Scanning Ion Conductance Microscopy Reveals Differences in the Ionic Environments of Gram-Positive and Negative Bacteria.

This paper reports on the use of scanning ion conductance microscopy (SICM) to locally map the ionic properties and charge environment of two live bacterial strains: the Gram-negative Escherichia coli and the Gram-positive Bacillus subtilis. SICM results find heterogeneities across the bacterial surface and significant differences among the Gram-positive and Gram-negative bacteria. The bioelectrical environment of the B. subtilis was found to be considerably more negatively charged compared to E. coli. SICM measurements, fitted to a simplified finite element method (FEM) model, revealed surface charge values of -80 to -140 mC m-2 for the Gram-negative E. coli. The Gram-positive B. subtilis show a much higher conductivity around the cell wall, and surface charge values between -350 and -450 mC m-2 were found using the same simplified model. SICM was also able to detect regions of high negative charge near B. subtilis, not detected in the topographical SICM response and attributed to the extracellular polymeric substance. To further explore how the B. subtilis cell wall structure can influence the SICM current response, a more comprehensive FEM model, accounting for the physical properties of the Gram-positive cell wall, was developed. The new model provides a more realistic description of the cell wall and allows investigation of the relation between its key properties and SICM currents, building foundations to further investigate and improve understanding of the Gram-positive cellular microenvironment.

MetQy-an R package to query metabolic functions of genes and genomes.

Summary: With the rapid accumulation of sequencing data from genomic and metagenomic studies, there is an acute need for better tools that facilitate their analyses against biological functions. To this end, we developed MetQy, an open-source R package designed for query-based analysis of functional units in [meta]genomes and/or sets of genes using the The Kyoto Encyclopedia of Genes and Genomes (KEGG). Furthermore, MetQy contains visualization and analysis tools and facilitates KEGG's flat file manipulation. Thus, MetQy enables better understanding of metabolic capabilities of known genomes or user-specified [meta]genomes by using the available information and can help guide studies in microbial ecology, metabolic engineering and synthetic biology. Availability and implementation: The MetQy R package is freely available and can be downloaded from our group's website (http://osslab.lifesci.warwick.ac.uk) or GitHub (https://github.com/OSS-Lab/MetQy).

Manganese Oxide Biomineralization Provides Protection against Nitrite Toxicity in a Cell-Density-Dependent Manner.

Manganese biomineralization is a widespread process among bacteria and fungi. To date, there is no conclusive experimental evidence for how and if this process impacts microbial fitness in the environment. Here, we show how a model organism for manganese oxidation is growth inhibited by nitrite, and that this inhibition is mitigated in the presence of manganese. We show that such manganese-mediated mitigation of nitrite inhibition is dependent on the culture inoculum size, and that manganese oxide (MnOX) forms granular precipitates in the culture, rather than sheaths around individual cells. We provide evidence that MnOX protection involves both its ability to catalyze nitrite oxidation into (nontoxic) nitrate under physiological conditions and its potential role in influencing processes involving reactive oxygen species (ROS). Taken together, these results demonstrate improved microbial fitness through MnOX deposition in an ecological setting, i.e., mitigation of nitrite toxicity, and point to a key role of MnOX in handling stresses arising from ROS.IMPORTANCE We present here a direct fitness benefit (i.e., growth advantage) for manganese oxide biomineralization activity in Roseobacter sp. strain AzwK-3b, a model organism used to study this process. We find that strain AzwK-3b in a laboratory culture experiment is growth inhibited by nitrite in manganese-free cultures, while the inhibition is considerably relieved by manganese supplementation and manganese oxide (MnOX) formation. We show that biogenic MnOX interacts directly with nitrite and possibly with reactive oxygen species and find that its beneficial effects are established through formation of dispersed MnOX granules in a manner dependent on the population size. These experiments raise the possibility that manganese biomineralization could confer protection against nitrite toxicity to a population of cells. They open up new avenues of interrogating this process in other species and provide possible routes to their biotechnological applications, including in metal recovery, biomaterials production, and synthetic community engineering.

Biodiversity-function relationships in methanogenic communities.

Methanogenic communities play a crucial role in carbon cycling and biotechnology (anaerobic digestion), but our understanding of how their diversity, or composition in general, determines the rate of methane production is very limited. Studies to date have been correlational because of the difficulty in cultivating their constituent species in pure culture. Here, we investigate the causal link between methanogenesis and diversity in laboratory anaerobic digesters by experimentally manipulating the diversity of cultures by dilution and subsequent equilibration of biomass. This process necessarily leads to the loss of the rarer species from communities. We find a positive relationship between methane production and the number of taxa, with little evidence of functional saturation, suggesting that rare species play an important role in methane-producing communities. No correlations were found between the initial composition and methane production across natural communities, but a positive relationship between species richness and methane production emerged following ecological selection imposed by the laboratory conditions. Our data suggest methanogenic communities show little functional redundancy, and hence, any loss of diversity-both natural and resulting from changes in propagation conditions during anaerobic digestion-is likely to reduce methane production.

Cooperation in microbial communities and their biotechnological applications.

Microbial communities are increasingly utilized in biotechnology. Efficiency and productivity in many of these applications depends on the presence of cooperative interactions between members of the community. Two key processes underlying these interactions are the production of public goods and metabolic cross-feeding, which can be understood in the general framework of ecological and evolutionary (eco-evo) dynamics. In this review, we illustrate the relevance of cooperative interactions in microbial biotechnological processes, discuss their mechanistic origins and analyse their evolutionary resilience. Cooperative behaviours can be damaged by the emergence of 'cheating' cells that benefit from the cooperative interactions but do not contribute to them. Despite this, cooperative interactions can be stabilized by spatial segregation, by the presence of feedbacks between the evolutionary dynamics and the ecology of the community, by the role of regulatory systems coupled to the environmental conditions and by the action of horizontal gene transfer. Cooperative interactions enrich microbial communities with a higher degree of robustness against environmental stress and can facilitate the evolution of more complex traits. Therefore, the evolutionary resilience of microbial communities and their ability to constraint detrimental mutants should be considered to design robust biotechnological applications.

An integrated computational-experimental approach reveals Yersinia pestis genes essential across a narrow or a broad range of environmental conditions.

BACKGROUND: The World Health Organization has categorized plague as a re-emerging disease and the potential for Yersinia pestis to also be used as a bioweapon makes the identification of new drug targets against this pathogen a priority. Environmental temperature is a key signal which regulates virulence of the bacterium. The bacterium normally grows outside the human host at 28 °C. Therefore, understanding the mechanisms that the bacterium used to adapt to a mammalian host at 37 °C is central to the development of vaccines or drugs for the prevention or treatment of human disease. RESULTS: Using a library of over 1 million Y. pestis CO92 random mutants and transposon-directed insertion site sequencing, we identified 530 essential genes when the bacteria were cultured at 28 °C. When the library of mutants was subsequently cultured at 37 °C we identified 19 genes that were essential at 37 °C but not at 28 °C, including genes which encode proteins that play a role in enabling functioning of the type III secretion and in DNA replication and maintenance. Using genome-scale metabolic network reconstruction we showed that growth conditions profoundly influence the physiology of the bacterium, and by combining computational and experimental approaches we were able to identify 54 genes that are essential under a broad range of conditions. CONCLUSIONS: Using an integrated computational-experimental approach we identify genes which are required for growth at 37 °C and under a broad range of environments may be the best targets for the development of new interventions to prevent or treat plague in humans.

Enzyme Sequestration as a Tuning Point in Controlling Response Dynamics of Signalling Networks.

Signalling networks result from combinatorial interactions among many enzymes and scaffolding proteins. These complex systems generate response dynamics that are often essential for correct decision-making in cells. Uncovering biochemical design principles that underpin such response dynamics is a prerequisite to understand evolved signalling networks and to design synthetic ones. Here, we use in silico evolution to explore the possible biochemical design space for signalling networks displaying ultrasensitive and adaptive response dynamics. By running evolutionary simulations mimicking different biochemical scenarios, we find that enzyme sequestration emerges as a key mechanism for enabling such dynamics. Inspired by these findings, and to test the role of sequestration, we design a generic, minimalist model of a signalling cycle, featuring two enzymes and a single scaffolding protein. We show that this simple system is capable of displaying both ultrasensitive and adaptive response dynamics. Furthermore, we find that tuning the concentration or kinetics of the sequestering protein can shift system dynamics between these two response types. These empirical results suggest that enzyme sequestration through scaffolding proteins is exploited by evolution to generate diverse response dynamics in signalling networks and could provide an engineering point in synthetic biology applications.

BioJazz: in silico evolution of cellular networks with unbounded complexity using rule-based modeling.

Systems biologists aim to decipher the structure and dynamics of signaling and regulatory networks underpinning cellular responses; synthetic biologists can use this insight to alter existing networks or engineer de novo ones. Both tasks will benefit from an understanding of which structural and dynamic features of networks can emerge from evolutionary processes, through which intermediary steps these arise, and whether they embody general design principles. As natural evolution at the level of network dynamics is difficult to study, in silico evolution of network models can provide important insights. However, current tools used for in silico evolution of network dynamics are limited to ad hoc computer simulations and models. Here we introduce BioJazz, an extendable, user-friendly tool for simulating the evolution of dynamic biochemical networks. Unlike previous tools for in silico evolution, BioJazz allows for the evolution of cellular networks with unbounded complexity by combining rule-based modeling with an encoding of networks that is akin to a genome. We show that BioJazz can be used to implement biologically realistic selective pressures and allows exploration of the space of network architectures and dynamics that implement prescribed physiological functions. BioJazz is provided as an open-source tool to facilitate its further development and use. Source code and user manuals are available at: http://oss-lab.github.io/biojazz and http://osslab.lifesci.warwick.ac.uk/BioJazz.aspx.

Metabolic modelling in a dynamic evolutionary framework predicts adaptive diversification of bacteria in a long-term evolution experiment.

BACKGROUND: Predicting adaptive trajectories is a major goal of evolutionary biology and useful for practical applications. Systems biology has enabled the development of genome-scale metabolic models. However, analysing these models via flux balance analysis (FBA) cannot predict many evolutionary outcomes including adaptive diversification, whereby an ancestral lineage diverges to fill multiple niches. Here we combine in silico evolution with FBA and apply this modelling framework, evoFBA, to a long-term evolution experiment with Escherichia coli. RESULTS: Simulations predicted the adaptive diversification that occurred in one experimental population and generated hypotheses about the mechanisms that promoted coexistence of the diverged lineages. We experimentally tested and, on balance, verified these mechanisms, showing that diversification involved niche construction and character displacement through differential nutrient uptake and altered metabolic regulation. CONCLUSION: The evoFBA framework represents a promising new way to model biochemical evolution, one that can generate testable predictions about evolutionary and ecosystem-level outcomes.

Phosphate sink containing two-component signaling systems as tunable threshold devices.

Synthetic biology aims to design de novo biological systems and reengineer existing ones. These efforts have mostly focused on transcriptional circuits, with reengineering of signaling circuits hampered by limited understanding of their systems dynamics and experimental challenges. Bacterial two-component signaling systems offer a rich diversity of sensory systems that are built around a core phosphotransfer reaction between histidine kinases and their output response regulator proteins, and thus are a good target for reengineering through synthetic biology. Here, we explore the signal-response relationship arising from a specific motif found in two-component signaling. In this motif, a single histidine kinase (HK) phosphotransfers reversibly to two separate output response regulator (RR) proteins. We show that, under the experimentally observed parameters from bacteria and yeast, this motif not only allows rapid signal termination, whereby one of the RRs acts as a phosphate sink towards the other RR (i.e. the output RR), but also implements a sigmoidal signal-response relationship. We identify two mathematical conditions on system parameters that are necessary for sigmoidal signal-response relationships and define key parameters that control threshold levels and sensitivity of the signal-response curve. We confirm these findings experimentally, by in vitro reconstitution of the one HK-two RR motif found in the Sinorhizobium meliloti chemotaxis pathway and measuring the resulting signal-response curve. We find that the level of sigmoidality in this system can be experimentally controlled by the presence of the sink RR, and also through an auxiliary protein that is shown to bind to the HK (yielding Hill coefficients of above 7). These findings show that the one HK-two RR motif allows bacteria and yeast to implement tunable switch-like signal processing and provides an ideal basis for developing threshold devices for synthetic biology applications.

Evolutionary systems biology: what it is and why it matters.

Evolutionary systems biology (ESB) is a rapidly growing integrative approach that has the core aim of generating mechanistic and evolutionary understanding of genotype-phenotype relationships at multiple levels. ESB's more specific objectives include extending knowledge gained from model organisms to non-model organisms, predicting the effects of mutations, and defining the core network structures and dynamics that have evolved to cause particular intracellular and intercellular responses. By combining mathematical, molecular, and cellular approaches to evolution, ESB adds new insights and methods to the modern evolutionary synthesis, and offers ways in which to enhance its explanatory and predictive capacities. This combination of prediction and explanation marks ESB out as a research manifesto that goes further than its two contributing fields. Here, we summarize ESB via an analysis of characteristic research examples and exploratory questions, while also making a case for why these integrative efforts are worth pursuing.

Metabolic tinker: an online tool for guiding the design of synthetic metabolic pathways.

One of the primary aims of synthetic biology is to (re)design metabolic pathways towards the production of desired chemicals. The fast pace of developments in molecular biology increasingly makes it possible to experimentally redesign existing pathways and implement de novo ones in microbes or using in vitro platforms. For such experimental studies, the bottleneck is shifting from implementation of pathways towards their initial design. Here, we present an online tool called 'Metabolic Tinker', which aims to guide the design of synthetic metabolic pathways between any two desired compounds. Given two user-defined 'target' and 'source' compounds, Metabolic Tinker searches for thermodynamically feasible paths in the entire known metabolic universe using a tailored heuristic search strategy. Compared with similar graph-based search tools, Metabolic Tinker returns a larger number of possible paths owing to its broad search base and fast heuristic, and provides for the first time thermodynamic feasibility information for the discovered paths. Metabolic Tinker is available as a web service at http://osslab.ex.ac.uk/tinker.aspx. The same website also provides the source code for Metabolic Tinker, allowing it to be developed further or run on personal machines for specific applications.

Bacterial drug tolerance under clinical conditions is governed by anaerobic adaptation but not anaerobic respiration.

Noninherited antibiotic resistance is a phenomenon whereby a subpopulation of genetically identical bacteria displays phenotypic tolerance to antibiotics. We show here that compared to Escherichia coli, the clinically relevant genus Burkholderia displays much higher levels of cells that tolerate ceftazidime. By measuring the dynamics of the formation of drug-tolerant cells under conditions that mimic in vivo infections, we show that in Burkholderia bacteria, oxygen levels affect the formation of these cells. The drug-tolerant cells are characterized by an anaerobic metabolic signature and can be eliminated by oxygenating the system or adding nitrate. The transcriptome profile suggests that these cells are not dormant persister cells and are likely to be drug tolerant as a consequence of the upregulation of anaerobic nitrate respiration, efflux pumps, ß-lactamases, and stress response proteins. These findings have important implications for the treatment of chronic bacterial infections and the methodologies and conditions that are used to study drug-tolerant and persister cells in vitro.

Split histidine kinases enable ultrasensitivity and bistability in two-component signaling networks.

Bacteria sense and respond to their environment through signaling cascades generally referred to as two-component signaling networks. These networks comprise histidine kinases and their cognate response regulators. Histidine kinases have a number of biochemical activities: ATP binding, autophosphorylation, the ability to act as a phosphodonor for their response regulators, and in many cases the ability to catalyze the hydrolytic dephosphorylation of their response regulator. Here, we explore the functional role of "split kinases" where the ATP binding and phosphotransfer activities of a conventional histidine kinase are split onto two distinct proteins that form a complex. We find that this unusual configuration can enable ultrasensitivity and bistability in the signal-response relationship of the resulting system. These dynamics are displayed under a wide parameter range but only when specific biochemical requirements are met. We experimentally show that one of these requirements, namely segregation of the phosphatase activity predominantly onto the free form of one of the proteins making up the split kinase, is met in Rhodobacter sphaeroides. These findings indicate split kinases as a bacterial alternative for enabling ultrasensitivity and bistability in signaling networks. Genomic analyses reveal that up 1.7% of all identified histidine kinases have the potential to be split and bifunctional.

Phosphorelays provide tunable signal processing capabilities for the cell.

Achieving a complete understanding of cellular signal transduction requires deciphering the relation between structural and biochemical features of a signaling system and the shape of the signal-response relationship it embeds. Using explicit analytical expressions and numerical simulations, we present here this relation for four-layered phosphorelays, which are signaling systems that are ubiquitous in prokaryotes and also found in lower eukaryotes and plants. We derive an analytical expression that relates the shape of the signal-response relationship in a relay to the kinetic rates of forward, reverse phosphorylation and hydrolysis reactions. This reveals a set of mathematical conditions which, when satisfied, dictate the shape of the signal-response relationship. We find that a specific topology also observed in nature can satisfy these conditions in such a way to allow plasticity among hyperbolic and sigmoidal signal-response relationships. Particularly, the shape of the signal-response relationship of this relay topology can be tuned by altering kinetic rates and total protein levels at different parts of the relay. These findings provide an important step towards predicting response dynamics of phosphorelays, and the nature of subsequent physiological responses that they mediate, solely from topological features and few composite measurements; measuring the ratio of reverse and forward phosphorylation rate constants could be sufficient to determine the shape of the signal-response relationship the relay exhibits. Furthermore, they highlight the potential ways in which selective pressures on signal processing could have played a role in the evolution of the observed structural and biochemical characteristic in phosphorelays.

Bistability in feedback circuits as a byproduct of evolution of evolvability.

Noisy bistable dynamics in gene regulation can underlie stochastic switching and is demonstrated to be beneficial under fluctuating environments. It is not known, however, if fluctuating selection alone can result in bistable dynamics. Using a stochastic model of simple feedback networks, we apply fluctuating selection on gene expression and run in silico evolutionary simulations. We find that independent of the specific nature of the environment-fitness relationship, the main outcome of fluctuating selection is the evolution of increased evolvability in the network; system parameters evolve toward a nonlinear regime where phenotypic diversity is increased and small changes in genotype cause large changes in expression level. In the presence of noise, the evolution of increased nonlinearity results in the emergence and maintenance of bistability. Our results provide the first direct evidence that bistability and stochastic switching in a gene regulatory network can emerge as a mechanism to cope with fluctuating environments. They strongly suggest that such emergence occurs as a byproduct of evolution of evolvability and exploitation of noise by evolution.

Formation and emergent dynamics of spatially organized microbial systems.

Spatial organization is the norm rather than the exception in the microbial world. While the study of microbial physiology has been dominated by studies in well-mixed cultures, there is now increasing interest in understanding the role of spatial organization in microbial physiology, coexistence and evolution. Where studied, spatial organization has been shown to influence all three of these aspects. In this mini review and perspective article, we emphasize that the dynamics within spatially organized microbial systems (SOMS) are governed by feedbacks between local physico-chemical conditions, cell physiology and movement, and evolution. These feedbacks can give rise to emergent dynamics, which need to be studied through a combination of spatio-temporal measurements and mathematical models. We highlight the initial formation of SOMS and their emergent dynamics as two open areas of investigation for future studies. These studies will benefit from the development of model systems that can mimic natural ones in terms of species composition and spatial structure.

Dynamics of co-substrate pools can constrain and regulate metabolic fluxes.

Cycling of co-substrates, whereby a metabolite is converted among alternate forms via different reactions, is ubiquitous in metabolism. Several cycled co-substrates are well known as energy and electron carriers (e.g. ATP and NAD(P)H), but there are also other metabolites that act as cycled co-substrates in different parts of central metabolism. Here, we develop a mathematical framework to analyse the effect of co-substrate cycling on metabolic flux. In the cases of a single reaction and linear pathways, we find that co-substrate cycling imposes an additional flux limit on a reaction, distinct to the limit imposed by the kinetics of the primary enzyme catalysing that reaction. Using analytical methods, we show that this additional limit is a function of the total pool size and turnover rate of the cycled co-substrate. Expanding from this insight and using simulations, we show that regulation of these two parameters can allow regulation of flux dynamics in branched and coupled pathways. To support these theoretical insights, we analysed existing flux measurements and enzyme levels from the central carbon metabolism and identified several reactions that could be limited by the dynamics of co-substrate cycling. We discuss how the limitations imposed by co-substrate cycling provide experimentally testable hypotheses on specific metabolic phenotypes. We conclude that measuring and controlling co-substrate dynamics is crucial for understanding and engineering metabolic fluxes in cells.

Metabolism powers individual cells and ultimately the body. It comprises a sequence of chemical reactions that cells use to break down substances and generate energy. These reactions are catalyzed by enzymes, which are proteins that speed up the rate of the reaction. Many reactions also involve co-substrates, which are themselves transformed by individual reactions but are eventually converted back into their original form in a series of steps. This process is known as co-substrate cycling. Scientists have long been interested in understanding what controls the rate at which metabolic reactions and metabolic pathways convert a substance into a final product. This is a difficult subject to study because of the complexity of the metabolic pathways, with their branched, linear or coupled structures. In the past, researchers have looked at the influence of enzymes on the rate of a metabolic pathway, but less has been known about the effect of co-substrate cycling. To find out more, West, Delattre et al. developed a series of mathematical models to describe different types of metabolic pathways in terms of the number of metabolites that enter and leave it, including the influence of co-substrates. They found that co-substrate cycling, when involved in a metabolic reaction, limits the speed with which the reaction happens. This is distinct from the limit that enzymes impose on the speed of the reaction. It depends on the total amount of co-substrates in the cell: changing the number of co-substrates in the cell influences the speed at which the metabolic reaction takes place. This study has increased our understanding of how metabolic pathways work, and what controls the speed at which reactions take place. It opens up a new potential method for explaining how cells control metabolic reaction rates and how metabolic substrates can be directed across different pathways. This research is likely to inspire future research into the influence of co-substrates in different cell types and conditions.

Thioflavin T indicates mitochondrial membrane potential in mammalian cells.

The fluorescent benzothiazole dye thioflavin T (ThT) is widely used as a marker for protein aggregates, most commonly in the context of neurodegenerative disease research and diagnosis. Recently, this same dye was shown to indicate membrane potential in bacteria due to its cationic nature. This finding prompted a question whether ThT fluorescence is linked to the membrane potential in mammalian cells, which would be important for appropriate utilization of ThT in research and diagnosis. Here, we show that ThT localizes into the mitochondria of HeLa cells in a membrane-potential-dependent manner. Specifically, ThT colocalized in cells with the mitochondrial membrane potential indicator tetramethylrhodamine methyl ester (TMRM) and gave similar temporal responses as TMRM to treatment with a protonophore, carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone (FCCP). Additionally, we found that presence of ThT together with exposure to blue light (λ = 405 nm), but neither factor alone, caused depolarization of mitochondrial membrane potential. This additive effect of the concentration and blue light was recapitulated by a mathematical model implementing the potential-dependent distribution of ThT and its effect on mitochondrial membrane potential through photosensitization. These results show that ThT can act as a mitochondrial membrane potential indicator in mammalian cells, when used at low concentrations and with low blue light exposure. However, it causes dissipation of the mitochondrial membrane potential depending additively on its concentrations and blue light exposure. This conclusion motivates a re-evaluation of ThT's use at micromolar range in live-cell analyses and indicates that this dye can enable future studies on the potential connections between mitochondrial membrane potential dynamics and protein aggregation.

Can Single Cell Respiration be Measured by Scanning Electrochemical Microscopy (SECM)?

Ultramicroelectrode (UME), or, equivalently, microelectrode, probes are increasingly used for single-cell measurements of cellular properties and processes, including physiological activity, such as metabolic fluxes and respiration rates. Major challenges for the sensitivity of such measurements include: (i) the relative magnitude of cellular and UME fluxes (manifested in the current); and (ii) issues around the stability of the UME response over time. To explore the extent to which these factors impact the precision of electrochemical cellular measurements, we undertake a systematic analysis of measurement conditions and experimental parameters for determining single cell respiration rates via the oxygen consumption rate (OCR) in single HeLa cells. Using scanning electrochemical microscopy (SECM), with a platinum UME as the probe, we employ a self-referencing measurement protocol, rarely employed in SECM, whereby the UME is repeatedly approached from bulk solution to a cell, and a short pulse to oxygen reduction reaction (ORR) potential is performed near the cell and in bulk solution. This approach enables the periodic tracking of the bulk UME response to which the near-cell response is repeatedly compared (referenced) and also ensures that the ORR near the cell is performed only briefly, minimizing the effect of the electrochemical process on the cell. SECM experiments are combined with a finite element method (FEM) modeling framework to simulate oxygen diffusion and the UME response. Taking a realistic range of single cell OCR to be 1 × 10-18 to 1 × 10-16 mol s-1, results from the combination of FEM simulations and self-referencing SECM measurements show that these OCR values are at, or below, the present detection sensitivity of the technique. We provide a set of model-based suggestions for improving these measurements in the future but highlight that extraordinary improvements in the stability and precision of SECM measurements will be required if single cell OCR measurements are to be realized.