Applications and impacts of stable isotope probing for analysis of microbial interactions

scMAR-Seq: a novel workflow for targeted single-cell genomics of microorganisms using radioactive labeling

IMPORTANCE

A central question in microbial ecology is which member of a community performs a particular metabolism. Several sophisticated isotope labeling techniques are available for analyzing the metabolic function of populations and individual cells in a community. However, these methods are generally either insufficiently sensitive or throughput-limited and thus have limited applicability for the study of complex environmental samples. Here, we present a novel approach that combines highly sensitive radioisotope tracking, microfluidics, high-throughput sorting, and single-cell genomics to simultaneously detect and identify individual microbial cells based solely on their in situ metabolic activity, without prior information on community structure.

Fungal key players of cellulose utilization: Microbial networks in aggregates of long-term fertilized soils disentangled using 13C-DNA-stable isotope probing

Long-term compost application accelerates organic carbon (C) accumulation and macroaggregate formation in soil. Stable aggregates and high soil organic C (SOC) content are supposed to increase microbiota activity and promote transformation of litter compounds (i.e., cellulose) into SOC. Here, we used ¹³C-DNA-stable isotope probing with subsequent high-throughput sequencing to characterize fungal succession and co-occurrence trends during ¹³C-cellulose decomposition in aggregate size classes in soils subjected to no fertilizer (control), nitrogen-phosphorus‑potassium (NPK) fertilizers, and compost (Compost) application for 27 years. Ascomycota (mostly saprotrophic fungi) were always highly competitive for cellulose in all aggregate size classes at the early stages of cellulose decomposition (20 days). Compost-treated soil was enriched with Ascomycota compared to the control soil, wherein Sordariomycetes, the majority, strongly dominated the cellulose utilization (¹³C incorporation in DNA). ¹³C-labeled fungal communities converged in the Compost soil, with lower abundance and diversity compared with the NPK and control soils. Such convergence led to greater cellulose decomposition, indicating that compost amendment increased the capacity of a few dominant fungal taxa to decompose litter. Compost soil had more ¹³C-labeled fungal decomposers in microaggregates and lower fungal decomposers in macroaggregates when compared with the levels in the NPK and control soils. This implies that compost application facilitates fungal colonization towards smaller aggregates. Fungal interactions were reinforced in microaggregates (<250 μm), with more positive associations than those in macroaggregates (>250 μm), indicating greater fungal synergism for recalcitrant resource utilization in microaggregates. The keystone taxa in the co-occurrence networks were not related to cellulose decomposition in microaggregates, but did in macroaggregates. The findings advance a process-based understanding of cellulose utilization by fungal key players based on C and energy availability and the regulation of microbial activity at the aggregate level.

New approaches for the characterization of plastic-associated microbial communities and the discovery of plastic-degrading microorganisms and enzymes

Plastics in the environment represent new substrates for microbial colonization, and recent methodological advances allow for in-depth characterization of plastic-associated microbial communities (PAMCs). Over the past several decades, discovery of plastic degrading enzymes (PDEs) and plastic degrading microorganisms (PDMs) has been driven by efforts to understand microbially-mediated plastic degradation in the environment and to discover biocatalysts for plastic processing. In this review, we discuss the evolution of methodology in plastic microbiology and highlight major advancements in the field stemming from computational microbiology. Initial research relied largely on culture-based approaches like clear-zone assays to screen for PDMs and microscopy to characterize PAMCs. New computational tools and sequencing technologies are accelerating discoveries in the field through culture-independent and multi-omic approaches, rapidly generating targets for protein engineering and improving the potential for plastic-waste management.

Methods for Studying Bacterial–Fungal Interactions in the Microenvironments of Soil

Due to their small size, microorganisms directly experience only a tiny portion of the environmental heterogeneity manifested in the soil. The microscale variations in soil properties constrain the distribution of fungi and bacteria, and the extent to which they can interact with each other, thereby directly influencing their behavior and ecological roles. Thus, to obtain a realistic understanding of bacterial–fungal interactions, the spatiotemporal complexity of their microenvironments must be accounted for. The objective of this review is to further raise awareness of this important aspect and to discuss an overview of possible methodologies, some of easier applicability than others, that can be implemented in the experimental design in this field of research. The experimental design can be rationalized in three different scales, namely reconstructing the physicochemical complexity of the soil matrix, identifying and locating fungi and bacteria to depict their physical interactions, and, lastly, analyzing their molecular environment to describe their activity. In the long term, only relevant experimental data at the cell-to-cell level can provide the base for any solid theory or model that may serve for accurate functional prediction at the ecosystem level. The way to this level of application is still long, but we should all start small.

Unveiling Cancer Metabolism through Spontaneous and Coherent Raman Spectroscopy and Stable Isotope Probing

Metabolic reprogramming is a common hallmark in cancer. The high complexity and heterogeneity in cancer render it challenging for scientists to study cancer metabolism. Despite the recent advances in single-cell metabolomics based on mass spectrometry, the analysis of metabolites is still a destructive process, thus limiting in vivo investigations. Being label-free and nonperturbative, Raman spectroscopy offers intrinsic information for elucidating active biochemical processes at subcellular level. This review summarizes recent applications of Raman-based techniques, including spontaneous Raman spectroscopy and imaging, coherent Raman imaging, and Raman-stable isotope probing, in contribution to the molecular understanding of the complex biological processes in the disease. In addition, this review discusses possible future directions of Raman-based technologies in cancer research.

Metabolism in action: stable isotope probing using vibrational spectroscopy and SIMS reveals kinetic and metabolic flux of key substrates

Microbial communities play essential functions which drive various ecosystems supporting animal and aquatic life. However, linking bacteria with specific metabolic functions is difficult, since microbial communities consist of numerous and phylogenetically diverse microbes. Stable isotope probing (SIP) combined with single-cell tools has emerged as a novel culture-independent strategy for unravelling microbial metabolic roles and intertwined interactions in complex communities. In this study, we applied Raman and Fourier-transform infrared (FT-IR) spectroscopies, secondary ion mass spectrometry (SIMS) with SIP to probe the rate of ¹³C incorporation in Escherichia coli at 37 and 25 °C. Our results indicate quantitative enrichment and flow of ¹³C into E. coli at various time points. Multivariate and univariate analyses of Raman and FT-IR data demonstrated distinctive ¹³C concentration-dependent trends that were due to vibrational bands shifting to lower frequencies and these shifts were a result of incubation time and metabolic rate. SIMS results were in complete agreement with the spectroscopy findings, and confirmed the detected levels of ¹³C incorporation into microbial biomass at the investigated conditions. Having established that FT-IR and Raman spectroscopy with SIP can measure metabolism kinetics in this simple system, we have applied the kinetics concept to study the metabolism of phenol by Pseudomonas putida and metabolic interactions within a two-species consortia with E. coli that could not degrade phenol. Raman spectroscopy combined with SIP identified quantitative shifts in P. putida due to temporal assimilation of phenol. Although E. coli was unable to grow on phenol, in co-culture with P. putida, general metabolic probing using deuterated water for SIP revealed that E. coli displayed increasing metabolic activity, presumably due to cross feeding from metabolites generated by P. putida. This study clearly demonstrates that Raman and FT-IR combined with SIP provide rapid and sensitive detection of carbon incorporation rates and microbial interactions. These novel findings may guide the identification of primary substrate consumers in complex microbial communities in situ, which is a key step towards the characterisation of novel genes, enzymes and metabolic flux analysis in microbial consortia.

Development overview of Raman-activated cell sorting devoted to bacterial detection at single-cell level

Understanding the metabolic interactions between bacteria in natural habitat at the single-cell level and the contribution of individual cell to their functions is essential for exploring the dark matter of uncultured bacteria. The combination of Raman-activated cell sorting (RACS) and single-cell Raman spectra (SCRS) with unique fingerprint characteristics makes it possible for research in the field of microbiology to enter the single cell era. This review presents an overview of current knowledge about the research progress of recognition and assessment of single bacterium cell based on RACS and further research perspectives. We first systematically summarize the label-free and non-destructive RACS strategies based on microfluidics, microdroplets, optical tweezers, and specially made substrates. The importance of RACS platforms in linking target cell genotype and phenotype is highlighted and the approaches mentioned in this paper for distinguishing single-cell phenotype include surface-enhanced Raman scattering (SERS), biomarkers, stable isotope probing (SIP), and machine learning. Finally, the prospects and challenges of RACS in exploring the world of unknown microorganisms are discussed. KEY POINTS: • Analysis of single bacteria is essential for further understanding of the microbiological world. • Raman-activated cell sorting (RACS) systems are significant protocol for characterizing phenotypes and genotypes of individual bacteria.

Predicting Nash equilibria for microbial metabolic interactions

MOTIVATION

Microbial metabolic interactions impact ecosystems, human health and biotechnology profoundly. However, their determination remains elusive, invoking an urgent need for predictive models seamlessly integrating metabolism with evolutionary principles that shape community interactions.

RESULTS

Inspired by the evolutionary game theory, we formulated a bi-level optimization framework termed NECom for which any feasible solutions are Nash equilibria of microbial community metabolic models with/without an outer-level (community) objective function. Distinct from discrete matrix games, NECom models the continuous interdependent strategy space of metabolic fluxes. We showed that NECom successfully predicted several classical games in the context of metabolic interactions that were falsely or incompletely predicted by existing methods, including prisoner's dilemma, snowdrift and cooperation. The improved capability originates from the novel formulation to prevent 'forced altruism' hidden in previous static algorithms while allowing for sensing all potential metabolite exchanges to determine evolutionarily favorable interactions between members, a feature missing in dynamic methods. The results provided insights into why mutualism is favorable despite seemingly costly cross-feeding metabolites and demonstrated similarities and differences between games in the continuous metabolic flux space and matrix games. NECom was then applied to a reported algae-yeast co-culture system that shares typical cross-feeding features of lichen, a model system of mutualism. 488 growth conditions corresponding to 3,221 experimental data points were simulated. Without training any parameters using the data, NECom is more predictive of species' growth rates given uptake rates compared with flux balance analysis with an overall 63.5% and 81.7% reduction in root-mean-square error for the two species.

AVAILABILITY

Simulation code and data are available at https://github.com/Jingyi-Cai/NECom.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Imaging the invisible-Bioorthogonal Raman probes for imaging of cells and tissues

A revolutionary avenue for vibrational imaging with super-multiplexing capability can be seen in the recent development of Raman-active bioortogonal tags or labels. These tags and isotopic labels represent groups of chemically inert and small modifications, which can be introduced to any biomolecule of interest and then supplied to single cells or entire organisms. Recent developments in the field of spontaneous Raman spectroscopy and stimulated Raman spectroscopy in combination with targeted imaging of biomolecules within living systems are the main focus of this review. After having introduced common strategies for bioorthogonal labeling, we present applications thereof for profiling of resistance patterns in bacterial cells, investigations of pharmaceutical drug-cell interactions in eukaryotic cells and cancer diagnosis in whole tissue samples. Ultimately, this approach proves to be a flexible and robust tool for in vivo imaging on several length scales and provides comparable information as fluorescence-based imaging without the need of bulky fluorescent tags.

A Retrospective Review of Microbiological Methods Applied in Studies Following the Deepwater Horizon Oil Spill

The Deepwater Horizon (DWH) oil spill in the Gulf of Mexico in 2010 resulted in serious damage to local marine and coastal environments. In addition to the physical removal and chemical dispersion of spilled oil, biodegradation by indigenous microorganisms was regarded as the most effective way for cleaning up residual oil. Different microbiological methods were applied to investigate the changes and responses of bacterial communities after the DWH oil spills. By summarizing and analyzing these microbiological methods, giving recommendations and proposing some methods that have not been used, this review aims to provide constructive guidelines for microbiological studies after environmental disasters, especially those involving organic pollutants.

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.

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.

Detection of sialic acid-utilising bacteria in a caecal community batch culture using RNA-based stable isotope probing

Sialic acids are monosaccharides typically found on cell surfaces and attached to soluble proteins, or as essential components of ganglioside structures that play a critical role in brain development and neural transmission. Human milk also contains sialic acid conjugated to oligosaccharides, glycolipids, and glycoproteins. These nutrients can reach the large bowel where they may be metabolised by the microbiota. However, little is known about the members of the microbiota involved in this function. To identify intestinal bacteria that utilise sialic acid within a complex intestinal community, we cultured the caecal microbiota from piglets in the presence of ¹³C-labelled sialic acid. Using RNA-based stable isotope probing, we identified bacteria that consumed ¹³C-sialic acid by fractionating total RNA in isopycnic buoyant density gradients followed by 16S rRNA gene analysis. Addition of sialic acid caused significant microbial community changes. A relative rise in Prevotella and Lactobacillus species was accompanied by a corresponding reduction in the genera Escherichia/Shigella, Ruminococcus and Eubacterium. Inspection of isotopically labelled RNA sequences suggests that the labelled sialic acid was consumed by a wide range of bacteria. However, species affiliated with the genus Prevotella were clearly identified as the most prolific users, as solely their RNA showed significantly higher relative shares among the most labelled RNA species. Given the relevance of sialic acid in nutrition, this study contributes to a better understanding of their microbial transformation in the intestinal tract with potential implications for human health.