Martin A. Hamilton

How to optimize the drop plate method for enumerating bacteria

The drop plate (DP) method can be used to determine the number of viable suspended bacteria in a known beaker volume. The drop plate method has some advantages over the spread plate (SP) method. Less time and effort are required to dispense the drops onto an agar plate than to spread an equivalent total sample volume into the agar. By distributing the sample in drops, colony counting can be done faster and perhaps more accurately. Even though it has been present in the laboratory for many years, the drop plate method has not been standardized. Some technicians use 10-fold dilutions, others use twofold. Some technicians plate a total volume of 0.1 ml, others plate 0.2 ml. The optimal combination of such factors would be useful to know when performing the drop plate method. This investigation was conducted to determine (i) the standard deviation of the bacterial density estimate, (ii) the cost of performing the drop plate procedure, (iii) the optimal drop plate design, and (iv) the advantages of the drop plate method in comparison to the standard spread plate method. The optimal design is the combination of factor settings that achieves the smallest standard deviation for a fixed cost. Computer simulation techniques and regression analysis were used to express the standard deviation as a function of the beaker volume, dilution factor, and volume plated. The standard deviation expression is also applicable to the spread plate method.

A microtiter-plate screening method for biofilm disinfection and removal

A quantitative spectrophotometric method was developed to measure the removal and killing efficacy of antibiofilm agents. Biofilms of Pseudomonas aeruginosa or Staphylococcus epidermidis were grown in 96-well plates, treated with an agent, then stained with either the biomass indicator crystal violet or the respiratory indicator 5-cyano-2,3-ditolyl tetrazolium chloride. This rapid screening method is sensitive enough to elucidate concentration-response relationships as well as differences between species responses to treatments. Using these assays, agents can be ranked by their ability to remove or kill biofilm.

Hypothesis for the Role of Nutrient Starvation in Biofilm Detachment

ABSTRACT A combination of experimental and theoretical approaches was used to investigate the role of nutrient starvation as a potential trigger for biofilm detachment. Experimental observations of detachment in a variety of biofilm systems were made with pure cultures of Pseudomonas aeruginosa. These observations indicated that biofilms grown under continuous-flow conditions detached after flow was stopped, that hollow cell clusters were sometimes observed in biofilms grown in flow cells, and that lysed cells were apparent in the internal strata of colony biofilms. When biofilms were nutrient starved under continuous-flow conditions, detachment still occurred, suggesting that starvation and not the accumulation of a metabolic product was responsible for triggering detachment in this particular system. A cellular automata computer model of biofilm dynamics was used to explore the starvation-dependent detachment mechanism. The model predicted biofilm structures and dynamics that were qualitatively similar to those observed experimentally. The predicted features included centrally located voids appearing in sufficiently large cell clusters, gradients in growth rate within these clusters, and the release of most of the biofilm with simulated stopped-flow conditions. The model was also able to predict biofilm sloughing resulting solely from this detachment mechanism. These results support the conjecture that nutrient starvation is an environmental cue for the release of microbes from a biofilm.

A method for growing a biofilm under low shear at the air-liquid interface using the drip flow biofilm reactor.

This protocol describes how to grow a Pseudomonas aeruginosa biofilm under low fluid shear close to the air–liquid interface using the drip flow reactor (DFR). The DFR can model environments such as food-processing conveyor belts, catheters, lungs with cystic fibrosis and the oral cavity. The biofilm is established by operating the reactor in batch mode for 6 h. A mature biofilm forms as the reactor operates for an additional 48 h with a continuous flow of nutrients. During continuous flow, the biofilm experiences a low shear as the media drips onto a surface set at a 10° angle. At the end of 54 h, biofilm accumulation is quantified by removing coupons from the reactor channels, rinsing the coupons to remove planktonic cells, scraping the biofilm from the coupon surface, disaggregating the clumps, then diluting and plating for viable cell enumeration. The entire procedure takes 13 h of active time that is distributed over 5 d.

The influence of surface features on bacterial colonization and subsequent substratum chemical changes of 316L stainless steel

Biofilm-forming bacteria were found to selectively colonize specific surface features of unpolished 316L stainless steel exposed to flowing aqueous media. Depending on the types of bacteria present, selective colonization resulted in significant depletion of Cr and Fe relative to Ni in the surface film at these features. No such depletion was observed on uncolonized surfaces exposed to sterile flowing aqueous medium. The results demonstrate that non-random, initial colonization of 316L stainless steel surfaces by these bacteria leads to changes in alloy elemental composition in the surface film that are enhanced with time. These chemical changes may be a critical step that weakens the oxide film at specific locations, allowing halides such as Cl− ions greater access to the underlying bulk alloy, and thereby facilitates localized attack and pit formation and propagation.

Quantifying biofilm structure

This article defines some quantitative parameters for describing the structure of a biofilm. The parameters can be calculated from a two-dimensional cross-sectional image on a plane parallel to the substratum within an in situ biofilm. Such images can be acquired using a confocal scanning laser microscope (CSLM). The parameters will eventually be used for eliciting relationships between the biofilms structure and its biochemical function, and for computer model evaluation. The results shown here indicate that the structural parameters appear to be reaching steady-state conditions as the biofilm grows to a steady state.

Modeling biocide action against biofilms

A phenomenological model of biocide action against microbial biofilms was derived. Processes incorporated in the model include bulk flow in and out of a well‐mixed reactor, transport of dissolved species into the biofilm, substrate consumption by bacterial metabolism, bacterial growth, advection of cell mass within the biofilm, cell detachment from the biofilm, cell death, and biocide concentration‐dependent disinfection. Simulations were performed to analyze the general behavior of the model and to perform preliminary sensitivity analysis to identify key input parameters. The model captured several general features of antimicrobial agent action against biofilms that have been observed widely by experimenters and practitioners. These included (1) rapid disinfection followed by biofilm regrowth, (2) slower detachment than disinfection, and (3) reduced susceptibility of microorganisms in biofilms. The results support the plausibility of a mechanism of biofilm resistance in which the biocide is neutralized by reaction with biofilm constituents, leading to a reduction in the bulk biocide concentration and, more significantly, biocide concentration gradients within the biofilm. Sensitivity experiments and analyses identified which input parameters influence key response variables. Each of three response variables was sensitive to each of the five input parameters, but they were most sensitive to the initial biofilm thickness and next most sensitive to the biocide disinfection rate coefficient. Statistical regression modeling produced simple equations for approximating the response variables for situations within the range of conditions covered by the sensitivity experiment. The model should be useful as a tool for studying alternative biocide control strategies. For example, the simulations suggested that a good interval between pulses of biocide is the time to minimum thickness.

Comparison of Fluorescence Microscopy and Solid-Phase Cytometry Methods for Counting Bacteria in Water

ABSTRACT Total direct counts of bacterial abundance are central in assessing the biomass and bacteriological quality of water in ecological and industrial applications. Several factors have been identified that contribute to the variability in bacterial abundance counts when using fluorescent microscopy, the most significant of which is retaining an adequate number of cells per filter to ensure an acceptable level of statistical confidence in the resulting data. Previous studies that have assessed the components of total-direct-count methods that contribute to this variance have attempted to maintain a bacterial cell abundance value per filter of approximately 106 cells filter−1. In this study we have established the lower limit for the number of bacterial cells per filter at which the statistical reliability of the abundance estimate is no longer acceptable. Our results indicate that when the numbers of bacterial cells per filter were progressively reduced below 105, the microscopic methods increasingly overestimated the true bacterial abundance (range, 15.0 to 99.3%). The solid-phase cytometer only slightly overestimated the true bacterial abundances and was more consistent over the same range of bacterial abundances per filter (range, 8.9 to 12.5%). The solid-phase cytometer method for conducting total direct counts of bacteria was less biased and performed significantly better than any of the microscope methods. It was also found that microscopic count data from counting 5 fields on three separate filters were statistically equivalent to data from counting 20 fields on a single filter.

Statistical Quantification of Detachment Rates and Size Distributions of Cell Clumps from Wild-Type (PAO1) and Cell Signaling Mutant (JP1) Pseudomonas aeruginosa Biofilms

ABSTRACT The detachment of cells from bacterial biofilms is an important, yet poorly understood and largely unquantified phenomenon. Detached cell clumps from medical devices may form microemboli and lead to metastasis, especially if they are resistant to host defenses and antibiotics. In manufacturing plants detached clumps entering a process stream decrease product quality. Two strains of Pseudomonas aeruginosa, a wild type (PAO1) and a cell signaling mutant (JP1), were studied to (i) quantify and model detachment patterns and (ii) determine the influence of cell signaling on detachment. We collected effluent from a biofilm flowthrough reactor and determined the size distribution for cell detachment events by microscopic examination and image analysis. The two strains were similar in terms of both biofilm structure and detachment patterns. Most of the detachment events were single-cell events; however, multiple-cell detachment events contributed a large fraction of the total detached cells. The rates at which events containing multiple cells detached from the biofilm were estimated by fitting a statistical model to the size distribution data. For events consisting of at least 1,000 cells, the estimated rates were 4.5 events mm−2 min−1 for PAO1 and 4.3 events mm−2 min−1 for JP1. These rates may be significant when they are scaled up to the total area of a real biofilm-contaminated medical device surface and to the hours or days of patient exposure.

Interval Estimation of the Density of Organisms Using a Serial-Dilution Experiment

SUMMARY The serial-dilution assay is a standard microbiological method for estimating the density of organisms in a solution. The commonly used interval-estimation procedures of Woodward and deMan are described and compared. A new method for the calculation of two-sided confidence intervals, which is superior to the standard procedures, is presented. The computations are illustrated for a decimal dilution assay with three samples at each of three dilutions.