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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.

 

Abstract In mammals, subcellular protein localization of factors like planar cell polarity proteins is a key driver of the multicellular organization of tissues. Bacteria also form organized multicellular communities, but these patterns are largely thought to emerge from regulation of whole-cell processes like growth, motility, cell shape, and differentiation. Here we show that a unique intracellular patterning of appendages known as type IV pili (T4P) can drive multicellular development of complex bacterial communities. Specifically, dynamic T4P appendages localize in a line along the long axis of the cell in the bacterium Acinetobacter baylyi . This long-axis localization is regulated by a functionally divergent chemosensory Pil-Chp system, and an atypical T4P protein homologue (FimV) bridges Pil-Chp signaling and T4P positioning. We further demonstrate through modeling and empirical approaches that subcellular T4P localization controls how individual cells interact with one another, independently of T4P dynamics, with different patterns of localization giving rise to distinct multicellular architectures. Our results reveal how subcellular patterning of single cells regulates the development of multicellular bacterial communities. 

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. 

The spread of pathogenic bacteria in unsaturated porous media, where air and liquid coexist in pore spaces, is the major cause of soil contamination by pathogens, soft rot in plants, food spoilage, and many pulmonary diseases. However, visualization and fundamental understanding of bacterial transport in unsaturated porous media are currently lacking, limiting the ability to address the above contamination- and disease-related issues. Here, we demonstrate a previously unreported mechanism by which bacterial cells are transported in unsaturated porous media. We discover that surfactant-producing bacteria can generate flows along corners through surfactant production that changes the wettability of the solid surface. The corner flow velocity is on the order of several millimeters per hour, which is the same order of magnitude as bacterial swarming, one of the fastest known modes of bacterial surface translocation. We successfully predict the critical corner angle for bacterial corner flow to occur based on the biosurfactant-induced change in the contact angle of the bacterial solution on the solid surface. Furthermore, we demonstrate that bacteria can indeed spread by producing biosurfactants in a model soil, which consists of packed angular grains. In addition, we demonstrate that bacterial corner flow is controlled by quorum sensing, the cell–cell communication process that regulates biosurfactant production. Understanding this previously unappreciated bacterial transport mechanism will enable more accurate predictions of bacterial spreading in soil and other unsaturated porous media.

 

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.