Organoid Modeling of Neuropsychiatric Disorders
March 30, 2023Information
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- 9773
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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.