The Shifting Shape and Functional Specializations of the Cell Cycle During Lineage Development

November 19, 2020

Merav Socolovsky, MBBS, PhD
Professor, Department of Molecular, Cell and Cancer Biology
University of Massachusetts Medical School
YCCEH Invited Speaker
November 12, 2020

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ID: 5910
To Cite: DCA Citation Guide

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00:00Everyone Jeannie Hendrickson I'm one
00:02of the Co directors of the enrichment
00:05program of the Yale Cooperative
00:07Center of Excellence in Hematology.
00:09Very excited to have our first virtual
00:11speaker sponsored by the Yale Cooperative
00:14Center of Excellence in Hematology,
00:16but hopefully broadcast to a lot of
00:18the other cooperative centers of
00:20excellence in hematology across the US.
00:23Whether you're looking at us Live
00:25Today or virtually on the video that
00:28we will eventually post of Mirage.
00:31Talk, we're happy that you could join
00:33us today and we're extremely grateful
00:35to Doctor Moore of Sokolovsky for
00:37being our kind of inaugural speaker,
00:40virtually for the YCCEH she's a
00:41professor at the University of
00:43Massachusetts Medical Center,
00:45she has had quite a distinguished career,
00:47really discovering fundamental insights
00:48into Areth row poesis in the process thereof.
00:51So again, we're very grateful.
00:53But she's our inaugural speaker this year.
00:56The way the web and R is going to work.
00:59She's going to give her talk.
01:01Then at the end of her talk,
01:04if you have questions.
01:05The questions in the chat or in
01:07the question and answer box.
01:09I also think I have the option
01:10to allow you to talk,
01:12so if you put in the chat you
01:14would like to talk in person.
01:15I can unmute your microphone
01:17and you can talk.
01:18So without further ado we will turn
01:19it over to Rob and thanks again Rob.
01:22We're excited to have you.
01:25Great, thank you so much.
01:27I'm truly honored to be invited to this
01:31forum and excited to tell you about
01:35the work that we've been doing so.
01:39I will attempt to start straightaway.
01:42Here we go so.
01:44When we think about the cell cycle,
01:48we think about a generic program
01:50whose purpose is to generate more
01:53cells to increase cell number.
01:56But when we look at a tissue,
01:59the cells that are cycling
02:02I usually transient cell.
02:04So long term teacher residents
02:07like terminally differentiated
02:09cells or stem cells.
02:11Do not cycle.
02:13So what does it mean that only transient
02:17cell states are undergoing cell cycle?
02:21These cells are continuously
02:23changing the genes that they express
02:26and our work suggests that they
02:28are also continuously changing
02:30the kind of cell cycle program
02:33that is expressed by these cells.
02:36It's likely that linear development.
02:39Had really coevolved with
02:41modifications in the cell cycle,
02:44so cell cycle programs probably
02:47adapted to specific tissue and cell
02:50cycles and developmental stage,
02:53and it is possible that in order
02:56to truly understand how a
02:59developmental process is regulated,
03:02we need to understand the
03:05specifics of its cell cycle.
03:08So here we're looking at
03:11the erythroid linear edge.
03:13The erythroid developmental trajectory
03:15has two phases earlier through
03:18pieces and terminal differentiation.
03:21What I'll show you today is that
03:24the cell cycle throughout this
03:26process varies in logs lockstep
03:30with differentiation stage both in
03:32terms of the cell cycle length,
03:35which here is represented vertically.
03:39And in terms of the ratio of
03:42S phase to the gap phases.
03:44So how did we set out on this question?
03:49We started out by asking.
03:54One question and that was how is a recruit
03:57terminal differentiation activated?
03:59We know that in earlier it releases
04:02we have progenitors that are already
04:04committed to their way through drainage,
04:07but that are not expressing any of the
04:10jeans that are present in red cells.
04:13At some point these progenitors undergo
04:16a selfhacked decision that switches on
04:19this specific program of red cell genes,
04:21and so the question is how does that happen?
04:26Of course, many labs are addressing
04:28this question, and the angle that
04:32we had was to ask.
04:35Can we identify the cell in
04:38which this activation happens?
04:40Our model system is the mouse fetal liver.
04:45And and that was,
04:46we also have experiments that show you
04:49later on in the mouse adult bone marrow.
04:52The fetal liver is a great system for
04:55working in original places because over 90%
04:57of the cells of the erythroid Lenny Edge.
05:01So we have two cells of his markers cities.
05:04Have anyone until 119 with just
05:07these two markers we can divide the
05:10fetal liver into a number of subsets
05:13that form a developmental sequence.
05:15So we find that in the S node
05:19subset and S1 subset,
05:21that's where we see colony
05:23forming progenitors.
05:24So this is where we see earlier with
05:28paresis and then in subsets as 22S5,
05:31we see progressively more
05:34differentiated erythroid precursors.
05:36And you can see this also here.
05:38The colonies that are formed by the S note
05:41cells and S1 cells are pretty similar.
05:43We wouldn't be able to tell them apart.
05:47But two important findings
05:48suggested to us over 10 years ago.
05:51Now that the S not see if you,
05:55we are really quite different
05:57to the those in S1.
05:59So the first finding was when
06:01we examined the fetal livers
06:03of a preceptor knockout cells.
06:05So here we're looking at two litter mates.
06:09This was published.
06:10The knockout was first published by Home
06:14Grow in the Lodish lab back in 1995 and.
06:17When we did flow cytometry on the
06:20fetal livers of embryos from a.
06:23Some this knockout we found that whilst
06:26in the wild type number we already see
06:30cells populating most of these subsets.
06:32This is Embedded Day 12.5.
06:35In the perception knockout,
06:37we see an absolute block at the
06:40transition from S No 2 S one,
06:42so that tells us that the transition
06:44is dependent on a preceptor signaling.
06:47These few cells that you see over here
06:50that tagline positive in the IP receptor
06:53knockout belong to the yolk SAC limit,
06:56and they're not part of
06:58definitive erythropoiesis.
06:58Now the second kind of experiment
07:01that we did that told us there's
07:03something different about S1 and S
07:06Note was a cell cycle experiment,
07:08and here we have a cartoon that illustrates
07:11typical cell cycle experiment that I'll
07:13show you a number of times during my talk,
07:17so.
07:17We take a mouse.
07:19In this case it's a pregnant female and
07:24injected with beyond you and then harvest
07:28hematopoietic tissue 30 minutes later.
07:31What we find is 2 things.
07:33First, we find which cells are positive.
07:36For Bru, these cells have
07:38incorporated be audio,
07:39which is a finding analog into their DNA.
07:43So these cells are in S phase
07:46during the 30 minutes of the pulse.
07:50A second finding. Is.
07:52Allows us to determine the speed of
07:55aspects and so we can compare the
07:58level of the audio incorporation in
08:02the cells of different populations.
08:05Cells that have incorporated less beardi.
08:08You must be synthesizing.
08:10DNA slower and have a longer
08:13essays than cells that incorporate
08:15the audio at a faster pace,
08:19and we've confirmed that with
08:21direct experiments that look that
08:24use a double family impulse to
08:27Measure S phase duration directly.
08:30And so when we did this experiment
08:32in the mouse fetal liver here we're
08:34looking at cells from each of the
08:37subsets and at the cell cycle status.
08:40We found 2 interesting things first.
08:44Whilst about 60% of the cells
08:47in S nought so these cells are.
08:52In S phase, at any one time.
08:53So this is a highly replicative tissue.
08:56When we look at S1 over here,
08:58nearly all of the cells, 90% in this case.
09:03Are in S phase of the cycle.
09:05So that's interesting finding number one.
09:09And the second finding was that
09:11the speed of S phase is about
09:1450% faster in S phase.
09:17Cells that are in S1 compared with S phase
09:21cells in the preceding as node subset.
09:25And so a number of.
09:27We've done a lot of work on this
09:30and I'll give you the summary.
09:33The way that we've interpreted our data.
09:37Is that the transition from S note to S1
09:40can only happen in S phase of the cycle.
09:44And this is based on a number of
09:47experiments where we have arrested as space,
09:50either genetically or using drugs
09:52that inhibit DNA polymerases.
09:53And when we do that,
09:55we totally prevent the transition
09:58from S note to S1.
10:00And.
10:00What we prevent isn't simply the
10:02upregulating I population of city 71,
10:04which is the marker of this transition,
10:06but all of the events that
10:08are associated with it,
10:10which is the induction activation of
10:13the array through transcriptional.
10:15Program their research differentiation
10:17program.
10:20The second thing that we found and I
10:23will talk about that a bit more later,
10:25running the talk is that S phase at the
10:29time of this transition is much shorter
10:32than S phase of preceding cycles.
10:35OK. And that the actual length of S
10:40phase is really only about four hours,
10:43which is pretty short for MA million S phase.
10:50Now what else is happening at the
10:52time of this suite at the time
10:55of transition from S note to S1,
10:58it turns out that there is a time
11:00when we see reconfiguration of
11:02chromating at the beta globin locus.
11:05We see a change in the timing of replication
11:07of the locals histone tail modifications.
11:10We see a loss in DNA methylation beginning
11:13at the time of this switch and more
11:16recent work from Dark Hexes Lab done by.
11:19Rob Bakery and others have shown
11:22that eight acsec and using tiled see,
11:26we see either changes in chromatin
11:29Accessibility promoter enhancer Contacts
11:31that begin with this transition,
11:33so this is clearly a key
11:36developmental switch.
11:40I haven't explained a color scheme here.
11:44What we think is happening is
11:47that CF uer undergoing expansion
11:50whilst in the S North subset.
11:54And then the very last generation of CFUE
11:58starts its life in the S note subset in G1.
12:02When it enters this specialized short space,
12:06it is at it up. Regulates city 71.
12:09And it is at that point that
12:12it undergoes commitment.
12:14The progeny of DCF.
12:15UE will no longer be safe UE.
12:17They will be priority through
12:19blast in the race for blast that
12:22undergo terminal differentiation.
12:24So when we asked next is is this
12:27CFU E2E TD transition true sweet?
12:30So it's all very well and good if
12:33we take all of us not cells and all
12:37of this one sells and compare them,
12:40large differences suggest to switch but
12:43is not is a heterogeneous population
12:45of cells and by taking all of them
12:49together we could be masking incremental
12:52changes within this population.
12:54The problem with addressing this
12:57question was that we really had no
13:02reliable way of taking apart this subset.
13:05And in fact,
13:07the entire trajectory starting
13:10with hematopoietic stem cells
13:12and ending at this point where
13:16terminal differentiation starts,
13:18this trajectory was really
13:21only partially understood,
13:23and so about four years ago the
13:27technology of single cell RNA SEQ had
13:32advanced massively by microfluidic.
13:35Approaches were introduced by
13:36a number of labs,
13:37including the lab of a long climb.
13:40And we were very fortunate in
13:42that he agreed to collaborate with
13:45us on this question.
13:47And so we took kit positive cells
13:49from the bone marrow or from fetal
13:52liver and we also took kid positive
13:54cells from the bone marrow of mice
13:57that were injected with Ipoh.
13:59Although I will not discuss that here.
14:02And we've undertaken single cell RNA
14:05sequencing on these projectors and
14:08what you're looking at here are two
14:11D projections of K nearest neighbor
14:14graphs of gene expression in the
14:17fetal liver and in the bone marrow.
14:21Topologically, these graphs are very similar.
14:24Each dot is a single cell,
14:27and the proximity of dots suggests
14:30a proximity in terms of their
14:33transcriptome similarity transcriptomes.
14:35And what you can see is that
14:38these transcriptomes are form
14:40one continuous structure.
14:41It's a branching structure.
14:43And in the fetal liver and bone marrow,
14:46the branches are pretty similar.
14:49Except that here we see a very
14:52large bulge compared with a much
14:54smaller bulge in the marrow.
14:55This bulge in fact contains the CFUE,
14:58as I will show you.
15:02I won't dwell on it today, but.
15:06We've used GNU algorithm that was
15:09developed by the client laboratory,
15:12especially Caleb Weinreb and some
15:15Wallach in the client lab called
15:18Population Balance analysis.
15:20And this algorithm allowed us
15:22to assign each cell within this
15:25structure with a fate probability.
15:28In fact, with a set of seven
15:31self check probabilities,
15:33which told us what is the probability
15:36of each cell to ultimately attain a cell
15:40fate in one of these seven branches and that?
15:46Really can be the result of our analysis
15:48can be collapsed into this structure,
15:51which is a hierarchical structure,
15:53not unlike the classical
15:55structure of hematopoiesis.
15:56The main difference is that
15:58we don't see discrete stages.
16:00We see a continuum which is meant to
16:03be represented by this kind of cloud.
16:09And one more point that I'd like to
16:12make is subsequent subsequent work
16:14of the client lab together with Luca
16:18Biosca Slab looking at single cell RNA
16:20seq of human marrow showed that the
16:24structure and topology is obtained
16:26from human bone marrow is actually very
16:29similar to that of the mouse and in
16:32terms of gene expression for each gene.
16:36Steam is aro.
16:38We see a very similar pattern
16:40in the mouse and in the human,
16:42which are represented here as mirror images.
16:46And so we can conclude that the
16:48mouse is a pretty good model for
16:51human hematopoiesis in general.
16:53Of course, that we know of some
16:56very clear differences as well.
16:58So now we were in a position to
17:01look at the erythroid trajectory,
17:03starting with multipotential progenitors
17:05in black and continuing along the
17:08array thread branch with the color
17:10representing every thread fate probability.
17:12We can use this probability to align
17:16the cells along a linear axis starting
17:19with MPP and ending with the end of ETD.
17:23And just to see that things look good.
17:29You can look at cells that we know our
17:32President Multipotential progenitor's
17:33like cities 34 gotta one which is
17:37expressed by the entire array thread
17:40branch and Alpha globin which is induced.
17:43Only with the activation of
17:47Arethra terminal differentiation.
17:49In order to be able to do experiments
17:53with transcriptome information we
17:55needed a way of learning how to
17:58isolate cells from specific regions
18:00of our single cell RNA SEQ graph.
18:03And so we've developed a fax approach
18:06that gives us five populations and then
18:09we sorted each of these five populations,
18:13repeated the single cell RNA SEQ work,
18:16and then projected them onto our.
18:19Original map and what you can see
18:22and what's relevant to today's talk
18:25is that the P1 and P2 subpopulations
18:28project pretty cleanly into this
18:30very narrow neck at the beginning
18:33of the urethra branch and then into
18:37this sort of bulge that follows up.
18:41And so we now have a way of isolating
18:44cells that correspond to these
18:46two regions of the graph.
18:48And the P5 population is also pretty
18:51good at giving us the multipotential
18:54progenitor cells right at the
18:57beginning of the erythroid branch.
18:59So with these three subpopulations,
19:02we can isolate cells from the entire.
19:06If we throw brunch and so now we're ready to
19:12do some assays and we find that P1 and P2.
19:18Almost my stroke of luck.
19:20Kivas populations at the highly enriched
19:25for CFUE. In fact, P1 contains.
19:29Almost all of the CFO is some
19:32small number also present in P2.
19:36And P2 is the only subpopulation that
19:40contains BFUE, so P2 is from the neck.
19:44Here represents cells with be
19:47a few potential.
19:48BF uer colonies that are multifocal either.
19:53Small bunches of small foci.
19:54Deezer called late be a few E and
19:57we see them around day four of.
20:00Culture or like might contain much
20:03larger column they might have might
20:06give rise to much larger colonies
20:09after about
20:10a week or even 10 days.
20:12See if you eat, give rise to 1.
20:15Focus of colonies that contain
20:17around 30 differentiated red cells,
20:20about two to three days after plating.
20:24OK, so we now have.
20:28Pretty complete set of tools
20:31to do our investigation.
20:33We can look at multipotential
20:36progenitors BFUS&CFUS at the
20:38transcriptome levels and we've given
20:41them transcriptome related names.
20:43You can correlate them with faith
20:46assays and isolate them by fax.
20:48And so we are in a position to ask,
20:52are we looking at a true switch when
20:56we transition from S Note 2 S one?
20:59Is this a few ITA ET transition
21:02at truth Switch?
21:03And so here we are looking at
21:05genes that are differentially
21:06expressed during the linear access.
21:09The linear suit I'm going from MPP
21:12to terminal differentiation and
21:14they arrange arrange story in terms
21:16of the peak expression along this.
21:19Access and without any really
21:21fancy analysis you could see that
21:24they form kind of three cohorts.
21:26There is a cohort of gene expression
21:29that happens during a very rapid change.
21:32Many streams are being switched on or off.
21:36Then we enter a period of relative
21:39stability of the ceep progenitors of
21:42correspond functionality to see a few E.
21:46Jeans and not many genes of
21:49turning on or off.
21:51Although you can see that there
21:53is a progressive slow change
21:55and this probably represents an
21:57amplification stage where there
21:59is little transcriptome change.
22:01And then we see.
22:05Rapid change or in fact I should say
22:07about sharp change from the CFD program
22:11to terminal differentiation program.
22:13Very few cells expressed genes
22:15of both terminal differentiation
22:17and sea Fury program.
22:18So that tells us that we're looking
22:21at a sharp transcriptional switch.
22:23So the answer is yes,
22:26the transition from this Cepheus
22:28Stage 2 terminal differentiation
22:29is a sharp transcriptional switch.
22:32So what is the context of that switch?
22:36And we're in the position to
22:38look at that as well.
22:40This is a busy slide,
22:41but let me take you through it slowly.
22:44So if we look at the medial
22:46medium panel first in red,
22:48we're looking at the expression of CD 71.
22:51This is the market we used by
22:53fax and we can see that there is
22:55a gradual increase in cities of
22:5771 throughout the trajectory.
22:59But at the time of the switch
23:02to terminal differentiation,
23:03this becomes a very rapid up regulation,
23:05and so the upregulation of CD 71.
23:08Which we previously took as a
23:10marker of activation of the switch.
23:13Acts as a marker of that,
23:15also at the single cell RNA level.
23:18When we look at either cells of
23:21his markers here we're looking at
23:24Ipoh receptor expression in blue.
23:26EPO receptor is expressed pretty
23:29early on in the trajectory in
23:31increases gradually subsequently.
23:33We can now look at transcription
23:36factors and we see in Grey gotta one
23:39is high initially and then is downregulated.
23:43Gotta sorry got it too in grey.
23:47Gotta one is low initially in
23:50multipotential parameters and then is
23:52expressed induced early in the trajectory.
23:55I'm sorry Ann is maintained at pretty
23:58high levels throughout the trajectory.
24:00Maybe going up a little bit
24:03at the time of the switch.
24:06Generally speaking,
24:07none of the key transcriptional
24:10regulators that we know of.
24:13Reporters of the timing of
24:16the switch from CFUE to ETD.
24:19When we look at the cell cycle,
24:21however, we see something that
24:23does seem to correlate very well
24:25with the timing of the switch.
24:27So what we're looking at here each
24:30each color denotes the average
24:32expression of genes in each that are
24:36characteristic of each cell cycle phase,
24:39either S Phase G,
24:412MG1S and so on,
24:43and there is very little difference there.
24:46Sensually, flat or uninteresting
24:48for most of the trajectory.
24:50And this really tells us that
24:53cells are cycling asynchronously
24:55through most of the trajectory.
24:58But as we approach the time of the switch,
25:01which is this dashed line,
25:03you can see the formation of a
25:06number of peaks starting with G1,
25:08S and then S in red and orange
25:11and then G2NG2M.
25:13So what we have here in fact
25:16is the cell cycle.
25:18And the first peak that is formed is S phase.
25:23And so it appears that the earliest event
25:27at the Switch from Seaview to ET D is
25:31marked by cells in S phase of the cycle.
25:35So this confirms our earlier functional data.
25:38That activation of the TD happens
25:42during S phase of the cycle.
25:45And So what we asked next is OK.
25:49Transcription factors are not good.
25:52Taught don't really report
25:53the timing of this switch.
25:55At least their expression doesn't.
25:57It's quite possible that post
26:00transcriptional post translational
26:01modifications of these do correlate
26:03with the timing of this switch,
26:05and that's an open question.
26:07Um?
26:09Can we find something else
26:12that might tell us?
26:14Something about the timing of the switch.
26:17So to do that we went back to the
26:20jeans that are expressed during this.
26:23See if you E program and we asked
26:26whether there are the slow progressive
26:29change that happens during this time,
26:32which streams are changing during that time?
26:35Which teams change their expression
26:37in a way that is correlated with
26:40progression along suit of time?
26:43And when we did that, the top five go.
26:48Terms that we got were essentially
26:51all to do with DNA replication.
26:54The cell cycle, S phase, and so on.
26:58And here are some examples.
27:01We're looking at cycling
27:038 two cycling E1 R&R.
27:06Units.
27:07Proteins that are associated
27:09with the origin of replication.
27:11All of these are ramping up their
27:15expression throughout the trajectory
27:16right through the BFUE&CFU stage
27:19and reach a peak at the time of the
27:22transcription of switch from CF UE2ET D.
27:27Now what does that mean functionally?
27:31So to understand the significance of
27:34this really quite impressive ramping
27:36up in cell cycle X gene expression,
27:39we went back to a functional experiment.
27:42So here we're looking at the same
27:45old experiment where we take a
27:48mouse injected with beyond you
27:50and then check the cell cycle
27:53status and the speed of space.
27:56But now we were armed with some more
27:59information about early erythropoiesis.
28:02We used the slow upregulation of CD 71
28:06as a way of measuring sudo time by fax.
28:10And we were able to also.
28:14Staying for the P1 and P2
28:18subpopulations which mark there
28:20be a few Ian CF Louise subsets.
28:23And so we sort of these cells and
28:26looked at the beardi you incorporation
28:28in here we're looking at individual
28:31cells Bru positive and Bru negative.
28:34We arrange them along the CD 71
28:37expression suit of time and if I did
28:40this sort of time into 14 different
28:44Gates 7 seven percentile gates.
28:46And then we can look at each of
28:50these gates and analyze cell cycle
28:52status as well as S phase speed.
28:55So the first really quite clear.
29:01Finding is that cells in
29:03S phase of this cycle.
29:05Increased markedly with progression
29:06along the earlier we throw trajectory,
29:09so maybe 20% of this other INS phase
29:11at the early parts of the trajectory,
29:14and as we approach this,
29:16which essentially all the cells are in
29:18space and you could see this right here.
29:21So at the time of this which nearly all the
29:24cells are in a space where is around here,
29:28most of the cells are not.
29:31When we look at S phase speed,
29:34we see there is there.
29:36There is an increment.
29:37There is an increase in the
29:40speed of West phase early,
29:42but then it stays quite stable until
29:44the point of the switch to ATD.
29:47And so this quite stable speed of essays.
29:50In other words,
29:52quite stable S faced length
29:54can't explain the change in
29:56the number of cells in space.
29:59And so our interpretation.
30:01Is that what's happening is?
30:05Shortening in G and in the G1 phase.
30:08Of course,
30:09this massive increase in the
30:11number of S phase cells explains
30:14why S phase genes are increased
30:16throughout the trajectory.
30:21And we suspect that the reason
30:23for that is G1 shortening,
30:26and so as a proportion the number
30:28of cells in S phase increases.
30:31The later we are in the Seaview stage
30:34as we approach the actual switch.
30:38G1 is pretty short.
30:40The switch itself we think involves
30:43S phase shortening. So.
30:48To summarize what I've told you so far.
30:51We use single cell RNA sequencing
30:54to identify the erythroid
30:57developmental trajectory in the mouse.
31:01We were able to match specific
31:04stages that are identified based on
31:08transcriptomes to fax populations
31:10and to functional subsets.
31:13Functional progenitors based on
31:16confirmation potential and using
31:19these tools we began to analyze.
31:22The factors that control
31:24the switch from CFUE to ET.
31:26We found that this is a
31:29true transcriptional switch.
31:31And that there is no real clear change
31:35in transcription factor levels that
31:39reports the timing of this switch.
31:42By contrast, we do see really quite
31:46marked changes in the cell cycle.
31:49Initially we see a gradual shortening in G1.
31:54And at the time of the switch we
31:57see a further shortening in S phase.
32:01So that at the time of this which
32:03we have a very short cell cycle,
32:06our measurements indicate that this
32:07cell cycle is about 6 hours long,
32:09with S phase being only four hours.
32:13So.
32:14What we next asked his first of all,
32:19what regulates this really quite
32:21dramatic remodeling of the cell cycle?
32:24And the second question is,
32:26is this cell cycle remodeling
32:29relevant to linear development?
32:31Does it play a role in these
32:34important cell fate decisions?
32:36For example, this switch from CFUE to ETD.
32:39Is it correlate or does it determine it?
32:45And so to begin to look at that.
32:49Just before I get to that,
32:52I just wanted to show you some of the
32:55expression of cell cycle regulators
32:58during the original trajectory and
33:01what's quite interesting is that.
33:03Our different shifting shape of the
33:06cell cycle is probably regulated
33:08through changing cell cycle regulators.
33:11And of course we have no idea
33:14how that happens at this point,
33:17but we know that for example,
33:19the dominant E2F four transcription factor
33:22during most of the trajectory is E2F four,
33:26but at the time of the switch
33:30to ETD it becomes E2F2.
33:33Other regulators, for example,
33:35when we look at the cycling dies cycling D2,
33:40is present early on.
33:42But it is gradually
33:44downregulated whilst cycling.
33:46D3 takes over at the time of this switch
33:49so it is quite possible that these.
33:53Part of them.
33:55So how do I that is able to generate these
34:00quite different shapes of the cell cycle?
34:04So the first thing that we asked
34:07is what are the mechanisms that
34:10underlie this very short space?
34:13And there are two ways of
34:15getting a short space.
34:16One is to have more origins of
34:19replication and the other is
34:22to have folks that move faster.
34:24We know from model organisms that
34:27have extremely short as phase.
34:30Is that the way they manage to
34:33replicate the genome very fast is
34:36by having all of the origins of
34:38replication firing synchronously,
34:41and they're all very closely spaced.
34:44So having efficient firing of origins
34:48of replication would clearly be one
34:51mechanism that has a president.
34:54At the time that we were doing this work,
34:57we started to collaborate with
34:59Nick Rind at UMass Medical School.
35:02Who is studies replication in yeast?
35:04And we through discussion with him for that.
35:08That is likely we will find because
35:11there was really no precedent for folks
35:14speed being regulated physiologically.
35:17But so to approach this question,
35:19we learned from the rind.
35:21Leiber technique called DNA combing here.
35:24You take cells,
35:25pulse them with finding analogs.
35:27We pass them with two distinct
35:30finding analogs that we could stay
35:32in with two different colors during
35:35the pulse with bio deoxy uridine,
35:38we were, we are able to follow.
35:42Into a incorporation of Iodo.
35:46The that by.
35:47Later on stretching DNA fibers
35:50along a cover sleep,
35:52the green regions are the regions that were
35:57replicating at the time of the iota pals.
36:00Then we followed that 10 minutes
36:02later with a pass of chloro,
36:04uh.
36:04Deoxy uridine and here we see the
36:07red regions are being labeled that
36:10this is where Clara gets incorporated
36:13and by doing that we get both the
36:16speed of the fork and its direction.
36:18Ality because we know that green
36:21replication proceeds read replication
36:23and with that we can place the origins.
36:26So here we have a fork moving
36:28in One Direction,
36:29another fork in another service
36:32must be a replication bubble
36:34with origin in the center.
36:36And with this approach.
36:38We discovered it was actually no difference.
36:42No significant difference in the
36:44distance between origins of replication,
36:46but there was a marked difference
36:49in the speed of replication Forks.
36:52So on average we're looking
36:55globally in the genome here.
36:59The replication folks in S1
37:01move at 50% faster speed than
37:05replication folks in S note,
37:08and that really entirely accounts for
37:12this speed of four S8 shortening.
37:17And So what might be regulating that?
37:19And is it really relevant to self it?
37:23Well, the answer is still not fully clear,
37:26but we know of examples where
37:28esfe shortening is at least
37:30associated with cell fate decisions.
37:32Here we're looking at gastrulation,
37:34in mammals where we know that
37:37there is dramatic space shortening
37:39from 7 hours to 2 1/2 hours.
37:42We know more recent examples
37:45running Yale from Shank Xingguo,
37:48where ultrafast cycles.
37:52Mark the cells that are most likely
37:55two week program into I PS cells,
37:58so this is clearly worth following up.
38:01So what might be regulating
38:04as they shortening?
38:06We looked at one key regulator P 57.
38:10Keep two. This is a CD K inhibitor.
38:16An IT regulates all it inhibits all
38:18city case, except I think City K 6.
38:22Um? What we found is it that
38:25it is present in S, not cells,
38:29but is rapidly downregulated in S1.
38:32And here you can see that,
38:35unusually, it's expressing S phase
38:37of those cells rather than in G1,
38:40suggesting it play some kind
38:42of role in S phase here.
38:45Looking at our Western and at Q.
38:48PCR, which both indicated the same thing,
38:50and so we looked at the P57.
38:53Keep two deficient embryos
38:55is in the imprinted gene,
38:57so we can look at the heterozygous and
39:01have essentially a knockout phenotype.
39:04And we see that these mice are anemic.
39:07They die in the prenatal stage.
39:10I have multiple developmental abnormalities.
39:12We found that they were anemic.
39:14They had fewer cells in the fetal
39:17liver and when we looked at the
39:20differentiation they had trouble
39:22generating differentiated and we
39:24throw blasts from the early CF you.
39:26So what's going on here Weekly looked
39:30thought to look at the S phase.
39:33So here we're looking at S
39:35phase again in CFUE.
39:37It is maintained as along as
39:39phase and we in wild type.
39:42Projectors see shortening at the
39:44time of the switch from C FE2ET D.
39:48And here we're looking at RC71
39:50so that I'm in the wild type
39:53and in knockout littermate and
39:55what you can see is that S phase
39:59speed is consistently faster.
40:01Prematurely fast in the CF,
40:03UE of the P57 knockout embryos,
40:06so we have a premature shortening of space.
40:13When we did DNA combing,
40:14we found that the folks were moving
40:17faster in their knockout in S not
40:20fork speed was already almost as
40:22fast as it would be in the wild
40:25types of in the water plasma cells.
40:28And in the X one of the P57 deficient
40:32embryo S1 cells will break,
40:34virtually breaking the speed
40:35limit on fork speed,
40:37and so we have very fast folks.
40:40What's the significance of that functionally?
40:44Anemia is 1,
40:45but why exactly do we get anemia?
40:48We didn't find that out until we
40:51looked at CF self renewal in the dish.
40:55So it turns out that CF UE can
41:00undergo self renewal in the dish
41:04for quite prolonged periods of time.
41:08Up to a month for adult.
41:12See a few ehad is documented in the
41:16literature in our hands up to about 2 weeks.
41:19And this contrasts with CF you,
41:21even they differentiate their form rec
41:24cells within a matter of two to three days.
41:27And what stops a fuse from differentiating
41:30it keeps them in itself for in
41:33your state are glucocorticoids.
41:34And so when we placed glucocorticoid
41:3857 knock hard to safely.
41:41In this self renewal cocktail.
41:44We found that they failed to
41:47undergo efficient self renewal.
41:49And so that is very likely
41:52the reason for the anemia.
41:54So for some reason they fail
41:57to sell from you,
41:58and the reason we can clear when we
42:01looked at the expression of P57 in cells
42:04in the presence of glucocorticoids.
42:07Here we're looking at using Dex,
42:09which is a synthetic glucocorticoid,
42:12so cells express P57 and then P 57
42:14isn't used further by DEX and so it
42:17seems that dex and glucocorticoids
42:19in general probably work at least
42:22in part by inducing P.
42:2557.
42:25Inhibiting Assface CK activity and
42:28that in turn allows a stabilizes
42:31and long essays and promotes
42:34the safe UE self renewal state.
42:37So here we see a connection
42:39between along S phase and self
42:42renewal or persistence of a
42:45particular transcriptional state.
42:47Suggesting that maybe a fast S
42:51phase is somehow destabilizing
42:53to transcriptional program.
42:56Now we were able to rescue.
42:59The situation by adding
43:01to the knockout cells P.
43:0457 knockout cells acidic A2 inhibiting
43:07drugs so this will reduce CD K
43:11activity will inhibit CK activity
43:13in S phase cells and you can see
43:17that compared to the city 57.
43:19Knock ourselves the knockout cells
43:22treated with the drug almost completely
43:25resume normal self renewal activity.
43:30And so, um. You can we have a
43:34bit more data here, so yeah,
43:37so here we're looking at wild type cells.
43:40We can take this paradigm,
43:42feather and say, well,
43:43OK we have CF uees long essays.
43:46Stable self renewal can
43:47make it even more stable.
43:49Can we enhance the sea of yourself
43:52on your potential by adding city
43:54K inhibitors to the medium and
43:56prolonging S phase even more?
43:58And that indeed happens so you
44:00can see that compared to control
44:02cells undergoing self renewal
44:04in just dexamethasone.
44:05When we add the city K2 inhibitor with,
44:08these cells are completely blocked.
44:10They don't up regulator 119.
44:13When the S phase is slower and
44:16we can amplify them much further,
44:19so in red at the cells that have
44:23sydicate 2 inhibitors added on so we
44:26can amplify the CFL stage even further.
44:30And clearly this may have some
44:33translational applications.
44:34Maybe Syndicate two inhibitors will assist,
44:37or perhaps replace glucocorticoids in
44:40therapeutic approaches that target the CFUE.
44:43But it also for fundamental
44:45biology point of view.
44:47It tells us that stabilizing and
44:50prolonging S phase delays the switch.
44:56So I will. I'm running a little
44:58bit out of time so I will quickly
45:02summarize what we what I've shown
45:04you in the second part of the talk.
45:07See if you self renewal.
45:09Depends on a long space.
45:12And when that is.
45:15Impaired, for example,
45:17in the P57 knockout mouse.
45:21There are insufficient cycles of
45:22self renewal, and that causes anemia.
45:25And we can rescue that and in fact enhance.
45:29See if you see few suffering you even
45:32in wild type progenitors by inhibiting
45:35SDK activity and prolonging essays.
45:39So it appears that exercise shortening
45:41may be causally related to the switch,
45:44although the underlying mechanism
45:47of course isn't clear yet.
45:50And then in the last few minutes of my talk,
45:53I'll take just five minutes to tell you
45:57knew story that isn't yet published.
46:00Again, that seems to highlight the
46:03importance of the cycle in this
46:07time in terminal differentiation.
46:09So we know that E praeceptor becomes
46:11essential during terminal differentiation.
46:13Although it begins to be expressed much
46:17earlier in earlier with the Preseas.
46:20And with that,
46:21the power sector I've shown you earlier,
46:23we have no cells in terminal differentiation.
46:26And so we asked.
46:27And this is work by Daniel Hidalgo in my lab.
46:31We asked whether.
46:35The emperor sector has added
46:36functions other than survival
46:38during terminal differentiation,
46:40and two answer that we developed a
46:42genetic model in which we take the
46:45perceptive nokut fetal livers and
46:48introduce back either the EPO receptor
46:50obiesie ELEX to stop them from dying.
46:53And with this model system we
46:55were able to see that in fact
46:57you can get full differentiation
46:59without any equal receptor at all,
47:01as long as you keep the cells from dying by.
47:06D. Transgenic expression of BCLX.
47:10However, there were various abnormalities,
47:11and the biggest one was that the
47:14Sea Fury colonies that were formed
47:16were much smaller than the area,
47:19which is much smaller.
47:22And the second finding was that
47:24S phase wasn't as fast,
47:26so it was still pretty fast,
47:29but not as fast as in cells that
47:32were expressing the E prospectors.
47:35So clearly E.
47:36Praeceptor can further accelerate
47:38space speed.
47:39When we look at cell growth,
47:41this is in vitro.
47:44And see that cells expressing the ape
47:47receptor grow much faster than cells
47:50expressing BCLX and the doubling time.
47:53Let's look at control cells.
47:55That doubling time is only six hours in
47:58our cells that express the equal sector,
48:00but it climbs to a towers in cells that
48:04don't have a protective signaling.
48:06We were fortunate to be able to
48:09collaborate with Sean James Goose Lab,
48:11who developed a beautiful Reporter mice.
48:14This mouse reports the length of the
48:17cell cycle using a Fusion protein.
48:20H2B fluorescence timer Fusion.
48:23These Fusion is fluorescent blue
48:25when it is first synthesized and
48:28then becomes a fluoresces red
48:29about an hour or two later.
48:32And so cells that have a short cycle IBB
48:36lower than cells that have a longer cycle.
48:40And so with her lab,
48:42we injected mice with either Ipoh or Saline.
48:45And found that two mice injected
48:48with Saline here in blue.
48:50Um?
48:52Here we're looking at the ratio
48:54of blue to red fluorescence.
48:56We see a shift in that ratio when we look
48:59at two mice that are injected with people.
49:03And here we're looking at a specific
49:05erythroblast earlier Race for Life stage,
49:07and we can look at a number of our
49:10way through glass stages and at
49:12each stage we see a clear difference
49:14in cell cycle length.
49:16So it seems that if receptor
49:18can shorten space even further,
49:20and in fact shorten the cycle.
49:23I'm going to skip this next part,
49:26which very briefly,
49:27you would think that if a preceptor
49:30drives as shortest cycle and more
49:33numerous cycles in terminal differentiation,
49:35the red cells that would result
49:38would be smaller.
49:39But in fact we find exactly the opposite.
49:42We find that the rattles are formed at
49:46larger and I won't go through the data,
49:49but we see a larger diameter in cells that.
49:53Experienced high ipho and so
49:56that's a sort of paradox.
49:58This is interesting in its own right.
50:01I'm.
50:03And so I will summarize
50:04what I've told you so far.
50:07And summarize my talk.
50:08What we find is that during
50:10the arethra developmental trajectory,
50:13the cell cycle takes up a
50:16number of different shapes.
50:17If you like, it is long,
50:20early in the trajectory with
50:23along for a space and along G1.
50:26During the gradual progression
50:28through to see if you re staged
50:31towards the switch to ETD we have
50:35initially gradual shortening of G1.
50:37And sorry, gradual shortening of D1.
50:40And then at the time of this which we have.
50:44An abrupt shortening of essays,
50:46the shortening West phase is the result
50:49of downregulation of P57 Kip too,
50:51which is acidic Lee,
50:53two inhibitor.
50:53It inhibits S phase city K activity
50:56and that leads to an increase in
51:00the speed of replication Forks.
51:02The long ass face in the CFG stage
51:06is critical for the stability of that
51:09stage in it for its self renewal,
51:12and it appears that glucocorticoids
51:15utilize that by enhancing expression
51:18of of the silicate to inhibit P 57 and
51:22prolonging the CFO is suffering you'll stage.
51:25And we may be able to use that
51:27therapeutically with silicate
51:29two inhibitors as well.
51:30Once we cross this this late
51:33into terminal differentiation,
51:34the initial cycles are very short.
51:36In fact I forgot to mention this cycles
51:40of the earlier throw blasts as some
51:43of this shortest in the bone marrow.
51:47And these can be even further
51:51shorter when we.
51:53Use that when we simulate these cells.
51:56With people so at high levels of people
51:59which we would find in stress situations,
52:03high altitude or disease or bleeding.
52:06Cycles become even shorter.
52:09And that in itself is raises
52:12the question of why is that?
52:15And I did not have time to discuss that,
52:20but all the work shows that the
52:23fast cycles of earlier reefer blasts
52:26contribute to rapid DNA demethylation,
52:29and that interns assist the speed
52:32of the ATD transcriptional program.
52:35So it's possible that that might
52:38be the reason why.
52:40This takes place here,
52:41but of course we don't know that for sure.
52:45So I'd like to thank the people that
52:49did the work and these people in my lab.
52:55Including include US Warrior, Sami, Nathan.
52:57Better bear to see who did a lot
53:00of the single cell RNA SEQ work.
53:03Melinda Futron who worked in the DNA
53:07combing together with young Kwang
53:09who did all the work on P57 and
53:11Daniel Hidalgo who did the work on
53:14the ape receptor signaling and our
53:17collaborators were very fortunate
53:19in our collaborators and old clients
53:21lab with a single cell RNA SEQ.
53:23Necrime at UMass Medical School.
53:26Who helped us with the DNA combing
53:29and shanting Google in Yale who
53:32had been a great colleague,
53:34was also interested in the cell cycle and
53:38we've collaborated with her transgenic mouse.
53:41So thank you.
53:46Thank you very much for a great talk.
53:48We have two questions for
53:50you in the Q&A session.
53:51I don't know if you can see them wrong,
53:54but I can read them to the group.
53:57The first is from Doctor Liu says great talk.
54:00Any speculation in how CD K2 and
54:02P57 regulate fork speed in S phase.
54:05We would like to know that yeah,
54:07so we don't know there are many.