Dimensional Control of DNA Nanostructures Enhances Cellular Uptake and Guides Tissue-Regenerative Responses

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Structural DNA nanotechnology enables the design of well-defined nanostructures, emerging as a promising platform in these biomedical applications. However, a clear understanding of how the dimensional properties of DNA nanostructures affect cellular uptake and biological responses remains limited. In this study, we constructed three distinct DNA nanostructures: a one-dimensional six-helix bundle (6HB), a two-dimensional three-point star, and a three-dimensional tetrahedron. We systematically evaluated their endocytic efficiency in five representative cell types: endothelial cells, dermal fibroblasts, myoblasts, chondrocytes, and osteoblasts. Among them, the 6HB exhibited the highest cellular uptake, with minimal variability across cell types in both 2D petri dish cultures and 3D multicellular spheroid invasion models. Moreover, DNA nanostructures were found to enhance cell proliferation in fibroblasts and chondrocytes, support chondrocyte phenotype maintenance, and, in the case of the 6HB, promote myoblast differentiation. These findings provide new insights into structure–function relationships in DNA nanomaterials and offer guidance for optimizing DNA-based platforms for drug delivery and regenerative medicine. DNA nanotechnology dimensional control six-helix bundle cellular uptake tissue regeneration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Cellular functional regulation is fundamental to tissue regeneration and drug delivery. During the interaction between biomaterials and cells, the physicochemical properties of the materials play a crucial role in modulating cell behaviors such as proliferation [ 1 ], secretion [ 2 ], differentiation [ 3 ], and stemness maintenance [ 4 ], thereby collectively promoting tissue repair and regeneration [ 5 ]. Integrating material design with drug delivery has emerged as an effective strategy for enhancing cellular functions and optimizing therapeutic outcomes. Consequently, material-based modulation of “seed cells” used in tissue engineering has become a widely adopted and promising approach in regenerative medicine. Over the past two decades, DNA has garnered growing interest as a programmable and biocompatible building block for nanoscale assemblies [ 6 , 7 ]. The advent of DNA origami in 2006 revolutionized structural DNA nanotechnology, enabling the precise folding of DNA strands into well-defined nanostructures [ 8 ]. These DNA nanostructures (DNs) possess several desirable features, including excellent biocompatibility, site-specific addressability, chemical modifiability, and the ability to be internalized by cells without the need for transfection agents [ 9 , 10 ]. Owing to these attributes, DNs are increasingly applied in biomedical fields, particularly in tissue engineering and drug delivery [ 11 , 12 ]. Through programmed self-assembly, DNs can be constructed into diverse one-dimensional (1D), two-dimensional (2D) [ 13 ], and three-dimensional (3D) [ 14 ] geometries. Previous research has shown that nanoparticle characteristics such as shape, size, and molecular weight significantly influence cellular uptake [ 15 – 17 ]. For instance, Bastings et al. demonstrated that among DNs with equivalent molecular weight, those with more compact geometries and lower aspect ratios achieved higher uptake in endothelial, epithelial, and immune cell lines [ 16 ]. Wang et al. reported that rod-shaped DNs and larger tetrahedral DNA nanostructures (TDNs) exhibited superior internalization in H1299 and DMS53 cells compared to their smaller counterparts [ 15 ]. Rajwar et al. found that TDNs were preferentially taken up by various carcinoma cell types relative to other 3D geometries [ 18 ]. Despite these advances, systematic studies on how dimensional parameters affect DN uptake across multiple tissue engineering-relevant cell types are still lacking. Moreover, most existing studies focus primarily on cellular uptake and subcellular localization, with limited exploration into the downstream functional effects of DNs on cell behavior [ 19 ]. In our previous work, we successfully applied a six-helix bundle (6HB) rod and a three-point star (3PS) structure for sorting sub-150-nm liposomes of different sizes [ 20 ]. Building upon this work, the current study investigates the endocytosis and biological effects of three DNs representing distinct dimensional configurations: a 1D 6HB, a 2D 3PS, and a 3D TDN (Fig. 1 a). Five representative cell lines were selected for this study: myoblasts (C2C12), endothelial cells (HUVEC), fibroblasts (HSF), chondrocytes (SW1353), and osteoblasts (MC3T3-E1). We systematically evaluated the internalization efficiency and biological influence of each DN geometry across these cell types, aiming to elucidate the role of structural dimensionality in cellular interactions and to provide valuable insights for the future development of DNA-based systems in tissue engineering and drug delivery. Results and Discussion Design, construction, characterization, and stability of DNs Three representative DNs—6HB, 3PS, and TDN—were designed and assembled as shown in Fig. 1 b–d [ 20 , 21 ]. The DNA sequences used for each structure are provided in Table S1 of the Supporting Information. To facilitate endocytosis tracking via confocal microscopy, one strand of each DN was labeled with Alexa Fluor 647 (Alexa 647-DNs). Following assembly, the DNs were purified using high-performance liquid chromatography (HPLC). Their structural integrity and assembly fidelity were confirmed by polyacrylamide gel electrophoresis (PAGE) and atomic force microscopy (AFM), as presented in Fig. 1 e–g. The structural stability of DNs is a critical factor for their effective performance in cellular and biological applications. Most tissue culture media are supplemented with fetal bovine serum (FBS), which contains a variety of nucleases known to degrade nucleic acid-based materials [ 22 , 23 ]. To evaluate the resistance of DNs to nuclease degradation, we assessed their stability in a cell culture environment containing 10% FBS (Figure S1 ). Among the tested structures, TDNs retained approximately 90% structural integrity after 1.5 hours, consistent with previous reports [ 18 ]. However, TDNs underwent progressive degradation over time, with only 5.8% remaining intact after 24 hours. In contrast, the 6HB exhibited markedly enhanced stability, maintaining over 95% integrity at the 24-hour time point. This suggests that densely packed nanostructures, such as 6HB, offer improved protection against nuclease activity compared to more open or hollow designs like TDNs [ 15 ]. Time- and concentration-dependent cellular uptake of DNs Due to their negatively charged phosphate backbones, single-stranded and double-stranded DNA (ssDNA and dsDNA) typically cannot cross the cellular membrane unaided. However, DNA organized into defined nanostructures can be internalized by cells without the use of transfection reagents [ 10 ]. To determine an optimal time point for subsequent experiments, we first evaluated the cellular uptake of DNs in HUVECs over a time course (0.5, 1.5, 3, 6, and 12 hours) (Fig. S2–S4). DNs were labeled with Alexa 647 (Alexa 647-DNs) to enable tracking, and their internalization was assessed by confocal microscopy and flow cytometry (Fig. 2 a–c). Untreated cells were used as negative controls and showed negligible fluorescence in the red channel, confirming that observed signals originated from internalized DNs. At 200 nM, 3PS uptake was detectable as early as 0.5 hours, while 6HB and TDN required 1.5–3 hours to become apparent. Internalization of all three DNs increased steadily over the 12-hour period, consistent with the quantitative data from flow cytometry. Based on these findings, a 3-hour incubation was selected as the standard time point for subsequent uptake experiments, as all DNs showed measurable cytoplasmic signals by this time. To examine concentration dependence, we then treated cells with increasing concentrations of Alexa 647-DNs (100, 200, 400, and 800 nM) (Fig. S5–S7). Uptake was positively correlated with concentration, and differences among the three DNs became more distinct at 400 nM, with some structures showing minimal fluorescence at lower concentrations. As a result, 400 nM was chosen as the optimal concentration for all subsequent experiments. 6HB exhibits the highest mean endocytosis efficiency across five cell lines The cellular uptake of DNs was systematically evaluated in five representative cell lines: HSF, MC3T3-E1, SW1353, C2C12, and HUVECs (Fig. 2 a–c). Notably, the endocytosis efficiency varied among both DN types and cell types. MC3T3-E1 and C2C12 cells demonstrated relatively high uptake capacities, whereas HUVECs displayed weaker internalization, consistent with previous studies [ 15 , 18 ]. Among the three DNA nanostructures, 6HB showed the highest endocytosis efficiency in HSF and SW1353 cells. Interestingly, 3PS exhibited superior uptake in HUVECs, aligning with its performance in earlier concentration- and time-dependent studies. In C2C12 cells, both 6HB and 3PS showed efficient internalization, while TDN achieved the highest uptake in MC3T3-E1 cells. To further quantify and compare their performance, the mean endocytosis efficiency and standard deviation of each DN across all five cell types were calculated based on flow cytometry data (Fig. 2 d). Among the three DN architectures, 6HB exhibited the highest average internalization efficiency with relatively low variability across cell types. In contrast, TDN demonstrated the lowest mean uptake and the highest variability, suggesting a stronger dependence on cell type for its internalization efficiency. These findings are in line with prior reports. Bastings et al. [ 14 ] found that compact nanostructures with low aspect ratios were internalized more effectively by HEK293, HUVECs, and dendritic cells. Similarly, Wang et al. [ 15 ] reported that rod-shaped DNs outperformed TDNs of similar size in DMS53 and H1299 cells. The relatively poor and inconsistent uptake of TDNs may be attributed to their three-dimensional configuration, which potentially limits membrane interactions compared to bundle-shaped or planar DNs. Taken together, our results highlight the superior and consistent internalization of 6HB across a diverse range of tissue-relevant cell types, supporting its potential as a broadly applicable scaffold for DNA-based biomedical applications. Endocytic mechanisms Endocytosis comprises two major processes: phagocytosis and pinocytosis. Phagocytosis typically involves the uptake of particles larger than 250 nm and is limited to specialized mammalian cells such as macrophages, neutrophils, dendritic cells, and monocytes [ 24 ]. Given that our DNs are within the 10–20 nm size range and non-phagocytic cells were used in this study, we focused our investigation on pinocytosis. This includes three primary pathways: macropinocytosis, clathrin-mediated endocytosis, and caveolin-mediated endocytosis. SW1353 cells were selected to study the mechanisms of DN internalization. To evaluate whether DN uptake is energy-dependent, we first conducted temperature-controlled uptake experiments. Endocytosis is known to be significantly reduced at 4°C due to the suppression of energy-dependent processes. Consistent with previous studies [ 25 , 26 ], our flow cytometry analysis revealed a dramatic reduction in uptake at 4°C compared to 37°C: 6HB by 78.6 ± 1.9%, 3PS by 82.1 ± 0.5%, and TDN by 90.2 ± 0.3%. These results confirm that internalization of all three DNs is an active, energy-dependent process. To further delineate the endocytic pathways involved, we employed pharmacological inhibitors: Wortmannin to block macropinocytosis [ 27 ]; Dynasore to inhibit dynamin and clathrin-mediated endocytosis [ 18 ]; and methyl-β-cyclodextrin (MβCD) to disrupt caveolin-mediated endocytosis by depleting membrane cholesterol [ 28 ]. Cells without inhibitors served as positive controls. Wortmannin reduced DN uptake by approximately 40–50%, with the strongest inhibitory effect observed for TDN. Treatment with Dynasore reduced uptake of all three DNs by about 30%, indicating partial involvement of clathrin-mediated pathways. MβCD had differential effects: 6HB uptake decreased by 32.2 ± 1.8%, 3PS by 44.8 ± 4.1%, and TDN by 56.8 ± 2.8%. These findings suggest that TDN uptake is predominantly caveolin-mediated, aligning with previous studies [ 9 , 10 ], while 3PS follows a similar trend. In contrast, 6HB appears to utilize both clathrin- and caveolin-mediated pathways, indicating a more balanced involvement of multiple mechanisms. Overall, our results indicate that multiple endocytic routes contribute to DN uptake, a phenomenon also observed with other nanoparticles [ 29 ]. Prior studies have shown that well-dispersed silica nanoparticles are primarily internalized via caveolin-mediated endocytosis, but agglomerated forms shift toward macropinocytosis as the dominant route [ 30 ]. Consistent with this, we observed that TDN exhibited aggregation after 3 hours in medium containing 10% FBS (Fig. S1 ), and similar tendencies may exist for 6HB and 3PS. These aggregated forms likely facilitate macropinocytosis, whereas monomeric DNs may preferentially enter cells via clathrin- and caveolin-mediated mechanisms. The relative contribution of each pathway may also be influenced by the specific cell type and DN dispersion state. 6HB exhibits the highest endocytosis efficiency in a multicellular spheroid model Traditional 2D monolayer cultures, such as petri dish-cultured cells, are widely used for cellular uptake studies. However, these systems often lack the structural and physiological complexity of native tissue microenvironments [ 31 ]. In contrast, 3D cell culture models, such as multicellular spheroids, more accurately replicate in vivo conditions, including cell–cell and cell–matrix interactions, diffusion gradients, and spatial organization [ 32 ]. To evaluate the endocytosis behavior of DNs in a physiologically relevant model, we constructed 3D spheroids using SW1353 cells and investigated the penetration and uptake of Alexa 647-labeled 6HB, 3PS, and TDN. After treating spheroids with DNs, we performed confocal microscopy with Z-stack imaging at 50 µm intervals across various incubation times. At 0.5 hours, weak fluorescence signals were observed in the outer spheroid layer for TDN, minimal signals for 3PS, and noticeably stronger fluorescence for 6HB (Fig. 3 ). After 1.5 hours, the intensity of Alexa 647 fluorescence increased across all DNs, with 6HB showing the highest accumulation in the outer layers, followed by 3PS and TDN. By the 3-hour time point, while fluorescence intensity at the spheroid periphery became more comparable among all three DNs, 6HB exhibited markedly deeper penetration into the spheroid core. After 6 and 12 hours, all DNs were able to reach the inner layers of the spheroid (Fig. S8). These observations suggest that 6HB enters multicellular spheroids more rapidly and penetrates more deeply than 3PS and TDN within the same time frame. Over extended incubation, however, the uptake of all DNs tends to converge, likely due to the eventual saturation of the spheroid’s uptake capacity. Collectively, both 2D and 3D experiments demonstrate that 6HB consistently exhibits superior endocytosis efficiency across various models and cell types. Effects of DNs on cell proliferation and cell cycle Given the broad potential of DNs in biomedical applications, it is essential to systematically evaluate their effects on cellular function prior to therapeutic use. In this study, we investigated the biological responses of three representative cell lines: HSF (soft tissue origin), SW1353 (hard tissue origin), and C2C12 (myogenic lineage with differentiation potential). To assess cellular proliferation, cells were incubated with 400 nM DNs for 24 hours, followed by CCK-8 assay (Fig. 4 a–c). HSF and SW1353 cells exhibited significantly increased proliferation upon DN treatment—approximately 1.5-fold and 1.2-fold higher, respectively, compared to vehicle controls. These findings are consistent with previous studies demonstrating that TDNs promote proliferation in mouse fibroblasts (L929) [ 33 ] and rat primary chondrocytes [ 12 ]. It has been proposed that DNs may upregulate cell-cycle-related regulators such as CDKL1 (cyclin-dependent kinase-like 1), which facilitates entry into the S phase [ 33 , 34 ]. To further elucidate the mechanism, flow cytometry was conducted to examine cell cycle progression (Fig. 4 e–g). DN treatment led to a marked increase in the S phase population and a corresponding decrease in G0/G1 in both HSF and SW1353 cells, indicating enhanced DNA replication and proliferative activity. These cell cycle alterations align with the proliferation results from the CCK-8 assay. In contrast, DN treatment did not enhance proliferation in C2C12 myoblasts. Instead, flow cytometry revealed an increased proportion of cells in the G0/G1 phase and a reduction in S phase, particularly after treatment with 6HB and TDN. This shift suggests a cell cycle arrest characteristic of differentiation onset, as myoblasts typically exit the cell cycle at G0/G1 during myogenic differentiation [ 35 ]. Effects of DNs on cell secretion and differentiation To further assess the biological effects of DNs, we investigated their influence on cell secretion and differentiation at both the cellular and molecular levels. Human skin fibroblasts (HSFs), widely utilized in skin reconstruction and wound healing models [ 36 , 37 ], were selected to examine DN-induced phenotypic changes, given their relevance in soft tissue engineering. Collagen types I and III (Col I and Col III) are abundant in human dermal tissue and are crucial for extracellular matrix formation during skin repair [ 38 , 39 ]. Fibroblasts also secrete cytokines and growth factors such as basic fibroblast growth factor (bFGF), which promotes angiogenesis and tissue regeneration [ 40 ]. Additionally, myofibroblast transformation, marked by α-smooth muscle actin (αSMA) expression, enhances contractility for wound closure [ 39 ]. In our study, HSFs were treated with 400 nM DNs. Gene expression analysis after 24 h and protein quantification after 72 h revealed a modest upregulation of bFGF mRNA by TDNs (1.36-fold), though no significant differences were observed among DN treatments. DN exposure had negligible effects on Col I , Col III , and αSMA either at the mRNA or protein levels (Fig. 5 b–d). These findings indicate that while DNs stimulate fibroblast proliferation, they do not significantly modulate wound healing–related secretory profiles. Chondrocyte phenotype maintenance is critical for cartilage regeneration. We used the human chondrocyte cell line SW1353 to evaluate the effects of DNs on cartilage-related functions. Type II collagen (Col II) and aggrecan are hallmark components of cartilage matrix, whereas matrix metalloproteinase 13 (MMP13) contributes to matrix degradation [ 41 – 43 ]. Upon 400 nM DN treatment, mRNA levels of Col II and aggrecan were upregulated, while MMP13 remained unchanged (Fig. 5 f), suggesting DN-driven enhancement of chondrocyte phenotype. Histological staining (Alcian blue and Toluidine blue) revealed increased glycosaminoglycan deposition in the extracellular matrix following DN exposure (Fig. 5 g–h). Quantitative analysis confirmed that all three DNs promoted matrix synthesis, with 6HB showing the most significant effect, consistent with prior studies on TDN-mediated cartilage repair [ 12 ]. Our results suggest that planar and rod-shaped DNs can support cartilage-specific matrix production and phenotype preservation. Finally, we examined the pro-differentiation potential of DNs using C2C12 myoblasts, a standard model in skeletal muscle regeneration studies [ 44 ]. Myogenic differentiation was induced with differentiation medium containing 400 nM DNs. After 24 h, 6HB treatment resulted in increased expression of both early ( MyoD ) and late ( MyoG ) myogenic markers by 1.25- and 1.50-fold, respectively (Fig. 5 j). All DNs enhanced embryonic myosin heavy chain ( eMyHC ) mRNA expression, a marker for nascent myofibers, with 6HB exerting the most pronounced effect (2.67-fold), followed by TDN (2.20-fold) and 3PS (1.66-fold). These findings align with the observed shift in C2C12 cell cycle, suggesting that DNs—especially 6HB—facilitate cell cycle withdrawal and differentiation. In summary, DNs not only exhibit favorable biocompatibility but also support or enhance key cellular functions, including proliferation, phenotype maintenance, and lineage-specific differentiation. These properties underscore their potential for further development in regenerative medicine and related biomedical applications. Given their versatility as platforms for delivering diverse cargos—including small molecules, aptamers, and nucleotide-based therapeutics [ 45 ], future efforts in structural design and functional modification are anticipated to broaden their biomedical utility and accelerate clinical translation. Conclusions In this study, we successfully designed, assembled, and characterized representative one-dimensional (6HB), two-dimensional (3PS), and three-dimensional (TDN) DNA nanostructures, and systematically evaluated their cellular uptake and biological effects across five representative cell lines relevant to soft and hard tissue engineering. Among the three constructs, the 1D 6HB consistently demonstrated the highest mean endocytosis efficiency across different cell types, both in traditional monolayer cultures and 3D multicellular spheroids. Furthermore, 6HB showed enhanced cell proliferation in fibroblasts and chondrocytes, supported chondrocyte phenotype maintenance, and promoted myogenic differentiation of myoblasts, highlighting its strong potential as a functional scaffold in regenerative contexts. Importantly, all DNA nanostructures tested showed excellent biocompatibility, with no observed cytotoxicity or negative impacts on cellular phenotype. These findings underscore the promise of structural DNA nanotechnology, particularly 6HB, in the development of next-generation biomaterials for tissue regeneration and programmable drug delivery. Looking ahead, tailoring the dimensionality and geometry of DNA nanostructures may further optimize their interaction with specific cell types and tissue microenvironments, offering a versatile platform for precision medicine and advanced regenerative therapies. Materials and methods Materials The ssDNA was provided by Sangon (China). Wortmannin and Dynasore were purchased from Sinopharm (China). MβCD was purchased from InnoChem (China). Cell counting kit-8 (CCK-8) was gained from Dojindo (Japan). Cell Cycle Analysis Kit was purchased from Beyotime (China). FBS was obtained from Gibco (USA). Preparation of DNs DNs were prepared as reported previously [ 20 , 21 ]. Dissolution of each HPLC-purified ssDNA was performed in deionized water, and was normalized to a concentration of 100 µM. All of the ssDNA were mixed in an equimolar ratio in buffer A (25 mM HEPES, 10 mM MgCl 2 , 150 mM KCl, pH 7.5) for 6HB and 3PS, and buffer B (20 mM Tris, 50 mM MgCl 2 , pH 8.0) for TDN. Thermal annealing of 6HB and 3PS was carried out from 95°C to 4°C (95°C for 5 min, 89°C for 1 min, and gradually reduce to 4°C through 170 cycles of decrement of 0.5°C). The TDN solution was maintained at 95°C for 10 min, and 4°C for 20 min. An Agilent 1260 HPLC (Agilent, USA) with a size exclusion chromatography column (Phenomenex, SEC-4000 LC Column 300 × 7.8 mm, USA) were used for purification of DNs, and chromatograms were detected at 260 nm. A buffer containing 25 mM Tris and 450 mM NaCl (pH 7.5) was used as the mobile phase, with a flow rate set to 1 mL/min. Subsequently, the HPLC fractions were concentrated at 2000 g for 10 min using Amicon Ultra-0.5 mL centrifugal filters with a 30 kDa molecular weight cutoff. After substituting the DN buffer with buffer A, samples were maintained at 4°C until further experimentation. All the DNs were used within 1 week. The UV-vis absorption spectrophotometer (Shimadzu, UV-2600i, Japan) was used for quantifying the concentration of each sample. Characterization of DNs Native 6% PAGE was conducted to validate the assembly. The electrophoresis was run at 70 V for 2 hours in 1× TAE buffer supplemented with 10 mM MgCl₂. DNs were imaged by atomic force microscope (AFM, Multimode 8, Bruker, Germany) after purification. Freshly cleaved mica was treated with 0.5% 3-aminopropyltriethoxysilane for 2 min, followed by triple rinsing with Milli-Q water and drying under compressed air. 10 nM DN monomer was spotted and absorbed for 5 min on treated mica and washed with water, after which 1× TAE buffer was added. Supersharp AFM tips of 2–3 nm radius was employed for the characterization. Fetal Bovine Serum (FBS) assay DNs were dissolved in DMEM medium containing 10% FBS, and were maintained at 37°C for different time points, followed by analysis using native 6% PAGE. Cell Culture C2C12, HUVECs, HSFs, SW1353 cells, and MC3T3-E1 cells were purchased from the Cell Bank of the Chinese Academy of Science. HSFs, C2C12 cells, and HUVECs were cultured in DMEM (Gibco, USA). MC3T3-E1 cells were cultured in α-MEM (Gibco, USA). SW1353 cells were cultured in DMEM/F-12 (Gibco, USA). All the medium contained 10% FBS, penicillin (100 U/mL), and streptomycin (100 µg/mL) (HyClone, USA). The cells were cultured at 37°C in a humidified 5% CO 2 atmosphere. Confocal fluorescence microscope imaging Cells were seeded into glass-bottomed culture dishes at 1 × 10 5 cells/mL and cultured for 24 hours. The medium was changed by fresh medium containing Alexa 647-DNs for appropriate concentration. After an appropriate time, the treated cells were triple rinsed with PBS and stained with Hoechst 33342 at 37°C for 20 min to visualize the nuclei. Confocal laser scanning microscopy (Leica STELLARIS 5, German) was used to capture imaging. Flow Cytometry For flow cytometry analysis, cells were plated on 24-well culture plates at 1×10 5 cells/mL and cultured for 24 hours. After being treated with Alexa 647-DNs, they were collected, washed, and resuspended in PBS. BD LSRFortessa flow cytometer (BD Biosciences, USA) was then used for analysis. Endocytosis pathway studies For chemical inhibition of endocytosis pathways, SW1353 cells were plated on 6-well culture plates at 1 × 10 5 cells/mL and maintained in culture for 24 hours. Then the cells were exposed to pharmacological inhibitors (0.2 µM Wortmannin [ 46 ], 80 µM Dynasore [ 18 ], 5 mM MβCD [ 18 ]) for 0.5 hours at 37°C. Subsequently, the cells were exposed to 400 nM Alexa 647-labeled DNs, and were cultured for another 3 hours. The drugs were maintained in the cell culture throughout the experiments. The cells cultured with DNs at 4°C were used in temperature experiment. Samples cultured at 37°C without inhibitors were taken as positive controls. After treatment, cells were thoroughly rinsed with PBS, harvested, and analyzed via flow cytometry (n = 3). FlowJo (10.8.1) was used to process the data. Culture of 3D multicellular spheroids In this study, SW1353 cells were used to prepare 3D multicellular spheroids. Cell suspensions (5,000 cells/well) were added to 96-well plates with U-bottom (Engineering for Life, China), where the wells were pretreated with the anti-adhesion coating solution. Within the next 72 hours of cell culture (37°C, 5% CO 2 ), the cells aggregated and formed spheroids (approximately 300 µm in diameter). Then the spheroids were incubated with 400 nM Alexa657-DNs for different times. After being washed with PBS for three times, the penetration of the spheroids by the DNs was examined by confocal microscopy in Z stacks with 50 µm Z-intervals from bottom to equator (n = 3). Quantitative analysis of DNs penetration into multicellular spheroids was performed with ImageJ software (version 1.52). Cell proliferation assay Cells were plated into 96-well culture plates at 1 × 10 5 cells/mL and maintained in culture for 24 hours. Then the cells were exposed to 400 nM DNs for another 24 hours, after which the cell proliferation was measured by CCK-8 (n = 5). Vehicle controls (containing a volume of buffer equal to the volume of DN of the experimental group) were taken as negative control. Cell cycle profile For cell cycle analysis, cells were seeded into 6-well culture plates at 1×10 5 cells/mL and maintained in culture for 24 hours. After being treated with the same procedure in cell proliferation assay, they were collected, rinsed, and preserved in 70% cold ethanol for a 12-hour fixation. Then the samples were stained with a pre-prepared propidium iodide (PI) staining solution of reagent kit, and kept in darkness at 37°C for 30 min. CytoFLEX S flow cytometer (Beckman Coulter, Brea, USA) was used to analyze cell cycle distribution by quantifying DNA content based on PI fluorescence. FlowJo was utilized to quantify the distribution of cell populations (n = 3). RNA isolation and real-time quantitative PCR (RT-qPCR) SteadyPure kit (Accurate Biology, China) was used to extract total RNA. It was reverse-transcribed into cDNA using PrimeScript RT Master Mix (Takara, Japan). The mRNA levels were evaluated through RT-qPCR, using the Hieff qPCR SYBR Green Master Mix (Yeason, China). The primers were summarized in Table S2. GAPDH mRNA served as the internal control. Western blot Cells were washed in PBS and were lysed using a RIPA solution containing both phosphatase and protease inhibitors (Beyotime, China). Western blot analysis was performed following established protocols in previous studies [ 47 ]. Specific primary antibodies included Collagen I (501352, Zenbio, China, 1:500), Collagen III (68320-1-Ig, Proteintech, 1:5,000), and GAPDH (60004-1-Ig, Proteintech, China, 1:500,000). Secondary antibodies included anti-Rabbit IgG HRP-conjugated antibody (AS014, Abclonal, China, 1:4000) and anti-Mouse IgG HRP-conjugated antibody (AS003, Abclonal, China, 1:4000). Alcian blue and Toluidine blue staining Alcian blue and Toluidine blue staining (Solarbio, China) were applied for assessing matrix production by SW1353 cells, specifically indicating the presence of acidic polysaccharides like glycosaminoglycans. SW1353 cells were placed into 24-well plates at 1 × 10 5 cells/mL. After paraformaldehyde fixation, staining was carried out using Alcian blue for 0.5 hours or Toluidine blue for 20 min. The cells were imaged microscopically (Nikon Eclipse Ti2, Japan). Analysis of stained regions was carried out with ImageJ. Myoblast differentiation C2C12 cells were plated in 6-well plates at 1 × 10 5 cells/mL and cultured until reaching 80% confluence. Differentiation was induced by replacing the medium with differentiation medium (DMEM, 2% horse serum, and 1% antibiotics), containing 400 nM DNs. The medium was refreshed every 24 hours. Vehicle controls were taken as negative control. Changes in mRNA levels were detected after 24 hours and changes in protein levels were detected after 96 hours. Statistical Analysis Statistical analysis and graphs of data were processed by GraphPad Prism 8.0.2 software. All data are presented as mean ± SD (standard deviation). P values were calculated using one-way ANOVA (confidence interval: 95%). A P value of ≥ 0.05 was considered to be nonsignificant (ns); * P < 0.05, ** P < 0.01, and *** P < 0.001 were considered statistically significant. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All data generated or analysed during this study are included in this published article and its supplementary information files. Competing interests The authors declare that they have no competing interests. Funding This work was supported by National Key Research and Development Program of China (2020YFA0908901), National Natural Science Foundation of China (82121002, U24A20377, and 32171348), the Program of Shanghai Academic Research Leader (22XD1421500), Innovative Research Team of High-level Local Universities in Shanghai (SHSMU- ZLCX20212402), Shanghai's Top Priority Research Center (2022ZZ01017), and Fundamental Research Program Funding of Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (JYZZ085). Authors' contributions X. T.: Conceptualization, Formal analysis, Data curation, Writing – original draft, Visualization. T. Z.: Conceptualization, Methodology, Writing – review & editing, Supervision, Visualization. T. L.: Methodology, Formal analysis, Investigation, Data curation, Visualization. Y. J.: Validation, Data curation. D. L.: Methodology, Formal analysis, Investigation. C. Z.: Validation, Visualization. L. 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Understanding the Biomedical Effects of the Self-Assembled Tetrahedral DNA Nanostructure on Living Cells . ACS Appl Mater Interfaces. 2016; 8(20): 12733-9. Pacek M, Prokhorova TA, Walter JC. Cdk1: unsung hero of S phase? Cell Cycle. 2004; 3(4): 401-3. Qu Z, Liu C, Li P, Xiong W, Zeng Z, Liu A, Xiao W, Huang J, Liu Z, Zhang S. Theaflavin Promotes Myogenic Differentiation by Regulating the Cell Cycle and Surface Mechanical Properties of C2C12 Cells . J Agric Food Chem. 2020; 68(37): 9978-9992. Matei AE, Chen CW, Kiesewetter L, Györfi AH, Li YN, Trinh-Minh T, Xu X, Tran Manh C, van Kuppevelt T, Hansmann J, Jüngel A, Schett G, Groeber-Becker F, Distler JHW. Vascularised human skin equivalents as a novel in vitro model of skin fibrosis and platform for testing of antifibrotic drugs . Ann Rheum Dis. 2019; 78(12): 1686-1692. Sriram G, Bigliardi PL, Bigliardi-Qi M. Fibroblast heterogeneity and its implications for engineering organotypic skin models in vitro . Eur J Cell Biol. 2015; 94(11): 483-512. Davison-Kotler E, Marshall WS, García-Gareta E. Sources of Collagen for Biomaterials in Skin Wound Healing . Bioengineering (Basel). 2019; 6(3). Younesi FS, Miller AE, Barker TH, Rossi FMV, Hinz B. Fibroblast and myofibroblast activation in normal tissue repair and fibrosis . Nat Rev Mol Cell Biol. 2024; 25(8): 617-638. Akita S, Akino K, Hirano A. Basic Fibroblast Growth Factor in Scarless Wound Healing . Adv Wound Care (New Rochelle). 2013; 2(2): 44-49. Bhosale AMRichardson JB. Articular cartilage: structure, injuries and review of management . Br Med Bull. 2008; 87: 77-95. Hu QEcker M. Overview of MMP-13 as a Promising Target for the Treatment of Osteoarthritis . Int J Mol Sci. 2021; 22(4). Alcaide-Ruggiero L, Cugat R, Domínguez JM. Proteoglycans in Articular Cartilage and Their Contribution to Chondral Injury and Repair Mechanisms . Int J Mol Sci. 2023; 24(13). Sun J, Lo HTJ, Fan L, Yiu TL, Shakoor A, Li G, Lee WYW, Sun D. High-efficiency quantitative control of mitochondrial transfer based on droplet microfluidics and its application on muscle regeneration . Sci Adv. 2022; 8(33): eabp9245. Zhao L, Hu HL, Ma XQ, Lyu Y, Yuan Q, Tan WH. Aptamer-based Membrane Protein Analysis and Molecular Diagnostics . Chemical Research in Chinese Universities. 2024; 40(2): 173-189. Andar AU, Hood RR, Vreeland WN, Devoe DL, Swaan PW. Microfluidic preparation of liposomes to determine particle size influence on cellular uptake mechanisms . Pharm Res. 2014; 31(2): 401-13. Jin Y, Li Z, Wu Y, Li H, Liu Z, Liu L, Ouyang N, Zhou T, Fang B, Xia L. Aberrant Fluid Shear Stress Contributes to Articular Cartilage Pathogenesis via Epigenetic Regulation of ZBTB20 by H3K4me3 . J Inflamm Res. 2021; 14: 6067-6083. Additional Declarations No competing interests reported. Supplementary Files SupportingInformation.docx floatimage1.jpeg Graphical Abstract Cite Share Download PDF Status: Published Journal Publication published 29 Sep, 2025 Read the published version in Journal of Nanobiotechnology → Version 1 posted Editorial decision: Revision requested 02 Aug, 2025 Reviews received at journal 29 Jul, 2025 Reviewers agreed at journal 27 Jul, 2025 Reviewers agreed at journal 26 Jul, 2025 Reviews received at journal 26 Jul, 2025 Reviewers agreed at journal 25 Jul, 2025 Reviewers agreed at journal 24 Jul, 2025 Reviewers invited by journal 24 Jul, 2025 Editor assigned by journal 22 Jul, 2025 Submission checks completed at journal 22 Jul, 2025 First submitted to journal 20 Jul, 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7170660","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":491441365,"identity":"fa4afef3-651e-4290-8cc9-9e6916367498","order_by":0,"name":"Xinyue Tang","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Xinyue","middleName":"","lastName":"Tang","suffix":""},{"id":491441366,"identity":"c78b4d4a-1795-4feb-b7ad-d73f19a3c65e","order_by":1,"name":"Tingting Zhai","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Zhai","suffix":""},{"id":491441367,"identity":"9fd2416f-88aa-4fbe-aed4-566a6327770c","order_by":2,"name":"Tiancheng Li","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Tiancheng","middleName":"","lastName":"Li","suffix":""},{"id":491441368,"identity":"656300be-20b4-4f34-9025-8f920f6c0395","order_by":3,"name":"Yu Jin","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Jin","suffix":""},{"id":491441369,"identity":"f9297740-48b3-4e5c-a5ec-31ca99f44b05","order_by":4,"name":"Dantong Lei","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Dantong","middleName":"","lastName":"Lei","suffix":""},{"id":491441370,"identity":"fa1efa76-79c4-4ba2-a198-b6616aa521d3","order_by":5,"name":"Cheng Zhu","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Zhu","suffix":""},{"id":491441371,"identity":"1fc8af41-e072-4a21-a60f-830a53d125dc","order_by":6,"name":"Luyao Qu","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Luyao","middleName":"","lastName":"Qu","suffix":""},{"id":491441372,"identity":"717c3a2a-3b88-4a90-af43-98d9139617d8","order_by":7,"name":"Yingfu Li","email":"","orcid":"","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Yingfu","middleName":"","lastName":"Li","suffix":""},{"id":491441373,"identity":"2bf00872-48b7-4a27-b79f-d948c65b396c","order_by":8,"name":"Yudong Wang","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Yudong","middleName":"","lastName":"Wang","suffix":""},{"id":491441374,"identity":"5e1b8b02-332b-451c-9e25-9733850bcf10","order_by":9,"name":"Hongzhou Gu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYBACAxDB2ABmMz5g4EEIEqWF2YCBx4A0LWwSUD5+LebsvYdf/txhk9jAv8as8ofMn8QG9uZtEgw1d3Bqsew5l2YheSYtsUHijdltHh6DxAaeY2USDMee4XbYjRwzA8O2w0AtZ8xuM4C0SOSYSTA2HMavJRGqpfAHSIv8G4JajB8cBGnh7zFjADtMgoeAljNnzBgb29KM2yTYiqV5eIyN23jSii0SjuHRcrzH+OPPNhvZfv7DGz/+7JGT7Wc/vPHGhxrcWhgg0QEiE4AR1ANkgHgJ+DQAI/0DmOI/ACR+4Fc6CkbBKBgFIxMAAHPqU932nrnTAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":true,"prefix":"","firstName":"Hongzhou","middleName":"","lastName":"Gu","suffix":""},{"id":491441375,"identity":"7b9f5837-410d-475a-82d9-4b935163c486","order_by":10,"name":"Bing Fang","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Fang","suffix":""}],"badges":[],"createdAt":"2025-07-20 15:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7170660/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7170660/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12951-025-03707-1","type":"published","date":"2025-09-29T15:57:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87829522,"identity":"ad4440de-3efc-4d9d-96b7-a37c14c59221","added_by":"auto","created_at":"2025-07-29 12:12:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":273888,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental design, formation, and characterization of DNs. a. Schematic overview of the experimental design investigating the cellular uptake and biological responses of DNs with varying dimensionality across multiple cell types relevant to soft and hard tissue engineering. b–d. Design schemes for the construction of DNA nanostructures: b Six-helix bundle (6HB, one-dimensional structure), c Three-point star (3PS, two-dimensional structure), and d Tetrahedral DNA nanostructure (TDN, three-dimensional structure). e–g. Characterization of DNs by 6% polyacrylamide gel electrophoresis (PAGE) and atomic force microscopy (AFM). PAGE lanes: M, DNA ladder; Lane 1, unassembled ssDNA; Lane 2, assembled DNs; Lane 3, purified DNs. AFM images confirm structural formation and morphology of DNs. Scale bar: 100 nm.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7170660/v1/b852c2f7ca21c7858bfe2f2a.png"},{"id":87827375,"identity":"cb2fe425-b882-4818-b2b9-22c24a308751","added_by":"auto","created_at":"2025-07-29 11:56:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":380557,"visible":true,"origin":"","legend":"\u003cp\u003eCellular uptake of DNs across multiple cell lines. a. Confocal microscopy images of five different cell types treated with 400 nM Alexa 647-labeled DNs for 3 hours under standard petri dish culture conditions. Magenta: Alexa 647-labeled DNs; blue: nuclei stained with Hoechst 33342. Scale bar: 40 µm. b. Flow cytometry analysis of cellular uptake of Alexa 647-labeled DNs across five cell types after 3-hour incubation. c. Quantification of mean fluorescence intensity from flow cytometry analysis (n = 3). d. Average internalization efficiency of the three DNs across the five cell types. e. Schematic illustration of potential endocytic pathways for DN internalization. f. Flow cytometry analysis of Alexa 647-DN uptake in SW1353 cells treated with various endocytosis inhibitors at 37 °C and under low-temperature (4 °C) conditions as a control. Data are presented as mean ± SD (n = 3). Statistical analysis was performed using one-way ANOVA with multiple comparisons. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; ns, not significant. Treatment duration: 3 hours.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7170660/v1/670312c5483db526f13b97d9.png"},{"id":87828426,"identity":"ff6df7ca-1f4f-45c8-aba4-0e5921921857","added_by":"auto","created_at":"2025-07-29 12:04:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":408910,"visible":true,"origin":"","legend":"\u003cp\u003e3D cellular uptake of DNA nanostructures in SW1353 multicellular spheroids. a. Schematic illustration of spheroid formation and imaging strategy. Spheroids were generated using U-bottom 96-well plates. Confocal microscopy images were acquired at 50 μm Z-stack intervals from the base to the equator of the spheroid, which had an approximate height of 300 μm. b–d. Representative confocal images showing Alexa 647-labeled DNs (magenta) within spheroids at 0.5, 1.5, and 3 hours post-incubation. Scale bar: 200 μm. e. Quantification of fluorescence intensity at a 100 μm cross-section from the spheroid base, illustrating comparative penetration depths of the DNs.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7170660/v1/70504641ec109fd526f653eb.png"},{"id":87827383,"identity":"60c12956-52ff-44b0-ba7c-1c0c8c9127dc","added_by":"auto","created_at":"2025-07-29 11:56:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":380547,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of DNs on cell proliferation and cell cycle. a–c, Cell proliferation analysis of HSF, SW1353, and C2C12 cells treated with 400 nM DNs for 24 h, assessed by CCK-8 assay (n = 5). d, Schematic illustration of the mammalian cell cycle. e–g, Flow cytometry analysis of cell cycle distribution in HSF, SW1353, and C2C12 cells after 24 h DN treatment (400 nM; n = 3). Corresponding percentages of cells in G0/G1, S, and G2/M phases are shown. Data are presented as mean ± SD. Statistical analysis was performed using one-way ANOVA with multiple comparisons. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; ns, not significant.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7170660/v1/236a99cd9f41dd065d47a88c.png"},{"id":87827378,"identity":"4fbd74fc-8603-4b8c-adf5-9ef6f96c8f15","added_by":"auto","created_at":"2025-07-29 11:56:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":484974,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of DNs on cell secretion and differentiation. a. Schematic illustration of DN-induced phenotypic modulation in human skin fibroblasts (HSFs). b. Relative mRNA expression of wound healing–related genes (\u003cem\u003eCol I\u003c/em\u003e, \u003cem\u003eCol III\u003c/em\u003e, \u003cem\u003ebFGF\u003c/em\u003e, and \u003cem\u003eαSMA\u003c/em\u003e) in HSFs following 24 h DN treatment. c, d. Protein expression of Col I and Col III in HSFs after 72 h DN treatment, analyzed by western blotting. e. Schematic illustration of DN-mediated effects on the chondrocyte phenotype in SW1353 cells. f. Relative mRNA expression of cartilage matrix–related markers (\u003cem\u003eAggrecan\u003c/em\u003e, \u003cem\u003eCol II\u003c/em\u003e, and \u003cem\u003eSox9\u003c/em\u003e) and a degradation marker (\u003cem\u003eMMP13\u003c/em\u003e) in SW1353 cells after 24 h DN treatment. g. Alcian blue and Toluidine blue staining of SW1353 cells after 72 h DN treatment (n = 3). Scale bar = 200 μm. h. Quantification of Alcian blue and Toluidine blue staining intensity. i. Schematic illustration of DN-induced myogenic differentiation in C2C12 cells. j. Relative mRNA expression of myogenic differentiation markers (\u003cem\u003eMyf5, MyoD, MyoG, and eMyHC\u003c/em\u003e) in C2C12 cells following 24 h treatment with DNs in differentiation medium. All data are presented as mean ± SD. Statistical analysis was performed using one-way ANOVA with multiple comparisons. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; ns, not significant.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7170660/v1/49b4d78fa99771b767a4d5f0.png"},{"id":92883801,"identity":"f4749049-9279-41e1-b7e2-4dfca77342b3","added_by":"auto","created_at":"2025-10-06 16:09:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2829175,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7170660/v1/3e414a23-6d0d-41a6-b754-066414ddb1b4.pdf"},{"id":87828428,"identity":"14697fc8-3025-47ca-873e-7f2f5be7f0db","added_by":"auto","created_at":"2025-07-29 12:04:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1519684,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7170660/v1/cb7e87f5315715576741fc44.docx"},{"id":87827385,"identity":"1487c6d6-1383-4b0b-b3d0-21e57c588a01","added_by":"auto","created_at":"2025-07-29 11:56:19","extension":"jpeg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":370788,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical Abstract\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7170660/v1/c70dffbafd845c4b1e351738.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dimensional Control of DNA Nanostructures Enhances Cellular Uptake and Guides Tissue-Regenerative Responses","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCellular functional regulation is fundamental to tissue regeneration and drug delivery. During the interaction between biomaterials and cells, the physicochemical properties of the materials play a crucial role in modulating cell behaviors such as proliferation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], secretion [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], differentiation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and stemness maintenance [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], thereby collectively promoting tissue repair and regeneration [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Integrating material design with drug delivery has emerged as an effective strategy for enhancing cellular functions and optimizing therapeutic outcomes. Consequently, material-based modulation of \u0026ldquo;seed cells\u0026rdquo; used in tissue engineering has become a widely adopted and promising approach in regenerative medicine.\u003c/p\u003e\u003cp\u003eOver the past two decades, DNA has garnered growing interest as a programmable and biocompatible building block for nanoscale assemblies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The advent of DNA origami in 2006 revolutionized structural DNA nanotechnology, enabling the precise folding of DNA strands into well-defined nanostructures [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These DNA nanostructures (DNs) possess several desirable features, including excellent biocompatibility, site-specific addressability, chemical modifiability, and the ability to be internalized by cells without the need for transfection agents [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Owing to these attributes, DNs are increasingly applied in biomedical fields, particularly in tissue engineering and drug delivery [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThrough programmed self-assembly, DNs can be constructed into diverse one-dimensional (1D), two-dimensional (2D) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and three-dimensional (3D) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] geometries. Previous research has shown that nanoparticle characteristics such as shape, size, and molecular weight significantly influence cellular uptake [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. For instance, Bastings et al. demonstrated that among DNs with equivalent molecular weight, those with more compact geometries and lower aspect ratios achieved higher uptake in endothelial, epithelial, and immune cell lines [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Wang et al. reported that rod-shaped DNs and larger tetrahedral DNA nanostructures (TDNs) exhibited superior internalization in H1299 and DMS53 cells compared to their smaller counterparts [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Rajwar et al. found that TDNs were preferentially taken up by various carcinoma cell types relative to other 3D geometries [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Despite these advances, systematic studies on how dimensional parameters affect DN uptake across multiple tissue engineering-relevant cell types are still lacking. Moreover, most existing studies focus primarily on cellular uptake and subcellular localization, with limited exploration into the downstream functional effects of DNs on cell behavior [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn our previous work, we successfully applied a six-helix bundle (6HB) rod and a three-point star (3PS) structure for sorting sub-150-nm liposomes of different sizes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Building upon this work, the current study investigates the endocytosis and biological effects of three DNs representing distinct dimensional configurations: a 1D 6HB, a 2D 3PS, and a 3D TDN (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Five representative cell lines were selected for this study: myoblasts (C2C12), endothelial cells (HUVEC), fibroblasts (HSF), chondrocytes (SW1353), and osteoblasts (MC3T3-E1). We systematically evaluated the internalization efficiency and biological influence of each DN geometry across these cell types, aiming to elucidate the role of structural dimensionality in cellular interactions and to provide valuable insights for the future development of DNA-based systems in tissue engineering and drug delivery.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e\u003cb\u003eDesign, construction, characterization, and stability of DNs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThree representative DNs\u0026mdash;6HB, 3PS, and TDN\u0026mdash;were designed and assembled as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb\u0026ndash;d [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The DNA sequences used for each structure are provided in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e of the Supporting Information. To facilitate endocytosis tracking via confocal microscopy, one strand of each DN was labeled with Alexa Fluor 647 (Alexa 647-DNs). Following assembly, the DNs were purified using high-performance liquid chromatography (HPLC). Their structural integrity and assembly fidelity were confirmed by polyacrylamide gel electrophoresis (PAGE) and atomic force microscopy (AFM), as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee\u0026ndash;g.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe structural stability of DNs is a critical factor for their effective performance in cellular and biological applications. Most tissue culture media are supplemented with fetal bovine serum (FBS), which contains a variety of nucleases known to degrade nucleic acid-based materials [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. To evaluate the resistance of DNs to nuclease degradation, we assessed their stability in a cell culture environment containing 10% FBS (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Among the tested structures, TDNs retained approximately 90% structural integrity after 1.5 hours, consistent with previous reports [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, TDNs underwent progressive degradation over time, with only 5.8% remaining intact after 24 hours. In contrast, the 6HB exhibited markedly enhanced stability, maintaining over 95% integrity at the 24-hour time point. This suggests that densely packed nanostructures, such as 6HB, offer improved protection against nuclease activity compared to more open or hollow designs like TDNs [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eTime- and concentration-dependent cellular uptake of DNs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDue to their negatively charged phosphate backbones, single-stranded and double-stranded DNA (ssDNA and dsDNA) typically cannot cross the cellular membrane unaided. However, DNA organized into defined nanostructures can be internalized by cells without the use of transfection reagents [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. To determine an optimal time point for subsequent experiments, we first evaluated the cellular uptake of DNs in HUVECs over a time course (0.5, 1.5, 3, 6, and 12 hours) (Fig. S2\u0026ndash;S4). DNs were labeled with Alexa 647 (Alexa 647-DNs) to enable tracking, and their internalization was assessed by confocal microscopy and flow cytometry (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea\u0026ndash;c). Untreated cells were used as negative controls and showed negligible fluorescence in the red channel, confirming that observed signals originated from internalized DNs.\u003c/p\u003e\u003cp\u003eAt 200 nM, 3PS uptake was detectable as early as 0.5 hours, while 6HB and TDN required 1.5\u0026ndash;3 hours to become apparent. Internalization of all three DNs increased steadily over the 12-hour period, consistent with the quantitative data from flow cytometry. Based on these findings, a 3-hour incubation was selected as the standard time point for subsequent uptake experiments, as all DNs showed measurable cytoplasmic signals by this time.\u003c/p\u003e\u003cp\u003eTo examine concentration dependence, we then treated cells with increasing concentrations of Alexa 647-DNs (100, 200, 400, and 800 nM) (Fig. S5\u0026ndash;S7). Uptake was positively correlated with concentration, and differences among the three DNs became more distinct at 400 nM, with some structures showing minimal fluorescence at lower concentrations. As a result, 400 nM was chosen as the optimal concentration for all subsequent experiments.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e6HB exhibits the highest mean endocytosis efficiency across five cell lines\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe cellular uptake of DNs was systematically evaluated in five representative cell lines: HSF, MC3T3-E1, SW1353, C2C12, and HUVECs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea\u0026ndash;c). Notably, the endocytosis efficiency varied among both DN types and cell types. MC3T3-E1 and C2C12 cells demonstrated relatively high uptake capacities, whereas HUVECs displayed weaker internalization, consistent with previous studies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Among the three DNA nanostructures, 6HB showed the highest endocytosis efficiency in HSF and SW1353 cells. Interestingly, 3PS exhibited superior uptake in HUVECs, aligning with its performance in earlier concentration- and time-dependent studies. In C2C12 cells, both 6HB and 3PS showed efficient internalization, while TDN achieved the highest uptake in MC3T3-E1 cells.\u003c/p\u003e\u003cp\u003eTo further quantify and compare their performance, the mean endocytosis efficiency and standard deviation of each DN across all five cell types were calculated based on flow cytometry data (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Among the three DN architectures, 6HB exhibited the highest average internalization efficiency with relatively low variability across cell types. In contrast, TDN demonstrated the lowest mean uptake and the highest variability, suggesting a stronger dependence on cell type for its internalization efficiency.\u003c/p\u003e\u003cp\u003eThese findings are in line with prior reports. Bastings et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] found that compact nanostructures with low aspect ratios were internalized more effectively by HEK293, HUVECs, and dendritic cells. Similarly, Wang et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] reported that rod-shaped DNs outperformed TDNs of similar size in DMS53 and H1299 cells. The relatively poor and inconsistent uptake of TDNs may be attributed to their three-dimensional configuration, which potentially limits membrane interactions compared to bundle-shaped or planar DNs.\u003c/p\u003e\u003cp\u003eTaken together, our results highlight the superior and consistent internalization of 6HB across a diverse range of tissue-relevant cell types, supporting its potential as a broadly applicable scaffold for DNA-based biomedical applications.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEndocytic mechanisms\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEndocytosis comprises two major processes: phagocytosis and pinocytosis. Phagocytosis typically involves the uptake of particles larger than 250 nm and is limited to specialized mammalian cells such as macrophages, neutrophils, dendritic cells, and monocytes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Given that our DNs are within the 10\u0026ndash;20 nm size range and non-phagocytic cells were used in this study, we focused our investigation on pinocytosis. This includes three primary pathways: macropinocytosis, clathrin-mediated endocytosis, and caveolin-mediated endocytosis. SW1353 cells were selected to study the mechanisms of DN internalization.\u003c/p\u003e\u003cp\u003eTo evaluate whether DN uptake is energy-dependent, we first conducted temperature-controlled uptake experiments. Endocytosis is known to be significantly reduced at 4\u0026deg;C due to the suppression of energy-dependent processes. Consistent with previous studies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], our flow cytometry analysis revealed a dramatic reduction in uptake at 4\u0026deg;C compared to 37\u0026deg;C: 6HB by 78.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9%, 3PS by 82.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5%, and TDN by 90.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3%. These results confirm that internalization of all three DNs is an active, energy-dependent process.\u003c/p\u003e\u003cp\u003eTo further delineate the endocytic pathways involved, we employed pharmacological inhibitors: Wortmannin to block macropinocytosis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]; Dynasore to inhibit dynamin and clathrin-mediated endocytosis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]; and methyl-β-cyclodextrin (MβCD) to disrupt caveolin-mediated endocytosis by depleting membrane cholesterol [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Cells without inhibitors served as positive controls.\u003c/p\u003e\u003cp\u003eWortmannin reduced DN uptake by approximately 40\u0026ndash;50%, with the strongest inhibitory effect observed for TDN. Treatment with Dynasore reduced uptake of all three DNs by about 30%, indicating partial involvement of clathrin-mediated pathways. MβCD had differential effects: 6HB uptake decreased by 32.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8%, 3PS by 44.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1%, and TDN by 56.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8%. These findings suggest that TDN uptake is predominantly caveolin-mediated, aligning with previous studies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], while 3PS follows a similar trend. In contrast, 6HB appears to utilize both clathrin- and caveolin-mediated pathways, indicating a more balanced involvement of multiple mechanisms.\u003c/p\u003e\u003cp\u003eOverall, our results indicate that multiple endocytic routes contribute to DN uptake, a phenomenon also observed with other nanoparticles [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Prior studies have shown that well-dispersed silica nanoparticles are primarily internalized via caveolin-mediated endocytosis, but agglomerated forms shift toward macropinocytosis as the dominant route [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Consistent with this, we observed that TDN exhibited aggregation after 3 hours in medium containing 10% FBS (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), and similar tendencies may exist for 6HB and 3PS. These aggregated forms likely facilitate macropinocytosis, whereas monomeric DNs may preferentially enter cells via clathrin- and caveolin-mediated mechanisms. The relative contribution of each pathway may also be influenced by the specific cell type and DN dispersion state.\u003c/p\u003e\u003cp\u003e\u003cb\u003e6HB exhibits the highest endocytosis efficiency in a multicellular spheroid model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTraditional 2D monolayer cultures, such as petri dish-cultured cells, are widely used for cellular uptake studies. However, these systems often lack the structural and physiological complexity of native tissue microenvironments [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In contrast, 3D cell culture models, such as multicellular spheroids, more accurately replicate in vivo conditions, including cell\u0026ndash;cell and cell\u0026ndash;matrix interactions, diffusion gradients, and spatial organization [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. To evaluate the endocytosis behavior of DNs in a physiologically relevant model, we constructed 3D spheroids using SW1353 cells and investigated the penetration and uptake of Alexa 647-labeled 6HB, 3PS, and TDN.\u003c/p\u003e\u003cp\u003eAfter treating spheroids with DNs, we performed confocal microscopy with Z-stack imaging at 50 \u0026micro;m intervals across various incubation times. At 0.5 hours, weak fluorescence signals were observed in the outer spheroid layer for TDN, minimal signals for 3PS, and noticeably stronger fluorescence for 6HB (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After 1.5 hours, the intensity of Alexa 647 fluorescence increased across all DNs, with 6HB showing the highest accumulation in the outer layers, followed by 3PS and TDN. By the 3-hour time point, while fluorescence intensity at the spheroid periphery became more comparable among all three DNs, 6HB exhibited markedly deeper penetration into the spheroid core. After 6 and 12 hours, all DNs were able to reach the inner layers of the spheroid (Fig. S8).\u003c/p\u003e\u003cp\u003eThese observations suggest that 6HB enters multicellular spheroids more rapidly and penetrates more deeply than 3PS and TDN within the same time frame. Over extended incubation, however, the uptake of all DNs tends to converge, likely due to the eventual saturation of the spheroid\u0026rsquo;s uptake capacity. Collectively, both 2D and 3D experiments demonstrate that 6HB consistently exhibits superior endocytosis efficiency across various models and cell types.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEffects of DNs on cell proliferation and cell cycle\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGiven the broad potential of DNs in biomedical applications, it is essential to systematically evaluate their effects on cellular function prior to therapeutic use. In this study, we investigated the biological responses of three representative cell lines: HSF (soft tissue origin), SW1353 (hard tissue origin), and C2C12 (myogenic lineage with differentiation potential).\u003c/p\u003e\u003cp\u003eTo assess cellular proliferation, cells were incubated with 400 nM DNs for 24 hours, followed by CCK-8 assay (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u0026ndash;c). HSF and SW1353 cells exhibited significantly increased proliferation upon DN treatment\u0026mdash;approximately 1.5-fold and 1.2-fold higher, respectively, compared to vehicle controls. These findings are consistent with previous studies demonstrating that TDNs promote proliferation in mouse fibroblasts (L929) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and rat primary chondrocytes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It has been proposed that DNs may upregulate cell-cycle-related regulators such as CDKL1 (cyclin-dependent kinase-like 1), which facilitates entry into the S phase [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo further elucidate the mechanism, flow cytometry was conducted to examine cell cycle progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee\u0026ndash;g). DN treatment led to a marked increase in the S phase population and a corresponding decrease in G0/G1 in both HSF and SW1353 cells, indicating enhanced DNA replication and proliferative activity. These cell cycle alterations align with the proliferation results from the CCK-8 assay.\u003c/p\u003e\u003cp\u003eIn contrast, DN treatment did not enhance proliferation in C2C12 myoblasts. Instead, flow cytometry revealed an increased proportion of cells in the G0/G1 phase and a reduction in S phase, particularly after treatment with 6HB and TDN. This shift suggests a cell cycle arrest characteristic of differentiation onset, as myoblasts typically exit the cell cycle at G0/G1 during myogenic differentiation [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eEffects of DNs on cell secretion and differentiation\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further assess the biological effects of DNs, we investigated their influence on cell secretion and differentiation at both the cellular and molecular levels. Human skin fibroblasts (HSFs), widely utilized in skin reconstruction and wound healing models [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], were selected to examine DN-induced phenotypic changes, given their relevance in soft tissue engineering. Collagen types I and III (Col I and Col III) are abundant in human dermal tissue and are crucial for extracellular matrix formation during skin repair [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Fibroblasts also secrete cytokines and growth factors such as basic fibroblast growth factor (bFGF), which promotes angiogenesis and tissue regeneration [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Additionally, myofibroblast transformation, marked by α-smooth muscle actin (αSMA) expression, enhances contractility for wound closure [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In our study, HSFs were treated with 400 nM DNs. Gene expression analysis after 24 h and protein quantification after 72 h revealed a modest upregulation of bFGF mRNA by TDNs (1.36-fold), though no significant differences were observed among DN treatments. DN exposure had negligible effects on \u003cem\u003eCol I\u003c/em\u003e, \u003cem\u003eCol III\u003c/em\u003e, and \u003cem\u003eαSMA\u003c/em\u003e either at the mRNA or protein levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb\u0026ndash;d). These findings indicate that while DNs stimulate fibroblast proliferation, they do not significantly modulate wound healing\u0026ndash;related secretory profiles.\u003c/p\u003e\u003cp\u003eChondrocyte phenotype maintenance is critical for cartilage regeneration. We used the human chondrocyte cell line SW1353 to evaluate the effects of DNs on cartilage-related functions. Type II collagen (Col II) and aggrecan are hallmark components of cartilage matrix, whereas matrix metalloproteinase 13 (MMP13) contributes to matrix degradation [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Upon 400 nM DN treatment, mRNA levels of \u003cem\u003eCol II\u003c/em\u003e and \u003cem\u003eaggrecan\u003c/em\u003e were upregulated, while \u003cem\u003eMMP13\u003c/em\u003e remained unchanged (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef), suggesting DN-driven enhancement of chondrocyte phenotype. Histological staining (Alcian blue and Toluidine blue) revealed increased glycosaminoglycan deposition in the extracellular matrix following DN exposure (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg\u0026ndash;h). Quantitative analysis confirmed that all three DNs promoted matrix synthesis, with 6HB showing the most significant effect, consistent with prior studies on TDN-mediated cartilage repair [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Our results suggest that planar and rod-shaped DNs can support cartilage-specific matrix production and phenotype preservation.\u003c/p\u003e\u003cp\u003eFinally, we examined the pro-differentiation potential of DNs using C2C12 myoblasts, a standard model in skeletal muscle regeneration studies [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Myogenic differentiation was induced with differentiation medium containing 400 nM DNs. After 24 h, 6HB treatment resulted in increased expression of both early (\u003cem\u003eMyoD\u003c/em\u003e) and late (\u003cem\u003eMyoG\u003c/em\u003e) myogenic markers by 1.25- and 1.50-fold, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ej). All DNs enhanced embryonic myosin heavy chain (\u003cem\u003eeMyHC\u003c/em\u003e) mRNA expression, a marker for nascent myofibers, with 6HB exerting the most pronounced effect (2.67-fold), followed by TDN (2.20-fold) and 3PS (1.66-fold). These findings align with the observed shift in C2C12 cell cycle, suggesting that DNs\u0026mdash;especially 6HB\u0026mdash;facilitate cell cycle withdrawal and differentiation.\u003c/p\u003e\u003cp\u003eIn summary, DNs not only exhibit favorable biocompatibility but also support or enhance key cellular functions, including proliferation, phenotype maintenance, and lineage-specific differentiation. These properties underscore their potential for further development in regenerative medicine and related biomedical applications. Given their versatility as platforms for delivering diverse cargos\u0026mdash;including small molecules, aptamers, and nucleotide-based therapeutics [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], future efforts in structural design and functional modification are anticipated to broaden their biomedical utility and accelerate clinical translation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we successfully designed, assembled, and characterized representative one-dimensional (6HB), two-dimensional (3PS), and three-dimensional (TDN) DNA nanostructures, and systematically evaluated their cellular uptake and biological effects across five representative cell lines relevant to soft and hard tissue engineering. Among the three constructs, the 1D 6HB consistently demonstrated the highest mean endocytosis efficiency across different cell types, both in traditional monolayer cultures and 3D multicellular spheroids. Furthermore, 6HB showed enhanced cell proliferation in fibroblasts and chondrocytes, supported chondrocyte phenotype maintenance, and promoted myogenic differentiation of myoblasts, highlighting its strong potential as a functional scaffold in regenerative contexts.\u003c/p\u003e\u003cp\u003eImportantly, all DNA nanostructures tested showed excellent biocompatibility, with no observed cytotoxicity or negative impacts on cellular phenotype. These findings underscore the promise of structural DNA nanotechnology, particularly 6HB, in the development of next-generation biomaterials for tissue regeneration and programmable drug delivery. Looking ahead, tailoring the dimensionality and geometry of DNA nanostructures may further optimize their interaction with specific cell types and tissue microenvironments, offering a versatile platform for precision medicine and advanced regenerative therapies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eMaterials\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe ssDNA was provided by Sangon (China). Wortmannin and Dynasore were purchased from Sinopharm (China). MβCD was purchased from InnoChem (China). Cell counting kit-8 (CCK-8) was gained from Dojindo (Japan). Cell Cycle Analysis Kit was purchased from Beyotime (China). FBS was obtained from Gibco (USA).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePreparation of DNs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDNs were prepared as reported previously [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Dissolution of each HPLC-purified ssDNA was performed in deionized water, and was normalized to a concentration of 100 \u0026micro;M. All of the ssDNA were mixed in an equimolar ratio in buffer A (25 mM HEPES, 10 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 150 mM KCl, pH 7.5) for 6HB and 3PS, and buffer B (20 mM Tris, 50 mM MgCl\u003csub\u003e2\u003c/sub\u003e, pH 8.0) for TDN. Thermal annealing of 6HB and 3PS was carried out from 95\u0026deg;C to 4\u0026deg;C (95\u0026deg;C for 5 min, 89\u0026deg;C for 1 min, and gradually reduce to 4\u0026deg;C through 170 cycles of decrement of 0.5\u0026deg;C). The TDN solution was maintained at 95\u0026deg;C for 10 min, and 4\u0026deg;C for 20 min.\u003c/p\u003e\u003cp\u003eAn Agilent 1260 HPLC (Agilent, USA) with a size exclusion chromatography column (Phenomenex, SEC-4000 LC Column 300 \u0026times; 7.8 mm, USA) were used for purification of DNs, and chromatograms were detected at 260 nm. A buffer containing 25 mM Tris and 450 mM NaCl (pH 7.5) was used as the mobile phase, with a flow rate set to 1 mL/min. Subsequently, the HPLC fractions were concentrated at 2000 g for 10 min using Amicon Ultra-0.5 mL centrifugal filters with a 30 kDa molecular weight cutoff. After substituting the DN buffer with buffer A, samples were maintained at 4\u0026deg;C until further experimentation. All the DNs were used within 1 week. The UV-vis absorption spectrophotometer (Shimadzu, UV-2600i, Japan) was used for quantifying the concentration of each sample.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCharacterization of DNs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNative 6% PAGE was conducted to validate the assembly. The electrophoresis was run at 70 V for 2 hours in 1\u0026times; TAE buffer supplemented with 10 mM MgCl₂. DNs were imaged by atomic force microscope (AFM, Multimode 8, Bruker, Germany) after purification. Freshly cleaved mica was treated with 0.5% 3-aminopropyltriethoxysilane for 2 min, followed by triple rinsing with Milli-Q water and drying under compressed air. 10 nM DN monomer was spotted and absorbed for 5 min on treated mica and washed with water, after which 1\u0026times; TAE buffer was added. Supersharp AFM tips of 2\u0026ndash;3 nm radius was employed for the characterization.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFetal Bovine Serum (FBS) assay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDNs were dissolved in DMEM medium containing 10% FBS, and were maintained at 37\u0026deg;C for different time points, followed by analysis using native 6% PAGE.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCell Culture\u003c/b\u003e\u003c/p\u003e\u003cp\u003eC2C12, HUVECs, HSFs, SW1353 cells, and MC3T3-E1 cells were purchased from the Cell Bank of the Chinese Academy of Science. HSFs, C2C12 cells, and HUVECs were cultured in DMEM (Gibco, USA). MC3T3-E1 cells were cultured in α-MEM (Gibco, USA). SW1353 cells were cultured in DMEM/F-12 (Gibco, USA). All the medium contained 10% FBS, penicillin (100 U/mL), and streptomycin (100 \u0026micro;g/mL) (HyClone, USA). The cells were cultured at 37\u0026deg;C in a humidified 5% CO\u003csub\u003e2\u003c/sub\u003e atmosphere.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConfocal fluorescence microscope imaging\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCells were seeded into glass-bottomed culture dishes at 1 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells/mL and cultured for 24 hours. The medium was changed by fresh medium containing Alexa 647-DNs for appropriate concentration. After an appropriate time, the treated cells were triple rinsed with PBS and stained with Hoechst 33342 at 37\u0026deg;C for 20 min to visualize the nuclei. Confocal laser scanning microscopy (Leica STELLARIS 5, German) was used to capture imaging.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFlow Cytometry\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor flow cytometry analysis, cells were plated on 24-well culture plates at 1\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells/mL and cultured for 24 hours. After being treated with Alexa 647-DNs, they were collected, washed, and resuspended in PBS. BD LSRFortessa flow cytometer (BD Biosciences, USA) was then used for analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEndocytosis pathway studies\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor chemical inhibition of endocytosis pathways, SW1353 cells were plated on 6-well culture plates at 1 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells/mL and maintained in culture for 24 hours. Then the cells were exposed to pharmacological inhibitors (0.2 \u0026micro;M Wortmannin [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], 80 \u0026micro;M Dynasore [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], 5 mM MβCD [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]) for 0.5 hours at 37\u0026deg;C. Subsequently, the cells were exposed to 400 nM Alexa 647-labeled DNs, and were cultured for another 3 hours. The drugs were maintained in the cell culture throughout the experiments. The cells cultured with DNs at 4\u0026deg;C were used in temperature experiment. Samples cultured at 37\u0026deg;C without inhibitors were taken as positive controls. After treatment, cells were thoroughly rinsed with PBS, harvested, and analyzed via flow cytometry (n\u0026thinsp;=\u0026thinsp;3). FlowJo (10.8.1) was used to process the data.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCulture of 3D multicellular spheroids\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, SW1353 cells were used to prepare 3D multicellular spheroids. Cell suspensions (5,000 cells/well) were added to 96-well plates with U-bottom (Engineering for Life, China), where the wells were pretreated with the anti-adhesion coating solution. Within the next 72 hours of cell culture (37\u0026deg;C, 5% CO\u003csub\u003e2\u003c/sub\u003e), the cells aggregated and formed spheroids (approximately 300 \u0026micro;m in diameter). Then the spheroids were incubated with 400 nM Alexa657-DNs for different times. After being washed with PBS for three times, the penetration of the spheroids by the DNs was examined by confocal microscopy in Z stacks with 50 \u0026micro;m Z-intervals from bottom to equator (n\u0026thinsp;=\u0026thinsp;3). Quantitative analysis of DNs penetration into multicellular spheroids was performed with ImageJ software (version 1.52).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCell proliferation assay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCells were plated into 96-well culture plates at 1 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells/mL and maintained in culture for 24 hours. Then the cells were exposed to 400 nM DNs for another 24 hours, after which the cell proliferation was measured by CCK-8 (n\u0026thinsp;=\u0026thinsp;5). Vehicle controls (containing a volume of buffer equal to the volume of DN of the experimental group) were taken as negative control.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCell cycle profile\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor cell cycle analysis, cells were seeded into 6-well culture plates at 1\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells/mL and maintained in culture for 24 hours. After being treated with the same procedure in cell proliferation assay, they were collected, rinsed, and preserved in 70% cold ethanol for a 12-hour fixation. Then the samples were stained with a pre-prepared propidium iodide (PI) staining solution of reagent kit, and kept in darkness at 37\u0026deg;C for 30 min. CytoFLEX S flow cytometer (Beckman Coulter, Brea, USA) was used to analyze cell cycle distribution by quantifying DNA content based on PI fluorescence. FlowJo was utilized to quantify the distribution of cell populations (n\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e\u003cp\u003e\u003cb\u003eRNA isolation and real-time quantitative PCR (RT-qPCR)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSteadyPure kit (Accurate Biology, China) was used to extract total RNA. It was reverse-transcribed into cDNA using PrimeScript RT Master Mix (Takara, Japan). The mRNA levels were evaluated through RT-qPCR, using the Hieff qPCR SYBR Green Master Mix (Yeason, China). The primers were summarized in Table S2. GAPDH mRNA served as the internal control.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWestern blot\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCells were washed in PBS and were lysed using a RIPA solution containing both phosphatase and protease inhibitors (Beyotime, China). Western blot analysis was performed following established protocols in previous studies [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Specific primary antibodies included Collagen I (501352, Zenbio, China, 1:500), Collagen III (68320-1-Ig, Proteintech, 1:5,000), and GAPDH (60004-1-Ig, Proteintech, China, 1:500,000). Secondary antibodies included anti-Rabbit IgG HRP-conjugated antibody (AS014, Abclonal, China, 1:4000) and anti-Mouse IgG HRP-conjugated antibody (AS003, Abclonal, China, 1:4000).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAlcian blue and Toluidine blue staining\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAlcian blue and Toluidine blue staining (Solarbio, China) were applied for assessing matrix production by SW1353 cells, specifically indicating the presence of acidic polysaccharides like glycosaminoglycans. SW1353 cells were placed into 24-well plates at 1 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells/mL. After paraformaldehyde fixation, staining was carried out using Alcian blue for 0.5 hours or Toluidine blue for 20 min. The cells were imaged microscopically (Nikon Eclipse Ti2, Japan). Analysis of stained regions was carried out with ImageJ.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMyoblast differentiation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eC2C12 cells were plated in 6-well plates at 1 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells/mL and cultured until reaching 80% confluence. Differentiation was induced by replacing the medium with differentiation medium (DMEM, 2% horse serum, and 1% antibiotics), containing 400 nM DNs. The medium was refreshed every 24 hours. Vehicle controls were taken as negative control. Changes in mRNA levels were detected after 24 hours and changes in protein levels were detected after 96 hours.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis and graphs of data were processed by GraphPad Prism 8.0.2 software. All data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (standard deviation). \u003cem\u003eP\u003c/em\u003e values were calculated using one-way ANOVA (confidence interval: 95%). A \u003cem\u003eP\u003c/em\u003e value of \u0026ge;\u0026thinsp;0.05 was considered to be nonsignificant (ns); *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Key Research and Development Program of China (2020YFA0908901), National Natural Science Foundation of China (82121002, U24A20377, and 32171348), the Program of Shanghai Academic Research Leader (22XD1421500), Innovative Research Team of High-level Local Universities in Shanghai (SHSMU- ZLCX20212402), Shanghai\u0026apos;s Top Priority Research Center (2022ZZ01017), and Fundamental Research Program Funding of Ninth People\u0026rsquo;s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (JYZZ085).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX. T.: Conceptualization, Formal analysis, Data curation, Writing \u0026ndash; original draft, Visualization. T. Z.: Conceptualization, Methodology, Writing \u0026ndash; review \u0026amp; editing, Supervision, Visualization. T. L.: Methodology, Formal analysis, Investigation, Data curation, Visualization. Y. J.: Validation, Data curation. D. L.: Methodology, Formal analysis, Investigation. C. Z.: Validation, Visualization. L. Q.: Formal analysis, Investigation. Y. L.: Formal analysis, Data curation. Y. W.: Formal analysis, Investigation. H. G.: Conceptualization, Resources, Writing \u0026ndash; review \u0026amp; editing, Supervision, Funding acquisition. B. F.: Conceptualization, Writing \u0026ndash; review \u0026amp; editing, Supervision, Project administration, Funding acquisition.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGarc\u0026iacute;a AJ, Vega MD, Boettiger D. 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Aberrant Fluid Shear Stress Contributes to Articular Cartilage Pathogenesis via Epigenetic Regulation of ZBTB20 by H3K4me3\u003cem\u003e.\u003c/em\u003e J Inflamm Res. 2021; 14: 6067-6083.\u003c/li\u003e\n\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":"journal-of-nanobiotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jnan","sideBox":"Learn more about [Journal of Nanobiotechnology](http://jnanobiotechnology.biomedcentral.com)","snPcode":"12951","submissionUrl":"https://submission.nature.com/new-submission/12951/3","title":"Journal of Nanobiotechnology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"DNA nanotechnology, dimensional control, six-helix bundle, cellular uptake, tissue regeneration","lastPublishedDoi":"10.21203/rs.3.rs-7170660/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7170660/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrecise regulation of cellular functions is fundamental for advancing tissue regeneration and drug delivery systems. Structural DNA nanotechnology enables the design of well-defined nanostructures, emerging as a promising platform in these biomedical applications. However, a clear understanding of how the dimensional properties of DNA nanostructures affect cellular uptake and biological responses remains limited. In this study, we constructed three distinct DNA nanostructures: a one-dimensional six-helix bundle (6HB), a two-dimensional three-point star, and a three-dimensional tetrahedron. We systematically evaluated their endocytic efficiency in five representative cell types: endothelial cells, dermal fibroblasts, myoblasts, chondrocytes, and osteoblasts. Among them, the 6HB exhibited the highest cellular uptake, with minimal variability across cell types in both 2D petri dish cultures and 3D multicellular spheroid invasion models. Moreover, DNA nanostructures were found to enhance cell proliferation in fibroblasts and chondrocytes, support chondrocyte phenotype maintenance, and, in the case of the 6HB, promote myoblast differentiation. These findings provide new insights into structure–function relationships in DNA nanomaterials and offer guidance for optimizing DNA-based platforms for drug delivery and regenerative medicine.\u003c/p\u003e","manuscriptTitle":"Dimensional Control of DNA Nanostructures Enhances Cellular Uptake and Guides Tissue-Regenerative Responses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 11:56:14","doi":"10.21203/rs.3.rs-7170660/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-02T07:36:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-29T06:45:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234822186035778210843066428828996347085","date":"2025-07-27T08:54:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215852519301679670930146918275937962326","date":"2025-07-26T14:46:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-26T04:03:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203087894529592126103253443945179460796","date":"2025-07-25T06:55:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317231926283001419330954971537296217874","date":"2025-07-24T14:57:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-24T14:42:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-22T18:38:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-22T09:04:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Nanobiotechnology","date":"2025-07-20T15:25:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-nanobiotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jnan","sideBox":"Learn more about [Journal of Nanobiotechnology](http://jnanobiotechnology.biomedcentral.com)","snPcode":"12951","submissionUrl":"https://submission.nature.com/new-submission/12951/3","title":"Journal of Nanobiotechnology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"da44f433-3ddd-497c-a274-d2fbafc0d3d9","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T16:03:25+00:00","versionOfRecord":{"articleIdentity":"rs-7170660","link":"https://doi.org/10.1186/s12951-025-03707-1","journal":{"identity":"journal-of-nanobiotechnology","isVorOnly":false,"title":"Journal of Nanobiotechnology"},"publishedOn":"2025-09-29 15:57:54","publishedOnDateReadable":"September 29th, 2025"},"versionCreatedAt":"2025-07-29 11:56:14","video":"","vorDoi":"10.1186/s12951-025-03707-1","vorDoiUrl":"https://doi.org/10.1186/s12951-025-03707-1","workflowStages":[]},"version":"v1","identity":"rs-7170660","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7170660","identity":"rs-7170660","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}