Dynamic control and quantification of bacterial population dynamics in droplets

Abstract

Culturing and measuring bacterial population dynamics are critical to develop insights into gene regulation or bacterial physiology. Traditional methods, based on bulk culture to obtain such quantification, have the limitations of higher cost/volume of reagents, non-amendable to small size of population and more laborious manipulation. To this end, droplet-based microfluidics represents a promising alternative that is cost-effective and high-throughput. However, difficulties in manipulating the droplet environment and monitoring encapsulated bacterial population for long-term experiments limit its utilization. To overcome these limitations, we used an electrode-free injection technology to modulate the chemical environment in droplets. This ability is critical for precise control of bacterial dynamics in droplets. Moreover, we developed a trapping device for long-term monitoring of population dynamics in individual droplets for at least 240 h. We demonstrated the utility of this new microfluidic system by quantifying population dynamics of natural and engineered bacteria. Our approach can further improve the analysis for systems and synthetic biology in terms of manipulability and high temporal resolution.

Keywords: Bacterial population dynamics; Droplet injection; Dynamic droplet manipulation; Microfluidics.

Fig. 1

A microfluidic platform for droplet production and injection. (A) Schematic of droplet injection chip. The microfluidic device consists of droplet production, injection and mixing units. The droplet injection is triggered by DC power supply. (B) Mechanism of droplet injection. A layer of surfactant molecules stabilizes the droplets after production. An electric field disrupts this protecting layer when power is turned ON, and the injection phase is injected into the original droplets when they move across the orifice. Real-time images depict the difference between power OFF and ON. Orange arrows indicate the individual droplets from injection phase without being injected. (C) Effects of electric field on cell survival. The droplets were injected with M9 media under varied voltage values. Mean fluorescence intensity of each droplet (log scale) was quantified over time. Error bars correspond to ±1 standard deviation of averaged fluorescence intensity of all droplets (n > 20). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Tunability of droplet injection technology. (A) Tunable injection efficiency by production rate of droplets. Voltage was set as 3 KV to trigger all injections, and flow rate of injection phase was 0.1 μl/min. Flow rates of oil and water phase were set as the same and varied from 0.5 μl/min to 3 μl/min 1.0 μl/min was chosen for subsequent experiments regarding the injection efficiency and production rate of droplets. (B) Tunable injection efficiency by voltage. Flow rates of oil and water phase were both set as 1.0 μl/min, and injection phase was 0.1 μl/min. Voltage was varied from 0 KV to 5 KV to trigger the injection. Regarding the injection efficiency and homogeneity of droplet size, 3.0 KV was chosen for subsequent experiments. (C) Correlation between injection efficiency and voltage value when production rate of droplets was varied. Flow rates of both oil and water phase were the same and varied from 0.5 μl/min to 2 μl/min, and voltage from 0 KV to 5 KV. Dash line denoted the threshold of voltage to obtain sufficient efficiency for the application of droplet injection. Error bars denoted ±1 standard deviation from multiple detecting positions (see details in SI).

Fig. 3

Droplets used for quantification of bacterial population dynamics. (A) Quantification of multiple droplets with population collapse and recovery with an ePop circuit. Each line represents one subpopulation in droplet started with low cell density (1–5 cells per droplet). Images are the representative time points of ePop oscillation. The top left inset represents schematic of the ePop circuit, GFP reporter works as an indicator of cell viability. (B) Schematic of inoculum effect (IE). Antibiotic (A) targets ribosome (Rs) to inhibit its accumulation and mislead protein synthesis. Concurrently, A causes heat shock response (HSR) to further destroy the ribosome feedback (Left). At low concentration, A cannot efficiently inhibit Rs, and population with both high and low initial densities (N0) will initiate growth. Conversely, high concentration of A effectively inhibits growth for both populations. Importantly, IE can only happen at intermediate concentration of A (Right). (C) Investigation of IE in droplets. The bacterial strain with GFP reporter were encapsulated in droplets with varied concentrations of antibiotics. Streptomycin (Strep) concentrations below 4 μg/ml were insufficient to inhibit growth of population with both low (green) and high (red) initial density (N0), while concentrations greater than 4 μg/ml effectively prevented growth of both conditions. IE was only observed when Step was 4 μg/ml. In comparison, chloramphenicol (Cm) did not generate IE within the same range of concentration. The curves indicate mean fluorescence intensity of sampled droplets (n > 20) versus time, and shades refer to the ±1 standard deviation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Droplet injection for control of population dynamics. (A) Schematics of PAD and NPD circuits. PAD consists of two modules, E lysis module and β-lactamase (BlaM) module. 6-APA partially breaks down cell murein, and generates the intermediate that can induce the synthesis of E protein. E protein further lyses the cell to release BlaM into the environment, which can then degrade 6-APA. BlaM is placed under an inducible IPTG promoter; constitutively expressed GFP is used as a reporter for cell viability. For control, the E lysis module is removed in the non-programmed altruistic death (NPD) circuit, and RFP is used as reporter. (B) Inhibition of PAD growth by 6-APA. PAD cells in the droplets that were injected with M9 (PAD – 6-APA) grew to high density after 24 h. In contrast, growth of PAD strain was fully inhibited in droplets when 400 μg/ml 6-APA was injected (PAD + 6-APA). This inhibition was not significant for NPD cells due to the deletion of E lysis module (NPD + 6-APA) compared to those without 6-APA (NPD - 6-APA). The insets demonstrated the normalized growth for the conditions with or without 6-APA treatment. Error bars represent ±1 standard deviation of average at 24th hour of culture (n > 20). (C) Representative images of (B). The scale bar represents 25 μm.

Fig. 5

Regulation of PAD by injecting different concentrations of 6-APA into droplets. (A) 25 μg/ml (B) 50 μg/ml and (C) 100 μg/ml 6-APA was injected into droplets with (red) and without (green) 1 mM IPTG rescuing. Black dash lines indicate inflection points when the populations (green) stop growing. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)