Tracking heavy water (D2O) incorporation for identifying and sorting active microbial cells

Studying the Human Microbiota

There are a range of methodologies available to study the human microbiota, ranging from traditional approaches such as culturing through to state-of-the-art developments in next generation DNA sequencing technologies. The advent of molecular techniques in particular has opened up tremendous new avenues for research, and has galvanised interest in the study of our microbial inhabitants. Given the dazzling array of available options, however, it is important to understand the inherent advantages and limitations of each technique so that the best approach can be employed to address the particular research objective. In this chapter we cover some of the most widely used current techniques in human microbiota research and highlight the particular strengths and caveats associated with each approach.

Visualizing in situ translational activity for identifying and sorting slow-growing archaeal-bacterial consortia

To understand the biogeochemical roles of microorganisms in the environment, it is important to determine when and under which conditions they are metabolically active. Bioorthogonal noncanonical amino acid tagging (BONCAT) can reveal active cells by tracking the incorporation of synthetic amino acids into newly synthesized proteins. The phylogenetic identity of translationally active cells can be determined by combining BONCAT with rRNA-targeted fluorescence in situ hybridization (BONCAT-FISH). In theory, BONCAT-labeled cells could be isolated with fluorescence-activated cell sorting (BONCAT-FACS) for subsequent genetic analyses. Here, in the first application, to our knowledge, of BONCAT-FISH and BONCAT-FACS within an environmental context, we probe the translational activity of microbial consortia catalyzing the anaerobic oxidation of methane (AOM), a dominant sink of methane in the ocean. These consortia, which typically are composed of anaerobic methane-oxidizing archaea (ANME) and sulfate-reducing bacteria, have been difficult to study due to their slow in situ growth rates, and fundamental questions remain about their ecology and diversity of interactions occurring between ANME and associated partners. Our activity-correlated analyses of >16,400 microbial aggregates provide the first evidence, to our knowledge, that AOM consortia affiliated with all five major ANME clades are concurrently active under controlled conditions. Surprisingly, sorting of individual BONCAT-labeled consortia followed by whole-genome amplification and 16S rRNA gene sequencing revealed previously unrecognized interactions of ANME with members of the poorly understood phylum Verrucomicrobia This finding, together with our observation that ANME-associated Verrucomicrobia are found in a variety of geographically distinct methane seep environments, suggests a broader range of symbiotic relationships within AOM consortia than previously thought.

Stable Isotope Phenotyping via Cluster Analysis of NanoSIMS Data As a Method for Characterizing Distinct Microbial Ecophysiologies and Sulfur-Cycling in the Environment

Stable isotope probing (SIP) is a valuable tool for gaining insights into ecophysiology and biogeochemical cycling of environmental microbial communities by tracking isotopically labeled compounds into cellular macromolecules as well as into byproducts of respiration. SIP, in conjunction with nanoscale secondary ion mass spectrometry (NanoSIMS), allows for the visualization of isotope incorporation at the single cell level. In this manner, both active cells within a diverse population as well as heterogeneity in metabolism within a homogeneous population can be observed. The ecophysiological implications of these single cell stable isotope measurements are often limited to the taxonomic resolution of paired fluorescence in situ hybridization (FISH) microscopy. Here we introduce a taxonomy-independent method using multi-isotope SIP and NanoSIMS for identifying and grouping phenotypically similar microbial cells by their chemical and isotopic fingerprint. This method was applied to SIP experiments in a sulfur-cycling biofilm collected from sulfidic intertidal vents amended with ¹³C-acetate, ¹⁵N-ammonium, and ³³S-sulfate. Using a cluster analysis technique based on fuzzy c-means to group cells according to their isotope (¹³C/¹²C, ¹⁵N/¹⁴N, and ³³S/³²S) and elemental ratio (C/CN and S/CN) profiles, our analysis partitioned ~2200 cellular regions of interest (ROIs) into five distinct groups. These isotope phenotype groupings are reflective of the variation in labeled substrate uptake by cells in a multispecies metabolic network dominated by Gamma- and Deltaproteobacteria. Populations independently grouped by isotope phenotype were subsequently compared with paired FISH data, demonstrating a single coherent deltaproteobacterial cluster and multiple gammaproteobacterial groups, highlighting the distinct ecophysiologies of spatially-associated microbes within the sulfur-cycling biofilm from White Point Beach, CA.

Single cell stable isotope probing in microbiology using Raman microspectroscopy

Microbial communities are essential for most ecosystem processes and interact in highly complex ways with virtually all eukaryotes. Thus, a detailed understanding of the function of such communities is a fundamental prerequisite for microbial ecologists, applied microbiologists and microbiome researchers. Using single cell Raman microspectroscopy, biochemical fingerprints of individual microbial cells can be obtained in an externally label-free and non-destructive manner. If combined with stable isotope probing (SIP), Raman spectroscopy can directly reveal functions of single microorganisms in their natural habitat. This review provides an update on various SIP-approaches suitable for combination with different Raman scattering techniques and illustrates how single cell Raman SIP can be directly combined with the omics-centric analysis pipelines to investigate microbial communities.

Multi-scale imaging of anticancer platinum(iv) compounds in murine tumor and kidney

Nano-scale secondary ion mass spectrometry (NanoSIMS) enables trace element and isotope analyses with high spatial resolution. This unique capability has recently been exploited in several studies analyzing the subcellular distribution of Au and Pt anticancer compounds. However, these studies were restricted to cell culture systems. To explore the applicability to the in vivo setting, we developed a combined imaging approach consisting of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), NanoSIMS and transmission electron microscopy (TEM) suitable for multi-scale detection of the platinum distribution in tissues. Applying this approach to kidney and tumor samples upon administration of selected platinum(iv) anticancer prodrugs revealed uneven platinum distributions on both the organ and subcellular scales. Spatial platinum accumulation patterns were quantitatively assessed by LA-ICP-MS in histologically heterogeneous organs (e.g., higher platinum accumulation in kidney cortex than in medulla) and used to select regions of interest for subcellular-scale imaging with NanoSIMS. These analyses revealed cytoplasmic sulfur-rich organelles accumulating platinum in both kidney and malignant cells. Those in the tumor were subsequently identified as organelles of lysosomal origin, demonstrating the potential of the combinatorial approach for investigating therapeutically relevant drug concentrations on a submicrometer scale.

Continuous cell sorting in a flow based on single cell resonance Raman spectra

Single cell Raman spectroscopy measures a spectral fingerprint of the biochemistry of cells, and provides a powerful method for label-free detection of living cells without the involvement of a chemical labelling strategy. However, as the intrinsic Raman signals of cells are inherently weak, there is a significant challenge in discriminating and isolating cells in a flowing stream. Here we report an integrated Raman-microfluidic system for continuous sorting of a stream of cyanobacteria, Synechocystis sp. PCC6803. These carotenoid-containing microorganisms provide an elegant model system enabling us to determine the sorting accuracy using the subtly different resonance Raman spectra of microorganism cultured in a (12)C or (13)C carbon source. Central to the implementation of continuous flow sorting is the use of "pressure dividers" that eliminate fluctuations in flow in the detection region. This has enabled us to stabilise the flow profile sufficiently to allow automated operation with synchronisation of Raman acquisition, real-time classification and sorting at flow rates of ca. <100 μm s(-1), without the need to "trap" the cells. We demonstrate the flexibility of this approach in sorting mixed cell populations with the ability to achieve 96.3% purity of the selected cells at a speed of 0.5 Hz.

Raman activated cell sorting

Single cell Raman spectra (SCRS) are intrinsic biochemical profiles and 'chemical images' of single cells which can be used to characterise phenotypic changes, physiological states and functions of cells. On the base of SCRS, Raman activated cell sorting (RACS) provides a label-free cell sorting approach, which can link single cells to their chemical or phenotypic profiles. Overcoming naturally weak Raman signals, establishing Raman biomarker as sorting criteria to RACS and improving specific sorting technology are three challenges of developing RACS. Advances on Raman spectroscopy such as stimulated Raman scattering (SRS) and pre-screening helped to increase RACS sorting speed. Entire SCRS can be characterised using pattern recognition methods, and specific Raman bands can be extracted as biomarkers for RACS. Recent advances on cell sorting technologies based on microfluidic device and surface-ejection enable accurate and reliable single cell sorting from complex samples. A high throughput RACS will be achievable in near future by integrating fast Raman detection system such as SRS with microfluidic RACS and Raman activated cell ejection (RACE).

Characterization of microbial associations with methanotrophic archaea and sulfate-reducing bacteria through statistical comparison of nested Magneto-FISH enrichments

Methane seep systems along continental margins host diverse and dynamic microbial assemblages, sustained in large part through the microbially mediated process of sulfate-coupled Anaerobic Oxidation of Methane (AOM). This methanotrophic metabolism has been linked to consortia of anaerobic methane-oxidizing archaea (ANME) and sulfate-reducing bacteria (SRB). These two groups are the focus of numerous studies; however, less is known about the wide diversity of other seep associated microorganisms. We selected a hierarchical set of FISH probes targeting a range of Deltaproteobacteria diversity. Using the Magneto-FISH enrichment technique, we then magnetically captured CARD-FISH hybridized cells and their physically associated microorganisms from a methane seep sediment incubation. DNA from nested Magneto-FISH experiments was analyzed using Illumina tag 16S rRNA gene sequencing (iTag). Enrichment success and potential bias with iTag was evaluated in the context of full-length 16S rRNA gene clone libraries, CARD-FISH, functional gene clone libraries, and iTag mock communities. We determined commonly used Earth Microbiome Project (EMP) iTAG primers introduced bias in some common methane seep microbial taxa that reduced the ability to directly compare OTU relative abundances within a sample, but comparison of relative abundances between samples (in nearly all cases) and whole community-based analyses were robust. The iTag dataset was subjected to statistical co-occurrence measures of the most abundant OTUs to determine which taxa in this dataset were most correlated across all samples. Many non-canonical microbial partnerships were statistically significant in our co-occurrence network analysis, most of which were not recovered with conventional clone library sequencing, demonstrating the utility of combining Magneto-FISH and iTag sequencing methods for hypothesis generation of associations within complex microbial communities. Network analysis pointed to many co-occurrences containing putatively heterotrophic, candidate phyla such as OD1, Atribacteria, MBG-B, and Hyd24-12 and the potential for complex sulfur cycling involving Epsilon-, Delta-, and Gammaproteobacteria in methane seep ecosystems.

Challenges in microbial ecology: building predictive understanding of community function and dynamics

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model–experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.

Optimization of metabolomics of defined in vitro gut microbial ecosystems

The metabolic functionality of a microbial community is a key to the understanding of its inherent ecological processes and the interaction with the host. However, the study of the human gut microbiota is hindered by the complexity of this ecosystem. One way to resolve this issue is to derive defined communities that may be cultured ex vivo in bioreactor systems and used to approximate the native ecosystem. Doing so has the advantage of experimental reproducibility and ease of sampling, and furthermore, in-depth analysis of metabolic processes becomes highly accessible. Here, we review the use of bioreactor systems for ex vivo modelling of the human gut microbiota with respect to analysis of the metabolic output of the microbial ecosystem, and discuss the possibility of mechanistic insights using these combined techniques. We summarize the different platforms currently used for metabolomics and suitable for analysis of gut microbiota samples from a bioreactor system. With the help of representative datasets obtained from a series of bioreactor runs, we compare the outputs of both NMR and mass spectrometry based approaches in terms of their coverage, sensitivity and quantification. We also discuss the use of untargeted and targeted analyses in mass spectroscopy and how these techniques can be combined for optimal biological interpretation. Potential solutions for linking metabolomic and phylogenetic datasets with regards to active, key species within the ecosystem will be presented.

Stable Isotope Probing Approaches to Study Anaerobic Hydrocarbon Degradation and Degraders

Stable isotope probing (SIP) techniques have become state-of-the-art in microbial ecology over the last 10 years, allowing for the targeted detection and identification of organisms, metabolic pathways and elemental fluxes active in specific processes within complex microbial communities. For studying anaerobic hydrocarbon-degrading microbial communities, four stable isotope techniques have been used so far: DNA/RNA-SIP, PLFA (phospholipid-derived fatty acids)-SIP, protein-SIP, and single-cell-SIP by nanoSIMS (nanoscale secondary ion mass spectrometry) or confocal Raman microscopy. DNA/RNA-SIP techniques are most frequently applied due to their most meaningful phylogenetic resolution. Especially using ¹³C-labeled benzene and toluene as model substrates, many new hydrocarbon degraders have been identified by SIP under various electron acceptor conditions. This has extended the current perspective of the true diversity of anaerobic hydrocarbon degraders relevant in the environment. Syntrophic hydrocarbon degradation was found to be a common mechanism for various electron acceptors. Fundamental concepts and recent advances in SIP are reflected here. A discussion is presented concerning how these techniques generate direct insights into intrinsic hydrocarbon degrader populations in environmental systems and how useful they are for more integrated approaches in the monitoring of contaminated sites and for bioremediation.

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.

Trace incorporation of heavy water reveals slow and heterogeneous pathogen growth rates in cystic fibrosis sputum

Effective treatment for chronic infections is undermined by a significant gap in understanding of the physiological state of pathogens at the site of infection. Chronic pulmonary infections are responsible for the morbidity and mortality of millions of immunocompromised individuals worldwide, yet drugs that are successful in laboratory culture are far less effective against pathogen populations persisting in vivo. Laboratory models, upon which preclinical development of new drugs is based, can only replicate host conditions when we understand the metabolic state of the pathogens and the degree of heterogeneity within the population. In this study, we measured the anabolic activity of the pathogen Staphylococcus aureus directly in the sputum of pediatric patients with cystic fibrosis (CF), by combining the high sensitivity of isotope ratio mass spectrometry with a heavy water labeling approach to capture the full range of in situ growth rates. Our results reveal S. aureus generation times with a median of 2.1 d, with extensive growth rate heterogeneity at the single-cell level. These growth rates are far below the detection limit of previous estimates of CF pathogen growth rates, and the rates are slowest in acutely sick patients undergoing pulmonary exacerbations; nevertheless, they are accessible to experimental replication within laboratory models. Treatment regimens that include specific antibiotics (vancomycin, piperacillin/tazobactam, tobramycin) further appear to correlate with slow growth of S. aureus on average, but follow-up longitudinal studies must be performed to determine whether this effect holds for individual patients.

Important Metabolic Pathways and Biological Processes Expressed by Chicken Cecal Microbiota

The gut microbiota plays important roles in its host. However, how each microbiota member contributes to the behavior of the whole population is not known. In this study, we therefore determined protein expression in the cecal microbiota in chickens of selected ages and in 7-day-old chickens inoculated with different cecal extracts on the day of hatching. Campylobacter, Helicobacter, Mucispirillum, and Megamonas overgrew in the ceca of 7-day-old chickens inoculated with cecal extracts from donor hens. Firmicutes were characterized by ABC and phosphotransferase system (PTS) transporters, extensive acyl coenzyme A (acyl-CoA) metabolism, and expression of l-fucose isomerase. Anaerostipes, Anaerotruncus, Pseudoflavonifractor, Dorea, Blautia, and Subdoligranulum expressed spore proteins. Firmicutes (Faecalibacterium, Butyrivibrio, Megasphaera, Subdoligranulum, Oscillibacter, Anaerostipes, and Anaerotruncus) expressed enzymes required for butyrate production. Megamonas, Phascolarctobacterium, and Blautia (exceptions from the phylum Firmicutes) and all Bacteroidetes expressed enzymes for propionate production pathways. Representatives of Bacteroidetes also expressed xylose isomerase, enzymes required for polysaccharide degradation, and ExbBD, TonB, and outer membrane receptors likely to be involved in oligosaccharide transport. Based on our data, Anaerostipes, Anaerotruncus, and Subdoligranulum might be optimal probiotic strains, since these represent spore-forming butyrate producers. However, certain care should be taken during microbiota transplantation because the microbiota may behave differently in the intestinal tract of a recipient depending on how well the existing communities are established.

Important Metabolic Pathways and Biological Processes Expressed by Chicken Cecal Microbiota

The gut microbiota plays important roles in its host. However, how each microbiota member contributes to the behavior of the whole population is not known. In this study, we therefore determined protein expression in the cecal microbiota in chickens of selected ages and in 7-day-old chickens inoculated with different cecal extracts on the day of hatching. Campylobacter, Helicobacter, Mucispirillum, and Megamonas overgrew in the ceca of 7-day-old chickens inoculated with cecal extracts from donor hens. Firmicutes were characterized by ABC and phosphotransferase system (PTS) transporters, extensive acyl coenzyme A (acyl-CoA) metabolism, and expression of l-fucose isomerase. Anaerostipes, Anaerotruncus, Pseudoflavonifractor, Dorea, Blautia, and Subdoligranulum expressed spore proteins. Firmicutes (Faecalibacterium, Butyrivibrio, Megasphaera, Subdoligranulum, Oscillibacter, Anaerostipes, and Anaerotruncus) expressed enzymes required for butyrate production. Megamonas, Phascolarctobacterium, and Blautia (exceptions from the phylum Firmicutes) and all Bacteroidetes expressed enzymes for propionate production pathways. Representatives of Bacteroidetes also expressed xylose isomerase, enzymes required for polysaccharide degradation, and ExbBD, TonB, and outer membrane receptors likely to be involved in oligosaccharide transport. Based on our data, Anaerostipes, Anaerotruncus, and Subdoligranulum might be optimal probiotic strains, since these represent spore-forming butyrate producers. However, certain care should be taken during microbiota transplantation because the microbiota may behave differently in the intestinal tract of a recipient depending on how well the existing communities are established.

Social dynamics within decomposer communities lead to nitrogen retention and organic matter build-up in soils

The chemical structure of organic matter has been shown to be only marginally important for its decomposability by microorganisms. The question of why organic matter does accumulate in the face of powerful microbial degraders is thus key for understanding terrestrial carbon and nitrogen cycling. Here we demonstrate, based on an individual-based microbial community model, that social dynamics among microbes producing extracellular enzymes (‘decomposers') and microbes exploiting the catalytic activities of others (‘cheaters') regulate organic matter turnover. We show that the presence of cheaters increases nitrogen retention and organic matter build-up by downregulating the ratio of extracellular enzymes to total microbial biomass, allowing nitrogen-rich microbial necromass to accumulate. Moreover, increasing catalytic efficiencies of enzymes are outbalanced by a strong negative feedback on enzyme producers, leading to less enzymes being produced at the community level. Our results thus reveal a possible control mechanism that may buffer soil CO₂ emissions in a future climate.

Microbial decomposers in soil provide the largest ecosystem flux of CO₂ to the atmosphere, but interactions at the microscale are poorly understood. Here, the authors use a computer modelling approach to show that social interactions among microbes buffer changing environmental conditions.

Advancements in the application of NanoSIMS and Raman microspectroscopy to investigate the activity of microbial cells in soils

The combined approach of incubating environmental samples with stable isotope-labeled substrates followed by single-cell analyses through high-resolution secondary ion mass spectrometry (NanoSIMS) or Raman microspectroscopy provides insights into the in situ function of microorganisms. This approach has found limited application in soils presumably due to the dispersal of microbial cells in a large background of particles. We developed a pipeline for the efficient preparation of cell extracts from soils for subsequent single-cell methods by combining cell detachment with separation of cells and soil particles followed by cell concentration. The procedure was evaluated by examining its influence on cell recoveries and microbial community composition across two soils. This approach generated a cell fraction with considerably reduced soil particle load and of sufficient small size to allow single-cell analysis by NanoSIMS, as shown when detecting active N2-fixing and cellulose-responsive microorganisms via (15)N2 and (13)C-UL-cellulose incubations, respectively. The same procedure was also applicable for Raman microspectroscopic analyses of soil microorganisms, assessed via microcosm incubations with a (13)C-labeled carbon source and deuterium oxide (D2O, a general activity marker). The described sample preparation procedure enables single-cell analysis of soil microorganisms using NanoSIMS and Raman microspectroscopy, but should also facilitate single-cell sorting and sequencing.

Microbial biofilms and the human intestinal microbiome

Since early life we are colonised by a myriad of microbes that make up our microbiome. This colonisation process starts at birth or even before, when the virtually sterile baby encounters new microbial environments. It is likely that at this time or at later moments in life, microbial communities are met that have high-level structures with a temporal and spatial organisation, termed biofilms. This perspective will focus on these biofilms and the microbes in the intestinal tract as these are the most numerous in the human body, are found in luminal and mucosal locations, and have a great impact on human health and disease.

Raman-activated cell sorting based on dielectrophoretic single-cell trap and release

Raman-activated cell sorting (RACS) is a promising single-cell technology that holds several significant advantages, as RACS is label-free, information-rich, and potentially in situ. To date, the ability of the technique to identify single cells in a high-speed flow has been limited by inherent weakness of the spontaneous Raman signal. Here we present an alternative pause-and-sort RACS microfluidic system that combines positive dielectrophoresis (pDEP) for single-cell trap and release with a solenoid-valve-suction-based switch for cell separation. This has allowed the integration of trapping, Raman identification, and automatic separation of individual cells in a high-speed flow. By exerting a periodical pDEP field, single cells were trapped, ordered, and positioned individually to the detection point for Raman measurement. As a proof-of-concept demonstration, a mixture of two cell strains containing carotenoid-producing yeast (9%) and non-carotenoid-producing Saccharomyces cerevisiae (91%) was sorted, which enriched the former to 73% on average and showed a fast Raman-activated cell sorting at the subsecond level.

Towards high-throughput microfluidic Raman-activated cell sorting

Raman-activated cell sorting (RACS) is a promising single-cell analysis technology that is able to identify and isolate individual cells of targeted type, state or environment from an isogenic population or complex consortium of cells, in a label-free and non-invasive manner.