Subpopulation-proteomics in prokaryotic populations

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.

Determination of elemental distribution and evaluation of elemental concentration in single Saccharomyces cerevisiae cells using single cell-inductively coupled plasma mass spectrometry

Single-cell analysis using inductively coupled plasma mass spectrometry (SC-ICP-MS) is a method to obtain qualitative and quantitative information of the elemental content and distribution of single cells. Six intrinsic target elements were analyzed in yeast cells at different cell growth phases cultured in medium with different phosphorus concentrations (0, 7, 14 mM) to study its effect on cell growth and composition. SC-ICP-MS results were compared with those obtained by the acid digestion and the average ratio was 0.81. The limits of detection of this method were 0.08, 2.54, 12.5, 0.02, 0.02, and 0.08 fg cell-1 for Mg, P, K, Mn, Cu, and Zn, respectively. During the exponential growth phase, the cells exhibited higher elemental contents, wider distribution for most elements, and larger cell size in comparison to the stationary growth phase. Phosphorus-free conditions reduced the average P content in single cells of stationary growth phase from 650 to 80 fg. Phosphorus deficiency led to decreasing intracellular concentrations not only of P but also of K and Cu, and to increasing Zn concentration after 48 h. Mg maintained its concentration at ∼0.11 fg µm-3 and did not change significantly under the three investigated conditions after 48 h. Accordingly, Mg content was successfully used to estimate the intracellular concentration of other intrinsic elements in single yeast cells. SC-ICP-MS is suited to determine target elements in single yeast cells, and allows the study of heterogeneity of cell composition and effects of stressors on the elemental content, distribution, and concentrations of intrinsic elements.

Computational Analysis of Microbial Flow Cytometry Data

Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. We place this overview in the context of a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research.

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.

Bacterial mock communities as standards for reproducible cytometric microbiome analysis

Flow cytometry has recently established itself as a tool to track short-term dynamics in microbial community assembly and link those dynamics with ecological parameters. However, instrumental configurations of commercial cytometers and variability introduced through differential handling of the cells and instruments frequently cause data set variability at the single-cell level. This is especially pronounced with microorganisms, which are in the lower range of optical resolution. Although alignment beads are valuable to generally minimize instrumental noise and align overall machine settings, an artificial microbial cytometric mock community (mCMC) is mandatory for validating lab workflows and enabling comparison of data between experiments, thus representing a necessary reference standard for the reproducible cytometric characterization of microbial communities, especially in long-term studies. In this study, the mock community consisted of two Gram-positive and two Gram-negative bacterial strains, which can be assembled with respective subsets of cells, including spores, in any selected ratio or concentration. The preparation of the four strains takes a maximum of 5 d, and the stains are storable with either PFA/ethanol fixation at -20 °C or drying at 4 °C for at least 6 months. Starting from this stock, an mCMC can be assembled within 1 h. Fluorescence staining methods are presented and representatively applied with two high-resolution cell sorters and three benchtop flow cytometers. Benchmarked data sets allow the use of bioinformatic evaluation procedures to decode community behavior or convey qualified cell sorting decisions for subsequent high-resolution sequencing or proteomic routines.

Discriminating Bacterial Phenotypes at the Population and Single-Cell Level: A Comparison of Flow Cytometry and Raman Spectroscopy Fingerprinting

Investigating phenotypic heterogeneity can help to better understand and manage microbial communities. However, characterizing phenotypic heterogeneity remains a challenge, as there is no standardized analysis framework. Several optical tools are available, such as flow cytometry and Raman spectroscopy, which describe optical properties of the individual cell. In this work, we compare Raman spectroscopy and flow cytometry to study phenotypic heterogeneity in bacterial populations. The growth stages of three replicate Escherichia coli populations were characterized using both technologies. Our findings show that flow cytometry detects and quantifies shifts in phenotypic heterogeneity at the population level due to its high-throughput nature. Raman spectroscopy, on the other hand, offers a much higher resolution at the single-cell level (i.e., more biochemical information is recorded). Therefore, it can identify distinct phenotypic populations when coupled with analyses tailored toward single-cell data. In addition, it provides information about biomolecules that are present, which can be linked to cell functionality. We propose a computational workflow to distinguish between bacterial phenotypic populations using Raman spectroscopy and validated this approach with an external data set. We recommend using flow cytometry to quantify phenotypic heterogeneity at the population level, and Raman spectroscopy to perform a more in-depth analysis of heterogeneity at the single-cell level. © 2019 International Society for Advancement of Cytometry.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

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.

Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling

Cellular heterogeneity influences bioprocess performance in ways that until date are not completely elucidated. In order to account for this phenomenon in the design and operation of bioprocesses, reliable analytical and mathematical descriptions are required. We present an overview of the single cell analysis, and the mathematical modeling frameworks that have potential to be used in bioprocess control and optimization, in particular for microbial processes. In order to be suitable for bioprocess monitoring, experimental methods need to be high throughput and to require relatively short processing time. One such method used successfully under dynamic conditions is flow cytometry. Population balance and individual based models are suitable modeling options, the latter one having in particular a good potential to integrate the various data collected through experimentation. This will be highly beneficial for appropriate process design and scale up as a more rigorous approach may prevent a priori unwanted performance losses. It will also help progressing synthetic biology applications to industrial scale.

Phenotypic Heterogeneity in Mycobacterium tuberculosis

The interaction between the host and the pathogen is extremely complex and is affected by anatomical, physiological, and immunological diversity in the microenvironments, leading to phenotypic diversity of the pathogen. Phenotypic heterogeneity, defined as nongenetic variation observed in individual members of a clonal population, can have beneficial consequences especially in fluctuating stressful environmental conditions. This is all the more relevant in infections caused by Mycobacterium tuberculosis wherein the pathogen is able to survive and often establish a lifelong persistent infection in the host. Recent studies in tuberculosis patients and in animal models have documented the heterogeneous and diverging trajectories of individual lesions within a single host. Since the fate of the individual lesions appears to be determined by the local tissue environment rather than systemic response of the host, studying this heterogeneity is very relevant to ensure better control and complete eradication of the pathogen from individual lesions. The heterogeneous microenvironments greatly enhance M. tuberculosis heterogeneity influencing the growth rates, metabolic potential, stress responses, drug susceptibility, and eventual lesion resolution. Single-cell approaches such as time-lapse microscopy using microfluidic devices allow us to address cell-to-cell variations that are often lost in population-average measurements. In this review, we focus on some of the factors that could be considered as drivers of phenotypic heterogeneity in M. tuberculosis as well as highlight some of the techniques that are useful in addressing this issue.

Tissue Specific Labeling in Proteomics

Mass spectrometry-based proteomics is a powerful tool for identifying and quantifying proteins in biological samples. While it is routinely used for the characterization of simple cell line systems, the analysis of the cell specific proteome in multicellular organisms and tissues poses a significant challenge. Isolating a subset of cells from tissues requires mechanical and biochemical separation or sorting, a process which can alter cellular signaling, and thus, the composition of the proteome. Recently, several approaches for cell selective labeling of proteins, that include bioorthogonal amino acids, biotinylating enzymes, and genetic tools, have been developed. These tools facilitate the selective labeling of proteins, their interactome, or of specific cell types within a tissue or an organism, while avoiding the difficult and contamination-prone biochemical separation of cells from the tissue. In this review, we give an overview of existing techniques and their application in cell culture models and whole animals.

Laser microdissection: A promising tool for exploring microorganisms and their interactions with hosts

Laser microdissection is a method that allows for the isolation of homogenous cell populations from their native niches in tissues for downstream molecular assays. This method is widely used for genomic analysis, gene expression profiling and proteomic and metabolite assays in various fields of biology, but it remains an uncommon approach in microbiological research. In spite of the limited number of publications, laser microdissection was shown to be an extremely useful method for studying host-microorganism interactions in animals and plants, investigating bacteria within biofilms, identifying uncultivated bacteria and performing single prokaryotic cell analysis. The current paper describes the methodological aspects of commercially available laser microdissection instruments and representative examples that demonstrate the advantages of this method for resolving a variety of issues in microbiology.

Copy number variability of expression plasmids determined by cell sorting and Droplet Digital PCR

Background

Plasmids are widely used for molecular cloning or production of proteins in laboratory and industrial settings. Constant modification has brought forth countless plasmid vectors whose characteristics in terms of average plasmid copy number (PCN) and stability are rarely known. The crucial factor determining the PCN is the replication system; most replication systems in use today belong to a small number of different classes and are available through repositories like the Standard European Vector Architecture (SEVA).

Results

In this study, the PCN was determined in a set of seven SEVA-based expression plasmids only differing in the replication system. The average PCN for all constructs was determined by Droplet Digital PCR and ranged between 2 and 40 per chromosome in the host organism Escherichia coli. Furthermore, a plasmid-encoded EGFP reporter protein served as a means to assess variability in reporter gene expression on the single cell level. Only cells with one type of plasmid (RSF1010 replication system) showed a high degree of heterogeneity with a clear bimodal distribution of EGFP intensity while the others showed a normal distribution. The heterogeneous RSF1010-carrying cell population and one normally distributed population (ColE1 replication system) were further analyzed by sorting cells of sub-populations selected according to EGFP intensity. For both plasmids, low and highly fluorescent sub-populations showed a remarkable difference in PCN, ranging from 9.2 to 123.4 for ColE1 and from 0.5 to 11.8 for RSF1010, respectively.

Conclusions

The average PCN determined here for a set of standardized plasmids was generally at the lower end of previously reported ranges and not related to the degree of heterogeneity. Further characterization of a heterogeneous and a homogeneous population demonstrated considerable differences in the PCN of sub-populations. We therefore present direct molecular evidence that the average PCN does not represent the true number of plasmid molecules in individual cells.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-016-0610-8) contains supplementary material, which is available to authorized users.

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.

Variability in subpopulation formation propagates into biocatalytic variability of engineered Pseudomonas putida strains

Pivotal challenges in industrial biotechnology are the identification and overcoming of cell-to-cell heterogeneity in microbial processes. While the development of subpopulations of isogenic cells in bioprocesses is well described (intra-population variability), a possible variability between genetically identical cultures growing under macroscopically identical conditions (clonal variability) is not. A high such clonal variability has been found for the recombinant expression of the styrene monooxygenase genes styAB from Pseudomonas taiwanensis VLB120 in solvent-tolerant Pseudomonas putida DOT-T1E using the alk-regulatory system from P. putida GPo1. In this study, the oxygenase subunit StyA fused to eGFP was used as readout tool to characterize the population structure in P. putida DOT-T1E regarding recombinant protein content. Flow cytometric analyses revealed that in individual cultures, at least two subpopulations with highly differing recombinant StyA-eGFP protein contents appeared (intra-population variability). Interestingly, subpopulation sizes varied from culture-to-culture correlating with the specific styrene epoxidation activity of cells derived from respective cultures (clonal variability). In addition, flow cytometric cell sorting coupled to plasmid copy number (PCN) determination revealed that detected clonal variations cannot be correlated to the PCN, but depend on the combination of the regulatory system and the host strain employed. This is, to the best of our knowledge, the first work reporting that intra-population variability (with differing protein contents in the presented case study) causes clonal variability of genetically identical cultures. Respective impacts on bioprocess reliability and performance and strategies to overcome respective reliability issues are discussed.

Experimental approaches to phenotypic diversity in infection

Microbial infections are burdening human health, even after the advent of antibiotics, vaccines and hygiene. Thus, infection biology has aimed at the molecular understanding of the pathogen-host interaction. This has revealed key virulence factors, host cell signaling pathways and immune responses. However, our understanding of the infection process is still incomplete. Recent evidence suggests that phenotypic diversity can have important consequences for the infection process. Diversity arises from the formation of distinct subpopulations of pathogen cells (with distinct virulence factor expression patterns) and host cells (with distinct response capacities). For technical reasons, such phenotypic diversity has often been overlooked. We are highlighting several striking examples and discuss the experimental approaches available for analyzing the different subpopulations. Single cell reporters and approaches from systems biology do hold much promise.

Making variability less variable: matching expression system and host for oxygenase-based biotransformations

Variability in whole-cell biocatalyst performance represents a critical aspect for stable and productive bioprocessing. In order to investigate whether and how oxygenase-catalyzed reactions are affected by such variability issues in solvent-tolerant Pseudomonas, different inducers, expression systems, and host strains were tested for the reproducibility of xylene and styrene monooxygenase catalyzed hydroxylation and epoxidation reactions, respectively. Significantly higher activity variations were found for biocatalysts based on solvent-tolerant Pseudomonas putida DOT-TIE and S12 compared with solvent-sensitive P. putida KT2440, Escherichia coli JM101, and solvent-tolerant Pseudomonas taiwanensis VLB120. Specific styrene epoxidation rates corresponded to cellular styrene monooxygenase contents. Detected variations in activity strictly depended on the type of regulatory system employed, being high with the alk- and low with the lac-system. These results show that the occurrence of clonal variability in recombinant gene expression in Pseudomonas depends on the combination of regulatory system and host strain, does not correlate with a general phenotype such as solvent tolerance, and must be evaluated case by case.

Bioprocess engineering for microbial synthesis and conversion of isoprenoids

Isoprenoids represent a natural product class essential to living organisms. Moreover, industrially relevant isoprenoid molecules cover a wide range of products such as pharmaceuticals, flavors and fragrances, or even biofuels. Their often complex structure makes chemical synthesis a difficult and expensive task and extraction from natural sources is typically low yielding. This has led to intense research for biotechnological production of isoprenoids by microbial de novo synthesis or biotransformation. Here, metabolic engineering, including synthetic biology approaches, is the key technology to develop efficient production strains in the first place. Bioprocess engineering, particularly in situ product removal (ISPR), is the second essential technology for the development of industrial-scale bioprocesses. A number of elaborate bioreactor and ISPR designs have been published to target the problems of isoprenoid synthesis and conversion, such as toxicity and product inhibition. However, despite the many exciting applications of isoprenoids, research on isoprenoid-specific bioprocesses has mostly been, and still is, limited to small-scale proof-of-concept approaches. This review presents and categorizes different ISPR solutions for biotechnological isoprenoid production and also addresses the main challenges en route towards industrial application.

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.

Cytometric fingerprints: evaluation of new tools for analyzing microbial community dynamics

Optical characteristics of individual bacterial cells of natural communities can be measured with flow cytometry (FCM) in high throughput. The resulting data are visualized in cytometric histograms. These histograms represent individual cytometric fingerprints of microbial communities, e.g., at certain time points or microenvironmental conditions. Up to now four tools for analyzing the variation in these cytometric fingerprints are available but have not yet been systematically compared regarding application: Dalmatian Plot, Cytometric Histogram Image Comparison (CHIC), Cytometric Barcoding (CyBar), and FlowFP. In this article these tools were evaluated concerning (i) the required experience of the operator in handling cytometric data sets, (ii) the detection level of changes, (iii) time demand for analysis, and (iv) software requirements. As an illustrative example, FCM was used to characterize the microbial community structure of electroactive microbial biofilms. Their cytometric fingerprints were determined, analyzed with all four tools, and correlated to experimental and functional parameters. The source of inoculum (four different types of wastewater samples) showed the strongest influence on the microbial community structure and biofilm performance while the choice of substrate (acetate or lactate) had no significant effect in the present study. All four evaluation tools were found suitable to monitor structural changes of natural microbial communities. The Dalmatian Plot was shown to be most sensitive to operator impact but nevertheless provided an overview on community shifts. CHIC, CyBar, and FlowFP showed less operator dependence and gave highly resolved information on community structure variation on different detection levels. In conclusion, experimental and productivity parameters correlated with the biofilm structures and practical process integration details were available from cytometric fingerprint analysis.