Holger Morschett

Simplified cryopreservation of the microalga Chlorella vulgaris integrating a novel concept for cell viability estimation

Microalgae currently receive growing attention as promising candidates for future bio‐economy concepts. However, the reliable maintenance of production strains remains challenging. The well‐established serial subculturing techniques suffer from low long‐time stability and high effort and are therefore stepwise being replaced by cryopreservation. Currently, available protocols are often deduced from cell culture technology and are rather complex. This study aimed to investigate if less complex approaches can be applied. We introduce an easy‐to‐use cryopreservation protocol based on the model organism Chlorella vulgaris. To overcome error‐prone viability estimation by plating techniques, an alternative method using growth pattern analysis was developed. As revealed by growth pattern analysis, the preservation of stationary phase cells proved superior to the commonly applied concept of freezing cells from the growing phase. Controlled‐rate cooling using simple devices resulted in reproducibly high post‐thawing viabilities in the range of 63 ± 2%. Moreover, the presented protocol highlights the potential of simplifying microalgal cryo‐preservation procedures, thereby reducing the required labor and material need to a minimum. Apart from the viability analysis of the cryopreserved microalga C. vulgaris, this approach seems to have the potential to be applied for other algae species and microorganisms, as well.

Design and validation of a parallelized micro-photobioreactor enabling phototrophic bioprocess development at elevated throughput

Microalgae offer great potential for the industrial production of numerous compounds, but most of the currently available processes fail on economic aspects. Due to the lack of appropriate microcultivation systems, especially screening and early stage laboratory process characterization limit throughput in process development. Consequently, a demand for high throughput photobioreactors has recently been identified upon which some prototype systems emerged. However, compared to microbial microbioreactors, the systems so far introduced suffer from at least one of several drawbacks, that is, inhomogeneous conditions, poor mixing or excessive evaporation. In this context, a microtiter plate based micro‐photobioreactor was developed enabling 48‐fold parallelized cultivation. Strict control of the process conditions enabled a high comparability between the distinct wells of one plate (±5% fluctuation in biomass formation). The small scale, resulting in a beneficial surface to volume ratio, as well as the fast mixing due to rigorous orbital shaking, ensured an excellent light supply of the cultures. Moreover, non‐invasive online biomass quantification was implemented via a scattered light analyzer that is capable of biomass measurements during continuous illumination of the cultures. The system was shown to be especially qualified for parallelized laboratory screening applications like for instance media optimization. Easy automation via integration into a liquid handling platform is given by design. Thereby, the presented micro‐photobioreactor system significantly contributes to improving the time efficiency during the development of phototrophic bioprocesses. Biotechnol. Bioeng. 2017;114: 122–131.

Laboratory scale photobiotechnology – Current trends and future perspectives

Phototrophic bioprocesses are a promising puzzle piece in future bioeconomy concepts but yet mostly fail for economic reasons. Besides other aspects, this is mainly attributed to the omnipresent issue of optimal light supply impeding scale-up and -down of phototrophic processes according to classic established concepts. This MiniReview examines two current trends in photobiotechnology, namely microscale cultivation and modeling and simulation. Microphotobioreactors are a valuable and promising trend with microfluidic chips and microtiter plates as predominant design concepts. Providing idealized conditions, chip systems are preferably to be used for acquiring physiological data of microalgae while microtiter plate systems are more appropriate for process parameter and medium screenings. However, these systems are far from series technology and significant improvements especially regarding flexible light supply remain crucial. Whereas microscale is less addressed by modeling and simulation so far, benchtop photobioreactor design and operation have successfully been studied using such tools. This particularly includes quantitative model-assisted understanding of mixing, mass transfer, light dispersion and particle tracing as well as their relevance for microalgal performance. The ultimate goal will be to combine physiological data from microphotobioreactors with hybrid models to integrate metabolism and reactor simulation in order to facilitate knowledge-based scale transfer of phototrophic bioprocesses.

Differential transcriptomic analysis reveals hidden light response in Streptomyces lividans

Recently, a comprehensive screening workflow for the filamentous bacterium Streptomyces lividans, a highly performant source for pharmaceutically active agents was introduced. This framework used parallelized cultivation in microtiter plates to efficiently accelerate early upstream process development. Focusing on growth performance, cultivation was successfully scaled‐up to 1 L stirred tank reactors. However, metabolic adaptation was observed on the transcriptomic level as among others, several genes incorporated in light response were upregulated during bioreactor cultivation. Despite it was assumed that this was attributed to the fact that reactor cultivations were performed in glass vessels exposed to daylight and artificial room light, this setup did not allow distinguishing exclusively between light and other effects. Upon that, the present study directly investigates the influence of light by defined illumination of microtiter plate cultures. Almost identical growth performance was observed for cultures grown in the dark or with illumination. Transcriptomics revealed the upregulation of seven genes of which 6 have previously been described to be relevant for carotenoid synthesis and its regulation. These pigments are effective quenchers of reactive oxygen species. The seventh transcript coded for a photo‐lyase incorporated in UV‐damage repair of DNA further confirming induced light response. However, this was fully compensated by metabolic adaptation on the transcriptomic level and overall process performance was maintained. Consequently, environmental conditions need extremely careful control and evaluation during in‐depth omics analysis of bioprocesses. Otherwise metabolic adaptation induced by such issues can easily be misinterpreted, especially during studies addressing cultivation system comparisons.

Accelerated development of phototrophic bioprocesses : a conceptual framework

Phototrophic systems regained interest as feedstocks for bio-economy but their industrial exploitation mostly fails for economic reasons until today. Due to lacking high throughput photobioreactors and supporting methodologies, especially early stage screening suffers from low efficiency. In this context, a framework for accelerated phototrophic process development was designed. Targeting lipid production by Chlorella vulgaris as a microalgal model process, the full spectrum from strain maintenance, via cultivation and analytics to experimental design was addressed, while parallelized cultivation was focused as key technology. Contrary to well-established serial subculturing, strain maintenance was done by cryopreservation of glucose-adapted cells. Thus, an easy-to-use protocol was set up and optimized. According to specially developed growth pattern analysis, post-thawing viabilities of 63 ± 2 % were achieved and phototrophic pre-cultivation lead to highly reproducible adaptation to light. Enabling elevated throughput cultivation, a 48-well microtiter plate-based micro-photobioreactor was designed and growth was precisely and non-invasively monitored via scattered light. Strictly controlled conditions permitted a high comparability along the wells of a plate (± 5 %) while small scale and fast mixing ensured excellent light supply of the cultures. The system was shown to offer good scalability to established laboratory-scale photobioreactors. To handle samples from microscale cultivation, a dye-based assay was set up and assay conditions were optimized towards efficient and precise quantification of C. vulgaris’ intracellular lipid accumulation. Assay automation resulted in minimal hands-on-times while errors due to fluctuating performance of manual operators could be excluded. These technologies were merged into a framework for the accelerated development of phototrophic processes while Kriging-assisted experimental design was used to take full advantage of the improved experimental capacity. Within as little as four experimental rounds, the volumetric productivity of the model lipid production process was approx. tripled solely via medium optimization and synergistic multi-parameter interaction could be revealed. Though this framework was suitable to accelerate phototrophic process development, it may only be seen as an initial blueprint. Future improvement of the micro-photobioreactor and intensified robotic integration will enable more complex processes and thus extend the range of application from screening tasks to the acquisition of kinetic data concerning metabolism of phototrophic microorganisms or even simulation of complex environmental conditions. Abbreviations and formula symbols abbreviation denotation ACP acyl-carrier-protein ADP adenosine diphosphate ATP adenosine triphosphate CA cellulose acetate CCC central composite face centred design CCF composite circumscribed design C. vulgaris Chlorella vulgaris CoA coenzyme A CZ Czech Republic DMSO dimethyl sulfoxide DoE Design of Experiments EDTA ethylenediaminetetraacetic acid EI Expected Improvement enBBM enriched Bold’s Basal Medium enBBMopt optimized enriched Bold’s Basal Medium enBBMopt,min optimized and minimized enriched Bold’s Basal Medium enBBMref enriched Bold’s Basal Medium with reference composition FA fatty acid G Germany GC-ToF-MS gas chromatography time-of-flight mass spectrometry G3P 3-phosphoglycerate KriKit Kriging ToolKit LED light emitting diode MCMC Markov Chain Monte Carlo MES 2-(N-morpholino)ethanesulfonic acid continued on next page continuation from previous page MBR microbioreactor MTP microtiter plate NADP nicotinamide adenine dinucleotide phosphate NADPH nicotinamide adenine dinucleotide phosphate, reduced state PES polyether sulfone PS polystyrene PTFE polytetrafluorethylene PYR pyruvate STR stirred tank reactor SW Sweden TAGs triacylglycerides US United States symbol denotation dimension acc accuracy % a.u. arbitrary unit CDW cell dry weight g L DOT dissolved oxygen tension % f dilution factor h Planck’s constant 6.626 · 10 W s Iav average light intensity μmol m -2 s I0 incident light intensity μmol m -2 s k number of levels per input variable KI light excess inhibition constant μmol m -2 s KM light affinity constant μmol m -2 s KS substrate affinity constant g L -1 lagP lag phase coefficient for product formation continued on next page continuation from previous page lagμ lag phase coefficient m average amount of water transferred mg mb brutto tube weight mg mB mass of extracted biomass mg mi amount of transferred water mg mI maintenance coefficient mn netto tube weight mg mt target amount of water to be transferred mg n number of replicates NA Avogadro’s number 6.022 · 10 23 mol nexp number of required experiments ngen apparent generation number ni number of input variables (n)LC (neutral) lipid content % (w w) OD(λ) optical density (at wavelength λ) p probability value PAR(λ) photosynthetically active radiation (at wavelength λ) μmol m -2 s pH lat. potentia hydrogenii Pλ illumination intensity (at wavelength λ) W m -2 Pmax maximal photosynthesis rate prec precision % Prel relative photosynthesis rate Pvol volumetric productivity mg L -1 d qP product formation rate % (w w ) h R correlation coefficient Ref reference value continued on next page continuation from previous page S substrate concentration g L t process time h tacc,P duration of acceleration phase for product formation h tacc,μ duration of acceleration phase h tD doubling time h tlag duration of lag phase h tlag,P duration of lag phase for product formation h tlag,μ duration of lag phase h tprod time from start of production phase h tstat time to stationary phase h v viability % var variance % VL liquid volume mL v v volume per volume w v weight per volume w w weight per weight X biomass concentration g L YX/S biomass yield g g -1 Δngen differential apparent generation number ε light absorption coefficient g L λnm wavelength nm μ(max) (maximal) exponential growth rate h -1 or d List of tables and figures Table 1.1: Cultivation conditions influence C. vulgaris’ biochemical composition ....................3 Table 1.2: Selected physico-chemical properties of microalgal and mineral diesel .................5 Table 1.3: Lipid content and productivity of selected microalgae ............................................8 Table 1.4: Comparison of selected commercial MBR systems ............................................. 18 Table 2.1: Overview of chemicals ......................................................................................... 27 Table 2.2: Overview of devices ............................................................................................. 28 Table 2.3: Preparation of enBBMref from stock solutions ....................................................... 30 Table 3.1: Key design aspects of the photobioreactors used ................................................ 67 Table 3.2: Comparison of cell dry weight, neutral lipid content and volumetric productivity ... 73 Table 3.3: Target molecule addressing by excitation and emission wavelength variation ..... 77 Table 3.4: Initial evaluation of the medium components ....................................................... 89 Table 3.5: Impact of medium optimization by key performance indicating parameters .......... 96 Table 6.1: Experimental design and measured data used in section 3.5.2.1 ....................... 137 Table 6.2: Comparison of the initial composition of enBBMref, enBBMopt and enBBMopt,min .. 137 Table 6.3: Experimental design and measured data used in section 3.5.2.2 ....................... 138 Table 6.4: Experimental design and measured data used in section 3.5.3 ......................... 138 Table 6.5: Experimental design and measured data used in section 3.5.2.4 ....................... 139 Figure 1.1: Microscopic view of C. vulgaris 211-11b fixed to agar slides ................................2 Figure 1.2: Daughter cell formation during vegetative autosporulation of C. vulgaris ..............2 Figure 1.3: Lipid biosynthesis as integral component of microalgal carbon capture ................6 Figure 1.4: Exemplary photobioreactors for large-scale phototrophic cultivation .....................8 Figure 1.5: Kinetic relation between light intensity and growth rate ....................................... 12 Figure 1.6: Course of phototrophic batch cultivation with its characteristic phases ............... 13 Figure 1.7: Comparison of conventional and accelerated bioprocess development .............. 15 Figure 1.8: Automated microscale bioprocess platform ........................................................ 20 Figure 1.9: Schematic process model ................................................................................... 21 Figure 1.10: Schematic representation of central composite designs for nonlinear models .. 23 Figure 1.11: Framework for accelerated phototrophic bioprocess development ................... 26 Figure 2.1: Workflow for the automated quantification of intracellular neutral lipids .............. 37 Figure 3.1: Heterotrophic growth of C. vulgaris after freezing preservation ........................... 45 Figure 3.2: Comparison of cultivation times necessary to reach stationary phase ................ 46 Figure 3.3: Cell count and biovolume before and after cryopreservation procedures ............ 47 Figure 3.4: Growth pattern-deviated assessment of cell viability ........................................... 49 Figure 3.5: Post-thawing viability after cryopreservation of stationary phase cells ................ 52 Figure 3.6: Phototrophic pre-cultivation of C. vulgaris in shake flasks ................................... 53 Figure 3.7: Design of the micro-photobioreactor prototype ................................................... 57 Figure 3.8: Tailoring of the photo module spectrum .............................................................. 59 Figure 3.9:

Beschleunigte Bioprozessentwicklung im automatisierten Mikromaßstab

Modern bioprocess development includes extensive screening tasks, but many tools tackling the resulting combinatorial explosion do not provide production scale relevant conditions. Thus, novel technologies are needed for scalable data acquisition via incorporation of miniaturization, automation and digitalization. The Microbial Bioprocess Lab–a Helmholtz Innovation Lab strives at designing such disruptive technologies and to catalyze their translation from science into industrial application.