Anaerobic Live-Imaging

Taking Things From the Macro-scale to Micro-scale

In an ideal world in anaerobic digestor systems we could easily

  • predict when a reactor was going to fail or perform sub-optimally - and predict why

  • control the process by making systems less vulnerable to variable / or harsh environmental parameters

  • and even know how a system would respond to a change (i.e. feed variation)

To me the answer to these solutions lies in truly knowing the microbial communities at a single-cell scale (like how individual cells respond to stress).

A lot of the time we use bulk or averaged-community data and have to use destructive sampling. We can’t observe cell-cell interactions or examine cells in in situ environments. To me the answer to delivering informed engineering solutions lies in truly knowing the microbial communities at a single-cell scale (like how individual cells respond to stress) in environments that mimic the complexity of the real-world.

There’s a difficulty here for anaerobic systems - the microbial communities are inhibited by even the smallest amount of oxygen. This has meant we have had limited methodological advancement in high throughput anaerobic microbiology. Driven by this need, I took a position with Prof. Bill Sloan at the University of Glasgow (UoG) as lead post doc on the anaerobic work package of a £5.2M EPSRC funded Frontier in Engineering grant. Here, we collaborated with biomedical engineers (led by Prof. Huabing Yin) who developed microfluidic devices (micro-scale and highly controlled lab on chip systems). I assembled an anaerobic live-imaging system embedded with these devices to visualise individual strictly anaerobic cells. This was a labour of love and a challenging project (and one that was hit by a pandemic!). The video (left) shows growth of bacterial cells in a previous version of the device and where cells have grown for 48+ hours to emphasise how cells are alive and active within the system. We’re currently in the process of writing a protocol to accompany a video training series with publication on the method development plus a lot of image analysis. I’m hoping to obtain more funding to take these ideas further.

I’m a strong believer in open science and so if you would like to learn more about the system or collaborate with me please get in touch!