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Welcome to CRISPR Unedited,

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a bite-sized bio podcast hosted by Anton Adamson.

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Today on CRISPR Unedited, we chat to Bernard Schmear,

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who heads the CRISPR Functional Genomics facility at the Carol Green Street in

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Sweden. We learn about the vast number of cells required for CRISPR screens.

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This means, uh, 500 cells to a thousand cells per guide.

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So you are handling roughly 80 million cells,

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Hear about must read literature on Christmas greens.

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Read the Review by John Dench. That's called, am I Ready for crispr?

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I think we also call it, uh, gospel according to John.

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And we discover what the future holds.

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We are looking a lot into, uh, base editing screens,

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uh, where you can now,

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instead of knocking out an entire gene or activating an entire gene,

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you can now actually introduce point mutations,

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All this and more in this episode of Crisp Bur Unedited.

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Hello everyone, and welcome to this podcast from Bite-Size Bio.

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My name is Anthony sson,

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and I run a core facility called the Genome Editing Unit,

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where we use CRISPR Cas nine to engineer, uh, cultured cells, uh,

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genetic modified mouse models and, and modified flies as well. Now,

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my facility focuses on one gene at a time. Um, we might use CRISPR to say,

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knock out that gene of interest in a cell line and explore its function

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afterwards. Maybe put some mutations in that, uh, same gene or tag it with, uh,

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a fluorescent protein. But today's podcast guest, um,

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he doesn't concern himself with one gene at a time.

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He operates at scale tags in the entire genome and knocking out every single

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gene using the so-called CRISPR knockout screens. So,

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I'd like to welcome Bernard, uh, schmear to the podcast. Welcome, Bernard.

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Thank you, Anthony.

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Uh, so to start with, maybe we could get an idea of your background.

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Tell us a little bit about yourself, uh,

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how you came to work in the field of performing Christmas greens.

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Yeah, sure. Um, so I am, I'm originally from Austria. I did my, uh,

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undergrad in biochemistry and my PhD at Vienna University. Um,

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and then moved on to London to do a postdoc, uh,

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to Cancer Research uk, London Research Institute. So,

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cancer Research UK had actually just been founded a year prior to,

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to me joining that was in 2003, I think. Uh,

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spent five years there working on T g PTA signaling mechanisms of TG F pta,

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signal transduction from the membrane to the nucleus.

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And while doing this,

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I slid a little into mathematical modeling of dynamic processes,

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and I pursued this, uh, by moving to Oxford, uh,

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where I did another postdoc with, uh, be Novak at, uh,

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department of Biochemistry in Oxford. Um,

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and since 2012, I have been in Stockholm.

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I came here initially to join as a senior scientist, the lab of uc,

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who is now also professor in Cambridge, actually. Um,

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and screening. Yeah. Um, I was,

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I was at the time supposed to run a project, uh,

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where we were looking for genes affecting, uh, cell size regulation,

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and we were doing this gene trapping that had been published in 2011, I think.

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Uh, but then immediately when, uh,

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the first Christmas screening papers came out in, in early 2014,

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we jumped on that bandwagon. And this was my start on crispr. Um,

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the cell size bit never flew,

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so never anything came out of that. Um, but yeah,

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I think I learned the, the CRISPR screening pretty from pretty early on.

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And then we did a lot of screens with transcription factor libraries on, uh,

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different, uh, cancer cell types. So it was essentially like, uh,

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a little bit of what, what depth map is now doing, of course, at,

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at a huge scale genome-wide with a lot of cells.

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And since 2017,

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I'm running a facility that's now called CRISPR Functional Genomics.

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We also do the precision editing just as you do, um, with varying success.

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Um, but we also, and our main focus actually is on, is on the,

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on the transcriptomic, sorry, on the, on the functional genomics. So really, I,

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I I tend to call it functional genomics, CRISPR based functional genomics,

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because it's not just knockout screens, but we might get to that.

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Yeah, absolutely. Um, so like I say, we,

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we tend to just focus on knocking out a single gene at time,

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and we haven't performed any knockout screens at all. Um,

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we are looking into it. We've got a number of people locally who are re uh,

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interested in applying this technology and their research. And from the outset,

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you know, as an outsider looking in, it looks quite daunting, you know,

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from what I can tell, there's a hell of a lot of cell culture involved. So,

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you know, how do you manage this?

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Yeah, so there are in principle two ways of doing this, right? Uh,

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there is an array, uh,

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there are array screens where you have each guide against each chain in one well

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of a microt title plate. Um, this is not what we do at all. So, um,

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as you were alluding to, we are doing pool screens, and for pool screens,

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as you say, you need quite a lot of cells.

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So for the genome-wide libraries that are around now and most commonly used,

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they contain about 80,000 guides. So four guides per gene.

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And in order to keep the library coverage through a screen,

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one commonly run the screen at a 500 x to a thousand x, this means,

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uh, 500 sales to a thousand sales per guide.

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So you are handling roughly 80 million sales,

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maybe 40 million minimally. Um,

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that can be quite daunting. Uh, one gets used to it.

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So I don't think it's the biggest issue really, but, uh,

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you should not be scared of having to handle like, uh,

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around 80 T 1 75 large cell culture flasks at a time.

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Um, yes, it also needs quite some incubator space sometimes if so,

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running at the same time. Um, but that's okay.

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Um, I think it's one of the, it's, it was one of the things you get used to, um,

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people find it quite scary in the beginning,

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but you get used to it fairly quickly.

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So how, how many members of staff do you have then?

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Does I assume it takes quite a few people to room with these screens if there's

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all this cell culture involved?

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Yeah, so we currently, we are six altogether, so including myself,

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I'm not in the lab ever, which is probably for the better. Um,

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but, uh, five people, no, sorry,

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four people actually work on the Christmas screens. Um, mainly.

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So we also do a lot of tech development and other things.

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So it is not just Christmas screens,

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and I have one person doing the precision genome editing focusing full-time on

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that, and the others help her out by commonly with that.

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Um, so it works very well. It is not so much limited by people.

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It can be limited by, as I said, like incubator space or you have to, or virus,

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virus room space and these kind of things. Uh,

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so you cannot run too many screens at the same time. It has to be one at a time.

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Just one a time. Yeah. Yeah.

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Yeah.

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And how long would each screen typically take?

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Um, yeah, um,

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brings me maybe to another challenge of the screen. So you,

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you normally do the screen such that you create a S nine expressing cell line

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first, uh,

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and creating a S nine expressing cell line that actually stably maintains S nine

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expression can be quite challenging. Um,

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so we do that by having a P F P on the CS nine.

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And we sort multiple times often over several weeks,

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multiple times until we get a cell line that is like at least kind of stable.

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So some, it depends. A So these

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Clonal though, these are mixed pools?

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No, they're not clonal. Okay. They're not clonal. So we just, uh,

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do a random integration by antivirus,

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which might of course contribute that a large proportion of them just silence

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over time.

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But this is something we see very commonly in many cell lines to silencing.

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So that can be, can take some time until you have a good cus nine line.

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Um, and then actual,

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So quite a lot of preliminary work before you even get to the screen, then you,

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you,

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you build the best possible cell line that's gonna be amenable to the screen

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being Yeah,

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Exactly. So that's important. Yeah, you have to have a good SSS line. Um,

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I mean if, you know, if like only like 80% or lower of your sales express cast,

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you will pick up all the guides that are in empty sales that don't have cast and

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it will cause a lot of background of course. So this needs to be good.

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So that's worth spending some time on. But altogether then, uh,

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the actual expansion of the sales library transaction, um,

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doesn't take that long normally, unless you have sales that grow like really,

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really slowly, which also happens sometimes. And then to get like, uh,

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240 million or whatever you need to transfuse per,

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per replicate can be daunting. How about with normal cells? It's not a big deal.

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So this takes maybe two months and, uh,

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normally we say to our customers when they come, they can expect data in,

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in four to six months. There's also the sequencing of course,

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you then need to do the next generation sequencing for which rely on another

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facility that does the sequencing. They also have a queue,

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so it doesn't necessarily go immediately. Alright. There's

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Elements that you have to out your elements of the process you have to outsource

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to of the course locally then. Yeah. Okay.

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Exactly. So we do everything other than the sequencing. Um, so

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Analysis of the sequencing as well. Does it do,

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is that something you guys can do or do you work with mathematicians for that

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process?

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Uh, data analysis we do ourselves, um, with mainly the magic package.

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That is a common thing. Uh, there are many others out there,

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but we use that one. Um, so we, in the end, there's not that much you can do,

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right? I mean, you end up with recounts in control and recounts in treatment.

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So you get two, two columns of numbers. Uh,

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there's not that much you can do with this, but of course, um,

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you can do chin set enrichment analysis then on the, on the data that you get,

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et cetera. So we could be better at that,

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but we do what we can to help to help our clients.

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So you, so you mentioned this other person who, who does the,

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the cell and engineering are, are they involved,

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like that functional validation?

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So if you've got some really exciting hits from your screen,

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will that person then go and knock out that cell individually? Sorry,

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knock out gene individually the cells to Yeah, I, I see.

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Yeah, we, we don't really do it. I mean, you know, the screening, it's,

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it's a high throughput method. I I,

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I used to say it doesn't actually give you any answers most commonly, but it,

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it teaches you to ask the right questions. I, right, uh,

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you get a lot of hits that some hits hopefully, that you expected,

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other hits that you have no idea why they come up. Um,

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so then the validation is the really challenging part. Then the screen is,

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uh, relatively straightforward. Um, but then the validation of course,

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a validation, make sure that your hits are real,

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and of course figuring out the mechanism,

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figuring out a story around the whole thing that you find that is way

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more time consuming and way more challenging. And that, of course,

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we just pass off to our customer again, is, okay, there you go, there your hits.

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Um, good luck. Um,

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which also contributes to the fact that it can take a very long time from a

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screen being performed and the publication coming out.

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So there's a often a several year gap.

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Yeah, I, I suppose just to comment on that,

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quite often you'll see in papers the screen is figure one and then yes,

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it follows you. It's probably, you know, a lot more work, uh,

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downstream of that. Yeah.

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That's how it is. Yeah.

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So four to six months is a significant amount of time amount investment

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for any investigator. And I assume it's, it's not necessarily cheap either.

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Is it quite expensive to run these, uh, these, these, these experiments?

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Um, they are, I think a pool screen is actually fairly cheap. Um,

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and array screen is considerably more expensive because you, you,

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the reagents are one, one time reagents, right? You just use a up. Um,

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whereas here everything is, there's no big instrumentation involved. Um,

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it's just, it's mainly sary cost because of course it is a lot of work,

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but a typical screen that we run, so we, we have a subsidy on that as well. But,

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um,

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it's probably around 10 to 15,000 pounds.

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Oh, okay. More reason I expected. Yeah,

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It is not terrible. Uh, it's affordable. Um,

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now, sorry, I lost, uh, did you, what, uh, what else did you ask?

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I lost your question.

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No, I, I was saying obviously a time investment as well, you know, oh,

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Time investment. Yeah,

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Sure. Or investigator colleagues ever get impatient, you know, do they, uh,

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start to ask questions after a couple months, that kind of thing?

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Yes, yes, yes. It happens, of course. But, uh, in a way it's,

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it's easy for them because it's, as you say, it's normally,

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it's the start of a project, right? So it is not, if you make a no cut line,

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like you for example, do, uh, then often people also come to us and say, oh, uh,

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we were asked for a paper revision to do that. And,

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and then of course it's urgent, super urgent. The screen is normally the,

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the start of a project, and I think they're bit more patient there. Mm-hmm.

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Maybe it's also important to get the expectations, right.

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So just tell them how long it's likely gonna take. Yeah,

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Absolutely. So I suppose, you know, do you always use the same screen?

228
00:13:38.460 --> 00:13:40.720
You know, because I know you, I've seen some of your papers use like, you know,

229
00:13:40.720 --> 00:13:44.720
the brunello screen screen, which obviously very popular, very well validated,

230
00:13:44.790 --> 00:13:47.800
very well used. But do you have a customized, or do you,

231
00:13:47.800 --> 00:13:49.640
do you generally speak and stick with that same screen?

232
00:13:51.340 --> 00:13:54.760
No, we do a lot of different things. So we also make the libraries ourselves.

233
00:13:55.300 --> 00:13:57.760
Oh, really? Um, depending what people want.

234
00:13:57.760 --> 00:14:01.840
So any custom library that can be smaller or bigger than the standard libraries,

235
00:14:02.740 --> 00:14:06.200
of course, I mean, the bread and butter screen is, is a knockout screen with,

236
00:14:06.230 --> 00:14:08.160
with brunello, this is what most people do.

237
00:14:08.260 --> 00:14:11.280
So we use brunello because we like it. I'm not saying it's the,

238
00:14:11.280 --> 00:14:15.870
necessarily the only good library that's around, but it's a good library. Um,

239
00:14:16.730 --> 00:14:20.950
we also do crisper activation, crisper inhibition, um,

240
00:14:21.420 --> 00:14:25.510
screens. We also have done, uh, screening with a CAST 13 D,

241
00:14:25.510 --> 00:14:30.310
so that's the one that actually targets, uh, R N A instead of, uh, D N A. Well,

242
00:14:30.310 --> 00:14:33.510
Let me ask you on that, on CA 13. So we've been dabbling in CA 13,

243
00:14:33.730 --> 00:14:36.350
and we haven't been very successful with it, not on a screen basis.

244
00:14:36.490 --> 00:14:41.030
We seem to get quite a lot of self death and we think a collateral damage of all

245
00:14:41.030 --> 00:14:43.190
the transcripts. Is that something you've seen as well?

246
00:14:44.570 --> 00:14:46.550
Not at all, actually. Okay.

247
00:14:47.030 --> 00:14:48.710
A mixed literature emerging on this, I think.

248
00:14:49.060 --> 00:14:50.870
Yeah. No, we, we targeted,

249
00:14:51.180 --> 00:14:56.040
so this screen was a relatively small screen for around 150 long non-coding

250
00:14:56.310 --> 00:14:59.120
RNAs. Uh, so I don't know, you know,

251
00:14:59.190 --> 00:15:03.440
they might not be that important for things we have not done really looked.

252
00:15:03.540 --> 00:15:05.280
One should actually do a proper screen, I think,

253
00:15:05.280 --> 00:15:08.080
where you actually hit essential genes as well, and then see how,

254
00:15:08.220 --> 00:15:12.200
how well it works. Um, hits came out of it, um,

255
00:15:13.340 --> 00:15:16.120
as always, I mean, people need to validate it now and see whether,

256
00:15:16.520 --> 00:15:20.600
whether it actually makes sense. Yeah. But it seemed to have worked,

257
00:15:20.600 --> 00:15:23.560
because normally if a screen doesn't work, it's just everything's flat, right?

258
00:15:23.600 --> 00:15:27.280
I mean, you just don't find any hits. Um, if you find hits,

259
00:15:27.280 --> 00:15:30.880
especially in this case where you have several more guides than we had,

260
00:15:30.880 --> 00:15:33.800
like probably 10 guides per transcript,

261
00:15:34.700 --> 00:15:39.320
and this significantly actually pops up. Uh, it's fairly trustworthy,

262
00:15:39.520 --> 00:15:42.480
I think. Yeah. Um, so then we also,

263
00:15:42.830 --> 00:15:44.080
Many, I'm gonna say,

264
00:15:44.080 --> 00:15:47.920
do you get many situations where the screen fails or generally most screens

265
00:15:47.920 --> 00:15:48.753
successful?

266
00:15:49.340 --> 00:15:53.880
Uh, yeah. Fail, it rarely fails. Fails.

267
00:15:53.990 --> 00:15:56.120
What it, what can happen? What,

268
00:15:56.120 --> 00:16:01.080
what changes in different screens is the effect size that you get, i e then the,

269
00:16:01.080 --> 00:16:02.320
the p values, uh,

270
00:16:02.380 --> 00:16:05.840
the significance that statistically significant significance of your, of your,

271
00:16:06.020 --> 00:16:10.760
of your hits. So sometimes it's all a bit blurry,

272
00:16:10.780 --> 00:16:14.040
and then that's what we call on the poor screen. Um,

273
00:16:14.070 --> 00:16:17.640
sometimes can be rescued by redoing the library prep. Uh,

274
00:16:17.640 --> 00:16:20.920
sometimes it happens there in the P C R for the, for the library prep,

275
00:16:20.980 --> 00:16:23.840
but sometimes it's just something else. Um,

276
00:16:24.540 --> 00:16:29.360
but I have to say that the vast majority actually work reasonably well.

277
00:16:29.940 --> 00:16:32.680
At least you can always say, okay, this could have, could have been clearer,

278
00:16:33.300 --> 00:16:35.000
but people always find something. Okay,

279
00:16:35.000 --> 00:16:37.200
there's a sort of confirmation bias there as well, right?

280
00:16:37.200 --> 00:16:38.480
People want to find something.

281
00:16:39.460 --> 00:16:42.920
So that's why I'm then when people try to validate it, say, okay,

282
00:16:42.950 --> 00:16:47.280
show me again what you want to validate so I can look in detail at the data.

283
00:16:47.380 --> 00:16:50.480
Is this really, do I buy this or do I not buy this?

284
00:16:51.110 --> 00:16:54.120
Yeah, I suppose that's where the validation comes in again, isn't it that,

285
00:16:54.420 --> 00:16:55.253
you know,

286
00:16:55.300 --> 00:16:59.280
you basically repeat the experiment in a different way to study the gene in a

287
00:16:59.280 --> 00:17:02.720
bit more detail. And I said, there is a bit of confirmation bias, isn't there?

288
00:17:02.960 --> 00:17:04.760
I suppose that is one of the risks here.

289
00:17:05.140 --> 00:17:07.720
You almost have too much data from what I can tell.

290
00:17:08.990 --> 00:17:13.280
Yeah, it's, uh, it can be a lot of data. It can be, you want,

291
00:17:13.630 --> 00:17:15.320
there's some sweet spot, right? You want,

292
00:17:15.320 --> 00:17:19.200
ideally you want to find like a couple of genes that you were expecting and a

293
00:17:19.200 --> 00:17:22.000
couple that we were not expecting. That's ideal. Mm-hmm. Uh,

294
00:17:22.580 --> 00:17:26.560
if you find only the ones you were expecting anyway, they're disappointed. Um,

295
00:17:26.660 --> 00:17:30.360
if you get 700 hits, um, then they go, oh,

296
00:17:30.550 --> 00:17:34.400
well what are we gonna do now? Uh, so yeah, difficult,

297
00:17:34.880 --> 00:17:36.640
I guess you can run then a follow up,

298
00:17:36.720 --> 00:17:39.800
a smaller follow up screen or something to see in a more,

299
00:17:40.150 --> 00:17:43.520
like followed maybe by single cell transcriptomic readout, for example,

300
00:17:43.580 --> 00:17:47.040
to get more information about the phenotype. And so there are things,

301
00:17:47.040 --> 00:17:48.040
one can narrow it down,

302
00:17:49.460 --> 00:17:52.600
but most people pick the hits they work on, on a,

303
00:17:52.700 --> 00:17:57.400
on a much less scientific, uh, in a much less scientific way.

304
00:17:57.400 --> 00:17:58.440
They just say, okay,

305
00:17:58.500 --> 00:18:01.360
I'm comfortable with working with stuff that's in the nucleus.

306
00:18:01.580 --> 00:18:05.080
I'm uncomfortable with working with stuff that is in the ghi.

307
00:18:05.700 --> 00:18:08.960
So if something from the GHI comes up, she goes, uh, no, uh,

308
00:18:08.990 --> 00:18:12.440
it's not my area of expertise. So we drop that one, we take another one. Right.

309
00:18:12.440 --> 00:18:14.400
So this is more how it works in practice.

310
00:18:15.190 --> 00:18:18.520
Well, I suppose when you, when you publish you, do you publish the, the data,

311
00:18:18.700 --> 00:18:22.840
the entire dataset so that if someone else around the world who is interested in

312
00:18:22.840 --> 00:18:24.960
that goal, you are in the other, so, you know,

313
00:18:24.980 --> 00:18:28.040
for the results in that compartment, they could take that finding forward.

314
00:18:29.110 --> 00:18:32.640
Yeah, ideally. So, um, we have also,

315
00:18:32.640 --> 00:18:37.200
so we are funded from the Swedish government mainly. We also have a mandate to,

316
00:18:37.620 --> 00:18:41.760
to push people into. So big we, we rarely publish ourselves. It's more that,

317
00:18:41.760 --> 00:18:42.960
that our customers publish,

318
00:18:43.780 --> 00:18:47.160
but we should push them into also publishing that the data is,

319
00:18:47.300 --> 00:18:49.760
is published in according to fair principles.

320
00:18:49.760 --> 00:18:53.160
So it'll all be there and it'll be findable and retrievable and, um,

321
00:18:53.750 --> 00:18:57.040
have the metadata present that it can actually be, um,

322
00:18:58.360 --> 00:18:59.600
refound and, and reused.

323
00:19:00.260 --> 00:19:04.160
So what definitely needs to be done is all the original sequencing files are

324
00:19:04.520 --> 00:19:07.680
uploaded to short treat archives so that this, this data is available,

325
00:19:07.740 --> 00:19:10.640
but without the proper metadata, of course this is not very useful,

326
00:19:11.220 --> 00:19:15.560
but there are several databases, uh, for CRISPR screening data that exist.

327
00:19:16.020 --> 00:19:19.120
And we encourage people to, to upload to these

328
00:19:20.960 --> 00:19:25.380
in a good way so that then the data is not lost in a sense. But it's,

329
00:19:25.380 --> 00:19:26.340
it is always hard, you know,

330
00:19:26.340 --> 00:19:30.980
if it comes from different labs done in different ways to make it, uh,

331
00:19:31.240 --> 00:19:32.980
really comparable, it's difficult.

332
00:19:34.230 --> 00:19:35.930
Mm-hmm. Um, I was gonna ask, does,

333
00:19:35.930 --> 00:19:39.210
does everyone at your institute use you guys for functional screening,

334
00:19:39.430 --> 00:19:42.050
or do they sometimes do it themselves in their own laboratories?

335
00:19:43.970 --> 00:19:48.870
Um, screening, I don't know much that people do screening.

336
00:19:48.870 --> 00:19:53.670
So some people have tried and then come to us. Uh,

337
00:19:53.770 --> 00:19:56.630
it, because commonly, you know, the first time you do it,

338
00:19:56.660 --> 00:20:00.470
it's likely gonna fail. Um, you need a practice run,

339
00:20:00.770 --> 00:20:04.110
at least one practice run. I say, okay, if you the second, third time,

340
00:20:04.190 --> 00:20:08.030
I think you're good. It's not that complicated. Yeah. Um, I

341
00:20:08.550 --> 00:20:12.350
Actually really quite nicely leads me onto another question I had in that you as

342
00:20:12.390 --> 00:20:16.230
a newcomer, what would be your, you know, main advice,

343
00:20:16.230 --> 00:20:18.710
obviously expect failure the first time is one bit of advice.

344
00:20:18.950 --> 00:20:22.470
I think you could say that, but, you know, what would you say to someone who's,

345
00:20:22.470 --> 00:20:25.630
you know, started to think about using these techniques in the laboratory?

346
00:20:27.400 --> 00:20:32.200
Um, yeah, assuming the user,

347
00:20:32.880 --> 00:20:35.200
assuming the user published library that you can get ready from,

348
00:20:35.560 --> 00:20:38.680
I think it is not so hard to amplify this. This is pretty easy, otherwise,

349
00:20:38.800 --> 00:20:42.760
library transformation cloning can already be a big problem. Um, okay.

350
00:20:43.020 --> 00:20:44.240
And you get all the guides in.

351
00:20:44.260 --> 00:20:48.290
So this is something where you need to have good protocols for, uh,

352
00:20:48.310 --> 00:20:51.290
for double stranding, the oligo pool and these kind of things. Uh,

353
00:20:51.630 --> 00:20:53.810
if you have a library ready, um,

354
00:20:54.590 --> 00:20:59.250
one advice that I would have is make sure that the way you count cells

355
00:20:59.730 --> 00:21:02.810
actually count cells correctly. Um, because, um,

356
00:21:03.270 --> 00:21:06.130
it seems that many also automatic cell counters,

357
00:21:06.250 --> 00:21:09.450
they seem to overestimate the number of cells by counting some debris in

358
00:21:09.450 --> 00:21:10.150
something.

359
00:21:10.150 --> 00:21:13.530
If you do this several times while you have a screen running and you every time

360
00:21:13.640 --> 00:21:15.810
underestimate by 20, 30%,

361
00:21:16.190 --> 00:21:19.090
you will then eventually pull nick your library and you will lose stuff.

362
00:21:19.710 --> 00:21:22.410
So having a lot of cells, counting them correctly, um,

363
00:21:22.630 --> 00:21:25.210
How, how do you count your cells? Are you the hemo, cytometer,

364
00:21:25.230 --> 00:21:30.010
the old old school? Really? Okay. Old school. Fascinating. You know,

365
00:21:30.010 --> 00:21:33.010
you've got this cutting edge technology and you're using the hemo cytometer.

366
00:21:33.010 --> 00:21:33.850
Do you sell counts?

367
00:21:34.440 --> 00:21:38.810
Yeah, we have, we have, we would have even a fax that could sort cells, uh,

368
00:21:38.810 --> 00:21:43.530
sorry, uh, count cells, but we, no, we rely on the hemo cytometer.

369
00:21:43.530 --> 00:21:48.170
People are used to it. They're good at it. It's quite consistent, so works fine.

370
00:21:49.230 --> 00:21:52.250
Um, yeah. Then if you do screening,

371
00:21:53.070 --> 00:21:54.250
if it's a live dead screen,

372
00:21:54.430 --> 00:21:56.930
so you have a drug screen or you just look for proliferation,

373
00:21:57.050 --> 00:22:00.770
I think it's also still pretty straightforward. Um,

374
00:22:00.770 --> 00:22:05.010
there are good protocols on ing how to prep the libraries and do this kind of

375
00:22:05.010 --> 00:22:08.730
things. So I think this is all kind of All right. Um, what we do a lot, however,

376
00:22:08.950 --> 00:22:13.370
is, uh, uh, screens where you do fax based sort thing,

377
00:22:13.370 --> 00:22:15.490
because in every, any pool screen, right,

378
00:22:15.490 --> 00:22:18.330
you need to separate your phenotypes of interest from each other.

379
00:22:18.470 --> 00:22:23.370
So if your live dead screen is easy, otherwise you need to sort, uh, and again,

380
00:22:23.370 --> 00:22:27.330
in order to not bottleneck, one needs to run, uh,

381
00:22:27.630 --> 00:22:30.770
around a hundred million cells through the facts. Um,

382
00:22:31.070 --> 00:22:35.090
so commonly we sort for two days for one experiment, two full days.

383
00:22:35.870 --> 00:22:37.890
So this is also something to take into account,

384
00:22:37.890 --> 00:22:42.640
especially if you don't have your own sort and rely on a facility needs to be

385
00:22:42.690 --> 00:22:44.320
pre-booked well in advance.

386
00:22:44.420 --> 00:22:48.720
And you have to also tell the poor staff there that they might not be able to go

387
00:22:48.720 --> 00:22:52.600
home at the time they are used to, because it can take even longer.

388
00:22:53.820 --> 00:22:56.480
So, so you use another co facility there in in

389
00:22:56.900 --> 00:22:59.920
No, we have our own sort of You have your own, yeah. Own sort. Yeah.

390
00:22:59.990 --> 00:23:02.000
Otherwise it's, it becomes too difficult.

391
00:23:02.390 --> 00:23:05.160
Yeah. You'd you'd be upsetting all your other colleagues who just wanna go.

392
00:23:05.160 --> 00:23:08.720
Exactly. Exactly. Yeah. 'cause those old screens are run in duplicate,

393
00:23:08.720 --> 00:23:10.720
so it's very important. There's also good advice. I mean,

394
00:23:10.720 --> 00:23:13.320
don't run a screen in only one replicate, um,

395
00:23:14.910 --> 00:23:18.160
because whatever you find, you can never be entirely sure it's correct.

396
00:23:18.300 --> 00:23:19.760
But if you have two replicates,

397
00:23:19.760 --> 00:23:23.320
then you can be sure about things that you would otherwise be unable to call.

398
00:23:23.320 --> 00:23:25.160
Right? If something comes up at position,

399
00:23:25.270 --> 00:23:29.880
rank two in one replicate and rank 150 in the other out of

400
00:23:30.220 --> 00:23:35.200
20,000 genes, then they rank 150. Even if it's maybe not significant,

401
00:23:35.230 --> 00:23:39.080
statistically it still means something if it comes up in post replicates.

402
00:23:39.420 --> 00:23:40.920
So that's really important, I think.

403
00:23:41.460 --> 00:23:44.960
Are your replicates really, generally speaking quite, you know, do you,

404
00:23:45.000 --> 00:23:48.360
you get very similar results outta them? Or is there a little bit of like,

405
00:23:48.360 --> 00:23:50.080
say a bit of a bit of wiggle room there?

406
00:23:50.980 --> 00:23:55.280
Uh, it depends a lot on the screen, the screen type, but um,

407
00:23:55.910 --> 00:24:00.510
they tend to be fairly okay. Um, wouldn't say tight,

408
00:24:00.850 --> 00:24:03.110
but there, there's wiggle room, definitely. Um,

409
00:24:04.090 --> 00:24:06.550
that's why I'm saying you then basically look, okay, uh, you,

410
00:24:06.570 --> 00:24:10.230
you can do an average rank of genes between the two replicates and then see

411
00:24:10.230 --> 00:24:14.870
where they end up. Um, often the, the calculation of p-values and,

412
00:24:14.870 --> 00:24:18.830
and false discovery rates are very, very stringent, often too stringent.

413
00:24:18.830 --> 00:24:21.910
So it's not easy to set the cutoff really, whereas, okay,

414
00:24:21.950 --> 00:24:25.430
this is still worth looking into or is no longer worth looking into.

415
00:24:27.590 --> 00:24:30.560
Yeah, I mean, again, it's down to the research, I suppose,

416
00:24:30.580 --> 00:24:34.080
who receives the data to decide what they're gonna do with it? Yeah,

417
00:24:34.460 --> 00:24:37.080
That's a common question. Where do I put the cutoffs? So, well,

418
00:24:37.480 --> 00:24:42.200
wherever you like, uh, it's a ranked list, um, whether you,

419
00:24:42.380 --> 00:24:46.600
it it seems make seems to make sense for you or not that this is still correct.

420
00:24:46.980 --> 00:24:49.160
And, and that's where the replicates help a lot, right?

421
00:24:49.360 --> 00:24:51.760
'cause otherwise you're totally lost, but if you have two replicates,

422
00:24:51.760 --> 00:24:52.640
then it's easier.

423
00:24:53.590 --> 00:24:53.880
Yeah.

424
00:24:53.880 --> 00:24:56.560
I suppose things that come out as being top ranked are very often things that we

425
00:24:56.560 --> 00:25:01.320
already know. Um, so Right. There's no real follow up for the research to do.

426
00:25:01.460 --> 00:25:02.190
So Yeah.

427
00:25:02.190 --> 00:25:02.760
Very often

428
00:25:02.760 --> 00:25:06.680
I assume people are looking in that, you know, ranks 10, you know,

429
00:25:06.680 --> 00:25:09.600
10 to 30 those kind of genes rather than one to 10. Yeah.

430
00:25:10.340 --> 00:25:12.920
Or if you do screens like we did with the long non-coding RNAs,

431
00:25:12.920 --> 00:25:16.000
of course you stick some protein coding genes in as positive controls and Yeah.

432
00:25:16.020 --> 00:25:19.040
Who already they come up as the top hit. Sure. Um,

433
00:25:20.730 --> 00:25:22.140
good in. But yeah,

434
00:25:23.680 --> 00:25:26.980
So, so you know, your advice would be, uh, you know,

435
00:25:27.070 --> 00:25:31.740
count cells properly use for first time and well published and well protocoled

436
00:25:31.740 --> 00:25:34.460
library, make sure you do things in duplicate, that kind of thing.

437
00:25:34.720 --> 00:25:39.300
Really all very good standard scientific advice in many ways. But yeah,

438
00:25:39.610 --> 00:25:43.540
Yeah. Uh, also read, read the Review by John Dench,

439
00:25:43.540 --> 00:25:45.420
that's called Am I Ready For crispr?

440
00:25:46.140 --> 00:25:49.980
I think we also call it the gospel according to John. Uh,

441
00:25:50.160 --> 00:25:53.980
so that has really, really good advice. It covers it really nicely.

442
00:25:54.370 --> 00:25:57.780
It's quite a couple years open. I I don't think things have changed a lot,

443
00:25:58.040 --> 00:26:00.420
so this is really very, very good advice.

444
00:26:01.160 --> 00:26:02.860
So, you know, speaking of change though,

445
00:26:02.860 --> 00:26:04.780
obviously you say things haven't changed loads,

446
00:26:04.800 --> 00:26:06.620
but do you see change on the horizon?

447
00:26:06.800 --> 00:26:10.900
Are things gonna get very different in the next few years? Uh, what,

448
00:26:10.900 --> 00:26:11.980
what do you anticipate?

449
00:26:14.400 --> 00:26:17.420
Um, yeah, I'm not sure actually. Uh,

450
00:26:18.080 --> 00:26:22.300
the knockout screens, I've, I don't know. I,

451
00:26:22.400 --> 00:26:26.500
the hope is that you could have some, some improvements in terms of, uh,

452
00:26:26.640 --> 00:26:29.380
effect size or have smaller libraries with better guides.

453
00:26:29.380 --> 00:26:32.580
So there can always be better guides, right? It can always improve the,

454
00:26:33.520 --> 00:26:37.620
the prediction of, of guide activity and, and these kind of things. But, um,

455
00:26:37.750 --> 00:26:39.660
there are also things, um,

456
00:26:39.970 --> 00:26:44.720
such as that if you cut with a guide and you re rely on

457
00:26:45.150 --> 00:26:48.040
alga and joining for, for, uh, repair,

458
00:26:48.040 --> 00:26:52.200
that of course you can always get in-frame repair and the problem might not be

459
00:26:52.200 --> 00:26:54.880
disrupted. So there's always some noise in this screen study.

460
00:26:55.560 --> 00:26:57.600
I don't think it's, it's even possible to get rid of,

461
00:26:57.690 --> 00:27:01.530
there will always be to a certain extent, noisy. Um,

462
00:27:01.760 --> 00:27:05.450
what is maybe coming more is rate screens.

463
00:27:05.470 --> 00:27:09.010
Now that is relatively easy to make these guide RNAs, uh,

464
00:27:09.070 --> 00:27:11.250
as synthetic guide RNAs. Um,

465
00:27:11.910 --> 00:27:16.210
is that now any better than was a, an original S RNA screen?

466
00:27:16.730 --> 00:27:17.810
I don't know. Um,

467
00:27:19.190 --> 00:27:22.170
of course it's a different situation if it's a knockout screen,

468
00:27:22.170 --> 00:27:24.410
if it's a crisp inhibition screen, maybe it's not different.

469
00:27:25.070 --> 00:27:28.290
But of course there are things like, uh, sca, um,

470
00:27:31.270 --> 00:27:36.210
CAS 13 D for example, that is even easier to do in,

471
00:27:36.270 --> 00:27:40.250
in an array format or CA 12 a because these have much shorter,

472
00:27:40.720 --> 00:27:44.610
much shorter guide r n a, so this is easier and cheaper to synthesize.

473
00:27:45.070 --> 00:27:48.600
So maybe this is something that's coming more. Um,

474
00:27:50.120 --> 00:27:55.060
and other modalities I think are interesting. So we are looking a lot into,

475
00:27:56.000 --> 00:27:59.620
uh, base editing screens, uh, where you can now,

476
00:27:59.650 --> 00:28:03.500
instead of knocking out an entire gene or activating an entire gene,

477
00:28:03.600 --> 00:28:08.020
you can now actually introduce point mutations. Uh,

478
00:28:08.160 --> 00:28:12.540
so tile and entire gene body with guides introduce point mutations then for

479
00:28:12.540 --> 00:28:15.740
example, C okay, which point mutations are resistant to a drug, uh,

480
00:28:16.240 --> 00:28:19.700
if you know the drug target, right, you would do use this on the drug target.

481
00:28:20.600 --> 00:28:23.220
So this is something we're looking into quite a lot now.

482
00:28:23.220 --> 00:28:28.140
We have not quite succeeded yet. Other people have. Uh, it's a bit tricky,

483
00:28:28.400 --> 00:28:31.140
but I think it's doable. And I think this is a,

484
00:28:31.160 --> 00:28:36.000
is a new frontier where we could get a lot of more information than you can get

485
00:28:36.000 --> 00:28:39.120
from a just whole element gene knockout screen.

486
00:28:39.660 --> 00:28:43.240
Mm. Absolutely. Could suppose its scanner function of the, you know,

487
00:28:43.420 --> 00:28:44.560
or change the function of the

488
00:28:44.880 --> 00:28:47.880
Yeah. Chain function. Exactly. You could also, I mean,

489
00:28:47.880 --> 00:28:51.920
you have to not only think of the drug binding to a target and exactly in the

490
00:28:51.920 --> 00:28:55.400
interface you make mutations. It could also be the steric, uh,

491
00:28:55.400 --> 00:29:00.280
interactions that you can identify what is, you can do epitope mapping,

492
00:29:00.900 --> 00:29:05.800
protein-protein interaction studies, all sorts of things. Uh,

493
00:29:05.830 --> 00:29:10.600
it's not very base editing, it's not very efficient often, but,

494
00:29:10.700 --> 00:29:13.640
uh, I think it's good enough, um, as it is to,

495
00:29:13.700 --> 00:29:15.280
to try to do these kind of screens.

496
00:29:16.870 --> 00:29:20.530
So, uh, you, you know, based it in s p Cass nine all the time,

497
00:29:20.550 --> 00:29:21.970
but you mentioned Cass 12 as well.

498
00:29:22.710 --> 00:29:25.330
Are you using the other casts proteins that cut D N A?

499
00:29:26.350 --> 00:29:30.290
Um, yeah, we have used CASS 12 A for screens. So in,

500
00:29:30.310 --> 00:29:34.010
we use this for multiplexing. So if you want to deliver two guides at once,

501
00:29:34.030 --> 00:29:36.330
you want to hit two genes at once. For example,

502
00:29:36.390 --> 00:29:40.410
if you are interested in synthetic lethality or in generally speaking,

503
00:29:40.480 --> 00:29:45.440
synergistic or buffering interactions between genes works as well with SS nine.

504
00:29:45.580 --> 00:29:50.080
But because of the long trace of SS nine, it's a bit trickier to get it to work.

505
00:29:50.420 --> 00:29:51.520
And with S 12 of a,

506
00:29:51.520 --> 00:29:55.240
it works really nicely also because S 12 of A actually chops its own, uh,

507
00:29:55.940 --> 00:30:00.680
two guide pair into single guides. The problem is,

508
00:30:01.020 --> 00:30:04.840
uh, is not a huge problem and can be, uh, will,

509
00:30:05.030 --> 00:30:09.160
will improve in time is that maybe the guides are not as good. So the,

510
00:30:09.160 --> 00:30:13.120
maybe the algorithms of guide design are not as advanced as for S nine.

511
00:30:13.740 --> 00:30:14.680
So there maybe a,

512
00:30:15.120 --> 00:30:17.680
a higher chance that you have guides in there that actually do not,

513
00:30:18.020 --> 00:30:20.600
do not really work that well. Um,

514
00:30:20.660 --> 00:30:24.400
but we did one screen where we looked at, uh,

515
00:30:25.360 --> 00:30:29.880
synthetically th between all the uase and all ubiquitins,

516
00:30:29.880 --> 00:30:34.320
so is a hundred, I think the screen about a hundred times a hundred, uh,

517
00:30:35.590 --> 00:30:40.030
proteins. And that worked reasonably well, I have to say. So I think it's, uh,

518
00:30:40.220 --> 00:30:44.750
it's a totally workable, um, thing. CAS 12 A,

519
00:30:44.750 --> 00:30:47.750
of course you can always in a, in a precision editing context,

520
00:30:47.890 --> 00:30:51.470
you can still use it if there is no Cass nine pam around, um,

521
00:30:51.850 --> 00:30:55.670
we have used Cass 12 A with good success. Um,

522
00:30:56.070 --> 00:31:00.310
I think it works pretty much the same, it seems.

523
00:31:01.590 --> 00:31:04.090
And obviously one part of this is you, we,

524
00:31:04.090 --> 00:31:07.010
we touched on it before when you mentioned about live dead screening versus

525
00:31:07.330 --> 00:31:08.163
functional assays.

526
00:31:08.610 --> 00:31:13.290
I suppose that assay is really critical in terms of how sensitive it is to

527
00:31:13.290 --> 00:31:16.850
making sure you're separating those population cells according to their

528
00:31:16.850 --> 00:31:17.683
different behaviors.

529
00:31:18.780 --> 00:31:21.670
Yeah, absolutely. I mean, this starts with life dead assays already.

530
00:31:21.690 --> 00:31:25.270
If you do life data asay on a, on a suspension line, for example,

531
00:31:25.660 --> 00:31:28.790
some suspension cells seem to hang out quite a long time,

532
00:31:28.790 --> 00:31:32.590
even though they're dead, but they're kind of intact. If you then harvest,

533
00:31:32.850 --> 00:31:36.230
you harvest also the dead cells with it. And then of course your,

534
00:31:36.380 --> 00:31:39.310
your dynamic range is gone. Um,

535
00:31:39.780 --> 00:31:44.070
more importantly with other screens where like reporter assays where we do fax

536
00:31:44.080 --> 00:31:46.870
based cell sorting, of course, um,

537
00:31:48.300 --> 00:31:49.560
if you do a staining or you,

538
00:31:49.560 --> 00:31:51.760
you do not have necessarily have to do a reporter line,

539
00:31:51.760 --> 00:31:55.440
you can also do staining. So we can also do screens on fixed cells. Um,

540
00:31:57.470 --> 00:32:00.760
then the staining has to be like really well set up. Um,

541
00:32:00.950 --> 00:32:04.960
another important thing with drug screens if you wanna do with drug screen is

542
00:32:04.960 --> 00:32:07.240
that your drug concentration is actually right. Uh,

543
00:32:07.240 --> 00:32:09.600
we do IC 50 and IC 19 normally,

544
00:32:10.300 --> 00:32:15.120
and this is also an important point that this gets tested to get determined

545
00:32:15.260 --> 00:32:19.880
on large culture flasks and not in a 96 will play because it never translates.

546
00:32:20.060 --> 00:32:24.200
Oh, really? Wow. On small play. And then you go like in a T 75 and,

547
00:32:24.220 --> 00:32:27.960
and the IC 50 is like totally different. Um, so that

548
00:32:27.960 --> 00:32:31.120
Something you do, do you, is that your lab testing in the IC 50?

549
00:32:31.380 --> 00:32:34.080
Uh, we tell, we tell our clients to do that. Uh,

550
00:32:34.910 --> 00:32:37.600
They build the assays as well. That kind of function assays too.

551
00:32:37.830 --> 00:32:41.320
Yeah, exactly. So the way we work practically is often that we give the,

552
00:32:41.420 --> 00:32:45.960
the library transducers back to the client who then does their assay

553
00:32:46.740 --> 00:32:49.160
and give us back cell pelles, and then we, um,

554
00:32:49.820 --> 00:32:51.800
we prep the genomic d n a into the wrist.

555
00:32:53.170 --> 00:32:55.050
'cause often they know their assay best, right?

556
00:32:55.520 --> 00:32:56.353
Yeah, exactly.

557
00:32:57.630 --> 00:32:58.463
Trying that.

558
00:32:58.750 --> 00:33:02.930
And sometimes they, are they developing bespoke brand new assays for just,

559
00:33:02.990 --> 00:33:06.210
you know, just for this application? Or is it generally speaking, people say,

560
00:33:06.340 --> 00:33:08.410
we've already got an assay, let's do a screen.

561
00:33:09.230 --> 00:33:09.450
No,

562
00:33:09.450 --> 00:33:13.970
often it's a new assays because you can even consider making a specific report

563
00:33:14.000 --> 00:33:17.530
line for screen. So we, we have projects where we made a T G P,

564
00:33:17.530 --> 00:33:21.810
the responsive reporter line, for example, that has A G F P under A T G F P,

565
00:33:21.810 --> 00:33:26.090
the responsive promoter, and only then you can actually do a,

566
00:33:26.770 --> 00:33:31.130
a proper pulled screen where you can sort out, um, high and low responders.

567
00:33:32.320 --> 00:33:33.780
So that's quite common actually.

568
00:33:33.780 --> 00:33:36.260
So that's where the precision edit goes into the screen, right?

569
00:33:36.320 --> 00:33:38.660
So you have to first do a knockin Yeah.

570
00:33:38.660 --> 00:33:42.100
Make a in line or people wanna compare a knockout line with the wild top lines.

571
00:33:42.120 --> 00:33:44.260
You first make a knockout, then compare these two,

572
00:33:44.510 --> 00:33:48.820
which counts with its own set of difficulties, but okay. Can be done.

573
00:33:49.720 --> 00:33:51.940
Sounds to me, honestly, there's so much, I I,

574
00:33:52.060 --> 00:33:56.020
I could talk to you all day about this and it, it sounds so exciting and I,

575
00:33:56.860 --> 00:34:00.460
I think I'm gonna walk away from this podcast with a renewed enthusiasm to

576
00:34:00.660 --> 00:34:01.030
actually, you know,

577
00:34:01.030 --> 00:34:05.220
start speaking to people locally and follow some of these knockout screens. Uh,

578
00:34:05.440 --> 00:34:07.540
but I think, you know, we've been acting now for just over half an hour.

579
00:34:07.540 --> 00:34:09.860
We probably should, you know, call, call it to a close.

580
00:34:10.690 --> 00:34:14.060
Been absolutely pleasure talking to you today. Really appreciate your time. Um,

581
00:34:14.120 --> 00:34:17.460
and I'm feeling a little less intimidated now about doing these screens,

582
00:34:18.240 --> 00:34:21.940
Uh, not enough in, are you still intimidated enough to not? No,

583
00:34:21.940 --> 00:34:24.460
I don't think so. I, I think, you know, I'm just intimidated thinking about,

584
00:34:24.460 --> 00:34:27.740
you know, my, my, my team probably say, we haven't got time for this.

585
00:34:27.920 --> 00:34:31.180
So I think maybe some conversations about how we, you know,

586
00:34:31.850 --> 00:34:34.940
move some our resources around that kind of thing to make sure we can support,

587
00:34:35.080 --> 00:34:37.460
uh, this, this, this type of experiment. But you know,

588
00:34:37.460 --> 00:34:38.900
there's so much you can do with it,

589
00:34:38.920 --> 00:34:42.540
and we're talking about things that might just scratch the surface and the

590
00:34:42.540 --> 00:34:45.260
things you've described today, how many different ways it can be applied.

591
00:34:45.760 --> 00:34:49.220
It just sounds absolutely fascinating. So thank you so much for your time today,

592
00:34:49.220 --> 00:34:53.140
Ben, Ben Schmear. I apologize again for pronouncing your name really badly. Uh,

593
00:34:53.290 --> 00:34:55.980
it's been brilliant and fascinating talking to you. See you soon.

594
00:34:56.270 --> 00:34:59.260
Thank you so much. It was my pleasure. See you soon.

595
00:35:00.550 --> 00:35:04.410
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596
00:35:04.480 --> 00:35:06.130
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597
00:35:06.340 --> 00:35:11.330
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