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.

Ammonia leakage can underpin nitrogen-sharing among soil microorganisms.

Soil microbial communities host a large number of microbial species that support important ecological functions such as biogeochemical cycling and plant nutrition. The extent and stability of these functions are affected by inter-species interactions among soil microorganisms, yet the different mechanisms underpinning microbial interactions in the soil are not fully understood. Here, we study the extent of nutrient-based interactions among two model, plant-supporting soil microorganisms, the fungi Serendipita indica, and the bacteria Bacillus subtilis. We found that S. indica is unable to grow with nitrate - a common nitrogen source in the soil - but this inability could be rescued, and growth restored in the presence of B. subtilis. We demonstrate that this effect is due to B. subtilis utilising nitrate and releasing ammonia, which can be used by S. indica. We refer to this type of mechanism as ammonia mediated nitrogen sharing (N-sharing). Using a mathematical model, we demonstrated that the pH dependent equilibrium between ammonia (NH3) and ammonium (NH+4) results in an inherent cellular leakiness, and that reduced amonnium uptake or assimilation rates could result in higher levels of leaked ammonia. In line with this model, a mutant B. subtilis - devoid of ammonia uptake - showed higher S. indica growth support in nitrate media. These findings highlight that ammonia based N-sharing can be a previously under-appreciated mechanism underpinning interaction among soil microorganisms and could be influenced by microbial or abiotic alteration of pH in microenvironments.

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.

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.