The mechanosensitive channel of large conductance, which serves as a model system for mechanosensitive channels, has previously been crystallized in the closed form, but not in the open form. Ensemble measurements and electrophysiological sieving experiments show that the open-diameter of the channel pore is >25 Å, but the exact size and whether the conformational change follows a helix-tilt or barrel-stave model are unclear. Here we report measurements of the distance changes on liposome-reconstituted MscL transmembrane α-helices, using a ‘virtual sorting’ single-molecule fluorescence energy transfer. We observed directly that the channel opens via the helix-tilt model and the open pore reaches 2.8 nm in diameter. In addition, based on the measurements, we developed a molecular dynamics model of the channel structure in the open state which confirms our direct observations.

Growth curve measurements are commonly used in microbiology, while the use of microplate readers for such measurements provides better temporal resolution and higher throughput. However, evaluating bacterial growth with microplate readers has been hurdled by barriers such as multiple scattering. Here, we report our development of a method based on the time derivatives of the optical density (OD) and/or fluorescence (FL) of bacterial cultures to overcome these barriers. First, we illustrated our method using quantitative models and numerical simulations, which predicted the number of bacteria and the number of fluorescent proteins in time as well as their time derivatives. Then, we systematically investigated how the time derivatives depend on the parameters in the models/simulations, providing a framework for understanding the FL growth curves. In addition, as a demonstration, we applied our method to study the lag time elongation of bacteria subjected to treatment with silver (Ag⁺) ions and found that the results from our method corroborated well with that from growth curve fitting by the Gompertz model that has been commonly used in the literature. Furthermore, this method was applied to the growth of bacteria in the presence of silver nanoparticles (AgNPs) at various concentrations, where the OD curve measurements failed. We showed that our method allowed us to successfully extract the growth behavior of the bacteria from the FL measurements and understand how the growth was affected by the AgNPs.

Immunofluorescence, a powerful technique to detect specific targets using fluorescently labeled antibodies, has been widely used in both scientific research and clinical diagnostics. The probes should be made with small antibodies and high brightness. We conjugated GFP binding protein (GBP) nanobodies, small single-chain antibodies from llamas, with new ∼7 nm quantum dots. These provide simple and versatile immunofluorescence nanoprobes with nanometer accuracy and resolution. Using the new probes we tracked the walking of individual kinesin motors and measured their 8 nm step sizes; we tracked Piezo1 channels, which are eukaryotic mechanosensitive channels; we also tracked AMPA receptors on living neurons. Finally, we used a new super-resolution algorithm based on blinking of (small) quantum dots that allowed ∼2 nm precision.

Silver nanoparticles (AgNPs) are well known to exhibit antimicrobial effects through plausible interactions with proteins and/or deoxyribonucleic acid inside the bacteria. Yet a quantitative understanding on the antimicrobial activities of AgNPs remains obscure. Here we conducted in-depth kinetic growth assays and colony-forming unit (CFU) assays on Escherichia coli (E. coli) cultured in AgNPs or Ag ion-containing growth media. Compared to the Ag-absent culture medium, it was found that the growth rate of the bacteria remained unaffected but the lag time of the bacterial growth was extended due to the presence of AgNPs or Ag ions. From the CFU-based time-kill curves, we observed that fractions of E. coli were killed exponentially in the presence of AgNPs or Ag ions. Based on the experimental data, a quantitative model was established to describe the antimicrobial activity of AgNPs. The predictions from this model agree well with the experimental results. We also showed that the parameters in our model as well as their dependence on the concentrations of Ag and bacteria could, in turn, be determined experimentally. It is expected that our quantitative model and the associated parameters provide an alternative means to minimum inhibitory concentration values for characterizing the antimicrobial activities of Ag ions and AgNPs.