Tracking bacterial lineages in complex and dynamic environments with applications to growth control and persistence

Abstract

As microbes deplete local resources and transition from exponential to stationary phase they can change greatly in size, morphology, growth and expression-profiles. These responses can also vary importantly between individual cells, as shown by population snapshots. However, it has been difficult to track individual cells along the growth curve to determine the progression of events or correlations between how cells enter and exit dormancy. We have developed a platform for tracking >10 5 parallel cell lineages in dense and changing cultures, independently validating that the imaged cells closely track the batch population. This provides a microcosm of bulk growth with exceptional resolution and control, while enabling conventional bulk assays on the same culture. We used the platform to show that for both Escherichia coli and Bacillus subtilis, growth changes from an 'adder' mode in exponential phase to a mixed 'adder-timer' entering stationary phase, and then a near-perfect 'sizer' upon exit -creating broadly distributed cell sizes in stationary phase -and rapid return to narrowly distributed sizes upon exit. By high-throughput tracking of single cells as they enter and exit stationary phase, we further show that the heterogeneity in entry and exit behavior has little consequence in regular wake up from overnight stationary phase but can play important role in determining population fitness after long periods of dormancy or survival against antibiotics.

Observation of non-dormant persister cells reveals diverse modes of survival in antibiotic persistence

“…S5 and Movie S17). Since it is reported that the time to regrowth of dormant cells after drug removal is heterogeneous among dormant cells [23], it is plausible that these cells would eventually regrow after long lags. However, these late regrowers cannot significantly contribute to the regrowing cell populations due to the exponential penalties from the delay of regrowth.…”

“…Hence, it is plausible that these cells would eventually regrow as persisters with long lags. In fact, it has been shown by the single-cell measurements with a mother machine microfluidic device that the time to regrowth after drug removal is heterogeneous among dormant cells [23]. It should be noted that these late regrowers are detectable in the killing curve assay but not in the single-cell measurements with the MCMA microfluidic device due to the overgrowth of the early regrowers.…”

“…For example, Balaban et al utilized high persistence (hip) mutants of Escherichia coli [22] and demonstrated that cells showing no or slow growth within actively growing populations survived ampicillin (Amp) exposure [17]. Bakshi et al performed time-lapse experiments with E. coli and applied Amp to cells regrowing from stationary phase [23], a known environmental condition that increases the frequency of persister cells [24]. They demonstrated that cells that remained dormant even after removing stationary phase conditions survived under subsequent Amp exposure [23].…”

“…Bakshi et al performed time-lapse experiments with E. coli and applied Amp to cells regrowing from stationary phase [23], a known environmental condition that increases the frequency of persister cells [24]. They demonstrated that cells that remained dormant even after removing stationary phase conditions survived under subsequent Amp exposure [23]. Despite these experimental demonstrations, it still remains elusive whether any cellular traits other than dormancy could cause bacterial persistence.…”

A universal trade-off between growth and lag in fluctuating environments

“…Experiment schematic. Cells were recovered in the mother machine using glucose medium, and then connected to a flask with culture growing in the same medium 28 . Media switch was performed the same way as for the batch cultures, and the flow was then restarted towards the mother machine.…”