Advanced tool for characterization of microbial cultures by combining cytomics and proteomics

Harnessing microbial heterogeneity for improved biosynthesis fueled by synthetic biology

Metabolic engineering-driven microbial cell factories have made great progress in the efficient bioproduction of biochemical and recombinant proteins. However, the low efficiency and robustness of microbial cell factories limit their industrial applications. Harnessing microbial heterogeneity contributes to solving this. In this review, the origins of microbial heterogeneity and its effects on biosynthesis are first summarized. Synthetic biology-driven tools and strategies that can be used to improve biosynthesis by increasing and reducing microbial heterogeneity are then systematically summarized. Next, novel single-cell technologies available for unraveling microbial heterogeneity and facilitating heterogeneity regulation are discussed. Furthermore, a combined workflow of increasing genetic heterogeneity in the strain-building step to help in screening highly productive strains - reducing heterogeneity in the production process to obtain highly robust strains (IHP-RHR) facilitated by single-cell technologies was proposed to obtain highly productive and robust strains by harnessing microbial heterogeneity. Finally, the prospects and future challenges are discussed.

Tracking defined microbial communities by multicolor flow cytometry reveals tradeoffs between productivity and diversity

Cross feeding between microbes is ubiquitous, but its impact on the diversity and productivity of microbial communities is incompletely understood. A reductionist approach using simple microbial communities has the potential to detect cross feeding interactions and their impact on ecosystem properties. However, quantifying abundance of more than two microbes in a community in a high throughput fashion requires rapid, inexpensive assays. Here, we show that multicolor flow cytometry combined with a machine learning-based classifier can rapidly quantify species abundances in simple, synthetic microbial communities. Our approach measures community structure over time and detects the exchange of metabolites in a four-member community of fluorescent Bacteroides species. Notably, we quantified species abundances in co-cultures and detected evidence of cooperation in polysaccharide processing and competition for monosaccharide utilization. We also observed that co-culturing on simple sugars, but not complex sugars, reduced microbial productivity, although less productive communities maintained higher community diversity. In summary, our multicolor flow cytometric approach presents an economical, tractable model system for microbial ecology using well-studied human bacteria. It can be extended to include additional species, evaluate more complex environments, and assay response of communities to a variety of disturbances.

Digital models in biotechnology: Towards multi-scale integration and implementation

Industrial biotechnology encompasses a large area of multi-scale and multi-disciplinary research activities. With the recent megatrend of digitalization sweeping across all industries, there is an increased focus in the biotechnology industry on developing, integrating and applying digital models to improve all aspects of industrial biotechnology. Given the rapid development of this field, we systematically classify the state-of-art modelling concepts applied at different scales in industrial biotechnology and critically discuss their current usage, advantages and limitations. Further, we critically analyzed current strategies to couple cell models with computational fluid dynamics to study the performance of industrial microorganisms in large-scale bioprocesses, which is of crucial importance for the bio-based production industries. One of the most challenging aspects in this context is gathering intracellular data under industrially relevant conditions. Towards comprehensive models, we discuss how different scale-down concepts combined with appropriate analytical tools can capture intracellular states of single cells. We finally illustrated how the efforts could be used to develop digitals models suitable for both cell factory design and process optimization at industrial scales in the future.

Single-Cell Technologies to Understand the Mechanisms of Cellular Adaptation in Chemostats

There is a growing interest in continuous manufacturing within the bioprocessing community. In this context, the chemostat process is an important unit operation. The current application of chemostat processes in industry is limited although many high yielding processes are reported in literature. In order to reach the full potential of the chemostat in continuous manufacture, the output should be constant. However, adaptation is often observed resulting in changed productivities over time. The observed adaptation can be coupled to the selective pressure of the nutrient-limited environment in the chemostat. We argue that population heterogeneity should be taken into account when studying adaptation in the chemostat. We propose to investigate adaptation at the single-cell level and discuss the potential of different single-cell technologies, which could be used to increase the understanding of the phenomena. Currently, none of the discussed single-cell technologies fulfill all our criteria but in combination they may reveal important information, which can be used to understand and potentially control the adaptation.

Proteomic study uncovers molecular principles of single-cell-level phenotypic heterogeneity in lipid storage of Nannochloropsis oceanica

BACKGROUND

Nannochloropsis oceanica belongs to a large group of photoautotrophic eukaryotic organisms that play important roles in fixation and cycling of atmospheric CO2. Its capability of storing solar energy and carbon dioxide in the form of triacylglycerol (TAG) of up to 60% of total weight under nitrogen deprivation stress sparked interest in its use for biofuel production. Phenotypes varying in lipid accumulation among an N. oceanica population can be disclosed by single-cell analysis/sorting using fluorescence-activated cell sorting (FACS); yet the phenomenon of single cell heterogeneity in an algae population remains to be fully understood at the molecular level. In this study, combination of FACS and proteomics was used for identification, quantification and differentiation of these heterogeneities on the molecular level.

RESULTS

For N. oceanica cultivated under nitrogen deplete (-N) and replete (+N) conditions, two groups differing in lipid content were distinguished. These differentiations could be recognized on the population as well as the single-cell levels; proteomics uncovered alterations in carbon fixation and flux, photosynthetic machinery, lipid storage and turnover in the populations. Although heterogeneity patterns have been affected by nitrogen supply and cultivation conditions of the N. oceanica populations, differentiation itself seems to be very robust against these factors: cultivation under +N, -N, in shaker bottles, and in a photo-bioreactor all split into two subpopulations. Intriguingly, population heterogeneity resumed after subpopulations were separately recultivated for a second round, refuting the possible development of genetic heterogeneity in the course of sorting and cultivation.

CONCLUSIONS

This work illustrates for the first time the feasibility of combining FACS and (prote)-omics for mechanistic understanding of phenotypic heterogeneity in lipid-producing microalgae. Such combinatorial method can facilitate molecular breeding and design of bioprocesses.

Alterations in the proteomic composition of Serratia marcescens in response to manganese (II)

Background

Proteomics is an important tool for the investigation of dynamic physiological responses of microbes under heavy metal stress. To gain insight into how bacteria respond to manganese (II) and identify the proteins involved in Mn (II) oxidation, the shotgun proteomics approach was applied to a potential Mn (II)-oxidizing Serratia marcescens strain cultivated in the absence and presence of Mn (II).

Results

The LG1 strain, which grew equally well in the two conditions, was found to express a set of proteins related to cellular processes vital for survival, as well as proteins involved in adaptation and tolerance to Mn (II). The multicopper oxidase CueO was identified, indicating its probable participation in the Mn (II) bio-oxidation; however, its expression was not modulated by the presence of Mn (II). A set of proteins related to cell and metabolic processes vital to the cells were downregulated in the presence of Mn (II), while cell membrane-related proteins involved in the maintenance of cell integrity and survival under stress were upregulated under this condition.

Conclusions

These findings indicate that the LG1 strain may be applied successfully in the bioremediation of Mn (II), and the shotgun approach provides an efficient means for obtaining the total proteome of this species.

Cytometry meets next-generation sequencing - RNA-Seq of sorted subpopulations reveals regional replication and iron-triggered prophage induction in Corynebacterium glutamicum

Phenotypic diversification is key to microbial adaptation. Currently, advanced technological approaches offer insights into cell-to-cell variation of bacterial populations at a spatiotemporal resolution. However, the underlying molecular causes or consequences often remain obscure. In this study, we developed a workflow combining fluorescence-activated cell sorting and RNA-sequencing, thereby allowing transcriptomic analysis of 106 bacterial cells. As a proof of concept, the workflow was applied to study prophage induction in a subpopulation of Corynebacterium glutamicum. Remarkably, both the phage genes and flanking genomic regions of the CGP3 prophage revealed significantly increased coverage upon prophage induction - a phenomenon that to date has been obscured by bulk approaches. Genome sequencing of prophage-induced populations suggested regional replication at the CGP3 locus in C. glutamicum. Finally, the workflow was applied to unravel iron-triggered prophage induction in early exponential cultures. Here, an up-shift in iron levels resulted in a heterogeneous response of an SOS (PdivS) reporter. RNA-sequencing of the induced subpopulation confirmed induction of the SOS response triggering also activation of the CGP3 prophage. The fraction of CGP3-induced cells was enhanced in a mutant lacking the iron regulator DtxR suffering from enhanced iron uptake. Altogether, these findings demonstrate the potential of the established workflow to gain insights into the phenotypic dynamics of bacterial populations.

Characterizing Microbiome Dynamics - Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical applications driven by microorganisms. Next generation sequencing methods are widely utilized to resolve microbiomes, but they are generally resource and time intensive and deliver mostly qualitative information. Flow cytometric microbiome analysis does not suffer from those disadvantages and can provide relative subcommunity abundances and absolute cell numbers at-line. Although it does not deliver direct phylogenetic information, it can enhance the analysis depth and resolution of sequencing approaches. In sharp contrast to medical applications in both research and routine settings, flow cytometry is still not widely used for microbiome analysis. Missing information on sample preparation and data analysis pipelines may create an entry barrier for the researchers facing microbiome analysis challenges that would often be textbook flow cytometry applications. Here, we present three comprehensive workflows for pure cultures, complex communities in clear medium and complex communities in challenging matrices, respectively. We describe individual sampling and fixation procedures and optimized staining protocols for the respective sample sets. We elaborate the cytometric analysis with a complex research centered and an application focused bench top device, describe the cell sorting procedure and suggest data analysis packages. We furthermore propose important experimental controls and apply the presented workflows to the respective sample sets.

Population heterogeneity in microbial bioprocesses: origin, analysis, mechanisms, and future perspectives

Population heterogeneity is omnipresent in all bioprocesses even in homogenous environments. Its origin, however, is only so well understood that potential strategies like bet-hedging, noise in gene expression and division of labour that lead to population heterogeneity can be derived from experimental studies simulating the dynamics in industrial scale bioprocesses. This review aims at summarizing the current state of the different parts of single cell studies in bioprocesses. This includes setups to visualize different phenotypes of single cells, computational approaches connecting single cell physiology with environmental influence and special cultivation setups like scale-down reactors that have been proven to be useful to simulate large-scale conditions. A step in between investigation of populations and single cells is studying subpopulations with distinct properties that differ from the rest of the population with sub-omics methods which are also presented here. Moreover, the current knowledge about population heterogeneity in bioprocesses is summarized for relevant industrial production hosts and mixed cultures, as they provide the unique opportunity to distribute metabolic burden and optimize production processes in a way that is impossible in traditional monocultures. In the end, approaches to explain the underlying mechanism of population heterogeneity and the evidences found to support each hypothesis are presented. For instance, population heterogeneity serving as a bet-hedging strategy that is used as coordinated action against bioprocess-related stresses while at the same time spreading the risk between individual cells as it ensures the survival of least a part of the population in any environment the cells encounter.

Proteomics of FACS-sorted heterogeneous Corynebacterium glutamicum populations

The metabolic status of individual cells in microbial cultures can differ, being relevant for biotechnology, environmental and medical microbiology. However, it is hardly understood in molecular detail due to limitations of current analytical tools. Here, we demonstrate that FACS in combination with proteomics can be used to sort and analyze cell populations based on their metabolic state. A previously established GFP reporter system was used to detect and sort single Corynebacterium glutamicum cells based on the concentration of branched chain amino acids (BCAA) using FACS. A proteomics workflow optimized for small cell numbers was used to quantitatively compare proteomes of a ΔaceE mutant, lacking functional pyruvate dehydrogenase (PD), and the wild type. About 800 proteins could be quantified from 1,000,000 cells. In the ΔaceE mutant BCAA production was coordinated with upregulation of the glyoxylate cycle and TCA cycle to counter the lack of acetyl CoA resulting from the deletion of aceE.

BIOLOGICAL SIGNIFICANCE

Metabolic pathways in C. glutamicum WT and ΔaceE, devoid of functional pyruvate dehydrogenase, were compared to understand proteome changes that contribute to the high production of branched chain amino acids (BCAA) in the ΔaceE strain. The data complements previous metabolome studies and corroborates the role of malate provided by the glyoxylate cycle and increased activity of glycolysis and pyruvate carboxylase reaction to replenish the TCA cycle. A slight increase in acetohydroxyacid synthase (ILV subunit B) substantiates the previously reported increased pyruvate pool in C. glutamicumΔaceE, and the benefit of additional ilv gene cluster overexpression for BCAA production.

Enhancing metaproteomics--The value of models and defined environmental microbial systems

Metaproteomics--the large-scale characterization of the entire protein complement of environmental microbiota at a given point in time--has provided new features to study complex microbial communities in order to unravel these "black boxes." New technical challenges arose that were not an issue for classical proteome analytics before that could be tackled by the application of different model systems. Here, we review different current and future model systems for metaproteome analysis. Following a short introduction to microbial communities and metaproteomics, we introduce model systems for clinical and biotechnological research questions including acid mine drainage, anaerobic digesters, and activated sludge. Model systems are useful to evaluate the challenges encountered within (but not limited to) metaproteomics, including species complexity and coverage, biomass availability, or reliable protein extraction. The implementation of model systems can be considered as a step forward to better understand microbial community responses and ecological functions of single member organisms. In the future, improvements are necessary to fully explore complex environmental systems by metaproteomics.

Heterogeneity in GFP expression in isogenic populations of P. putida KT2440 investigated using flow cytometry and bacterial microarrays

Fluorescence intensities were determined using both flow cytometry and bacterial microarrays for Pseudomonas putida expressing GFP upon addition of an inducer. Fluorescence micrographs revealed static inter-cell differences in fluorescence emission.

Transcription factor-based biosensors in biotechnology: current state and future prospects

Living organisms have evolved a plethora of sensing systems for the intra- and extracellular detection of small molecules, ions or physical parameters. Several recent studies have demonstrated that these principles can be exploited to devise synthetic regulatory circuits for metabolic engineering strategies. In this context, transcription factors (TFs) controlling microbial physiology at the level of transcription play a major role in biosensor design, since they can be implemented in synthetic circuits controlling gene expression in dependency of, for example, small molecule production. Here, we review recent progress on the utilization of TF-based biosensors in microbial biotechnology highlighting different areas of application. Recent advances in metabolic engineering reveal TF-based sensors to be versatile tools for strain and enzyme development using high-throughput (HT) screening strategies and adaptive laboratory evolution, the optimization of heterologous pathways via the implementation of dynamic control circuits and for the monitoring of single-cell productivity in live cell imaging studies. These examples underline the immense potential of TF-based biosensor circuits but also identify limitations and room for further optimization.

Metaproteomics of complex microbial communities in biogas plants

Production of biogas from agricultural biomass or organic wastes is an important source of renewable energy. Although thousands of biogas plants (BGPs) are operating in Germany, there is still a significant potential to improve yields, e.g. from fibrous substrates. In addition, process stability should be optimized. Besides evaluating technical measures, improving our understanding of microbial communities involved into the biogas process is considered as key issue to achieve both goals. Microscopic and genetic approaches to analyse community composition provide valuable experimental data, but fail to detect presence of enzymes and overall metabolic activity of microbial communities. Therefore, metaproteomics can significantly contribute to elucidate critical steps in the conversion of biomass to methane as it delivers combined functional and phylogenetic data. Although metaproteomics analyses are challenged by sample impurities, sample complexity and redundant protein identification, and are still limited by the availability of genome sequences, recent studies have shown promising results. In the following, the workflow and potential pitfalls for metaproteomics of samples from full-scale BGP are discussed. In addition, the value of metaproteomics to contribute to the further advancement of microbial ecology is evaluated. Finally, synergistic effects expected when metaproteomics is combined with advanced imaging techniques, metagenomics, metatranscriptomics and metabolomics are addressed.

GenoBase: comprehensive resource database of Escherichia coli K-12

Comprehensive experimental resources, such as ORFeome clone libraries and deletion mutant collections, are fundamental tools for elucidation of gene function. Data sets by omics analysis using these resources provide key information for functional analysis, modeling and simulation both in individual and systematic approaches. With the long-term goal of complete understanding of a cell, we have over the past decade created a variety of clone and mutant sets for functional genomics studies of Escherichia coli K-12. We have made these experimental resources freely available to the academic community worldwide. Accordingly, these resources have now been used in numerous investigations of a multitude of cell processes. Quality control is extremely important for evaluating results generated by these resources. Because the annotation has been changed since 2005, which we originally used for the construction, we have updated these genomic resources accordingly. Here, we describe GenoBase (http://ecoli.naist.jp/GB/), which contains key information about comprehensive experimental resources of E. coli K-12, their quality control and several omics data sets generated using these resources.

Applications of flow cytometry to characterize bacterial physiological responses

Although reports of flow cytometry (FCM) applied to bacterial analysis are increasing, studies of FCM related to human cells still vastly outnumber other reports. However, current advances in FCM combined with a new generation of cellular reporter probes have made this technique suitable for analyzing physiological responses in bacteria. We review how FCM has been applied to characterize distinct physiological conditions in bacteria including responses to antibiotics and other cytotoxic chemicals and physical factors, pathogen-host interactions, cell differentiation during biofilm formation, and the mechanisms governing development pathways such as sporulation. Since FCM is suitable for performing studies at the single-cell level, we describe how this powerful technique has yielded invaluable information about the heterogeneous distribution of differently and even specialized responding cells and how it may help to provide insights about how cell interaction takes place in complex structures, such as those that prevail in bacterial biofilms.

Subpopulation-proteomics reveal growth rate, but not cell cycling, as a major impact on protein composition in Pseudomonas putida KT2440

Population heterogeneity occurring in industrial microbial bioprocesses is regarded as a putative effector causing performance loss in large scale. While the existence of subpopulations is a commonly accepted fact, their appearance and impact on process performance still remains rather unclear. During cell cycling, distinct subpopulations differing in cell division state and DNA content appear which contribute individually to the efficiency of the bioprocess. To identify stressed or impaired subpopulations, we analyzed the interplay of growth rate, cell cycle and phenotypic profile of subpopulations by using flow cytometry and cell sorting in conjunction with mass spectrometry based global proteomics. Adjusting distinct growth rates in chemostats with the model strain Pseudomonas putida KT2440, cells were differentiated by DNA content reflecting different cell cycle stages. The proteome of separated subpopulations at given growth rates was found to be highly similar, while different growth rates caused major changes of the protein inventory with respect to e.g. carbon storage, motility, lipid metabolism and the translational machinery. In conclusion, cells in various cell cycle stages at the same growth rate were found to have similar to identical proteome profiles showing no significant population heterogeneity on the proteome level. In contrast, the growth rate clearly determines the protein composition and therefore the metabolic strategy of the cells.

Proteomic analysis of carbon concentrating chemolithotrophic bacteria Serratia sp. for sequestration of carbon dioxide

A chemolithotrophic bacterium enriched in the chemostat in presence of sodium bicarbonate as sole carbon source was identified as Serratia sp. by 16S rRNA sequencing. Carbon dioxide sequestering capacity of bacterium was detected by carbonic anhydrase enzyme and ribulose-1, 5- bisphosphate carboxylase/oxygenase (RuBisCO). The purified carbonic anhydrase showed molecular weight of 29 kDa. Molecular weight of RuBisCO was 550 kDa as determined by fast protein liquid chromatography (FPLC), however, sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) showed presence of two subunits whose molecular weights were 56 and 14 kDa. The Western blot analysis of the crude protein and purified sample cross reacted with RuBisCO large-subunit polypeptides antibodies showed strong band pattern at molecular weight around 56 kDa regions. Whole cell soluble proteins of Serratia sp. grown under autotrophic and heterotrophic conditions were resolved by two-dimensional gel electrophoresis and MALDI-TOF/MS for differential expression of proteins. In proteomic analysis of 63 protein spots, 48 spots were significantly up-regulated in the autotrophically grown cells; seven enzymes showed its utilization in autotrophic carbon fixation pathways and other metabolic activities of bacterium including lipid metabolisms indicated sequestration potency of carbon dioxide and production of biomaterials.

Interactions in the microbiome: communities of organisms and communities of genes

A central challenge in microbial community ecology is the delineation of appropriate units of biodiversity, which can be taxonomic, phylogenetic, or functional in nature. The term 'community' is applied ambiguously; in some cases, the term refers simply to a set of observed entities, while in other cases, it requires that these entities interact with one another. Microorganisms can rapidly gain and lose genes, potentially decoupling community roles from taxonomic and phylogenetic groupings. Trait-based approaches offer a useful alternative, but many traits can be defined based on gene functions, metabolic modules, and genomic properties, and the optimal set of traits to choose is often not obvious. An analysis that considers taxon assignment and traits in concert may be ideal, with the strengths of each approach offsetting the weaknesses of the other. Individual genes also merit consideration as entities in an ecological analysis, with characteristics such as diversity, turnover, and interactions modeled using genes rather than organisms as entities. We identify some promising avenues of research that are likely to yield a deeper understanding of microbial communities that shift from observation-based questions of 'Who is there?' and 'What are they doing?' to the mechanistically driven question of 'How will they respond?'

Recovery of soil unicellular eukaryotes: an efficiency and activity analysis on the single cell level

Eukaryotic unicellular organisms are an important part of the soil microbial community, but they are often neglected in soil functional microbial diversity analysis, principally due to the absence of specific investigation methods in the special soil environment. In this study we used a method based on high-density centrifugation to specifically isolate intact algal and yeast cells, with the aim to analyze them with flow cytometry and sort them for further molecular analysis such as deep sequencing. Recovery efficiency was tested at low abundance levels that fit those in natural environments (10(4) to 10(6) cells per g soil). Five algae and five yeast morphospecies isolated from soil were used for the testing. Recovery efficiency was between 1.5 to 43.16% and 2 to 30.2%, respectively, and was dependent on soil type for three of the algae. Control treatments without soil showed that the majority of cells were lost due to the method itself (58% and 55.8% respectively). However, the cell extraction technique did not much compromise cell vitality because a fluorescein di-acetate assay indicated high viability percentages (73.3% and 97.2% of cells, respectively). The low abundant algae and yeast morphospecies recovered from soil were cytometrically analyzed and sorted. Following, their DNA was isolated and amplified using specific primers. The developed workflow enables isolation and enrichment of intact autotrophic and heterotrophic soil unicellular eukaryotes from natural environments for subsequent application of deep sequencing technologies.

Monitoring of population dynamics of Corynebacterium glutamicum by multiparameter flow cytometry

Phenotypic variation of microbial populations is a well-known phenomenon and may have significant impact on the success of industrial bioprocesses. Flow cytometry (FC) and the large repertoire of fluorescent dyes bring the high-throughput analysis of multiple parameters in single bacterial cells into reach. In this study, we evaluated a set of different fluorescent dyes for suitability in FC single cell analysis of the biotechnological platform organism Corynebacterium glutamicum. Already simple scattering properties of C. glutamicum cells in the flow cytometer were shown to provide valuable information on the growth activity of analysed cells. Furthermore, we used DAPI staining for a FC-based determination of the DNA content of C. glutamicum cells grown on standard minimal or complex media. Characteristic DNA patterns were observed mirroring the typical uncoupled DNA synthesis in the logarithmic (log) growth phase and are in agreement with a symmetric type of cell division of C. glutamicum. Application of the fluorescent dyes Syto 9, propidium iodide, and DiOC₂(3) allowed the identification of subpopulations with reduced viability and membrane potential within early log and stationary phase populations. The presented data highlight the potential of FC-based analyses for online monitoring of C. glutamicum bioprocesses and provide a first reference for future applications and protocols.

Proteomics approaches for the analysis of enriched microbial subpopulations and visualization of complex functional information

Advances in the separation of microbial subpopulations and in proteomics technologies have paved the way for the global molecular characterization of microbial cells that share common functional characteristics. Quantitative characterization of the dynamics of microbial proteomes enables an unprecedented view of the adaptive responses of microbes to environmental stimuli or during interaction with other species or host cells. However, the intrinsic complexity of such data requires sophisticated visualization methods for the display, mining, interpretation and efficient exploitation of these data resources. In this review, we discuss how new approaches in data visualization such as streamgraphs, network graphs or Voronoi treemaps are being used in the field to provide new insights into the functional complexity of microbial cells, populations and multispecies consortia.

Getting the big beast to work--systems biotechnology of Bacillus megaterium for novel high-value proteins

The high industrial relevance of the soil bacterium Bacillus megaterium as host for recombinant proteins is driving systems-wide analyses of its metabolic and regulatory networks. The present review highlights novel systems biology tools available to unravel the various cellular components on the level of metabolic and regulatory networks. These provide a rational platform for systems metabolic engineering of B. megaterium. In line, a number of interesting studies have particularly focused on studying recombinant B. megaterium in its industrial bioprocess environment thus integrating systems metabolic engineering with systems biotechnology and providing the full picture toward optimal processes.

Flow cytometry for the development of biotechnological processes with microalgae

The current interest in microalgae as a sustainable source of next generation biofuels and other valuable substances is driving exploration of their use as unique biotechnological production systems. To design and optimise appropriate production strategies, the behaviour of particular microalgal species should be well characterised under different culture conditions. Thus, flow cytometric (FCM) methods, which are already well established in environmental and toxicological studies of microalgae, are also useful for analysing the physiological state of microalgae, and have the potential to contribute to the rapid development of feasible bioprocesses. These methods are commonly based on the examination of intrinsic features of individual cells within a population (such as autofluorescence or size). Cells possessing the desired physiological or morphological features, which are detectable with or without fluorescent staining, are counted or isolated (sorted) using an FCM device. The options for implementation of FCM in the development of biotechnological processes detailed in this review are (i) analysing the chemical composition of biomass, (ii) monitoring cellular enzyme activity and cell viability, and (iii) sorting cells to isolate those overproducing the target compound or for the preparation of axenic cultures.

Key players and team play: anaerobic microbial communities in hydrocarbon-contaminated aquifers

Biodegradation of anthropogenic pollutants in shallow aquifers is an important microbial ecosystem service which is mainly brought about by indigenous anaerobic microorganisms. For the management of contaminated sites, risk assessment and control of natural attenuation, the assessment of in situ biodegradation and the underlying microbial processes is essential. The development of novel molecular methods, "omics" approaches, and high-throughput techniques has revealed new insight into complex microbial communities and their functions in anoxic environmental systems. This review summarizes recent advances in the application of molecular methods to study anaerobic microbial communities in contaminated terrestrial subsurface ecosystems. We focus on current approaches to analyze composition, dynamics, and functional diversity of subsurface communities, to link identity to activity and metabolic function, and to identify the ecophysiological role of not yet cultured microbes and syntrophic consortia. We discuss recent molecular surveys of contaminated sites from an ecological viewpoint regarding degrader ecotypes, abiotic factors shaping anaerobic communities, and biotic interactions underpinning the importance of microbial cooperation for microbial ecosystem services such as contaminant degradation.

Where has all the message gone?

We provide a brief history of polyribosomes, ergosomes, prosomes, informosomes, maternal mRNA, stored mRNA, and RNP particles. Even though most published research focuses on total mRNA rather than polysomal mRNA and often assumes they are synonymous - i.e., if a functional mRNA is present, it must be translated - results from our laboratories comparing polysomal RNA and total mRNA in a range of "normal" issues show that some transcripts are almost totally absent from polysomes while others are almost entirely associated with polysomes. We describe a recent model from yeast showing various destinies for polysomal mRNA once it has been released from polysomes. The main points we want to emphasize are; a) when mRNA leaves polysomes to go to prosomes, P-bodies, stress granules, etc., it is not necessarily destined for degradation - it can be re-utilized; b) "normal" tissue, not just seeds and stressed tissue, contains functional non-polysomal mRNA; c) association of mRNA with different classes of polysomes affects their sub-cellular location and translatability; and d) drawbacks, misinterpretations, and false hopes arise from analysis of total mRNA rather than polysomal mRNA and from presuming that all polysomes are "created equal".

Single cell analysis applied to antibody fragment production with Bacillus megaterium: development of advanced physiology and bioprocess state estimation tools

BACKGROUND

Single cell analysis for bioprocess monitoring is an important tool to gain deeper insights into particular cell behavior and population dynamics of production processes and can be very useful for discrimination of the real bottleneck between product biosynthesis and secretion, respectively.

RESULTS

Here different dyes for viability estimation considering membrane potential (DiOC2(3), DiBAC4(3), DiOC6(3)) and cell integrity (DiBAC4(3)/PI, Syto9/PI) were successfully evaluated for Bacillus megaterium cell characterization. It was possible to establish an appropriate assay to measure the production intensities of single cells revealing certain product secretion dynamics. Methods were tested regarding their sensitivity by evaluating fluorescence surface density and fluorescent specific concentration in relation to the electronic cell volume. The assays established were applied at different stages of a bioprocess where the antibody fragment D1.3 scFv production and secretion by B. megaterium was studied.

CONCLUSIONS

It was possible to distinguish between live, metabolic active, depolarized, dormant, and dead cells and to discriminate between high and low productive cells. The methods were shown to be suitable tools for process monitoring at single cell level allowing a better process understanding, increasing robustness and forming a firm basis for physiology-based analysis and optimization with the general application for bioprocess development.

Single cell analytics: an overview

The research field of single cell analysis is rapidly expanding, driven by developments in flow cytometry, microscopy, lab-on-a-chip devices, and many other fields. The promises of these developments include deciphering cellular mechanisms and the quantification of cell-to-cell differences, ideally with spatio-temporal resolution. However, these promises are challenging as the analytical techniques have to cope with minute analyte amounts and concentrations. We formulate first these challenges and then present state-of-the-art analytical techniques available to investigate the different cellular hierarchies--from the genome to the phenome, i.e., the sum of all phenotypes.