Dynamic gene expression and growth underlie cell-to-cell heterogeneity in Escherichia coli stress response

Abstract

Significance Individual bacteria that share identical genomes and growth environments can display substantial cell-to-cell differences in expression of stress-response genes and single-cell growth rates. This phenotypic heterogeneity can impact the survival of single cells facing sudden stress. However, the windows of time that cells spend in vulnerable or tolerant states are often unknown. We quantify the temporal expression of a suite of stress-response reporters, while simultaneously monitoring growth. We observe pulsatile expression across genes with a range of stress-response functions, finding that single-cell growth rates are often anticorrelated with reporter levels. These dynamic phenotypic differences have a concrete link to function, in which individual cells undergoing a pulse of elevated expression and slow growth are predisposed to survive antibiotic exposure.

Environmental feedback drives oxidative stress response heterogeneity in bacterial populations

“…Studies on the oxidative stress response in bacteria have revealed characteristics that partially support each of these contrasting models (Cochran et al, 2000;de Martino et al, 2016;Hong et al, 2020;Patange et al, 2018;Sampaio et al, 2022). To resolve this uncertainty, we characterized the spatio-temporal behaviour of single E. coli cells growing in defined structured populations under a constant treatment of H2O2.…”

“…This indicates that while cells can reliably sense the onset of H2O2 stress and respond rapidly, the response magnitude depends on an unidentified factor that varies substantially between cells. This heterogeneity could be caused by a variety of mechanisms, including molecular stochasticity in the specific regulatory circuits or general gene expression machinery (Golding et al, 2005;Uphoff et al, 2016), cell-to-cell variation in growth or morphology (Łapińska et al, 2022;Ojkic et al, 2022;Sampaio et al, 2022) , variable cellto-cell interactions (Dal Co et al, 2020;Snoussi et al, 2018;van Gestel et al, 2021), or differences in the local environment of cells (Snoussi et al, 2018;van Vliet et al, 2018). To pinpoint the mechanisms, we designed a machine learning model using random forest regression [Figure 2A].…”

“…Besides instantaneous responses to current conditions, cells also maintain memory of past conditions (Mathis & Ackermann, 2016;Veening, Stewart, et al, 2008;Wolf et al, 2008). The duration of memory is generally set by the cell growth rate, which overall determines the balance between the production and dilution of molecules as cells elongate and divide (Kiviet et al, 2014;Sampaio et al, 2022). Because stress conditions affect growth rates, complex feedback arises between the stress level and gene expression dynamics.…”

“…Variation in cell growth and morphology can also affect the influx, dilution, and reaction-diffusion dynamics of stressor molecules such as ROS (Łapińska et al, 2022;Ojkic et al, 2022;Sampaio et al, 2022). Furthermore, cells in a population can interact and modulate their environment in response to stress (Dal Co et al, 2020;Snoussi et al, 2018;van Gestel et al, 2021).…”

Massively-parallel Microbial mRNA Sequencing (M3-Seq) reveals heterogeneous behaviors in bacteria at single-cell resolution

“…After assigning reads to barcode combinations, we filtered out droplets barcodes in which a given droplet barcode had more than 8 associated plate barcodes, which corresponds to a barcode collision rate greater than 25%. We then split barcode combinations by condition (plate barcodes), and then performed another filtering step using the knee method for each condition 16 . We note that this step is important because bacteria in different conditions have different amounts of mean mRNA expression.…”