Selective trapping of bacteria in porous media by cell length

TL;DR

This study addresses how bacterial cell length interacts with porous geometry to govern transport in heterogeneous media. The authors use an inducible elongation system in $E.\ coli$ and microfluidic devices that create ordered and disordered pore networks to track single-cell trajectories and quantify transport metrics such as $MSD$ and the straightness index ($SI$). They show that elongation enhances exploration in ordered, anisotropic lattices but promotes trapping in disordered networks, with concave microstructures selectively sequestering elongated cells. This morphology–geometry coupling enables passive length-based sorting of bacterial subpopulations and has implications for microbial colonization, biofilm initiation, and diagnostics in natural porous habitats.

Abstract

Bacteria commonly inhabit porous environments such as host tissues, soil, and marine sediments, where complex geometries constrain and redirect their motion. Although bacterial motility has been studied in porous media, the roles of cell length and pore shape in navigating these environments remain poorly understood. Here, we investigate how cell morphology and pore architecture jointly determine bacterial spreading behavior. Using genetically engineered E. coli with tunable cell length, we performed single-cell tracking in microfluidic devices that mimic ordered and disordered porous structures. We find that elongated bacteria traverse ordered pore networks more effectively than short cells, exhibiting straighter paths, greater directional persistence, and enhanced exploration efficiency. In contrast, in disordered porous media, elongated bacteria become trapped in dead-end regions for extended periods, resulting in markedly reduced navigational efficiency. Together, these results reveal how cell shape and environmental geometry interact to govern bacterial transport. Moreover, we suggest a new mechanism for separating antimicrobial-resistant (AMR) bacteria from elongated susceptible cells in designer porous media.

Figures (5)

• Figure 1: Bacterial elongation and motility characterization. (A) Schematic illustration of bacterial behaviors in porous media, including free swimming, sliding, and trapping. (B) Genetic construction of the elongation strain. The sulA gene is PCR-amplified and inserted into the pBAD24 plasmid using EcoRI and HindIII restriction sites. The resulting recombinant plasmid is transformed into E. coli MG1655, enabling arabinose-inducible expression of sulA for controlled cell elongation. (C) Experimental workflow for elongation induction by L-arabinose. (D--E) Representative SEM images of E. coli at 0 hr and 1 hr induction, showing elongation with increasing induction time. (F) Mean cell length measured under bright-field microscopy as a function of induction time $T_{\mathrm{induce}}$. Longer induction results in significantly increased cell length. (G--H) Representative trajectories of short (0 hr, green) and elongated (1 hr, magenta) cells in unconstrained motility chambers. Short cells exhibit circular motion, while elongated cells display straighter, more persistent trajectories. (I) Violin plots of swimming speed $v_{\mathrm{swim}}$ showing minimal dependence on elongation. (J) Straightness index (SI = $r/D_p$), where $r$ is the net displacement and $D_p$ is the total path length, and curvature $\kappa$ as functions of induction time, illustrating that elongated cells swim along straighter trajectories.
• Figure 2: Effect of cell length and confinement on motility in ordered porous media. (A) Bright-field image of an ordered microfluidic lattice composed of circular pillars with radius $R$ and spacing $S$. (B--C) Trajectories of short ($L = 4~\mu\mathrm{m}$, 0 hr induction) and elongated ($L = 16~\mu\mathrm{m}$, 2 hr induction) cells within ordered pillar arrays ($R = 25~\mu\mathrm{m}$, $S = 10~\mu\mathrm{m}$). Short cells frequently become trapped around pillars, whereas elongated cells display straighter, more persistent trajectories. Each color corresponds to a distinct bacterium. (D) Mean-squared displacement (MSD) versus lag time $\tau$ for cells of different lengths under fixed confinement ($R = 25~\mu\mathrm{m}$, $S = 10~\mu\mathrm{m}$); elongated cells exhibit higher MSD values. Inset: log--log representation showing reference slopes of $2$ (ballistic) and $1$ (diffusive). (E) Straightness index (SI) increases with induction time, indicating enhanced directional persistence with increasing cell length. (C, F, I) Representative trajectories of elongated cells within ordered pillar arrays of varying confinement levels ($S = 10$, 25, and 50 $\mu\mathrm{m}$, respectively). As the pillar spacing increases, trajectories become more curved and less persistent. (G) MSD of elongated cells under different pillar spacings ($S = 10$--$50~\mu\mathrm{m}$), showing enhanced diffusivity under tighter confinement. (H) SI increases with decreasing pillar spacing, consistent with greater trajectory persistence under stronger confinement.
• Figure 3: Trapping of elongated cells in disordered porous media. (A--C) Single-frame, averaged, and normalized density maps for short cells (0 hr induction) in disordered pillar networks, showing a nearly uniform distribution. (D--F) Corresponding maps for elongated cells (1 hr induction), revealing pronounced clustering near dead ends and concave surfaces. (G) Magnified view highlighting elongated cells trapped in concave pore regions. (H) Illustration of concave and convex curvature boundaries used for density analysis. (I) Mean normalized density as a function of local curvature $\kappa$, showing preferential accumulation of elongated cells in concave regions. Positive $\kappa$ corresponds to concave boundaries, and negative $\kappa$ to convex ones.
• Figure 4: Trap duration analysis for short and long cells in disordered porous media. (A) Representative regions within disordered porous media used to quantify trap durations, with four example trap locations highlighted. (B) Trap duration distributions for short (0 hr induction, $L \approx 4µm$) and long (1 hr induction, $L \approx 10µm$) cells. Each point represents an individual trapping event, and different symbols indicate different regions of data collection. A significant difference was observed between groups (****, $p < 0.0001$). (C) Example trajectory of a short cell navigating a pillar dead end, color-coded by time. (D) Mean speed of short cells aligned to the trap entry time $t_{0}$ and exit time $t_{1}$, with the mean trap duration indicated by shading. Here, $t_{0}$ is defined as the first time the cell speed drops below 10µms, marking the start of trapping, and $t_{1}$ is defined as the first time after trapping that the cell speed rises above 10µms, marking the end of trapping. (E) Example trajectory of a long cell navigating a pillar dead end. (F) Mean speed of long cells aligned to $t_{0}$ and $t_{1}$, with the mean trap duration indicated by shading.
• Figure 5: Navigation of short and long cells in ordered and disordered porous media. (A) In ordered porous media, short cells ($L = 4µm$) frequently slide along pillar surfaces, resulting in inefficient exploration. (B) Elongated cells ($L = 10µm$) in ordered media follow directed, low-curvature trajectories, promoting efficient exploration. (C) In disordered porous media, short cells readily escape dead-end regions by sliding along curved pillar boundaries. (D) In contrast, elongated cells become trapped in concave microstructures within disordered porous media, as their limited turning ability prevents effective reorientation. Time–colored trajectories illustrate characteristic navigation patterns, and schematic diagrams highlight the distinct escape and trapping behaviors associated with each condition.