Fig 1.
This study examines a range of drugs targeting different cellular functions in three bacterial species, using various system-level readouts to analyze their effects.
A Diagram of the antibiotics used in this study, ranked by the population growth rate heterogeneity (PGRH) they induce, alongside their cellular targets. Protein synthesis inhibitors, which directly limit protein production, induce the lowest PGRH, while cell wall synthesis inhibitors induce the highest. This suggests a potential link between PGRH and the functional distance from protein production. B Schematic plot illustrating that morphological changes occur only at antibiotic concentrations that impact growth. While the magnitude of these changes varies greatly (as shown in C), normalization reveals a consistent general pattern across antibiotics, irrespective of their mechanism of action. This novel link between morphology change and growth inhibition led us to develop a new heuristic metric, MOR50, which can be used for rapid and high throughput MIC estimation. C Schematic showing the morphological impacts of different antibiotics for the three tested bacterial species. The white cell masks in the centre represent the typical morphologies of each bacteria species without an antibiotic present, while the coloured cells represent changes induced by the antibiotics. Some antibiotics increase cell size, while others decrease it. Data for this plot is provided in Section 2.3.
Fig 2.
Different antibiotics affect bacterial growth rate and population growth rate heterogeneity (PGRH) differently, as illustrated by E.coli exposed to three antibiotics from our test set.
The concentration selected to represent PGRH for subsequent analysis has a white-centred marker. A Effect of tetracycline (a protein synthesis inhibitor) on E.coli colony growth rates after 2.5 hours of growth on the MAP. A Hill equation was used to fit the data and determine the concentrations corresponding to 10% (IC10) and 90% (IC90) growth inhibition. Each point represents the mean and standard deviation from four biological repeats. B Effect of rifampicin (an RNA synthesis inhibitor) on E.coli growth rates. Data represent five biological repeats. C Effect of ampicillin (a cell wall synthesis inhibitor) on E.coli growth rates. Data represent six biological repeats. D Effect of tetracycline on the PGRH of E.coli during 1 to 2.5 hours of growth on the MAP. PGRH is defined as the standard deviation of colony growth rates at a given time point, averaged across multiple time points and biological repeats. E Effect of rifampicin on E.coli PGRH. F Effect of ampicillin on E.coli PGRH. G Control showing how growth rate typically varies between microcolonies when there is no antibiotic present. Colony images after 1 hour of growth on the MAP are shown alongside their segmentation masks. The mask from an additional 1.5 hours of growth is overlaid in a lighter hue, highlighting differences in colony growth. H Example showing the heterogeneous effects of the selected tetracycline concentration on colony growth. I Example of heterogeneous effects of rifampicin. J Example of heterogeneous effects of ampicillin.
Fig 3.
Protein synthesis inhibitor antibiotics cause the smallest population growth rate heterogeneity (PGRH), while cell wall synthesis inhibitors cause the largest PGRH.
A PGRH, normalized to the growth rate in the absence of antibiotics, averaged across E.coli, S.aureus and P.aeruginosa and grouped by antibiotic target. The antibiotic classes are ordered by PGRH from least to greatest. Bars represent the mean and standard deviation across antibiotic/species combinations. A detailed version of this plot, showing individual antibiotics and species, is provided in S8 Fig. B Correlation between PGRH and the Hill coefficient for each antibiotic. Data points represent the mean and standard deviation across species for each antibiotic.
Fig 4.
The morphology of E.coli changes significantly when exposed to antibiotics.
A Representative colonies from pads with varying ciprofloxacin (DNA synthesis inhibitor) concentrations demonstrate the antibiotic’s impact on cell morphology after 2.5 hours of incubation. Single-cell segmentation masks are overlaid, with each cell shown in a different colour. Ciprofloxacin induces clear filamentous growth. The bottom row provides a 4× zoom of the corresponding frames from the top row. B Mecillinam (cell wall synthesis inhibitor) causes the cells to grow into a more spherical shape. C Chloramphenicol (protein synthesis inhibitor) causes the cells to grow larger. D Filamentous growth becomes evident over time, with the most pronounced differences observed around 2 hours of growth for ciprofloxacin. Each line represents the mean cell area at a given antibiotic concentration, with shaded areas indicating the standard deviation from four repeats. Darker colours represent higher antibiotic concentrations, and data points are binned at 30-minute intervals. E Changes induced by mecillinam occur with a similar time delay. Data is based on four repeats. F Changes induced by chloramphenicol occur more quickly, becoming evident within the first hour of growth. Data is based on four repeats. G At 2.5 hours of growth, morphology data show that antibiotic concentrations near the MIC induce the most substantial changes in cell morphology, with significant area increases primarily driven by cell elongation. The MIC, averaged across four repeats, is indicated by the vertical line. The boxplot displays the median (line) and interquartile range (box), with whiskers extending to 1.5 times the interquartile range. Data is based on four repeats. H For mecillinam, morphology data at 2.5 hours reveal that the most pronounced change is an increase in cell width, with minimal impact on length. These changes are most prominent at concentrations close to the MIC. Data is based on four repeats. I Chloramphenicol induces moderate increases in both cell length and width, with the largest magnitude close to the MIC. Data is based on four repeats.
Fig 5.
All antibiotics induce changes in morphology, though to varying degrees. These plots show data at the antibiotic concentration closest to IC50 after 2.5 hours of imaging.
A Images illustrating the response of E.coli to antibiotics. All antibiotics, except vancomycin, produce an increase in cell size. See Fig 4 for images from ciprofloxacin, mecillinam and chloramphenicol. B Morphological responses of S.aureus to a sample of antibiotics. Ampicillin, gentamicin, norfloxacin, and trimethoprim increase cell area compared to the control, while vancomycin reduces cell size. C Morphological responses of P.aeruginosa to a selection of antibiotics. All antibiotics induce subtle increases in cell area. D Scatterplot showing how the test set of antibiotics affects the mean cell width and length for E.coli. Markers represent the mean and standard deviation across three or more replicates per antibiotic. Cell wall and DNA synthesis inhibitors induce the largest morphological changes. E Scatterplot showing the impact of cell wall and nucleic acid synthesis inhibitors on S.aureus morphology, with significant effects observed for all tested antibiotics. Protein synthesis inhibitors induce smaller changes, with kanamycin, neomycin, and gentamicin approaching the noise floor of the measurements. The dashed line represents x = y, where perfect spheres would fall. F Scatterplot showing the morphological effects of antibiotics on P.aeruginosa. With the exception of cecropin A, all tested antibiotics induce small but significant increases in both cell length and width.
Fig 6.
There are trends in morphology and growth characteristics between the antibiotic classes, but there are also exceptions.
This plot shows how the mean cross-sectional area at IC50 is affected for the three species and for different antibiotics, with a symlog x-scale. The antibiotics are grouped by functional target. The bacteriostatic antibiotics are marked with *. Data is shown for E.coli, S.aureus, and P.aeruginosa. The error bars indicate a 95% confidence interval.
Fig 7.
The magnitude of morphological change correlates closely with growth inhibition.
Morphological change 50 (MOR50) metric estimates the 50% inhibitory concentration (IC50) of an antibiotic from single-cell morphology data after 2.5 hours of antibiotic exposure. A Ciprofloxacin concentration vs cross-sectional cell area for E.coli. Markers show mean ± SD across repeats, capturing natural cell size variation due to the cell cycle. The MOR50 threshold (horizontal line) is halfway between mean areas at no antibiotic and maximum change. The MOR50 concentration (blue line) closely aligns with IC50 (grey line). Data from four biological repeats. B Vancomycin concentration vs cell area for E.coli. Markers show mean ± SD across repeats. The MOR50 concentration is where the curve crosses the threshold from above, aligning with IC50. Data from six biological repeats. C Antibiotic concentration (normalized to IC50) vs cell area (normalized so that mean area without antibiotics is zero, and mean area at the concentration of max area change is one) for all antibiotics in our test set applied to E.coli. The MOR50 threshold is shown at 50% change, and the MOR50 concentrations are where the area curves first cross this threshold. Morphology change varies between antibiotics, with noise evident in antibiotics inducing small changes. Each line consists of data from three or more biological repeats. D-F Scatterplots of IC50 vs MOR50 for E.coli, S.aureus, and P.aeruginosa show strong correlations (Pearson r, Spearman ρ). Dashed line: y = x. Markers: mean ± SD between repeats, with 3 or more repeats per condition.
Table 1.
Showing average MIC values obtained through growth rate as outlined in [32] (IC90), and single-cell morphology using the MOR50 metric.
All MIC values are reported in mg L−1. Values are reported as mean and standard deviation between biological repeats. The combinations of species and antibiotics marked with “-” were not tested. We note that our strain of S.aureus carries a plasmid encoding for Kanamcycin resistance.
Table 2.
Outlining the strain, growth media and analysis statistics for the different bacteria species used for these experiments.
The growth media was Luria-Bertani (LB) Broth and Mueller Hinton (MH) Broth. We started using LB Broth with E.coli, but switched to MH Broth for the later experiments with P.aeruginosa and S.aureus as we wanted to test with this media as well, given it is recommended for use with AST by EUCAST [44]. The same growth media was used for precultures and preparing pads on the MAP platform. Pad count, microcolony count and bacteria count are reported from a single timepoint around 2.5 hours after imaging was started, meaning each microcolony and cell counted are unique. For the analysis, data from a few consecutive time steps is typically used.