Dynamic light scattering: A fast and reliable method to analyze bacterial growth during the lag phase

Vargas, Susana; Millán-Chiu, Blanca E.; Arvizu-Medrano, Sofía M.; Loske, Achim M.; Rodrı́guez, Rogelio

doi:10.1016/j.mimet.2017.04.004

Cited by 22 publications

(9 citation statements)

References 20 publications

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“…The size measurement of E. coli cells resuspended incubated in buffer was of ca. 1.2 nm with low polydispersity of 0.18 ( Table S1 ), which compares well with previous DLS studies( Loske et al, 2014 , Vargas et al, 2017 ). E. coli cells incubated in the presence of Mac1 showed a slightly increased diameter of 1.4 nm with a greater polydispersity of 0.4, which within experimental error indicates that the cells are not drastically fragmente, albeit this does not confirm a full cellular integrity.…”

Section: Resultssupporting

confidence: 91%

In-cell DNP NMR reveals multiple targeting effect of antimicrobial peptide

Separovic

Hofferek

Duff

et al. 2022

Journal of Structural Biology: X

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Section: Resultssupporting

confidence: 91%

In-cell DNP NMR reveals multiple targeting effect of antimicrobial peptide

Separovic

Hofferek

Duff

et al. 2022

Journal of Structural Biology: X

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“…Recently, dynamic light scattering was shown to be as accurate as plate counting to quantify viable cells, so it is foreseeable that DLS might be able to provide similar data with a one to one correlation with cell viability. 44 Therefore, while the data quality herein may not be transferable, the parameters and models used to extract time-series behavior for fast-screening of resistance development and other undesirable behavior of antibacterial agents are likely transferable.…”

Section: Discussionmentioning

confidence: 99%

Statistical classification of dynamic bacterial growth with sub-inhibitory concentrations of nanoparticles and its implications for disease treatment

Jones

Medina-Cruz

Kim

et al. 2020

Preprint

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Nanoparticles are promising alternatives to antibiotics since nanoparticles are easy to manufacture, non-toxic, and do not promote resistance. Nanoparticles act via physical disruption of the bacterial membrane and/or the generation of high concentrations of reactive-oxygen species locally. Potential for physical disruption of the bacterial membrane may be quantified by free energy methods, such as the extended Derjuan-Landau-Verwey-Overbeek theory, which predicts the initial surface-material interactions. The generation of reactive-oxygen species may be quantified using enthalpies of formation to predict minimum inhibitory concentrations. Neither of these two quantitative structure- activity values describes the dynamic, in situ behavioral changes in the bacteria's struggle to survive. In this paper, borrowing parameters from logistic, oscillatory, and diauxic growth models, we use principal component analysis and agglomerative hierarchical clustering to classify survival modes across nanoparticle types and concentrations. We compare the growth parameters of 170 experimental interactions between nanoparticles and bacteria. The bacteria studied include Escherichia coli, Staphylococcus aureus, Methicillin-Resistant Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas aeruginosa, and Helicobacter pylori, and were tested across multiple concentrations of liposomal drug delivery systems, amphiphilic peptide, and silver and selenium nanoparticles. Clustering reveals specific pairs of bacteria and nanoparticles where the nanoparticle induced growth dynamics could potentially spread the infection through the development of resistance and tolerance. This rapid screening also shows that bacteria generated nanoparticles do not induce growth modes indicative of the development of resistance. This methodology can be used to rapidly screen for novel therapeutics that do not induce resistance before using more robust intracellular content screening. This methodology can also be used as a quality check on batch manufactured nanoparticles.

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“…Along with measurement techniques based on the analysis of turbidity, light-scattering methods are an alternative option to plate counting as well, and can be used to discriminate between live and dead cells [ 36 , 37 ]. Other than that, light scattering has long been used to characterize cell population parameters, the morphological properties of cells and even subcellular structures [ 38 ].…”

Section: Spectroscopy-based Techniques For On-line Monitoring Of Viab...mentioning

confidence: 99%

“…The light undergoes a frequency shift when it is diffracted by moving particles contained in the suspension. Using the Doppler effect, it is possible to determine the particle size if the temperature is kept constant [ 37 ]. Since the scattered light intensity is proportional to the concentration of the light-scattering particles, the slope of the scattered light intensity signal correlates directly with the growth rate of living bacteria acting as scattering particles.…”

Section: Spectroscopy-based Techniques For On-line Monitoring Of Viab...mentioning

confidence: 99%

Sensors and Techniques for On-Line Determination of Cell Viability in Bioprocess Monitoring

et al. 2022

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In recent years, the bioprocessing industry has experienced significant growth and is increasingly emerging as an important economic sector. Here, efficient process management and constant control of cellular growth are essential. Good product quality and yield can only be guaranteed with high cell density and high viability. Whereas the on-line measurement of physical and chemical process parameters has been common practice for many years, the on-line determination of viability remains a challenge and few commercial on-line measurement methods have been developed to date for determining viability in industrial bioprocesses. Thus, numerous studies have recently been conducted to develop sensors for on-line viability estimation, especially in the field of optical spectroscopic sensors, which will be the focus of this review. Spectroscopic sensors are versatile, on-line and mostly non-invasive. Especially in combination with bioinformatic data analysis, they offer great potential for industrial application. Known as soft sensors, they usually enable simultaneous estimation of multiple biological variables besides viability to be obtained from the same set of measurement data. However, the majority of the presented sensors are still in the research stage, and only a few are already commercially available.

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Dynamic light scattering: A fast and reliable method to analyze bacterial growth during the lag phase

Cited by 22 publications

References 20 publications

In-cell DNP NMR reveals multiple targeting effect of antimicrobial peptide

In-cell DNP NMR reveals multiple targeting effect of antimicrobial peptide

Statistical classification of dynamic bacterial growth with sub-inhibitory concentrations of nanoparticles and its implications for disease treatment

Sensors and Techniques for On-Line Determination of Cell Viability in Bioprocess Monitoring

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