Handfield, L., Chong, Y., Simmons, J., Andrews, B., & Moses, A. (2013). Unsupervised Clustering of Subcellular Protein Expression Patterns in High-Throughput Microscopy Images Reveals Protein Complexes and Functional Relationships between Proteins PLoS Computational Biology, 9 (6) DOI: 10.1371/journal.pcbi.1003085
This is an excellent paper that came out in PLoS CompBio last week.
The authors present a high-throughput analysis of yeast fluorescent microscopy images of tagged proteins. Figure 8, panel B (doi:10.1371/journal.pcbi.1003085.g008) shows a few example images from their collection
One interesting aspect is that they work on the dynamic aspects of protein distributions only from snapshots. I was previously involved in a similar project (ref. 18 in the paper ) and so I was happy to see others working in this fashion.
Budding yeast, as the name says, buds. A mother cell will create a new bud, that bud will grow and eventually it will split off and become a new daughter cell.
By leveraging the bud size as a marker of cell stage, the authors can build dynamic protein profiles and cluster these. This avoids the need for either (i) chemical synchronization [which has other side-effects in the cell] or (ii) movie acquisition [which besides taking longer, itself damages the cells through photoxicity].
In all of the examples above, you can see a change in protein distribution as the bud grows.
They perform an unsupervised analysis of their data, noting that
Unsupervised analysis also has the advantage that it is unbiased by prior ‘expert’ knowledge, such as the arbitrary discretization of protein expression patterns into easily recognizable classes.
Part of my research goals is to move beyond supervised/unsupervised into mixed models (take the supervision, but take it with a grain of salt). However, this is not yet something that we can do with current machine learning technologies.
The clusters are obtained are found to group together functionally similar genes (details in the paper).
The authors are Bayesian about their estimates in a very interesting way. They evaluate their segmentations against training data, which gives them a confidence measure:
Our confidence measure allows us to distinguish correctly identified cells from artifacts and misidentified objects, without specifying what the nature of artifacts might be.
This is because their measure is a density estimate derived from training based on features of the shape. Now, comes the nice Bayesian point:
This allows us to weight probabilistically data points according to the posterior probability. For classes of cells where our model does not fit as well, such as very early non-ellipsoidal buds, we expect to downweight all the data points, but we can still include information from these data points in our analysis. This is in contrast to the situation where we used a hard threshold to exclude artifacts.
Unlike the authors, I do not tend to care so much about interpretable features in my work. However, it is interesting that such a small number (seven) of features got such good results.
There is more in the paper which I did not mention here: the image processing pipeline (which is fairly standard if you’re familiar with the field, but this unglamorous aspect of the business is where you always spend a lot of time);
One of my goals is to raise the profile of Bioimage Informatics, so I will try to have more papers in this field on the blog.
|||We worked on mammalian cells, not budding yeast. Their cell cycles are very different and the methods that work in one do not necessarily work in the other.|