Pathology Grand Rounds, January 11, 2024 - Diane Krause, MD, PhD

Name: Pathology Grand Rounds, January 11, 2024 - Diane Krause, MD, PhD
Uploaded: 2024-01-12T13:45:09.8633333Z
Duration: 1 h 2 min 21 s
Description: Pathology Grand Rounds, January 11, 2024. Diane Krause, MD, PhD, presents on, "Mechanisms of Hematopoietic Fate Specification to Megakaryocytes."

January 12, 2024

Pathology Grand Rounds, January 11, 2024. Diane Krause, MD, PhD, presents on, "Mechanisms of Hematopoietic Fate Specification to Megakaryocytes."

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00:00Hey, good afternoon, everyone.
00:02So it's a great pleasure to introduce
00:06today's Grand Round speaker,
00:07a speaker that many of us know
00:09extremely well, Doctor Diane Krause,
00:11the Anthony N Brady Professor of
00:13Laboratory Medicine, Pathology
00:15and Cell Biology here at Yale. Diane
00:17did her MD PhD training at 10 at
00:20Penn and she also followed this
00:22by clinical pathology training
00:24at Penn as well. She
00:26moved on to postdoctoral training at Johns
00:29Hopkins and joined the Yale faculty in 1997,
00:32and since then she's developed an
00:34internationally recognized research
00:36program focusing on leukemiogenesis
00:38and hematopoietic differentiation.
00:40Some major research areas in Diane's
00:44group have included functionally
00:46characterizing gene products involved
00:49in acute megakaryoblastic leukemias,
00:51defining transcriptional mechanisms
00:53that regulate megacaryocyte maturation,
00:56and elucidating factors that regulate
00:58how the erythroid megacaryocyte
00:59precursor cell in the bone marrow
01:02differentiates down the erythroid versus
01:04the platelet lineage.
01:05Diane wears many hats at Yale.
01:07As many of you know,
01:08she's director of the Wine HH Stem
01:11Cell Processing Laboratory, associate
01:12director of the Blood Bank, Associate
01:14Director of the Yale Stem Cell Center Co,
01:17Director of Yale's Immunohematology
01:19T32 training Grant.
01:21And she's also the director of an
01:24NIHU 54 grant that has established the
01:26Yale Cooperative Center of
01:27Excellence in Hematology,
01:28one of five centers nationwide
01:31funded to increase to provide
01:33resources for investigators in the
01:35field of hematology and to provide
01:37training to promote, you know,
01:39a field of growing investigators
01:41in non legit heme. Diana
01:43is a recipient of numerous awards and
01:45just to name a few, the Klaus Meyer
01:46Award from Morial Sloan Kettering,
01:49the Tibor Greenwald Award from the American
01:50Association of Blood Banks,
01:52and she's been also inducted into
01:54the National Blood Foundation
01:55Hall of Fame. There's one local
01:58award that I'd really like to mention.
01:59In 2018, she received the Yale
02:02Postdoctoral Mentoring Award,
02:03and I think this award really
02:05speaks to her complete dedication
02:07to advance the success of women
02:10and those from underrepresented
02:11groups in science and medicine.
02:13She's extremely generous with her time,
02:16and despite her many responsibilities,
02:19she always finds time to serve as a
02:21truly dedicated mentor to a large number
02:23of trainees and many junior faculty,
02:25including Pallavi and myself.
02:27So we are really delighted
02:28that she's taken the time today
02:30to accept her invitation
02:31and present her work to you. Welcome, Dan.
02:39Thanks so much, Karen, for that really
02:41nice introduction I should have.
02:43I do have a recording of it.
02:44I can. That's me. Name my CD.
02:47I really wanted to start
02:47with the title slide.
02:48Because of this beautiful picture.
02:50I'm going to give too much in this talk.
02:52More than one should put into a one
02:54hour talk because I'm talking to
02:56pathology and I just couldn't not
02:57present some of the stuff in our lab
02:59that is just so visually beautiful
03:01and really maybe even attract some
03:03pathology trainees and faculty to
03:05collaborate on some of the the work.
03:08But I'll tell you mostly what's
03:10going on in lab.
03:11This picture is a mega karyocyte,
03:13a primary human mega karyocyte.
03:14And what you can see is that
03:16there's a lot of detail.
03:18You can even see the Golgi,
03:20the the Golgi and the endoplasmic reticulum.
03:23And what this is, is expansion microscopy.
03:25So this was taken with the confocal,
03:27but you really have a lot of
03:29the kind of detail that you can
03:30get with electron microscopy.
03:32So it's a pretty picture,
03:33but what I'll be telling you about
03:35today is hematopoiesis For those
03:36of you who don't think about this,
03:37it in our bone marrow there's a
03:39hematopoietic stem cell Like other
03:40stem cells it self renews for the
03:42life of the Organism and it can
03:44differentiate the hematopoietic
03:45stem cell differentiates into all
03:46of the cells in our peripheral blood
03:49leukocytes as well as the red,
03:50red blood cells and platelets.
03:53And my lab really focuses on this
03:55bright orange cell which we have here as MEP.
03:58I'm going to try not to talk in
04:00too many abbreviations,
04:02but the name of the MEP is a
04:05megacarycytic erythroid precursor cell,
04:06and it's kind of a mouthful,
04:08so I'll only sometimes say the whole thing.
04:10So this is the bipotent precursor of
04:12megacarycytes that make platelets and
04:14the erythroid lineage that ends up
04:16making enucleated red blood cells.
04:20And just to remind you,
04:21we make about 2,000,000 platelets and
04:232 million red blood cells every second.
04:25So this cell is very busy making its
04:27progenitors and trying to decide.
04:29I I don't really love using the word decide,
04:31but it really helps you ask the
04:34question which lineage to go down.
04:35So what is determining the fate
04:38specification of this bipotent progenitor?
04:41Just because I wouldn't be
04:42complete without saying this,
04:44there is evidence in the literature that
04:45megacary sites can also be derived directly
04:48from a hematopoietic stem cell population.
04:50So if that is the case,
04:52then I'm not talking about that lineage
04:54to megacarycytes, I'm talking about
04:56this bipotent lineage to megacarycytes.
04:58Why did we pick MEP?
04:59Well, first of all,
05:00it's a model of bipotent fate specification
05:02which is important in all of the
05:04stem and progenitor cell biology,
05:06tissue repair and response to injury.
05:10Secondly,
05:10it's important in regenerative medicine.
05:12As most of you are aware,
05:14the place that we get our red cells
05:16and platelets that we transfuse into
05:17patients is from healthy donors and
05:19there really aren't enough of them.
05:20And there's a huge amount of work
05:22in finding and collecting cells from
05:24healthy donors in order to maintain an
05:27adequate supply for the recipients.
05:29And sometimes we really run low
05:31on platelets in red blood cells,
05:32particularly in the last year or so.
05:35We've had several times when we're
05:36near crisis situation.
05:37So if we could figure out a way to
05:39make them in vitro, that would be great.
05:41And finally,
05:41just as potential therapeutics
05:43might be identified in erythroid
05:46and megacary acidic diseases,
05:48so how does one distinguish
05:50whether you have a bipotent MEP?
05:52What you have to do is a colony
05:54forming assay.
05:55Just if you think about a bacterium,
05:57it's going to form a colony.
05:58When we do him out of aquatic assays,
06:00we take a stemmer progenitor cell,
06:02we put it into a semi solid medium
06:03in a very dilute fashion and if that
06:06cell divides and differentiates,
06:07it's going to form a colony of cells.
06:10And what we do is we over the course
06:12of the two weeks that cell makes 2
06:14cell types with one of them being
06:15megakaryocytes and other cells of
06:17the erythroid lineage.
06:18Then the cell that started that
06:20process is the MEP,
06:21the bipotent progenitor that is
06:24the assay we used.
06:25We identified in in this paper
06:28from 2016 a really good sorting
06:31strategy for primary human MEP.
06:33What we did is we worked out the
06:36assay and then tested different flow
06:39sorting approaches to come up with
06:41the best possible way of isolating the cells.
06:43What happens is after the course of two
06:45weeks a single cell forms a colony.
06:47This is a colony of cells.
06:48It's been stained with anti glycoporin
06:50A which is a surface marker for
06:52red blood cell lineage.
06:53This and this colony is entirely
06:55made-up of cells that are committed
06:57to the erythroid lineage.
06:58Here's a colony that's stained
07:00with anti CD 41 only
07:02CD 41 is on the mega carry site lineage.
07:04So this is a colony of cells that are
07:06mega carry site only and then we often
07:08get colonies that have cells of both the
07:11megakaryocyte and the erythroid lineage.
07:13And just to be more complete,
07:14my lab has now switched to an assay and
07:16rather than using immunohistochemistry for
07:17glia in 41 we now do immunofluorescence.
07:23Based on the data obtained we now
07:25can get a population of primary human
07:27mega karyocyte erythroid progenitor
07:29cells where if you played 100 cells
07:31in a plate you get about 70 colonies
07:34and of those colonies about 50%
07:36shown here in blue are cells are
07:39comprised of cells with both of cells
07:41of both the mega karyocyte and the
07:43erythroid lineage with the remainder
07:45being erythroid only and mega only.
07:46We also came up with sorting strategies
07:48for the mega karyocyte progenitor,
07:50with most of the colonies are mega only
07:52and the erythroid progenitis under your
07:54similarly where they're mostly erythroid.
07:55One of the questions you may ask you
07:57may be asking yourself and will kind
07:59of be answered throughout the course
08:00of the talk is do we really have a
08:02good sorting strategy for the MEP?
08:04Because it looks like half of the
08:05colonies are E only and MK only.
08:07And what I'm going to tell you is
08:09that the data very strongly suggest
08:11that what we have is quite a pure
08:13population and that there is a
08:15probability that a bipotent cell,
08:17when put into the culture will,
08:19with the First Division,
08:21come up with two cells that then
08:23subsequently all decide Erythroid
08:24or subsequently all decide Meg.
08:26And it doesn't mean that the starting
08:28cell didn't have the potential
08:29to go down both lineages,
08:30and I'll try to convince you of that.
08:33So this enrichment of these populations
08:35has allowed us to study the fate
08:37transitions from the bipotent
08:38progenitor to the Meg progenitor
08:40and from the bipotent progenitor
08:41to the erythroid progenitor.
08:45I'm going to tell you four stories today,
08:47hopefully not too quickly,
08:48but quickly enough that I'm
08:49done by the end of the hour.
08:51The 1st is some really novel data
08:53that came out of our single cell RNA
08:56sequencing of these populations that
08:58revealed that the cell cycle speed
09:00of the MEP actually seems to predict
09:03whether that's going to be megacaryocyte
09:05output or erythroid output and that we
09:08can actually toggle the fate of the
09:10MEP by toggling its cell cycle speed.
09:13Then I'll tell you about the role of
09:14the Runks 1 transcription factor and
09:16how it's post translational modification
09:18effects MEP fate and then we'll talk about,
09:20we'll show show you some really cool
09:22data watching MEP fate specification that
09:24really gave us those probabilities that
09:26I told you about that a bipotent cell
09:28can form an E only or an MK only colony.
09:31And finally expansion microscopy
09:32that I already introduced
09:37what we did once we had fact sort facts,
09:39gating strategies for enriching MEP,
09:42Meg progenitors and erythroid progenitors.
09:44We also sorted the upstream common
09:46myeloid progenitors and we sent these
09:48for single cell RNA SEC analysis.
09:50And this was work done by Yi Shan
09:51Liu in the lab, an amazing post doc
09:53who published this work in 2018.
09:56What you can see when you look
09:57at the single cell RNA SEC and if
09:59you're not used to looking at this,
10:00the data from the individual cells has
10:04now been categorized into four groups.
10:07The CMP, the common myeloid progenitor group,
10:10the MEP or the Meg erythroid
10:12progenitor group,
10:12the Meg progenitors or the Meg
10:14committed and the erythroid
10:15progenitors or Erythroid committed.
10:17And what you can see is when
10:19we fact sort out these MEP,
10:20it's really a distinct population.
10:22There's a bit of a graduation to it,
10:24but it's a distinct population.
10:25It looks very different from CMPMKP or ERP,
10:28but it looks like it had still has
10:30some genes that are still on from
10:32the CMP that are going to be turned
10:34off and some genes that are on in
10:36erythroid and mega caries like Destin
10:39cells that are just coming on.
10:41So it really is a transitional state.
10:44When we looked at the gene expression
10:47analysis and compared MEP to the
10:49other populations,
10:50what we found that the pathways
10:52that were over represented in the
10:54differentially expressed genes
10:56were almost always the cell cycle.
10:58And so you can see it's here from
11:00the MEP to the Meg progenitor cell
11:02cycle shows up,
11:02from the MEP to the erythroid shows up
11:04and the other things were were less specific.
11:07We weren't entirely surprised by
11:09this because we had preliminary
11:10data that were consistent with this.
11:12What we had done prior to getting
11:14the single cell RNA C data is we had
11:16tried a candidate approach where
11:17we would add various drugs and
11:19cytokines to the MEP to see if it
11:22affected their hematopoietic output.
11:23We already knew that in response to
11:25all trans retinoic acid which goes
11:27to the nucleus and binds directly as
11:29a transcription factor on the DNA,
11:30that we had a dose dependent increase
11:33in megacaryocyte only colonies when
11:35we added ATRA.
11:36We also knew that when we added
11:38rapamycin which is an mtor inhibitor,
11:40it's affecting metabolism.
11:41We had a a similarly A dose dependent
11:44increase in megacaryocyte biased and
11:46what we realized is that both ATRA
11:49and rapamycin can slow the cell cycle.
11:51So we tested that.
11:53What we've done here is a dilution assay,
11:57CFSE, dilution assay,
11:58for those of you who are not
12:00familiar with this,
12:00you stain all your cells at time
12:02zero with a fluorescent dye.
12:04Each time the cells divide,
12:05they have less of the fluorescent dye.
12:07So the further to the left they are,
12:09the more division there's been.
12:11And what you can see is the
12:12controls here are shown in blue.
12:14When you treat with ATRA,
12:15there's less division. Similarly,
12:16when you treat with rapamycin,
12:18there's been less division proving that
12:20they're both slowing the cell cycle.
12:22Now I'm not necessarily talking
12:23about the speed of the cell cycle,
12:24we haven't tested that, but there's
12:26they're dividing less frequently.
12:30What we did next then is just
12:31add a cell cycle inhibitor.
12:33We used CDK 46 inhibitor.
12:34These cells completely stopped dividing.
12:37We then washed that out and put them
12:39into the colony assays and again saw
12:42this dose dependent increase in the
12:45mega carrier site lineage specification
12:47of the MEP that proved this long.
12:49The cell cycle gave us a Meg bias.
12:51But what happens if you speed
12:52up the cell cycle?
12:53Well how do you speed up the cell cycle?
12:54One thing is that you can
12:57knock down CDK inhibitors.
12:58The CDK is that was pretty much toxic
13:01to the cells and didn't turn out.
13:03What we ended up getting to work
13:05is when we over expressed cyclins.
13:07So we got two different vectors from
13:09Claudia Vaskal's group in Germany,
13:11one that expresses CDK 2 and cycling
13:14E So this is the cycling dependent
13:16kinase 2 and the cyclin here the cyclin
13:19E that activates it and separately
13:21the CDK four and it's cyclin CDK
13:23Cyclin D We call this guy 2E and
13:26this one 4D for obvious reasons.
13:28And what we found is both 2E and
13:314D accelerated the cycling of MEP
13:33getting more more cycling in vitro.
13:36And when we looked at the output of
13:38those MEP you can see that whether we
13:41gave them 2E or 4D on a cell by cell
13:43basis now we had an erythroid bias.
13:45So the opposite with more cell cycle,
13:48more E fate specification and we
13:50did not see this effect if we took
13:53cells that were already MK committed
13:55or already Erythroid committed.
13:57So part one is when we slow the
13:59cell cycle we get more MKP.
14:00When we speed up the cell cycle we
14:02get more Erythroid. Why, how there?
14:04We have a lot of ideas.
14:06I'm going to show you that where
14:07we are in terms of answering that
14:09which is the runks one story
14:13and we I'm not showing you the data but
14:15we've shown that MEP actually cycle more
14:17slowly than both Meg or Erythroid cells.
14:19So that's kind of an interesting
14:20concept that they have to speed up
14:22whether they're going Meg or Erythroid,
14:23it's just the degree to which they speed up.
14:27So I want to tell you about Runx 1.
14:28Runx one also was revealed in our
14:31single cell RNA seq data and then
14:33subsequently in bulk RNA seq data.
14:35When we looked at the single cell
14:37RNA seq data and said what's what
14:39is likely regulating the genes,
14:41the change from MEP to MKP and
14:43from MEP to ERP,
14:45from Meg to erythroid and Meg to,
14:47I'm sorry, from the bipotent to the Meg
14:49and from the bipotent to the erythroid.
14:51Ronx One was the predicted transcription
14:53factor that would be regulating this.
14:56Of the genes that are down regulated
14:58from CMP to MEP and down regulated from
15:00the MEP to the erythroid progenitor,
15:02it was the number one ranked
15:05transcription factor that was able
15:06to regulate the target genes that
15:09were differentially expressed.
15:10It was also the number three potential
15:13regulator of genes that are up
15:14regulated in the megacaryocyte fate
15:16specification and amongst those
15:18is Mipple which is thrombopotin
15:19receptor and FLEA one which is a
15:22known transcription factor that's
15:24critical for megacaryois what we oops,
15:29this is supposed to come next.
15:30What we did then was we over
15:32expressed Runks 1 and when we do
15:34that you can see that we actually
15:37caused those bipotent cells to go
15:39towards the megacaryocyte lineage.
15:41And then when we inhibited Bronx 1,
15:44Runks 2 and Runks 3 with a drug,
15:46we could see the opposite effect where we
15:48see an increased in E fate specification
15:51which really proved that the Runcs one
15:54activity is promoting the MK fate in the MEP.
15:58However,
15:58when we looked at Runcs One RNA and
16:01protein levels in these three lineages,
16:03the Meg erythroid progenitor and then the
16:04Meg and the erythroid committed cells,
16:06there was no difference in either
16:08protein or RNA expression between
16:10the Meg committed cells and
16:12the erythroid committed cells.
16:13Which told us it's not happening
16:15at the transcriptional level
16:16or the translational level.
16:18It's probably post translational.
16:19So we started looking at post
16:22translational modifications of Runx one.
16:24And one that has been heavily studied
16:26before is serine and threonine.
16:28Phosphorylation of Runx one is known
16:30to be necessary for its activation for
16:33its ability to activate transgenes.
16:36So activate transcription.
16:38I'm sorry.
16:39What we did is we got antibodies
16:41that are specific for different
16:43phosphoserines on Ronks.
16:44One from our collaborator,
16:46Alan Friedman,
16:47he'd published this and pulled them out of
16:49the freezer for us and they work beautifully.
16:51And what you can see,
16:52and this is work that was done by two
16:54very talented people in the laboratory.
16:56I already introduced you to Yi
16:58Shen and Nayeong Kwan is a grad
17:01student in the lab and she's really
17:03been the mastermind between all of
17:05all the work I'm about to show you.
17:08What she did is she did intracellular
17:11flow cytometry for total runks
17:13one and phosphoserine runks 1.
17:15And I'm going to show you data
17:16for several of the phosphoserines.
17:17This is phosphoserine 76.
17:18I just bother to show you.
17:20The 276 is here and phosphoserine 303
17:23which is here in the Runks one protein.
17:26And what she showed and this is just
17:28representative data on the left and
17:29then graphed here on the right for
17:31multiple replicates is that either
17:33with commitment to erythroid or
17:35megacarocyte fate specification,
17:36you see an increase in the phosphosurine
17:41levels of Bronx One and there's a
17:44significantly higher increase when
17:46you go to the Meg Fate specification.
17:48Similarly with the Erythroid, Similarly,
17:51I'm sorry, with phosphosurine 303,
17:53you see this increase and then a further
17:56significant increase between erythroid
17:58progenitors and Meg progenitors.
18:00So that's just shown schematically here.
18:02The phosphoserine runcs 1 levels go up
18:04from MEP to MKP and go down or don't
18:08go up with when you go to Erythroid.
18:11So this is where we are.
18:12Is there a link now with the cell
18:14cycle data that I showed you?
18:16I'll just show you one example
18:18to for this link.
18:19The link is basically that the slowing
18:21of the cell cycle requires Runcs 1.
18:25You if you slow the cell cycle and
18:26there's no Runx One activity then you
18:28don't get the Meg Fate specification.
18:30They still go down the erythroid lineage.
18:32But let me show that to you slowly.
18:33So here's your control with the bipotent,
18:35the erythroid only and the Meg only.
18:37This is the effect of the Runx 1 inhibitor
18:40that gives us more erythroid only.
18:41This is the effect I showed you
18:43previously of ATRA or rapamycin
18:44where they slow the cell cycle and
18:46you get more Meg Fate specification.
18:48And here I'm showing you the combination.
18:50You do not see this increase in
18:53Meg phase specification with ATRA
18:55or with rapamycin in the presence
18:56of the inhibitor of the Ronx one.
18:59Really suggesting that we have a link
19:01now between slowing the cell cycle and
19:04getting increased Ronx 1 phosphorylation
19:05and increased MK phase specification.
19:12Oh, so I did include this,
19:13I wasn't sure if I'd show this.
19:14So then we actually did prove that
19:15if you slow the cell cycle like
19:17with the Pelvicyclib that actually
19:19slowed the cell cycle and then
19:20you wash it out and then you show
19:22the cells are Meg fate specified,
19:24you actually get increased levels of
19:27phosphosurine runks one at both 276 and 303.
19:30It ends up you also get increased
19:32levels of total runks one,
19:33but the ratios suggest that we probably
19:35have a higher percentage of the
19:38total runks that is phosphorylated.
19:40So that's our link for now with fate
19:43specification and the cell cycle.
19:45We then wanted to test the effects
19:47on primary cells if we get rid of
19:50the serines and threonines that
19:51are phosphorylated in the Runx 1,
19:54So I'd shown you previously,
19:55when we overexpressed Runx one,
19:56we get more MK fate specification.
19:58What if we mutate these four residues,
20:013 serines and one threonine
20:04to alanine in that case?
20:06We didn't get quite no effect.
20:08We got some effect,
20:09but it was a less strong effect
20:10than in the wild type.
20:11And in contrast when we changed the
20:14serines and threonines to aspartic
20:15acid which mimics the phospho serine,
20:18so all of the overexpressed brunx one
20:20is pre in a pre phosphorylated state.
20:23We got a far stronger effect with
20:25almost no erythroid fate specification
20:27and a lot of MK only suggesting
20:30that this is really playing a
20:32role in MK fate specification.
20:34In order to study this we
20:35then used a cell line model.
20:37So human erythro leukemia
20:39cells are an OK model.
20:41When you add TPA they
20:42go down the Meg lineage,
20:44when you add hemen they kind of sort
20:46of go down the erythroid lineage.
20:48Anyway it's the best system we have
20:49for looking at this and what we wanted
20:51to do is over express the wild type,
20:53the 4A mutant that has the alanine mutations,
20:56the 4D mutant with the aspartic acid
20:58mutations and so that we can do some
21:00molecular studies like cut and run
21:02and and gene expression changes.
21:04And first thing you can see is even
21:06without inducing these cells to
21:08differentiate with TPA we just we
21:10get them to go down the Meg lineage.
21:12CD 42 comes on in more mature megakaryocytes.
21:15You can see that they already start
21:17to mature down the Meg lineage
21:19just by over expressing this pre
21:21phosphorylated Bronx one compared
21:23to the 4A or the wild type.
21:26We then looked at gene expression changes
21:28and cut and run in these health cells.
21:30I'm just showing you gene expression changes.
21:32First glycoprotein 1B beta is a
21:33gene that's very important in Meg
21:35maturation and what you can see
21:37and this is just two duplicates of
21:38each for the empty vector cells.
21:40For the cells that we're expressing
21:424A that cannot be phosphorylated
21:44on this four residues,
21:45the wild type and the 4D that's
21:48pre phosphorylated.
21:48And what you can see is
21:50this gradual increase in
21:51the glycoprotein 1B beta consistent with
21:53the increased CD 42 that we had seen when
21:56we looked at where is that Runx one bound.
21:58So the over expressed 4A4D and wild
22:00tape are all HA tagged and when we
22:02did anti HA cut and run what we
22:05found is there's no difference,
22:06they all bind just fine.
22:08This isn't a complete surprise because
22:11the DNA binding domain of Runx One
22:13is not near those phosphocytes.
22:15But what it strongly suggests is
22:17that Runx one can bind but the post
22:20translational modification is what's
22:22affecting its effect on transcription.
22:24And keep that in mind because I think
22:26we we start to have clues now as
22:29to where that might be taking us.
22:31This is just showing you that when
22:32we combine the cut and run and the
22:34RNA seek data that we have this group
22:36of genes that are activated by both
22:38wild type and 4D in the health cells
22:41but not as much by the 4A mutant.
22:43But yeah,
22:44the 4A mutant and those genes tend
22:46to be genes that we know are very
22:48important in Meg maturation.
22:49So just consistent with what I
22:51already showed you on the other two,
22:53I'm not going to show you a whole
22:54lot of data and a whole lot of
22:56work on the cut and run data,
22:57except to say that there really was no
23:00significant difference in binding of
23:02the four a the wild type in the 4D.
23:04So the next question is what phosphorylates
23:06the runks one and this has been,
23:08this is very recent data,
23:09it's not yet published.
23:10A lot of this isn't published,
23:12but this is like we got it in
23:13the last few months.
23:15Multiple kinases were published
23:17that phosphorylate runks one,
23:19and problem is whether when
23:20we knock down any of them,
23:22we had no loss of phosphorylation on Runx 1.
23:27So what's going on?
23:28We decided what has to be another kinase,
23:31so I'm going to take you through
23:32that a little bit.
23:33The predicted kinases for Runx
23:36one include CD,
23:38all of the cycling dependent kinases,
23:40and CDKS 1-2 and six had all been
23:42proven to phosphorylate it in vitro.
23:44Similarly with the SIP,
23:46K2 and the URC.
23:47But all of their activity was shown
23:48in reporter assays and it didn't end
23:50up being relevant for our primary
23:51cells where the phospho levels
23:52didn't change when we knocked down
23:54these genes or inhibited them with
23:56with with very small molecules.
24:00In fact,
24:00if you what we found
24:02is if if you
24:04inhibit CDK 9,
24:06which is completely different CDK,
24:08that's when you lose it.
24:09So I'm going to show you first,
24:10this is what happens when
24:11we inhibit CDK four or six.
24:13So they were predicted.
24:14CDK six was predicted to
24:16be a kinase for Runx One.
24:18I previously showed you these
24:19data in a different context.
24:21When you inhibit CDK four and six,
24:22you actually get more
24:24phosphorylation of Runx One.
24:25Remember that was consistent with slowing
24:27the cell cycle more Runx 1 phosphorylation.
24:29So CDK 6 is not the thing that's
24:32phosphorylating Runx One in our cells.
24:34But when we inhibited CDK 9,
24:37which was another predicted kinase
24:38that would phosphorylate these cells,
24:40then we saw something really interesting.
24:42Then the total level of Bronx
24:44One didn't change,
24:45but the levels of both phosphoserine
24:473O3 and phosphoserine 276 did change.
24:49Now this is one of several flavopyridol
24:52is one of several CDK 9 inhibitors,
24:54but none of them is absolutely
24:57specific for CDK 9:00.
24:58So we ended up getting a different
25:00CDK 9 inhibitor that is more specific.
25:02It's called phallus NSO 3 two,
25:04and it induces degradation of CDK 9,
25:07which I'm not showing you, but it does.
25:09And when we added the Thou,
25:11we also got the loss of the
25:14phosphoserine 3O3 and phosphoserine 276.
25:16And when we added the Thou to the cells,
25:19just as we had expected,
25:20we got an erythroid bias to our MEP.
25:23Really.
25:24Now connecting CDK 9 activity to Ronx 1
25:29phosphorylation to MEP Fate specification.
25:33Now for those of you who know what CDK 9 is,
25:35this is just like,
25:36Oh my God, what's it doing?
25:37And I the answer is I don't know.
25:38But for those of you who
25:40don't know what CDK 9 is,
25:41the reason this is exciting is
25:43CDK 9 is part of just the general
25:46transcriptional control apparatus.
25:49It's part of activating RNA polymerase too,
25:52but in published data from years
25:54ago that has never been explained.
25:57Knock down the CDK 9 causes you to lose
26:00megacary sites and people never knew why.
26:02So I think we now have a link between CDK 9,
26:05Runx One and Meg Fate specification
26:07that we have a grant to look at.
26:10So the summary of Part 2 is that
26:13phosphosurine RUNX 1 promotes Meg
26:16Fate specification that's through
26:18phosphorylation by CDK 9 which is part of
26:21the transcriptional regulatory complex.
26:22And the work that we're in the process
26:25of doing that I I don't know the
26:27answer to it yet is what is their
26:30differential binding as phosphosirring
26:31runks 1 to different target proteins.
26:33And we really want to do RNA seek and
26:35cut and run on these various different
26:37runks mutants in primary cells because
26:39everything I showed you for that
26:41so far was done in health cells.
26:43And then really,
26:44how does the CDK 9 Pol 2 Runks 1
26:47regulate transcriptional elongation
26:49to promote Meg BAKED specification.
26:52OK Act 3.
26:52So act three is I showed you
26:55that we get colonies and what we do is we
26:58read those colonies out after two weeks.
27:01So you put cells in two weeks later
27:02you say what colony types do we have,
27:04but we really then are not not,
27:07don't know for sure what's happening
27:08with all the cells in between.
27:10For example, is there more rapid
27:12proliferation in the cells before they
27:14pick the erythroid fate and slower
27:17proliferation before they pick the Meg fate?
27:19How are we going to look at that?
27:20We have to actually watch them
27:23undergoing this fate specification.
27:25So what Vanessa Scanlon lab and did in
27:28my lab and Vanessa has now moved on.
27:30She was an amazing post doc and
27:32she's now an assistant professor at
27:34University of Connecticut and what
27:36she did is she developed a time lapse
27:38microscopy to watch individual human MEP
27:41undergo fate specification in vitro.
27:47So here's what she did.
27:48She took her facts, sorted MEP.
27:50She put very few of them in a
27:52very small volume in a in a plate
27:54covered that and that in the same
27:55semi solid medium that we use
27:57for our colony forming essays.
27:58But it has it's very flat.
28:00She puts a cover slip on top of
28:02that puts it into the Viva view.
28:03This is an Olympus apparatus
28:05we still have in the lab.
28:07It works beautifully.
28:08They don't make it anymore.
28:09So for now we have it and then she
28:11can watch these cells undergoing
28:13fate specification and add the
28:15antibodies towards the end of making
28:16the movie so that the erythroid cells
28:18under are showing in red and the
28:20megacuria sites are showing in green.
28:22So here you have a bipotent colony,
28:25a mega only colony and an erythroid colony.
28:26But they're all very flat because
28:28we're looking at this and we're going
28:30to want to look at this over time.
28:32Here's an example of an MEP colony
28:34of an MEP ending up making a mega
28:37carry site in erythroid colony.
28:39The little dots that color them,
28:41we put those in, that's part of our analysis.
28:43So I don't have that pre dotted.
28:46But anyway, so that's a single cell.
28:47We're starting with a single MEP
28:49and then what you're going to
28:51see is that that cell over time,
28:53and this is over the course
28:54of about seven days,
28:56undergoes state specification.
28:57If it's blue,
28:58it means that downstream of that cell
29:00there are both Meg and Erythroid cells.
29:02If it's red,
29:03it means everything downstream of
29:04that is Erythroid and if it's green,
29:06it means everything downstream
29:07of that is going to be Meg.
29:10And there are a lot of things
29:11that you can see here.
29:12One of them maybe you saw
29:14those streaky green lines,
29:15the Meg progenitors move a whole lot
29:17more than the erythroid progenitors.
29:19We're not sure yet what that means
29:21and whether it's relevant for
29:22what's going on in the bone marrow.
29:23But what we do know is in the bone marrow,
29:25people have looked at it,
29:26Erythroid maturation tends to hurt
29:29occur in bundles, whereas megs,
29:31they tend to be all over the place.
29:33So we think that this might have
29:34something to do with the fact
29:36that the Meg destined cell is
29:37still quite motile and there are
29:38other things that you can see.
29:39I'll just let's go take show you
29:42quickly where you can see that there
29:44are blue cells that are still present
29:46after multiple rounds of division,
29:48but fewer and fewer of them.
29:50Some of the blue cells are still
29:52here even pretty late when
29:53the other ones still haven't
29:55undergone fate specification.
29:57When we analyze these,
29:58one of the first things we saw is.
30:00So this is now a tree where the
30:01blue cells are bipotent,
30:02the red cells are erythroid committed
30:04and the green cells are Meg committed.
30:06When I say committed,
30:07I should probably say destined.
30:08We don't really know when they committed.
30:10We just know what the
30:11cells became at the end.
30:12One thing you can see though
30:13is that MEP self renewal,
30:15this is not something anybody had
30:17ever known before and it was kind
30:19of questionable when you look at
30:20the single cell RNA seek data.
30:22If you remember we had this graduation,
30:24I didn't know how long that graduation took.
30:26Maybe cells just become an MEP and then
30:28the next day they're mega erythroid.
30:30But here you can see that the
30:31bipotent cells can self renew
30:33and make more bipotent cells.
30:35Sometimes where one bipotent cell
30:36makes 2 bipotent cells and times where
30:38sometimes where it makes 1 bipotent cell
30:41and one fate Destin cell unique fate.
30:43And when we and this is just looking
30:45at the the sometimes when we played
30:47at MEP we got MK only colonies,
30:49sometimes when we got we played
30:51at MEP we got E only colonies.
30:53So this was another opportunity
30:54for us to say, well,
30:55is this different from when we plate
30:57an erythroid progenitor that we already
30:59know is E committed or a Meg progenitor?
31:02And the answer is yes.
31:05This is a sample tree from an
31:07MEP that's going to undergo fate
31:09specification down both lineages.
31:11Here's one where it's going to
31:13undergo Meg only or Erythroid only.
31:15If you compare that when we
31:18plate the Meg progenitors,
31:19there aren't very many divisions.
31:21They make teeny tiny colonies and
31:23when we play erythroid progenitors,
31:24what we see is that they reach
31:27this faster proliferation sooner.
31:29So they really are downstream of this
31:31cell that we're seeing here that is
31:34making a much larger colony with,
31:36and it doesn't speed up its
31:37cell division quite so early.
31:41This is another way of looking at
31:43the data where what you can see is we
31:45were able to follow these cells for
31:47up to 13 generations, a single cell,
31:49what happens over 13 generations in
31:51vitro and what you can see is expansion.
31:53When one MEP makes 2 ME PS tends to
31:56occur but one is that's where we started.
31:58We only looked at colonies that
32:00were going to make both here.
32:01But what you can see is that you
32:03really get MEP self renewal where
32:05you're going to get two expansion from
32:08MET one MEP to two MEP for the 1st 3
32:11divisions and then that gradually goes
32:13away and by the 6th division you're
32:16not getting one MEP making two MEP.
32:18In contrast this maintenance division
32:20where one daughter cells going to be a Meg,
32:22an MEP and one is going to be fate
32:24destined that starts to occur at
32:26approximately the 4th generation and
32:27that's what we have until the end.
32:29And with each time you have one
32:31of these yellow divisions,
32:32that's when one MEP makes 1 Erythroid
32:34fate committed and one MK fate committed.
32:36That's going to be the end of the
32:38line because we're not going to
32:39keep following MEP.
32:40So it really gives us a nice way of looking
32:42at the changes that occur over time,
32:44which ends up being highly relevant
32:46for our predictive models.
32:48What we wanted to do is come up with
32:50a mathematical model that gave us the
32:52outcome that we saw so that we could
32:55understand the probability that a cell
32:57would undergo a specific fate decision.
33:00And this is work done by Everett Thompson
33:01in my lab who's an amazing graduate student.
33:04And what he realized is if he used a
33:07Markov model of these cells that are
33:09MEP that are expanding to make two
33:11MEP exhaustion where the MEP makes
33:131 erythroid and 1 Meg fate specified
33:16versus these two maintenance divisions.
33:18He could model the data that we got
33:20as long as he had that model change
33:23over time,
33:24which is consistent with what I just
33:25showed you.
33:25It does change over time the the
33:28probability that the MEP will self
33:30renew and expand.
33:31So when he did that he got the data
33:33that are plotted here.
33:34So what you're seeing here is the
33:37the broadbands shown here in blue,
33:39purple, Aqua and yellow.
33:41That is the data predicted by the model.
33:44And then in the dotted line is the.
33:48I want to make sure I say the right thing.
33:50Yeah.
33:50And the dotted line is the in blue
33:52is the observed data.
33:54So what you can see is we really
33:56are very closely modeling what the
33:58actual data are for the exhaustion,
34:01expansion, maintenance and maintenance.
34:03The way to look at this is,
34:05for example,
34:05if you just look at Generation 4,
34:08if you have an MEP,
34:09their chances are 46% chance of
34:12expansion or one MEP makes 2 MEP,
34:1528% chance that you're
34:16going to get maintenance
34:18plus E, 9% chance of maintenance plus
34:20MK and a 17% chance of exhaustion.
34:22Well, that kind of models our
34:24outcome in our CFU where we get about
34:2650% of the colonies have Mega and
34:28Erythroid and the other ones are
34:30Unilineage MK only and Erythroid only.
34:32And then similarly you can look at another
34:35generation and get additional data.
34:39She Vanessa got a huge amount of
34:41data out of this and I just want to
34:43show you one other part of that.
34:45And what she did is she analyzed the
34:48length of the cell cycle and whether
34:50that predicted output and it wasn't
34:53as simple as we had hoped, but we did
34:56get some statistically significant data.
34:57The data that we got is that MEP
35:00that are cycling slower are going
35:04to be the MK destined.
35:07Remember MEP cycling?
35:08I have to remember exactly.
35:10So there was no difference.
35:11And this is where this was disappointing.
35:13There was no difference in the cell cycle
35:16interval between MEP and E destined cells.
35:20I thought that we would have seen that the E
35:22destined cells had a faster proliferation,
35:24but that's not what we saw.
35:26But we did.
35:27What we did see is that once we and with
35:30MK destined it was a little slower.
35:32That's the point I wanted to make.
35:34So there was a slowing,
35:35as if the cell was dividing more
35:37slowly there was a very good chance
35:39that it was going to be MK destined.
35:41And then if you looked at the MKP themselves,
35:43they are,
35:43they're known to have a slower cell cycle.
35:44I already told you that.
35:45But this was really the the new
35:47data was this MK Destined having
35:48a slightly slower cell cycle.
35:50So not quite as clear as we would have liked,
35:52but that's what the data show
35:56this. So just this is this time
35:58lapse imaging is now a tool in the
36:00laboratory that we are enjoying using.
36:01If anybody wants to collaborate and
36:03use this tool just let us know.
36:05It's one of the tools that's offered by the
36:08Yale Center of Excellence in Hematology.
36:12So last story, plenty of time I
36:14wanted to tell you about expansion
36:16microscopy to probe hematopoietic cells.
36:18So what is expansion microscopy?
36:20This is a way of doing super
36:23resolution microscopy using a confocal
36:25microscope and that really opens
36:27up the door to all of those of us
36:30who don't do electron microscopy.
36:31And even if you do do electron microscopy,
36:33you know it's very difficult to do any
36:36kind of immuno analysis because you're
36:38really limited to the size of the gold
36:40balls that are attached to your antibody.
36:42So you maybe can look at two things at the
36:44same time and maybe can see where they are.
36:47Here you have a confocal you can do
36:49immunofluorescence from for some antigens,
36:51not for every antigen with the expansion.
36:55So this is just to get you guys interested,
36:57if you're not a pathologist
36:59in looking at mega karyocytes,
37:00they happen to be the most beautiful
37:02cell in the body according to me.
37:04And what you can see is they're very,
37:06very large, hence the name mega karyocyte.
37:09What we're looking at here
37:10is a bunch of blood cells.
37:12These are your normal neutrophils.
37:14You can see the size of their nucleus,
37:15it's about 8 microns and
37:17this is a mega karyocyte.
37:19It's a single cell.
37:21It's got this gigantic nucleus and a
37:24gigantic cell and what this nucleus is,
37:27is it's polyploid.
37:28It's got the cell has divide,
37:29the DNA has divided and the cell
37:31has gotten bigger and bigger,
37:33but the cell has not divided.
37:34So you have many.
37:35You can get four and eight and 1632,
37:37whatever, up to 128 clearly.
37:41And then this part of this cell,
37:43which is super interesting
37:44and hard to describe,
37:45but you're about to see what it is.
37:47It's not a single cell membrane
37:50surrounding a cytopus.
37:52Well, it is,
37:53but the cell membrane is invaginated
37:55all throughout that cytoplasm.
37:58And way you can see that is from this movie.
38:00So this is a movie.
38:01It was published in 1999 by Joe Italiano,
38:04who's an amazing mega karyocyte
38:05scientist up at Harvard.
38:07This is a single mega karyocyte.
38:09Here's its nucleus.
38:10It's starting to make pro platelets.
38:12And the thing that's amazing about
38:13this movie is you can see that
38:15the cytoplasm is basically going
38:17to unravel to release the pro
38:19platelets that then become platelets.
38:26So all that membrane system was inside,
38:28it was all packaged and then it just
38:30had to be induced to to unravel itself
38:33and release these pro platelets.
38:35So yeah, it's a very cool movie.
38:38When people then look at megacary sites, they
38:41want to see that demarcation membrane system,
38:43that invagination of the plasma membrane.
38:46And we're doing this using
38:48expansion microscopy.
38:48So what is it, expansion microscopy?
38:50It's been developed in multiple laboratories.
38:54Neither of these labs,
38:55York Broersdorf or Yong Shinzhao's,
38:57was the first to do it.
38:58But these are the two people
38:59that we're collaborating with.
39:00Many of you may know York.
39:01He's here at Yale.
39:02He does beautiful work with Pan XM
39:04that I'll show you the I And Yong
39:06Shinzhao is at Carnegie Mellon.
39:08He has a different approach called magnify.
39:12And what you can see is that you take
39:15your cell and here we're just looking
39:17at different the mitochondria and
39:19the Golgi here in the cell and you
39:22polymerize polyacrylamide gel into the cell,
39:25hit it and it cross links with it.
39:27You then expand that because there's
39:30acrylamide in there and sodium acrylate.
39:32Sodium acrylate is what's in babies diapers.
39:35It's very,
39:36very absorptive.
39:36So if you have sodium acrylate and then
39:39you add water everything expands so
39:41you get this huge expansion then what
39:43they do in the boomers Dorf's lab is
39:47they stop that get rid of the cross
39:50linking re embedded and do it again.
39:52So they can get up to 16 to 20
39:54fold expansion of a single cell.
39:57With Magnify you get about a 10 fold
39:59expansion and I'll tell you about the
40:00differences but we we do both in the lab.
40:02I mean the idea is you type take
40:05one thing that was really little
40:07and now it's really big.
40:09This is data from your Goersdorf's lab using
40:12the Pan XM his two fold expansion approach.
40:16What you can see in these cells is
40:18an NHS Ester just stains proteins.
40:21So it gives you something that's very
40:23similar to what you might see on EM.
40:25And you see this beautiful
40:27Golgi apparatus in a cell.
40:28This is just he LA cells.
40:31They can actually get antibodies to
40:34work that allow them to localize whether
40:36a protein is on the outside or the
40:39inside of this of the mitochondria.
40:41And So what you can see here is
40:44when they stain with anti Cox four,
40:46it's on the inside of the mitochondria.
40:48When they stain with anti Tom 20
40:50which is known to be on the outside
40:51of the mitochondria,
40:52you can see this different
40:53pattern and it's really,
40:54really beautiful how you
40:56can clearly see the Cox,
40:57the Tom 20 is on the outside and
40:59the Cox 9 is on the inside Cox four,
41:02sorry.
41:02So just beautiful imaging that we
41:05want to be able to use in now in
41:07mega carry sites and platelets.
41:09This is a comparison of Magnify which
41:12is from Yong Shin Zhao's lab and the
41:15Pan XM that is in your Boomer source lab.
41:17And we really takes the best of both in some
41:21of our assays York Boomersdorf's approach.
41:23The Pan XM gives you much better resolution.
41:26No doubt you're getting 16X expansion and
41:29you're really preserving morphology better.
41:31However, it takes a lot of time and effort.
41:35In contrast,
41:37Yongshin's approach called Magnify,
41:38just takes one to three days.
41:39It's less than an hour of hands on time per
41:42day and there's no special equipment needed.
41:44You don't need this nitrogen
41:45tank and you get less expansion,
41:47but it's still quite beautiful.
41:48So I'll show you some data
41:49that we have for each.
41:50And this is not an expensive thing to do.
41:54This is just a beautiful image
41:56that comes from the that we did in
41:59collaboration with your Brewers.
42:00Dorf's lab and your runs the imaging core
42:02for the Center of Excellence in Hematology.
42:04And what you see here is a pan XM image.
42:07So that's the 16 fold increase,
42:1020 fold increase.
42:10And they pan stained it with the NHS Ester,
42:13which stains all proteins and with M cling.
42:17The nice thing about M cling is it bind,
42:18you stain the cells before you expand them.
42:20It binds to membranes,
42:22it binds to lipids.
42:23And this is allowing us to start to see this
42:26invaginated membrane throughout the cell.
42:29And we're getting better and better
42:31images of this invagination that
42:33tells that shows us the demarcation
42:35membrane system of the megakaryocytes.
42:39So here's another way of looking at
42:41this demarcation membrane system.
42:42Now not with the M cling but just with
42:44the pan stain of all the proteins.
42:46This is an electron microscopy image and
42:48this is from our expanded whole bone marrow.
42:52This is Mina Shu gave us this slide.
42:54So this is expanded bone
42:55marrow from human FFPE tissue.
42:57And what you can see is that this PAN
43:00XM really shows you the demarcation
43:02membrane system similarly to what you
43:05can see with the electron microscopy.
43:07Here's another expanded thing.
43:09This is now from magnify,
43:11showing that we have some antigens
43:12that we can identify.
43:13We can identify CD 61 shown
43:15in green and thrombospondin.
43:16So these are megacaryocytes and
43:19these green and red vesicles are
43:22actually the granules that are going
43:23to become the platelet granules,
43:25the alpha granules that have
43:26within them the thrombus bonded.
43:30And this is an image.
43:31I just can't get it out of my mind.
43:32But we haven't seen this again,
43:34we haven't done this.
43:35Again, this is again the formal
43:37and fixed paraffin embedded tissue
43:38from Mina shoe where we just
43:41did a pan stain after expansion.
43:44And I can't get over this little
43:45hole in the megacaryocyte.
43:47I really think that this might be
43:49where the invagination is happening,
43:50but we have to see it more.
43:52But I'm showing it to you
43:53because this is a pathology,
43:54grand rounds and it's so beautiful.
43:56These are autofluorescent red blood cells.
43:57On the on the outside it's
44:00just your gigantic nucleus.
44:01What what we've been quite
44:04successful at is using this to
44:06look at platelets and this is
44:07work that was done by Max Carlino.
44:09Some of you may know he's a first
44:11year graduate student of pathology,
44:12but he worked in my lab before
44:14that and he worked on this
44:16expansion microscopy on platelets.
44:17This is just an ultra an electromic
44:20graph view of a platelet and you can
44:22see that there are dense granules
44:24and there are alpha granules.
44:25So I didn't mean to go to the next
44:27one so quite so quickly at but you
44:29need electron microscopy to see the details.
44:31So what Max was able to do was expand
44:34primary human platelets and then just
44:36this was just the pan staining with
44:39the protein stain you can see granules.
44:41Then he used antibody against thrombospondin.
44:45Oops,
44:45it's supposed to be playing Oh well then
44:47he used antibiotic and there you go.
44:49So sorry.
44:51This is the thrombospondin
44:53which is in alpha granules.
44:55This is staining for tubulin which
44:57is on the outside of platelets.
45:00So and just the way you expect,
45:01we can see this tubulin ring and
45:03this is showing you both the tubulin
45:05ring and the thrombus bonded.
45:07Beautiful. What can we use this for?
45:09Well,
45:09one of the things we can use it
45:11for is to try to quantitate alpha
45:13granules within the platelets.
45:14And what I'm showing you here on the
45:16left is some of the classic work
45:19where they were quantifying alpha
45:20granules in platelets using electron
45:22microscopy and they got about 50 such
45:25granules per platelet on average.
45:28This is our data,
45:30not counting them using electron
45:31microscopy where you it's a huge
45:33amount of time and effort to try to
45:35get this three-dimensional microscopy.
45:37Here he can look at 151 platelets
45:39in that stained slide that in the
45:41slide I just showed you and he can
45:43say how many total granules are
45:44there and how many granules are there
45:46that have thromaspondon in them.
45:48And he could see that there were a
45:49little bit more than 50 granules
45:50on average per platelet looking
45:51very similar to this.
45:52And then he could even look at
45:54what percentage of those platelets
45:55have thromaspondon.
45:56So again something that I think
45:57can be useful clinically,
45:58certainly it's interesting scientifically.
46:02So finally,
46:03this is what I've told you today that
46:05single cell RNA seek reveals MEP
46:07as a unique transitional state in
46:10hematopoietic fate specification.
46:12That cell cycle differences really
46:13seem to regulate MEP fate and
46:15we're trying to figure out how.
46:17One of the ways that seems to be working
46:20is through in a slower cycling cell
46:23there's more phosphoserine Ronx one,
46:25and that phosphoserine Ronx
46:261 activates Meg genes,
46:28so you get Meg fate specification.
46:30I showed you time lapse imaging
46:32that really showed at least that
46:34statistically significant slowing
46:36of the cell cycle speed predicts
46:39MK fade specification and that
46:41we can predict the probability
46:43of MEP fade specification over
46:45time with this Markov model.
46:47And finally that we're very excited about
46:50the power of using expansion microscopy.
46:53I wanted to take a minute to tell
46:54you about the cooperative centers
46:55of excellence in hematology,
46:57which Karen already mentioned,
46:58but who listens to the intro.
46:59So YCCEH is funded by the NIDDK.
47:04Yale is one of five such centers
47:07nationwide and we all provide
47:09cores that can help people who
47:11do non malignant hematology.
47:13And some of what I showed you
47:15today is available in our core
47:17including the expansion microscopy,
47:18the time lapse microscopy,
47:21CDC's colony forming assays.
47:22We can help you with hematopoietic
47:25assays and other across the country.
47:27There's a metabolomics core for
47:28any non malignant heme work
47:30that you're doing in at Utah.
47:32There's an imaging core that
47:34does codecs in Indiana.
47:35You can get as many CD34 cells as
47:38you would ever need from multiple
47:40types of donors in Seattle,
47:43so do contact me if you want to be part
47:45of that or look it up at cceh dot IO.
47:48Finally, there are grants available
47:50through ICCEH Money,
47:51Money, Money, money.
47:52They have Type A grants and Type B grants.
47:54The Type A grants give you $12,000 worth
47:58of services at any one of the five cores,
48:01and those are it's a rolling submission.
48:04Anytime you have one of these just
48:06submit it and we'll we review
48:08the monthly and then the Type
48:09B grants are up to 70,000.
48:11They take an 8% overhead out of that
48:14$70,000 for you for your research
48:16for non malignant hematology.
48:18And those Type B grants are due
48:21February 15th I think don't quote me
48:23on that go to I go to CCH dot IO.
48:26But really it's it's they're good grants.
48:28So finally thank you to the lab,
48:33everybody's pictured here and hopefully
48:34I gave them credit as we went along.
48:36Thanks so much.
48:46This is open for questions.
48:50Yeah,
48:53expansion by class is 34.
48:56I'm wondering do you know if
48:59that expansion material disrupt
49:01like protein public interaction,
49:03did you try to see that colonization
49:05of sort of things? Yeah. So
49:11the answer is that proteins stay
49:12intact and protein interactions they
49:15they're they say look Co localized,
49:17but I don't know if they stay
49:18negative if it's not prevailed.
49:20What you what you can do though with
49:23extended microscopy is Co localized
49:252 proteins that you cannot clearly
49:28visualize if you don't have extension.
49:30So if you stain them after you've extended,
49:33you'll really be able to see
49:34that they were right next to each
49:36other and all of the epitopes will
49:38still be there because they're not
49:40blocking one another by being bad.
49:42So people have done Co localization
49:44studies with expansion that weren't
49:46feasible prior to having expansion.
49:48But if you're asking other things when we
49:51don't know what happens to DNA and RNA,
49:53some people have gotten fish to work,
49:55but I don't really know what the
49:57stretching does and what exactly gets
49:59stretched at that tiny molecular level.
50:01I've asked the same questions to the answer,
50:04but I'm not sure you know,
50:07with your increase in drugs on
50:10causing the increase in accuracy.
50:13Do you know that this later gets rise
50:17to functional increase in platelets?
50:21No. But what we do know,
50:23so our work was unique in
50:25starting with the bipodent progenitor
50:27and what we were always looking
50:29for is just which fake did it pick.
50:31And so you're right, we're only
50:32looking like the first part of it,
50:35but we didn't come up with
50:36rocks all by ourselves.
50:38Bronx One is known in a mouse.
50:40If you knock down Bronx One,
50:41you have lower meds, lower platelets.
50:43If you over fresh rocks,
50:45you have more meds and more platelets.
50:46What wasn't known is where was
50:48that acting and that it might
50:50be acting literally at the Fate
50:52specification level of an MEP.
50:53So I think they would,
50:55but I can't tell you for sure.
50:56I ask this because of our patients
50:58with rocks one journal on mutation
51:02and they in the bone marrow have the
51:04creation of abnormal and carrying sites,
51:07but then they have Bronx therapy.
51:10Those patients actually are
51:13hemisitis for inactivating mutation.
51:16They have decreased Bronx activity.
51:20So their mutant Bronx is either hypo
51:23functioning or not functioning at all.
51:25I wasn't aware then more plate, more megs.
51:28I know they have lower ploying megs because
51:30they have a defect in Meg maturation
51:32and they have lower platelets. Yeah,
51:37yes, yes, it's really beautiful.
51:40I was wondering that the the
51:42the common progenitor and in vitro,
51:44the lineage commitment from the Detroit
51:47and and Medicare is obviously driven by
51:49the cell cycle towards that it's right.
51:52Could you understand your situation in
51:55vivo where those things are altered in
51:58a way that the the ratio you know the
52:01cell has to decide it's going to make?
52:02How many RPCS and how many pavements?
52:05In which situation does it go awry
52:07and is it truly lineage commitment
52:12between or is it just stochastic? Well,
52:15we think it's truly lineage commitment
52:17on a stochastic low because
52:19there's always probability.
52:20We don't see that if we overspress rocks,
52:23everything goes in it.
52:24It's just some ability to go overthrow it.
52:25In fact you need rocks one
52:28for erythroid maturation.
52:29So I think it's stochastic but biased
52:32that you know you have one ratio.
52:34In the absence of over expressing
52:36wrongs you get a ratio that's very
52:37Meg biased when you over express
52:39wrongs and more Meg bias if you have
52:41normal. But in a normal progenitor
52:43what is the ratio of commitment
52:45towards something like a carrier
52:46site and the unit for itself?
52:48You're asking in vivo and I can
52:49only tell you in vitro, yeah,
52:51or even in vitro. In vitro,
52:53it seems that they're about equal and
52:56there's a there's a good reason for that.
52:59What happens downstream is the
53:01erythroid progenitor proliferates
53:03log fold multiple times very quickly
53:05to make a lot of erythroid cells.
53:08The Meg progenitor doesn't
53:10proliferate very many times,
53:12but each mega carry site makes
53:1410 to the three platelets.
53:16So you have a three log production
53:20per mega Carrison.
53:21So it kind of works mathematically.
53:24If you say you make one play
53:25then one or it's all about you
53:27know 1 to 1 ratios that
53:28that's how it would go. And
53:29we know if in older adults where
53:32there is minority buys that
53:34that there is a a differential
53:36response to this commitment between
53:38megataryocytes and heart disease. I
53:39don't know, I'd love to actually get access
53:42to marrow from patients with different
53:44diseases and that's been problematic.
53:46We have looked at MPNS and in MPNS if they
53:50have essential thrombocytosis then they
53:52do tend to have a Meg bias to their MEP
53:55and the opposite for polysychemia Vera.
53:57But it's very subtle.
53:58I think a lot of that is downstream of
54:01the MEP and the FATE certification.
54:04Jack 2:00 and from and Teepo, they're there.
54:07They're acted the whole time.
54:09So it's not going to just toggle
54:10it. Yeah. So
54:12along those lines that you looked or
54:14what do you know about CHIP and actually
54:18mutations in Ronczuan,
54:18a lot of things in terms of their
54:21cell cycling and their biases,
54:23nothing but. But patients with Ronczuan
54:26familial mutations in Ronczuan
54:28do have an increased risk of Chip
54:33that might just be because they
54:34have abnormal Hemato policies.
54:35And so the few, the better cells
54:37are the ones that are taking over.
54:39But I I don't know for sure.
54:40It's a good question,
54:42really good question.
54:43It'll be fun to look at that.
54:45We have a lot of such patients
54:46that we can get access to cells.
54:53That question
54:56hypothetically speaking, eventually
54:57the red blood cells will be Euclided.
55:01Is there part of the process that they
55:03don't have to have a nucleus in the end
55:05that allows them to proliferate so fast?
55:07Is the ability of proliferation is is
55:10reduced because they don't have to
55:12maintain the full sort of nucleus.
55:14They can just go faster by being
55:16more efficient in that way.
55:17They keep
55:17it absolutely as well they're.
55:20But content is the content the same?
55:21Do you know what the size
55:23as they as they go forward?
55:24It's a good question for PAD Gallup.
55:26Here we go. What we do know is that
55:31as these erythroid cells are matured,
55:33they're proliferating.
55:34Again, matured that with the maturation,
55:37the nucleus shuts down and the histones
55:40get spit out. But prior to that,
55:42when they're so proliferating,
55:44I'm not aware of what's changing at
55:46the chromatin level, but correct.
55:48But that's been published.
55:49I should know.
55:50It's because Pat's published,
55:53I think what they,
55:54I think if I remember correctly,
55:55they express fewer and fewer genes
55:57and higher and higher levels of
55:59the erythroid genes and you know,
56:00like globins because it's going
56:02to need all that globin for
56:03when it doesn't have a nucleus
56:04if they give them a timing advantage, if
56:06they're going to that moment. So they can. I
56:08don't know why it sounds like
56:09being so fast. It's part of their
56:12own. Yeah, throughout that. So you can see,
56:18so can you see any advantage to
56:19cycling faster or that you can cycle
56:21faster because you don't need so much
56:23activity going on in your nucleus,
56:29which is like slow down. Yeah,
56:35sticking outside. Oh, I like it,
56:36I like it. Let me know when you
56:38have to go to that conference.
56:40So in addition to intrinsic
56:42things that would be
56:46differentiate, you can see downstream
56:48what about contribution from other
56:50cell types either from other chromatic
56:52cells and signals or thrombo cells.
56:56We have looked really hard for other parts
56:58of the micro environment that might affect
57:01MEP fate and I did not include those data,
57:04but we've done a lot of work and and
57:06Vanessa's published quite a bit on it.
57:07One thing we know is there are two growth
57:10factors that may many of you may be
57:12aware of erythropodent and thrombopodent.
57:14Thrombopodin sounds like it's making
57:16platelets, right, Thrombopodin,
57:17erythropodin making erythroid but
57:19they actually act super differently
57:22on different cells.
57:23Thrombopodin is the thrombopodin
57:25receptor is is on hematopoietic stem
57:28cell and all of those progenitors.
57:30So they all need thrombopodin
57:31and they're it's binding in the
57:33middle of the thrombopod receptor.
57:35When you get to the anti P level though,
57:37the erythroid progenitor loses its erythroid,
57:42its thrombopodin receptor,
57:44so it does not have ****** on it,
57:47and the MEP progenitor has increased ******.
57:51What we thought then is if we add
57:52****** or remove ****** we're going
57:54to affect faith specification.
57:56No, what happened is when you
57:58remove thrombocodin,
57:59I'm sorry saying that one.
58:00When you remove thrombocodin you get
58:02exactly the same ratio of colony types,
58:05but way fewer colonies and the
58:08colonies are teeny tiny.
58:09And when we look at the time
58:11lapse microscopy of that,
58:12what we see is that the cells are dying.
58:14So they're trying to,
58:15they're doing everything right
58:16at the beginning and then you
58:18can just see apoptosis.
58:19I don't know,
58:20I didn't prove it was after you see
58:21the cells dying with erythropoietin,
58:23again no difference in fate specification,
58:27but a lack of erythroid maturation,
58:30absolutely no difference
58:31in the ratio of output.
58:33We have seen an effect in this and
58:35this is part of Vanessa Scanlon's
58:37work now in the lab that when she Co
58:40cultures the cells with endothelial
58:42cells then she also sees an an
58:45erythroid phase specification.
58:46It's it's subtle but she can
58:48see a statistically significant
58:50increase in E phase specification.
58:52What she wants to do now is very
58:54methodically add different cell types
58:56that are in the bone marrow micro
58:58environment and determine how they affect
59:01NDP phase specification individually
59:03and together and then determine how.
59:05But that's as much as we know,
59:06but it's definitely not TECO and ECO.
59:09Has she tried macrophages
59:10since there's the, you know,
59:16I think she did once but
59:18she didn't have really good
59:19macrophages to use.
59:21We're much better in my lab at
59:23making urine macrophages than human.
59:24So I'd say we haven't done that adequately.
59:28I am here,
59:30very nice talk. So I think this
59:31is just a
59:37using the IPS cell, it's with the
59:41fringe and all kind of other cells,
59:42but they cannot differentiate
59:44that character. Is that true?
59:47They can make mix.
59:48They can make mix and in fact there's
59:50even one really good scientist who
59:53has made IPS derived megacarius like
59:56progenitor cell line that is a it's a
59:59really beautiful model for studying.
01:00:01You know you can get different mutations
01:00:03from the patients make IPSC make this
01:00:06Meg cell line and basically it doesn't
01:00:09look renty until you then induce
01:00:13the movie show. When you see that, terrified,
01:00:19do you see that? The size,
01:00:22you know, every size
01:00:23comes in the same size. Yeah,
01:00:25yeah, the arithmetics are always small
01:00:26and round and the bags get bigger and bigger
01:00:28and bigger and bigger. But when when
01:00:29do you see the immersion? Just like
01:00:32self started right
01:00:33about the same time that they
01:00:34express the C41. So by the time
01:00:37they're they're they're 41,
01:00:38they're they're kind of bigger now.
01:00:42We haven't looked at that carefully.
01:00:44If we really looked at nuclear size
01:00:46carefully, I would bet we would see
01:00:48something different because they're
01:00:49undergoing different nuclear changes.
01:00:50But we haven't looked that
01:00:51carefully. Thank you.
01:00:55One last question, the
01:00:58cell regulatory volume is it's super, super
01:01:04tightly regulated and that carrier sets
01:01:06something very unique in that regard.
01:01:08So I was wondering can you stall
01:01:10that process and or carrier sets
01:01:14that they do not and you know
01:01:18how long can can
01:01:19that be done or if that
01:01:21happens in any pathologies
01:01:25it hasn't been done but many people
01:01:27have tried not specifically with cell
01:01:29volume because many period sites can
01:01:32make platelets as a 2N cell as 4,
01:01:33N cell as 8, N as 16 and 32.
01:01:36So the question is,
01:01:37what tells the men stop undergoing this
01:01:40end of mitosis and start making platelets?
01:01:44All we know so far is that if you
01:01:47take the inside of a magnet making
01:01:49platelets set up and Joe Battalion
01:01:52and you inject it into a 2NA,
01:01:54it'll make platelets.
01:01:56So there's something that says go.
01:01:59And once you have it,
01:02:00you can transplant it into
01:02:01another bag and it'll tell
01:02:03it to go. So you can give a hypertonic
01:02:05shock to a melancharyocyte.
01:02:06Would it make platelets? I don't know.
01:02:11I don't know if that would
01:02:12be done. I don't know. Thank
01:02:17you. Next.