Synthetic developmental biology: Engineering approaches to guide multicellular organization

Engineering plant photoreceptors towards enhancing plant productivity

Light is a critical environmental factor that governs the growth and development of plants. Plants have specialised photoreceptor proteins, which allow them to sense both quality and quantity of light and drive a wide range of responses critical for optimising growth, resource use and adaptation to changes in environment. Understanding the role of these photoreceptors in plant biology has opened up potential avenues for engineering crops with enhanced productivity by engineering photoreceptor activity and/or action. The ability to manipulate plant genomes through genetic engineering and synthetic biology approaches offers the potential to unlock new agricultural innovations by fine-tuning photoreceptors or photoreceptor pathways that control plant traits of agronomic significance. Additionally, optogenetic tools which allow for precise, light-triggered control of plant responses are emerging as powerful technologies for real-time manipulation of plant cellular responses. As these technologies continue to develop, the integration of photoreceptor engineering and optogenetics into crop breeding programs could potentially revolutionise how plant researchers tackle challenges of plant productivity. Here we provide an overview on the roles of key photoreceptors in regulating agronomically important traits, the current state of plant photoreceptor engineering, the emerging use of optogenetics and synthetic biology, and the practical considerations of applying these approaches to crop improvement. This review seeks to highlight both opportunities and challenges in harnessing photoreceptor engineering approaches for enhancing plant productivity. In this review, we provide an overview on the roles of key photoreceptors in regulating agronomically important traits, the current state of plant photoreceptor engineering, the emerging use of optogenetics and synthetic biology, and the practical considerations of applying these approaches to crop improvement.

Organoids from pluripotent stem cells and human tissues: When two cultures meet each other

Human organoids are a widely used tool in cell biology to study homeostatic processes, disease, and development. The term organoids covers a plethora of model systems from different cellular origins that each have unique features and applications but bring their own challenges. This review discusses the basic principles underlying organoids generated from pluripotent stem cells (PSCs) as well as those derived from tissue stem cells (TSCs). We consider how well PSC- and TSC-organoids mimic the different intended organs in terms of cellular complexity, maturity, functionality, and the ongoing efforts to constitute predictive complex models of in vivo situations. We discuss the advantages and limitations associated with each system to answer different biological questions including in the field of cancer and developmental biology, and with respect to implementing emerging advanced technologies, such as (spatial) -omics analyses, CRISPR screens, and high-content imaging screens. We postulate how the two fields may move forward together, integrating advantages of one to the other.

Programming the elongation of mammalian cell aggregates with synthetic gene circuits

A key goal of synthetic morphogenesis is the identification and implementation of methods to control morphogenesis. One line of research is the use of synthetic genetic circuits guiding the self-organization of cell ensembles. This approach has led to several recent successes, including control of cellular rearrangements in 3D via control of cell-cell adhesion by user-designed artificial genetic circuits. However, the methods employed to reach such achievements can still be optimized along three lines: identification of circuits happens by hand, 3D structures are spherical, and effectors are limited to cell-cell adhesion. Here we show the identification, in a computational framework, of genetic circuits for volumetric axial elongation via control of proliferation, tissue fluidity, and cell-cell signaling. We then seek to implement this design in mammalian cell aggregates in vitro. We start by identifying effectors to control tissue growth and fluidity in vitro. We then combine these new modules to construct complete circuits that control cell behaviors of interest in space and time, resulting in measurable tissue deformation along an axis that depends on the engineered signaling modules. Finally, we contextualize in vitro and in silico implementations within a unified morphospace to suggest further elaboration of this initial family of circuits towards more robust programmed axial elongation. These results and integrated in vitro/in silico pipeline demonstrate a promising method for designing, screening, and implementing synthetic genetic circuits of morphogenesis, opening the way to the programming of various user-defined tissue shapes.

Control of spatio-temporal patterning via cell growth in a multicellular synthetic gene circuit

A major goal in synthetic development is to build gene regulatory circuits that control patterning. In natural development, an interplay between mechanical and chemical communication shapes the dynamics of multicellular gene regulatory circuits. For synthetic circuits, how non-genetic properties of the growth environment impact circuit behavior remains poorly explored. Here, we first describe an occurrence of mechano-chemical coupling in synthetic Notch (synNotch) patterning circuits: high cell density decreases synNotch-gated gene expression in different cellular systems in vitro. We then construct, both in vitro and in silico, a synNotch-based signal propagation circuit whose outcome can be regulated by cell density. Spatial and temporal patterning outcomes of this circuit can be predicted and controlled via modulation of cell proliferation, initial cell density, and/or spatial distribution of cell density. Our work demonstrates that synthetic patterning circuit outcome can be controlled via cellular growth, providing a means for programming multicellular circuit patterning outcomes.

Ex Vivo Tools and Models in MASLD Research

Metabolic dysfunction-associated fatty liver disease (MASLD) presents a growing global health challenge with limited therapeutic choices. This review delves into the array of ex vivo tools and models utilized in MASLD research, encompassing liver-on-a-chip (LoC) systems, organoid-derived tissue-like structures, and human precision-cut liver slice (PCLS) systems. Given the urgent need to comprehend MASLD pathophysiology and identify novel therapeutic targets, this paper aims to shed light on the pivotal role of advanced ex vivo models in enhancing disease understanding and facilitating the development of potential therapies. Despite challenges posed by the elusive disease mechanism, these innovative methodologies offer promise in reducing the utilization of in vivo models for MASLD research while accelerating drug discovery and biomarker identification, thereby addressing critical unmet clinical needs.

Evaluation of bacteriophages for the alleviation of potential bacterial contamination risks in developmental engineering

This research aimed to address the potential bacterial contamination risks in developmental engineering (DE) using bacteriophages. To compare and contrast the exemplar Escherichia coli T4 and M13 bacteriophages, human dermal fibroblasts cultivated on culture plates, natural cellulosic scaffolds, and poly(methyl methacrylate) (PMMA) particles were utilized as two-dimensional (2D) cell, three-dimensional (3D) tissue, and modular tissue culture models, respectively. When directly introduced into these distinct culture systems, both phages survived, exhibited no significant effects on the cultured cells or tissues, yet displayed their potentials to alleviate the infections caused by corresponding bacterial host cells. Apart from direct addition into the culture medium, both phages were also coated on PMMA, polystyrene, poly(lactic acid) particles with different diameters (5, 10, 30, and 100 µm) and cellulosic scaffolds. The coated phages endured the coating processes and demonstrated their viabilities in plaque assays. Further testing indicated that the phages coated on the PMMA particles tolerated multiple deliberate rinses and centrifugations, but not thermal treatment at 60-80°C. In summary, T4 and M13 bacteriophages not only manifested their antibacterial functions in diverse 2D cell, 3D tissue, and modular tissue culture systems, but also demonstrated their potentials of coating modular scaffolds to alleviate the bacterial contamination risks in DE.

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.

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.

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.