In uncertain environments, phenotypic diversity can be advantageous for survival. However, as the environmental uncertainty decreases, the relative advantage of having diverse phenotypes decreases. Here, we show how populations of E. coli integrate multiple chemical signals to adjust sensory diversity in response to changes in the prevalence of each ligand in the environment. Measuring kinase activity in single cells, we quantified the sensitivity distribution to various chemoattractants in different mixtures of background stimuli. We found that when ligands bind uncompetitively, the population tunes sensory diversity to each signal independently, decreasing diversity when the signal ambient concentration increases. However, amongst competitive ligands, the population can only decrease sensory diversity one ligand at a time. Mathematical modeling suggests that sensory diversity tuning benefits E. coli populations by modulating how many cells are committed to tracking each signal proportionally as their prevalence changes.

In-vivo single-cell FRET microscropy

Bacteria were grown to mid-exponential phase with inducer to express a YFP/RFP FRET pair. FRET imaging was performed with an inverted microscope (Eclipse Ti-E, Nikon) equipped with an oil immersion objective (CFI Apo TIRF 60X Oil, Nikon). Yellow fluorescent proteins were illuminated with a light-emitting diode system (SOLA SE, Lumencore) through one excitation filter (59026x, Chroma), then another (FF01-550/24-25, Semrock) and a dichroic mirror (F01-542/27-25F, Semrock). Emission was fed into an emission image splitter (OptoSplit II, Cairn) where it was split into donor and acceptor channels with a dichroic mirror (FF580-FDi01-25x36, Semrock) and collected through emission filters (FF520-Di02-25x36 and FF593-Di03-25x36, Semrock) with a scientific CMOS camera (ORCA-Flash 4.0 V2, Hammatsu). Red fluorescent protein mRFP1 was imaged in the same way as YFP, except with a different second excitation filter (FF01-575/05-25) and dichroic mirror (FF593-Di03-25x36, Semrock). For both fluorophores, images were taken with 50ms exposure time.

FRET data-analysis

Images were segmented and single-cell fluorescent signals determined with in-house software. Photobleaching was corrected by fitting donor and acceptor time-series with a bi-exponential function and subtracting out the decay to yield donor  and acceptor  time-series. To calculate FRET from fluorescence time-series, we employed the E-FRET method as described in the paper associated with this dataset. To convert FRET to kinase activity, FRET values were normalized by the minimum and maximum values attained during a saturating response at the beginning of the experiment. Data were detrended by subtracting a line fit to the minimum FRET levels attained during saturating stimuli at the beginning and end of the experiment.