ARTseq-FISH reveals position-dependent differences in gene expression of micropatterned mESCs.

Differences in gene-expression profiles between individual cells can give rise to distinct cell fate decisions. Yet how localisation on a micropattern impacts initial changes in mRNA, protein, and phosphoprotein abundance remains unclear. To identify the effect of cellular position on gene expression, we developed a scalable antibody and mRNA targeting sequential fluorescence in situ hybridisation (ARTseq-FISH) method capable of simultaneously profiling mRNAs, proteins, and phosphoproteins in single cells. We studied 67 (phospho-)protein and mRNA targets in individual mouse embryonic stem cells (mESCs) cultured on circular micropatterns. ARTseq-FISH reveals relative changes in both abundance and localisation of mRNAs and (phospho-)proteins during the first 48 hours of exit from pluripotency. We confirm these changes by conventional immunofluorescence and time-lapse microscopy. Chemical labelling, immunofluorescence, and single-cell time-lapse microscopy further show that cells closer to the edge of the micropattern exhibit increased proliferation compared to cells at the centre. Together these data suggest that while gene expression is still highly heterogeneous position-dependent differences in mRNA and protein levels emerge as early as 12 hours after LIF withdrawal.

Iterative design of training data to control intricate enzymatic reaction networks.

Kinetic modeling of in vitro enzymatic reaction networks is vital to understand and control the complex behaviors emerging from the nonlinear interactions inside. However, modeling is severely hampered by the lack of training data. Here, we introduce a methodology that combines an active learning-like approach and flow chemistry to efficiently create optimized datasets for a highly interconnected enzymatic reactions network with multiple sub-pathways. The optimal experimental design (OED) algorithm designs a sequence of out-of-equilibrium perturbations to maximize the information about the reaction kinetics, yielding a descriptive model that allows control of the output of the network towards any cost function. We experimentally validate the model by forcing the network to produce different product ratios while maintaining a minimum level of overall conversion efficiency. Our workflow scales with the complexity of the system and enables the optimization of previously unobtainable network outputs.

Noise Minimization in Cell-Free Gene Expression.

Biochemical reactions that involve small numbers of molecules are accompanied by a degree of inherent randomness that results in noisy reaction outcomes. In synthetic biology, the ability to minimize noise particularly during the reconstitution of future synthetic protocells is an outstanding challenge to secure robust and reproducible behavior. Here we show that by encapsulation of a bacterial cell-free gene expression system in water-in-oil droplets, in vitro-synthesized MazF reduces cell-free gene expression noise >2-fold. With stochastic simulations we identify that this noise minimization acts through both increased degradation and the autoregulatory feedback of MazF. Specifically, we find that the expression of MazF enhances the degradation rate of mRNA up to 18-fold in a sequence-dependent manner. This sequence specificity of MazF would allow targeted noise control, making it ideal to integrate into synthetic gene networks. Therefore, including MazF production in synthetic biology can significantly minimize gene expression noise, impacting future design principles of more complex cell-free gene circuits.

Cell-Free Expression System Derived from a Near-Minimal Synthetic Bacterium.

Cell-free expression (CFE) systems are fundamental to reconstituting metabolic pathways in vitro toward the construction of a synthetic cell. Although an Escherichia coli-based CFE system is well-established, simpler model organisms are necessary to understand the principles behind life-like behavior. Here, we report the successful creation of a CFE system derived from JCVI-syn3A (Syn3A), the minimal synthetic bacterium. Previously, high ribonuclease activity in Syn3A lysates impeded the establishment of functional CFE systems. Now, we describe how an unusual cell lysis method (nitrogen decompression) yielded Syn3A lysates with reduced ribonuclease activity that supported in vitro expression. To improve the protein yields in the Syn3A CFE system, we optimized the Syn3A CFE reaction mixture using an active machine learning tool. The optimized reaction mixture improved the CFE 3.2-fold compared to the preoptimized condition. This is the first report of a functional CFE system derived from a minimal synthetic bacterium, enabling further advances in bottom-up synthetic biology.

Dynamics and Composition of Small Heat Shock Protein Condensates and Aggregates.

Small heat shock proteins (sHSPs) are essential ATP-independent chaperones that protect the cellular proteome. These proteins assemble into polydisperse oligomeric structures, the composition of which dramatically affects their chaperone activity. The biomolecular consequences of variations in sHSP ratios, especially inside living cells, remain elusive. Here, we study the consequences of altering the relative expression levels of HspB2 and HspB3 in HEK293T cells. These chaperones are partners in a hetero-oligomeric complex, and genetic mutations that abolish their mutual interaction are associated with myopathic disorders. HspB2 displays three distinct phenotypes when co-expressed with HspB3 at varying ratios. Expression of HspB2 alone leads to formation of liquid nuclear condensates, while shifting the stoichiometry towards HspB3 resulted in the formation of large solid-like aggregates. Only cells co-expressing HspB2 with a limited amount of HspB3 formed fully soluble complexes that were distributed homogeneously throughout the nucleus. Strikingly, both condensates and aggregates were reversible, as shifting the HspB2:HspB3 balance in situ resulted in dissolution of these structures. To uncover the molecular composition of HspB2 condensates and aggregates, we used APEX-mediated proximity labelling. Most proteins interact transiently with the condensates and were neither enriched nor depleted in these cells. In contrast, we found that HspB2:HspB3 aggregates sequestered several disordered proteins and autophagy factors, suggesting that the cell is actively attempting to clear these aggregates. This study presents a striking example of how changes in the relative expression levels of interacting proteins affects their phase behavior. Our approach could be applied to study the role of protein stoichiometry and the influence of client binding on phase behavior in other biomolecular condensates and aggregates.

A microfluidic optimal experimental design platform for forward design of cell-free genetic networks.

Cell-free protein synthesis has been widely used as a "breadboard" for design of synthetic genetic networks. However, due to a severe lack of modularity, forward engineering of genetic networks remains challenging. Here, we demonstrate how a combination of optimal experimental design and microfluidics allows us to devise dynamic cell-free gene expression experiments providing maximum information content for subsequent non-linear model identification. Importantly, we reveal that applying this methodology to a library of genetic circuits, that share common elements, further increases the information content of the data resulting in higher accuracy of model parameters. To show modularity of model parameters, we design a pulse decoder and bistable switch, and predict their behaviour both qualitatively and quantitatively. Finally, we update the parameter database and indicate that network topology affects parameter estimation accuracy. Utilizing our methodology provides us with more accurate model parameters, a necessity for forward engineering of complex genetic networks.

Sigma Factor-Mediated Tuning of Bacterial Cell-Free Synthetic Genetic Oscillators.

Cell-free transcription-translation provides a simplified prototyping environment to rapidly design and study synthetic networks. Despite the presence of a well characterized toolbox of genetic elements, examples of genetic networks that exhibit complex temporal behavior are scarce. Here, we present a genetic oscillator implemented in an E. coli-based cell-free system under steady-state conditions using microfluidic flow reactors. The oscillator has an activator-repressor motif that utilizes the native transcriptional machinery of E. coli: the RNAP and its associated sigma factors. We optimized a kinetic model with experimental data using an evolutionary algorithm to quantify the key regulatory model parameters. The functional modulation of the RNAP was investigated by coupling two oscillators driven by competing sigma factors, allowing the modification of network properties by means of passive transcriptional regulation.

Bottom-Up Construction of an Adaptive Enzymatic Reaction Network.

The reproduction of emergent behaviors in nature using reaction networks is an important objective in synthetic biology and systems chemistry. Herein, the first experimental realization of an enzymatic reaction network capable of an adaptive response is reported. The design is based on the dual activity of trypsin, which activates chymotrypsin while at the same time generating a fluorescent output from a fluorogenic substrate. Once activated, chymotrypsin counteracts the trypsin output by competing for the fluorogenic substrate and producing a non-fluorescent output. It is demonstrated that this network produces a transient fluorescent output under out-of-equilibrium conditions while the input signal persists. Importantly, in agreement with mathematical simulations, we show that optimization of the pulse-like response is an inherent trade-off between maximum amplitude and lowest residual fluorescence.

Designing synthetic networks in silico: a generalised evolutionary algorithm approach.

BACKGROUND: Evolution has led to the development of biological networks that are shaped by environmental signals. Elucidating, understanding and then reconstructing important network motifs is one of the principal aims of Systems & Synthetic Biology. Consequently, previous research has focused on finding optimal network structures and reaction rates that respond to pulses or produce stable oscillations. In this work we present a generalised in silico evolutionary algorithm that simultaneously finds network structures and reaction rates (genotypes) that can satisfy multiple defined objectives (phenotypes). RESULTS: The key step to our approach is to translate a schema/binary-based description of biological networks into systems of ordinary differential equations (ODEs). The ODEs can then be solved numerically to provide dynamic information about an evolved networks functionality. Initially we benchmark algorithm performance by finding optimal networks that can recapitulate concentration time-series data and perform parameter optimisation on oscillatory dynamics of the Repressilator. We go on to show the utility of our algorithm by finding new designs for robust synthetic oscillators, and by performing multi-objective optimisation to find a set of oscillators and feed-forward loops that are optimal at balancing different system properties. In sum, our results not only confirm and build on previous observations but we also provide new designs of synthetic oscillators for experimental construction. CONCLUSIONS: In this work we have presented and tested an evolutionary algorithm that can design a biological network to produce desired output. Given that previous designs of synthetic networks have been limited to subregions of network- and parameter-space, the use of our evolutionary optimisation algorithm will enable Synthetic Biologists to construct new systems with the potential to display a wider range of complex responses.