Latent Space: The AI Engineer Podcast
00:06:07 · Fabian Theis
[00:06:07] S0: I mean, for for me, if I may, you stop me when I'm sort of getting to put to put too much into these these these old stories. Like, you know, I'm sort of... I'm I'm I'm I've been trained machine learning scientist for the longest time doing all kinds of things, but, you know, I started with single cell early on, qPCR. Maybe initially, if I'm really honest, not so much because it was like the biology that was interested in, but because we had high sample numbers purely for that. Right? Because even early on, it was sort of clear that the number of QPCRs at scale, but then, of course, transcriptomes was just getting really big. And, you know, when I started my career early on, we had, a few bug RNA. Now we sort of had thousands of them or whatever. Right? So this is, early times. But now, you know, we we go into to a regime, and that was sort of one of these eye opening moments when when Parse Bio had, like, I think they call it the Gigalab or something like that. And, know, there's sort of similar ones from competing companies now. But when they had, like, a single experiment, 10,000,000 transcriptomes measured, right? I thought that was like next level stuff. And you know, this step from just getting more PBMCs as a control tool, like let's actually do something at scale to it, sort of having here the cell space, but then having here the perturbation space, which typically was very scarce. It was like control versus disease or so. So this sort of number of points, what you had here in terms of sampling on the perturbation space was typically much, much more scarce than the number of observations in terms of cells that you had. But now this is exploding. Now you sort of have, I don't know, 100 different cell lines where you do sort of full epitope seek, like full CRISPR, or you have, like, batteries of a thousand drugs or so added, and suddenly this space gets really rich, right, and then you can sort of start doing all these fancy perturbation prediction type of things, and this is currently happening. It's crazy what scale is happening.
00:07:45 · Fabian Theis, RJ Hanaki, Brandon Anderson
We ourselves involved in a whole bunch of things where we actually go beyond cell lines and do organoid perturbations, and maybe if you have time we can can talk a bit more about that, because I'm quite convinced that we need to go to multicellular systems to have actually really interesting readouts, but I think everyone is scaling these these these levels up, and at some point, you know, we're gonna go maybe even to human genetics as a perturbation. [00:08:07] S1: Can you just, like, explain what is a perturbation? What do you mean by that? Just for the people who are not familiar. [00:08:13] S0: Yeah. Yeah. [00:08:13] S2: Yeah. Actually, would you mind maybe backing up even one step more than that? Like, what do we even mean by, like, measuring transcriptomics? And you were talking about, like, PCR based and then also perturbed seq.
00:08:25 · Fabian Theis
[00:08:25] S0: And When I give sort of public talks, I typically say, you know, human genome project, we sort of have the genome for the body, but it's pretty much the same everywhere, right? So, like, why do we have all these different cell types, right? What's sort of happening specifically? And so first level, what we typically have, one of the first levels is that, you know, per cell different stuff is being read off of the genome, right, and these messenger RNAs, which then sort of encode the various downstream proteins, while these are sort of differently expressed as one says in in in different cells. And to say in a human maybe there's something like 20,000 different types of these, splicing many, many more, but there's sort of a rough level. So what you de facto have is per cell a 20,000 dimensional vector that has a whole bunch of zeros when stuff is not there, but then a whole bunch of sort of numbers, how many of these counts of these messenger RNAs you have. Because these things are called transcripts. Right? This is called the transcriptome. Yeah. And and and de facto, then you have this sort of 20,000 dimensional space that we very often encode in a sort of nicer latent space that sort of has like an... A sub manifold, like sort of this this subspace where these actual cell cloud lives. Right? And if the cell cloud then sort of like these sort of 20,000 dimensional vectors, each point then being one cell, right, if they sort of cluster up, then we call these things cell types or something like that. And if they sort of go into a sort of sausage or whatever, right, we call them in trajectories, like a cell differentiation, like the fertilized cell then making all the cell types in the body or something like that.
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?