Synthetic developmental biology: Engineering approaches to guide multicellular organization

Mathematical and In Silico Analysis of Synthetic Inhibitory Circuits That Program Self-Organizing Multicellular Structures

Bottom-up approaches are becoming increasingly popular for studying multicellular self-organization and development. In contrast to the classic top-down approach, where parts of the organization/developmental process are broken to understand the process, the goal is to build the process to understand it. For example, synthetic circuits have been built to understand how cell-cell communication and differential adhesion can drive multicellular development. The majority of current bottom-up efforts focus on using activatory circuits to engineer and understand development, but efforts with inhibitory circuits have been minimal. Yet, inhibitory circuits are ubiquitous and vital to native developmental processes. Thus, inhibitory circuits are a crucial yet poorly studied facet of bottom-up multicellular development. To demonstrate the potential of inhibitory circuits for building and developing multicellular structures, several synthetic inhibitory circuits that combine engineered cell-cell communication and differential adhesion were designed, and then examined for synthetic development capability using a previously validated in silico framework. These designed inhibitory circuits can build a variety of patterned, self-organized structures and even morphological oscillations. These results support that inhibitory circuits can be powerful tools for building, studying, and understanding developmental processes.

Automatic design of gene regulatory mechanisms for spatial pattern formation

Gene regulatory mechanisms (GRMs) control the formation of spatial and temporal expression patterns that can serve as regulatory signals for the development of complex shapes. Synthetic developmental biology aims to engineer such genetic circuits for understanding and producing desired multicellular spatial patterns. However, designing synthetic GRMs for complex, multi-dimensional spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given two-dimensional spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target spatial pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover complex genetic circuits producing spatial patterns.

The Promise and Potential of Brain Organoids

Brain organoids are 3D in vitro culture systems derived from human pluripotent stem cells that self-organize to model features of the (developing) human brain. This review examines the techniques behind organoid generation, their current and potential applications, and future directions for the field. Brain organoids possess complex architecture containing various neural cell types, synapses, and myelination. They have been utilized for toxicology testing, disease modeling, infection studies, personalized medicine, and gene-environment interaction studies. An emerging concept termed Organoid Intelligence (OI) combines organoids with artificial intelligence systems to generate learning and memory, with the goals of modeling cognition and enabling biological computing applications. Brain organoids allow neuroscience studies not previously achievable with traditional techniques, and have the potential to transform disease modeling, drug development, and the understanding of human brain development and disorders. The aspirational vision of OI parallels the origins of artificial intelligence, and efforts are underway to map a roadmap toward its realization. In summary, brain organoids constitute a disruptive technology that is rapidly advancing and gaining traction across multiple disciplines.

Programming Juxtacrine-Based Synthetic Signaling Networks in a Cellular Potts Framework

Synthetic development is a synthetic biology subfield aiming to reprogram higher-order eukaryotic cells for tissue formation and morphogenesis. Reprogramming efforts commonly rely upon implementing custom signaling networks into these cells, but the efficient design of these signaling networks is a substantial challenge. It is difficult to predict the tissue/morphogenic outcome of these networks, and in vitro testing of many networks is both costly and time-consuming. We therefore developed a computational framework with an in silico cell line (ISCL) that sports basic but modifiable features such as adhesion, motility, growth, and division. More importantly, ISCL can be quickly engineered with custom genetic circuits to test, improve, and explore different signaling network designs. We implemented this framework in a free cellular Potts modeling software CompuCell3D. In this chapter, we briefly discuss how to start with CompuCell3D and then go through the steps of how to make and modify ISCL. We then go through the steps of programming custom genetic circuits into ISCL to generate an example signaling network.

Distinct HAND2/HAND2-AS1 Expression Levels May Fine-Tune Mesenchymal and Epithelial Cell Plasticity of Human Mesenchymal Stem Cells

We previously developed several successful decellularization strategies that yielded porcine cardiac extracellular matrices (pcECMs) exhibiting tissue-specific bioactivity and bioinductive capacity when cultured with various pluripotent and multipotent stem cells. Here, we study the tissue-specific effects of the pcECM on seeded human mesenchymal stem cell (hMSC) phenotypes using reverse transcribed quantitative polymerase chain reaction (RT-qPCR) arrays for cardiovascular related gene expression. We further corroborated interesting findings at the protein level (flow cytometry and immunological stains) as well as bioinformatically using several mRNA sequencing and protein databases of normal and pathologic adult and embryonic (organogenesis stage) tissue expression. We discovered that upon the seeding of hMSCs on the pcECM, they displayed a partial mesenchymal-to-epithelial transition (MET) toward endothelial phenotypes (CD31+) and morphologies, which were preceded by an early spike (~Day 3 onward after seeding) in HAND2 expression at both the mRNA and protein levels compared to that in plate controls. The CRISPR-Cas9 knockout (KO) of HAND2 and its associated antisense long non-coding RNA (HAND2-AS1) regulatory region resulted in proliferation arrest, hypertrophy, and senescent-like morphology. Bioinformatic analyses revealed that HAND2 and HAND2-AS1 are highly correlated in expression and are expressed in many different tissue types albeit at distinct yet tightly regulated expression levels. Deviation (downregulation or upregulation) from these basal tissue expression levels is associated with a long list of pathologies. We thus suggest that HAND2 expression levels may possibly fine-tune hMSCs' plasticity through affecting senescence and mesenchymal-to-epithelial transition states, through yet unknown mechanisms. Targeting this pathway may open up a promising new therapeutic approach for a wide range of diseases, including cancer, degenerative disorders, and aging. Nevertheless, further investigation is required to validate these findings and better understand the molecular players involved, potential inducers and inhibitors of this pathway, and eventually potential therapeutic applications.

Application of colloidal photonic crystals in study of organoids

As alternative disease models, other than 2D cell lines and patient-derived xenografts, organoids have preferable in vivo physiological relevance. However, both endogenous and exogenous limitations impede the development and clinical translation of these organoids. Fortunately, colloidal photonic crystals (PCs), which benefit from favorable biocompatibility, brilliant optical manipulation, and facile chemical decoration, have been applied to the engineering of organoids and have achieved the desirable recapitulation of the ECM niche, well-defined geometrical onsets for initial culture, in situ multiphysiological parameter monitoring, single-cell biomechanical sensing, and high-throughput drug screening with versatile functional readouts. Herein, we review the latest progress in engineering organoids fabricated from colloidal PCs and provide inputs for future research.

Generative models of morphogenesis in developmental biology

Understanding the mechanism by which cells coordinate their differentiation and migration is critical to our understanding of many fundamental processes such as wound healing, disease progression, and developmental biology. Mathematical models have been an essential tool for testing and developing our understanding, such as models of cells as soft spherical particles, reaction-diffusion systems that couple cell movement to environmental factors, and multi-scale multi-physics simulations that combine bottom-up rule-based models with continuum laws. However, mathematical models can often be loosely related to data or have so many parameters that model behaviour is weakly constrained. Recent methods in machine learning introduce new means by which models can be derived and deployed. In this review, we discuss examples of mathematical models of aspects of developmental biology, such as cell migration, and how these models can be combined with these recent machine learning methods.

Synthetic symmetry breaking and programmable multicellular structure formation

During development, cells undergo symmetry breaking into differentiated subpopulations that self-organize into complex structures.1,2,3,4,5 However, few tools exist to recapitulate these behaviors in a controllable and coupled manner.6,7,8,9 Here, we engineer a stochastic recombinase genetic switch tunable by small molecules to induce programmable symmetry breaking, commitment to downstream cell fates, and morphological self-organization. Inducers determine commitment probabilities, generating tunable subpopulations as a function of inducer dosage. We use this switch to control the cell-cell adhesion properties of cells committed to each fate.10,11 We generate a wide variety of 3D morphologies from a monoclonal population and develop a computational model showing high concordance with experimental results, yielding new quantitative insights into the relationship between cell-cell adhesion strengths and downstream morphologies. We expect that programmable symmetry breaking, generating precise and tunable subpopulation ratios and coupled to structure formation, will serve as an integral component of the toolbox for complex tissue and organoid engineering.

Automatic design of gene regulatory mechanisms for spatial pattern formation

Synthetic developmental biology aims to engineer gene regulatory mechanisms (GRMs) for understanding and producing desired multicellular patterns and shapes. However, designing GRMs for spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover pattern-producing genetic circuits.

Synthetic developmental biology: molecular tools to re-design plant shoots and roots

Plant morphology and anatomy strongly influence agricultural yield. Crop domestication has strived for desirable growth and developmental traits, such as larger and more fruits and semi-dwarf architecture. Genetic engineering has accelerated rational, purpose-driven engineering of plant development, but it can be unpredictable. Developmental pathways are complex and riddled with environmental and hormonal inputs, as well as feedback and feedforward interactions, which occur at specific times and places in a growing multicellular organism. Rational modification of plant development would probably benefit from precision engineering based on synthetic biology approaches. This review outlines recently developed synthetic biology technologies for plant systems and highlights their potential for engineering plant growth and development. Streamlined and high-capacity genetic construction methods (Golden Gate DNA Assembly frameworks and toolkits) allow fast and variation-series cloning of multigene transgene constructs. This, together with a suite of gene regulation tools (e.g. cell type-specific promoters, logic gates, and multiplex regulation systems), is starting to enable developmental pathway engineering with predictable outcomes in model plant and crop species.

Systems bioengineering approaches for developmental toxicology

Developmental toxicology is the field of study that examines the effects of chemical and physical agents on developing organisms. By using principles of systems biology and bioengineering, a systems bioengineering approach could be applied to study the complex interactions between developing organisms, the environment, and toxic agents. This approach would result in a holistic understanding of the effects of toxic agents on organisms, by considering the interactions between different biological systems and the impacts of toxicants on those interactions. It would be useful in identifying key biological pathways and mechanisms affected by toxic agents, as well as in the development of predictive models to assess potential risks of exposure to toxicants during development. In this review, we discuss the relevance of systems bioengineering to the field of developmental toxicity and provide up-to-date examples that illustrate the use of engineering principles for this application.

Advances of microfluidic lung chips for assessing atmospheric pollutants exposure

Atmospheric pollutants, including particulate matters, nanoparticles, bioaerosols, and some chemicals, have posed serious threats to the environment and the human's health. The lungs are the responsible organs for providing the interface betweenthecirculatory system and the external environment, where pollutant particles can deposit or penetrate into bloodstream circulation. Conventional studies to decipher the mechanismunderlying air pollution and human health are quite limited, due to the lack of reliable models that can reproduce in vivo features of lung tissues after pollutants exposure. In the past decade, advanced near-to-native lung chips, combining cell biology with bioengineered technology, present a new strategy for atmospheric pollutants assessment and narrow the gap between 2D cell culture and in vivo animal models. In this review, the key features of artificial lung chips and the cutting-edge technologies of the lung chip manufacture are introduced. The recent progresses of lung chip technologies for atmospheric pollutants exposure assessment are summarized and highlighted. We further discuss the current challenges and the future opportunities of the development of advanced lung chips and their potential utilities in atmospheric pollutants associated toxicity testing and drug screening.

Scaling up complexity in synthetic developmental biology

The application of synthetic biology approaches to study development opens the possibility to build and manipulate developmental processes to understand them better. Researchers have reconstituted fundamental developmental processes, such as cell patterning and sorting, by engineering gene circuits in vitro. Moreover, new tools have been created that allow for the control of developmental processes in more complex organoids and embryos. Synthetic approaches allow testing of which components are sufficient to reproduce a developmental process and under which conditions as well as what effect perturbations have on other processes. We envision that the future of synthetic developmental biology requires an increase in the diversity of available tools and further efforts to combine multiple developmental processes into one system.