Profile rare cell populations with the sacrifice of 1 (one) lab animal!

 

If you've never done it, it absolutely sucks to kill a lab animal to get samples. Not only is it awful but an increasing body of evidence suggests that those millions of years or evolution make it not make very much sense at all to do it for many mechanisms. However, there are some systems where you absolutely have to do it to get that information. What if instead of needing to murder dozens of animals you could learn everything you wanted from just one? 

Hey, wait, this is one of the few labs on earth who are doing real single cell proteomics with mass spectrometers? What are they doing using more than one cell? 

Answer: I suspect having a much better day with their 500 cells than they had with most of their days of working with one! 

Besides being easier, this makes a lot of sense, right? Outside of marketing literature and some people studying single cells that are larger than my dogs, we aren't getting what we expect in terms of today's proteomic depth. But you can FACs sort 500 cells (assuming you have good cell markers) from just about anything.

This group goes through and quantifies something north of 7,000 proteins from every known cell type in rare c-kit+ progenitor population! (review on that here, I didn't know what it was either). 

Now, a fair argument that you could draw from reading other work from this group is that we might not actually know every cell type that is there, but at 500 cell resolution and as good as they appear to be at FACs you can learn a ton. 

While saving animals is a super worthy endeavor and some biologists might be like - ummm....what about animal to animal variation.... - it's one hell of a proof of concept. Some of those things where we can't possibly do human research is because taking a big chunk out of someone is generally not good for them. But you can lose 500 cells from just about anywhere without it being a big deal. Pretty easy to start imaging the future of low input proteomic diagnostics, right? 

Stellar preprint. Highly recommend you spend more time on it than I did this morning!