Optimising the 3-channel microfluidic system to investigate chemical gradient impacts on bacterial chemotaxis in fluid and near surfaces

TL;DR

The paper presents a three-channel microfluidic gradient platform to study bacterial chemotaxis with stable, controlled chemical gradients. By tracking single-cell trajectories in the gradient, it derives local chemotactic velocity v_c and susceptibility χ from a flux-balance perspective, enabling rapid transient and local measurements. The results reveal a log-sensing chemotactic response for MeAsp and casamino acids and show that surfaces suppress chemotactic drift, highlighting key considerations for chemotaxis in porous or confined environments. The approach yields high data density and spatial resolution, outperforming stationary analyses and offering a generalizable tool for characterizing chemotaxis across bacterial species and gradient types.

Abstract

Bacteria can adjust their swimming behaviour in response to chemical variations, a phenomenon known as chemotaxis. This process is characterised by a drift velocity that depends non-linearly on the concentration of the chemical species and its local gradient. To study this process more effectively, we optimised a 3-channel microfluidic device designed to create a stable gradient of chemoattractants. This setup allows us to simultaneously monitor the response of Escherichia coli to casamino acids or alpha-methyl-DL-aspartic acid at the individual level.

Figures (9)

• Figure 1: Sketch of the experimental setup. (a) The microfluidic chip, placed on an inverted microscope and observed under $20 \times$ magnification, consists of three parallel channels. The channels ① and ③ are filled with a chemo-attractant at concentration $c_1$ and $c_3$ diluted in the motility buffer (MB). (b) The chemo-attractant diffuses through the agarose layer, creating a linear gradient between channels ① and ③, notably in channel ② where fluorescent E. coli bacteria are injected. At $T = 0$, the bacteria are homogeneously distributed along the y-axis, before starting to accumulate towards the source of chemoattractant.
• Figure 2: Bacterial tracks and concentration profiles for two experiments. Trajectories of E. coli bacteria are shown at three different times $T$ during two experiments: without any gradient in the bulk $z=h/2$ (a-c), and with a 200 MeAsp gradient either in the bulk $z=h/2$ (e-g) or on the bottom agar surface $z=0$ (i-k). The tracks are colour-coded with the time $t$ from 020. The corresponding bacterial profiles, measured at $T = \qty{0}{\minute}$ (●), 5 (●) and 25(●), are displayed at the bottom of each column (d,h,l).
• Figure 3: Evolution of the average net, diffusive and chemotactic velocities in a typical experiment measured at $z=h/2$. All velocities are normalised by the swimming speed $v_s$: blue circles correspond to the net velocity $\tilde{v}$, orange triangles to the diffusive velocity $\tilde{v}_\mu$, and green triangles to the chemotactic velocity $\tilde{v}_c$. The vertical bars indicate the standard deviation of the values on the bands, while the horizontal bars correspond to film acquisition duration. The net and diffusive velocities are fitted by an exponential function $\tilde{v} \propto \exp(-t / \tau^\mathrm{stat})$ (dashed lines), with $\tau^\mathrm{stat}$ the characteristic time to reach the stationary state: $\tau^\mathrm{stat} \simeq \qty{7}{\minute}$ for the net velocity, and $\tau^\mathrm{stat}_\mu \simeq \qty{9}{\minute}$ for the diffusive velocity. Green band: average and standard deviation of each average chemotactic velocity value. Grey band: threshold of $\tilde{v}_c$ over which chemotaxis happens. This experiment was performed using MeAsp with $\bar{c} = \qty{100}{\uM}$ and $\nabla c = \qty{200}{\uM\per\mm}$.
• Figure 4: Measuring the bacterial velocity bias in the transient state allows for a much quicker quantification of the chemotactic bias. Chemotactic susceptibility $\chi(\bar{c})$ measured with (a) MeAsp and (b) casamino acids as a function of the average concentration in the channel $\bar{c}$. Blue circles: susceptibility deduced in the transient state ($T \leq \qty{3}{\minute}$) from the bias in bacterial velocities along the gradient. Red squares: susceptibility deduced from the steady spatial profile of the bacteria Gargasson2025.
• Figure 5: Transient and local measurements allow for a quicker and more accurate quantification of the log-sensing response. Chemotactic susceptibility $\chi(c)$ of chemoattractants (a) MeAsp and (b) casamino acids, respectively, as a function of their local concentration $c$. Each small shaded blue circle corresponds to the value of the chemotactic susceptibility $\chi(Y,T)$ measured for each band $Y$ of local concentration $c(Y)$ at each time $T$ for each experiment, while the larger blue circles with error bars correspond to their binning in $\log(c)$. Data from the steady spatial profile of the bacteria Gargasson2025 are shown as ■, one point corresponding to only one experiment. Solid lines: fit of the data as $\chi(c) = \chi_0/((1+c/c_-)(1+c/c_+))$, with (a) $\chi_0 = \qty{2.5(20)e3}{\um\per\uM}$, $c_- = \qty{5(3)e-2}{\uM}$ and $c_+ = \qty{5(3)e4}{\uM}$ for MeAsp, and (b) $\chi_0 = \qty{0.7(4)}{\um\per\uM}$, $c_- = \qty{2(1)e2}{\uM}$ and $c_+ = \qty{2(1)e5}{\uM}$ for casamino acids. Dashed lines: $\chi(c) \propto 1/c$.