Unmixing of fluorescence spectra to resolve quantitative time-series measurements of gene expression in plate readers

Room-temperature mL-to-μL quantitative liquid concentration device for cyclone flow

Highly sensitive quantitative analysis of liquids is required in various fields. Analytical instruments and devices such as chromatography, spectroscopic analysis, DNA sequencers, immunoassay, mass spectrometry, and microfluidic devices are utilized for this purpose. Typically, the sample volume is at the milliliter scale, while the analysis volume is at the microliter scale. Consequently, most of the sample is discarded. Therefore, a universal volume interface is required to quantitatively concentrate samples from milliliter to microliter volume. This study introduces a liquid quantitative function to the cyclone concentration method using a millimeter-scale channel, which is highly suitable for controlling liquids at the microliter scale due to its high fluidic resistance against cyclone flow. This method enables the effective control of liquid concentration by cyclone flow. The optimum channel structure is investigated, and a 33-fold concentration of aqueous solutions is demonstrated. Finally, the concentration device is applied to measure molybdenum ions in a river.

Yeast Surface-Displayed Quenchbody as a Novel Whole-Cell Biosensor for One-Step Detection of Influenza A (H1N1) Virus

Timely surveillance of airborne pathogens is essential to preventing the spread of infectious diseases and safeguard human health. Methods for sensitive, efficient, and cost-effective detection of airborne viruses are needed. With advances in synthetic biology, whole-cell biosensors have emerged as promising platforms for environmental monitoring and medical diagnostics. However, the current design paradigm of whole-cell biosensors is mostly based on intracellular detection of analytes that can transport across the cell membrane, which presents a critical challenge for viral pathogens and large biomolecules. To address this challenge, we developed a new type of whole-cell biosensor by expressing and displaying VHH-based quenchbody (Q-body) on the surface of the yeast Saccharomyces cerevisiae for simple one-step detection of influenza A (H1N1) virus. Seventeen VHH antibody fragments targeting the hemagglutinin protein H1N1-HA were displayed on the yeast cells and screened for the H1N1-HA binding affinity. The functionally displayed VHHs were selected to create surface-displayed Q-body biosensors. The surface-displayed Q-body exhibiting the highest quenching and dequenching efficiency was identified. The biosensor quantitatively detected H1N1-HA in a range from 0.5 to 16 μg/mL, with a half-maximal concentration of 2.60 μg/mL. The biosensor exhibited high specificity for H1N1-HA over other hemagglutinin proteins from various influenza A virus subtypes. Moreover, the biosensor succeeded in detecting the H1N1 virus at concentrations from 2.4 × 104 to 1.5 × 107 PFU/mL. The results from this study demonstrated a new whole-cell biosensor design that circumvents the need for transport of analytes into biosensor cells, enabling efficient detection of the target virus particles.

Metabolic impact of heterologous protein production in Pseudomonas putida: Insights into carbon and energy flux control

For engineered microorganisms, the production of heterologous proteins that are often useless to host cells represents a burden on resources, which have to be shared with normal cellular processes. Within a certain metabolic leeway, this competitive process has no impact on growth. However, once this leeway, or free capacity, is fully utilized, the extra load becomes a metabolic burden that inhibits cellular processes and triggers a broad cellular response, reducing cell growth and often hindering the production of heterologous proteins. In this study, we sought to characterize the metabolic rearrangements occurring in the central metabolism of Pseudomonas putida at different levels of metabolic load. To this end, we constructed a P. putida KT2440 strain that expressed two genes encoding fluorescent proteins, one in the genome under constitutive expression to monitor the free capacity, and the other on an inducible plasmid to probe heterologous protein production. We found that metabolic fluxes are considerably reshuffled, especially at the level of periplasmic pathways, as soon as the metabolic load exceeds the free capacity. Heterologous protein production leads to the decoupling of anabolism and catabolism, resulting in large excess energy production relative to the requirements of protein biosynthesis. Finally, heterologous protein production was found to exert a stronger control on carbon fluxes than on energy fluxes, indicating that the flexible nature of P. putida's central metabolic network is solicited to sustain energy production.

Targeting Transcriptional and Translational Hindrances in a Modular T7RNAP Expression System in Engineered Pseudomonas putida

The T7 RNA polymerase is considered one of the most popular tools for heterologous gene expression in the gold standard biotechnological host Escherichia coli. However, the exploitation of this tool in other prospective hosts, such as the biotechnologically relevant bacterium Pseudomonas putida, is still very scarce. The majority of the existing T7-based systems in P. putida show low expression strengths and possess only weak controllability. A fundamental understanding of these systems is necessary in order to design robust and predictable biotechnological processes. To fill this gap, we established and characterized a modular T7 RNA polymerase-based system for heterologous protein production in P. putida, using the enhanced Green Fluorescent Protein (eGFP) as an easy-to-quantify reporter protein. We have effectively targeted the limitations associated with the initial genetic setup of the system, such as slow growth and low protein production rates. By replacing the T7 phage-inherent TΦ terminator downstream of the heterologous gene with the synthetic tZ terminator, growth and protein production rates improved drastically, and the T7 RNA polymerase system reached a productivity level comparable to that of an intrinsic RNA polymerase-based system. Furthermore, we were able to show that the system was saturated with T7 RNA polymerase by applying a T7 RNA polymerase ribosome binding site library to tune heterologous protein production. This saturation indicates an essential role for the ribosome binding sites of the T7 RNA polymerase since, in an oversaturated system, cellular resources are lost to the synthesis of unnecessary T7 RNA polymerase. Eventually, we combined the experimental data into a model that can predict the eGFP production rate with respect to the relative strength of the ribosome binding sites upstream of the T7 gene.

ACtivE: Assembly and CRISPR-Targeted in Vivo Editing for Yeast Genome Engineering Using Minimum Reagents and Time

Thanks to its sophistication, the CRISPR/Cas system has been a widely used yeast genome editing method. However, CRISPR methods generally rely on preassembled DNAs and extra cloning steps to deliver gRNA, Cas protein, and donor DNA. These laborious steps might hinder its usefulness. Here, we propose an alternative method, Assembly and CRISPR-targeted in vivo Editing (ACtivE), that only relies on in vivo assembly of linear DNA fragments for plasmid and donor DNA construction. Thus, depending on the user's need, these parts can be easily selected and combined from a repository, serving as a toolkit for rapid genome editing without any expensive reagent. The toolkit contains verified linear DNA fragments, which are easy to store, share, and transport at room temperature, drastically reducing expensive shipping costs and assembly time. After optimizing this technique, eight loci proximal to autonomously replicating sequences (ARS) in the yeast genome were also characterized in terms of integration and gene expression efficiencies and the impacts of the disruptions of these regions on cell fitness. The flexibility and multiplexing capacity of the ACtivE were shown by constructing a β-carotene pathway. In only a few days, >80% integration efficiency for single gene integration and >50% integration efficiency for triplex integration were achieved on Saccharomyces cerevisiae BY4741 from scratch without using in vitro DNA assembly methods, restriction enzymes, or extra cloning steps. This study presents a standardizable method to be readily employed to accelerate yeast genome engineering and provides well-defined genomic location alternatives for yeast synthetic biology and metabolic engineering purposes.

Absolute protein quantification using fluorescence measurements with FPCountR

This paper presents a generalisable method for the calibration of fluorescence readings on microplate readers, in order to convert arbitrary fluorescence units into absolute units. FPCountR relies on the generation of bespoke fluorescent protein (FP) calibrants, assays to determine protein concentration and activity, and a corresponding analytical workflow. We systematically characterise the assay protocols for accuracy, sensitivity and simplicity, and describe an 'ECmax' assay that outperforms the others and even enables accurate calibration without requiring the purification of FPs. To obtain cellular protein concentrations, we consider methods for the conversion of optical density to either cell counts or alternatively to cell volumes, as well as examining how cells can interfere with protein counting via fluorescence quenching, which we quantify and correct for the first time. Calibration across different instruments, disparate filter sets and mismatched gains is demonstrated to yield equivalent results. It also reveals that mCherry absorption at 600 nm does not confound cell density measurements unless expressed to over 100,000 proteins per cell. FPCountR is presented as pair of open access tools (protocol and R package) to enable the community to use this method, and ultimately to facilitate the quantitative characterisation of synthetic microbial circuits.

Reliable online measurement of population dynamics for filamentous co-cultures

Understanding population dynamics is a key factor for optimizing co-culture processes to produce valuable compounds. However, the measurement of independent population dynamics is difficult, especially for filamentous organisms and in presence of insoluble substrates like cellulose. We propose a workflow for fluorescence-based online monitoring of individual population dynamics of two filamentous microorganisms. The fluorescent tagged target co-culture is composed of the cellulolytic fungus Trichoderma reesei RUT-C30-mCherry and the pigment-producing bacterium Streptomyces coelicolor A3(2)-mNeonGreen (mNG) growing on insoluble cellulose as a substrate. To validate the system, the fluorescence-to-biomass and fluorescence-to-scattered-light correlation of the two strains was characterized in depth under various conditions. Thereby, especially for complex filamentous microorganisms, microbial morphologies have to be considered. Another bias can arise from autofluorescence or pigments that can spectrally interfere with the fluorescence measurement. Green autofluorescence of both strains was uncoupled from different green fluorescent protein signals through a spectral unmixing approach, resulting in a specific signal only linked to the abundance of S. coelicolor A3(2)-mNG. As proof of principle, the population dynamics of the target co-culture were measured at varying inoculation ratios in presence of insoluble cellulose particles. Thereby, the respective fluorescence signals reliably described the abundance of each partner, according to the variations in the inocula. With this method, conditions can be fine-tuned for optimal growth of both partners along with natural product formation by the bacterium.

Maturation models of fluorescent proteins are necessary for unbiased estimates of promoter activity

Fluorescent proteins (FPs) are a powerful tool to quantitatively monitor gene expression. The dynamics of a promoter and its regulation can be inferred from fluorescence data. The interpretation of fluorescent data, however, is strongly dependent on the maturation of FPs since different proteins mature in distinct ways. We propose a novel approach for analyzing fluorescent reporter data by incorporating maturation dynamics in the reconstruction of promoter activities. Our approach consists of developing and calibrating mechanistic maturation models for distinct FPs. These models are then used alongside a Bayesian approach to estimate promoter activities from fluorescence data. We demonstrate by means of targeted experiments in Escherichia coli that our approach provides robust estimates and that accounting for maturation is, in many cases, essential for the interpretation of gene expression data.

Accurate characterization of dynamic microbial gene expression and growth rate profiles

Genetic circuits are subject to variability due to cellular and compositional contexts. Cells face changing internal states and environments, the cellular context, to which they sense and respond by changing their gene expression and growth rates. Furthermore, each gene in a genetic circuit operates in a compositional context of genes which may interact with each other and the host cell in complex ways. The context of genetic circuits can, therefore, change gene expression and growth rates, and measuring their dynamics is essential to understanding natural and synthetic regulatory networks that give rise to functional phenotypes. However, reconstruction of microbial gene expression and growth rate profiles from typical noisy measurements of cell populations is difficult due to the effects of noise at low cell densities among other factors. We present here a method for the estimation of dynamic microbial gene expression rates and growth rates from noisy measurement data. Compared to the current state-of-the-art, our method significantly reduced the mean squared error of reconstructions from simulated data of growth and gene expression rates, improving the estimation of timing and magnitude of relevant shapes of profiles. We applied our method to characterize a triple-reporter plasmid library combining multiple transcription units in different compositional and cellular contexts in Escherichia coli. Our analysis reveals cellular and compositional context effects on microbial growth and gene expression rate dynamics and suggests a method for the dynamic ratiometric characterization of constitutive promoters relative to an in vivo reference.

Tunable population dynamics in a synthetic filamentous coculture

Microbial cocultures are used as a tool to stimulate natural product biosynthesis. However, studies often empirically combine different organisms without a deeper understanding of the population dynamics. As filamentous organisms offer a vast metabolic diversity, we developed a model filamentous coculture of the cellulolytic fungus Trichoderma reesei RUT-C30 and the noncellulolytic bacterium Streptomyces coelicolor A3(2). The coculture was set up to use α-cellulose as a carbon source. This established a dependency of S. coelicolor on hydrolysate sugars released by T. reesei cellulases. To provide detailed insight into coculture dynamics, we applied high-throughput online monitoring of the respiration rate and fluorescence of the tagged strains. The respiration rate allowed us to distinguish the conditions of successful cellulase formation. Furthermore, to dissect the individual strain contributions, T. reesei and S. coelicolor were tagged with mCherry and mNeonGreen (mNG) fluorescence proteins, respectively. When evaluating varying inoculation ratios, it was observed that both partners outcompete the other when given a high inoculation advantage. Nonetheless, adequate proportions for simultaneous growth of both partners, cellulase, and pigment production could be determined. Finally, population dynamics were also tuned by modulating abiotic factors. Increased osmolality provided a growth advantage to S. coelicolor. In contrast, an increase in shaking frequency had a negative effect on S. coelicolor biomass formation, promoting T. reesei. This comprehensive analysis fills important knowledge gaps in the control of complex cocultures and accelerates the setup of other tailor-made coculture bioprocesses.

SAIBR: a simple, platform-independent method for spectral autofluorescence correction

Biological systems are increasingly viewed through a quantitative lens that demands accurate measures of gene expression and local protein concentrations. CRISPR/Cas9 gene tagging has enabled increased use of fluorescence to monitor proteins at or near endogenous levels under native regulatory control. However, owing to typically lower expression levels, experiments using endogenously tagged genes run into limits imposed by autofluorescence (AF). AF is often a particular challenge in wavelengths occupied by commonly used fluorescent proteins (GFP, mNeonGreen). Stimulated by our work in C. elegans, we describe and validate Spectral Autofluorescence Image Correction By Regression (SAIBR), a simple platform-independent protocol and FIJI plug-in to correct for autofluorescence using standard filter sets and illumination conditions. Validated for use in C. elegans embryos, starfish oocytes and fission yeast, SAIBR is ideal for samples with a single dominant AF source; it achieves accurate quantitation of fluorophore signal, and enables reliable detection and quantification of even weakly expressed proteins. Thus, SAIBR provides a highly accessible low-barrier way to incorporate AF correction as standard for researchers working on a broad variety of cell and developmental systems.

Analysing and meta-analysing time-series data of microbial growth and gene expression from plate readers

Responding to change is a fundamental property of life, making time-series data invaluable in biology. For microbes, plate readers are a popular, convenient means to measure growth and also gene expression using fluorescent reporters. Nevertheless, the difficulties of analysing the resulting data can be a bottleneck, particularly when combining measurements from different wells and plates. Here we present omniplate, a Python module that corrects and normalises plate-reader data, estimates growth rates and fluorescence per cell as functions of time, calculates errors, exports in different formats, and enables meta-analysis of multiple plates. The software corrects for autofluorescence, the optical density’s non-linear dependence on the number of cells, and the effects of the media. We use omniplate to measure the Monod relationship for the growth of budding yeast in raffinose, showing that raffinose is a convenient carbon source for controlling growth rates. Using fluorescent tagging, we study yeast’s glucose transport. Our results are consistent with the regulation of the hexose transporter (HXT) genes being approximately bipartite: the medium and high affinity transporters are predominately regulated by both the high affinity glucose sensor Snf3 and the kinase complex SNF1 via the repressors Mth1, Mig1, and Mig2; the low affinity transporters are predominately regulated by the low affinity sensor Rgt2 via the co-repressor Std1. We thus demonstrate that omniplate is a powerful tool for exploiting the advantages offered by time-series data in revealing biological regulation.

Time series of growth and of gene expression via fluorescent reporters are rich ways to characterise the behaviours of cells. With plate readers, it is straightforward to measure 96 independent time series in a single experiment, with readings taken every 10 minutes and each time series lasting tens of hours. Analysing such data can become challenging, particularly if multiple plate-reader experiments are required to characterise a phenomenon, which then should be analysed simultaneously. Taking advantage of existing packages in Python, we have written code that automates this analysis but yet still allows users to develop custom routines. Our omniplate software corrects both measurements of optical density to become linear in the number of cells and measurements of fluorescence for autofluorescence. It estimates growth rates and fluorescence per cell as continuous functions of time and enables tens of plate-reader experiments to be analysed together. Data can be exported in text files in a format immediately suitable for public repositories. Plate readers are a convenient way to study cells; omniplate provides an equally convenient yet powerful way to analyse the resulting data.

Multiple nutrient transporters enable cells to mitigate a rate-affinity tradeoff

Eukaryotic genomes often encode multiple transporters for the same nutrient. For example, budding yeast has 17 hexose transporters (HXTs), all of which potentially transport glucose. Using mathematical modelling, we show that transporters that use either facilitated diffusion or symport can have a rate-affinity tradeoff, where an increase in the maximal rate of transport decreases the transporter’s apparent affinity. These changes affect the import flux non-monotonically, and for a given concentration of extracellular nutrient there is one transporter, characterised by its affinity, that has a higher import flux than any other. Through encoding multiple transporters, cells can therefore mitigate the tradeoff by expressing those transporters with higher affinities in lower concentrations of nutrients. We verify our predictions using fluorescent tagging of seven HXT genes in budding yeast and follow their expression over time in batch culture. Using the known affinities of the corresponding transporters, we show that their regulation in glucose is broadly consistent with a rate-affinity tradeoff: as glucose falls, the levels of the different transporters peak in an order that mostly follows their affinity for glucose. More generally, evolution is constrained by tradeoffs. Our findings indicate that one such tradeoff often occurs in the cellular transport of nutrients.

From yeast to humans, cells often express multiple different types of transporters for the same nutrient, and it is puzzling why a single high-affinity transporter is not expressed instead. Here we initially use mathematical modelling to demonstrate that transporters facilitating diffusion and those powered by the proton motive force can both exhibit a rate-affinity tradeoff, for quite general conditions. A transporter with a higher affinity necessarily has a lower rate, and vice versa. The tradeoff implies that there is a range of nutrient concentrations for which a transporter, characterised by its affinity, has a higher import flux than any other transporter with a different affinity. To mitigate the tradeoff, genomes may therefore encode multiple different transporters, and cells that express each transporter in the concentrations where it imports best will uptake nutrients at higher rates. Consistently, we show that as cells of budding yeast consume glucose, they express five types of hexose transporters in an order that follows the transporters’ affinities.

Live-cell fluorescence spectral imaging as a data science challenge

Live-cell fluorescence spectral imaging is an evolving modality of microscopy that uses specific properties of fluorophores, such as excitation or emission spectra, to detect multiple molecules and structures in intact cells. The main challenge of analyzing live-cell fluorescence spectral imaging data is the precise quantification of fluorescent molecules despite the weak signals and high noise found when imaging living cells under non-phototoxic conditions. Beyond the optimization of fluorophores and microscopy setups, quantifying multiple fluorophores requires algorithms that separate or unmix the contributions of the numerous fluorescent signals recorded at the single pixel level. This review aims to provide both the experimental scientist and the data analyst with a straightforward description of the evolution of spectral unmixing algorithms for fluorescence live-cell imaging. We show how the initial systems of linear equations used to determine the concentration of fluorophores in a pixel progressively evolved into matrix factorization, clustering, and deep learning approaches. We outline potential future trends on combining fluorescence spectral imaging with label-free detection methods, fluorescence lifetime imaging, and deep learning image analysis.

Measurement Techniques to Resolve and Control Population Dynamics of Mixed-Culture Processes

Microbial mixed cultures are gaining increasing attention as biotechnological production systems, since they offer a large but untapped potential for future bioprocesses. Effects of secondary metabolite induction and advantages of labor division for the degradation of complex substrates offer new possibilities for process intensification. However, mixed cultures are highly complex, and, consequently, many biotic and abiotic parameters are required to be identified, characterized, and ideally controlled to establish a stable bioprocess. In this review, we discuss the advantages and disadvantages of existing measurement techniques for identifying, characterizing, monitoring, and controlling mixed cultures and highlight promising examples. Moreover, existing challenges and emerging technologies are discussed, which lay the foundation for novel analytical workflows to monitor mixed-culture bioprocesses.

FlopR: An Open Source Software Package for Calibration and Normalization of Plate Reader and Flow Cytometry Data

The measurement of gene expression using fluorescence markers has been a cornerstone of synthetic biology for the past two decades. However, the use of arbitrary units has limited the usefulness of these data for many quantitative purposes. Calibration of fluorescence measurements from flow cytometry and plate reader spectrophotometry has been implemented previously, but the tools are disjointed. Here we pull together, and in some cases improve, extant methods into a single software tool, written as a package in the R statistical framework. The workflow is validated using Escherichia coli engineered to express green fluorescent protein (GFP) from a set of commonly used constitutive promoters. We then demonstrate the package's power by identifying the time evolution of distinct subpopulations of bacteria from bulk plate reader data, a task previously reliant on laborious flow cytometry or colony counting experiments. Along with standardized parts and experimental methods, the development and dissemination of usable tools for quantitative measurement and data analysis will benefit the synthetic biology community by improving interoperability.

A Nitrate-Blind P. putida Strain Boosts PHA Production in a Synthetic Mixed Culture

One of the major challenges for the present and future generations is to find suitable substitutes for the fossil resources we rely on today. In this context, cyanobacterial carbohydrates have been discussed as an emerging renewable feedstock in industrial biotechnology for the production of fuels and chemicals. Based on this, we recently presented a synthetic bacterial co-culture for the production of medium-chain-length polyhydroxyalkanoates (PHAs) from CO₂. This co-cultivation system is composed of two partner strains: Synechococcus elongatus cscB which fixes CO₂, converts it to sucrose and exports it into the culture supernatant, and a Pseudomonas putida strain that metabolizes this sugar and accumulates PHAs in the cytoplasm. However, these biopolymers are preferably accumulated under conditions of nitrogen limitation, a situation difficult to achieve in a co-culture as the other partner, at best, should not perceive any limitation. In this article, we will present an approach to overcome this dilemma by uncoupling the PHA production from the presence of nitrate in the medium. This is achieved by the construction of a P. putida strain that is no longer able to grow with nitrate as nitrogen source -is thus nitrate blind, and able to grow with sucrose as carbon source. The deletion of the nasT gene encoding the response regulator of the NasS/NasT two-component system resulted in such a strain that has lost the ability use nitrate, but growth with ammonium was not affected. Subsequently, the nasT deletion was implemented in P. putida cscRABY, an efficient sucrose consuming strain. This genetic engineering approach introduced an artificial unilateral nitrogen limitation in the co-cultivation process, and the amount of PHA produced from light and CO₂ was 8.8 fold increased to 14.8% of its CDW compared to the nitrate consuming reference strain. This nitrate blind strain, P. putidaΔnasT attTn7:cscRABY, is not only a valuable partner in the co-cultivation but additionally enables the use of other nitrate containing substrates for medium-chain-length PHA production, like for example waste-water.

Fluorescent reporter plasmids for single-cell and bulk-level composition assays in E. faecalis

Fluorescent reporters are an important tool for monitoring dynamics of bacterial populations at the single cell and community level. While there are a large range of reporter constructs available–particularly for common model organisms like E. coli–fewer options exist for other species, including E. faecalis, a gram-positive opportunistic pathogen. To expand the potential toolkit available for E. faecalis, we exchanged the original fluorescent reporter in a previously developed plasmid (pBSU101) with one of eight fluorescent reporters and confirmed that all constructs exhibited detectable fluorescence in single E. faecalis cells and mixed biofilm communities. To identify promising constructs for bulk-level experiments, we then measured the fluorescence spectra from E. faecalis populations in microwell plate (liquid) cultures during different phases of aerobic growth. Cultures showed density- and reporter-specific variations in fluorescent signal, though spectral signatures of all reporters become clear in late-exponential and stationary-phase populations. Based on these results, we identified six pairs of reporters that can be combined with simple spectral unmixing to accurately estimate population composition in 2-strain mixtures at or near stationary phase. This approach offers a simple and scalable method for selection and competition experiments in simple two-species populations under aerobic growth conditions. Finally, we incorporated codon-optimized variants of blue (BFP) and red (RFP) reporters and show that they lead to increased fluorescence in exponentially growing cells. As a whole, the results inform the scope of application of different reporters and identify both single reporters and reporter pairs that are promising for fluorescence-based assays at bulk and single-cell levels in E. faecalis.

In vivo measurements reveal a single 5'-intron is sufficient to increase protein expression level in Caenorhabditis elegans

Introns can increase gene expression levels using a variety of mechanisms collectively referred to as Intron Mediated Enhancement (IME). IME has been measured in cell culture and plant models by quantifying expression of intronless and intron-bearing reporter genes in vitro. We developed hardware and software to implement microfluidic chip-based gene expression quantification in vivo. We altered position, number and sequence of introns in reporter genes controlled by the hsp-90 promoter. Consistent with plant and mammalian studies, we determined a single, natural or synthetic, 5'-intron is sufficient for the full IME effect conferred by three synthetic introns, while a 3'-intron is not. We found coding sequence can affect IME; the same three synthetic introns that increase mcherry protein concentration by approximately 50%, increase mEGFP by 80%. We determined IME effect size is not greatly affected by the stronger vit-2 promoter. Our microfluidic imaging approach should facilitate screens for factors affecting IME and other intron-dependent processes.

WellInverter: a web application for the analysis of fluorescent reporter gene data

Background

Fluorescent reporter genes have become widely used for monitoring gene expression in living cells. When a microbial strain carrying a reporter gene is grown in a microplate reader, the fluorescence and the absorbance (optical density) of the culture can be automatically measured every few minutes in a highly parallelized way. The extraction of useful information from the resulting large amounts of data is not easy to achieve, because the fluorescence and absorbance measurements are only indirectly related to promoter activities and protein concentrations, requiring mathematical models of the expression of reporter genes for their interpretation. Although the principles of the analysis of reporter gene data are well-established today, there is a lack of general-purpose bioinformatics tools based on generic measurement models and sound inference procedures. This has motivated the development of WellInverter, a web application based on well-known methods for regularized linear inversion.

Results

We present a new version of WellInverter that considerably improves the performance and usability of the original application. In particular, we have put in place a parallel computing architecture with a load balancer to distribute analysis queries over several back-end servers, we have completely redesigned the graphical user interface to better support the different analysis steps, and we have developed a plug-in system for the parsing of data files produced by microplate readers from different manufacturers. We illustrate the functioning of WellInverter by analyzing data of the expression of a fluorescent reporter gene controlled by a phage promoter in growing Escherichia coli populations. We show that the expression pattern in different growth media, supporting different growth rates, corresponds to the pattern expected for a constitutive gene.

Conclusions

The new version of WellInverter is a robust, easy-to-use and scalable web application, which has been deployed on two publicly accessible web servers and which can also be installed locally. A demo version of the application with two sample datasets is available on-line.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2920-4) contains supplementary material, which is available to authorized users.

"Do It Yourself" Microbial Cultivation Techniques for Synthetic and Systems Biology: Cheap, Fun, and Flexible

With the emergence of inexpensive 3D printing technology, open-source platforms for electronic prototyping and single-board computers, "Do it Yourself" (DIY) approaches to the cultivation of microbial cultures are becoming more feasible, user-friendly, and thus wider spread. In this perspective, we survey some of these approaches, as well as add-on solutions to commercial instruments for synthetic and system biology applications. We discuss different cultivation designs, including capabilities and limitations. Our intention is to encourage the reader to consider the DIY solutions. Overall, custom cultivation devices offer controlled growth environments with in-line monitoring of, for example, optical density, fluorescence, pH, and dissolved oxygen, all at affordable prices. Moreover, they offer a great degree of flexibility for different applications and requirements and are fun to design and construct. We include several illustrative examples, such as gaining optogenetic control and adaptive laboratory evolution experiments.

Macroscale fluorescence imaging against autofluorescence under ambient light

Macroscale fluorescence imaging is increasingly used to observe biological samples. However, it may suffer from spectral interferences that originate from ambient light or autofluorescence of the sample or its support. In this manuscript, we built a simple and inexpensive fluorescence macroscope, which has been used to evaluate the performance of Speed OPIOM (Out of Phase Imaging after Optical Modulation), which is a reference-free dynamic contrast protocol, to selectively image reversibly photoswitchable fluorophores as labels against detrimental autofluorescence and ambient light. By tuning the intensity and radial frequency of the modulated illumination to the Speed OPIOM resonance and adopting a phase-sensitive detection scheme that ensures noise rejection, we enhanced the sensitivity and the signal-to-noise ratio for fluorescence detection in blot assays by factors of 50 and 10, respectively, over direct fluorescence observation under constant illumination. Then, we overcame the strong autofluorescence of growth media that are currently used in microbiology and realized multiplexed fluorescence observation of colonies of spectrally similar fluorescent bacteria with a unique configuration of excitation and emission wavelengths. Finally, we easily discriminated fluorescent labels from the autofluorescent and reflective background in labeled leaves, even under the interference of incident light at intensities that are comparable to sunlight. The proposed approach is expected to find multiple applications, from biological assays to outdoor observations, in fluorescence macroimaging.

Light-controlled gene expression in yeast using photocaged Cu2

The manipulation of cellular function, such as the regulation of gene expression, is of great interest to many biotechnological applications and often achieved by the addition of small effector molecules. By combining effector molecules with photolabile protecting groups that mask their biological activity until they are activated by light, precise, yet minimally invasive, photocontrol is enabled. However, applications of this trendsetting technology are limited by the small number of established caged compound-based expression systems. Supported by computational chemistry, we used the versatile photolabile chelator DMNP-EDTA, long-established in neurobiology for photolytic Ca²⁺ release, to control Cu²⁺ release upon specific UV-A irradiation. This permits light-mediated control over the widely used Cu²⁺-inducible pCUP1 promoter from S. cerevisiae and thus constitutes the first example of a caged metal ion to regulate recombinant gene expression. We screened our novel DMNP-EDTA-Cu system for best induction time and expression level of eYFP with a high-throughput online monitoring system equipped with an LED array for individual illumination of every single well. Thereby, we realized a minimally invasive, easy-to-control, parallel and automated optical expression regulation via caged Cu²⁺ allowing temporal and quantitative control as a beneficial alternative to conventional induction via pipetting CuCl₂ as effector molecule.

Inferring time derivatives including cell growth rates using Gaussian processes

Often the time derivative of a measured variable is of as much interest as the variable itself. For a growing population of biological cells, for example, the population's growth rate is typically more important than its size. Here we introduce a non-parametric method to infer first and second time derivatives as a function of time from time-series data. Our approach is based on Gaussian processes and applies to a wide range of data. In tests, the method is at least as accurate as others, but has several advantages: it estimates errors both in the inference and in any summary statistics, such as lag times, and allows interpolation with the corresponding error estimation. As illustrations, we infer growth rates of microbial cells, the rate of assembly of an amyloid fibril and both the speed and acceleration of two separating spindle pole bodies. Our algorithm should thus be broadly applicable.

Probing unnatural amino acid integration into enhanced green fluorescent protein by genetic code expansion with a high-throughput screening platform

BACKGROUND

Genetic code expansion has developed into an elegant tool to incorporate unnatural amino acids (uAA) at predefined sites in the protein backbone in response to an amber codon. However, recombinant production and yield of uAA comprising proteins are challenged due to the additional translation machinery required for uAA incorporation.

RESULTS

We developed a microtiter plate-based high-throughput monitoring system (HTMS) to study and optimize uAA integration in the model protein enhanced green fluorescence protein (eGFP). Two uAA, propargyl-L-lysine (Plk) and (S)-2-amino-6-((2-azidoethoxy) carbonylamino) hexanoic acid (Alk), were incorporated at the same site into eGFP co-expressing the native PylRS/tRNAPylCUA pair originating from Methanosarcina barkeri in E. coli. The site-specific uAA functionalization was confirmed by LC-MS/MS analysis. uAA-eGFP production and biomass growth in parallelized E. coli cultivations was correlated to (i) uAA concentration and the (ii) time of uAA addition to the expression medium as well as to induction parameters including the (iii) time and (iv) amount of IPTG supplementation. The online measurements of the HTMS were consolidated by end point-detection using standard enzyme-linked immunosorbent procedures.

CONCLUSION

The developed HTMS is powerful tool for parallelized and rapid screening. In light of uAA integration, future applications may include parallelized screening of different PylRS/tRNAPylCUA pairs as well as further optimization of culture conditions.

Green autofluorescence, a double edged monitoring tool for bacterial growth and activity in micro-plates

The intrinsic green autofluorescence of an Escherichia coli culture has long been overlooked and empirically corrected in green fluorescent protein (GFP) reporter experiments. We show here, by using complementary methods of fluorescence analysis and HPLC, that this autofluorescence, principally arise from the secreted flavins in the external media. The cells secrete roughly 10 times more than what they keep inside. We show next that the secreted flavin fluorescence can be used as a complementary method in measuring the cell concentration particularly when the classical method, based on optical density measure, starts to fail. We also demonstrate that the same external flavins limit the dynamical range of GFP quantification and can lead to a false interpretation of lower global dynamic range of expression than what really happens. In the end we evaluate different autofluorescence correction methods to extract the real GFP signal.

Robust reconstruction of gene expression profiles from reporter gene data using linear inversion

MOTIVATION

Time-series observations from reporter gene experiments are commonly used for inferring and analyzing dynamical models of regulatory networks. The robust estimation of promoter activities and protein concentrations from primary data is a difficult problem due to measurement noise and the indirect relation between the measurements and quantities of biological interest.

RESULTS

We propose a general approach based on regularized linear inversion to solve a range of estimation problems in the analysis of reporter gene data, notably the inference of growth rate, promoter activity, and protein concentration profiles. We evaluate the validity of the approach using in silico simulation studies, and observe that the methods are more robust and less biased than indirect approaches usually encountered in the experimental literature based on smoothing and subsequent processing of the primary data. We apply the methods to the analysis of fluorescent reporter gene data acquired in kinetic experiments with Escherichia coli. The methods are capable of reliably reconstructing time-course profiles of growth rate, promoter activity and protein concentration from weak and noisy signals at low population volumes. Moreover, they capture critical features of those profiles, notably rapid changes in gene expression during growth transitions.

AVAILABILITY AND IMPLEMENTATION

The methods described in this article are made available as a Python package (LGPL license) and also accessible through a web interface. For more information, see https://team.inria.fr/ibis/wellinverter.

The emerging use of in vivo optical imaging in the study of neurodegenerative diseases

The detection and subsequent quantification of photons emitted from living tissues, using highly sensitive charged-couple device (CCD) cameras, have enabled investigators to noninvasively examine the intricate dynamics of molecular reactions in wide assortment of experimental animals under basal and pathophysiological conditions. Nevertheless, extrapolation of this in vivo optical imaging technology to the study of the mammalian brain and related neurodegenerative conditions is still in its infancy. In this review, we introduce the reader to the emerging use of in vivo optical imaging in the study of neurodegenerative diseases. We highlight the current instrumentation that is available and reporter molecules (fluorescent and bioluminescent) that are commonly used. Moreover, we examine how in vivo optical imaging using transgenic reporter mice has provided new insights into Alzheimer's disease, amyotrophic lateral sclerosis (ALS), Prion disease, and neuronal damage arising from excitotoxicity and inflammation. Furthermore, we also touch upon studies that have utilized these technologies for the development of therapeutic strategies for neurodegenerative conditions that afflict humans.