# Cell fate decisions in hematopoiesis

February 22, 2021## Information

Marjorie Brand, PhD

Professor, Department of Cellular and Molecular Medicine, University of Ottawa

Senior Scientist, Ottawa Hospital Research Institute

YCCEH Invited Speaker

February 19, 2021

ID6217

To CiteDCA Citation Guide

- 00:00Why don't we go ahead and get started?
- 00:03So I'm Jeannie Hendrickson.
- 00:04I'm representing the enrichment
- 00:06program of the Yale Cooperative
- 00:08Center of Excellence in Hematology,
- 00:10or super excited on this Snowy Friday
- 00:12afternoon to have Marjorie Brands
- 00:14visiting us for a talk that's called
- 00:16cell fate decisions in hematopoiesis.
- 00:18She's visiting us,
- 00:19and virtually she's a professor in the
- 00:22Department of Cellular and Molecular
- 00:24Medicine and a senior scientist at the
- 00:27Ottawa Hospital Research Institute,
- 00:28and really enjoy the picture of her lab here.
- 00:31So we're thrilled.
- 00:33She's with us this afternoon.
- 00:35And at the end of her talk we
- 00:37should be able to have some
- 00:40questions and answers as well.
- 00:42So thanks again Marjorie for visiting.
- 00:44We really appreciate it.
- 00:46Thank you very much.
- 00:48It's a great pleasure to be here.
- 00:50Thanks Pat for the invitation and I really
- 00:53enjoyed my discussions that I had today,
- 00:56so I'm going to start by 10.
- 00:59So the as we all know,
- 01:01hematopoiesis is a multi step process
- 01:04through which the hematopoietic stem cell.
- 01:06Shades to give rise to all the cells
- 01:09that are present in the blood,
- 01:12and this process is highly regulated
- 01:14by a number of transcription
- 01:16factors and cofactors,
- 01:17and these factors work together as
- 01:20networks to promote differentiation to
- 01:22our specific lineages such As for example,
- 01:25the average ridlen age that I'm going
- 01:28to focus on today and at the same
- 01:32time these transcription factors.
- 01:34Inhibiting the other alternate
- 01:36hematopoietic lineages.
- 01:36Now what's interesting is that the
- 01:39transcription factors that drive
- 01:41every trade or other lineages
- 01:43drive differentiation.
- 01:44There are expressed at very high levels
- 01:47at the specific stage when they are
- 01:50important to drive differentiation,
- 01:52usually towards the later stages.
- 01:54But most of these factors are
- 01:57also expressed in hematopoietic
- 01:59stem and progenitor cells,
- 02:01although at very low levels.
- 02:03Which suggests that the amount also
- 02:07dosage of transcription factors play
- 02:09critical role for cell fate determination.
- 02:13Now the importance of the dosage
- 02:15of transcription factors for the
- 02:17process of cell fate decision
- 02:19has been established early on by
- 02:22reprogramming experiments in cell lines.
- 02:24So, for example,
- 02:25the lab of tumors graph in 1995
- 02:28was able to reprogram myeloblastic
- 02:30cell lines 2 hours different fate
- 02:33depending on the amount of the data.
- 02:36One footings that was Ectopically
- 02:38expressed so specifically,
- 02:39the lab showed that if that high
- 02:42levels of get a one.
- 02:44We drive differentiation to us every
- 02:46trade lineages or megakaryocytes,
- 02:48while intermediate levels of get
- 02:50a one will drive differentiation
- 02:53too as the elzina fear.
- 02:54Now these early experiment really
- 02:57demonstrated that different
- 02:58amounts of the data one put in
- 03:01can promote alternate cell fate.
- 03:03Now, more recently,
- 03:04linear programming experiment in
- 03:06in primary cells have also shown
- 03:09the importance of the dosage of
- 03:12transcription factors for the
- 03:13process of cell fate decisions.
- 03:15So for example, in this publication here,
- 03:19the authors have reprogrammed
- 03:21human fibroblasts into every trade
- 03:23progenitors by using this cocktail
- 03:26of four transcription factors get
- 03:28away until one element 2 and C myc,
- 03:31which they called the GTLM.
- 03:33Cocktail, and for these reprogramming
- 03:35experiments they have used a retroviruses,
- 03:37and each retrovirus expresses one
- 03:39of these transcription factors.
- 03:41And what's very interesting is they
- 03:43found that depending on the relative
- 03:46levels between their retroviruses
- 03:47that they use in their experiments,
- 03:50they can obtain completely different results.
- 03:52So for example,
- 03:53if they use a GT LM ratio of 2111,
- 03:57then these these reprogram cells will
- 03:59produce exclusively red cell colony,
- 04:02which suggests that those are
- 04:04functionally which would progenitors.
- 04:06However, if they use a
- 04:07slightly different GT LM ratio,
- 04:09for example 1112,
- 04:11then the reprogram cells will
- 04:12not produce red cells colonies,
- 04:14suggesting that these are not functional.
- 04:17So these these experiments have
- 04:18shown that it's not only the dosage
- 04:21of the amount of transcription
- 04:23factors that's important,
- 04:24but really the whole ative levels,
- 04:27or just to comma tree of
- 04:29transcription factors.
- 04:30That is key for reprogramming efficiency,
- 04:32and it is the same for native hematopoiesis.
- 04:36Where it has been proposed that the
- 04:38transcription factors that drive
- 04:40differentiation towards alternate
- 04:42hematopoietic lineages Co expressing
- 04:45bipotential progenitors and that those
- 04:47changes in us to comma tree is what
- 04:50is important to drive differentiation
- 04:53to US1 fate or another phase.
- 04:57Now a number of gene regulatory
- 04:59networks have been established in
- 05:01an attempt to model this self,
- 05:03a choice that occur in this
- 05:05bipotential progenitors,
- 05:06and for example,
- 05:07here is a network model of the
- 05:09selfish choice that occur in the MVP,
- 05:12just the megakaryocytes every
- 05:14trade progenitors and those cells
- 05:16must choose between an arbitrate
- 05:18fate or a megakaryocytic faith.
- 05:20Now,
- 05:20this hematopoietic tree of
- 05:22differentiation that I have shown
- 05:24you so far was a commonly accepted
- 05:27model for hematopoiesis until
- 05:28about 6:00 or seven years ago,
- 05:30when the use of single cell RNA
- 05:33seek transformed it into something
- 05:35that looks more like this.
- 05:37Now,
- 05:37in this knew continuous model
- 05:39of hematopoiesis,
- 05:40there are no differentiation steps process,
- 05:42but instead the cells are gradually
- 05:45transitioning along hematopoietic lineages.
- 05:46And really in these types of models,
- 05:49the.
- 05:49Hematopoiesis is actually based on a
- 05:52continuum of changing failed probability.
- 05:54So in in that type of model they are
- 05:57known by potential opportunities,
- 05:59but they're only hematopoietic.
- 06:01For generators with sulfate
- 06:02probabilities that are becoming
- 06:04more and more restricted as the
- 06:05cells gradually transition along
- 06:07the hematopoietic trajectories.
- 06:08Now in his new types of models
- 06:11for him at a crisis,
- 06:13and the exact exact role of
- 06:15transcription factors and the
- 06:17importance of their quantitative
- 06:18changes for the process of self.
- 06:21Decision remains unclear,
- 06:22so our goal is to establish a dynamic
- 06:26model of erythropoiesis that can
- 06:29integrate those quantitative changes in
- 06:32the level of transcription factors overtime.
- 06:35Now I will.
- 06:36I will model system to study human.
- 06:38Every troop Oasis is an ex vivo
- 06:41differentiation protocol where we
- 06:42isolate the city certified positive
- 06:44stomach progenitor cells from cord
- 06:46blood or peripheral blood or bone
- 06:48marrow and those cells are then
- 06:50differentiated to observe Richard
- 06:52lineages by using a cocktail of
- 06:54growth factors and cytokines.
- 06:56Now here are the sum of the
- 06:58morphological changes that occur
- 06:59during this ex vivo electrophoresis.
- 07:01You can see at the at the beginning at
- 07:03Day zero we can see the hematopoietic
- 07:05stem and progenitor cells and then there
- 07:08are dramatic changes in morphology and
- 07:10at the end of differentiation we obtain
- 07:12complete indication on those cells.
- 07:14That we wanted to further study every
- 07:17true policies at the single cell level.
- 07:19And for this we decided to use
- 07:21my cytometry or site off,
- 07:23which allows one to measure
- 07:25putains in single cell.
- 07:27So for the site of experiment,
- 07:29we harvested cells at regular
- 07:31intervals during the crossover.
- 07:33Every trait differentiation and
- 07:34then we barcoded those cells are
- 07:37separately at different time points
- 07:39with Palladium isotopes prior to
- 07:41combining them and standing them
- 07:43together with a cocktail of antibodies
- 07:46contain including cell surface markers
- 07:48as well as transcription factors.
- 07:51And then we did a clustering analysis
- 07:53on the 27 markers at a different time
- 07:56points and this allowed us to identify
- 07:5818 clusters or cell populations.
- 08:01And because we did a temporal
- 08:02because we did re bar coded the
- 08:05samples at different time points,
- 08:07we could do a temporal analysis of
- 08:10those cell populations which allowed us
- 08:12to reconstruct the dynamic trajectory
- 08:14from the early March put on progenitor
- 08:16cells to the differentiated auto chromatic,
- 08:18which replies.
- 08:19And then we looked at.
- 08:21The transcription factors expression
- 08:23in single cells within these different
- 08:26populations along the retreat trajectory.
- 08:28And our first question was whether
- 08:30there is Co expression of antagonist
- 08:33transcription factors in early progenitors.
- 08:35So we looked at fly one which
- 08:37promotes differentiation towards
- 08:39megakaryocytes and PLF,
- 08:40one which promotes differentiation
- 08:41to repair itself,
- 08:43and we looked at them in the MVP
- 08:45populations that we gated with this
- 08:48combination of seven cell surface
- 08:50markers and what we found is that the
- 08:53vast majority of the MVP Dooku Express
- 08:56is antagonist transcription factors,
- 08:57KLF one and fly one.
- 09:00In single serve.
- 09:01So next we wanted to follow the
- 09:03changes in put in levels of KLF one
- 09:05and fly one as the sales proceeds
- 09:08towards irritated differentiation
- 09:10and you can see these results at
- 09:12the single cell level here and when
- 09:15we aggregate the results we found
- 09:17that there is a gradual increase
- 09:19in put in levels of KLF one and
- 09:22at the same time gradual decrease
- 09:24in foot in levels of fly one.
- 09:26But there is no abrupt switch
- 09:28after a specific population.
- 09:30What happens is that those
- 09:32gradual changes actually.
- 09:33Taking several populations to occur
- 09:36belongs in between the trajectory.
- 09:40And then we we decided to Ectopically
- 09:43Express a flag tag version of fly,
- 09:46one in early progenitors.
- 09:48And then again,
- 09:50we followed differentiation by
- 09:51site of analysis and this time we
- 09:54identified not one but two trajectories
- 09:56in addition to the average rate
- 09:58trajectory that you can see here.
- 10:00By high level of expression of the
- 10:02Alpha globin we also found a second
- 10:05trajectory here which correspond to
- 10:07the megakaryocytic trajectory as
- 10:08shown by high level of expression of
- 10:11the city 41 marker.
- 10:13And then when we followed the cells
- 10:16that express flag fly one by including
- 10:18a flag antibody in our cocktail,
- 10:21we could determine that only the
- 10:23cells that express flag fly one shown
- 10:26in blue are differentiating towards
- 10:28the megakaryocytic trajectory,
- 10:30while the cells that do not
- 10:32express factor one shown here in
- 10:34green continue to differentiate
- 10:36along the imagery trajectory.
- 10:38So this experiment showed that the
- 10:41ectopic expression of Flight 1 is
- 10:43able to deviate the cells from their
- 10:45preferred every trade tragic tree
- 10:47to take on a megakaryocytic faith.
- 10:50So overall,
- 10:50these results are supports the
- 10:53concepts that quantitative changes
- 10:54in transcription factor put in
- 10:57levels in individual hematopoietic
- 10:59progenitors are key determinants
- 11:00of the cell fate decisions.
- 11:02Now in our site of experiment we
- 11:05could account for all older cell
- 11:07populations that were present
- 11:09in our differentiation media and
- 11:12that includes the hematopoetic
- 11:14progenitors erythroid cells.
- 11:15Also some megakaryocytes and Milo itself.
- 11:18However,
- 11:19there was one major cell population here
- 11:22for which the identity was less clear.
- 11:26And as you can see,
- 11:28the cell population starts to accumulate
- 11:30from day four of differentiation and
- 11:32then it increases until the 11th
- 11:34and then starts to progressively
- 11:36decrease and then completely
- 11:38disappears from our differentiation.
- 11:42Such a medium.
- 11:43Now looking at the cell surface
- 11:45markers for this population,
- 11:47we notice that it expresses
- 11:48very high levels of CD 44,
- 11:50which you can see here on the heat map,
- 11:54but also on this Disney plot.
- 11:56And we also notice that this
- 11:58sector population expresses
- 11:59intermediate levels of CD123.
- 12:00But when we look closer on a Disney product,
- 12:03you can see the expression
- 12:05of CD123 is heterogeneous,
- 12:06with some cells expressing
- 12:08extremely high levels an other
- 12:10cells are moderate levels of CD123.
- 12:12We also notice that these cells
- 12:14express very high levels of data.
- 12:16Two,
- 12:16it's actually the cell population that
- 12:19expresses the highest level of of getting 2.
- 12:22In in our experiment,
- 12:24and it does express graded levels
- 12:27of the transferring receptor CD
- 12:3071 but is completely negative
- 12:33for the early marker CD 34.
- 12:36So based on these combination
- 12:38of cell surface markers,
- 12:40it suggested that these populations
- 12:42could represent battlefields which
- 12:44at first we found a surprising
- 12:46because basil fees are supposed
- 12:48to emerge from the granulocytic.
- 12:50So the model with branch of differentiation,
- 12:52not the average rate branch.
- 12:54However,
- 12:55these results are consistent with
- 12:57a single scientific data that have
- 12:59been published both from mouse bone
- 13:01marrow and from human bone marrow
- 13:04that have identified a cell population.
- 13:06In close proximity to the error rate,
- 13:09cells that with the gene expression
- 13:10profile that that is consistent with the
- 13:13battlefield identity transcription factors.
- 13:15Now why do we want to
- 13:17know Putin's documetary?
- 13:18First of all,
- 13:19because we want to better understand
- 13:21how this changes in stock.
- 13:23Yama tree, Dr,
- 13:24Cell fate decisions.
- 13:25But also because despite everything
- 13:27that we have learned from genomic
- 13:29studies and cheap seek about
- 13:31the binding of transcription
- 13:32factors to their target side,
- 13:34we still don't know how
- 13:36many transcription factors.
- 13:37Are there compared to the number of
- 13:39binding sites and we still don't
- 13:42know if cofactors are limiting
- 13:43compared to transcription factors,
- 13:46in which case the transcription
- 13:48factors must compete to recruit them,
- 13:50or whether cofactors are
- 13:52present in large excess,
- 13:53in which case there equipment would
- 13:56be highly facilitated.
- 13:58Now, if one wants to measure
- 14:00documetary between Putin's,
- 14:02it is necessary to use approaches
- 14:04that provide an absolute
- 14:05quantification of these proteins.
- 14:07So we decided to use a targeted
- 14:10mass spectrometry approach,
- 14:12which is called selected reaction
- 14:14monitoring our SRM, which,
- 14:15when coupled with the spiking of known
- 14:18amounts of isotopically labeled peptides,
- 14:20can provide an absolute
- 14:22quantification of footings.
- 14:23Now recently,
- 14:24a number of publications came
- 14:26out using quantitative mass
- 14:28spectrometry approaches.
- 14:29Which we are not SRM,
- 14:31so I just wanted to take a few
- 14:34minutes to explain why we have
- 14:37decided to use this SRM approach
- 14:39for our for our own purposes.
- 14:42So first of all I will start by
- 14:44saying that my spectrometry is not
- 14:46inherently quantitative an what it
- 14:48means is that the intensity of the
- 14:51signal that is measured by the mass
- 14:53spectrometer does not only depend
- 14:55on the abundance of the peptide,
- 14:57but also on a number of other criteria,
- 15:00such as the amino acid composition
- 15:02or the ionization efficiency,
- 15:03or other parameters that are
- 15:05not fully understood and what it
- 15:08means is that it's not unusual to
- 15:10be in situation like this one.
- 15:12When you have a very high abundant
- 15:14peptide like the blue peptide
- 15:16producing a low intensity signal by
- 15:19mass spectrometry or lower opponents
- 15:21peptide like the red peptide,
- 15:23producing a very high intensity signal.
- 15:27Now to overcome this limitation and
- 15:29still being able to extract quantitative
- 15:31information from my spec data,
- 15:33a number of strategies have been proposed.
- 15:36Anna very popular strategy is
- 15:38called the Proteomic Ruler Method,
- 15:40which has been established by the
- 15:42lab of Matthias Mann to estimate
- 15:44copy number of proteins preserve,
- 15:46so this method is using the mass
- 15:49spectrometry signals from histones
- 15:50as an internal standard to quantify
- 15:52all the other footings,
- 15:54and this is this is a very effective
- 15:57method to estimate putting copy numbers.
- 16:00It's also high throughput.
- 16:01You can measure thousands of
- 16:03proteins in a single experiment,
- 16:05and it is quite easy to do and it
- 16:08has been used in and in a lot of
- 16:11different publications and I have
- 16:13highlighted some of these publications
- 16:16here for high throughput studies.
- 16:18So,
- 16:19however,
- 16:19in our case we want it to be
- 16:21able to distinguish even subtle
- 16:24differences in the circulatory
- 16:26between transcription factors.
- 16:28So so instead we decided to
- 16:30use a different approach,
- 16:32which is based on the spiking of
- 16:35known amount of isotopically labeled
- 16:37peptides that are used as internal controls.
- 16:40So in this approach,
- 16:42each individual peptide is
- 16:44quantified using an isotopically
- 16:45labeled version of itself,
- 16:47as illustrated here.
- 16:49Which we called the the silk
- 16:51peptide and this approach has been
- 16:54shown to be extremely sensitive
- 16:56to dynamic range,
- 16:57is very large and it's currently
- 16:59considered as a gold standard for putting
- 17:03quantification by mass spectrometry.
- 17:05So to use to establish SRM assay
- 17:08for putting quantification,
- 17:09we collaborated with Jeff and Ishan
- 17:12Margulis pee from the Institute
- 17:14for Systems Biology in Seattle,
- 17:16and we developed SRM assay and
- 17:19optimize Sri massive over 100 Putin's
- 17:21and we decided to use a different
- 17:24types of proteins, including.
- 17:26DNA binding transcription factors,
- 17:28but also compressors, coactivators,
- 17:30some chromatin remodeling enzyme,
- 17:32as well as some subunits of the
- 17:35general transcription machinery.
- 17:36And overall we quantified 103
- 17:38proteins at 13 time points during the
- 17:41course of erythroid differentiation.
- 17:43Now I should mention that even though
- 17:46these SRM assay are quite expensive
- 17:48to develop and they take time,
- 17:51but once they have been developed
- 17:55they can be reused.
- 17:57Much cheaper way and fast way to be
- 18:00able to quantify the same protein in
- 18:04in a different cellular environment.
- 18:06So because we wanted to build
- 18:09gene regulatory network,
- 18:10we also had to measure changes
- 18:12in our near levels.
- 18:13So again,
- 18:14we have just excels at regular intervals
- 18:17during the course of their true policies,
- 18:19and we measured our in a level by RNA
- 18:24sequencing and put in levels by SRAM.
- 18:27And first,
- 18:27focusing on the RNA data.
- 18:29Here I'm showing this irony Sickbay
- 18:32Suretrade trajectory overtime along
- 18:33these two principal components,
- 18:35and as you can see,
- 18:37there is a nice purple nice progression
- 18:39from the hematopoietic stem and
- 18:42progenitor cells at day zero to the
- 18:44differentiated erythroid cells shown here.
- 18:47Now looking at the proteins that we
- 18:49have measured by hospital clustering,
- 18:51you can see that there are different
- 18:54clusters and these clusters appear
- 18:57to be a temporary Co regulated.
- 19:00Now the first question we wanted
- 19:02to ask was whether there is a good
- 19:05correlation between putting an M
- 19:06RNA during erythropoiesis and 1st.
- 19:09We looked at global correlation
- 19:11as illustrated here.
- 19:12Looking at M RNA level versus
- 19:14protein levels and what we found is
- 19:17that there is really quite a good
- 19:19correlation in differentiating cells.
- 19:21However,
- 19:22the correlation is extremely low in
- 19:24hematopoietic stem and progenitor cells,
- 19:26which is consistent with published
- 19:28data that have shown that the
- 19:30protein translation rate.
- 19:32Is extremely low in the hematopoietic
- 19:34stem and progenitor cells.
- 19:36So next we wanted to look at the
- 19:39correlation between our input in
- 19:41changes overtime during differentiation
- 19:43as illustrated here and what we
- 19:45found is that for most genes there
- 19:48is a positive correlation.
- 19:49However,
- 19:49for some genes there is low correlation
- 19:52and even sometimes a negative correlation.
- 19:54So for example here if we look
- 19:57at the fly one or get the two.
- 20:00She and you can see that there is
- 20:02a very good correlation between
- 20:04changes in putting an M RNA levels.
- 20:07However,
- 20:08for other genes such as get our Nortel one,
- 20:11you can see the Putin level is
- 20:13increasing much faster than the our name,
- 20:16suggesting an important contribution
- 20:17of the post transcriptional
- 20:19regulatory mechanisms.
- 20:20So if you are interested
- 20:22in looking at other genes
- 20:24that we have measured,
- 20:25we have we have done a website here
- 20:28which I'm going to try to access and I
- 20:31hope it is going to work so I'm just
- 20:35gonna stop sharing for a few seconds.
- 20:38And try to share.
- 20:46My other sites. OK,
- 20:47so I hope you can see it so so perfect.
- 20:52Thank you so on these websites we
- 20:54have to drop down menu here with the
- 20:57proteins that you have measured.
- 20:59So I just wanted to show you an
- 21:01example of proteins that going up
- 21:03during differentiation where there
- 21:05is extremely good correlation so
- 21:07you can see so on the left side.
- 21:09Here we have put in copy number per
- 21:12cell and the blue graph correspond to
- 21:15put in an on the right we have the RNA.
- 21:18And from left to right we have
- 21:21a differentiation from MPP to
- 21:22Basel figurative glass,
- 21:23so you can see this two hour and then
- 21:26put in correlate extremely well for six,
- 21:296, but actually for for a lot of
- 21:31proteins there is a very low correlation
- 21:33or or even a negative correlation.
- 21:36I just want to show you one more one.
- 21:39Another example kept away which
- 21:41is a histo nasty transfer is also
- 21:44called GCN 5 and you can see for
- 21:46this put in the RNA and put in.
- 21:49Growth curves have almost
- 21:51nothing to do with each other,
- 21:54suggesting a highly complex
- 21:57posttranscriptional regulation mechanism.
- 21:59So.
- 22:01I will just go back to my presentation now.
- 22:06OK, perfect,
- 22:06so as you could see,
- 22:08we found that there are really
- 22:11major discrepancies between
- 22:13mRNA and put in and put in.
- 22:15Abundance is an.
- 22:16This is for master regulators
- 22:18of erythropoiesis,
- 22:19suggesting that gene regulatory networks
- 22:21should not be limited to M RNA but
- 22:25should also integrate put things.
- 22:27And this is what we tried to
- 22:29do in collaboration with the
- 22:31computational biology step.
- 22:32Perkins and Daniel Sanchez start
- 22:34level from our Institute and
- 22:36basically they they wanted to build
- 22:38a dynamic network model of every
- 22:40trade commitments that incorporates
- 22:41the quantitative changes in
- 22:43transcription factor protein levels.
- 22:45So in the model,
- 22:46M RNA are considered as the targets and
- 22:49Putin's are considered as the regulators.
- 22:51Putins can be activated so
- 22:53they can be repressors.
- 22:55And basically what they did is to
- 22:59use ordinary differential equations
- 23:01to try to predict the kinetic
- 23:03of the change in M RNA levels by
- 23:07the changes in the quantitative
- 23:09changes in protein levels.
- 23:10And here is the the models that
- 23:13they they established around 14
- 23:16transcription factors at different
- 23:18days during differentiation.
- 23:20Now what distinguishes this model
- 23:22from previously published model is
- 23:24that in quantifies the strength of
- 23:27the identified regulatory relationships
- 23:29as a measure of the contribution of
- 23:32all proteins and this is shown by
- 23:35different levels of transparency.
- 23:37So for example here,
- 23:38if we look at ranks one and get it to,
- 23:41you can see that the activation
- 23:43of getting 2 by ranks one is much
- 23:45stronger than the activation of ranks.
- 23:47One by getting 2.
- 23:49The model is also dynamic in
- 23:51that it reveals changes in these
- 23:54regulatory relationships overtime,
- 23:55so you can see, for example,
- 23:58at day zero,
- 23:59the strongest regulatory relationships.
- 24:01Involve proteins that are important
- 24:03in progenitor cells,
- 24:04but those links are progressively
- 24:07decreasing in strength and they
- 24:09are fading away and that
- 24:11they tend in committed cells.
- 24:13They have been replaced by other links.
- 24:17That that binds proteins that are known
- 24:19to be important in differentiated cells.
- 24:22We also notice that our model is able to
- 24:25correctly recapitulates the timing of the
- 24:27transcription factor across antagonisms
- 24:29that underlies the cell fate decisions.
- 24:32So here you can see at day zero,
- 24:35the model was able to capture the P1,
- 24:38get a one across antagonism,
- 24:39which regulates a very early self,
- 24:42a choice between my
- 24:44Louisiana Richard lineage.
- 24:45And that they too were able to capture
- 24:48a second subsequent cross antagonism,
- 24:50the KLF one,
- 24:52Fly 1 cross antagonism that regulates
- 24:54cell fate decision that took on later
- 24:57between the erythroid cells Anna
- 24:59megakaryocytes and both of these
- 25:01cross antagonisms have completely
- 25:03disappeared at day 10 in committed cells.
- 25:07Now I will another interesting
- 25:09aspect of the model is that it allows
- 25:11us to compare the strength of the
- 25:13different transcription factors to
- 25:15the regulation of their target genes.
- 25:17So for example,
- 25:18if we look at the data 1 gene,
- 25:21you can see that the contribution of
- 25:23tell one to the activation of get
- 25:26a one is about twice as important
- 25:28compared to the contribution of GATA 2.
- 25:31And surprisingly,
- 25:32this is not because tell one is a
- 25:34stronger activator of this gene,
- 25:36because when we look.
- 25:38Get the activation strength and one
- 25:40is in fact a weaker activator of the
- 25:42ghetto engine compared to get that too.
- 25:44But what happens is that 10 one is
- 25:463 times more abundant than get a 2.
- 25:49So basically what this shows is
- 25:51that Taiwan is a week,
- 25:53but opponents of activators of the
- 25:55data 1 gene and get a 2 even though
- 25:58it's a stronger activator of the gene.
- 26:01Overall it contributes less to its
- 26:03activation because it is less abundant.
- 26:05So with this type of models we
- 26:07are really able to dissect the
- 26:09quantitative relationships between
- 26:11these transcription factors.
- 26:12And that's very important if we
- 26:15want to be able to control every
- 26:18trapezes either in vitro or in vivo.
- 26:21So because they built different gene
- 26:24regulatory networks at different time points,
- 26:26we pieced them all together and
- 26:29made little movies where you can see
- 26:32the quantitative changes in this
- 26:35regulatory relationships as the cells
- 26:37proceed towards every trait differentiation.
- 26:40So you can see that this early
- 26:43regulatory here relationships
- 26:44decreases with time and are replaced
- 26:47by other regulatory relationships
- 26:49in more differentiated cells.
- 26:54OK, So what about cofactors?
- 26:56So everything I showed you so far was about
- 26:59the DNA binding transcription factors,
- 27:02but we know that these proteins works
- 27:04with the recruitment of cofactors,
- 27:06so we also wanted to measure cofactors by SM.
- 27:11And we started with the
- 27:13histone acety transfers P300.
- 27:15Now, according to the current literature,
- 27:17P300 has been shown to interact with
- 27:20dozens of transcription factors,
- 27:22so we expected it to be highly abundant
- 27:24in the nucleus, but much more,
- 27:27well, surprise.
- 27:27We found that not only P 300 is not in
- 27:30excess of the transcription factors,
- 27:33it is in fact present at a lower
- 27:35opponents than almost every single
- 27:38transcription factor taken individually.
- 27:40So then we looked at the other histone
- 27:44acetyltransferase is CBP and catuara
- 27:46GCN 5 and as you can see those are
- 27:50just as well in the nucleus as P300.
- 27:53So the next logical thing was
- 27:55to look at the antagonist,
- 27:57enzymes, histone deacetylase,
- 27:58and as you can see,
- 28:00those are much more abundant than
- 28:02the histo nasty transparency.
- 28:03So there is a quantitative
- 28:05imbalance between the edge ducks
- 28:07and their hearts in the nucleus.
- 28:09And then we looked at a broader
- 28:12range of cofactors.
- 28:13And what we found is all the
- 28:15coactivators that we have measured
- 28:17are actually really low abundance
- 28:19compared to the compressors.
- 28:21So for example,
- 28:22if we focus on CHD for sociology,
- 28:24far is one of the enzymatic subunits
- 28:26of the Nerd core oppressor complex.
- 28:29And as you can see,
- 28:31there is an almost 50 fold excess
- 28:33of CHD 4 compared to the to
- 28:35the Co activators in nucleus,
- 28:37and these results likely explain the
- 28:39fact that they're not complex has been
- 28:42identified as an interacting Putin.
- 28:44In almost every single put on the screen,
- 28:47now that have been published so far.
- 28:50So we found that the compressors
- 28:52are highly opponent.
- 28:53The coactivators are rare,
- 28:55so where do the transcription
- 28:58factor of fit on that scale?
- 29:00And the answer is that they
- 29:02fit somewhere in the middle.
- 29:04So here on this graph we have the
- 29:06copy number of Putin's in log scale,
- 29:09and as you can see,
- 29:11there is about an order of magnitude
- 29:13difference in your balance between Co
- 29:15activators and transcription factors.
- 29:17And then another order of magnitude
- 29:19difference between the transcription
- 29:21factors and the Co oppressors.
- 29:22And Interestingly,
- 29:23we could detect these differences
- 29:25in abundance only because we
- 29:27looked at Putin's because when we
- 29:29do the same analysis with M RNA.
- 29:31As you can see,
- 29:33there is no statistically significant
- 29:35differences in the abundance of
- 29:37the transcripts that code for
- 29:39these different classes of factors,
- 29:41which suggests that these very
- 29:43large differences in abundance are
- 29:45regulated post transcriptionally.
- 29:47And indeed, consistent with that,
- 29:49we found that when we inhibits
- 29:51protein translation by using by
- 29:53treating the cells with cycloheximide,
- 29:55you can see that the coactivators
- 29:57appear to be much less stable compare.
- 30:00This occurred pressers.
- 30:02So at least one of the reason why
- 30:05the coactivators are so where in
- 30:08a cell is
- 30:09because those appears to be
- 30:12extremely unstable footing.
- 30:14And this is true throughout every trip Oasis.
- 30:17Here I have plotted the Putin
- 30:20transcript average and as you can see
- 30:23the coactivators here are present
- 30:25between 100 and 1000 copies per
- 30:28Nicholas there transcription factors.
- 30:31On average, between 1000 and 10,000 and
- 30:35the oppressors between 10,000 and 100,000.
- 30:39So overall, these results suggest that
- 30:41the nucleus is a highly repressive
- 30:43environment where coactivators are limiting,
- 30:46which implies that the DNA bound
- 30:49transcription factors must compete with
- 30:51each other to mediate the recruitment
- 30:53of this limited number of coactivators.
- 30:56Now, one way through which the
- 30:59transcription factors can increase their
- 31:01likelihood of being able to recruit them,
- 31:04the limiting coactivators is through
- 31:06assembling themselves onto enhancers.
- 31:08So we wanted to ask the question,
- 31:11how do the number of active enhancers
- 31:13and coactivator molecules compare?
- 31:15So we looked at Co activators that are
- 31:18known to associate to active enhancers,
- 31:20which are the histone acetyl transferases,
- 31:22CPP 300 and the HTK for monometer
- 31:26transfer is is a mileage 3 MLN 4?
- 31:29And we have estimated the number of
- 31:32active enhancers by doing a taxic
- 31:34experiments which we have analyzed by
- 31:36hint Attack to identify transcription
- 31:39factor footprint and then intersected
- 31:42these with predicted enhancers from database.
- 31:44And this is the number of estimated
- 31:48active enhancers that we have obtained
- 31:50and as you can see these numbers are
- 31:54in the same order of magnitude as the
- 31:57number of molecules of coactivators.
- 31:59Suggesting that maybe the formation of
- 32:02these active in home sales depend on
- 32:05the availability of these coactivators.
- 32:07So this is of all our model where we have
- 32:10the transcription factors recruiting
- 32:12the RECO activators and the rest of
- 32:16the nucleus containing corepressor's.
- 32:18And we think that this.
- 32:23Hello hello hi how are you doing?
- 32:28Well, not there are you in
- 32:30the wilderness or in the camp.
- 32:32Sorry, my treat doctor Wiseman.
- 32:34Can you hear me OK?
- 32:37Actually going out on Sunday.
- 32:39So OK, so you missed some buddies
- 32:42working before Doctor Wiseman.
- 32:44Please mute yourself.
- 32:46I muted him. Thank you.
- 32:49I I'm almost done anyway.
- 32:52Thank you so so we think basically
- 32:55that this restriction of abundance
- 32:57of coactivators could be an
- 32:59important mechanism for concerted
- 33:01gene regulation and we think it
- 33:04may be particularly important in
- 33:06multipotent progenitors to prevent
- 33:08high levels of Co expression of
- 33:10genes from different lineages.
- 33:12So basically decreasing the
- 33:14transcriptional noise as well.
- 33:18And I will stop here.
- 33:20And I've knowledge people who did the work,
- 33:24so I already acknowledged all
- 33:26our collaborators an from my lab.
- 33:28This project was done mainly by two
- 33:31very talented postdoctoral fellow,
- 33:32common Poly who did most of the wet lab work.
- 33:36Xena Motory, who did the informatic.
- 33:39Analysis, With some help
- 33:41from a Kathy Silverman,
- 33:42who also did the informatic analysis,
- 33:44and I thank you all for listening and
- 33:47I'll be happy to take any question.