Quantification of cell growth is central to any study of photoautotrophic microorganisms. However, cellular self-shading and limited CO2 control in conventional photobioreactors lead to heterogeneous conditions that obscure distinct correlations between the environment and cellular physiology. Here we present a microfluidic cultivation platform that enables precise analysis of cyanobacterial growth with spatio-temporal resolution. Since cyanobacteria are cultivated in monolayers, cellular self-shading does not occur, allowing homogeneous illumination and precise knowledge of the photonflux density at single-cell resolution. A single chip contains multiple channels, each connected to several hundred growth chambers. In combination with an externally applied light gradient, this setup enables high-throughput multi-parameter analysis in short time. In addition, the multilayered microfluidic design allows continuous perfusion of defined gas mixtures. Transversal CO2 diffusion across the intermediate polydimethylsiloxane membrane results in homogeneous CO2 supply, with a unique exchange-surface to cultivation-volume ratio. Three cyanobacterial model strains were examined under various, static and dynamic environmental conditions. Phase-contrast and chlorophyllfluorescence images were recorded by automated time-lapse microscopy. Deep-learning trained cell segmentation was used to efficiently analyse large image stacks, thereby generating statistically reliable data. Cell division was highly synchronized, and growth was robust under continuous illumination but stopped rapidly upon initiating dark phases. CO2-limitation, often a limiting factor in photobioreactors, was only observed when the device was operated under reduced CO2 between 50 and 0 ppm. Here we provide comprehensive and precise data on cyanobacterial growth at single-cell resolution, accessible for further growth studies and modeling.

Microbial metabolism and growth are tightly linked to oxygen (O2). Microbes experience fluctuating O2 levels in natural environments; however, our understanding of how cells respond to fluctuating O2 over various time scales remains limited due to challenges in observing microbial growth at single-cell resolution under controlled O2 conditions and in linking individual cell growth with the specific O2 microenvironment. We performed time-resolved microbial growth analyses at single-cell resolution under a temporally controlled O2 supply. A multilayer microfluidic device was developed, featuring a gas supply above a cultivation layer, separated by a thin membrane enabling efficient gas transfer. This platform allows microbial cultivation under constant, dynamic, and oscillating O2 conditions. Automated time-lapse microscopy and deep-learning-based image analysis provide access to spatiotemporally resolved growth data at the single-cell level. O2 switching within tens of seconds, coupled with precise microenvironment monitoring, allows us to accurately correlate cellular growth with local O2 concentrations. Growing Escherichia coli microcolonies subjected to varying O2 oscillation periods show distinct growth dynamics characterized by response and recovery phases. The comprehensive growth data and insights gained from our unique platform are a crucial step forward to systematically study cell response and adaptation to fluctuating O2 environments at single-cell resolution.

Microfluidic cultivation, with its high level of environmental control and spatio-temporal resolution of cellular behavior, is a well-established tool in today’s microfluidics. Yet, reliable retention of (randomly) motile cells inside designated cultivation compartments still represents a limitation, which prohibits systematic single-cell growth studies. To overcome this obstacle, current approaches rely on complex multilayer chips or on-chip valves, which makes their application for a broad community of users infeasible. Here, we present an easy-to-implement cell retention concept to withhold cells inside microfluidic cultivation chambers. By introducing a blocking structure into a cultivation chamber’s entrance and nearly closing it, cells can be manually pushed into the chamber during loading procedures but are unable to leave it autonomously in subsequent long-term cultivation. CFD simulations as well as trace substance experiments confirm sufficient nutrient supply within the chamber. Through preventing recurring cell loss, growth data obtained from Chinese hamster ovary cultivation on colony level perfectly match data determined from single-cell data, which eventually allows reliable high throughput studies of single-cell growth. Due to its transferability to other chamber-based approaches, we strongly believe that our concept is also applicable for a broad range of cellular taxis studies or analyses of directed migration in basic or biomedical research.

The biotechnological production of fine chemicals, proteins and pharmaceuticals is usually hampered by loss of microbial performance during scale-up. This challenge is mainly caused by discrepancies between homogeneous environmental conditions at laboratory scale, where bioprocesses are optimized, and inhomogeneous conditions in large-scale bioreactors, where production takes place. Therefore, to improve strain selection and process development, it is of great interest to characterize these fluctuating conditions at large-scale and to study their effects on microbial cells. In this paper, we demonstrate the potential of computational fluid dynamics (CFD) simulation of large-scale bioreactors combined with dynamic microfluidic single-cell cultivation (dMSCC). Environmental conditions in a 200 L bioreactor were characterized with CFD simulations. Computational lifelines were determined by combining simulated turbulent multiphase flow, mass transport and particle tracing. Glucose availability for Corynebacterium glutamicum cells was determined. The reactor was simulated with average glucose concentrations of 6 g m −3 , 10 g m −3 and 16 g m −3 . The resulting computational lifelines, discretized into starvation and abundance regimes, were used as feed profiles for the dMSCC to investigate how varying glucose concentration affects cell physiology and growth rate. In this study, each colony in the dMSCC device represents a single cell as it travels through the reactor. Under oscillating conditions reproduced in the dMSCC device, a decrease in growth rate of about 40% was observed compared to continuous supply with the same average glucose availability. The presented approach provides insights into environmental conditions observed by microorganisms in large-scale bioreactors. It also paves the way for an improved understanding of how inhomogeneous environmental conditions influence cellular physiology, growth and production.

A consecutive three-component Heck-Knoevenagel synthesis of phenothiazinyl merocyanines was developed by statistical design of experiments (DoE) and Bayesian optimization furnishing nine red orange dyes starting from heteroaryl halides, acrolein and ethyl cyanoacetate. Besides scrutinizing the electronic properties of these merocyanines by cyclic voltammetry, absorption and emission spectroscopy, and (TD)DFT calculations their saponified carboxylic acids were employed in dye-sensitized solar cell (DSSC) experiments showing promising solar cell performances with relative efficiencies of up to 93% compared to the standard ruthenium dye N3.

Fluid flow rate and total throughput are the major controlling parameters to calculate the required size of membrane-based filter equipment for manufacturing of pharmaceuticals. Filtration equipment comprises several resistances to flow such as pipes, connectors and the filter construction itself. The incorporated membrane is a main factor that determines the flow rate through the filter element. With larger membrane area, its resistance to flow declines and total filter throughput increases. Yet, additional hydrodynamic resistances in the filter device lead to lower flow rates than expected from the hydrodynamic resistances of the membrane. Especially the membrane pleats and the spacer material in-between can cause additional flow restrictions. This study investigates the causes of these pleat resistances in manufacturing scale filters. First, manufacturing scale filter flow rates were metered to quantify the effects of pleat geometry, filtration pressure and liquid viscosity on pleat resistance. Subsequent computed tomography (CT) scans of filter devices, performed under simulated operating conditions, reveal so far unreported pleat compressions that rise with increasing differential pressure up to 50% at 1.5 bar. In-plane flow resistances of the nonwoven spacer material between the pleats were determined. Finally, these pleat geometries, measured under pressure, and the in-plane nonwoven resistances were implemented into CFD simulations. These simulations show that reduced fluid flow in the nonwoven due to the compression of pleats can explain the previously observed hydrodynamic pleat resistances.

•CFD simulations reveal hydrodynamics and impact of non-woven layers.•Pleat geometry reconstructed from microscopic images.•Plastic cage is negligible for performance of standard cartridge designs.•Non-woven permeability identified as crucial for cartridge performance.•CFD estimated and directly measured non-woven permeability compared.

The performance of three membrane adsorber formats containing the same membrane type is comparatively analyzed. Membrane volumes range from 3 mL to 1.6 L, and above in parallel operation. Orthogonal modeling of internal flow patterns and binding properties is crucial for transferring model parameters across devices and scales. The binding rates are found to depend on the flow rate through convective transport in the macropores of the membrane. Breakthrough curves of large scale devices are successfully predicted using information acquired at smaller scales provided membrane properties, particularly binding capacity, are identical. (C) 2018 Elsevier Ltd. All rights reserved.

Enzymatic parameter determination is an essential step in biocatalytic process development. Therefore higher throughput in miniaturized devices is urgently needed. An ideal microfluidic device should combine easy immobilization and retention of a minimal amount of biocatalyst with a well-mixed reaction volume. Together, all criteria are hardly met by current tools. Here we describe a microfluidic reactor (mu MORE) which employs magnetic particles for both enzyme immobilization and efficient mixing using two permanent magnets placed in rotating cylinders next to the a glass chip reactor. The chip geometry and agitation speed was optimized by investigation of the mixing and retention characteristics using simulation and dye distribution analysis. Subsequently, the mu MORE was successfully applied to determine critical biocatalytic process parameters in a parallelized manner for the carboligation of benzaldehyde and acetaldehyde to (S)-2-hydroxy-1-phenylpropan-1-one with less than 5 mu g of benzoylformate decarboxylase from Pseudomonas putida immobilized on magnetic beads. Here, one run of the device in six parallelized glass reactors took only 2-3 h for an immobilized enzyme with very low activity (similar to 2 U/mg). The optimized parameter set was finally tested in a 10 mL enzyme membrane reactor, demonstrating that the mu MORE provides a solid data base for biocatalytic process optimization. (C) 2016 Elsevier B.V. All rights reserved.

Cell‐to‐cell heterogeneity typically evolves due to a manifold of biological and environmental factors and special phenotypes are often relevant for the fate of the whole population but challenging to detect during conventional analysis. We demonstrate a microfluidic single‐cell cultivation platform that incorporates several hundred growth chambers, in which isogenic bacteria microcolonies growing in cell monolayers are tracked by automated time‐lapse microscopy with spatiotemporal resolution. The device was not explicitly developed for a specific organism, but has a very generic configuration suitable for various different microbial organisms. In the present study, we analyzed Corynebacterium glutamicum microcolonies, thereby generating complete lineage trees and detailed single‐cell data on division behavior and morphology in order to demonstrate the platform's overall capabilities. Furthermore, the occurrence of spontaneously induced stress in individual C. glutamicum cells was investigated by analyzing strains with genetically encoded reporter systems and optically visualizing SOS response. The experiments revealed spontaneous SOS induction in the absence of any external trigger comparable to results obtained by flow cytometry (FC) analyzing cell samples from conventional shake flask cultivation. Our microfluidic setup delivers detailed single‐cell data with spatial and temporal resolution; complementary information to conventional FC results. © 2015 International Society for Advancement of Cytometry

A microfluidic device for microbial single-cell cultivation of bacteria was modeled and simulated using COMSOL Multiphysics. The liquid velocity field and the mass transfer within the supply channels and cultivation chambers were calculated to gain insight in the distribution of supplied nutrients and metabolic products secreted by the cultivated bacteria. The goal was to identify potential substrate limitations or product accumulations within the cultivation device. The metabolic uptake and production rates, colony size, and growth medium composition were varied covering a wide range of operating conditions. Simulations with glucose as substrate did not show limitations within the typically used concentration range, but for alternative substrates limitations could not be ruled out. This lays the foundation for further studies and the optimization of existing picoliter bioreactor systems.

Fluorescence-activated droplet sorting (FADS) has become a widely used technique for high-throughput screening applications. However, existing methods are very sensitive to fluctuating flow rates at the sorting junction, which can be caused by the pulsing effects of mechanical pumps, droplet aggregates or the accumulation of precipitates during lengthy biological screening applications. Furthermore, existing sorting devices allow only 2-way sorting. We present here a dielectrophoretic sorting system in which the droplets are sorted along multiple electrode pairs that run parallel to the channels. This enables highly reliable sorting (no errors were detected for more than 2000 sorting events) even when inverting the relative flow rates at a 2-way sorting junction from 80 : 20 to 20 : 80. Furthermore, our toolbox is scalable: we demonstrate on the example of a triple-colour sorting experiment with a total of four decoupled electrodes that multi-way sorting is feasible.

Packed bed chromatography is commonly applied for the separation of large molecules in biopharmaceutical industry. A technical chromatography system is typically composed of a cylindrical column that is filled with porous spheres. Particularly in small columns, which are increasingly applied for parallel experiments on lab robotic platforms, the impacts of inhomogeneous packing and wall effects on separation performance can be quite significant. We hence study mass transfer by convection, diffusion and adsorption in three-dimensional sphere packings. Random packings are externally generated and imported into COMSOL where the model equations are easy to implement. However, the COMSOL functions for automatic meshing and for iteratively solving the resulting equation systems fail to work with default settings. We have previously established a semi-automated and half-manual meshing procedure for rather small packings with less than 150 spheres that works with the direct PARDISO solver. The present contribution addresses the evaluation and optimization of the iterative equation solvers that are provided by COMSOL for the given spatial geometries with up to ten million degrees of freedom. The presented results illustrate that we can iteratively solve systems with up to 750 spheres using less memory and less computational time.

Summary: 13C-based metabolic flux analysis (13C-MFA) is the state-of-the-art method to quantitatively determine in vivo metabolic reaction rates in microorganisms. 13CFLUX2 contains all tools for composing flexible computational 13C-MFA workflows to design and evaluate carbon labeling experiments. A specially developed XML language, FluxML, highly efficient data structures and simulation algorithms achieve a maximum of performance and effectiveness. Support of multicore CPUs, as well as compute clusters, enables scalable investigations. 13CFLUX2 outperforms existing tools in terms of universality, flexibility and built-in features. Therewith, 13CFLUX2 paves the way for next-generation high-resolution 13C-MFA applications on the large scale. Availability and implementation: 13CFLUX2 is implemented in C++ (ISO/IEC 14882 standard) with Java and Python add-ons to run under Linux/Unix. A demo version and binaries are available at www.13cflux.net. Contact: info@13cflux.net or k.noeh@fz-juelich.de Supplementary information: Supplementary data are available at Bioinformatics online.