Jacob Kimmel: evaluation

Transcript context

…This is a dumb question, but it will help me understand why an AI model is necessary to do any of this work. You mentioned the Yamanaka factors. From my understanding, the way he identified these four transcription factors was that he found the 24 transcription factors that have high expression in embryonic cells, and then he just turned them all on in a somatic cell. Basically, he systematically removed from this set until he found the minimal set that still induces a cell to become a stem cell. That doesn't require any fancy AI models. Why can't we do the same things for the transcription factors that are expressed more in younger cells as opposed to older cells, and then keep eliminating from them until we find the ones that are necessary to just make a cell young? I wish it were so easy. You're entirely right. Shinya Yamanaka was able to do this with a relatively small team, with relatively few resources, and achieve this remarkable feat. It's entirely worth asking. Why can't a similar procedure work for arbitrary problems in reprogramming cell state? Whether it be trying to make an aged cell act like a young one, a disease cell act like a healthy one, why can't you just take 24 transcription factors and randomly sort through them? There were two features of Shinya's problem that I think make it amenable to that sort of interrogation that aren't present for many other types of problems. This is why he's such a remarkable scientist. Most of science is problem selection. You don't actually get better at pipetting or running experiments after a certain age, but you do get better at picking what to do. He's amazing at this. The first feature is that measuring your success criterion is trivial in the particular case he was investigating. He's starting with somatic cells that, in this case, were a type of fibroblast, which literally is defined as cells that stick to glass and grow in a dish when you grind up a tissue. It sounds fancy, but it's a very simplistic thing. He's starting with fibroblasts, you can look at them under a microscope, and you can see they’re fibroblasts just based on how they look. Then the cells he's reprogramming toward are embryonic stem cells. These are tiny cells, they're mostly nucleus. They grow really fast. They look different, they detach from a dish, they grow up into a 3D structure. They express some genes that will just never be turned on in a fibroblast by definition. How he ran the experiment was he just set up a simple reporter system. He took a gene that should never be on in a fibroblast, should only be on in the embryo, and he put a little reporter behind it so that these cells would actually turn blue when you dumped a chemical on them. Then he ran this experiment in many, many dishes with millions upon millions of cells. The second really key feature of the problem is this notion that those cells he's converting into amplify. They divide and grow really quickly. In order for you to find a successful combination, you don't actually need it to be efficient almost at all. The original efficiency Yamanaka published, the number of cells in the dish that convert from somatic to an induced pluripotent state, back into a stem cell, is something like a basis point or a tenth of a basis point, so 0.01%, 0.001%. If these cells were not growing and they were not proliferating like mad, you probably would never be able to detect that you had actually found anything successful. It's only because success is easy to measure once you have it and—even being successful in very rare cases, one in a million—amplifies and you can detect it, that this was amenable to his particular approach. y because success is easy to measure once you have it and—even being successful in very rare cases, one in a million—amplifies and you can detect it, that this was amenable to his particular approach. In practice, what he would do is dump these factors or this group of 24 minus some number, eventually whittling it down to four. He would dump these onto a group of cells and over the course of about 30 days, just a few cells in that dish, like a countable number on your fingers, would actually reprogram. But they would proliferate like mad. They form these big colonies. It's a single cell that just proliferates and forms a bunch of copies of itself. They form these colonies. You can see with your eyeballs by holding the dish up to the light and looking for opaque little dots on the bottom. You don't need any fancy instruments. Then you could stain them with this particular stain and they would turn blue based on the genetic reporter he had. We look at those key features of the problem and we pick any other problem we're interested in. I'm interested in aging, so that's the one I'm going to pick for explanation. How difficult is it to measure the likelihood of success or whether you've achieved success for cell age? It turns out age is much more complicated in terms of discriminating function than actually just comparing two types of cells. An old liver cell and a young liver cell, prima facie, actually look pretty darn similar. It's actually quite nuanced the ways in which they're distinct. There isn't a simple, trivial system where you just label your one favorite gene or you can just……

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