Nethika R.
Ariyasinghe
a,
Caitlin H.
Reck
a,
Alyssa A.
Viscio
a,
Andrew P.
Petersen
a,
Davi M.
Lyra-Leite
a,
Nathan
Cho
a and
Megan L.
McCain
*ab
aLaboratory for Living Systems Engineering, Department of Biomedical Engineering, USC Viterbi School of Engineering, University of Southern California, 1042 Downey Way, DRB 140, Los Angeles, CA 90089, USA. E-mail: mlmccain@usc.edu; Tel: +1 213-821-0791
bDepartment of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90033, USA
First published on 12th July 2017
Cardiovascular diseases are a leading cause of death, in part due to limitations of existing models of the myocardium. Myocardium consists of aligned, contractile cardiac myocytes interspersed with fibroblasts that synthesize extracellular matrix (ECM). The cellular demographics and biochemical and mechanical properties of the ECM remodel in many different cardiac diseases. However, the impact of diverse cellular and extracellular remodeling on the contractile output of the myocardium are poorly understood. To address this, we micropatterned 13 kPa and 90 kPa polyacrylamide gels with aligned squares of fibronectin (FN) or laminin (LN). We seeded gels with two concentrations of primary neonatal rat ventricular myocytes, which naturally contain fibroblasts. Cells assembled into aligned “μMyocardia” with fibroblast:myocyte ratios dependent on initial seeding concentration. Using traction force microscopy (TFM), we found that the peak systolic longitudinal cross-sectional force was similar across conditions, but the peak systolic work was significantly lower on 90 kPa gels. This indicates that ECM elasticity dominates over ECM ligand and cell demographics in regulating contractile output. Because our platform provides independent control over cell–cell and cell–matrix interactions, it has many applications for cardiac disease modeling.
Insight, innovation, integrationThe myocardium consists of aligned cardiac myocytes interspersed with fibroblasts that synthesize the extracellular matrix (ECM), a protein network with defined biochemical and mechanical properties. In many cardiac pathologies, it is challenging to pinpoint the precise cellular and extracellular factors that underlie declines in contractility due to their coordinated and coupled remodeling. To overcome this, we engineered aligned, micro-scale tissues comprised of cardiac myocytes and fibroblasts on hydrogels with tunable elasticity, modular composition, and an integrated contractility assay. Our engineered μMyocardia demonstrate that peak systolic work, a correlate to stroke work, is regulated primarily by ECM elasticity, but is independent of ECM ligand and cell demographics. This highly-tunable, engineered model of myocardium with quantitative functional outputs offers many benefits over existing platforms. |
Recently, tissue engineering has been leveraged to develop more sophisticated in vitro models that mimic key features of native myocardium and integrate built-in assays for quantifying contractility. For example, stresses generated by single and paired cardiac myocytes have been measured with traction force microscopy (TFM) by culturing cells on FN-micropatterned hydrogels.10–13 The advantage of this approach is that both cell shape and the ECM are relatively tunable. However, these one- and two-cell myocyte constructs lack the diverse cell demographics and cell–cell interactions seen in native myocardium. Aligned, multi-cellular cardiac tissues have been engineered on FN-micropatterned PDMS14–16 or micromolded gelatin hydrogels17 pre-cut into mm-scale cantilevers parallel to the orientation of the patterning. After tissue formation, these cantilevers can be peeled up to measure stresses generated by the tissue based on cantilever curvature.15,18,19 However, because the cantilevers must be manually released, this assay is relatively low-throughput. Three-dimensional cardiac tissues have been generated by encapsulating cells in ECM hydrogels within fabricated molds.20,21 These constructs are likely closer representations of the cell–cell interactions seen in native myocardium, but the architecture of these constructs is difficult to control and measuring contractility is cumbersome. These tissues also require relatively large quantities of cells, which increases cost and limits scalability. Thus, there is a need for a biomimetic myocardial contractility platform that offers tunability over the ECM and improved scalability compared to current methods.
To address the limitations of existing models of the myocardium, we engineered a medium-throughput platform that outputs contractile forces generated by aligned cardiac myocyte and fibroblast tissues engineered on ECM substrates with tunable mechanical and biochemical properties. To achieve this, we microcontact printed 200 μm-sided squares of arrayed 15 μm-wide lines of FN or LN onto polyacrylamide gels with elastic moduli tuned to mimic healthy (13 kPa) or fibrotic (90 kPa) myocardium.22 We seeded gels with two different densities of neonatal rat ventricular cardiac myocytes, which naturally contain cardiac fibroblasts. The cardiac myocytes and fibroblasts self-assembled into aligned patches of tissue, which we refer to as μMyocardia, with distinct fibroblast:myocyte ratios based on cell seeding density. Next, we used TFM to quantify peak systolic displacement, cross-sectional force, and work generated by μMyocardia as a function of ECM elasticity, ECM ligand, and cell demographics. Our results indicate that peak systolic longitudinal cross-sectional force was relatively independent of ECM elasticity, ECM ligand, or cell demographics. However, peak systolic longitudinal displacement and work were significantly lower on more rigid gels. Thus, ECM elasticity appears to be the most dominant factor regulating contractile output. Because this platform offers independent control over ECM ligand, ECM elasticity, and cell demographics, it can be used to engineer customized μMyocardia that mimic unique aspects of healthy and diseased myocardium. This platform is also relatively scalable compared to existing in vitro contractility assays and thus can be extended to screening drug toxicity and/or efficacy.
Fig. 2 Cell demographics and actin alignment in engineered μMyocardia. (A) Representative images of micropatterned ECM proteins (i and iv) and μMyocardia engineered using low (ii and v) and high (iii and vi) seeding densities on FN- and LN-patterned 13 kPa gels. Scale bars, 20 μm; blue, nuclei; red, sarcomeric α-actinin; green, actin. (B) Total number of cells per μMyocardium (mean ± standard error of the mean; *p < 0.05 for the total cell number and number of fibroblasts compared to high seeding density for the same ECM elasticity and ligand, ANOVA followed by Tukey's test for multiple comparisons). (C) Average actin orientational order parameter for each condition (mean ± standard error of the mean; *p < 0.05 compared to high seeding density for the same ECM elasticity and ligand, Kruskal–Wallis followed by the Bonferroni method for multiple comparisons). Details of statistical analyses are located in Tables S1–S4 (ESI†). |
Because native myocardium is highly aligned, we next determined if our engineered μMyocardia mimicked this hallmark architectural feature by calculating the orientational order parameter of actin fibers15,19 in multiple μMyocardia for each condition. As shown in Fig. 2C, the average actin orientational order parameter for all the conditions was 0.4–0.7, which is a relatively high level of alignment consistent with values seen previously in aligned tissues.25,26 We found no statistical differences in alignment between the conditions, except for low density and high density μMyocardia patterned with FN on 13 kPa gels, suggesting that ECM ligand and elasticity do not considerably impact cytoskeletal alignment.
To characterize the spatial distribution of force transmission from μMyocardia to the ECM, we next summed and normalized the magnitudes of peak systolic longitudinal displacement vectors at each x-position for each μMyocardium. As shown in Fig. 5, for all conditions, the average spatial distribution of longitudinal displacement vectors was similar, with displacement concentrated at the longitudinal edges of the tissues. Conversely, in the center of the tissue, minimal amounts of displacement were detected. Thus, in all conditions, μMyocardia consistently transmitted force predominantly at the longitudinal boundaries of the tissue.
Fig. 5 Spatial distributions of x-displacement vectors in μMyocardia. For each condition, the normalized x-displacement was calculated at each x-position for all μMyocardia, averaged, and plotted. |
Fig. 6 Average longitudinal contractile parameters for μMyocardia. Time to peak systole (A), peak systolic longitudinal displacement (B), peak systolic longitudinal cross-sectional force Fx (C), and peak systolic work W (D) for each condition (*p < 0.05 compared to 13 kPa, same ECM ligand, same seeding density; †p < 0.05 compared to FN, 13 kPa, low seeding density; ‡p < 0.05 compared to LN, 13 kPa, high seeding density; Kruskal–Wallis followed by the Bonferroni method for multiple comparisons). Details of statistical analyses are located in Tables S5–S8 (ESI†). |
With each contraction cycle, left ventricular myocardium must both shorten and generate stress to eject blood into the aorta. Stroke work is defined as the amount of work needed for this process and is calculated by multiplying stroke volume times aortic pressure, or calculating the area of a pressure–volume loop over one contraction cycle.27 In our system, peak systolic W is analogous to stroke work because it considers both displacement and stress from diastole to systole. Peak systolic W was independent of both ECM ligand and seeding density (Fig. 6D). Thus, μMyocardia generated equivalent levels of work, independent of the number of cardiac myocytes. However, peak systolic W was significantly higher for μMyocardia on 13 kPa gels compared to those on 90 kPa gels (Fig. 6D). Thus, peak systolic W generated by μMyocardia was inversely regulated by ECM elastic modulus, but was independent of both ECM ligand and cell seeding density for the tested ranges.
Myocardium is powered by cardiac myocytes that are highly aligned and densely packed with myofibrils,24 which maximizes force generation parallel to the long axis of the tissue.15,28,29 To mimic this critical architectural feature, we used photolithography, soft lithography, and microcontact printing to micropattern arrayed lines of biotinylated FN or LN onto streptavidin-doped polyacrylamide gels. Although this approach has previously been used for FN,11–13 our results show that it is also compatible with LN. On both 13 kPa and 90 kPa gels, micropatterned FN and LN successfully induced cells to self-assemble into aligned tissues. However, in many pathological settings, such as the border zone surrounding a myocardial infarction,30 myocytes lose their hallmark alignment. Because ECM patterning can be manipulated simply by altering photomask design, our platform can be easily tuned to engineer cardiac disease models with physiologically-relevant architectural features. Fine-tuning tissue architecture could also reveal further insight into relationships between myocyte shape, alignment, and contractility to complement previous studies in single cells10,12 and multicellular, mm-scale tissues.15,19,29 Furthermore, in this study, we only tested FN and LN individually. However, the basement membrane of healthy myocardium contains both ligands in defined concentrations and ratios, which are often altered in many pathological settings.3,5,6 The biochemical characteristics of physiological and pathological ECM could easily be mimicked in our platform by mixing and customizing the concentration of FN and LN in the stamping solution. Thus, our approach enables users to independently dictate tissue alignment, ECM elasticity, and ECM composition, which is highly beneficial for cardiac disease modeling.
Cardiac myocytes occupy the most volume in myocardium (75%) but comprise only 30–40% of the myocardium in terms of cell numbers. Fibroblasts comprise the majority of the remaining cells.23 Fibroblasts play mostly a supportive role in the myocardium by synthesizing, depositing, and degrading the ECM.4 In many pathological settings, such as after a myocardial infarction, the number of fibroblasts at and/or near the affected area increases due to myocyte necrosis and fibroblast proliferation.23 Because we utilized primary neonatal rat ventricular myocytes in this study, we naturally had a small percentage of fibroblasts present in our engineered μMyocardia. Since fibroblasts are proliferative, and myocytes are not, the number and ratio of these two cell types was ultimately regulated by the initial cell seeding density. Specifically, our high seeding density μMyocardia had 10–15% fibroblasts and low seeding density μMyocardia had 30–35% fibroblasts. These fibroblast:myocyte ratios are lower than both healthy and diseased myocardium, but it is challenging to form tissues that maintain electrical coupling between cardiac myocytes in 2-D culture if 60–70% of the tissue is fibroblasts. This inability to truly replicate the 3-D architecture and cell positioning of native myocardium is one limitation of our in vitro system. However, our results still have important implications for understanding the impact of fibroblasts on myocytes. For example, we found that the initial cell seeding density, and thus the number of cardiac myocytes, had relatively minimal impact on the contractile parameters that we measured. This could have a variety of explanations, as detailed below.
One potential explanation for the independence of contractile output on the number of myocytes is that fibroblasts had a beneficial effect on the cardiac myocytes. As described above, we found that the fibroblast:myocyte ratio was higher in the tissues seeded at low density. Previous studies have shown that increasing the fibroblast:myocyte ratio improves cardiac myocyte morphology and electrical31 and contractile32 phenotype in a variety of engineered tissue systems. This could be because fibroblasts secrete ECM molecules, which provide additional mechanical and structural support to cardiac myocytes. Evidence also suggests that fibroblasts can form functional cell–cell junctions with cardiac myocytes33,34 and therefore likely affect cardiac myocytes in many ways beyond ECM synthesis and remodeling. Together, our results combined with these previous studies indicate that fibroblasts likely do play an important role in maintaining and promoting the structural and functional phenotype of cardiac myocytes in vitro and should not be perceived only as a “contamination” of primary cultures. These hypotheses can be explored in future studies by more deliberately altering μMyocardia cellular composition. Our results also suggest that fibroblasts could potentially enhance the functionality and/or maturity of stem cell-derived cardiac myocytes. Currently, stem cell-derived cardiac myocytes are typically differentiated, purified, and maintained in monoculture.35,36 However, previous studies have shown that co-culturing stem cell-derived cardiac myocytes with fibroblasts has a beneficial effect on the adhesion and functionality of the myocytes,37–39 which is in-line with our current results. Collectively, our study provides further evidence that fibroblasts are important components to include in both healthy and diseased cardiac tissue models.
Our previous studies in pairs of cardiac myocytes showed that, at the cell–cell interface, myocytes predominantly release their focal adhesions and form cohesive cell–cell junctions.11,13 Thus, at the cell–cell interface, minimal stresses are transmitted to the ECM, which are the only stresses that we can measure using TFM. However, we can safely assume that stresses transmitted at cell–matrix adhesions are appropriately balanced by stresses transmitted to cell–cell adhesions due to the need for forces to balance.11,40 Due to this feature, we previously observed that traction stresses generated by a single myocyte and a pair of myocytes is relatively equivalent, if the cell(s) occupy the same surface area and are arranged linearly.11,12 Thus, the surface area of tissue adhesion dominates the amount of stress transmitted to the ECM compared to the actual number of myocytes. In our μMyocardia, we similarly observed that traction stresses were concentrated at the longitudinal ends of the tissues over a relatively consistent surface area, suggesting that cell–cell adhesions were dominant over cell–matrix adhesions in the interior of the tissue. Furthermore, we observed that contractile parameters were independent of the number of myocytes. Together, our observations in μMyocardia further support the idea that, up to a certain point, the geometry and surface area of the tissue, rather than the overall number of myocytes, dominates the amount of contractile stress transmitted to the ECM, given that ECM elasticity is kept constant. Overall, our data spanning from single myocyte to myocyte pair to multi-cellular μMyocardium further demonstrate that the myocardium functions as a mechanical syncytium, where myocytes form robust cell–cell adhesions that seamlessly transmit force across the tissue.
The ECM is a network of proteins and molecules that have both structural and non-structural functions. Myocardial ECM is dominated by Type I collagen fibers, which serve largely structural and stabilizing roles.6 The basement membrane that directly surrounds myocytes and links myocytes to collagen fibers consists primarily of the glycoproteins FN and LN,24 which are adhesive ligands for integrin receptors.41,42 Concurrent remodeling of both ECM composition and elastic modulus have been reported in many disease settings.3 For example, many cardiac diseases are associated with fibrosis and/or scar tissue formation,4 which alters ECM composition,5,43 including elevated expression of FN,44,45 and increases the elastic modulus of the tissue beyond 50 kPa.22 With in vivo models, it is challenging, if not impossible, to truly de-couple ECM composition and elasticity to identify the structural and functional impact of these parameters. With our in vitro approach, we independently controlled ECM elasticity and ECM ligand such that we could quantify how these two parameters alter the contractile output of myocardium. Our results indicate that ECM elasticity has a greater functional impact on μMyocardia compared to ECM ligand. One explanation for this result is that, although FN and LN are ligands for distinct integrin receptors,42 the engagement of different integrin receptors had minimal impact on contractility. A second explanation is that, because myocytes and predominantly fibroblasts secrete FN and LN, ultimately both ligands were present in our constructs, regardless of the identity of the original micropatterned ligand. For example, our immunostained tissues do show evidence of cell-generated FN in LN-patterned tissues, and vice versa. Thirdly, our use of serum in the media could have confounded our results because serum contains many ECM molecules. However, polyacrylamide gels are non-fouling surfaces, which is the reason for using biotin–streptavidin linkages to adhere micropatterned FN and LN to the surface. As a result, cells did not adhere to any regions of the gel not subjected to micropatterning, suggesting that ECM molecules from the serum did not deposit on the surface of the gel. However, we cannot exclude any interactions between soluble ECM molecules in the serum with the micropatterned ECM molecules or with the cells themselves. Thus, ECM synthesis by the cells and the presence of ECM molecules in the serum could potentially have masked any differences due to FN versus LN.
Our results indicate that, in μMyocardia, peak systolic displacement and W were lower on 90 kPa gels compared to 13 kPa gels, but Fx was preserved. Interestingly, we observed slightly different trends in single and paired cardiac myocytes: peak systolic displacement and various metrics for peak systolic force were lower on 90 kPa gels compared to 13 kPa gels, but peak systolic work was preserved.11,12 Thus, in all cases, peak systolic displacement was lowest on the more rigid gels. However, in μMyocardia, the difference in displacement between 13 kPa and 90 kPa gels was much greater compared to this difference in single cells and cell pairs. This ultimately led to a smaller difference in the traction stress and Fx generated on 13 kPa and 90 kPa gels, as this value is dependent on both gel elasticity and displacement. Consequently, peak systolic work in μMyocardia was also much lower on the 90 kPa gels compared to 13 kPa gels, as this is a function of both displacement and traction stress. Thus, relationships between displacement, force, and work do not perfectly scale from single cell to cell pair to multi-cellular tissue. One key difference between μMyocardia and single and paired myocytes is the presence of fibroblasts, which could potentially add greater resistance to contraction in μMyocardia. Another important consideration is that we did not measure changes in diastolic parameters in our μMyocardia, as all measurements reflect changes from diastole to systole. There could be differences in baseline diastolic displacement, force, and/or work that are important for understanding how the parameters we tested potentially contribute to diseases such as diastolic heart failure.46 These measurements are an important topic for follow-up studies.
Many limitations of our study are inherent to in vitro systems. For example, we used polyacrylamide gels functionalized with ECM proteins such that we could independently modulate mechanical and biochemical properties of the ECM. However, these gels are highly synthetic and limit the ability of cells to interact with and remodel the ECM, which is an important process in native myocardium. Ideally, we would use hydrogels consisting purely of our ECM molecule of interest, but this is cost-prohibitive. Another limitation is that we only tested FN and LN, but the basal lamina in native myocardium has many other molecules, such as Collagen IV.24 However, our biotin–streptavidin approach should be compatible with testing other ECM molecules and combinations of ECM molecules in future studies. Future studies could also investigate lower elastic moduli, such as 1 kPa,11 to establish how modulating ECM elasticity and composition impacts contractility in more of a developmental context.47,48
Another limitation is that our constructs are two-dimensional. However, three-dimensional constructs require large quantities of cells and are less scalable, so although we compromise physiological relevance, our two-dimensional approach offers many practical advantages. Furthermore, μMyocardia remain planar throughout the experimental measurements, require minimal user intervention, and are not endpoint experiments. Thus, our μMyocardia are well-suited to be enclosed in a fluidic device to create a “Myocardium on a Chip” platform that could be used for extended drug testing or coupling with other “Organs on Chip” platforms.14,49 Our platform is also relatively versatile and should be compatible with measuring contractile parameters in other types of muscle cells, such as vascular or bronchial smooth muscle.
Our study is also limited by the use of neonatal rat cardiac myocytes, which are non-human. For future studies, we plan to use this same platform with human induced pluripotent stem cell (hiPSC)-derived cardiac myocytes sourced from wildtype patients and patients with inherited cardiomyopathies, similar to previous studies.16,50,51 By combining our μMyocardia platform with hiPSC-derived cardiac myocytes, we can build robust, human-relevant, patient-specific platforms to study inherited cardiomyopathies, identify novel therapeutic targets, and screen drugs with quantitative functional outputs. Importantly, because our platform offers control over the ECM, we can also determine how diverse forms of ECM remodeling potentially synergize with genetic mutations in inherited diseases. For example, merosin-deficient congenital muscular dystrophy is an inherited striated muscle myopathy caused by a mutation in the LAMA2 gene, which encodes for the LN-α2 chain.52 Our platform would be especially powerful for elucidating intracellular and extracellular mechanisms of this disease because we could independently tune the LN composition of the ECM for both wildtype and LAMA2-deficient hiPSC-derived cardiac myocytes.
In summary, we have fabricated a scalable platform for engineering and interrogating customizable μMyocardia, which has revealed novel insight into how remodeling of the ECM and cellular composition impact the contractile function of myocardium. Our platform has many applications for functional disease modeling and drug screening, especially when combined with hiPSC-derived cardiac myocytes.
Silicon wafers were fabricated in a Class 100 Cleanroom using standard photolithography protocols.54 Briefly, cleaned silicon wafers were spin coated with hexamethyldisilazane and then SU-8 2005 negative photoresist (MicroChem). The wafers were exposed to UV light through the chromium photomask using a mask aligner. The non-cross-linked portions of the wafers were removed by submerging the wafers in propylene glycol monomethyl ether acetate. Last, the wafers were silanized overnight in a desiccator with a drop of trichloro (1H,1H,2H,2H-perfluorooctyl) silane. PDMS stamps were created by mixing the base and curing agents of Sylgard 184 (Dow Corning) at a 10:1 mass ratio and then pouring this mixture over the wafer in a Petri dish. The PDMS was degassed, cured at 65 °C for four hours, and peeled off the wafer and cut into square stamps.
To microcontact print the gels, PDMS stamps were sonicated, dried, coated with biotinylated FN or LN, and incubated for at least one hour at room temperature. The polyacrylamide gels were placed in a 37 °C incubator for approximately ten minutes to remove excess moisture from the gel surface, taking care not to over-dry the gels. Then, the stamps were dried using compressed air and inverted onto the gels such that the patterned side of the stamp was in contact with the gel. Patterned gels were stored in PBS at 4 °C until cell seeding.
Videos were cropped using ImageJ so that each video started at diastole and comprised exactly two contractions. In MATLAB, fluorescent bead videos were used to calculate longitudinal (x-direction) and lateral (y-direction) displacement vectors, ux,n and uy,n, respectively, discretized to a grid with n field elements. The dimensions of each field element were 10.4 μm in the x-direction (Δx) and 10.4 μm in the y-direction (Δy). Displacement was calculated as the average magnitude of the three highest displacement vectors for the first and second contractions. Longitudinal (tx,n) and lateral (ty,n) traction stress vector fields were determined using Fourier transform traction cytometry, as described in previous publications.10,55 Longitudinal force vectors at each field element (fx,n) were calculated by multiplying the traction stress vectors (tx,n) at each field element n by the surface area of the field element (ΔxΔy):
fx,n = tx,nΔxΔy | (1) |
fy,n = ty,nΔxΔy | (2) |
(3) |
(4) |
(5) |
Footnote |
† Electronic supplementary information (ESI) available. See DOI: 10.1039/c7ib00081b |
This journal is © The Royal Society of Chemistry 2017 |