Quantifying the effects of antibiotic treatment on the extracellular polymer network of antimicrobial resistant and sensitive biofilms using multiple particle tracking

Novel therapeutics designed to target the polymeric matrix of biofilms requires innovative techniques to accurately assess their efficacy. Here, multiple particle tracking (MPT) was developed to characterize the physical and mechanical properties of antimicrobial resistant (AMR) bacterial biofilms and to quantify the effects of antibiotic treatment. Studies employed nanoparticles (NPs) of varying charge and size (40–500 nm) in Pseudomonas aeruginosa PAO1 and methicillin-resistant Staphylococcus aureus (MRSA) biofilms and also in polymyxin B (PMB) treated Escherichia coli biofilms of PMB-sensitive (PMBSens) IR57 and PMB-resistant (PMBR) PN47 strains. NP size-dependent and strain-related differences in the diffusion coefficient values of biofilms were evident between PAO1 and MRSA. Dose-dependent treatment effects induced by PMB in PMBSensE. coli biofilms included increases in diffusion and creep compliance (P < 0.05), not evident in PMB treatment of PMBRE. coli biofilms. Our results highlight the ability of MPT to quantify the diffusion and mechanical effects of antibiotic therapies within the AMR biofilm matrix, offering a valuable tool for the pre-clinical screening of anti-biofilm therapies.

Multiple particle tracking detects changes in brain extracellular matrix structure and predicts neurodevelopmental age

SummaryBrain extracellular matrix (ECM) structure mediates many aspects of neuronal function. Probing changes in ECM structure could provide insights into aging and neurological disease. Herein, we demonstrate the ability to characterize changes in brain ECM structure using multiple particle tracking (MPT). MPT was carried out in organotypic rat brain slices to detect induced and naturally occurring changes in ECM structure. Induced degradation of neural ECM led to a significant increase in nanoparticle diffusive ability in the brain extracellular space. For structural changes that occur naturally during development, an inverse relationship existed between age and nanoparticle diffusion. Using the age-dependent dataset, we applied extreme gradient boosting (XGBoost) to generate models capable of classifying nanoparticle trajectories. Collectively, this work demonstrates the utility of MPT combined with machine learning for measuring changes in brain ECM structure and predicting associated complex features such as developmental age.

Microrheology imaging of fiber suspensions – a case study for lyophilized collagen I in HCl solutions

Where conventional microscopy fails, overlaying subsequent images of multiple particle tracking (MPT) videos including short trajectories allowed for direct visualization of the network structure of lyophilized collagen I.