Latent Space: The AI Engineer Podcast
00:09:46 · Brandon Anderson, Fabian Theis, RJ Hanaki
[00:09:46] S2: So, like, DNA is sort of like a recipe book, or maybe it's a bunch of genes or a bunch of ingredients. And then as you... Each cell has its own sort of meal or dish or something, which is a combination of different ingredients. And now the trans... Transcriptomics is sort of figuring out... Taking different meals and then sort of decomposing them, like, oh, what ingredients went in to make this meal? So it used to set a dumb analogy. [00:10:12] S0: I have a bunch bunch of foodies as kids. I will use this metaphor a bit more often. Like it. Thanks, Brandon. [00:10:18] S1: I love it. And and then and then perturbations is when you somehow perturb the the genome or the transcriptome so that it has different transcripts in it. And then typically, that's done with a CRISPR knockout where you remove a gene from from the genome, and that causes it to not express that gene, obviously. And then from there, then you see what are the phenotypic or the actual changes in the physical biology that occur because of a perturbation. Is that more or less?
00:10:49 · Fabian Theis
[00:10:49] S0: Maybe I can take take a step back there, because I think, Ajay, you're you're fully right. But but this is sort of one of the hardcore type of perturbations, like fully knocking out a gene, sort of in evolution happens a bit, but mostly these things are sort of a tad more subtle. But of course, in order to understand the system, it's a good idea to take out bits and pieces. I think what happens sort of on on a normal development or normal sort of us being in homeostasis, is of course that these these cells sort of talk to each other all the time, you know, they sort of live in these complex multicellular states, right, so there's all type of communications happening between cells, so they always have stuff coming from the outside and then sort of doing something to them. So for me, a perturbation space, in fact, is just an input space to stuff that happens to a cell, just maybe because it just sits to another one and there's like some signaling happening, right? Or some gradient, some hormones coming in, whatever. Right? And all this stuff, of course, you can now sort of tune up or tune down. Maybe even something as basic as when you go these in vitro systems where pharma does drug screens, when you sort of put them in a dish, you know, you can do different what's called media conditions, and they have so much impact. Right? And it's like a bit of, for me, still like a a bio driven, maybe even like an alchemy, what type of cocktails people come up with to then sort of maybe take, you know, skin cells and turn them into more or less embryo like state cells, right, where you can then sort of make new cells to heal different stuff in the body, or these what are called Yamanaka factors, for example, you know, these were Nobel laureate type of stuff, right. So I think having a space of perturbations, essentially of things that you can do to a cell, this can be a very rich thing. It can go on as much as actually changes in the gene, which would be like a full knockout or something more subtle, which is sort of human genetics. And I think all of that we need to put in a big space of stuff that we can do that has an influence of cell state. And and like a virtual cell model should take all of it, right, and also combinations thereof, of course, and maybe, you know, disease happening, time, whatever, right, that then impacts.
00:12:40 · RJ Hanaki
[00:12:40] S1: That's a great connection to the the organoids, idea. So can you talk a little bit about... Okay. So an organoid is is what? And and how does that connect to this concept of perturbation space? And what do you do to it in addition to adjust this context?
00:12:58 · Fabian Theis
[00:12:58] S0: Dude, when I when I first heard about... Learned about this concept, I should say, you know, I'm I'm an auto trained biologist. So I typically learn about things when I read a bit or when I talk to colleagues and and friends. And when I first saw that people are able to take essentially cells out of a body, out of a cancer cell or whatever, right, and let them then grow into small multicellular systems that actually sort of start, connect, and do things. I was super super impressed. So, you know, people can now, for example, take some cells, put them in a dish, and then grow little mini brains, essentially set of neurons that essentially really start connecting, talking to each other, and people have shown that they can get those sort of mini brains in a dish to compute. Similarly, people can take heart cells, grow them into little heart organoids, and we have a bunch of local corporations with fantastic partners that actually start beating. Like, you have beating little heart cell clumps in a dish, right, and then of course, once you have these, and you're same thing as I spoke about brain, there's even simple systems like the colon where this works very well. There's a whole bunch of cancer systems. But you know, also for... Let's say if you wanna study diseases with hard to acquire material, let's say you wanna look into the genetics of some type of of neurodegenerative diseases or so. Actively, it's very hard to get access to tissue. Right? So you take stem cells from the skin... You take cells from the skin, differentiate them, make... Turn them into these mid little brains, and then you can potentially study some some things there. So I think this concept of replicating little bits of organs, that's why they call organoids, in a dish is very fascinating. There's all kinds of limitations, obviously, of an immune cell... Immune system is missing, there's like no, maybe feeding them or something like that. So there's all kinds of problems, right? But it's much, much more than just a cell line, which is like one cell type.
00:14:34 · RJ Hanaki
[00:14:34] S1: Moving to the modeling side, how does... How do you model that?
00:14:37 · Fabian Theis
[00:14:37] S0: So that's, I think, bit the research frontier. So I don't think people have agreed on what the optimal way to waddle is, particularly because there's not as much data as one wants. Jay, right, you're involved, if I understand correctly, in a whole bunch of spatial omics slash also sort of H and E sort of pathology type of effort. So like pathology, there's like so much samples available, right? And now increasingly we sort of do a bit more spatial omics. So I think how we even represent spatially connected data sets, and then you sort of break it down on an organoid level and so on, people are not as clear as it is with disassociated cells. So again, taking this a bit simpler, I think if we look at the space of disassociated cells and we look at these groups, we have this beautiful metaphor of what the humans call a cell type, which you know nature doesn't care about, This is just us putting categories on top of stuff, but it helps us understand this complexity. I don't think we have a similar metaphor for local niches of cells, maybe a little bit, but also in H and E, right. Whenever I talk and see what pathologists do, it's often very qualitative and it's maybe not at that level yet. So I think we need to develop a bit of a categorization language for us. Maybe computationally, maybe we don't need this so much, but computationally we also need to sort of think a bit how we deal with this. At the moment, we essentially deal with this as distributions of of of cells. So this is sort of the solution at the moment, but I think it can be more much more subtle, like graphs or something like that.