Biology subjects

Durham, W. M.

Publications and source records attributed to Durham, W. M..

3 recordsLinked to original sources

bioRxiv2021

Reconfigurable microfluidic circuits for isolating and retrieving cells of interest

Microfluidic devices are widely used in many fields of biology, but a key limitation is that cells are typically surrounded by solid walls, making it hard to access those that exhibit a specific phenotype for further study. Here, we provide a general and flexible solution to this problem that exploits the remarkable properties of microfluidic circuits with fluid walls - transparent interfaces between culture media and an immiscible fluorocarbon that are easily pierced with pipets. We provide two proofs-of-concept in which specific cell sub-populations are isolated and recovered: i) murine macrophages chemotaxing towards complement component 5a, and ii) bacteria (Pseudomonas aeruginosa) in developing biofilms that migrate towards antibiotics. We build circuits in minutes on standard Petri dishes, add cells, pump in laminar streams so molecular diffusion creates attractant gradients, acquire time-lapse images, and isolate desired sub-populations in real-time by building fluid walls around migrating cells with an accuracy of tens of micrometres using 3D-printed adaptors that convert conventional microscopes into wall-building machines. Our method allows live cells of interest to be easily extracted from microfluidic devices for downstream analyses.

bioengineering↗

bioRxiv2021

Suicidal chemotaxis in bacteria

Bacteria commonly live in communities on surfaces where steep gradients of antibiotics and other chemical compounds routinely occur. While many species of bacteria can move on surfaces, we know surprisingly little about how such antibiotic gradients affect cell motility. Here we study the behaviour of the opportunistic pathogen Pseudomonas aeruginosa in stable spatial gradients of a range of antibiotics by tracking thousands of cells in microfluidic devices as they form biofilms. Unexpectedly, these experiments reveal that individual bacteria use pili-based ( twitching) motility to actively navigate towards regions with higher antibiotic concentrations. Our analyses suggest that this biased migration is driven, at least in part, by a direct response to the antibiotics. Migrating cells can reach antibiotic concentrations hundreds of times higher than their minimum inhibitory concentration in a few hours and remain highly motile. However, isolating these cells - using fluid-walled microfluidic devices that can be reconfigured in situ - suggests that these bacteria are terminal and not able to reproduce. In spite of moving towards their death, we show that migrating cells are capable of entering a suicidal program to release bacteriocins that are used to kill other bacteria. Our work suggests that bacteria respond to antibiotics as if they come from a competing colony growing in the neighbourhood, inducing them to invade and attack. As a result, clinical antibiotics have the potential to serve as a bait that lures bacteria to their death.

microbiology↗

bioRxiv2021

Tracking bacteria at high density with FAST, the Feature-Assisted Segmenter/Tracker

Most bacteria live attached to surfaces in densely-packed communities1,2. While new experimental and imaging techniques are beginning to provide a window on the complex processes that play out in these communities, resolving the behaviour of individual cells through time and space remains a major challenge. Although a number of different software solutions have been developed to track microorganisms3-8, these approaches typically rely on a large number of user-defined parameters that must be carefully tuned to effectively track cells. Testing a given parameter combination can take hours to days depending on the size of the dataset, making iterative optimisation impractical. To overcome these limitations, we have developed FAST, the Feature-Assisted Segmenter/Tracker, which uses unsupervised machine learning to optimise tracking while maintaining ease of use. Our approach, rooted in information theory, largely eliminates the need for users to iteratively adjust parameters manually and make qualitative assessments of the resulting cell trajectories. Instead, FAST measures multiple distinguishing "features" for each cell and then autonomously quantifies the amount of unique information each feature provides. We then use these measurements to determine how data from different features should be combined to minimize tracking errors. Comparing our algorithm with a naive approach that uses cell position alone revealed that FAST produced 4 to 10 times fewer tracking errors. The modular design of FAST combines our novel tracking method with tools for segmentation, extensive data visualisation, lineage assignment, and manual track correction. It is also highly extensible, allowing users to extract custom information from images and seamlessly integrate it into downstream analyses. FAST therefore enables high-throughput, data-rich analyses with minimal user input. It has been released for use either in Matlab or as a compiled stand-alone application, and is available at https://bit.ly/3vovDHn, along with extensive tutorials and detailed documentation.

microbiology↗