The topology of metabolic isotope labeling networks

<p>Abstract</p> <p>Background</p> <p>Metabolic Flux Analysis (MFA) based on isotope labeling experiments (ILEs) is a widely established tool for determining fluxes in metabolic pathways. Isotope labeling networks (ILNs) contain all essential information required to describe the flow of labeled material in an ILE. Whereas recent experimental progress paves the way for high-throughput MFA, large network investigations and exact statistical methods, these developments are still limited by the poor performance of computational routines used for the evaluation and design of ILEs. In this context, the global analysis of ILN topology turns out to be a clue for realizing large speedup factors in all required computational procedures.</p> <p>Results</p> <p>With a strong focus on the speedup of algorithms the topology of ILNs is investigated using graph theoretic concepts and algorithms. A rigorous determination of all cyclic and isomorphic subnetworks, accompanied by the global analysis of ILN connectivity is performed. Particularly, it is proven that ILNs always brake up into a large number of small strongly connected components (SCCs) and, moreover, there are natural isomorphisms between many of these SCCs. All presented techniques are universal, i.e. they do not require special assumptions on the network structure, bidirectionality of fluxes, measurement configuration, or label input. The general results are exemplified with a practically relevant metabolic network which describes the central metabolism of <it>E. coli </it>comprising 10390 isotopomer pools.</p> <p>Conclusion</p> <p>Exploiting the topological features of ILNs leads to a significant speedup of all universal algorithms for ILE evaluation. It is proven in theory and exemplified with the <it>E. coli </it>example that a speedup factor of about 1000 compared to standard algorithms is achieved. This widely opens the door for new high performance algorithms suitable for high throughput applications and large ILNs. Moreover, for the first time the global topological analysis of ILNs allows to comprehensively describe and understand the general patterns of label flow in complex networks. This is an invaluable tool for the structural design of new experiments and the interpretation of measured data.</p

Automated Characterization of Catalytically Active Inclusion Body Production in Biotechnological Screening Systems

We here propose an automated pipeline for the microscopy image-based characterization of catalytically active inclusion bodies (CatIBs), which includes a fully automatic experimental high-throughput workflow combined with a hybrid approach for multi-object microbial cell segmentation. For automated microscopy, a CatIB producer strain was cultivated in a microbioreactor from which samples were injected into a flow chamber. The flow chamber was fixed under a microscope and an integrated camera took a series of images per sample. To explore heterogeneity of CatIB development during the cultivation and track the size and quantity of CatIBs over time, a hybrid image processing pipeline approach was developed, which combines an ML-based detection of in-focus cells with model-based segmentation. The experimental setup in combination with an automated image analysis unlocks high-throughput screening of CatIB production, saving time and resources. Biotechnological relevance - CatIBs have wide application in synthetic chemistry and biocatalysis, but also could have future biomedical applications such as therapeutics. The proposed hybrid automatic image processing pipeline can be adjusted to treat comparable biological microorganisms, where fully data-driven ML-based segmentation approaches are not feasible due to the lack of training data. Our work is the first step towards image-based bioprocess control

Rapid inoculation of single bacteria into parallel picoliter fermentation chambers

Probst C, Grünberger A, Braun N, et al. Rapid inoculation of single bacteria into parallel picoliter fermentation chambers. Analytical methods. 2015;7(1):91-98.Microfluidic single-cell cultivation devices have been successfully utilized in a variety of biological research fields. One major obstacle to the successful implementation of high throughput single-cell cultivation technology is the requirement for a simple, fast and reliable cell inoculation procedure. In the present report, an air-bubble-based cell loading methodology is described and validated for inoculating single bacteria into multiple picoliter sized growth chambers arranged in a highly parallel manner. It is shown that the application of the injected air bubble can serve as a reproducible mechanism to modify laminar flow conditions. In this way, convective flow was temporarily induced in more than 1000 cultivation chambers simultaneously, which under normal conditions operate exclusively under diffusive mass transport. Within an inoculation time of 100 s, Corynebacterium glutamicum cells were inoculated by convection at minimal stress level and single bacteria remain successfully trapped by cell-wall interactions. The procedure is easy, fast, gentle and requires only minimal fluidic control and equipment. The technique is well suited for microbial cell loading into commonly used microfluidic growth sites arranged in parallel intended for high throughput single-cell analysis

The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis

13C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. 13C MFA, however, stands out from other “-omics sciences,” in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying 13C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of 13C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an “all-around carefree package” for 13C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of 13C MFA

Robustifying Experimental Tracer Design for13C-Metabolic Flux Analysis

13C metabolic flux analysis (MFA) has become an indispensable tool to measure metabolic reaction rates (fluxes) in living organisms, having an increasingly diverse range of applications. Here, the choice of the13C labeled tracer composition makes the difference between an information-rich experiment and an experiment with only limited insights. To improve the chances for an informative labeling experiment, optimal experimental design approaches have been devised for13C-MFA, all relying on some a priori knowledge about the actual fluxes. If such prior knowledge is unavailable, e.g., for research organisms and producer strains, existing methods are left with a chicken-and-egg problem. In this work, we present a general computational method, termed robustified experimental design (R-ED), to guide the decision making about suitable tracer choices when prior knowledge about the fluxes is lacking. Instead of focusing on one mixture, optimal for specific flux values, we pursue a sampling based approach and introduce a new design criterion, which characterizes the extent to which mixtures are informative in view of all possible flux values. The R-ED workflow enables the exploration of suitable tracer mixtures and provides full flexibility to trade off information and cost metrics. The potential of the R-ED workflow is showcased by applying the approach to the industrially relevant antibiotic producer Streptomyces clavuligerus, where we suggest informative, yet economic labeling strategies

microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation

In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly Python-based cell segmentation tool with a graphical user interface and OMERO data management. microbeSEG utilizes a state-of-the-art deep learning-based segmentation method and can be used for instance segmentation of a wide range of cell morphologies and imaging techniques, e.g., phase contrast or fluorescence microscopy. The main focus of microbeSEG is a comprehensible, easy, efficient, and complete workflow from the creation of training data to the final application of the trained segmentation model. We demonstrate that accurate cell segmentation results can be obtained within 45 minutes of user time. Utilizing public segmentation datasets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures

Non-Invasive Microbial Metabolic Activity Sensing at Single Cell Level by Perfusion of Calcein Acetoxymethyl Ester

Krämer CEM, Singh A, Helfrich S, et al. Non-Invasive Microbial Metabolic Activity Sensing at Single Cell Level by Perfusion of Calcein Acetoxymethyl Ester. PLoS one. 2015;10(10): e0141768.Phase contrast microscopy cannot give sufficient information on bacterial metabolic activity, or if a cell is dead, it has the fate to die or it is in a viable but non-growing state. Thus, a reliable sensing of the metabolic activity helps to distinguish different categories of viability. We present a non-invasive instantaneous sensing method using a fluorogenic substrate for online monitoring of esterase activity and calcein efflux changes in growing wild type bacteria. The fluorescent conversion product of calcein acetoxymethyl ester (CAM) and its efflux indicates the metabolic activity of cells grown under different conditions at real-time. The dynamic conversion of CAM and the active efflux of fluorescent calcein were analyzed by combining microfluidic single cell cultivation technology and fluorescence time lapse microscopy. Thus, an instantaneous and non-invasive sensing method for apparent esterase activity was created without the requirement of genetic modification or harmful procedures. The metabolic activity sensing method consisting of esterase activity and calcein secretion was demonstrated in two applications. Firstly, growing colonies of our model organism Corynebacterium glutamicum were confronted with intermittent nutrient starvation by interrupting the supply of iron and carbon, respectively. Secondly, bacteria were exposed for one hour to fatal concentrations of antibiotics. Bacteria could be distinguished in growing and non-growing cells with metabolic activity as well as non-growing and non-fluorescent cells with no detectable esterase activity. Microfluidic single cell cultivation combined with high temporal resolution time-lapse microscopy facilitated monitoring metabolic activity of stressed cells and analyzing their descendants in the subsequent recovery phase. Results clearly show that the combination of CAM with a sampling free microfluidic approach is a powerful tool to gain insights in the metabolic activity of growing and non-growing bacteria

Enabling oxygen-controlled microfluidic cultures for spatiotemporal microbial single-cell analysis

Microfluidic cultivation devices that facilitate O2 control enable unique studies of the complex interplay between environmental O2 availability and microbial physiology at the single-cell level. Therefore, microbial single-cell analysis based on time-lapse microscopy is typically used to resolve microbial behavior at the single-cell level with spatiotemporal resolution. Time-lapse imaging then provides large image-data stacks that can be efficiently analyzed by deep learning analysis techniques, providing new insights into microbiology. This knowledge gain justifies the additional and often laborious microfluidic experiments. Obviously, the integration of on-chip O2 measurement and control during the already complex microfluidic cultivation, and the development of image analysis tools, can be a challenging endeavor. A comprehensive experimental approach to allow spatiotemporal single-cell analysis of living microorganisms under controlled O2 availability is presented here. To this end, a gas-permeable polydimethylsiloxane microfluidic cultivation chip and a low-cost 3D-printed mini-incubator were successfully used to control O2 availability inside microfluidic growth chambers during time-lapse microscopy. Dissolved O2 was monitored by imaging the fluorescence lifetime of the O2-sensitive dye RTDP using FLIM microscopy. The acquired image-data stacks from biological experiments containing phase contrast and fluorescence intensity data were analyzed using in-house developed and open-source image-analysis tools. The resulting oxygen concentration could be dynamically controlled between 0% and 100%. The system was experimentally tested by culturing and analyzing an E. coli strain expressing green fluorescent protein as an indirect intracellular oxygen indicator. The presented system allows for innovative microbiological research on microorganisms and microbial ecology with single-cell resolution

13C labeling experiments at metabolic nonstationary conditions: An exploratory study

<p>Abstract</p> <p>Background</p> <p>Stimulus Response Experiments to unravel the regulatory properties of metabolic networks are becoming more and more popular. However, their ability to determine enzyme kinetic parameters has proven to be limited with the presently available data. In metabolic flux analysis, the use of ¹³C labeled substrates together with isotopomer modeling solved the problem of underdetermined networks and increased the accuracy of flux estimations significantly.</p> <p>Results</p> <p>In this contribution, the idea of increasing the information content of the dynamic experiment by adding ¹³C labeling is analyzed. For this purpose a small example network is studied by simulation and statistical methods. Different scenarios regarding available measurements are analyzed and compared to a non-labeled reference experiment. Sensitivity analysis revealed a specific influence of the kinetic parameters on the labeling measurements. Statistical methods based on parameter sensitivities and different measurement models are applied to assess the information gain of the labeled stimulus response experiment.</p> <p>Conclusion</p> <p>It was found that the use of a (specifically) labeled substrate will significantly increase the parameter estimation accuracy. An overall information gain of about a factor of six is observed for the example network. The information gain is achieved from the specific influence of the kinetic parameters towards the labeling measurements. This also leads to a significant decrease in correlation of the kinetic parameters compared to an experiment without ¹³C-labeled substrate.</p

Germination and Growth Analysis of Streptomyces lividans at the Single-Cell Level Under Varying Medium Compositions

Quantitative single-cell cultivation has provided fundamental contributions to our understanding of heterogeneity among industrially used microorganisms. Filamentous growing Streptomyces species are emerging platform organisms for industrial production processes, but their exploitation is still limited due to often reported high batch-to-batch variations and unexpected growth and production differences. Population heterogeneity is suspected to be one responsible factor, which is so far not systematically investigated at the single-cell level. Novel microfluidic single-cell cultivation devices offer promising solutions to investigate these phenomena. In this study, we investigated the germination and growth behavior of Streptomyces lividans TK24 under varying medium compositions on different complexity levels (i.e., mycelial growth, hyphal growth and tip elongation) on single-cell level. Our analysis reveals a remarkable stability within growth and germination of spores and early mycelium development when exposed to constant and defined environments. We show that spores undergo long metabolic adaptation processes of up to &gt; 30 h to adjust to new medium conditions, rather than using a “persister” strategy as a possibility to cope with rapidly changing environments. Due to this uniform behavior, we conclude that S. lividans can be cultivated quite robustly under constant environmental conditions as provided by microfluidic cultivation approaches. Failure and non-reproducible cultivations are thus most likely to be found in less controllable larger-scale cultivation workflows and as a result of environmental gradients within large-scale cultivations