We show, using differential dynamic microscopy, that the diffusivity of nonmotile cells in a three-dimensional (3D) population of motile E. coli is enhanced by an amount proportional to the active cell flux. While nonmotile mutants without flagella and mutants with paralyzed flagella have quite different thermal diffusivities and therefore hydrodynamic radii, their diffusivities are enhanced to the same extent by swimmers in the regime of cell densities explored here. Integrating the advective motion of nonswimmers caused by swimmers with finite persistence-length trajectories predicts our observations to within 2%, indicating that fluid entrainment is not relevant for diffusion enhancement in 3D.

The flagellated bacterium Escherichia coli is increasingly used experimentally as a self-propelled swimmer. To obtain meaningful, quantitative results that are comparable between different laboratories, reproducible protocols are needed to control, 'tune' and monitor the swimming behaviour of these motile cells. We critically review the knowledge needed to do so, explain methods for characterising the colloidal and motile properties of E. coli cells, and propose a protocol for keeping them swimming at constant speed at finite bulk concentrations. In the process of establishing this protocol, we use motility as a high-throughput probe of aspects of cellular physiology via the coupling between swimming speed and the proton motive force. Keywords:Escherichia coli, active colloids, motility, differential dynamic microscopy, metabolism, bioenergetics, proton motive force Some time ago, our lab wanted to culture motile bacteria as 'model active colloids'. We obtained a strain of Escherichia coli with the full complement of motility genes and a culturing protocol from a local microbiologist. For some time, we thought we were experimenting with motile E. coli, until one day we checked in the microscope. Few, if any, of the cells were swimming! So we set out to learn how to modify the standard protocol to optimise motility by collating literature, talking to other researchers and trial and error; we also implemented differential dynamic microscopy (DDM) to quantify motility.This article reviews what we have learnt. Some of the material is previously known, but seldom critically discussed in one place. We have explained some basic bacterial bioenergetics and genetics, because physical scientists can use E. coli and collaborate with biologists more effectively if these topics are understood. Much of the materials is new, arising from using DDM to quantify motility. While we aim primarily at researchers working on active colloids [1], this article should also be useful to others studying motility biophysics [2].From the outset, we refer to various culture media (BMB, TB, LB) and protocols, and freely use terminology related to molecular biology (plasmid, gene names, etc.) and measurement techniques (OD, DDM, etc.). Readers should refer to Section 5 on matters of cell culture, Sections 3 and 4 for methodology, and Section 10 and Appendix C for biological jargon. We also provide a table of symbols in Appendix F.

We measured the minimum inhibitory concentration (MIC) of the antimicrobial peptide pexiganan acting on Escherichia coli, and report an intrinsic variability in such measurements. These results led to a detailed study of the effect of pexiganan on the growth curve of E. coli, using a plate reader and manual plating (i.e. time-kill curves). The measured growth curves, together with single-cell observations and peptide depletion assays, suggested that addition of a sub-MIC concentration of pexiganan to a population of this bacterium killed a fraction of the cells, reducing peptide activity during the process, while leaving the remaining cells unaffected. This pharmacodynamic hypothesis suggests a considerable inoculum effect, which we quantified. Our results cast doubt on the use of the MIC as 'a measure of the concentration needed for peptide action' and show how 'coarse-grained' studies at the population level give vital information for the correct planning and interpretation of MIC measurements.

We report a high-throughput technique for characterising the motility of spermatozoa using differential dynamic microscopy. A movie with large field of view (∼10mm 2 ) records thousands of cells (e.g. ≈ 5000 cells even at a low cell density of 20 × 10 6 cells/ml) at once and yields averaged measurements of the mean ( ) and standard deviation ( σ ) of the swimming speed, head oscillation amplitude ( A 0 ) and frequency ( f 0 ), and the fraction of motile spermatozoa ( α ). Interestingly, we found that the measurement of α is facilitated because the swimming spermatozoa enhance the motion of the non-swimming population. We demonstrate the ease and rapidity of our method by performing on-farm characterisation of bull spermatozoa motility, and validate the technique by comparing laboratory measurements with tracking. Our results confirm the long-standing theoretical prediction that for swimming spermatozoa.