skip to main content


Title: 3D bioprinting of high cell-density heterogeneous tissue models through spheroid fusion within self-healing hydrogels
Abstract

Cellular models are needed to study human development and disease in vitro, and to screen drugs for toxicity and efficacy. Current approaches are limited in the engineering of functional tissue models with requisite cell densities and heterogeneity to appropriately model cell and tissue behaviors. Here, we develop a bioprinting approach to transfer spheroids into self-healing support hydrogels at high resolution, which enables their patterning and fusion into high-cell density microtissues of prescribed spatial organization. As an example application, we bioprint induced pluripotent stem cell-derived cardiac microtissue models with spatially controlled cardiomyocyte and fibroblast cell ratios to replicate the structural and functional features of scarred cardiac tissue that arise following myocardial infarction, including reduced contractility and irregular electrical activity. The bioprinted in vitro model is combined with functional readouts to probe how various pro-regenerative microRNA treatment regimes influence tissue regeneration and recovery of function as a result of cardiomyocyte proliferation. This method is useful for a range of biomedical applications, including the development of precision models to mimic diseases and the screening of drugs, particularly where high cell densities and heterogeneity are important.

 
more » « less
NSF-PAR ID:
10212447
Author(s) / Creator(s):
; ;
Publisher / Repository:
Nature Publishing Group
Date Published:
Journal Name:
Nature Communications
Volume:
12
Issue:
1
ISSN:
2041-1723
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. The epigenetic landscape and the responses to pharmacological epigenetic regulators in each human are unique. Classes of epigenetic writers and erasers, such as histone acetyltransferases, HATs, and histone deacetylases, HDACs, control DNA acetylation/deacetylation and chromatin accessibility, thus exerting transcriptional control in a tissue- and person-specific manner. Rapid development of novel pharmacological agents in clinical testing—HDAC inhibitors (HDACi)—targets these master regulators as common means of therapeutic intervention in cancer and immune diseases. The action of these epigenetic modulators is much less explored for cardiac tissue, yet all new drugs need to be tested for cardiotoxicity. To advance our understanding of chromatin regulation in the heart, and specifically how modulation of DNA acetylation state may affect functional electrophysiological responses, human-induced pluripotent stem-cell-derived cardiomyocyte (hiPSC-CM) technology can be leveraged as a scalable, high-throughput platform with ability to provide patient-specific insights. This review covers relevant background on the known roles of HATs and HDACs in the heart, the current state of HDACi development, applications, and any adverse cardiac events; it also summarizes relevant differential gene expression data for the adult human heart vs. hiPSC-CMs along with initial transcriptional and functional results from using this new experimental platform to yield insights on epigenetic control of the heart. We focus on the multitude of methodologies and workflows needed to quantify responses to HDACis in hiPSC-CMs. This overview can help highlight the power and the limitations of hiPSC-CMs as a scalable experimental model in capturing epigenetic responses relevant to the human heart. 
    more » « less
  2. Abstract

    Human pluripotent stem cell‐derived cardiomyocytes (hPSC‐CMs) have emerged as an exciting new tool for cardiac research and can serve as a preclinical platform for drug development and disease modeling studies. However, these aspirations are limited by current culture methods in which hPSC‐CMs resemble fetal human cardiomyocytes in terms of structure and function. Herein we provide a novel in vitro platform that includes patterned extracellular matrix with physiological substrate stiffness and is amenable to both mechanical and electrical analysis. Micropatterned lanes promote the cellular and myofibril alignment of hPSC‐CMs while the addition of micropatterned bridges enable formation of a functional cardiac syncytium that beats synchronously over a large two‐dimensional area. We investigated the electrophysiological properties of the patterned cardiac constructs and showed they have anisotropic electrical impulse propagation, as occurs in the native myocardium, with speeds 2x faster in the primary direction of the pattern as compared to the transverse direction. Lastly, we interrogated the mechanical function of the pattern constructs and demonstrated the utility of this platform in recording the strength of cardiomyocyte contractions. This biomimetic platform with electrical and mechanical readout capabilities will enable the study of cardiac disease and the influence of pharmaceuticals and toxins on cardiomyocyte function. The platform also holds potential for high throughput evaluation of drug safety and efficacy, thus furthering our understanding of cardiovascular disease and increasing the translational use of hPSC‐CMs.

     
    more » « less
  3. Abstract

    This study employed machine learning (ML) models to predict the cardiomyocyte (CM) content following differentiation of human induced pluripotent stem cells (hiPSCs) encapsulated in hydrogel microspheroids and to identify the main experimental variables affecting the CM yield. Understanding how to enhance CM generation using hiPSCs is critical in moving toward large‐scale production and implementing their use in developing therapeutic drugs and regenerative treatments. Cardiomyocyte production has entered a new era with improvements in the differentiation process. However, existing processes are not sufficiently robust for reliable CM manufacturing. Using ML techniques to correlate the initial, experimentally specified stem cell microenvironment's impact on cardiac differentiation could identify important process features. The initial tunable (controlled) input features for training ML models were extracted from 85 individual experiments. Subsets of the controlled input features were selected using feature selection and used for model construction. Random forests, Gaussian process, and support vector machines were employed as the ML models. The models were built to predict two classes of sufficient and insufficient for CM content on differentiation day 10. The best model predicted the sufficient class with an accuracy of 75% and a precision of 71%. The identified key features including post‐freeze passage number, media type, PF fibrinogen concentration, CHIR/S/V, axial ratio, and cell concentration provided insight into the significant experimental conditions. This study showed that we can extract information from the experiments and build predictive models that could enhance the cell production process by using ML techniques.

     
    more » « less
  4. The heart has a dynamic mechanical environment contributed by its unique cellular composition and the resultant complex tissue structure. In pathological heart tissue, both the mechanics and cell composition can change and influence each other. As a result, the interplay between the cell phenotype and mechanical stimulation needs to be considered to understand the biophysical cell interactions and organization in healthy and diseased myocardium. In this work, we hypothesized that the overall tissue organization is controlled by varying densities of cardiomyocytes and fibroblasts in the heart. In order to test this hypothesis, we utilized a combination of mechanical strain, co-cultures of different cell types, and inhibitory drugs that block intercellular junction formation. To accomplish this, an image analysis pipeline was developed to automatically measure cell type-specific organization relative to the stretch direction. The results indicated that cardiac cell type-specific densities influence the overall organization of heart tissue such that it is possible to model healthy and fibrotic heart tissue in vitro. This study provides insight into how to mimic the dynamic mechanical environment of the heart in engineered tissue as well as providing valuable information about the process of cardiac remodeling and repair in diseased hearts. 
    more » « less
  5. Abstract

    Cardiac fibrosis is a pathological process characterized by excessive tissue deposition, matrix remodeling, and tissue stiffening, which eventually leads to organ failure. On a cellular level, the development of fibrosis is associated with the activation of cardiac fibroblasts into myofibroblasts, a highly contractile and secretory phenotype. Myofibroblasts are commonly identified in vitro by the de novo assembly of alpha-smooth muscle actin stress fibers; however, there are few methods to automate stress fiber identification, which can lead to subjectivity and tedium in the process. To address this limitation, we present a computer vision model to classify and segment cells containing alpha-smooth muscle actin stress fibers into 2 classes (α-SMA SF+and α-SMA SF-), with a high degree of accuracy (cell accuracy: 77%, F1 score 0.79). The model combines standard image processing methods with deep learning techniques to achieve semantic segmentation of the different cell phenotypes. We apply this model to cardiac fibroblasts cultured on hyaluronic acid-based hydrogels of various moduli to induce alpha-smooth muscle actin stress fiber formation. The model successfully predicts the same trends in stress fiber identification as obtained with a manual analysis. Taken together, this work demonstrates a process to automate stress fiber identification in in vitro fibrotic models, thereby increasing reproducibility in fibroblast phenotypic characterization.

     
    more » « less