Scalable Generation of Nanovesicles from Human-Induced Pluripotent Stem Cells for Cardiac Repair

Abstract

Extracellular vesicles (EVs) from stem cells have shown significant therapeutic potential to repair injured cardiac tissues and regulate pathological fibrosis. However, scalable generation of stem cells and derived EVs for clinical utility remains a huge technical challenge. Here, we report a rapid size-based extrusion strategy to generate EV-like membranous nanovesicles (NVs) from easily sourced human iPSCs in large quantities (yield 900× natural EVs). NVs isolated using density-gradient separation (buoyant density 1.13 g/mL) are spherical in shape and morphologically intact and readily internalised by human cardiomyocytes, primary cardiac fibroblasts, and endothelial cells. NVs captured the dynamic proteome of parental cells and include pluripotency markers (LIN28A, OCT4) and regulators of cardiac repair processes, including tissue repair (GJA1, HSP20/27/70, HMGB1), wound healing (FLNA, MYH9, ACTC1, ILK), stress response/translation initiation (eIF2S1/S2/S3/B4), hypoxia response (HMOX2, HSP90, GNB1), and extracellular matrix organization (ITGA6, MFGE8, ITGB1). Functionally, NVs significantly promoted tubule formation of endothelial cells (angiogenesis) (p < 0.05) and survival of cardiomyocytes exposed to low oxygen conditions (hypoxia) (p < 0.0001), as well as attenuated TGF-β mediated activation of cardiac fibroblasts (p < 0.0001). Quantitative proteome profiling of target cell proteome following NV treatments revealed upregulation of angiogenic proteins (MFGE8, MYH10, VDAC2) in endothelial cells and pro-survival proteins (CNN2, THBS1, IGF2R) in cardiomyocytes. In contrast, NVs attenuated TGF-β-driven extracellular matrix remodelling capacity in cardiac fibroblasts (ACTN1, COL1A1/2/4A2/12A1, ITGA1/11, THBS1). This study presents a scalable approach to generating functional NVs for cardiac repair.

Keywords: extracellular vesicles; human iPSCs; nanovesicles; proteomics; tissue repair.

Figure 1

Validation of human-induced pluripotent stem cell lines. (A) CL2 and CERA iPSCs are reprogrammed from human dermal fibroblasts using lentiviral and episomal reprograming method, respectively. Pluripotency markers as identified in current study by cell-based proteomic profiling of each iPSC model (CL2/CERA). (B) Brightfield microscopy reveals derived iPSCs grow in an island-like pattern (20× magnification). (C) iPSC viability as assessed by IP/Hoechst staining (n = 3). (D) iPSC proliferation determined by Ki67 and Hoechst staining (n = 3). (E) Mass spectrometry-based proteomic profiling workflow, including sera-bead sample preparation with nano-liquid chromatography tandem mass spectrometry and data processing/informatics. (F) Protein identification and LFQ (normalised) intensity for iPSCs and HUVECs (n = 4). (G) Pearson correlation (R-score) of iPSCs (CL2, CERA) and HUVECs (n = 4). (H) Differential abundance (LFQ centred intensity, log2) of pluripotency and endothelial markers as determined by proteomic profiling (n = 4). (I) Hierarchical cluster analysis of iPSCs and HUVECs (ANOVA, p < 0.05, fold change (FC): 1.5), reveals three clusters of differential protein expression; high abundant iPSCs, low abundant iPSCs. Gene Ontology enrichment analysis using (J) Reactome and (K) Cellular Component for cluster high and low groups. Enrichment map highlighting proteins associated with each differential protein expression cluster (high/low in iPSCs) [48,49].

Figure 2

Generation and characterisation of NVs generated from human-induced pluripotent stem cells. (A) iPSC nanovesicle generation using serial extrusion (10, 5, 1 µm filter, 13× each membrane) with the cell suspension purified using density-cushion ultracentrifugation to obtain fractions (F) 1–7 of increasing density. (B) The buoyant densities of seven fractions collected for each iPSC preparation were determined by absorbance at 244 nm using a molar extinction coefficient of 320 L g⁻¹cm⁻¹. Protein yield determined based on micro-BCA protein quantification (as a % of total), revealing major fraction F5 as NV-containing. Data presented as mean ± s.e.m. (C) Cryo-electron microscopic analysis of CL2- and CERA-derived NVs (F5), scale 100 nm. (D) Scatter plot distribution of NV particle diameter as determined by cryo-EM images. Data presented as mean ± s.e.m (standard error of mean). (E) Size distribution profiles of NVs determined by single particle tracking analysis (ZetaView), indicating mean ~110 nm. (F) Protein yields (determined by microBCA) of NVs (F5, density-based preparation) and natural EVs (EVs, density-based preparation) from CL2 and CERA iPSCs. Yields correspond to the same starting cell number for each cell model. (G) Workflow for mass spectrometry-based proteomic profiling of NVs. (H) Protein identification and LFQ (normalised) intensity for NVs and cell lysates from each iPSC model (n = 4). (I) Pearson correlation (R-score) of NVs and iPSCs (n = 4). (J) Abundance distribution (waterfall plots, LFQ intensity, log₁₀) of proteins identified in iPSCs (cells) and derived NVs. Several examples of proteins similarly identified between both cells and NVs are shown. (K) Selected proteins identified in NVs associated with wound healing, hypoxia response, extracellular matrix organisation and implicated in tissue repair found in natural EVs. (L) Hierarchical cluster analysis of iPSCs and NVs (p < 0.05, fold change (FC): 1.5), reveals three clusters of differential protein expression; high abundant NVs, low abundant NVs, similarly expressed (common) between NVs and iPSCs. (M) Gene Ontology enrichment analysis (ranked, p < 0.05) of each cluster based on cellular component, biological processes, and Reactome pathway analyses.

Figure 3

Internalisation of NVs by cardiac fibroblasts and cardiomyocytes. Uptake of iPSC-derived NVs by (A) human cardiomyocytes (CMs) and (B) human primary cardiac fibroblasts (hCFs). Fluorescence microscopy analysis of cells incubated with NVs labelled with lipophilic dye (DiI) for 2 h. DiI vehicle (PBS) control. Nuclei (blue) were stained with Hoechst. Scale bar, * 100 μm, ≠ 10 μm.

Figure 4

iPSC NVs functionally regulate aspects of cardiac repair in vitro. (A) Cardiomyocyte (CM) survival assay, where cells are exposed to hypoxia/normoxia (4 h) and treated with NVs under normoxic condition for 24 h followed by fluorescence microscopy. (B) NV single dose treatment (6 µg in 200 µL) confers reduced CM cell death based on % propidium iodide (PI) stain, Scale bar, 500 μm. (C) Bar plot for cell survival shown as percentage of cell death for NV treatments (CL and CERA) and vehicle and untreated controls (p < 0.0001). (n = 2 biological, 3 technical). Data represented as mean ± s.e.m. (D) Endothelial (HUVEC) tube formation assay in response to NV treatment following hypoxia/reoxygenation. (E) Brightfield microscopy images of tube formation assay (normoxic and hypoxic) in response to NV treatment (single dose, 1.5 µg in 50 µL), Scale bar, 200 μm. (F) Histogram analysis of the number of tubules formed for NV treatments (CL and CERA) and vehicle and untreated controls (p < 0.05 and <0.005, respectively). (n = 2 biological, 5 technical), Data represented as mean ± s.e.m. (G) TGF-β-mediated (5 ng/mL) human primary cardiac fibroblast (hCF) activation (24 h) or PBS vehicle; smooth muscle actin activation (α-SMA) assessed at 72 h. (H) NV single dose treatment (24 h post TGF-β stimuli); 9 µg in 300 µL confers reduced α-SMA expression based on immunoblot analysis compared to TGF-β alone and GAPDH. (I) Quantitation of differential α-SMA expression (n = 2 biological, 3 technical). Data represented as mean ± s.e.m. (p < 0.0001). * p < 0.05, ** p < 0.005, *** p < 0.0005, **** p < 0.0001.

Figure 5

iPSC NVs induce target cell proteome reprogramming to support repair functions. (A) Cell proteome analysis of cell assays following NV treatment: cardiomyocyte (CM) survival, endothelial (HUVEC) tube formation, and TGF-β-mediated human primary cardiac fibroblast (hCF) activation. Protein identification, LFQ (normalised) intensity, and Pearson correlation for cells in response to NV treatment, and vehicle control for each assay (n = 4). (B) MS-based quantitation (LFQ intensity) of ACTC1 (actin alpha cardiac muscle 1) or GAPDH expression in hCFs in response to TGF-β (vehicle, UT) and following NV treatment (CERA- or CL2-NVs). Data represented as mean ± s.e.m. *** p < 0.0005, ns, non-significant. (C) Differential protein abundance (fold change, of LFQ intensity, log₂) of selected protein markers for each assay following NV treatment (CERA or CL2), relative to vehicle controls (yellow) (p < 0.05). Proteins selected include pro-survival, pro-angiogenic, and anti-fibrotic associated markers identified in study. (D) Gene Ontology enrichment analysis (ranked, p < 0.05) (Reactome and GO cellular component) of proteins identified differentially enriched for each assay. Hierarchical cluster analysis of NV treatments and vehicle controls for each assay were determined (Supplementary Figure S7, ANOVA, p < 0.05, fold change (FC): 1.5), to reveal distinct clusters of differential protein expression for each assay. Differential protein subsets for each assay (Supplementary Figure S7) were then mapped using Reactome and GO cellular component and plotted as adj. p-val for each category/assay/treatment.