Flow Cytometry Quantification of Transient Transfections in Mammalian Cells.

Flow cytometry is a powerful quantitative assay supporting high-throughput collection of single-cell data with a high dynamic range. For flow cytometry to yield reproducible data with a quantitative relationship to the underlying biology, however, requires that (1) appropriate process controls are collected along with experimental samples, (2) these process controls are used for unit calibration and quality control, and (3) data are analyzed using appropriate statistics. To this end, this chapter describes methods for quantitative flow cytometry through the addition of process controls and analyses, thereby enabling better development, modeling, and debugging of engineered biological organisms. The methods described here have specifically been developed in the context of transient transfections in mammalian cells but may in many cases be adaptable to other categories of transfection and other types of cells.

Standardized Representation of Parts and Assembly for Build Planning.

The design and construction of genetic systems, in silico, in vitro, or in vivo, often involve the handling of various pieces of DNA that exist in different forms across an assembly process: as a standalone "part" sequence, as an insert into a carrier vector, as a digested fragment, etc. Communication about these different forms of a part and their relationships is often confusing, however, because of a lack of standardized terms. Here, we present a systematic terminology and an associated set of practices for representing genetic parts at various stages of design, synthesis, and assembly. These practices are intended to represent any of the wide array of approaches based on embedding parts in carrier vectors, such as BioBricks or Type IIS methods (e.g., GoldenGate, MoClo, GoldenBraid, and PhytoBricks), and have been successfully used as a basis for cross-institutional coordination and software tooling in the iGEM Engineering Committee.

Role of Endometrial Extracellular Vesicles in Mediating Cell-to-Cell Communication in the Uterus: A Review.

There are several critical events that occur in the uterus during early pregnancy which are necessary for the establishment and maintenance of pregnancy. These events include blastocyst implantation, uterine decidualization, uterine neoangiogenesis, differentiation of trophoblast stem cells into different trophoblast cell lineages, and formation of a placenta. These processes involve several different cell types within the pregnant uterus. Communication between these cell types must be intricately coordinated for successful embryo implantation and the formation of a functional maternal-fetal interface in the placenta. Understanding how this intricate coordination transpires has been a focus of researchers in the field for many years. It has long been understood that maternal endometrial tissue plays a key role in intercellular signaling during early pregnancy, sending signals to nearby tissues in a paracrine manner. Recently, insights have been obtained into the mechanisms by which these signaling events occur. Notably, the endometrium has been shown to secrete extracellular vesicles (EVs) that contain crucial cargo (proteins, lipids, RNA, miRNA) that are taken up by recipient cells to initiate a response leading to the occurrence of critical events during implantation and placentation. In this review, we aim to summarize the role that endometrium-derived EVs play in mediating cell-to-cell communications within the pregnant uterus to orchestrate the events that must occur to establish and maintain pregnancy. We will also discuss how aberrant endometrial EV signaling may lead to pathophysiological conditions, such as endometriosis and infertility.

Runx1 regulates critical factors that control uterine angiogenesis and trophoblast differentiation during placental development.

During early pregnancy in humans and rodents, uterine stromal cells undergo a remarkable differentiation to form the decidua, a transient maternal tissue that supports the growing fetus. It is important to understand the key decidual pathways that orchestrate the proper development of the placenta, a key structure at the maternal-fetal interface. We discovered that ablation of expression of the transcription factor Runx1 in decidual stromal cells in a conditional Runx1-null mouse model (Runx1d/d) causes fetal lethality during placentation. Further phenotypic analysis revealed that uteri of pregnant Runx1d/d mice exhibited severely compromised decidual angiogenesis and a lack of trophoblast differentiation and migration, resulting in impaired spiral artery remodeling. Gene expression profiling using uteri from Runx1d/d and control mice revealed that Runx1 directly controls the decidual expression of the gap junction protein connexin 43 (also known as GJA1), which was previously shown to be essential for decidual angiogenesis. Our study also revealed that Runx1 controls the expression of insulin-like growth factor (IGF) 2 and IGF-binding protein 4 (IGFBP4) during early pregnancy. While Runx1 deficiency drastically reduced the production of IGF2 by the decidual cells, we observed concurrent elevated expression of the IGFBP4, which regulates the bioavailability of IGFs, thereby controlling trophoblast differentiation. We posit that dysregulated expression of GJA1, IGF2, and IGFBP4 in Runx1d/d decidua contributes to the observed defects in uterine angiogenesis, trophoblast differentiation, and vascular remodeling. This study therefore provides unique insights into key maternal pathways that control the early phases of maternal-fetal interactions within a critical window during placental development.

Studying pathogens degrades BLAST-based pathogen identification.

As synthetic biology becomes increasingly capable and accessible, it is likewise increasingly critical to be able to make accurate biosecurity determinations regarding the pathogenicity or toxicity of particular nucleic acid or amino acid sequences. At present, this is typically done using the BLAST algorithm to determine the best match with sequences in the NCBI nucleic acid and protein databases. Neither BLAST nor any of the NCBI databases, however, are actually designed for biosafety determination. Critically, taxonomic errors or ambiguities in the NCBI nucleic acid and protein databases can also cause errors in BLAST-based taxonomic categorization. With heavily studied taxa and frequently used biotechnology tools, even low frequency taxonomic categorization issues can lead to high rates of errors in biosecurity decision-making. Here we focus on the implications for false positives, finding that BLAST against NCBI's protein database will now incorrectly categorize a number of commonly used biotechnology tool sequences as the pathogens or toxins with which they have been used. Paradoxically, this implies that problems are expected to be most acute for the pathogens and toxins of highest interest and for the most widely used biotechnology tools. We thus conclude that biosecurity tools should shift away from BLAST against general purpose databases and towards new methods that are specifically tailored for biosafety purposes.

Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop.

Computational tools addressing various components of design-build-test-learn (DBTL) loops for the construction of synthetic genetic networks exist but do not generally cover the entire DBTL loop. This manuscript introduces an end-to-end sequence of tools that together form a DBTL loop called Design Assemble Round Trip (DART). DART provides rational selection and refinement of genetic parts to construct and test a circuit. Computational support for experimental process, metadata management, standardized data collection and reproducible data analysis is provided via the previously published Round Trip (RT) test-learn loop. The primary focus of this work is on the Design Assemble (DA) part of the tool chain, which improves on previous techniques by screening up to thousands of network topologies for robust performance using a novel robustness score derived from dynamical behavior based on circuit topology only. In addition, novel experimental support software is introduced for the assembly of genetic circuits. A complete design-through-analysis sequence is presented using several OR and NOR circuit designs, with and without structural redundancy, that are implemented in budding yeast. The execution of DART tested the predictions of the design tools, specifically with regard to robust and reproducible performance under different experimental conditions. The data analysis depended on a novel application of machine learning techniques to segment bimodal flow cytometry distributions. Evidence is presented that, in some cases, a more complex build may impart more robustness and reproducibility across experimental conditions. Graphical Abstract.

Functional Synthetic Biology.

Synthetic biologists have made great progress over the past decade in developing methods for modular assembly of genetic sequences and in engineering biological systems with a wide variety of functions in various contexts and organisms. However, current paradigms in the field entangle sequence and functionality in a manner that makes abstraction difficult, reduces engineering flexibility and impairs predictability and design reuse. Functional Synthetic Biology aims to overcome these impediments by focusing the design of biological systems on function, rather than on sequence. This reorientation will decouple the engineering of biological devices from the specifics of how those devices are put to use, requiring both conceptual and organizational change, as well as supporting software tooling. Realizing this vision of Functional Synthetic Biology will allow more flexibility in how devices are used, more opportunity for reuse of devices and data, improvements in predictability and reductions in technical risk and cost.

Runx1 regulates critical factors that control uterine angiogenesis and trophoblast differentiation during placental development.

During early pregnancy in humans and rodents, uterine stromal cells undergo a remarkable differentiation to form the decidua, a transient maternal tissue that supports the growing fetus. It is important to understand the key decidual pathways that orchestrate the proper development of the placenta, a key structure at the maternal-fetal interface. We discovered that ablation of expression of the transcription factor Runx1 in decidual stromal cells in a conditional Runx1 -null mouse model ( Runx1 d/d ) causes fetal lethality during placentation. Further phenotypic analysis revealed that uteri of pregnant Runx1 d/d mice exhibited severely compromised decidual angiogenesis, and a lack of trophoblast differentiation and migration, resulting in impaired spiral artery remodeling. Gene expression profiling using uteri from Runx1 d/d and control mice revealed that Runx1 directly controls the decidual expression of the gap junction protein connexin 43 (also known as GJA1), which was previously shown to be essential for decidual angiogenesis. Our study also revealed a critical role of Runx1 in controlling insulin-like growth factor (IGF) signaling at the maternal-fetal interface. While Runx1-deficiency drastically reduced the production of IGF2 by the decidual cells, we observed concurrent elevated expression of the IGF-binding protein 4 (IGFBP4), which regulates the bioavailability of IGFs thereby controlling trophoblast differentiation. We posit that dysregulated expression of GJA1, IGF2, and IGFBP4 in Runx1 d/d decidua contributes to the observed defects in uterine angiogenesis, trophoblast differentiation, and vascular remodeling. This study therefore provides unique insights into key maternal pathways that control the early phases of maternal-fetal interactions within a critical window during placental development. Significance: A clear understanding of the maternal pathways that ensure coordination of uterine differentiation and angiogenesis with embryonic growth during the critical early stages of placenta formation still eludes us. The present study reveals that the transcription factor Runx1 controls a set of molecular, cellular, and integrative mechanisms that mediate maternal adaptive responses controlling uterine angiogenesis, trophoblast differentiation, and resultant uterine vascular remodeling, which are essential steps during placenta development.

Excel-SBOL Converter: Creating SBOL from Excel Templates and Vice Versa.

Standards support synthetic biology research by enabling the exchange of component information. However, using formal representations, such as the Synthetic Biology Open Language (SBOL), typically requires either a thorough understanding of these standards or a suite of tools developed in concurrence with the ontologies. Since these tools may be a barrier for use by many practitioners, the Excel-SBOL Converter was developed to facilitate the use of SBOL and integration into existing workflows. The converter consists of two Python libraries: one that converts Excel templates to SBOL and another that converts SBOL to an Excel workbook. Both libraries can be used either directly or via a SynBioHub plugin.

Extracellular Vesicles Secreted by Mouse Decidual Cells Carry Critical Information for the Establishment of Pregnancy.

The mouse decidua secretes many factors that act in a paracrine/autocrine manner to critically control uterine decidualization, neovascularization, and tissue remodeling that ensure proper establishment of pregnancy. The precise mechanisms that dictate intercellular communications among the uterine cells during early pregnancy remain unknown. We recently reported that conditional deletion of the gene encoding the hypoxia-inducible transcription factor 2 alpha (Hif2α) in mouse uterus led to infertility. Here, we report that HIF2α in mouse endometrial stromal cells (MESCs) acts via the cellular trafficking regulator RAB27b to control the secretion of extracellular vesicles (EVs) during decidualization. We also found that Hif2α-regulated pathways influence the biogenesis of EVs. Proteomic analysis of EVs secreted by decidualizing MESCs revealed that they harbor a wide variety of protein cargoes whose composition changed as the decidualization process progressed. The EVs enhanced the differentiation capacity of MESCs and the production of angiogenic factors by these cells. We also established that matrix metalloproteinase-2, a prominent EV cargo protein, modulates uterine remodeling during decidualization. Collectively, our results support the concept that EVs are central to the mechanisms by which the decidual cells communicate with each other and other cell types within the uterus to facilitate successful establishment of pregnancy.

Highly-automated, high-throughput replication of yeast-based logic circuit design assessments.

We describe an experimental campaign that replicated the performance assessment of logic gates engineered into cells of Saccharomyces cerevisiae by Gander et al. Our experimental campaign used a novel high-throughput experimentation framework developed under Defense Advanced Research Projects Agency's Synergistic Discovery and Design program: a remote robotic lab at Strateos executed a parameterized experimental protocol. Using this protocol and robotic execution, we generated two orders of magnitude more flow cytometry data than the original experiments. We discuss our results, which largely, but not completely, agree with the original report and make some remarks about lessons learned. Graphical Abstract.

Extracellular vesicles secreted by human uterine stromal cells regulate decidualization, angiogenesis, and trophoblast differentiation.

In humans, the uterus undergoes a dramatic transformation to form an endometrial stroma-derived secretory tissue, termed decidua, during early pregnancy. The decidua secretes various factors that act in an autocrine/paracrine manner to promote stromal differentiation, facilitate maternal angiogenesis, and influence trophoblast differentiation and development, which are critical for the formation of a functional placenta. Here, we investigated the mechanisms by which decidual cells communicate with each other and with other cell types within the uterine milieu. We discovered that primary human endometrial stromal cells (HESCs) secrete extracellular vesicles (EVs) during decidualization and that this process is controlled by a conserved HIF2α-RAB27B pathway. Mass spectrometry revealed that the decidual EVs harbor a variety of protein cargo, including cell signaling molecules, growth modulators, metabolic regulators, and factors controlling endothelial cell expansion and remodeling. We tested the hypothesis that EVs secreted by the decidual cells mediate functional communications between various cell types within the uterus. We demonstrated that the internalization of EVs, specifically those carrying the glucose transporter 1 (GLUT1), promotes glucose uptake in recipient HESCs, supporting and advancing the decidualization program. Additionally, delivery of HESC-derived EVs into human endothelial cells stimulated their proliferation and led to enhanced vascular network formation. Strikingly, stromal EVs also promoted the differentiation of trophoblast stem cells into the extravillous trophoblast lineage. Collectively, these findings provide a deeper understanding of the pleiotropic roles played by EVs secreted by the decidual cells to ensure coordination of endometrial differentiation and angiogenesis with trophoblast function during the progressive phases of decidualization and placentation.

Multicolor plate reader fluorescence calibration.

Plate readers are commonly used to measure cell growth and fluorescence, yet the utility and reproducibility of plate reader data is limited by the fact that it is typically reported in arbitrary or relative units. We have previously established a robust serial dilution protocol for calibration of plate reader measurements of absorbance to estimated bacterial cell count and for green fluorescence from proteins expressed in bacterial cells to molecules of equivalent fluorescein. We now extend these protocols to calibration of red fluorescence to the sulforhodamine-101 fluorescent dye and blue fluorescence to Cascade Blue. Evaluating calibration efficacy via an interlaboratory study, we find that these calibrants do indeed provide comparable precision to the prior calibrants and that they enable effective cross-laboratory comparison of measurements of red and blue fluorescence from proteins expressed in bacterial cells.

Building the Plant SynBio Toolbox through Combinatorial Analysis of DNA Regulatory Elements.

While the installation of complex genetic circuits in microorganisms is relatively routine, the synthetic biology toolbox is severely limited in plants. Of particular concern is the absence of combinatorial analysis of regulatory elements, the long design-build-test cycles associated with transgenic plant analysis, and a lack of naming standardization for cloning parts. Here, we use previously described plant regulatory elements to design, build, and test 91 transgene cassettes for relative expression strength. Constructs were transiently transfected into Nicotiana benthamiana leaves and expression of a fluorescent reporter was measured from plant canopies, leaves, and protoplasts isolated from transfected plants. As anticipated, a dynamic level of expression was achieved from the library, ranging from near undetectable for the weakest cassette to a ∼200-fold increase for the strongest. Analysis of expression levels in plant canopies, individual leaves, and protoplasts were correlated, indicating that any of the methods could be used to evaluate regulatory elements in plants. Through this effort, a well-curated 37-member part library of plant regulatory elements was characterized, providing the necessary data to standardize construct design for precision metabolic engineering in plants.

pySBOL3: SBOL3 for Python Programmers.

The Synthetic Biology Open Language version 3 (SBOL3) provides a data model for representation of synthetic biology information across multiple scales and throughout the design-build-test-learn workflow. To support practical use of this data model, we have developed pySBOL3, a Python library that allows programmers to create and edit SBOL3 documents. Here we describe this library and key engineering decisions in its design. The resulting implementation is a compact and maintainable core that provides both a familiar, pythonic interface for manipulating SBOL3 objects as well as mechanisms for building additional extensions and representations on this base.

Simulation-Based Engineering of Time-Delayed Safety Switches for Safer Gene Therapies.

CRISPR-based gene editing is a powerful tool with great potential for applications in the treatment of many inherited and acquired diseases. The longer that CRISPR gene therapy is maintained within a patient, however, the higher the likelihood that it will result in problematic side effects such as off-target editing or immune response. One approach to mitigating these issues is to link the operation of the therapeutic system to a safety switch that autonomously disables its operation and removes the delivered therapeutics after some amount of time. We present here a simulation-based analysis of the potential for regulating the time delay of such a safety switch using one or two transcriptional regulators and/or recombinases. Combinatorial circuit generation identifies 30 potential architectures for such circuits, which we evaluate in simulation with respect to tunability, sensitivity to parameter values, and sensitivity to cell-to-cell variation. This modeling predicts one of these circuit architectures to have the desired dynamics and robustness, which can be further tested and applied in the context of CRISPR therapeutics.

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.

Round Trip: An Automated Pipeline for Experimental Design, Execution, and Analysis.

Synthetic biology is a complex discipline that involves creating detailed, purpose-built designs from genetic parts. This process is often phrased as a Design-Build-Test-Learn loop, where iterative design improvements can be made, implemented, measured, and analyzed. Automation can potentially improve both the end-to-end duration of the process and the utility of data produced by the process. One of the most important considerations for the development of effective automation and quality data is a rigorous description of implicit knowledge encoded as a formal knowledge representation. The development of knowledge representation for the process poses a number of challenges, including developing effective human-machine interfaces, protecting against and repairing user error, providing flexibility for terminological mismatches, and supporting extensibility to new experimental types. We address these challenges with the DARPA SD2 Round Trip software architecture. The Round Trip is an open architecture that automates many of the key steps in the Test and Learn phases of a Design-Build-Test-Learn loop for high-throughput laboratory science. The primary contribution of the Round Trip is to assist with and otherwise automate metadata creation, curation, standardization, and linkage with experimental data. The Round Trip's focus on metadata supports fast, automated, and replicable analysis of experiments as well as experimental situational awareness and experimental interpretability. We highlight the major software components and data representations that enable the Round Trip to speed up the design and analysis of experiments by 2 orders of magnitude over prior ad hoc methods. These contributions support a number of experimental protocols and experimental types, demonstrating the Round Trip's breadth and extensibility. We describe both an illustrative use case using the Round Trip for an on-the-loop experimental campaign and overall contributions to reducing experimental analysis time and increasing data product volume in the SD2 program.

Intent Parser: A Tool for Codification and Sharing of Experimental Design.

Communicating information about experimental design among a team of collaborators is challenging because different people tend to describe experiments in different ways and with different levels of detail. Sometimes, humans can interpret missing information by making assumptions and drawing inferences from information already provided. Doing so, however, is error-prone and typically requires a high level of interpersonal communication. In this paper, we present a tool that addresses this challenge by providing a simple interface for incremental formal codification of experiment designs. Users interact with a Google Docs word-processing interface with structured tables, backed by assisted linking to machine-readable definitions in a data repository (SynBioHub) and specification of available protocols and requests for execution in the Open Protocol Interface Language (OPIL). The result is an easy-to-use tool for generating machine-readable descriptions of experiment designs with which users in the DARPA SD2 program have collected data from 80 208 samples using a variety of protocols and instruments over the course of 181 experiment runs.

Synthetic Biology Curation Tools (SYNBICT).

Much progress has been made in developing tools to generate component-based design representations of biological systems from standard libraries of parts. Most biological designs, however, are still specified at the sequence level. Consequently, there exists a need for a tool that can be used to automatically infer component-based design representations from sequences, particularly in cases when those sequences have minimal levels of annotation. Such a tool would assist computational synthetic biologists in bridging the gap between the outputs of sequence editors and the inputs to more sophisticated design tools, and it would facilitate their development of automated workflows for design curation and quality control. Accordingly, we introduce Synthetic Biology Curation Tools (SYNBICT), a Python tool suite for automation-assisted annotation, curation, and functional inference for genetic designs. We have validated SYNBICT by applying it to genetic designs in the DARPA Synergistic Discovery & Design (SD2) program and the International Genetically Engineered Machines (iGEM) 2018 distribution. Most notably, SYNBICT is more automated and parallelizable than manual design editors, and it can be applied to interpret existing designs instead of only generating new ones.