates Myocardial Endoplasmic Reticulum Stress and Cardiac Dysfunction During Ischemia/Reperfusion Injury. J Am Heart Assoc. ; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/JAHA.124.035193\u003c/span\u003e\u003cspan address=\"10.1161/JAHA.124.035193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZahid, M. A., Abdelsalam, S. S., Ra\u0026iuml;q, H., Parray, A., Korashy, H. M., Zeidan, A., et al. (2023). Sestrin2 as a Protective Shield against Cardiovascular Disease. \u003cem\u003eInternational Journal Of Molecular Sciences\u003c/em\u003e, 24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms24054880\u003c/span\u003e\u003cspan address=\"10.3390/ijms24054880\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang, W., Li, Y., Bai, J., You, T., Yi, K., Xie, D., et al. (2021). A Functional Variant Rs492554 Associated With Congenital Heart Defects Modulates SESN2 Expression Through POU2F1. \u003cem\u003eFront Cell Dev Biol\u003c/em\u003e, 9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcell.2021.668474\u003c/span\u003e\u003cspan address=\"10.3389/fcell.2021.668474\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu, X., Jin, K., Bais, A. S., Zhu, W., Yagi, H., Feinstein, T. N., et al. (2022). Uncompensated mitochondrial oxidative stress underlies heart failure in an iPSC-derived model of congenital heart disease. \u003cem\u003eCell Stem Cell\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e, 840\u0026ndash;855e7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.stem.2022.03.003\u003c/span\u003e\u003cspan address=\"10.1016/j.stem.2022.03.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu, Y., Wang, C., Ye, S., Qin, H., Alonzo, M., Onorato, A., et al. (2025). Common and divergent cellular aetiologies underlying hypoplastic left heart syndrome and hypoplastic right heart syndrome. \u003cem\u003eEuropean Heart Journal\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/eurheartj/ehaf121\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehaf121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoyle, E. A., Li, Y. I., \u0026amp; Pritchard, J. K. (2017). An Expanded View of Complex Traits: From Polygenic to Omnigenic. \u003cem\u003eCell\u003c/em\u003e, \u003cem\u003e169\u003c/em\u003e, 1177\u0026ndash;1186. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2017.05.038\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2017.05.038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eErgul, A. (2011). Endothelin-1 and diabetic complications: focus on the vasculature. \u003cem\u003ePharmacological Research\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e, 477\u0026ndash;482. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.phrs.2011.01.012\u003c/span\u003e\u003cspan address=\"10.1016/j.phrs.2011.01.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParadis, A., \u0026amp; Zhang, L. (2013). Role of endothelin in uteroplacental circulation and fetal vascular function. \u003cem\u003eCurrent Vascular Pharmacology\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 594\u0026ndash;605. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2174/1570161111311050004\u003c/span\u003e\u003cspan address=\"10.2174/1570161111311050004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiao, Y., Tian, L., Martin, M., Paige, S. L., Galdos, F. X., Li, J., et al. (2020). Intrinsic Endocardial Defects Contribute to Hypoplastic Left Heart Syndrome. \u003cem\u003eCell Stem Cell\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e, 574\u0026ndash;589e8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.stem.2020.07.015\u003c/span\u003e\u003cspan address=\"10.1016/j.stem.2020.07.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBohlmeyer, T. J., Helmke, S., Ge, S., Lynch, J., Brodsky, G., Sederberg, J. H., et al. (2003). Hypoplastic left heart syndrome myocytes are differentiated but possess a unique phenotype. \u003cem\u003eCardiovascular Pathology\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 23\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S1054-8807(02)00127-8\u003c/span\u003e\u003cspan address=\"10.1016/S1054-8807(02)00127-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNielsen, T., Kervadec, A., Theis, J. L., Missinato, M. A., Marchant, J., Lynott, M. (2022). Functional analysis across model systems implicates ribosomal proteins in growth and proliferation defects associated with hypoplastic left heart syndrome. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2022.07.01.22277112\u003c/span\u003e\u003cspan address=\"10.1101/2022.07.01.22277112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSedmera, D., Hu, N., Weiss, K. M., Keller, B. B., Denslow, S., \u0026amp; Thompson, R. P. (2002). Cellular changes in experimental left heart hypoplasia. \u003cem\u003eAnatomical Record\u003c/em\u003e, \u003cem\u003e267\u003c/em\u003e, 137\u0026ndash;145. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ar.10098\u003c/span\u003e\u003cspan address=\"10.1002/ar.10098\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYagi, H., Liu, X., Gabriel, G. C., Wu, Y., Peterson, K., Murray, S. A., et al. (2018). The Genetic Landscape of Hypoplastic Left Heart Syndrome. \u003cem\u003ePediatric Cardiology\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e, 1069\u0026ndash;1081. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00246-018-1861-4\u003c/span\u003e\u003cspan address=\"10.1007/s00246-018-1861-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eServatius, H., Porro, A., Pless, S. A., Schaller, A., Asatryan, B., Tanner, H., et al. (2018). Phenotypic Spectrum of HCN4 Mutations. \u003cem\u003eCirc Genom Precis Med\u003c/em\u003e, 11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCGEN.117.002033\u003c/span\u003e\u003cspan address=\"10.1161/CIRCGEN.117.002033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang, Y., Li, J., Lin, X., Yang, Y., Hong, K., Wang, L., et al. (2009). Novel KCNA5 loss-of-function mutations responsible for atrial fibrillation. \u003cem\u003eJournal Of Human Genetics\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e, 277\u0026ndash;283. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/jhg.2009.26\u003c/span\u003e\u003cspan address=\"10.1038/jhg.2009.26\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlson, T. M., Alekseev, A. E., Liu, X. K., Park, S., Zingman, L. V., Bienengraeber, M., et al. (2006). Kv1.5 channelopathy due to KCNA5 loss-of-function mutation causes human atrial fibrillation. \u003cem\u003eHuman Molecular Genetics\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e, 2185\u0026ndash;2191. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/hmg/ddl143\u003c/span\u003e\u003cspan address=\"10.1093/hmg/ddl143\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChristophersen, I. E., Olesen, M. S., Liang, B., Andersen, M. N., Larsen, A. P., Nielsen, J. B., et al. (2013). Genetic variation in KCNA5: impact on the atrial-specific potassium current IKur in patients with lone atrial fibrillation. \u003cem\u003eEuropean Heart Journal\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e, 1517\u0026ndash;1525. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/eurheartj/ehs442\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehs442\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlaschke, R. J., Monaghan, A. P., Schiller, S., Schechinger, B., Rao, E., Padilla-Nash, H., et al. (1998). SHOT, a SHOX-related homeobox gene, is implicated in craniofacial, brain, heart, and limb development. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e, \u003cem\u003e95\u003c/em\u003e, 2406\u0026ndash;2411. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.95.5.2406\u003c/span\u003e\u003cspan address=\"10.1073/pnas.95.5.2406\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, N., Wang, Z-S., Wang, X-H., Xu, Y-J., Qiao, Q., Li, X-M., et al. (2018). A SHOX2 loss-of-function mutation underlying familial atrial fibrillation. \u003cem\u003eInternational Journal Of Medical Sciences\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e, 1564\u0026ndash;1572. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7150/ijms.27424\u003c/span\u003e\u003cspan address=\"10.7150/ijms.27424\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlaschke, R. J., Hahurij, N. D., Kuijper, S., Just, S., Wisse, L. J., Deissler, K., et al. (2007). Targeted Mutation Reveals Essential Functions of the Homeodomain Transcription Factor Shox2 in Sinoatrial and Pacemaking Development. \u003cem\u003eCirculation\u003c/em\u003e, \u003cem\u003e115\u003c/em\u003e, 1830\u0026ndash;1838. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCULATIONAHA.106.637819\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.106.637819\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarbassi, E., Fenix, A., Marchiano, S., Muraoka, N., Nakamura, K., Yang, X., et al. (2020). Cardiomyocyte maturation: advances in knowledge and implications for regenerative medicine. \u003cem\u003eNature Reviews. Cardiology\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e, 341\u0026ndash;359. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41569-019-0331-x\u003c/span\u003e\u003cspan address=\"10.1038/s41569-019-0331-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"stem-cell-reviews-and-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"stcr","sideBox":"Learn more about [Stem Cell Reviews and Reports](https://www.springer.com/journal/12015)","snPcode":"12015","submissionUrl":"https://submission.nature.com/new-submission/12015/3","title":"Stem Cell Reviews and Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8250379/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8250379/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eHypoplastic left heart syndrome (HLHS) is a severe congenital heart defect characterised by underdevelopment of left-sided cardiac structures. While genetic predisposition contributes to HLHS, the relevance of environmental stressors is increasingly recognised, yet the cellular mechanisms linking genetic susceptibility to environmental vulnerability remain unclear. We aimed to identify molecular and functional differences between cardiomyocytes derived from HLHS patients and healthy controls to uncover potential susceptibilities contributing to the HLHS phenotype.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eHuman induced pluripotent stem cell\u0026ndash;derived cardiomyocytes (hiPSC-CMs) from HLHS patients and healthy controls were used to examine intrinsic cellular differences. Single-cell RNA sequencing compared baseline transcriptional profiles. Functional assays assessed responses to endothelin-1 (ET-1)\u0026ndash;induced stress, cyclic mechanical stretch, and basal or mitogen-stimulated proliferation. These approaches were used to identify intrinsic functional impairments and altered stress responses in HLHS cardiomyocytes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSingle-cell transcriptomics revealed downregulation of gene networks associated with cardiac stress responses, metabolic resilience, and rhythm regulation in HLHS cardiomyocytes. Regulon analysis revealed broad reductions in transcription factor activity across key cardiac regulatory networks. Functionally, HLHS cells showed heightened vulnerability to ET-1, with exaggerated proBNP induction compared with controls. No significant differences were observed following cyclic mechanical stretch. Basal proliferation varied across HLHS lines, while mitogen-induced proliferation remained comparable to controls.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThese findings support a model in which intrinsic molecular and functional vulnerabilities in HLHS cardiomyocytes might reduce resilience to developmental stressors. Such gene\u0026ndash;environment interactions may contribute to HLHS pathogenesis, underscoring the interplay between genetic predisposition and environmental influences in congenital heart disease.\u003c/p\u003e","manuscriptTitle":"Hypoplastic left heart syndrome cardiomyocytes exhibit intrinsic stress vulnerabilities and augmented stress responses in vitro","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 10:56:45","doi":"10.21203/rs.3.rs-8250379/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-23T21:45:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-23T21:41:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241506067010111044071674282020899592788","date":"2026-01-23T19:59:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-26T06:04:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114534655196066568029846795681564114542","date":"2025-12-18T01:37:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-02T18:25:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-02T03:59:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-02T03:59:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Stem Cell Reviews and Reports","date":"2025-12-01T12:15:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"stem-cell-reviews-and-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"stcr","sideBox":"Learn more about [Stem Cell Reviews and Reports](https://www.springer.com/journal/12015)","snPcode":"12015","submissionUrl":"https://submission.nature.com/new-submission/12015/3","title":"Stem Cell Reviews and Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"16f0d288-4156-417e-b4d9-659bde4c7f09","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-10T04:54:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 10:56:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8250379","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8250379","identity":"rs-8250379","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}