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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.

Here, we describe a protocol for automated extraction of genomic DNA (gDNA) from yeast liquid culture and colonies. The protocol uses a Hamilton NGS-STAR automated liquid handler equipped with thermal-regulated microplate shakers. We have tested this automated protocol with 1.5 mL aliquots of liquid culture (OD600= 1) to demonstrate that it can provide high-quality gDNA at high efficiency. The protocol can also be implemented with manual pipetting, and/or to extract gDNA from larger volumes of culture by proportionally increasing the volumes of reagents and using an appropriate magnet separation rack.

Single-cell and single-transcript measurement methods have elevated our ability to understand and engineer biological systems. However, defining and comparing performance between methods remains a challenge, in part due to the confounding effects of experimental variability. Here, we propose a generalizable framework for performing multiple methods in parallel using split samples, so that experimental variability is shared between methods. We demonstrate the utility of this framework by performing 12 different methods in parallel to measure the same underlying reference system for cellular response. We compare method performance using quantitative evaluations of bias and resolvability. We attribute differences in method performance to steps along the measurement process such as sample preparation, signal detection, and choice of measurand. Finally, we demonstrate how this framework can be used to benchmark different methods for single-transcript detection. The framework we present here provides a practical way to compare performance of any methods., Rammohan et al. propose a generalizable framework for performing multiple methods in parallel using split samples, so that experimental variability is defined and compared between methods. Their framework provides a practical solution for benchmarking and comparing the performance of any method, illustrated by analysing single-cell and single-transcript methods.

As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors:in silicoselection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show thatin silicoselection can be used to engineer sensors with a wide range of dose-response curves. To demonstratein silicoselection for precise, multi-objective engineering, we simultaneously tune a genetic sensor’s sensitivity (EC50) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specifiedEC50. Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.

Accurate measurements of promoter activities are crucial for predictably building genetic systems. Here we report a method to simultaneously count plasmid DNA, RNA transcripts, and protein expression in single living bacteria. From these data, the activity of a promoter in units of RNAP/s can be inferred. This work facilitates the reporting of promoters in absolute units, the variability in their activity across a population, and their quantitative toll on cellular resources, all of which provide critical insights for cellular engineering., A quantitative assessment of promoter function can improve the precision of cellular engineering. Here the authors develop a method to simultaneously count plasmid DNA, RNA transcripts and protein expression in single living bacteria.

Here, we describe a protocol for automated extraction of genomic DNA (gDNA) from yeast liquid culture and colonies. The protocol uses a Hamilton NGS-STAR automated liquid handler equipped with thermal-regulated microplate shakers. We have tested this automated protocol with 1.5 mL aliquots of liquid culture (OD600= 1) to demonstrate that it can provide high-quality gDNA at high efficiency. The protocol can also be implemented with manual pipetting, and/or to extract gDNA from larger volumes of culture by proportionally increasing the volumes of reagents and using an appropriate magnet separation rack.

Concentrated, intact, and biologically-active DNA templates are essential for protein production using cell-free expression. We present automated methods for DNA template preparation and quantitation towards improving reproducibility in cell-free expression.

DNA templates for protein production remain an unexplored source of variability in the performance of cell-free expression (CFE) systems. To characterize this variability, we investigated the effects of two common DNA extraction methodologies, a postprocessing step and manual versus automated preparation on protein production using CFE. We assess the concentration of the DNA template, the quality of the DNA template in terms of physical damage and the quality of the DNA solution in terms of purity resulting from eight DNA preparation workflows. We measure the variance in protein titer and rate of protein production in CFE reactions associated with the biological replicate of the DNA template, the technical replicate DNA solution prepared with the same workflow and the measurement replicate of nominally identical CFE reactions. We offer practical guidance for preparing and characterizing DNA templates to achieve acceptable variability in CFE performance.

Performance variability is a common challenge in cell-free protein production and hinders a wider adoption of these systems for both research and biomanufacturing. While the inherent stochasticity and complexity of biology likely contributes to variability, other systematic factors may also play a role, including the source and preparation of the cell extract, the composition of the supplemental reaction buffer, the facility at which experiments are conducted, and the human operator (Cole et al. ACS Synth Biol 8:2080-2091, 2019). Variability in protein production could also arise from differences in the DNA template-specifically the amount of functional DNA added to a cell-free reaction and the quality of the DNA preparation in terms of contaminants and strand breakage. Here, we present protocols and suggest best practices optimized for DNA template preparation and quantitation for cell-free systems toward reducing variability in cell-free protein production.

RNA degradation plays a major role in cellular function, but current methods for measuring RNA degradation require RNA purification or are low throughput. Here we show how a flow-FISH assay can be used for high-throughput, in situ measurement of RNA degradation without RNA purification. We demonstrate how this approach can be used to simultaneously measure RNA degradation rates of different RNA sequences in a single assay and explore how the assay can be used to examine the effect of cellular context on RNA degradation rates. This assay will be generally useful to quantitatively measure how natural and engineered biological function depends on RNA half-life.

Allostery is a fundamental biophysical mechanism that underlies cellular sensing, signaling, and metabolism. Quantitative methods to characterize the genotype-phenotype relationships for allosteric proteins would provide data needed to improve engineering of biological systems, to uncover the role of allosteric mis-regulation in disease, and to develop allosterically targeted drugs1. Here we report the large-scale measurement of the genotype-phenotype landscape for an allosteric protein: the lac repressor from Escherichia coli, LacI. Using a method that combines long-read and short-read DNA sequencing, we quantitatively determine the dose-response curves for nearly 105 variants of the LacI sensor. With the resulting data, we train a deep neural network (DNN) capable of predicting the dose-response curves for additional LacI genotypes in silico. We then map the impact of amino acid substitutions on the allosteric function of LacI. Additionally, we demonstrate engineering of allosteric function with unprecedented accuracy by identifying LacI variants from the measured landscape with quantitatively specified dose-response curves. Finally, we discover sensors with previously unreported band-stop dose-response curves. Overall, our results provide the first high-coverage, quantitative view of genotype-phenotype relationships for an allosteric protein, revealing a surprising diversity of phenotypes and showing that each phenotype is accessible via multiple distinct genotypes.

Here, we describe a protocol for automated library preparation for DNA barcode sequencing. The protocol uses a Hamilton NGS-STAR automated liquid handler equipped with an automated thermocycler. This protocol requires an automated liquid handler (Hamilton Robotics, NGS-STAR) equipped with an automated thermocycler.

Here, we describe a protocol for automated extraction of plasmid DNA from 24 E. coli cultures. The protocol uses an automated liquid handler and a positive pressure filter press. It is based on the QIAprep Spin Miniprep Kit (Qiagen, 27106). This protocol requires an automated liquid handler (Hamilton Robotics, STAR) with both 8-channel and 96-channel pipetting heads and positive pressure filter press (Hamilton, MPE2). It also requires a centrifuge with a 96-well plate rotor capable of at least 3878 g.

Here, we describe protocols for assembly of two different plasmids used for the measurement of the genotype-phenotype landscape of the LacI protein in E. coli: a Library Plasmid (pTY1, Figure 1a) used for the landscape measurement of the sensor library, and a Verification Plasmid (pVER, Figure 1b) used to verify the function of approximately 120 sensors from the library chosen to test the accuracy of the landscape measurement and to demonstrate the range of sensor function. We also describe the protocol used for the creation of the LacI sensor library in E. coli.

Here, we present a transformational approach to genome engineering of herpes simplex virus type 1 (HSV-1), which has a large DNA genome, using synthetic genomics tools. We believe this method will enable more rapid and complex modifications of HSV-1 and other large DNA viruses than previous technologies, facilitating many useful applications. Yeast transformation-associated recombination was used to clone 11 fragments comprising the HSV-1 strain KOS 152 kb genome. Using overlapping sequences between the adjacent pieces, we assembled the fragments into a complete virus genome in yeast, transferred it into an Escherichia coli host, and reconstituted infectious virus following transfection into mammalian cells. The virus derived from this yeast-assembled genome, KOSYA, replicated with kinetics similar to wild-type virus. We demonstrated the utility of this modular assembly technology by making numerous modifications to a single gene, making changes to two genes at the same time and, finally, generating individual and combinatorial deletions to a set of five conserved genes that encode virion structural proteins. While the ability to perform genome-wide editing through assembly methods in large DNA virus genomes raises dual-use concerns, we believe the incremental risks are outweighed by potential benefits. These include enhanced functional studies, generation of oncolytic virus vectors, development of delivery platforms of genes for vaccines or therapy, as well as more rapid development of countermeasures against potential biothreats.

Most microbes have not been cultured, and many of those that are cultivatable are difficult, dangerous or expensive to propagate or are genetically intractable. Routine cloning of large genome fractions or whole genomes from these organisms would significantly enhance their discovery and genetic and functional characterization. Here we report the cloning of whole bacterial genomes in the yeast Saccharomyces cerevisiae as single-DNA molecules. We cloned the genomes of Mycoplasma genitalium (0.6 Mb), M. pneumoniae (0.8 Mb) and M. mycoides subspecies capri (1.1 Mb) as yeast circular centromeric plasmids. These genomes appear to be stably maintained in a host that has efficient, well-established methods for DNA manipulation.

Mycoplasma genitalium has the smallest genome of any organism that can be grown in pure culture. It has a minimal metabolism and little genomic redundancy. Consequently, its genome is expected to be a close approximation to the minimal set of genes needed to sustain bacterial life. Using global transposon mutagenesis, we isolated and characterized gene disruption mutants for 100 different nonessential protein-coding genes. None of the 43 RNA-coding genes were disrupted. Herein, we identify 382 of the 482 M. genitalium protein-coding genes as essential, plus five sets of disrupted genes that encode proteins with potentially redundant essential functions, such as phosphate transport. Genes encoding proteins of unknown function constitute 28% of the essential protein-coding genes set. Disruption of some genes accelerated M. genitalium growth.

During the 2009 H1N1 influenza pandemic, vaccines for the virus became available in large quantities only after human infections peaked. To accelerate vaccine availability for future pandemics, we developed a synthetic approach that very rapidly generated vaccine viruses from sequence data. Beginning with hemagglutinin (HA) and neuraminidase (NA) gene sequences, we combined an enzymatic, cell-free gene assembly technique with enzymatic error correction to allow rapid, accurate gene synthesis. We then used these synthetic HA and NA genes to transfect Madin-Darby canine kidney (MDCK) cells that were qualified for vaccine manufacture with viral RNA expression constructs encoding HA and NA and plasmid DNAs encoding viral backbone genes. Viruses for use in vaccines were rescued from these MDCK cells. We performed this rescue with improved vaccine virus backbones, increasing the yield of the essential vaccine antigen, HA. Generation of synthetic vaccine seeds, together with more efficient vaccine release assays, would accelerate responses to influenza pandemics through a system of instantaneous electronic data exchange followed by real-time, geographically dispersed vaccine production.

Character Transplant When engineering bacteria, it can be advantageous to propagate the genomes in yeast. However, to be truly useful, one must be able to transplant the bacterial chromosome from yeast back into a recipient bacterial cell. But because yeast does not contain restriction-modification systems, such transplantation poses problems not encountered in transplantation from one bacterial cell to another. Bacterial genomes isolated after growth in yeast are likely to be susceptible to the restriction-modification system(s) of the recipient cell, as well as their own. Lartigue et al. (p. 1693 , published online 20 August) describe multiple steps, including in vitro DNA methylation, developed to overcome such barriers. A Mycoplasma mycoides large-colony genome was propagated in yeast as a centromeric plasmid, engineered via yeast genetic systems, and, after specific methylation, transplanted into M. capricolum to produce a bacterial cell with the genotype and phenotype of the altered M. mycoides large-colony genome.

As a step toward propagation of synthetic genomes, we completely replaced the genome of a bacterial cell with one from another species by transplanting a whole genome as naked DNA. Intact genomic DNA from Mycoplasma mycoides large colony (LC), virtually free of protein, was transplanted into Mycoplasma capricolum cells by polyethylene glycol–mediated transformation. Cells selected for tetracycline resistance, carried by the M. mycoides LC chromosome, contain the complete donor genome and are free of detectable recipient genomic sequences. These cells that result from genome transplantation are phenotypically identical to the M. mycoides LC donor strain as judged by several criteria.

Toward achieving rapid and large scale genome modification directly in a target organism, we have developed a new genome engineering strategy that uses a combination of bioinformatics aided design, large synthetic DNA and site-specific recombinases. Using Cre recombinase we swapped a target 126-kb segment of the Escherichia coli genome with a 72-kb synthetic DNA cassette, thereby effectively eliminating over 54 kb of genomic DNA from three non-contiguous regions in a single recombination event. We observed complete replacement of the native sequence with the modified synthetic sequence through the action of the Cre recombinase and no competition from homologous recombination. Because of the versatility and high-efficiency of the Cre-lox system, this method can be used in any organism where this system is functional as well as adapted to use with other highly precise genome engineering systems. Compared to present-day iterative approaches in genome engineering, we anticipate this method will greatly speed up the creation of reduced, modularized and optimized genomes through the integration of deletion analyses data, transcriptomics, synthetic biology and site-specific recombination.