Sensing and guiding cell-state transitions by using genetically encoded endoribonuclease-mediated microRNA sensors.

Precisely sensing and guiding cell-state transitions via the conditional genetic activation of appropriate differentiation factors is challenging. Here we show that desired cell-state transitions can be guided via genetically encoded sensors, whereby endogenous cell-state-specific miRNAs regulate the translation of a constitutively transcribed endoribonuclease, which, in turn, controls the translation of a gene of interest. We used this approach to monitor several cell-state transitions, to enrich specific cell types and to automatically guide the multistep differentiation of human induced pluripotent stem cells towards a haematopoietic lineage via endothelial cells as an intermediate state. Such conditional activation of gene expression is durable and resistant to epigenetic silencing and could facilitate the monitoring of cell-state transitions in physiological and pathological conditions and eventually the 'rewiring' of cell-state transitions for applications in organoid-based disease modelling, cellular therapies and regenerative medicine.

LNP-RNA-engineered adipose stem cells for accelerated diabetic wound healing.

Adipose stem cells (ASCs) have attracted considerable attention as potential therapeutic agents due to their ability to promote tissue regeneration. However, their limited tissue repair capability has posed a challenge in achieving optimal therapeutic outcomes. Herein, we conceive a series of lipid nanoparticles to reprogram ASCs with durable protein secretion capacity for enhanced tissue engineering and regeneration. In vitro studies identify that the isomannide-derived lipid nanoparticles (DIM1T LNP) efficiently deliver RNAs to ASCs. Co-delivery of self-amplifying RNA (saRNA) and E3 mRNA complex (the combination of saRNA and E3 mRNA is named SEC) using DIM1T LNP modulates host immune responses against saRNAs and facilitates the durable production of proteins of interest in ASCs. The DIM1T LNP-SEC engineered ASCs (DS-ASCs) prolong expression of hepatocyte growth factor (HGF) and C-X-C motif chemokine ligand 12 (CXCL12), which show superior wound healing efficacy over their wild-type and DIM1T LNP-mRNA counterparts in the diabetic cutaneous wound model. Overall, this work suggests LNPs as an effective platform to engineer ASCs with enhanced protein generation ability, expediting the development of ASCs-based cell therapies.

Extracellular carriers control lipid-dependent secretion, delivery, and activity of WNT morphogens.

WNT morphogens trigger signaling pathways fundamental for embryogenesis, regeneration, and cancer. WNTs are modified with palmitoleate, which is critical for binding Frizzled (FZD) receptors and activating signaling. However, it is unknown how WNTs are released and spread from cells, given their strong lipid-dependent membrane attachment. We demonstrate that secreted FZD-related proteins and WNT inhibitory factor 1 are WNT carriers, potently releasing lipidated WNTs and forming active soluble complexes. WNT release occurs by direct handoff from the membrane protein WNTLESS to the carriers. In turn, carriers donate WNTs to glypicans and FZDs involved in WNT reception and to the NOTUM hydrolase, which antagonizes WNTs by lipid moiety removal. WNT transfer from carriers to FZDs is greatly facilitated by glypicans that serve as essential co-receptors in Wnt signaling. Thus, an extracellular network of carriers dynamically controls secretion, posttranslational regulation, and delivery of WNT morphogens, with important practical implications for regenerative medicine.

Synthetic genetic circuits to uncover the OCT4 trajectories of successful reprogramming of human fibroblasts.

Reprogramming human fibroblasts to induced pluripotent stem cells (iPSCs) is inefficient, with heterogeneity among transcription factor (TF) trajectories driving divergent cell states. Nevertheless, the impact of TF dynamics on reprogramming efficiency remains uncharted. We develop a system that accurately reports OCT4 protein levels in live cells and use it to reveal the trajectories of OCT4 in successful reprogramming. Our system comprises a synthetic genetic circuit that leverages noise to generate a wide range of OCT4 trajectories and a microRNA targeting endogenous OCT4 to set total cellular OCT4 protein levels. By fusing OCT4 to a fluorescent protein, we are able to track OCT4 trajectories with clonal resolution via live-cell imaging. We discover that a supraphysiological, stable OCT4 level is required, but not sufficient, for efficient iPSC colony formation. Our synthetic genetic circuit design and high-throughput live-imaging pipeline are generalizable for investigating TF dynamics for other cell fate programming applications.

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.

Rapid Development of Cell State Identification Circuits with Poly-Transfection.

Mammalian genetic circuits have demonstrated the potential to sense and treat a wide range of disease states, but optimization of the levels of circuit components remains challenging and labor-intensive. To accelerate this process, our lab developed poly-transfection, a high-throughput extension of traditional mammalian transfection. In poly-transfection, each cell in the transfected population essentially performs a different experiment, testing the behavior of the circuit at different DNA copy numbers and allowing users to analyze a large number of stoichiometries in a single-pot reaction. So far, poly-transfections that optimize ratios of three-component circuits in a single well of cells have been demonstrated; in principle, the same method can be used for the development of even larger circuits. Poly-transfection results can be easily applied to find optimal ratios of DNA to co-transfect for transient circuits or to choose expression levels for circuit components for the generation of stable cell lines. Here, we demonstrate the use of poly-transfection to optimize a three-component circuit. The protocol begins with experimental design principles and explains how poly-transfection builds upon traditional co-transfection methods. Next, poly-transfection of cells is carried out and followed by flow cytometry a few days later. Finally, the data is analyzed by examining slices of the single-cell flow cytometry data that correspond to subsets of cells with certain component ratios. In the lab, poly-transfection has been used to optimize cell classifiers, feedback and feedforward controllers, bistable motifs, and many more. This simple but powerful method speeds up design cycles for complex genetic circuits in mammalian cells.

Synthetic genetic circuits to uncover and enforce the OCT4 trajectories of successful reprogramming of human fibroblasts.

Reprogramming human fibroblasts to induced pluripotent stem cells (iPSCs) is inefficient, with heterogeneity among transcription factor (TF) trajectories driving divergent cell states. Nevertheless, the impact of TF dynamics on reprogramming efficiency remains uncharted. Here, we identify the successful reprogramming trajectories of the core pluripotency TF, OCT4, and design a genetic controller that enforces such trajectories with high precision. By combining a genetic circuit that generates a wide range of OCT4 trajectories with live-cell imaging, we track OCT4 trajectories with clonal resolution and find that a distinct constant OCT4 trajectory is required for colony formation. We then develop a synthetic genetic circuit that yields a tight OCT4 distribution around the identified trajectory and outperforms in terms of reprogramming efficiency other circuits that less accurately regulate OCT4. Our synthetic biology approach is generalizable for identifying and enforcing TF dynamics for cell fate programming applications.

Cholesterol-Amino-Phosphate (CAP) Derived Lipid Nanoparticles for Delivery of Self-Amplifying RNA and Restoration of Spermatogenesis in Infertile Mice.

Male infertility caused by genetic mutations is an important type of infertility. Currently, there is no reliable method in the clinic to address this medical need. The emergence of mRNA therapy provides a possible strategy for restoring mutant genes in the reproductive system. However, effective delivery of mRNA to spermatocytes remains a formidable challenge. Here a series of cholesterol-amino-phosphate (CAP) lipids are reported by integrating three bioactive moieties into a geometric structure, which is favorable for mRNA delivery. The results demonstrate that CAP-derived lipid nanoparticles (CAP LNPs) can deliver RNA including traditional mRNA and self-amplifying RNA (saRNA) encoding DNA Meiotic Recombinase 1 (Dmc1) protein in spermatocytes and treat male infertility caused by the Dmc1 gene mutation. Notably, the delivery efficiency of CAP LNPs is significantly higher than that of the MC3 and ALC-0315 LNPs, which is consistent with the design of CAP molecules. More importantly, a single injection of CAP LNPs-saRNA can produce Dmc1 protein for an extended period, which restores the spermatogenesis in the Dmc1 gene knockout mouse model. Overall, this study proves the concept of LNPs for the delivery of mRNA to spermatocytes, which provides a unique method to probe male infertility caused by the genetic mutation.

The sound of silence: Transgene silencing in mammalian cell engineering.

To elucidate principles operating in native biological systems and to develop novel biotechnologies, synthetic biology aims to build and integrate synthetic gene circuits within native transcriptional networks. The utility of synthetic gene circuits for cell engineering relies on the ability to control the expression of all constituent transgene components. Transgene silencing, defined as the loss of expression over time, persists as an obstacle for engineering primary cells and stem cells with transgenic cargos. In this review, we highlight the challenge that transgene silencing poses to the robust engineering of mammalian cells, outline potential molecular mechanisms of silencing, and present approaches for preventing transgene silencing. We conclude with a perspective identifying future research directions for improving the performance of synthetic gene circuits.

A synthetic transcription platform for programmable gene expression in mammalian cells.

Precise, scalable, and sustainable control of genetic and cellular activities in mammalian cells is key to developing precision therapeutics and smart biomanufacturing. Here we create a highly tunable, modular, versatile CRISPR-based synthetic transcription system for the programmable control of gene expression and cellular phenotypes in mammalian cells. Genetic circuits consisting of well-characterized libraries of guide RNAs, binding motifs of synthetic operators, transcriptional activators, and additional genetic regulatory elements express mammalian genes in a highly predictable and tunable manner. We demonstrate the programmable control of reporter genes episomally and chromosomally, with up to 25-fold more activity than seen with the EF1α promoter, in multiple cell types. We use these circuits to program the secretion of human monoclonal antibodies and to control T-cell effector function marked by interferon-γ production. Antibody titers and interferon-γ concentrations significantly correlate with synthetic promoter strengths, providing a platform for programming gene expression and cellular function in diverse applications.

Synthetic neuromorphic computing in living cells.

Computational properties of neuronal networks have been applied to computing systems using simplified models comprising repeated connected nodes, e.g., perceptrons, with decision-making capabilities and flexible weighted links. Analogously to their revolutionary impact on computing, neuro-inspired models can transform synthetic gene circuit design in a manner that is reliable, efficient in resource utilization, and readily reconfigurable for different tasks. To this end, we introduce the perceptgene, a perceptron that computes in the logarithmic domain, which enables efficient implementation of artificial neural networks in Escherichia coli cells. We successfully modify perceptgene parameters to create devices that encode a minimum, maximum, and average of analog inputs. With these devices, we create multi-layer perceptgene circuits that compute a soft majority function, perform an analog-to-digital conversion, and implement a ternary switch. We also create a programmable perceptgene circuit whose computation can be modified from OR to AND logic using small molecule induction. Finally, we show that our approach enables circuit optimization via artificial intelligence algorithms.

PERSIST platform provides programmable RNA regulation using CRISPR endoRNases.

Regulated transgene expression is an integral component of gene therapies, cell therapies and biomanufacturing. However, transcription factor-based regulation, upon which most applications are based, suffers from complications such as epigenetic silencing that limit expression longevity and reliability. Constitutive transgene transcription paired with post-transcriptional gene regulation could combat silencing, but few such RNA- or protein-level platforms exist. Here we develop an RNA-regulation platform we call "PERSIST" which consists of nine CRISPR-specific endoRNases as RNA-level activators and repressors as well as modular OFF- and ON-switch regulatory motifs. We show that PERSIST-regulated transgenes exhibit strong OFF and ON responses, resist silencing for at least two months, and can be readily layered to construct cascades, logic functions, switches and other sophisticated circuit topologies. The orthogonal, modular and composable nature of this platform as well as the ease in constructing robust and predictable gene circuits promises myriad applications in gene and cell therapies.

Robust and tunable signal processing in mammalian cells via engineered covalent modification cycles.

Engineered signaling networks can impart cells with new functionalities useful for directing differentiation and actuating cellular therapies. For such applications, the engineered networks must be tunable, precisely regulate target gene expression, and be robust to perturbations within the complex context of mammalian cells. Here, we use bacterial two-component signaling proteins to develop synthetic phosphoregulation devices that exhibit these properties in mammalian cells. First, we engineer a synthetic covalent modification cycle based on kinase and phosphatase proteins derived from the bifunctional histidine kinase EnvZ, enabling analog tuning of gene expression via its response regulator OmpR. By regulating phosphatase expression with endogenous miRNAs, we demonstrate cell-type specific signaling responses and a new strategy for accurate cell type classification. Finally, we implement a tunable negative feedback controller via a small molecule-stabilized phosphatase, reducing output expression variance and mitigating the context-dependent effects of off-target regulation and resource competition. Our work lays the foundation for establishing tunable, precise, and robust control over cell behavior with synthetic signaling networks.

Principles for the design of multicellular engineered living systems.

Remarkable progress in bioengineering over the past two decades has enabled the formulation of fundamental design principles for a variety of medical and non-medical applications. These advancements have laid the foundation for building multicellular engineered living systems (M-CELS) from biological parts, forming functional modules integrated into living machines. These cognizant design principles for living systems encompass novel genetic circuit manipulation, self-assembly, cell-cell/matrix communication, and artificial tissues/organs enabled through systems biology, bioinformatics, computational biology, genetic engineering, and microfluidics. Here, we introduce design principles and a blueprint for forward production of robust and standardized M-CELS, which may undergo variable reiterations through the classic design-build-test-debug cycle. This Review provides practical and theoretical frameworks to forward-design, control, and optimize novel M-CELS. Potential applications include biopharmaceuticals, bioreactor factories, biofuels, environmental bioremediation, cellular computing, biohybrid digital technology, and experimental investigations into mechanisms of multicellular organisms normally hidden inside the "black box" of living cells.

Meeting Measurement Precision Requirements for Effective Engineering of Genetic Regulatory Networks.

Reliable, predictable engineering of cellular behavior is one of the key goals of synthetic biology. As the field matures, biological engineers will become increasingly reliant on computer models that allow for the rapid exploration of design space prior to the more costly construction and characterization of candidate designs. The efficacy of such models, however, depends on the accuracy of their predictions, the precision of the measurements used to parametrize the models, and the tolerance of biological devices for imperfections in modeling and measurement. To better understand this relationship, we have derived an Engineering Error Inequality that provides a quantitative mathematical bound on the relationship between predictability of results, model accuracy, measurement precision, and device characteristics. We apply this relation to estimate measurement precision requirements for engineering genetic regulatory networks given current model and device characteristics, recommending a target standard deviation of 1.5-fold. We then compare these requirements with the results of an interlaboratory study to validate that these requirements can be met via flow cytometry with matched instrument channels and an independent calibrant. On the basis of these results, we recommend a set of best practices for quality control of flow cytometry data and discuss how these might be extended to other measurement modalities and applied to support further development of genetic regulatory network engineering.

Quantitative characterization of recombinase-based digitizer circuits enables predictable amplification of biological signals.

Many synthetic gene circuits are restricted to single-use applications or require iterative refinement for incorporation into complex systems. One example is the recombinase-based digitizer circuit, which has been used to improve weak or leaky biological signals. Here we present a workflow to quantitatively define digitizer performance and predict responses to different input signals. Using a combination of signal-to-noise ratio (SNR), area under a receiver operating characteristic curve (AUC), and fold change (FC), we evaluate three small-molecule inducible digitizer designs demonstrating FC up to 508x and SNR up to 3.77 dB. To study their behavior further and improve modularity, we develop a mixed phenotypic/mechanistic model capable of predicting digitizer configurations that amplify a synNotch cell-to-cell communication signal (Δ SNR up to 2.8 dB). We hope the metrics and modeling approaches here will facilitate incorporation of these digitizers into other systems while providing an improved workflow for gene circuit characterization.

An engineered protein-phosphorylation toggle network with implications for endogenous network discovery.

Synthetic biological networks comprising fast, reversible reactions could enable engineering of new cellular behaviors that are not possible with slower regulation. Here, we created a bistable toggle switch in Saccharomyces cerevisiae using a cross-repression topology comprising 11 protein-protein phosphorylation elements. The toggle is ultrasensitive, can be induced to switch states in seconds, and exhibits long-term bistability. Motivated by our toggle's architecture and size, we developed a computational framework to search endogenous protein pathways for other large and similar bistable networks. Our framework helped us to identify and experimentally verify five formerly unreported endogenous networks that exhibit bistability. Building synthetic protein-protein networks will enable bioengineers to design fast sensing and processing systems, allow sophisticated regulation of cellular processes, and aid discovery of endogenous networks with particular functions.

Context-aware synthetic biology by controller design: Engineering the mammalian cell.

The rise of systems biology has ushered a new paradigm: the view of the cell as a system that processes environmental inputs to drive phenotypic outputs. Synthetic biology provides a complementary approach, allowing us to program cell behavior through the addition of synthetic genetic devices into the cellular processor. These devices, and the complex genetic circuits they compose, are engineered using a design-prototype-test cycle, allowing for predictable device performance to be achieved in a context-dependent manner. Within mammalian cells, context effects impact device performance at multiple scales, including the genetic, cellular, and extracellular levels. In order for synthetic genetic devices to achieve predictable behaviors, approaches to overcome context dependence are necessary. Here, we describe control systems approaches for achieving context-aware devices that are robust to context effects. We then consider cell fate programing as a case study to explore the potential impact of context-aware devices for regenerative medicine applications.

TASBE Image Analytics: A Processing Pipeline for Quantifying Cell Organization from Fluorescent Microscopy.

Laboratory automation now commonly allows high-throughput sample preparation, culturing, and acquisition of microscopy images, but quantitative image analysis is often still a painstaking and subjective process. This is a problem especially significant for work on programmed morphogenesis, where the spatial organization of cells and cell types is of paramount importance. To address the challenges of quantitative analysis for such experiments, we have developed TASBE Image Analytics, a software pipeline for automatically segmenting collections of cells using the fluorescence channels of microscopy images. With TASBE Image Analytics, collections of cells can be grouped into spatially disjoint segments, the movement or development of these segments tracked over time, and rich statistical data output in a standardized format for analysis. Processing is readily configurable, rapid, and produces results that closely match hand annotation by humans for all but the smallest and dimmest segments. TASBE Image Analytics can thus provide the analysis necessary to complete the design-build-test-learn cycle for high-throughput experiments in programmed morphogenesis, as validated by our application of this pipeline to process experiments on shape formation with engineered CHO and HEK293 cells.