Biophysical differentiation of susceptibility and chemical differences in Staphylococcus aureus.

Differentiating bacteria strains using biophysical forces has been the focus of recent studies using dielectrophoresis (DEP). The refinement of these studies has created high-resolution separations such that very subtle properties of the cells are enough to induce significant differences in measurable biophysical properties. These high-resolution capabilities build upon the advantages of DEP which include small sample sizes and fast analysis times. Studies focusing on differentiating antimicrobial resistant and susceptible bacteria potentially have significant impact on human health and medical care. A prime example is Staphylococcus aureus, which commonly colonizes adults without ill effects. However, the pathogen is an important cause of infections, including surgical site infections. Treatment of S. aureus infections is generally possible with antimicrobials, but antimicrobial resistance has emerged. Of special importance is resistance to methicillin, an antimicrobial created in response to resistance to penicillin. Here, dielectrophoresis is used to study methicillin-resistant (MRSA) and -susceptible S. aureus (MSSA) strains, both with and without the addition of a fluorescent label. The capture onset potential of fluorescently-labeled MRSA (865 ± 71 V) and thus the ratio of electrokinetic to dielectrophoretic mobility, was found to be higher than that of fluorescently-labeled MSSA (685 ± 61 V). This may be attributable to the PBP2a enzyme present in the MRSA strain and not in the MSSA bacteria. Further, unlabeled MRSA was found to have a capture onset potential of 732 ± 44 V, while unlabeled MSSA was found to have a capture onset potential of 562 ± 59 V. This shows that the fluorescently-labeled bacteria require a higher applied potential, and thus ratio of mobilities, to capture than the unlabeled bacteria.

A mathematical model of dielectrophoretic data to connect measurements with cell properties.

Dielectrophoresis (DEP) brings about the high-resolution separations of cells and other bioparticles arising from very subtle differences in their properties. However, an unanticipated limitation has arisen: difficulty in assignment of specific biological features which vary between two cell populations. This hampers the ability to interpret the significance of the variations. To realize the opportunities made possible by dielectrophoresis, the data and the diversity of structures found in cells and bioparticles must be linked. While the crossover frequency in DEP has been studied in-depth and exploited in applications using AC fields, less attention has been given when a DC field is present. Here, a new mathematical model of dielectrophoretic data is introduced which connects the physical properties of cells to specific elements of the data from potential- or time-varied DEP experiments. The slope of the data in either analysis is related to the electrokinetic mobility, while the potential at which capture initiates in potential-based analysis is related to both the electrokinetic and dielectrophoretic mobilities. These mobilities can be assigned to cellular properties for which values appear in the literature. Representative examples of high and low values of properties such as conductivity, zeta potential, and surface charge density for bacteria including Streptococcus mutans, Rhodococcus erythropolis, Pasteurella multocida, Escherichia coli, and Staphylococcus aureus are considered. While the many properties of a cell collapse into one or two features of data, for a well-vetted system the model can indicate the extent of dissimilarity. The influence of individual properties on the features of dielectrophoretic data is summarized, allowing for further interpretation of data. Graphical abstract.

Isolation and identification of Listeria monocytogenes utilizing DC insulator-based dielectrophoresis.

Foodborne pathogens pose one of the greatest challenges facing public health in the modern day. One important pathogen, Listeria monocytogenes, is known to be challenging to detect and identify. Three serovars cause most of the Listeria related food-borne illnesses, which the Centers for Disease Control currently utilizes a combination of pulsed-field gel electrophoresis and whole genome sequencing for identification and the determination of clusters and outbreaks. There is a potential method for rapid collection of epidemiological information by exploiting the electrokinetic and dielectrophoretic properties of the L. monocytogenes serovars. Using dielectrophoresis, the three most commonly identified serovars of L. monocytogenes can be distinguished from each other. The electrokinetic and dielectrophoretic mobilities of each serovar was determined through a combination of electrokinetic velocity and dielectrophoretic trapping assessments, in conjunction with finite element multi-physics modeling. A mathematical model of the data, which defines the various factors of dielectrophoretic trapping, is utilized and verified based on the behavior of L. monocytogenes in the microchannel. The trapping condition for the serovars were evaluated as 2.8±0.2×109, 2.2±0.2×109, and 2.2±0.3×109Vm-2 and the electrokinetic mobility was assessed to be 19±0.7, 17±0.7, and for the L. monocytogenes serovars 1/2a, 1/2b, and 4b, respectively.