In unconfined environments, bacterial motility patterns are an explicit expression of the internal states of the cell. Bacteria operating a run-and-tumble behavioral program swim forward when in a 'run' state, and are stalled in place when in a reorienting 'tumble' state. However, in natural environments, motility dynamics often represent a convolution of bacterial behavior and environmental constraints. Recent investigations showed that Escherichia coli swimming through highly confined porous media exhibit extended periods of 'trapping' punctuated by forward 'hops', a seemingly drastic restructuring of run-and-tumble behavior. We introduce a microfluidic device to systematically explore bacterial movement in a range of spatially structured environments, bridging the extremes of unconfined and highly confined conditions. We observe that trajectories reflecting unconstrained expression of run-and-tumble behavior and those reflecting 'hop-and-trap' dynamics coexist in all structured environments considered, with ensemble dynamics transitioning smoothly between these two extremes. We present a unifying 'swim-and-stall' framework to characterize this continuum of observed motility patterns and demonstrate that bacteria employing a consistent set of behavioral rules can present motility patterns that smoothly transition between the two extremes. Our results indicate that the control program underlying run-and-tumble motility is robust to changes in the environment, allowing flagellated bacteria to navigate and adapt to a diverse range of complex, dynamic habitats using the same set of behavioral rules.
Bacterial motility, E. coli, complex environments, microbial active matter, microfluidics
The repository has five folders: 'tracks', 'sorted_tracks', 'analysis', 'simulation', and 'protocols'.
--The folder 'tracks' contains raw trajectory data in csv files. Raw trajectory data from raw imaging data is generated by the ImageJ plugin 'TrackMate' and stored in csv files under the folder 'tracks'. The naming of these csv files is described as follows: It has four components and each component is connected by a dash line. 'dev' represents microfluidic device. 'pil' stands for pillar confinement. 'dis' stands for disorder. The higher the number after 'pil' or 'dis', the higher the confinement or disorder of the region. Thus, 'pil1', 'pil2', and 'pil3' respresent pillar confinement C = 6 μm, C = 2.6 μm, and C = 1.3 μm, respectively. Similarly, 'dis1', 'dis2', 'dis3', and 'dis4' represent disorder level D = 0 (no disorder), D = 1, D = 2, and D = 3, respectively. The only exception is the unconfined region where no pillar is present. For example, 'dev1-pil0-dis0-rep1' corresponds to microfluidic device No.1, pillar confinement C = 0 (no confinement), disorder D = 0 (no disorder), and the replicate No. 1 for the same experimental condition. For pillared regions, the example 'dev1-pil3-dis2-rep7' corresponds to microfluidic device No.1, pillar confinement C = 1.3 μm, disorder D = 1, and the replicate No. 7.
--The folder 'sorted tracks' stores the sorted data. The raw trajectory data under the folder 'tracks' has no order in track IDs and trajectory times. Thus, sorting is necessary for data analysis. Soring is based on the entry 'TRACK_ID' and 'POSITION_T'. The sorted files are named based on the previous naming method and the only difference is the addition of 'sorted' as the beginning. There are four key entries in the csv files: 'TRACK_ID', 'POSITION_X', 'POSITION_Y', and 'POSITION_T'. They represent trajectory ID, x position data, y position data, and time information.
--The folder 'analysis' contains imaging analysis in Python. To run the code, download this repository. Open the terminal and navigate to the 'analysis' folder in the downloaded repository. Then type 'python xx.py' in the terminal where 'xx' represents the specific analysis file in Python.
--The folder 'simulation' contains simulation code written in Python. To run the code, download the file 'R&T_sim_032825.py' and open the terminal. Type 'python R&T_sim_032825.py' in the terminal.
--The folder 'protocols' contains experimental protocol as well as tracking protocol, both in pdf.
Note that all raw images used for this research is publicly accessible in Dropbox: https://www.dropbox.com/scl/fo/sfdxub50ny3niq647s0m1/AP2umhdJng3Pm1bxp02KQpc?rlkey=xad6wdjedgs7uwg8h2ty53sz0&e=1&st=9ozcb7om&dl=0.
Haibei Zhang†1,2, Miles T. Wetherington†3,4, Hungtang Ko5, Cody E. FitzGerald2,6, Leone V. Luzzatto2,7, István A. Kovács2,6,7,8, Edwin M. Munro2,9, Jasmine A. Nirody2,10
- Graduate Program in Biophysical Sciences, University of Chicago, 929 East 57th St, Chicago, IL 60637
- NSF-Simons National Institute for Theory and Mathematics in Biology, Chicago, IL 60611
- School of Physics, Georgia Institute of Technology, 837 State St NW, Atlanta, GA 30332
- School of Applied and Engineering Physics, Cornell University, Clark Hall, 271, 142 Sciences Dr, Ithaca, NY 14853
- Department of Mechanical and Aerospace Engineering, Princeton University, 41 Olden St, 401 Princeton, NJ 08544
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, 404 2145 Sheridan Rd, Evanston, IL 60208
- Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208
- Department of Molecular Genetics and Cell Biology, University of Chicago, 920 E. 58th St, Chicago, Il 60637
- Department of Organismal Biology and Anatomy, University of Chicago, 1027 E 57th St, 407 Chicago, IL 60637
†These authors contributed equally to this work. *Corresponding author. Email: jnirody@uchicago.edu
H.Z. and J.A.N. acknowledge funding support by NSF-Simons National Institute for Theory and Mathematics in Biology, which is jointly supported by the U.S. National Science Foundation (Award 2235451) and the Simons Foundation (Award MP-TMPS-00005320). H.Z. is also grateful for her graduate program in Biophysical Sciences at the University of Chicago. M.T.W., H.K., C.E.F., and J.A.N. acknowledge support from The Santa Fe Institute and The James S. McDonnell Foundation Postdoctoral Fellowship Award in Complex Systems (M.T.W: https://doi.org/10.37717/2020-1543; H.K.: https://doi.org/10.37717/2021-3524; C.E.F.: https://doi.org/10.37717/2020-1591; J.A.N.: https://doi.org/10.37717/220020527). C.E.F. is supported by the NSF-Simons Center for Quantitative Biology at Northwestern University (NSF: 1764421 and Simons Foundation/SFARI 597491-RWC). J.A.N. and E.M.M. acknowledge support from the National Science Foundation through the Center for Living Systems (Grant No. 2317138). This work was performed in part at the Cornell NanoScale Facility, a member of the National Nanotechnology Coordinated Infrastructure (NNCI), which is supported by the National Science Foundation (Grant NNCI-2025233). We thank Tom Pennell for his guidance and advice throughout the fabrication process at the Cornell NanoScale Facility. We also thank Benjamin R. Epley, Erin Brandt, and Peter Yunker for their insights and many thoughtful discussions.
This project is licensed under the CC-BY-NC 4.0 International License - see the LICENSE.md file for details.