Search for: All records

Bacterial populations are highly adaptive. They can respond to stress and survive in shifting environments. How the behaviours of individual bacteria vary during stress, however, is poorly understood. To identify and characterize rare bacterial subpopulations, technologies for single-cell transcriptional profiling have been developed. Existing approaches show some degree of limitation, for example, in terms of number of cells or transcripts that can be profiled. Due in part to these limitations, few conditions have been studied with these tools. Here we develop massively-parallel, multiplexed, microbial sequencing (M3-seq)—a single-cell RNA-sequencing platform for bacteria that pairs combinatorial cell indexing with post hoc rRNA depletion. We show that M3-seq can profile bacterial cells from different species under a range of conditions in single experiments. We then apply M3-seq to hundreds of thousands of cells, revealing rare populations and insights into bet-hedging associated with stress responses and characterizing phage infection.

 

The ability of eukaryotic cells to differentiate surface stiffness is fundamental for many processes like stem cell development. Bacteria were previously known to sense the presence of surfaces, but the extent to which they could differentiate stiffnesses remained unclear. Here we establish that the human pathogen Pseudomonas aeruginosa actively measures surface stiffness using type IV pili (TFP). Stiffness sensing is nonlinear, as induction of the virulence factor regulator is peaked with stiffness in a physiologically important range between 0.1 kPa (similar to mucus) and 1,000 kPa (similar to cartilage). Experiments on surfaces with distinct material properties establish that stiffness is the specific biophysical parameter important for this sensing. Traction force measurements reveal that the retraction of TFP is capable of deforming even stiff substrates. We show how slow diffusion of the pilin PilA in the inner membrane yields local concentration changes at the base of TFP during extension and retraction that change with substrate stiffness. We develop a quantitative biomechanical model that explains the transcriptional response to stiffness. A competition between PilA diffusion in the inner membrane and a loss/gain of monomers during TFP extension/retraction produces substrate stiffness-dependent dynamics of the local PilA concentration. We validated this model by manipulating the ATPase activity of the TFP motors to change TFP extension and retraction velocities and PilA concentration dynamics, altering the stiffness response in a predictable manner. Our results highlight stiffness sensing as a shared behavior across biological kingdoms, revealing generalizable principles of environmental sensing across small and large cells. 

Type IV pili (TFP) function through cycles of extension and retraction. The coordination of these cycles remains mysterious due to a lack of quantitative measurements of multiple features of TFP dynamics. Here, we fluorescently label TFP in the pathogenPseudomonas aeruginosaand track full extension and retraction cycles of individual filaments. Polymerization and depolymerization dynamics are stochastic; TFP are made at random times and extend, pause, and retract for random lengths of time. TFP can also pause for extended periods between two extension or two retraction events in both wild-type cells and a slowly retracting PilT mutant. We developed a biophysical model based on the stochastic binding of two dedicated extension and retraction motors to the same pilus machine that predicts the observed features of the data with no free parameters. We show that only a model in which both motors stochastically bind and unbind to the pilus machine independent of the piliation state of the machine quantitatively explains the experimentally observed pilus production rate. In experimental support of this model, we show that the abundance of the retraction motor dictates the pilus production rate and that PilT is bound to pilus machines even in their unpiliated state. Together, the strong quantitative agreement of our model with a variety of experiments suggests that the entire repetitive cycle of pilus extension and retraction is coordinated by the competition of stochastic motor binding to the pilus machine, and that the retraction motor is the major throttle for pilus production.

 

Mechanical properties of the extracellular matrix are important determinants of cellular migration in diverse processes, such as immune response, wound healing, and cancer metastasis. Moreover, recent studies indicate that even bacterial surface colonization can depend on the mechanics of the substrate. Here, we focus on physical mechanisms that can give rise to substrate-rigidity dependent migration. We study a “twitcher”, a cell driven by extension–retraction cycles, to idealize bacteria and perhaps eukaryotic cells that employ a slip-stick mode of motion. The twitcher is asymmetric and always pulls itself forward at its front. Analytical calculations show that the migration speed of a twitcher depends non-linearly on substrate rigidity. For soft substrates, deformations do not lead to build-up of significant force and the migration speed is therefore determined by stochastic adhesion unbinding. For rigid substrates, forced adhesion rupture determines the migration speed. Depending on the force-sensitivity of front and rear adhesions, forced bond rupture implies an increase or a decrease of the migration speed. A requirement for the occurrence of rigidity-dependent stick-slip migration is a “sticky” substrate, with binding rates being an order of magnitude larger than unbinding rates in absence of force. Computer simulations show that small stall forces of the driving machinery lead to a reduced movement on high rigidities, regardless of force-sensitivities of bonds. The simulations also confirm the occurrence of rigidity-dependent migration speed in a generic model for slip-stick migration of cells on a sticky substrate.