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Data from: Signal integration and adaptive sensory diversity tuning in Escherichia coli chemotaxis

Cite this dataset

Moore, Jeremy et al. (2024). Data from: Signal integration and adaptive sensory diversity tuning in Escherichia coli chemotaxis [Dataset]. Dryad. https://doi.org/10.5061/dryad.nvx0k6dzz

Abstract

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.

README: Sensory diversity tuning in E. coli chemotaxis

https://doi.org/10.5061/dryad.nvx0k6dzz

Signal Integration and Adaptive Sensory Diversity Tuning in Escherichia coli Chemotaxis
Jeremy Philippe Moore, Keita Kamino, Rafaela Kottou, Thomas S. Shimizu, Thierry Emonet

Dataset contents: This dataset contains single-cell FRET time series measuring activity of the chemotaxis signaling pathway of E. coli.

Description of the data and file structure

The dataset is a collection of .mat files which each contain a matlab struct with processed single-cell FRET data from different experiments. The structure of each .mat file is the same, but the experimental conditions are different. A list of experimental conditions (constant background ligand and foreground ligand whose concentration changes during the experiment) is provided in the file BackgroundList.txt.

Opening one of the .mat files in matlab will load the struct 'reorgData'. This struct contains the field 'resp_data', which is a [1x'number of cells'] struct containing the processed FRET measurements for each cell as entries. That is, the processed fret data for cell i can be accessed in resp_data(i).

Each entry in resp_data has several fields:

FRET [35x20] is a matrix where each row is a single FRET response measured over 10 seconds at 2Hz.

t [35x20] matrix containing the time relative to the start of the experiment in seconds when each measurement in FRET was taken.

s [35x20] matrix containing the stimulus concentration during each measurement in FRET

FRET_min_max [2x40] is a matrix where each row is a FRET response to a saturating stimulus used to normalize the data between 0 (minmum FRET and 1 (maximum FRET).

t_min_max [2x40] matrix containing the time relative to the start of the experiment in seconds when each measurement in FRET_min_max was taken.

s_min_max [2x40] matrix containing the stimulus concentration during each measurement in FRET_min_max

a [35x20] is the FRET data normalized to the maximum and minimum values per cell.

a_min_max [2x40] is the data in FRET_min_max normalized to the maximum and minimum FRET values per cell.

A [35x20] is the FRET data normalized by the maximum value per cell only.

A_min_max [2x40] is the data in FRET_min_max normalized to the maximum FRET values per cell.

Sharing/Access information

Raw data are available upon request. Send all inquiries to the corresponding author, Thierry Emonet (thierry.emonet@yale.edu).

Code/Software

Code to analyze the FRET data is available on GitHub https://github.com/emonetlab/SensoryDiversityTuningAnalysis

Methods

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.

Funding

National Institute of General Medical Sciences, Award: R01GM106189

National Institute of General Medical Sciences, Award: R01GM138533

National Science Foundation, Award: DGE-2139841