This week, I had two first author papers published:
- Quantitative 3D-imaging for cell biology and ecology of environmental microbial eukaryotes
- Jug: Software for Parallel Reproducible Computation in Python
I intend to post on both of them over the next week or so, but I will start with the first one.
The basic idea is that just as metagenomics was the application of lab techniques (sequencing) that had been developed for pure cultures to environmental samples, we are moving from imaging cell cultures (the type of work I did during my PhD and shortly afterwards) to imaging environmental samples. These are, thus, mixed samples of microbes (micro-eukaryotes, not bacteria, but remember: protists are microbes too).
Figure 1 from the paper depicting the process (a) and the results (b & c).
The result is a phenotypic view of the whole community, not just the elements that you can easily grow in the lab. As it is not known apriori which organisms will be present, we use generic eukaryotic dyes, tagging DNA, membranes, and the exterior. In addition, chlorophyll is auto-fluorescence, so we get a free extra channel.
With automated microscopes and automated analysis, we obtained images of 300,000 organisms, which were classified into 155 classes. A simple machine-learning system can perform this classification with 82% accuracy, which is similar to (or better than) the inter-operator variability in similar problems.
The result is both a very large set of images as well as a large set of features, which can be exploited for understanding the microbial community.