When the York iGEM 2017 team first got together, we knew that co-culturing of microorganisms has become an extremely promising approach, in Biology. In particular, this is true with regards to understanding natural and synthetic cell population interactions and applications in Industrial Biotechnology (e.g. manufacturing and drug research). Our original plan was to use a co-culture comprising Chlamydomonas reinhardtii and Escherichia coli in order to create a somewhat self-sustaining bioreactor that could cost-efficiently produce biofuels using cheap feedstocks. In such an instance, C. reinhardtii would be engineered to export maltose, which E. coli could use in its production of biofuel. Since the crux of this challenge lay in ensuring that C. reinhardtii would export maltose, we planned to use a strain of E. coli that produces ethanol, rather than over-complicate the experiment by using biofuel producing strains that may not have grown as efficiently.

We soon became acutely aware that, along with the maintenance of stable and productive co-cultures being technically challenging, the usual methods of counting cells are often inaccurate, expensive, slow or destructive when applied to cultures of more than one organism. Thence, we have come to consider the lack of a Quicker Way to Analyse Co-Cultures (QWACC) a problem that must be rectified in order to advance the possibilities of research with co-cultures. QWACC is an acronym we will use, henceforth, to refer to the hardware and software that perform the Digital Inline Holographic Microscopy and cell counting in this project. We have not, however, abandoned the idea of producing cheaper biofuels with the C. reinhardtii and E. coli co-culture and it remains a centre piece of this project. It has served as a stark and ever-present reminder that the hardware and software we were developing should be easily used in conjunction with actual experiments involving co-cultures.

So, in the beginning, our project became a bipartite entity. One half of the greater whole was the construction of hardware and software related to using a DIHM to count cells in a co-culture. This would include the microscope itself, software that can automatically count cells and an milli-fluidic chamber to hold samples. The other half was the genetic modification of C. reinhardtii such that it would export maltose in a co-culture with E. coli and the monitoring of the numbers of each organism in a sample with a view to optimising ethanol production.

Why DIHM?

So, we began the development of an inexpensive DIHM and accompanying software which is able to automatically count cells. Due to the inherently digital nature of this type of microscopy, software can easily be created and adapted such that the DIHM can not only count organisms but, also, differentiate between those that are distinguishable by physical appearance. Among the most important motivators of this hardware/software combination were speed of measurement (our target was the order of seconds or minute per measurement) and low costs for setup and maintenance. Further, in co-cultures wherein neither organism contains a fluorescent marker, the process of counting each type can become a rather complex endeavour. With our QWACC, however, there exists the potential for real-time counting of all physically distinct organisms within a sample.

In the table, below, we have compared several methods of cell counting that are able to distinguish between organisms. This is not quantitative and simply shows how each technique stacks up compared to the rest of those on the list. This is denoted through a traffic light system - green indicates that the method is desirable for the given quality, yellow corresponds to a reasonably desirable quality and red indicates that the technique is undesirable with respect to the quality.

In the absence of a DIHM, the technique that we would have most likely used is flow cytometry. This would have been suitable for our needs since, in our modification of Chlamydomonas we inserted a fluorescence gene - Venus "YFP" - and E. coli also fluoresces due to mCherry. This would allow us to count the two organisms by exciting with different wavelengths and measuring the responses. This is a relatively accurate procedure and is not overly time consuming. On the other hand, not every organism is modifiable and therefore it is not always possible to discern the numbers of organisms using this technique. For more thorough comparisons between the techniques specified in the above table, see here.

Experiments in Versatility

Once we had created our QWACC, we wanted to test its capabilities with respect to analysing co-cultures. To do this, we performed several experiments with a variety of co-cultures. The organism combinations were:

1. E. coli (ethanol production strain LW06) and C. reinhardtii (strain CC-4533)
2. E. coli (ethanol production strain LW06) and E. coli (strain BL21(DE3) with increased motility via parts BBa_K777100 to BBa_K777108)
3. Galdieria Sulphuraria (strain currently not known) and E. coli (ethanol production strain LW06)
4. Galdieria Sulphuraria (strain currently not known) and C. reinhardtii (strain CC-4533)

A sample of each liquid co-culture was pumped through a milli-fluidic chamber (whose design can be found here) using a peristaltic pump. Then the chamber was positioned over the camera of our QWACC. An image was then taken every few minutes in order to create a hologram. The hologram was then processed into a single PNG containing all the information necessary for counting cells. A blob-detection/circle-detection algorithm was then used to locate and count the cells, with size and shape being used to differentiate between organisms. In the case of co-culture 2 (in the above list), the distinction could not be made via size or shape since both organisms were E. coli and, therefore, physically identical. Instead, we took videos of the co-culture and deciphered, by eye, which were moving faster/more. These, we marked as the E. coli that had been modified to have increased motility. This further tested the versatility of co-culture analysis with the QWACC since it confirms/refutes the possibility that physically identical organisms can also be distinguished through this method.

Results

Co-Culture

The second half of the project involves the creation of a co-culture comprising Escherichia coli and Chlamydomonas reinhardtii. Since E. coli and C. reinhardtii are separated in size by an order of magnitude, it is possible to use so-called blob detection algorithms to locate, count and distinguish between the two organisms in holograms formed by a DIHM.