Understanding social networks (like Gusto's, above!) is big business and requires sophisticated big data approaches to fully understand and capitalize on. All the algorithms being developed to study social networks must have applications besides the freakishly accurate predictions in my Inbox of when Gusto and Bernie need another case of Deedle Dudes
Leave it to those brilliant people at Max Planck to divert them to understand something as ridiculously complex as the human immune system response! You can check it out in Nature Immunology here
How'd they do it? First they needed the data. So they flow sorted 28 different types of immune cells (!!!) from a person and did deep proteomics on all of them. The raw instrument data is all available at PRIDE here
(PXD004352), but they also set up a beautiful web resource for the full output data at www.immprot.org
This is the Immprot front page image that describes the experimental design.
This looks like a big matrix already, right? They do proteomics to a depth of around 10,000 proteins on all of these cell lines -- but get this -- it is bigger than this. They take the flow sorted immune cells, culture them and activate them. I'm no immunologist and it's too early in the morning to call and ask any friends about it, but from a rough understanding those cells in their native state floating around aren't all that interesting, right? They expose them to cytokines in culture that initiates something like the response they'd have to pathogens or other things immune cells destroy. Interesting to the non-immunologist here -- the different cells are activated with different compounds.
These activated cells are studied by doing proteomics on the cells as well as doing proteomics on the media. If you are considering doing something like this -- proteins excreted by growing cells -- I strongly suggest the method section. I wouldn't have thought to remove contaminating background materials from the culture supernatants and they go to great lengths to remove cellular material that might interfere with their results. If nothing else, this section is beautifully written. It's a Max Planck paper for sure...
It's easy to miss here that they also did transcriptomics as well, but the proteomics was all single shot on a 50cm column with 180min linear gradient. I'm glad to see they are still employing the SprayQC instrument software
(I haven't heard of it in a while and wasn't sure it was still updated for new instruments!) The MS1 is 120k resolution and MS/MS was set for more sensitivity over perfect transient matching with 55ms.
The data was expertly processed with MaxQuant and Andromeda and the MaxQuant output is also available at the PRIDE location above. But the stuff that really knocks your socks off is what they do afterward. Maybe it is less impressive to computational people, but this all falls under bioinformaGics for me that you can check out at ImmProt (but the figures in the paper are just stunning!)
Examples of me just looking around this morning..wait.... You seriously have to check this out. And send this link to any immunologists you know (I just did!) this has got to be an amazing asset for them. I'll skip the volcano plots (which are awesome!) and just go for the protein networks.
I went after my favorite Integrin protein (cause they're involved in everything!)
I love the note at the bottom! There is obviously some serious computational power behind the scenes here. Being too greedy will cost you some time. It populated with interaction partners, and you update the interaction wheel.
And it constructs the network map wheel for you! I wasn't too greedy, this thing was like 10 seconds. Please note I clipped the cell key and output to make them fit here, so scaling looks better on the page. You can output the plot as a high res PDF, or you can go straight to searchable tables.
Wow. This post is really long, if you can't tell, I really like this resource. It shows just another place where we can come in with our technology and techniques and impact researchers who maybe haven't explored what we can do now. Is this a resource that dedicated immunologists can mine for years and continually find new things to explore? It sure looks like it!