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Organoid Modeling of Neuropsychiatric Disorders

March 30, 2023
ID
9773

Transcript

  • 00:00So where I transition into next speaker,
  • 00:02which is Flora Vacarina,
  • 00:03I'm going to introduce her.
  • 00:05So Flora Flora received the MD
  • 00:08from the University of Padua in
  • 00:11Italy and then she spent a few
  • 00:13years and europharma call as a
  • 00:15neuro pharmacology fellow at NIH,
  • 00:17where she completed internship
  • 00:18and residency in psychiatry.
  • 00:19Then she completed internship and residency,
  • 00:23residency in psychiatry at Yale and.
  • 00:28Then she did a research fellowship
  • 00:30in developmental genetic of
  • 00:31the Yale Child Study Center,
  • 00:33where she subsequently became an assistant
  • 00:35associate and then a full professor.
  • 00:38And in 2010 she was appointed as a
  • 00:42Harris Harris professor as a child Study
  • 00:44Center for Development of Neuroscience
  • 00:46at Yale University School of Medicine.
  • 00:48And since 2009 she has been the
  • 00:50director of the program in the
  • 00:53neurodevelopment and regression.
  • 00:54And I'm very happy.
  • 00:56So he's also my great collaborator and
  • 00:58I'm happy looking forward to talk.
  • 01:01Thank you, Alexei.
  • 01:08Okay. Well, thank you very much.
  • 01:10Let's move on.
  • 01:13So let me start by saying that
  • 01:17humans are a mosaic of germline
  • 01:20and somatic genomic variations.
  • 01:23And we're all very different from
  • 01:25each other for different reasons.
  • 01:27And similarly, genetic risk for human
  • 01:31disease rarely map to a single gene.
  • 01:33I'm not going to say that this is unheard of.
  • 01:36We just heard two very good examples of that,
  • 01:39but it's a rare phenomenon.
  • 01:40Most most disorders map to multiple genes,
  • 01:44risk genes.
  • 01:45So one effort of our lab is being to find
  • 01:49convergence in biological mechanisms.
  • 01:52In complex developmental disorders,
  • 01:57of course, this present challenges,
  • 01:59particularly when you're studying the brain.
  • 02:01So let me explain to you what I mean
  • 02:04by convergence. So here you see.
  • 02:07The promoter with the gene downstream of it.
  • 02:10And that could be thought of a convergence,
  • 02:13right? Because the promoter eventually
  • 02:16synthesize regulates product synthesis
  • 02:17which regulates cellular function.
  • 02:19But you can see up here, there could
  • 02:22be a number of different mutations,
  • 02:24right, that could actually lead to
  • 02:26the exactly similar or same phenotype.
  • 02:29And this could be a close by
  • 02:31like it could be in a promoter,
  • 02:32but it could be very far away like
  • 02:34in an enhancer that could be hundreds
  • 02:36of kilo base away sometimes or it
  • 02:38could be in different chromosomes
  • 02:39for the transcription factor that
  • 02:41binds to that enhancer.
  • 02:42And so this very disparate different
  • 02:45mutation could actually lead to
  • 02:47essentially the very same phenotype.
  • 02:49So how are,
  • 02:50how are we going to deal with that?
  • 02:53And one way to study this is exactly
  • 02:56what we've been talking about today
  • 02:59is to study personal genomes and the
  • 03:03way these personal genomes can develop
  • 03:06in vitro in different ways and what's
  • 03:08the influence on this development.
  • 03:10So we've been using induced pretty buttons
  • 03:13themselves to generate these organoids,
  • 03:15which are 3D aggregates of neuro
  • 03:18progenitors that can be interrogated using.
  • 03:21Different assay at the genomic level,
  • 03:23at the transcriptomic level and
  • 03:26at the epgenomic level, right.
  • 03:28And and hopefully we could also look
  • 03:31at the 3D DNA confirmation to try to
  • 03:34put all this together and come back and
  • 03:37and derive a model for intersection
  • 03:40between genes and phenotypes.
  • 03:43So this sounds easy.
  • 03:46It's not.
  • 03:47Let me first go through what organoids
  • 03:49are and why do we want to use them.
  • 03:51So obviously they are a longitudinal
  • 03:54model of brain development,
  • 03:56so they respect individual
  • 03:59genetic background largely,
  • 04:01which is very important.
  • 04:03And let's not forget they can be
  • 04:05developed from living people,
  • 04:07so we can still try to do
  • 04:11correlations between phenotypes.
  • 04:12And in vitro development of these systems,
  • 04:17this is how they look like.
  • 04:18We've been growing them for over
  • 04:2010 years now and we found ways
  • 04:23to grow lots of them in batches.
  • 04:25If you section them,
  • 04:27they have these complex layers
  • 04:28of progenitors.
  • 04:29This is, these are cortical organoids.
  • 04:31So you see cortical progenitors,
  • 04:33you see immature neurons piling
  • 04:35up here on the external side.
  • 04:37And then if you grow them for very long time,
  • 04:41you eventually see something that looks
  • 04:42more similar to the actual cortex.
  • 04:44In Cortex,
  • 04:45you see layer five and six developing
  • 04:48before layer two and three.
  • 04:49So it's an inverse development.
  • 04:51You can see that at one month you
  • 04:54see layer 6 neurons here on the
  • 04:56outside of the progenitors in blue.
  • 04:59And then at five months you
  • 05:01start seeing not only layer 6,
  • 05:03but also layer two and three in red.
  • 05:06So really the development of this
  • 05:09system seems to recapitulate,
  • 05:10at least in great lines,
  • 05:14what's happening in the real brain.
  • 05:16And eventually you even have glial cells.
  • 05:18You see here astrocytes of 5 1/2 months
  • 05:21that develop in the organoids as well.
  • 05:23So a long time ago, but.
  • 05:25Five years ago we asked the crucial
  • 05:27question right to what extent
  • 05:29organoids look beautiful but do they
  • 05:31are really similar to the real brain?
  • 05:34So we did a paper where we took three
  • 05:36fetal specimen and had a cortical
  • 05:39specimen for those fetal specimen at
  • 05:43about 1617 postconceptional weeks.
  • 05:45And then we developed organo.
  • 05:48We developed in just pretty bottom
  • 05:50stem sets from skin fibroblast from
  • 05:52those specimen generated organoid.
  • 05:55Can analyze them over a time course of
  • 05:57three time courses and compare them to the
  • 06:01isogenic cortices and what we found here.
  • 06:03You can see these are the
  • 06:05actual samples at the bottom.
  • 06:07These are the cortex themselves and these
  • 06:09are the organoids over three time points.
  • 06:12Compared to a large data sets of
  • 06:15gene expression across human stages,
  • 06:18you can see that while the brains the
  • 06:21cortices of this specimen are a snapshot
  • 06:25of development because they map exactly
  • 06:28to a 1617 post conceptual weak cortex,
  • 06:31the organoids are a range.
  • 06:34Here they present a range of
  • 06:36similarities that go back.
  • 06:38Not only to the 16 postconceptional week,
  • 06:41but back to April postconceptional week,
  • 06:43and even possibly earlier for
  • 06:45stages for which we don't have
  • 06:48human brain to compare them to.
  • 06:50So organoids are a way to look in back
  • 06:53from stem cells to later developmental,
  • 06:56fetal and late fetal developmental stages.
  • 06:58So let me show you a few slides on an
  • 07:01ongoing study on autism spectrum disorder.
  • 07:04This is a data set of
  • 07:1014 families where we take the problem
  • 07:13with autism spectrum disorder and
  • 07:15we compare to the unaffected Father.
  • 07:18And we do this to avoid a spuriously
  • 07:20negative background comparison between
  • 07:22groups that may be very different.
  • 07:24So we do intra family comparisons here.
  • 07:28And another thing we're doing,
  • 07:29we separated head circumference size.
  • 07:32So we separated patients into
  • 07:35macrocephalic which have a larger brain,
  • 07:37larger brain size versus those that don't.
  • 07:40And the reason for doing that is that
  • 07:43about 20% of people with all these
  • 07:47marmacrocephalic and they're often
  • 07:49have higher severity of symptoms.
  • 07:52So here you see a single cell data sets
  • 07:55of this that we generated in this study.
  • 07:58Each dot representing this U
  • 08:00map represent a single cell,
  • 08:02and they're grouped by
  • 08:04transcriptome similarities,
  • 08:05and this is one of the largest data sets,
  • 08:08if not the largest of organoids by
  • 08:10single cell sequencing represents about
  • 08:14650,000 / 650,000 cells.
  • 08:16And you can see that they're
  • 08:18grouped into various cell types.
  • 08:20And here you see at the
  • 08:22bottom radial glial cells,
  • 08:23there is a trajectory
  • 08:24between radial glial cells,
  • 08:26intermediate progenitors and
  • 08:27eventually cortical excitatory
  • 08:29neuron and inhibitory neuron.
  • 08:31And they're annotated by canonical markers.
  • 08:34And one thing I like to point out
  • 08:37that over time you see that there is a
  • 08:40trajectory where the progenitors decrease
  • 08:43in quantity and neurons increase.
  • 08:45Which is what's to be expected.
  • 08:48And let me highlight an important
  • 08:51distinction of two particular cell
  • 08:54groups that in this organ or that
  • 08:57reflect actual development and
  • 08:58one is the pre plate during early
  • 09:01neurogenesis and the cortical plate
  • 09:02which will form the actual cerebral cortex.
  • 09:05So the pre plate is a transient layer
  • 09:08of cells that develop very early.
  • 09:11And serves as has various developmental
  • 09:14functions but then eventually disappears.
  • 09:17And then soon after that you
  • 09:19have the actual cortical plate,
  • 09:21the six layer cortical plate developing
  • 09:23from the same regular real cells and we
  • 09:26have both type of cells in this organoids.
  • 09:30And of course another thing that I
  • 09:32want to highlight is the viability.
  • 09:34This is given the large data
  • 09:36set that we have,
  • 09:37we could actually assess that.
  • 09:39You can see by color each color
  • 09:42represent a cell type and one.
  • 09:45One source of variability of course is age.
  • 09:48And you see light green are TD0 and in in
  • 09:53darker green TD30 and TD60 various stages.
  • 09:57And so of course that TD0 you have
  • 09:59more progenitor regular cells in pink
  • 10:01and later you have more neurons,
  • 10:02but still there is a large
  • 10:05variability between different preps.
  • 10:07And so how do we deal with that?
  • 10:09That's that's an important phenomenon in
  • 10:12in this field that we need to understand
  • 10:15and we need to possibly study, right.
  • 10:18So I was talking about
  • 10:20variability earlier on.
  • 10:21So,
  • 10:22so what's you this variability what,
  • 10:24what is,
  • 10:25what is the origin of this variability and
  • 10:28so one thing obviously could be a number
  • 10:31of factors like reprogramming like you know.
  • 10:36And anything that has to do
  • 10:37with batch effect of cultures.
  • 10:39And of course some of this may
  • 10:41have an effect,
  • 10:41but we largely excluded them and
  • 10:43it seems that one important source
  • 10:46of viability is genetic background,
  • 10:48because if we culture IPS line
  • 10:51organoid from the same individual.
  • 10:54Even if they're cultured in
  • 10:56different batches or differentiation,
  • 10:57they're still displaying more
  • 10:59similarity than all the other ones.
  • 11:01So we believe that any background is
  • 11:04an important driver of these differences.
  • 11:07And these differences,
  • 11:08of course, they're not random.
  • 11:09If we have different percentage, say,
  • 11:12excitatory neuron or inhibitory neurons,
  • 11:14it's not just a serendipitous phenomenon.
  • 11:17And you can see here, for example,
  • 11:19that is highly correlated.
  • 11:21Back down here with expression of
  • 11:24certain genes in progenital cells.
  • 11:26So the reason why we have different
  • 11:29percentage of a excitatory neuron
  • 11:32and inhibitory neuron is because
  • 11:34there is a different programming
  • 11:35of this transcription factors in
  • 11:37the progenital cells in those
  • 11:39spreads. So they do reflect
  • 11:42differences within each organoid,
  • 11:44within each organ that's derived from
  • 11:46a particular person and perhaps.
  • 11:48Reflects intrinsic difference in in
  • 11:51the development of each one of us.
  • 11:53So what happens if we compare people
  • 11:56with autism with their father?
  • 12:06So when we compared gene expression,
  • 12:09single cell gene expression
  • 12:11between problems and their father,
  • 12:13we got the first surprise and that was
  • 12:16that when we analyzed macrocephalic
  • 12:18and normal cephalic separately.
  • 12:21We found that they don't intersect
  • 12:23or they intersect very minimum.
  • 12:25That means that the differential
  • 12:28gene expression is largely
  • 12:30specific to which AST subgroup,
  • 12:32and you can see examples of that here.
  • 12:34So for example,
  • 12:35in red you see differential gene
  • 12:37expressions that are increased,
  • 12:39in blue that are decreased and the one
  • 12:41that are increased in macrosympalic
  • 12:43which reflect largely dorsal cortical
  • 12:45plate neurons and their progenitors.
  • 12:48And a decrease in transcript or inhibitory
  • 12:51neuron are actually not the same that
  • 12:54are in fact they're the opposite.
  • 12:55So if these are increase in macrocephalic,
  • 12:58those are decreased and the enormous
  • 13:00ephalic also don't have any significant
  • 13:03change in interneurons and that's reflected
  • 13:06also by the relative abundance of cells.
  • 13:09So in macrocephalic individuals
  • 13:10you see an increase in these dorsal
  • 13:13cortical plate neurons,
  • 13:14a decrease in preplate.
  • 13:16And in the normal cephalic,
  • 13:18you if you have the opposite phenomenon.
  • 13:20So this was quite puzzling,
  • 13:22quite interesting and do we
  • 13:24have an explanation of that.
  • 13:27So here just to show you that even by
  • 13:31immunocytochemistry we reproduce these
  • 13:33differences that I just described.
  • 13:36So what we think this reflects is
  • 13:39actually a different difference
  • 13:41in the actual pathogenesis.
  • 13:43Because if I go back to the pre plate
  • 13:45and cord and those are cortical plate
  • 13:48enormous epalic people that we have
  • 13:50an increase in pre plate neurons that
  • 13:52basically say what does it say that
  • 13:55this radio glia says prematurely exit
  • 13:57the cell cycle generate more pre plate.
  • 14:00These are transient population and
  • 14:02there is less of course progenitor that
  • 14:05generating the subsequent cortical plate.
  • 14:07Whereas the microcephalic
  • 14:09of the opposite phenomenon,
  • 14:10this progenitor generates fewer pre
  • 14:12plate and there is more progenitors,
  • 14:14there is more later on to generate
  • 14:17an exuberant cortical plate
  • 14:19neuron generation and so why?
  • 14:23Why do we think this is important?
  • 14:24Why is this something that's of interest?
  • 14:26Because obviously this could reflect
  • 14:30differences in the actual pathogenesis of
  • 14:33what we call homogeneously autism people.
  • 14:36They may actually be not reflecting the
  • 14:38same pathogenic phenomenon in development.
  • 14:41So just to summarize this part,
  • 14:43organoid.
  • 14:43Reproduce the lineages and cell type,
  • 14:47at least the major one that we
  • 14:49see in protocol development.
  • 14:50There is great variability that in
  • 14:52genetic programs of differentiation
  • 14:54across individual and there are two
  • 14:57different formal ASD that perhaps
  • 15:00are different in pathogenesis
  • 15:02and potentially they could have
  • 15:05potential implications of treatment.
  • 15:07So the next question was why?
  • 15:09Why do we have these differences?
  • 15:11What the transcriptome is without?
  • 15:13What's the origin of these
  • 15:15transcriptomic differences?
  • 15:16And this brought up,
  • 15:17this is ongoing studies,
  • 15:19still unpublished bring us to
  • 15:22the next step which is the non
  • 15:24coding element of the genome.
  • 15:26So as you know those are
  • 15:28the portion of the genome,
  • 15:30the regular gene expression.
  • 15:31So to analyze those what we did,
  • 15:33we took the non holding genome
  • 15:36segmented by using cheap seek data,
  • 15:39chromatic immunoprecipitation
  • 15:40in various regions,
  • 15:42mainly enhancers, promoters,
  • 15:44repressed regions and mixed regions.
  • 15:48And then correlated them with gene
  • 15:52derived the data sets of about
  • 15:55173,000 gene linked enhancers.
  • 15:56So took the enhancers,
  • 15:58linked them to genes and then perform
  • 16:01correlation analysis where we could
  • 16:04actually correlate the enhancer
  • 16:06activity to the transcription factor
  • 16:07that was bound to that enhanced.
  • 16:09So correlation between activity of an
  • 16:12enhancer and and transcription factor RN,
  • 16:16A/C levels for those.
  • 16:18The transcription factor,
  • 16:19the bound to it and correlation
  • 16:22between the enhancers and
  • 16:24the downstream link chain.
  • 16:26And by doing that we built the
  • 16:28regular and what we mean by regular
  • 16:31is a map of this gene enhancer
  • 16:33transcription factor interaction.
  • 16:36And we could identify two
  • 16:37different type of enhancers,
  • 16:39ones we call activating enhancers
  • 16:41because they're positively correlated
  • 16:43with the downstream genes.
  • 16:45And whereas the repressing enhancers are
  • 16:47those that are negatively correlated
  • 16:49with the downstream genes and you see
  • 16:52an example of this phenomenon here.
  • 16:54So this is the regulatory graph for emx one.
  • 16:57This is one of the genes that was
  • 16:59up regulated in microcephalic AST.
  • 17:01And you can see that there
  • 17:02is this enhancer here
  • 17:06693906 which is the major
  • 17:08enhancers that activates emx one.
  • 17:10This is the correlation coefficients.
  • 17:12There are other,
  • 17:13but they're less less powerful at
  • 17:16activating this gene transcription and
  • 17:18this enhances upstream of this enhances.
  • 17:21There are five transcription factors okay,
  • 17:24and four are inhibiting this enhancer
  • 17:26and one IOM which is another gene
  • 17:28that was appregulated in ASD models,
  • 17:30sophalic is actually activating
  • 17:32that enhanced so.
  • 17:34So that's one example of going
  • 17:38upstream of gene expression and trying
  • 17:41to find out what's happening above.
  • 17:43And then another thing that we've
  • 17:45been doing is actually looking
  • 17:47at the transcription factor that
  • 17:48drives the type specification.
  • 17:50So we looked at the single cell rnac.
  • 17:53Derived gene markers that are
  • 17:56specific for certain cell types,
  • 17:58say excitatory neuron for example,
  • 18:01you see they're not here or
  • 18:03inhibitory neuron or radial glia.
  • 18:04And then found those transcription
  • 18:06factors that actually can explain
  • 18:08or are correlated with this cell
  • 18:10type specific gene expression.
  • 18:12And we derive sets of transcription factor
  • 18:14for example that can activate all neurons.
  • 18:16You see these are all neurons.
  • 18:18And repressing already or perhaps
  • 18:20the activator of a certain type of
  • 18:23excitatory in human or a certain
  • 18:25type of inhibitory in human.
  • 18:27So this,
  • 18:27this really improves our ability to go
  • 18:30upstream of cell type differences and
  • 18:32try to find out what are the upstream
  • 18:35mechanism that regulate those cell types.
  • 18:38And finally this is the
  • 18:42regular of macrocephalic AST.
  • 18:44So what we did here we displayed
  • 18:47in the regular.
  • 18:48The differential gene expression
  • 18:49in macrocephalic ASD,
  • 18:51and you see here a few genes that
  • 18:53are upstream that are actually their
  • 18:55transcription factor that regulate
  • 18:56exactly for neuron development.
  • 18:58You see emx one that I talked about before,
  • 19:01and you see this analysis 693906
  • 19:05that as you saw before,
  • 19:07is regulated by IOMS,
  • 19:08which is also regulated in ASD.
  • 19:11And then these are connected to
  • 19:13other transcription factor through
  • 19:15enhancing that as you can see here
  • 19:17the red means they're activated.
  • 19:19So we're really going upstream and
  • 19:21explaining this gene expression
  • 19:23differences by the activity of
  • 19:25those enhances.
  • 19:26So we have enhancers that activate
  • 19:28genes and we have enhancers like this
  • 19:30one here that are downstream of genes.
  • 19:32So for example this enhancers
  • 19:34downstream activated by Neuro D2,
  • 19:36by eoms and by Vhlh E22 which
  • 19:40are all up regulated.
  • 19:41So this is a self reinforcing network
  • 19:44that is giving us some ideas of what's
  • 19:48going on in the development of of
  • 19:51this organized in these patients,
  • 19:54but you might ask.
  • 19:55Why do we care about this?
  • 19:57Why do we want to show all these enhancers?
  • 20:00What the reason is this again right?
  • 20:02Because how do we know that one enhancer,
  • 20:06say here,
  • 20:06activate a certain genes if we
  • 20:09don't make a regular?
  • 20:10We need to make a regular in
  • 20:12order to actually make these
  • 20:14connections meaningful and possible.
  • 20:15And not only that,
  • 20:17we need to make this regular
  • 20:18mean different individuals in
  • 20:20as many individual as we can.
  • 20:22In order to be able to figure
  • 20:25out this relationship
  • 20:26and to figure out how the non coding
  • 20:29genome relates to the coding regions,
  • 20:31we already found some intersection
  • 20:33between our differential express
  • 20:36genes and the spy genes and other
  • 20:38data sets of ASD risk genes.
  • 20:40But these are coding genes we need to
  • 20:42do this work for the non coding part,
  • 20:45so that's being our next step so.
  • 20:50I don't have much time,
  • 20:51but let me say that perhaps differential
  • 20:55regular activity can explain one day ASD,
  • 20:58differential gene expression and
  • 20:59self state and can point to no coding
  • 21:02element that are enacting these changes.
  • 21:04And in the last few slides,
  • 21:06let me go back to something that
  • 21:10Andrea Chenko spoke to you just
  • 21:13a few minutes ago. And in fact,
  • 21:16we by chance have this very same figure
  • 21:18here about Peter Lawrence French flag,
  • 21:21which basically says that
  • 21:22gradients are important, right?
  • 21:24And these are the collab young
  • 21:26collaborators in his lab and my lab
  • 21:28that they've made possible this project.
  • 21:30And we're all very grateful to them.
  • 21:32But basically they built this Chamber
  • 21:34which allows us to look at the
  • 21:37orthogonal effect of two gradients,
  • 21:38and we intagonist,
  • 21:39which is posteriorizing the organoids
  • 21:41and a Sonic a joke agonies, which is.
  • 21:44Ventralizing them and these are the
  • 21:46organoid cultures in this area and this
  • 21:49is a slide that you already showed,
  • 21:50so I'm not going too much in detail.
  • 21:51We're reading the gradient by
  • 21:53using gene expression.
  • 21:55And just let me say that it's
  • 21:58fantastic what we see because dorsal
  • 22:01genes which are up here like TBR
  • 22:04one and Fox G1 are expressing the
  • 22:07dorsal portion of the chambers.
  • 22:09And not in the ventral and
  • 22:11cortical genes like Phase 2 E MX2,
  • 22:13they're expressing the anterior chamber
  • 22:15which is C5D is anterior and C1 is posterior.
  • 22:19And then ventral gene like nkx
  • 22:222.1 which you see here in mouse,
  • 22:24it's a ventral gene in the basal
  • 22:26ganglia instead is expressed in
  • 22:28the basal ganglia in the ventral
  • 22:31regions of the of the chamber.
  • 22:32So this Chamber really seems
  • 22:34to be able to make.
  • 22:38Brain regions,
  • 22:39specific brain regions
  • 22:40different from one another,
  • 22:42and we only need 5 days of
  • 22:44exposure to this gradient.
  • 22:45And after that we can remove
  • 22:47the organo from the Chamber and
  • 22:49culture them in the usual way.
  • 22:51But let me say one thing.
  • 22:53Individual variation.
  • 22:54We've been talking about that, well,
  • 22:56it turns out the out of seven IPS
  • 22:59line that we tried in this gradient,
  • 23:01they're all different.
  • 23:02They don't respond in the same way.
  • 23:04So these are the chambers,
  • 23:06these are the gradients.
  • 23:08Anterior, C5, posterior C1,
  • 23:10dorsal and ventral.
  • 23:11And you can see that the slope,
  • 23:13the response is similar but
  • 23:15the slope is different.
  • 23:16What does that suggest?
  • 23:18That at least in this type of
  • 23:21assay we don't behave the same way?
  • 23:23Organized from different people respond
  • 23:25in slightly different way and perhaps
  • 23:28that's due to genetic background and other.
  • 23:31Epigenetic and other phenomenon
  • 23:32that are peculiar to each one of us,
  • 23:35so the our brains are not constructed
  • 23:38according to this in exactly the same way.
  • 23:40And that's my last slide,
  • 23:43is EU map for this Chamber or
  • 23:47derived organoid altogether?
  • 23:49If we combine all the organic together
  • 23:51make single cell RN A/C can make a U map.
  • 23:53Well, obviously it doesn't look like
  • 23:55EU map I showed you before, right?
  • 23:57It's not just cortex.
  • 23:59You have palamus, you have subparium,
  • 24:01your midbrain with dopaminenergic
  • 24:02neurons in there.
  • 24:04You have floor plate,
  • 24:05you have medium structure septum
  • 24:07with corioplexus in there developing
  • 24:09and cortex of course.
  • 24:11And these regions come from different
  • 24:14regions of the Chamber, right?
  • 24:15So the pallium,
  • 24:16which is the cortex,
  • 24:17come from the anterior regions and
  • 24:20if we project them to the mouse brain
  • 24:23using a software called Box Hunt.
  • 24:26Again, you see that the C5 anterior
  • 24:29map mostly to anterior mouse
  • 24:31regions and the posterior regions.
  • 24:34Instead, C1 maps to posterior
  • 24:36regions of the mouse brain and
  • 24:38the same is for the ventral side.
  • 24:40So in conclusion.
  • 24:43This is a new system that we really want to
  • 24:46exploit to make our organo more credible,
  • 24:49to build organoids that are more similar,
  • 24:52developed in a more in a way that is
  • 24:54more similar towards the actual brain.
  • 24:56Human brain actually develops
  • 24:57and and and that's using organo,
  • 25:00it is not using tissue culture
  • 25:02dishes with with factors added.
  • 25:04So let me finish by highlighting the
  • 25:08contribution of all my colleagues in my lab.
  • 25:11All of them have greatly contributed
  • 25:13to this work.
  • 25:15Jessica Mariani was the first one
  • 25:17who developed an organo in my
  • 25:19lab back more than 10 years ago.
  • 25:21And Alex and so I have greatly
  • 25:23contributed to the Chamber project.
  • 25:25And then of course Alexei is
  • 25:27collaborated with us for many years.
  • 25:29You hear him soon.
  • 25:31And Andrei Levchenko and his people
  • 25:33have greatly collaborated with
  • 25:36us for the Chamber project.
  • 25:38And with that, thank you very much.