A New Generation of Tools For Experimental Modeling Of Brain Structure And Function

Transcript

• 00:00Let me introduce Doctor Andrelevchenko,
• 00:03who is the next speaker.
• 00:05He graduated from the Moscow
• 00:08Institute of Physics and Technology
• 00:10and obtained his doctor,
• 00:12obtained his doctoral degree from
• 00:14Columbia University and after
• 00:17postdoctoral experience at Caltech,
• 00:19he started his independent position at
• 00:21Johns Hopkins in 2001 and was recruited
• 00:24as the founding director of the Ill
• 00:27Systems Biology Institute in 2015.
• 00:29And he's also the John Malone
• 00:32professor of biomedical Engineering
• 00:34and professor of physics.
• 00:37Please.
• 00:44Hello and thanks for inviting me.
• 00:46And as as Angelica just said for
• 00:50engineers and physicists to be in the
• 00:53in the room with all of you is, is,
• 00:55is always the great pleasure and and very,
• 00:58very interesting to us.
• 01:01It's of course I'm an engineer,
• 01:02but I'm actually we do do quite a
• 01:04bit of biology and what you learn
• 01:07of course is being an engineer.
• 01:09If you develop tools and we know,
• 01:11all know with with tools you
• 01:15get to discover new tools,
• 01:17new discoveries and that's something
• 01:19that we've really enjoyed thoroughly
• 01:22on different different scales.
• 01:24And what I'd like to illustrate
• 01:26today is the use of these tools.
• 01:29And some insights that we can gain
• 01:31from them and the fact that you
• 01:33actually can do it on multiple
• 01:35different scales in terms of sales,
• 01:39small organoids or even larger structures,
• 01:45okay. So let's see if this. Yeah.
• 01:49So I always run out of time,
• 01:51hopefully not today.
• 01:52So I'd like to immediately thank all
• 01:55the people who helped us do this,
• 01:58as our collaborators as well as the
• 02:00members of the lab were truly in the
• 02:02trenches doing all of this work.
• 02:03Particularly for a lot of the
• 02:08work towards the ends of flora,
• 02:10because this has been a very,
• 02:11very exciting collaboration
• 02:12with the factory in the lab.
• 02:15And a lot of things are not shown are also
• 02:18very interesting to us and are always,
• 02:21almost always products of collaboration.
• 02:23And as on the hill,
• 02:24like I mentioned for example,
• 02:25we have been doing a lot of
• 02:27interesting things with her lab,
• 02:28which has been amazing.
• 02:30So what I'd like to really focus on
• 02:35is this combination of difference
• 02:37approaches and how they come together
• 02:40more specifically in the context of.
• 02:43The fact that as we've already
• 02:45heard the tissues and you know,
• 02:48even if you go smaller on the
• 02:50level of digital cells,
• 02:52what you see is that the
• 02:54environment is not uniform.
• 02:55The environment can present cells
• 02:57and tissues with gradients,
• 02:59for example of cues of morphogens.
• 03:03And this can occur both for stages
• 03:06in development or in cancer,
• 03:09for example, progression where
• 03:10cells may migrate gradients.
• 03:12Could be in collective cell migration or
• 03:16reorganization of tissues in development.
• 03:19It could be in homeostasis and wound repair.
• 03:23So many, many instances.
• 03:25And of course if you take
• 03:28developmental biology,
• 03:29I guess more or less,
• 03:32you know, almost anywhere,
• 03:33at least where I took it,
• 03:35you find that there is this model,
• 03:37beautiful model due to Lewis Wolpert.
• 03:40Of the French flag,
• 03:42essentially suggesting that in
• 03:43the gradient of morphogens,
• 03:45you can have multiple different
• 03:48fates emerging due to the overall
• 03:51level of morphogens,
• 03:52sort of sort of like the cells
• 03:54responding to the doses,
• 03:55different doses of the input.
• 03:57And it does frequently happen in
• 04:00tissues and hopefully towards the
• 04:02end I'm going to show you an example
• 04:04that Flora has really introduced us to.
• 04:09Now,
• 04:09so how do you do experiments to
• 04:12try to understand the influence
• 04:14of graded
• 04:15inputs, whether you look at cell migration
• 04:18or this developmental processes where
• 04:21you have multiple different responses.
• 04:25So there are there's a tradition
• 04:27in by engineering that you may or
• 04:29may not be aware of for kind of a
• 04:31progression of different steps in
• 04:32development of different tools.
• 04:34And these are just some of the examples
• 04:37essentially historically how the
• 04:39gradient studies have been done and
• 04:41almost always what you see and the ones
• 04:43at the bottom are the ones that we developed.
• 04:47The ones at the top are the
• 04:49ones that were used before.
• 04:50You see that it's a kind of
• 04:52two different ideas.
• 04:53One is the flow.
• 04:54In the flow you have mixing of
• 04:57liquids and within liquids you
• 04:59can have different doses of.
• 05:01Compound that you're interested in and
• 05:03that gradually may generate the gradient.
• 05:06The issue is that of course
• 05:07cells don't like flow of liquid.
• 05:09For the most part,
• 05:11they die almost immediately.
• 05:12They're very,
• 05:13very sensitive to what happens
• 05:15with the environment.
• 05:16If you shake, you know your flask.
• 05:19Sometimes you'll see a cell death,
• 05:21and that's what you face when you try
• 05:23to introduce cells into anything that
• 05:25flows when you hear microfluidics,
• 05:27microfluidics.
• 05:29Fluidics parts should really generate
• 05:32immediately some trepidation for you
• 05:35because anything again that flows
• 05:38almost always there are instances
• 05:40where flow is important for sure,
• 05:43for example in the material cells,
• 05:44but generally cells are really sensitive
• 05:46to that and they really don't like it.
• 05:48So that is something that we recognized
• 05:51almost immediately started when
• 05:53we started working with cells and
• 05:55then we had to develop a different.
• 05:57Series of devices where it's
• 06:00really all diffusion, right?
• 06:02It's not nothing flows.
• 06:03The cells are actually in this beautiful,
• 06:05very steady environment,
• 06:07but there are gradients and so with that.
• 06:11So you have this example on
• 06:13the left here where you have.
• 06:17I'm not, I'm not going to mess it. Okay.
• 06:19So you have this idea of a source,
• 06:22for example of growth factory GF.
• 06:25And the cells in between
• 06:26looking like fish here,
• 06:28but this is a cell and there are this
• 06:31nice channels connecting source of
• 06:33the GF and medium without the GF and
• 06:36the certain gradient developing and
• 06:38the the cells actually do respond.
• 06:40And so you start seeing how they run
• 06:43very happily, they run into each other,
• 06:45they collide.
• 06:46You can study what happens when that
• 06:49when that occurs and you see this
• 06:51beautiful chains of cells going up and down.
• 06:56And let me try again,
• 06:58maybe it will work, maybe not.
• 07:02So we wanted to really extend
• 07:04that a little bit to you know,
• 07:07we've heard that cells actually live in
• 07:09softer media and they're surrounded by
• 07:11the cells that communicate the form tissues,
• 07:14tissue ensembles.
• 07:15And so can we really extend
• 07:19this analysis now to?
• 07:21What really is at the center of today's
• 07:23workshop and that is organized.
• 07:25And so this paper was the first
• 07:27attempt to do that where it wasn't.
• 07:30It was an organoid of breast tissue
• 07:33that you'll see next and rather than.
• 07:37Brain tissue that we'll see a bit
• 07:40later and this was surf the question,
• 07:42okay.
• 07:42So what happens,
• 07:44we know that in the breast there's branching,
• 07:48there's in the periods of lactation
• 07:51especially or around that time
• 07:53there may be a very significant
• 07:55reorganization of the breast tissue.
• 07:57And in that process in the adults
• 08:00what happens is there is a growth
• 08:02and branching of various surf.
• 08:04Parts of the of the tissue and that
• 08:08is is triggered by e.g F the same
• 08:11same compound that I showed you in
• 08:13the in the last movie can trigger
• 08:15migration of cells in a directive way.
• 08:18So what happens with tissues?
• 08:21And So what you can see is that
• 08:22you again you can sort of extend
• 08:24this technology to the same idea,
• 08:26have a gradient now if e.g F over
• 08:29much larger distance and organized
• 08:31are embedded now in the space.
• 08:34And you start seeing that they
• 08:36actually start branching.
• 08:37This organoid that initially was
• 08:38the kind of a spherical thing,
• 08:41begins to branch in a very directed
• 08:43way towards the source of e.g F
• 08:46what's interesting is that you can
• 08:48either induce it or naturally have
• 08:50some single cells around and they
• 08:52actually don't sense these gradients.
• 08:54So in spite of the movie that I
• 08:56just showed you in the previous,
• 08:58the the ligand concentration
• 08:59gradients in this case are so shallow.
• 09:02That individual cells just don't respond,
• 09:04they don't sense the gradients,
• 09:05only the tissues can sense the gradients,
• 09:08which implies that there is some
• 09:09sort of cell cell communication.
• 09:11And so our analysis suggested that's
• 09:15really the cell cell communication
• 09:18mechanisms in this process,
• 09:20this one branch forming here that
• 09:23will branch more is,
• 09:25is really all of that is mediated
• 09:28by calcium signaling.
• 09:29And calcium communication between the
• 09:32channels connecting the cells.
• 09:33So one thing that I want to
• 09:36emphasize already in the two
• 09:38examples that I showed you where
• 09:40you in both cases you saw movies,
• 09:42is that you really benefit not
• 09:44only from the ability to generate
• 09:46gradients or grows such organoids.
• 09:49But also from the fact that
• 09:51you can really peer into life,
• 09:53either life cells or life in
• 09:55this case life organoias,
• 09:56and see the dynamics of the processes
• 09:58that are of interest to you.
• 10:00So these devices really allow you to analyze,
• 10:04you know,
• 10:05communication between the cells and
• 10:07what happens with the cells and
• 10:10shoot movies like that and analyze
• 10:12in this case the calcium signaling.
• 10:15So we wanted to really continue doing
• 10:17this and of course the challenge the the
• 10:20more interesting structures the more
• 10:21complex structures occur in the brain.
• 10:23And in the brain there may be different
• 10:26types of analysis and this was termed
• 10:29for the first time brain on the chip.
• 10:31Now in the Community of Engineers,
• 10:35anything on a chip, you'll,
• 10:36you'll hear longer on the chip,
• 10:37brain on chip it's just a big name
• 10:40it's it's not more than that.
• 10:41But here it's,
• 10:43it's,
• 10:43it's it's something that allows
• 10:46you to start modeling the presence
• 10:49of multiple cell types in the self
• 10:53organizing networks and again do it
• 10:55in such a way that can visualize this
• 10:57in great detail and see what happens.
• 11:01So here again you can start
• 11:03with the progenitor cells.
• 11:04They will not necessarily form and organize,
• 11:07they can form clusters of cells that
• 11:09are connected by bundles of axons.
• 11:11At some point you can couple that to a
• 11:14layer of endothelial cells and mimic the
• 11:17blood brain barrier that actually forms here.
• 11:20And you can introduce some drugs here,
• 11:22for example,
• 11:23on this side,
• 11:24and study how they can potentially be
• 11:27penetrating this blood brain barrier.
• 11:29There's a basement membrane
• 11:31that will form here.
• 11:32And so even though of course
• 11:33it's not the real tissue,
• 11:35it starts having some interesting
• 11:36features that you can use.
• 11:38To start exploring what happens,
• 11:40there is a communication as was just
• 11:43mentioned by antalicate between the
• 11:45neuronal cells as they different
• 11:48shades and endothelial cells.
• 11:50And it can introduce,
• 11:51which we did in this case.
• 11:52Also project cells into a more
• 11:55developed network and see how the
• 11:57presence of both neuronal cells and
• 12:00in the field cells make may control
• 12:03the behavior of this progenitor
• 12:05cells neural progeneral cells.
• 12:07And you can again introduce the gradients
• 12:09now of variety of different cues.
• 12:12So I could be B&P we've heard about some
• 12:16examples have already been enunciated
• 12:19and so you can have introduction
• 12:21of multiple gradients can attended
• 12:23that can potentially either Dr.
• 12:25migration of the cells and how they
• 12:27position them themselves now in the
• 12:29more realistic model of this brain
• 12:32tissue or what happens to them in different.
• 12:35Concentration within this gradient,
• 12:37right.
• 12:38And so you can study that and of
• 12:40course again you can visualize
• 12:42what happens within
• 12:43the clusters. So again,
• 12:44this is calcium imaging before you
• 12:46saw that in the memory tissue,
• 12:48organoid in this case,
• 12:50it's neural tissue, not yet organoid,
• 12:52it's cluster of cells,
• 12:54but you can already see how they
• 12:58show this neuronal phenotypes,
• 12:59how they communicate with each
• 13:00other and they can visualize
• 13:01calcium signaling in them.
• 13:05So of course I already mentioned
• 13:08flora multiple times, and I think
• 13:11she's going to talk about that more.
• 13:13So I'm not going to go into biology of
• 13:17of what happens with embryos and what
• 13:19happens with the development of the brain
• 13:21very much other than to say that again,
• 13:24there are gradients, of course,
• 13:26in the developing embryo.
• 13:28Of multiple morphogens and
• 13:30multiple signals that really
• 13:32define axis for developing embryo.
• 13:35And it again is of interest to see
• 13:38what happens either with cells,
• 13:39single cells or cell ensembles
• 13:41that they just showed you,
• 13:42or even with organoids that may be
• 13:46exposed the gradients of various
• 13:48cues and it could be the actual.
• 13:50Signaling molecules for something
• 13:52that mimics them.
• 13:53For example,
• 13:53this G SK3 inhibitor is widely used
• 13:55and used by flora quite a bit.
• 13:57And so we tried to develop this radiance
• 14:00and analyze the outcomes of that.
• 14:03And again as I said,
• 14:05Flora will likely,
• 14:06I don't know exactly what
• 14:07she's going to talk about,
• 14:08but she will talk a little
• 14:10bit more about this.
• 14:12And so this devices again are
• 14:13very similar in some sense,
• 14:15but but have now been optimized
• 14:17and developed and that does
• 14:19take quite a bit of time.
• 14:20Forebrain organize that are much
• 14:22more complex than the ensembles
• 14:24that they showed you before.
• 14:26And this is what you do,
• 14:28is you try to optimize all of this.
• 14:29You do both analysis and experiments and
• 14:33modeling of what happens in such devices.
• 14:35Ultimately,
• 14:36of course what you look at is
• 14:39the outcome of the genetic level
• 14:42of expression of different
• 14:43markers of different tissues you
• 14:45can play with concentrations,
• 14:46different concentrations you can.
• 14:49Do different types of analysis in
• 14:53terms of for example different
• 14:55hydrogel composition of collagen.
• 14:57We just again heard from Angelica
• 15:00that the extra cell metrics is
• 15:02very important and so how does
• 15:04it affected how the gradients of
• 15:06different morphogens affected.
• 15:07And of course even visually you
• 15:10can see that depending on where
• 15:11you are in the gradient,
• 15:13in this case still 1 dimensional gradient.
• 15:17It really defines the outcome in
• 15:19terms of the differentiation and
• 15:21expression of the differentiation markers.
• 15:23Now we want to really take it
• 15:25beyond this and since there are
• 15:28multiple axis for differentiation,
• 15:29if you have dorsal ventral AT axis and so on,
• 15:34it would be wonderful to develop
• 15:36now fields of this morphogens
• 15:38that may be 2 dimensional or
• 15:40high dimensional so that embryos
• 15:42positioned in different parts of this.
• 15:44Field can have different combinations
• 15:47of morphogens affecting their
• 15:51differentiation and So what you really
• 15:55get if you succeed in experiment
• 15:57like this is a really snapshot of
• 16:00not just one combination of different
• 16:02inputs or different factors,
• 16:04but multiple combinations present in
• 16:06a sort of A2 dimensional those distribution.
• 16:10And of course by modulating what you.
• 16:13Do with these devices you can get
• 16:15all sorts of different gradients
• 16:17and gradient distributions,
• 16:19and again you can do various
• 16:21types of experimental tests
• 16:23and simulations of all this.
• 16:24But ultimately, again as we make
• 16:26hopefully we'll hear from Flora and
• 16:28this is done very much with her lab,
• 16:31you can really now begin to examine
• 16:35within this two-dimensional fields
• 16:37what happens with the expression
• 16:39now of markers that will tell you.
• 16:43About the AP&DV or Dorsodential
• 16:47here access imposed by Sony
• 16:50Hedgehog and this compound that
• 16:51they mentioned before which is the
• 16:54G SK3 inhibitor and really look at
• 16:56the rated changes in distribution
• 17:00of multiple expression markers and
• 17:02ultimately how it corresponds to the
• 17:06differentiation in the actual embryo now.
• 17:10I meant to insert conclusion slide
• 17:12but it didn't come out and so well
• 17:14I'll tell you a little bit just
• 17:16to summarize a couple of thoughts,
• 17:17I'll leave you with that and also
• 17:20with what we want to do next.
• 17:23So again I think one of the things I
• 17:26would like to, I wanted to illustrate
• 17:28it's no not the only challenge,
• 17:30but one of the challenges again is how
• 17:32do you screen multiple conditions,
• 17:34how do you explore the influence of graded?
• 17:38Inputs, it could be attractants,
• 17:40growth factors, it could be morphogens,
• 17:43could be other things.
• 17:45So how do you study that and do you
• 17:47really have technologies to do that?
• 17:49And the answer is that the technology,
• 17:51at least for that part,
• 17:52is becoming more and more mature
• 17:55and applicable to multiple scales.
• 17:57You know, you can play with cells,
• 17:58you can play with cell small cell
• 18:01ensembles or even larger organized
• 18:04and you really can explore that.
• 18:06One huge limitation of course,
• 18:08is the actual size of the organoids
• 18:10that we are playing with, which is,
• 18:13as already has been mentioned,
• 18:14is really limited.
• 18:16For example by the vascularization.
• 18:17We don't really have vascularization
• 18:20of organoids,
• 18:21and therefore whatever nutrients
• 18:23or oxygen they get,
• 18:25they get by diffusion from
• 18:27the surrounding medium,
• 18:28and that means that you cannot
• 18:30grow them beyond a certain size
• 18:32before they become necrotic.
• 18:33In the at the center and that means
• 18:36that that's actually one of the
• 18:38bigger challenges that flora and I
• 18:40have been discussing quite a bit.
• 18:41And then and Helica mentioned as well,
• 18:43how do you really vascularize organoids
• 18:45and how do you introduce that component,
• 18:49especially because vascular
• 18:51component can affect formation
• 18:53of this structures as well.
• 18:55So there's crosstalk between
• 18:58vasculature and neural tissue.
• 19:01So this it's a huge challenge for
• 19:03the whole community and we're
• 19:04trying to tackle it now.
• 19:05And beyond that of course,
• 19:07how can we really build it up in
• 19:10terms of the complexity of both the
• 19:12tissues themselves and the fields
• 19:14of different inputs that this such
• 19:16tissues can experience and do it in
• 19:19the relatively high throughput fashion.
• 19:20So this is what we are super excited
• 19:22right now about and this technology
• 19:24really has been progressing.
• 19:25So hopefully it will be widely used.
• 19:27As as it becomes available to you.
• 19:29Thank you very much.