Pathology Grand Rounds: May 11, 2023 - Jennifer C. Jones, MD, PhD

Transcript

• 00:00Hi, everyone. Thank you so much
• 00:02for joining us today. I'm Tiffany,
• 00:05I'm a fourth year PhD student and
• 00:07the Pathology Grand Round Student
• 00:08Committee is very excited to have Dr.
• 00:11Jennifer Jones here with us today.
• 00:14It's not. Maybe I'll just use the.
• 00:18Hello.
• 00:23Hello. Great. Okay, great.
• 00:27Thank you again for joining us today.
• 00:30The Pathology Grand Rand Student Committee
• 00:32is really excited to host Doctor Jones.
• 00:34Doctor Jones received her
• 00:37Bachelor's at Princeton University
• 00:39and her MD&PHD at Stanford.
• 00:41She also spends a few years at Harvard
• 00:43and is currently at the National Center,
• 00:46the NCI and the CCR at NIH.
• 00:50And she is also the head of the
• 00:54Translational Nanobiology section.
• 00:57Currently, her lab does wonderful
• 00:59work on extracellular vesicles and
• 01:01identifying the different types
• 01:03of vesicles secreted by distinct
• 01:05tumor types and analyzing how they
• 01:08affect downstream immune pathways.
• 01:10She is also working on the
• 01:12development of the characterization
• 01:13and analysis of these extracellular
• 01:15vesicles and we are very excited to
• 01:17hear more about her research.
• 01:19So please join me in welcoming Doctor Jones.
• 01:26So thank you very much.
• 01:29In one of the chats that I
• 01:32had with one of your faculty
• 01:33members earlier this morning,
• 01:34the comment was made.
• 01:36This all sounds kind of like a mess,
• 01:39and actually it is.
• 01:40And what I'm going to show you is
• 01:43what we're going to try to make
• 01:45sense of the mess that is all of
• 01:48the extracellular structures and
• 01:49complexes and what they can tell us.
• 01:52So there is a book called The Commotion
• 01:57in the Blood that is a bit of a back
• 02:02story of of how I got started with this.
• 02:05First, these are the the folks
• 02:07who have done the the legwork,
• 02:09the hard work,
• 02:10the experiments behind these things
• 02:12that I'm going to show you and.
• 02:14So they really deserve all the
• 02:16credit for for getting this done.
• 02:19And I hope I'll show you
• 02:21collectively where this ends up.
• 02:24I'm a radiation oncologist,
• 02:26so I just wanted to loop you in
• 02:29on my perspective and interest on
• 02:34these problems.
• 02:35There's a a phenomenon called abscopal
• 02:38immune responses where you radiate one
• 02:41side of a tumor and the immune system.
• 02:44Causes other sites to regress.
• 02:47We have had some clinical trials at at
• 02:50NIH looking at this and as you know with
• 02:53immunotherapy it doesn't always work.
• 02:55In fact, it often doesn't work
• 02:56as well as you might want it to
• 02:58and the abscope will response.
• 03:00The combination of radiation
• 03:01and immunotherapy works even
• 03:03less often than that.
• 03:06So I want to find tools that
• 03:09help me unpack and understand.
• 03:11What's happening when those
• 03:13immune responses are going right,
• 03:15what we need to do to drive
• 03:16them in the right direction.
• 03:18This is the structure of the talk.
• 03:19I'm going to talk about not just the
• 03:21motivations that they just went through,
• 03:23but also the basics about extracellular
• 03:25vesicles and other extracellular things.
• 03:27The technology development we've
• 03:28been doing to crack the nut.
• 03:30Some basics discoveries that we've
• 03:32made as we're beginning to actually
• 03:33start to leverage these tools now
• 03:35and some conclusions that really
• 03:37are not really what we expected,
• 03:39but where we're going.
• 03:40So for those of you who haven't seen it,
• 03:42you may or may not have a book
• 03:44or two in your past that sort of
• 03:46stoked your interest in the field
• 03:48that you went into in science.
• 03:49This was a book in the 90s about
• 03:52tumor immunology in the early days and
• 03:55Cole's Toxin and all of these things.
• 03:57And that really led me to want to
• 04:00follow a path of studying these things.
• 04:01Overall,
• 04:04it's all about cells in the immune
• 04:06system of the cells in the immune system
• 04:08and the way that that book frames it.
• 04:10So there's more in our blood
• 04:12than just the cells.
• 04:13There is an assortment of vesicles,
• 04:16things with lipid bilayers and
• 04:17various cargo released in different
• 04:19ways from different cells.
• 04:20They're also obviously lipoproteins.
• 04:22They're also ribonuclear proteins complexes.
• 04:25They're also classes of extracellular
• 04:28things called exomeres and
• 04:31supermeres isolated in different
• 04:33ways from these broadly speaking,
• 04:37I'm going to focus on.
• 04:39The vesicles,
• 04:40with a caveat that I can't guarantee to you
• 04:44with the technology that anybody is using,
• 04:48that every single vesicle is actually
• 04:50vesicle and not just a particle that
• 04:52happens to have the same density,
• 04:54size, or other properties, but.
• 04:57This is this is where we are with the field.
• 05:00So Ev's are heterogeneous exosomes.
• 05:03Perche tends to be less than 150 nanometers.
• 05:07They have protein surface markers.
• 05:09They have nucleic acid cargo
• 05:11inside and sometimes outside,
• 05:13stuck to the surface,
• 05:15and there's widespread interest in them
• 05:17from tumors and other types of cells.
• 05:20People are interested in them because
• 05:22everybody who's interested in some tissue,
• 05:25some disease,
• 05:25some something wants to look at the
• 05:28vesicles coming from that tissue,
• 05:29that disease.
• 05:30And so it's really a great framework
• 05:33for potentially doing systematic
• 05:35systems biology if we could be
• 05:37organized and structured about this.
• 05:42So I want you to remember this
• 05:44figure because it's important.
• 05:45Strictly speaking exosomes come
• 05:48from multi vesicular bodies.
• 05:50In the cell and released into
• 05:53the extracellular space,
• 05:55other vessels are set from the surface.
• 05:57Those might be called micro
• 05:58vessels or microparticles.
• 05:59But exosomes imply a certain Biogenesis
• 06:05in the liquid biopsy community.
• 06:07So most of my clinical colleagues,
• 06:08when they do exosome studies or
• 06:10liquid biopsies for biomarkers,
• 06:12they'll take a biofluid,
• 06:14they'll isolate the exosomes often with
• 06:16some kit and then they'll do some other.
• 06:18Cargo assay RNA or DNA,
• 06:20some sequencing to identify some biomarkers.
• 06:25The reason there's so much huge
• 06:28excitement about this is because
• 06:31robust consistency in the protocols
• 06:35and a useful payload in the readouts
• 06:40has had some big successes.
• 06:42So Exosome diagnostics has
• 06:45prostate cancer assays.
• 06:47Which can help predict the the aggressiveness
• 06:53and discriminate between high grade
• 06:55and low grade prostate cancer and
• 06:58indicate to a patient based on their PS:.
• 07:01A and this test whether or not
• 07:04they need to get a biopsy.
• 07:06This was the result of one of the
• 07:09randomized control trials where they
• 07:11looked at this and showed the benefit.
• 07:15Of combining this test with standard of care.
• 07:19There have been since 3 randomized
• 07:21control trials and so I'm not saying
• 07:23that this kind of assay is not useful.
• 07:26But if you talk to Johan Skog who
• 07:29developed these assays you'll be
• 07:30the first to tell you yes 3 Two of
• 07:33the three Rna's that they isolated
• 07:35in the first versions of this test
• 07:38were not actually in vesicles at all.
• 07:41They were Co isolated with those.
• 07:44And so
• 07:47remember I showed you the figure
• 07:48where it really means something very
• 07:50specific in terms of Biogenesis.
• 07:51We have another part of the field that's
• 07:54really approaching this strictly as
• 07:56a I don't know about the Biogenesis,
• 07:57I don't really care.
• 07:59I'm taking a blood sample and and
• 08:02doing a procedural based thing.
• 08:04So there is an ontology initiative
• 08:07that we've started because if you
• 08:09want to create atlases you have
• 08:11to be speaking the same language.
• 08:13And I'll get to that at the very
• 08:15end because I know ontology talks
• 08:16make everybody fall asleep.
• 08:17So I just got a couple of slides to to
• 08:20share with you and thank those of you
• 08:22who feel about the the survey for us.
• 08:26But back to the original idea,
• 08:27So how do we do this systematically
• 08:31and correctly?
• 08:32What we want to do is to sort
• 08:36subsets of the vesicles and then
• 08:37look at the the cargo and the
• 08:39messages that those sets contain.
• 08:43So the state of the field is taking
• 08:46the whole mess of vesicles from all
• 08:49the different cells and we want to
• 08:51parse them out into vesicles from
• 08:53particular sources and and study those.
• 08:58So we need to know how do we reliably
• 09:01identify the specific subsets.
• 09:04So with immune cell substate sets
• 09:06you might use CD3 for a T cell.
• 09:11CD14 for a type of monocyte,
• 09:14which are the right markers to use for
• 09:16different types of vesicles and then
• 09:18how to reliably assay them Once you
• 09:20know which ones you want to assay,
• 09:22how do you do the assay in a reliable way.
• 09:25So we're in an in between space.
• 09:28There's a lot of cellular biology and
• 09:30this is pretty mature at this point and
• 09:32there's a lot of molecular diagnostics.
• 09:34It's individual molecule assessed in mass,
• 09:37these are packets and so.
• 09:40We're looking at packets of informations
• 09:41and sets of packets of those informations.
• 09:43So it's a fundamentally different
• 09:46type of bioinformatics.
• 09:48And Joshua Welch,
• 09:49in my group Staff Scientist,
• 09:52has led the development of three software
• 09:56tools which help improve the rigor and
• 09:59reproducibility of three important
• 10:00tools that we use for characterizing.
• 10:03Individual E v's and repertoires of E
• 10:06V's and I'm going to walk through these.
• 10:08One that's targeted at EV flow
• 10:11cytometry for single E V's,
• 10:13resisted pulse sensing that
• 10:14measures size and concentration,
• 10:16and MPA pass and Multiplex analysis system,
• 10:20which is useful for assessing
• 10:22repertoires broadly.
• 10:26So these are the specific
• 10:30tonologies I'm going to talk about.
• 10:34So for single EV studies we've produced
• 10:40these advanced protocols for labeling,
• 10:43sorting, a framework which I'll show
• 10:46you or how to do and organize and
• 10:49report the the studies and then also
• 10:52do your assays in a calibrated way.
• 10:54So rather than an arbitrary
• 10:55unit sharing calibrated units,
• 10:56which if in everybody probably
• 10:59does philositometry,
• 11:00you know your skills are actually.
• 11:01Arbitrary, they're not
• 11:03calibrated for essence units.
• 11:07And then I'm going to show you how
• 11:09we're stepping towards integrating
• 11:10this into a comprehensive Atlas.
• 11:12So our labeling protocols and
• 11:16sorting we're done on the Astrios,
• 11:20basically a next generation Moflow XDP
• 11:23den air system with this we showed here.
• 11:29Remember this picture?
• 11:29I'm going to come back to it.
• 11:31These are vesicles that we
• 11:33can see from DC 2.4 cells.
• 11:35And this bottom thing is not
• 11:37a population of vesicles,
• 11:38it's the noise of the instrument.
• 11:41So you can see how we're really
• 11:43hugging the bottom limits of detection.
• 11:46We can't separate the vesicles
• 11:48from the noise any better because
• 11:50of the limits of detection and.
• 11:52Polystyrene beads can't be used as
• 11:55calibrators because they refract.
• 11:56They have a different refractive index,
• 11:58so they they fundamentally can't
• 12:01be actual calibrators.
• 12:03This is what happens when your particles
• 12:06are lower than the limited fraction.
• 12:09Fortunately,
• 12:09HIV and most of our extracellular
• 12:12vesicles is about the same size,
• 12:14so we took advantage of that and
• 12:16took two different HIV variants,
• 12:20one that was.
• 12:23CCR5 trophic and one that's CX
• 12:26CR4 trophic and labeled 1 red,
• 12:28labeled 1 green,
• 12:29which showed that we could sort
• 12:31them and that they remain retain
• 12:33their trophism for their specific
• 12:38for their specific type and
• 12:40the recipient cell line.
• 12:42So this shows fidelity.
• 12:44It's not something you're ever
• 12:46going to do if you're producing.
• 12:48Therapeutic vessels and
• 12:49you want to produce a lot.
• 12:51It's feasible to do this
• 12:52if you're studying viruses,
• 12:53but I think our group,
• 12:55Vanderbilt's group and a group and
• 12:57the Netherlands may be the only
• 12:59groups who've ever really done this
• 13:01and it takes like 48 hours nonstop.
• 13:04We set up COTS in the lab and all of that.
• 13:08So it's it's not a
• 13:12scalable approach.
• 13:17So when we did this we we had
• 13:19so many people look at our data
• 13:22and say how did you do that?
• 13:23I just, I just don't believe those results.
• 13:27And so we we took that challenge
• 13:29on and we said okay,
• 13:31we're going to prove it and we
• 13:34need to help each other be able
• 13:36to look at each other's data
• 13:38and know what we can believe,
• 13:39what we what we can trust in terms
• 13:41of the integrity of the data.
• 13:43So this led to this formation of
• 13:45a Tri society ISAF ISAC ISTH flow
• 13:48cytometry group where we worked on
• 13:51how can we improve the rigor in
• 13:53the field so that we can speak the
• 13:55same language for EV flow cytometry
• 13:57and improve our data approach.
• 14:01So this was the product of a
• 14:06surprisingly long time working with
• 14:08groups who do things differently.
• 14:11And we basically set out these
• 14:15guidelines to help basically tell you
• 14:20when you're designing your experiment,
• 14:22you're setting things up.
• 14:22What do you need to do to
• 14:24help people reproduce it?
• 14:25How do you prove that what
• 14:27you're looking at is Ev's?
• 14:28How do you validate it across
• 14:29instruments and settings?
• 14:30And how do you make your data shareable,
• 14:34transparent, and ideally interoperable?
• 14:40So this is where Josh's coding
• 14:42and technology development skills
• 14:44have really come into play.
• 14:45He tackled both the single EV
• 14:48analysis problem and the EV
• 14:50repertoire problem in flow cytometry.
• 14:52Single EV analysis Low is highly
• 14:55quantitative, but it has terrible
• 14:58sensitivity for single EV's.
• 15:00If you do it on a bead based
• 15:02way like a Multiplex way,
• 15:03it's high throughput.
• 15:04It's multi, multi, parametric but.
• 15:06It's only semi quantitative and
• 15:08you can't really assess the full
• 15:11range of complexity.
• 15:12So for single EV flow cytometry
• 15:16he developed FCM passive
• 15:17software that basically
• 15:21derives the collection angle of the
• 15:23actual optics of the actual machine
• 15:25at the time that you're doing.
• 15:27So if the engineer came in and
• 15:29fiddled with the alignment,
• 15:30it would you'd have to,
• 15:32it wouldn't be the same collection angle.
• 15:34But once you've derived the collection angle,
• 15:36if you collect the proper calibrators,
• 15:38then you can convert your data from those
• 15:41flow cytometry arbitrary units using ME
• 15:45theory to calibrated SI standard units
• 15:49of nanometers and for your fluorescence.
• 15:51You can also calibrate with molecular
• 15:53equivalence of soluble fluorescents and
• 15:56generate calibrated fluorescence as well.
• 16:02This led to. A bunch of papers,
• 16:04a bunch of our reports.
• 16:06This is still something that we're trying
• 16:09to get out and use more commonly so
• 16:11that we can more actively engage with
• 16:13and sort of share data with each other.
• 16:16For the EV repertoire analysis this involves.
• 16:21We prototyped a lot of this using
• 16:23the Miltonie Multiplex Exosome Kit,
• 16:25which is a bead set of almost 40 beads.
• 16:30Several micronic piece which capture
• 16:33based on one antibody type 1 epitope type
• 16:36captures the vesicles and then you go in
• 16:39and you detect with a different antibody.
• 16:42And so with this I'm going to walk you
• 16:45through some of the results that we see.
• 16:47But he's written the software to really
• 16:50facilitate the complexity of this and
• 16:52all of that data that has to get analyzed
• 16:54all at once in a calibrated way compared
• 16:56between experiments etcetera, so.
• 17:00This is a heat map showing several
• 17:03experiments we did with different
• 17:06antibody capture B combinations,
• 17:08different biofluid types and it's all
• 17:12calibrated again and with fluorescence
• 17:17and and the appropriate controls.
• 17:19So what you can see is CSF is unique,
• 17:23it's it's sort of standing off on its own,
• 17:25it's very different from plasm and serum.
• 17:28Plasma and Serum are relatively similar
• 17:30to each other and in the way that
• 17:32PCA and RT Sneeze are parsing them.
• 17:35So that's what we've done for that
• 17:37and now we have worked,
• 17:39we're working on stitching those together
• 17:42into a more comprehensive Atlas type
• 17:44approach where we can integrate single EV
• 17:47data with Multiplex EV repertoire data.
• 17:52There's also. Resistive pulse sensing.
• 17:54So if any of you are doing small
• 17:57particle work you may use a Nano
• 17:59site nanoparticle tracking analyzer.
• 18:00Resistive pulse sensing like
• 18:01a Spectradine or an eyes on.
• 18:03SO this is specifically resistive pulse
• 18:06sensing that works with the output and
• 18:08interface of the spectradine instruments.
• 18:10Those use little chips and what we
• 18:12found was if we took the same sample
• 18:15and reran it on a set of chips
• 18:18we get a different result every
• 18:20time just with the standard beads.
• 18:22And that's not good.
• 18:24So we developed a way to use this
• 18:27bike in and then reanalyze the
• 18:31data to normalize the data.
• 18:33So essentially it appropriately scales
• 18:39so that it is calibrated and it
• 18:41makes a difference in your data.
• 18:42So the plot on your left is of data
• 18:48that was not processed with RPS pass,
• 18:50and you can see there's a huge.
• 18:52Variation in those when we look at
• 18:56that with where the the spike has been
• 18:59used to appropriately scale the data,
• 19:01you can see that we can more
• 19:03clearly discriminate the the, the,
• 19:07the qualities of the size of and
• 19:10the concentration of those Ev's.
• 19:13So all three of these we're working with.
• 19:17Baylor and other collaborators
• 19:18to to work on integrating these
• 19:21into tools that people can access
• 19:23comprehensively and shared data.
• 19:30So this has been relatively quick.
• 19:33You know, this is something
• 19:34we've really been working hard
• 19:36on for the last 5-6 years,
• 19:39but that has made the difference
• 19:41as the field has gone from being
• 19:43able to go from Western lots.
• 19:44To flow cytometry where we don't
• 19:46know our limits of detection,
• 19:48now we know our limits of detection,
• 19:50we can articulate them and really
• 19:52reproducibly state what the results are.
• 19:57So then okay, we've done it.
• 19:59Can the whole field do it.
• 20:02In November we had a an EV
• 20:07reference material study.
• 20:08This was really spearheaded by
• 20:10Joshua Walsh who who recognized
• 20:12that as much as we try to.
• 20:15Teach everybody what they need to know.
• 20:19And we want the data to
• 20:21actually be consistent,
• 20:22to be calibrated, to be well,
• 20:25to 1st be capable,
• 20:26but then to be calibrated,
• 20:28to have the whole data set
• 20:31reproducible, etcetera.
• 20:36It's too complex when it's a really
• 20:38complex sample, so we use this.
• 20:41Fluorescent recombinant EV reference
• 20:44material from Anne Hendricks,
• 20:45which is now available from Sigma Millipore.
• 20:50It's called recombinant exosomes.
• 20:52From them, they rebranded it just because
• 20:55they thought that would sell better.
• 20:57Sigma did not, not.
• 21:00And and to really get at the heart of
• 21:03where we need to make inroads in the field,
• 21:06we went to the manufacturers.
• 21:08So Josh basically offered all of
• 21:11the instrument manufacturers the
• 21:12opportunity to take a sample.
• 21:14We shipped it off to them and send us back
• 21:18the results with a a set of sort of criteria.
• 21:21We want you to report back this,
• 21:23this, this and this.
• 21:25So everything was fair.
• 21:26It was transparent up front.
• 21:27This is what we wanted because
• 21:30when you buy instruments,
• 21:31you need the manufacturer to be able
• 21:33to tell you how to properly use it.
• 21:36In this study,
• 21:37this is only the beginnings,
• 21:40but this shows I won't go into
• 21:41all of the results.
• 21:43But basically in that wheel of all
• 21:45the criteria we'd like to have met,
• 21:49some are very good and others
• 21:51are in the process of learning.
• 21:54And so if we redid this today,
• 21:57some of those on the bottom row.
• 21:59Would be either nearly completely
• 22:02filled in or filled in.
• 22:04So this was really a good
• 22:05opportunity to work with industry
• 22:07to start trying to pioneer this.
• 22:09So OK, so if we can do it,
• 22:13how do we share this with the field?
• 22:15So this needs to be centralized,
• 22:17accessible.
• 22:18So we've been working with Baylor as
• 22:21part of the ERCC common fund effort
• 22:25to develop the Nanoflow repository.
• 22:29And so that's the beginnings of a
• 22:34shared way to deposit the data.
• 22:38This Baylor Group is also as
• 22:42in parallel the XRNA Alice.
• 22:44So you can see what I was talking
• 22:46about before where we want to
• 22:47tie the surface phenotyping data,
• 22:48the individual EV phenotyping data,
• 22:50the repertoire phenotyping data
• 22:52then along with the RNA cargo.
• 22:55Data.
• 22:57They do have the the skeleton and
• 23:01the background and the infrastructure
• 23:03of the xrna Atlas there.
• 23:08So this is moving us closer to
• 23:11doing what we want to do,
• 23:12which is to be able to look at subsets,
• 23:14identify markers to pull out
• 23:16subsets to look at the RNA.
• 23:18Then we hit a roadblock which
• 23:21was one I expected, but.
• 23:24You know,
• 23:26most RNA seq methods require
• 23:29nanogram levels of RNA.
• 23:32When you get to the subsets,
• 23:34you're probably in less than 100
• 23:36picogram kind of range of of RNA.
• 23:39So we tried to we decided to
• 23:40test whether or not we could use
• 23:43single cell sequencing methods,
• 23:44not in the single cell mode but in.
• 23:48Bulk using that as the library preparation
• 23:51method for looking at EVRN A's.
• 23:57And remember I showed you the picture
• 23:59of the DC 2.4 E V's on the flow
• 24:01cytometer and I said remember these?
• 24:03That's the cell line we chose.
• 24:06It grows like weeds.
• 24:07It's a little mouse dendritic cell
• 24:09line that Ken Rock made back in the
• 24:111990s and it feeds itself GMCSF.
• 24:13So these are the happiest
• 24:14cells you could ever.
• 24:16Want they grow like weeds,
• 24:20and they've also had a lot of
• 24:22manipulation in their background.
• 24:23So I outlined here all of the background
• 24:27that I kind of ignored until one of
• 24:30our reviewers pushed us to instead of
• 24:34TEM get cryoem to really hammer out
• 24:38the exact size of these and what we
• 24:41couldn't see in the TEM on the left.
• 24:44You can see really clearly on the
• 24:46right we have retroviral capsules or
• 24:48something that looks awfully a lot like them.
• 24:50And I got a call from the lab
• 24:53who was helping us with this.
• 24:55Not a call,
• 24:56it was worse than that,
• 24:58an e-mail that was carbon copied to
• 25:01the then scientific director of all of
• 25:04NIH saying what do you not understand
• 25:07about B SL1 samples for a B SL1 lab.
• 25:13This cell line is sold by Sigma and
• 25:17mercury pour has a B SL1 cell line.
• 25:21And I say look, I'm really sorry,
• 25:24I don't know what that is.
• 25:25It could be a mishmash of
• 25:27rearrangements of any of those things.
• 25:29In this background it's also a
• 25:31mouse cell line and they have
• 25:33lots of endogenous rector viruses,
• 25:35so I have no idea what that is, but.
• 25:38I'll I'll get to the bottom of it.
• 25:41And this is now.
• 25:42I've lost count of how many years later
• 25:46we decided we wanted to apply this
• 25:47RN A/C approach to those because I
• 25:49wanted to figure out what's what is it.
• 25:51I don't want to just do a PCR for this,
• 25:53that and the other thing,
• 25:54I want to know what's in it.
• 25:57So we've done Proteomics and we've
• 25:59done RNAC and it turns out we find
• 26:01a dominant species and it turns
• 26:03out it's Mouse Maloney virus which
• 26:05was part of its background.
• 26:10So that is a xenotropic virus,
• 26:14meaning it doesn't go from mouse cells to us,
• 26:16it just stays between mouse cells and it
• 26:18doesn't go from mouse cell to mouse cell
• 26:20unless the cells are actively dividing,
• 26:22which, well, those do. And so
• 26:29another reason I wanted
• 26:31to go down this crazy Rd.
• 26:33is because of the hers where
• 26:35we know that those modulate.
• 26:37Responses to immunotherapy or
• 26:39their indications that they do.
• 26:41And so we wanted to have a pipeline,
• 26:43a method that would allow us to elucidate
• 26:46the presence or absence or the types
• 26:49of herbs in our human EV samples.
• 26:52So we're collaborating with
• 26:54Kendall Jensen at Tijan and
• 26:56Yasmine Belkade's group at NIAID.
• 26:58She's just accepted the position
• 27:00to run the Pasteur Institute.
• 27:02So unfortunately we're going
• 27:03to lose her soon.
• 27:04But we're working very hard to get
• 27:06this all tied together before she
• 27:09goes to have a comprehensive pipeline
• 27:12that would include conventional RN,
• 27:14A's and the Hearse.
• 27:15So I want to show you some results
• 27:18of all these tools that we've been.
• 27:20Working on and here a couple of the examples.
• 27:23I'll show you a little bit of kidney cancer,
• 27:27prostate cancer, colon cancer, CNS diseases.
• 27:32But first,
• 27:34if you could live a day in my shoes,
• 27:37you get a question just about every day.
• 27:42I want to start a study and
• 27:43I want to look at exosomes.
• 27:45That's what people say to me and I want
• 27:48to know what kind of blood tube I need.
• 27:50And that's a really hard what
• 27:52do you want to do with it?
• 27:53What do you, what do you want to look at.
• 27:56So to help us figure out what is
• 27:59our right blood collection tube,
• 28:01we decided to compare for the SST tubes,
• 28:05EDTA tubes and the strect
• 28:08DNA&RNA complete tubes.
• 28:09This is the comparisons that we
• 28:11did to suss out the impacts of
• 28:14platelets and not platelets and.
• 28:17Ways that you do the spins,
• 28:18we counted the particles that were remaining
• 28:21after we did the depletions etcetera.
• 28:23And what you see is that we had
• 28:28a surprise which is that CD62 P,
• 28:33CD242A,
• 28:34some platelet markers were not
• 28:36only elevated in samples where
• 28:38you froze the sample and then
• 28:40you spin out the platelets,
• 28:41which is a terrible idea,
• 28:42but a lot of people do it.
• 28:45It was also elevated in the struck DNA tubes.
• 28:48So maybe something with the
• 28:49fixation of the struck DNA tube
• 28:51that's causing shedding of these
• 28:55these vesicles.
• 28:55And so we've we've looked at this further.
• 28:58But you know it's this kind of
• 29:00quantitative analysis that helps us
• 29:02assess the not only the integrity
• 29:04but also the repertoire and the
• 29:06relative abundance of these different
• 29:08types of vesicles in the solution.
• 29:11So for us, for our lab,
• 29:12for our protocols,
• 29:13we're doing SST tubes and complete RNA tubes.
• 29:18I I actually think that plasma
• 29:20DTA tubes are also great.
• 29:24I spoke to somebody earlier
• 29:26today about oncosomes.
• 29:27These are large vesicles shed
• 29:29by tumor cells which are like
• 29:31larger than 800 nanometers,
• 29:33sometimes larger than a Micron.
• 29:35So every platelet depleting protocol
• 29:37that you do to spin out the platelets
• 29:39is going to remove the oncosomes.
• 29:43I I don't have a good solution.
• 29:46If you want to study those,
• 29:48I think you have to go directly to
• 29:51processing the onpisomes separately.
• 29:53So our approach is showing us good
• 29:58fidelity and differences in tumor types.
• 30:01So Long story short we compared a bunch
• 30:04of different EV's from different tumors.
• 30:06This is something that
• 30:08we've already published and.
• 30:10You can see Epcam is more commonly spread
• 30:12or sort of more highly expressed in these
• 30:15samples from the epithelial tumors and
• 30:17from the Seglio bus, I mean, it's good.
• 30:20You wouldn't expect Epicam so much in those.
• 30:23The tetraspanins and CD44 are
• 30:26up in the in in both.
• 30:30So for kidney cancers,
• 30:33there's not a great molecular handle.
• 30:37For pulling out kidney cancers.
• 30:39So Marsha Lenahan and Maria Marino
• 30:43have this amazing set of cohorted
• 30:46patients and data and studies and
• 30:48information they've learned about
• 30:50one hippo window and and other
• 30:53forms of hereditary kidney cancers.
• 30:57So we worked with them to look at some
• 31:00of the different tumor types that we
• 31:03could prototype with and then begin to
• 31:05look at those samples and patients.
• 31:07And so we tried a battery of different
• 31:11markers and we found some that
• 31:15really hadn't been expected and they
• 31:17have some of the same features.
• 31:19Some of them are also Tetra spannons
• 31:21and they're also Stemmus markers.
• 31:23So this is consistent with what we saw
• 31:25was really elevated in the other two types.
• 31:27So maybe we're finding that there's kind
• 31:29of a a malignant signature as opposed
• 31:33to a specific type of tumor signature.
• 31:37In the types of markers they express.
• 31:41Then we worked with collaborators to
• 31:43look at the EV profiles in malignant CSF
• 31:47samples and other CSF samples including
• 31:52autoimmune diseases and viral diseases.
• 31:55And you can see again here
• 31:57we see that same pattern.
• 32:01And so
• 32:04once we have the markers,
• 32:05we do the pull down.
• 32:06Do we actually see differences in the RNA?
• 32:09So this was one of my early proof of
• 32:13principles examples where we just took
• 32:17a thoracentesis sample from
• 32:19a patient of mine who had
• 32:23very metastatic prostate cancer,
• 32:25which is PSMA positive the therapeutic
• 32:29tap and in the therapeutic tap in
• 32:31the biospecimen protocol we're able
• 32:33to pull down the PSMA positive EV's.
• 32:36Compared to the PSMA negative
• 32:39EV's compared to the bulk sample.
• 32:41And you can see there are several
• 32:44RNA's which are highly associated
• 32:46with the PSMA positive ones,
• 32:48which you would have missed if
• 32:50you were looking at the soup of
• 32:52everything because there's so many
• 32:54other kinds of vesicles that compete
• 32:56in that type of identification.
• 32:58So I thought PSMA was going
• 33:00to be a great marker,
• 33:01but really what marker should we be using?
• 33:07I insinuated and I really feel like
• 33:09the markers that we choose are not
• 33:11going to be the same markers that we
• 33:14use in the context of intact tissue.
• 33:16It may relate more to their phenotype.
• 33:18So we did a large screen of 170
• 33:22different EV surface markers across
• 33:25some of those kidney cancer patients.
• 33:28Other CSF sample was just a massive cohort.
• 33:31So if you're squinting at this
• 33:32from the back of the room,
• 33:33you can see there's sort of a
• 33:35tartan Plaid kind of pattern.
• 33:38There's a a sample down here
• 33:40where it's all blown out.
• 33:42It turned out,
• 33:43turned out that person had a radioisotopic
• 33:46treatment for metastatic prostate
• 33:48cancer couple weeks before and was
• 33:52having ramped up marrow production.
• 33:53I don't have any other samples like that,
• 33:56but clearly this is. Not,
• 33:58we're not going to understand much from that.
• 34:01But then there are sections where
• 34:03you see more of some workers,
• 34:05less of other markers and in sets.
• 34:07And if you break down those sets and
• 34:10you look and you say CSF versus serum,
• 34:12they're really different.
• 34:15Looking at PC A's,
• 34:16if you look at tumors versus immune,
• 34:19this tumor, that tumor,
• 34:21they're also very separable.
• 34:23Well,
• 34:23some of them are separable more than others.
• 34:27And then looking at the CSF samples from
• 34:31patients with or without brain tumors,
• 34:33you can also see that there
• 34:36are differences that we see.
• 34:41So we also worked with Steve Jacobson
• 34:45in NININDS and he studies both Ms.
• 34:50and he MTSP, the HTLV associated
• 34:54tropical Myelotis myelo.
• 35:00******* peripheresis.
• 35:03So these are CSF samples from those
• 35:07patients and patients also with who carry
• 35:10the HTLV virus but are asymptomatic.
• 35:12That's what the AC's are or other
• 35:15viral diseases and you can see
• 35:18that turns out that hand patients,
• 35:21the ones with active disease associated
• 35:24with HTLV in the nervous system.
• 35:26Have higher CDA than CD2E V counts.
• 35:31We've followed that up with
• 35:34another set of samples,
• 35:36again that size and as as well
• 35:38as other markers and we still
• 35:40see that robust difference.
• 35:41It's it's it's very,
• 35:43it's not a massive magnitude,
• 35:45but it's very consistent.
• 35:47So which of these EV markers
• 35:49relate to the biological state,
• 35:51meaning the biological state
• 35:52of the cell that made them?
• 35:55And this is work that was done with a
• 35:59colleague who had a really interesting
• 36:02biological phenotype they were studying.
• 36:05They made some knockout cell lines.
• 36:07And what you see here is our
• 36:09B plus antibody control,
• 36:10the knockout line, the control line,
• 36:12the knockout line, the control line.
• 36:14And what you can see is that
• 36:17there are some genes that are just
• 36:20missing from the knockouts and there
• 36:23are some genes that are missing.
• 36:25From the controls.
• 36:26So we're really getting a sense of
• 36:29changes in these related to that.
• 36:32So we're really just starting
• 36:35to apply these and learn more.
• 36:37We have also found a pattern
• 36:40in metastatic potential,
• 36:42so match sets of cell lines that have
• 36:44different metastatic potential on
• 36:46their markers and so now we want to
• 36:51move forward further with that so.
• 36:54You know,
• 36:54I started talking about the
• 36:56commotion in the blood,
• 36:58or as one of the earlier professor said,
• 37:02the the mess that is the
• 37:04extracellular space these days.
• 37:08I think the reason why we've wrangled and
• 37:11learned so much from the immune system is
• 37:14being able to be so systematic about it.
• 37:17And so I've tried to begin to wrangle the
• 37:20extracellular space into the same way,
• 37:23to establish some foundations
• 37:24to make a consistent Atlas,
• 37:26to then begin to study the specific
• 37:29markers related to tumors,
• 37:31relating them to phenotypes and
• 37:32all of these other things.
• 37:34So the survey that I sent you guys
• 37:37and thank you for those who who went
• 37:41slog through it to to to humor me.
• 37:44The the bottom line is,
• 37:45do we need an extracellular ontology?
• 37:48We have a cellular ontology.
• 37:50But when you take a liquid biopsy,
• 37:53you have no idea.
• 37:54There's nothing that's a single
• 37:56marker that can tell you that a
• 37:58vesicle came from an exosomal pathway.
• 38:01In fact,
• 38:02the biologists are really kind
• 38:03of working out all the specifics
• 38:05of the exosomal pathway anyway.
• 38:07So then you try to frame the
• 38:09ontology of the extracellular
• 38:11space in the related ontologies.
• 38:13So I just mentioned,
• 38:15do we need an extra cellular one?
• 38:17There's a cellular one,
• 38:21I don't know, you guys can tell me,
• 38:22but I have asked my liquid biopsy colleagues,
• 38:25is it probably pretty true that
• 38:28you categorize the things that
• 38:31you're using for biomarkers,
• 38:34classify them really by what it is
• 38:37that you isolated or how you isolated?
• 38:39I say yeah, okay so.
• 38:43And the nano material
• 38:45field is super detailed.
• 38:47They have a Nano Nano Particle Ontology,
• 38:51the NPO, that's all about formulation,
• 38:54this is the shell, this is the surface,
• 38:56this is the, it's extensive.
• 38:59So how do we just approach
• 39:01the mess that's in between?
• 39:03So hence the survey and I didn't ask
• 39:08the question I wanted to ask because
• 39:10it was so strongly objected to.
• 39:13My first question was going to be
• 39:15what do you think an exozone is?
• 39:17A BCD? But people decided
• 39:20that was too controversial,
• 39:22so instead we asked more obliquely,
• 39:28maybe obtusely.
• 39:31This is a selection of ways to
• 39:33classify extracellular vesicles.
• 39:35Which one do you think is most central
• 39:38to harmonizing with later system?
• 39:414 vesicles,
• 39:42the largest proportion that the
• 39:45highest answer is based on biological
• 39:49considerations like Biogenesis.
• 39:51And so I think that message of the
• 39:54EV community of what distinguishes
• 39:57A vesicle from a non vesicle and
• 40:01an exosome and microparticles
• 40:03or other things it's getting
• 40:06through in response to the non
• 40:09vesicular extracellular particles.
• 40:12Even the EV people, the ISAF people,
• 40:16say we don't know what the Biogenesis is.
• 40:19For the most part, the top answer is based
• 40:21on biochemical considerations, composition.
• 40:24Is it a lipid biolayer? What's in it?
• 40:29Informally? And I don't know if this
• 40:31is ever going to get published or not,
• 40:32but we did a we did a beta test.
• 40:35I used my friends and colleagues
• 40:37at NIH as Guinea pigs.
• 40:39We have a couple of listservs for
• 40:42the liquid biopsy group and the EV
• 40:44interest group and we sent it to them
• 40:46and it was even more extreme when we
• 40:50focused on the liquid biopsy groups.
• 40:53It's about composition, what is it,
• 40:54what we're looking at and the
• 40:57EV folks about everything,
• 40:59not just vesicles and when asked about
• 41:03everything without dividing into vesicles
• 41:05or non vehicular extracellular particles.
• 41:08The EV group still focused on Biogenesis,
• 41:12so I'm working on the analysis of who
• 41:15answered what and it should be interesting.
• 41:18But I've been at meetings where
• 41:22people stand up and they ask me
• 41:23why do you care what it's called,
• 41:26if it's a good biomarker?
• 41:27And honestly,
• 41:28if the biomarkers is a good biomarker,
• 41:30that's great.
• 41:31It's just if you want to stitch
• 41:33the data together and understand
• 41:36how our data relates to each other.
• 41:39Everybody who does omics and assays
• 41:42and atlases knows that there has to
• 41:44be a common framework it's set on.
• 41:47So I just want to.
• 41:49In addition,
• 41:49I really have to thank you all
• 41:52for inviting me to come speak.
• 41:53It's really an honor for me as a
• 41:55young scientist to speak to you guys
• 41:58learn from you, get your feedback.
• 42:01I also need to thank the laboratory
• 42:04pathology kind of that be my mentors.
• 42:08Past, present and current.
• 42:10As you know I was thinking last night
• 42:14I couldn't say this takes a village.
• 42:16This actually takes like lots of villages.
• 42:19So these are some of the villages
• 42:24who have and they're continuing to
• 42:26help me and I'll take questions.
• 42:28But as a sneak peek I had bought
• 42:32on behalf of our residency program
• 42:34director some slides about the.
• 42:37Residency program at at NIH If there
• 42:41are folks who are interested in it
• 42:43at lunch and I'll just e-mail it to
• 42:47anybody who's interested, thank you.
• 42:59Should I open the chat and see if
• 43:01there are questions in the chat? Okay
• 43:11act stating for CME credit.
• 43:14Texting for CME credit, so I don't
• 43:17think those are questions. Yeah,
• 43:21refer to analyze the EV in
• 43:25the context of that area.
• 43:29Yeah, there's a whole group of ISA which
• 43:33is interested in not only the Ev's,
• 43:38the host Ev's, but also the Ev's.
• 43:41Of you know, across the microbiome
• 43:45or infections, that's become a very
• 43:48interesting part of COVID work.
• 43:50Kendall's done some work on that at Tgen.
• 43:53Several people have have done
• 43:55a lot of work on that. Yeah.
• 43:58In that context how do you
• 44:01differentiate the post PR?
• 44:05Yeah, so it depends on your assay, right.
• 44:08So if you. Have species specific
• 44:12antibody clones that can begin to
• 44:14differentiate some of it and that's
• 44:16been applied in some model systems.
• 44:20I don't know if it's been
• 44:23applied in clinical settings.
• 44:27And then in terms of the informatics
• 44:31for you know like RNA analysis it
• 44:32would it would be based on the genomes.
• 44:37There's certainly overlap where you can't
• 44:40discriminate I would imagine my final
• 44:43question on the basis of the buy markers,
• 44:49the efforts pull down subset,
• 44:52yeah that's what we're doing and
• 44:56that's why we had we've had such an
• 44:58extensive focus on which markers to use.
• 45:01And then once we do the pull downs,
• 45:03how do you make that work robustly for
• 45:05the very small amount that you pull down?
• 45:08So one thing that struck me and
• 45:11I think anybody who's interested
• 45:14in doing liquid biopsies of EV's
• 45:17should probably understand this in
• 45:19a milliliter of blood, you know,
• 45:21you might have 3 circulating tumor cells,
• 45:2410 circulating tumor cells.
• 45:26There's something on the order
• 45:28of about a billion EV's.
• 45:30And there's something on the order of
• 45:3510 to the 16th versus 10 to the 18th,
• 45:38like a billion billion lipoprotein particles.
• 45:42So, So those since they're so close and
• 45:45overlapping in size with the vesicles,
• 45:47those become the main complicator.
• 45:52And it's what I like about the affinity
• 45:55pull down part is that you can.
• 46:00Directly interrogate A membrane receptor,
• 46:02another membrane receptor,
• 46:03and know that you're dealing with
• 46:05something that is likely something that
• 46:07has a little bit by later because it
• 46:09has a Tetra span and then thanks, yeah,
• 46:17the best questions.
• 46:47Yeah. So, So, yes, yes and yes.
• 46:50So the the question for folks
• 46:52online who maybe didn't hear it was
• 46:56are there. Impacts of the cellular,
• 46:59the state of the cell in terms of its
• 47:02metabolism or other stressors that
• 47:04affect the type of vesicles produced
• 47:07And are there impacts also on the ways
• 47:10that cells receive vesicles. So you know
• 47:172004 Arnie Levine showed that P53 was
• 47:21central regulator of producing exosome.
• 47:23So there's. From way back there's there's
• 47:26been an understanding that genotoxic stress
• 47:29hence my interest as a radiation oncologist.
• 47:31Radiation kicks off a surge of these and
• 47:38no so you can give a sublethal dose and
• 47:43it it stimulates the exozone pathway.
• 47:47So so this is there's clearly a.
• 47:51Very wide heterogeneous range
• 47:53of types of vesicles.
• 47:56There are these exosomes,
• 47:57they're small ones made in the vesicles or
• 47:58other types that are shut off the surface.
• 48:00There are the,
• 48:01I guess you could call them apoptosomes,
• 48:03the ones that are shut in
• 48:05the context of apoptosis.
• 48:08I think we are only scratching the
• 48:11surface of those different types the
• 48:15in the 80s or 90s they originally described.
• 48:19These little vesicles and microscopy
• 48:21is platelet dust where they just
• 48:23kind of kick out the garbage.
• 48:25So there was first an idea that these
• 48:27are garbage bags and there was this idea
• 48:30that they're sophisticated endocrine
• 48:32systems of communicating between cells.
• 48:34I think it's both and and a
• 48:36lot of stuff in between.
• 48:38So for me, I'm going to be
• 48:41looking for different types of
• 48:43vesicles with different types of.
• 48:46Aberrant DNA damage,
• 48:50Addux and other things.
• 48:53So yes genotoxic stress increases
• 48:58exosome production per se.
• 49:01Also probably stress and loving
• 49:04There also is starving cells to this
• 49:10is really kind of related to some
• 49:12work that Raghu Glory has talked
• 49:14a lot about which is that the.
• 49:16Pancreatic cells,
• 49:17which are essentially just
• 49:20ravenous for resources,
• 49:22take up these therapeutic
• 49:24vesicles that he produces.
• 49:26And he thinks that that's because
• 49:28of their metabolic state and
• 49:30receptor affinity for vesicles
• 49:31compared to surrounding tissue,
• 49:33which doesn't seem to pick up
• 49:35those therapeutic vesicles as well.
• 49:37But any type of vesicle that you look at,
• 49:42you can find.
• 49:45Yin and Yang and a lot of these things.
• 49:47So there are the E V's or exosomes that
• 49:53cause essentially vaccinating effects,
• 49:55tumor stimulation.
• 49:57There are other vesicles which are
• 49:59clearly inhibitory that promote a more
• 50:03mildly suppressor type phenotype,
• 50:05which are which which do what?
• 50:07Until we systematically start
• 50:09breaking the groups apart,
• 50:11there are a lot of mysteries
• 50:12that are hard to unravel.
• 50:23Yeah.
• 50:25So that's a really good question.
• 50:28As with all of it, part of
• 50:29the answer is it depends.
• 50:34So if you make synthetic
• 50:37ones and you inject them,
• 50:39it depends on how you made them.
• 50:42They could just.
• 50:43Go first pass and get largely
• 50:46taken up in the spleen or the
• 50:48liver and they may make only
• 50:49kind of one round through,
• 50:50so it might really matter
• 50:52which way you inject them.
• 50:56In other cases where you've made
• 50:58them under other conditions,
• 51:00they circle around quite
• 51:01a bit longer beforehand.
• 51:03I I think the common understanding
• 51:07is that probably the turnover
• 51:09overall is something like 6 hours.
• 51:12But it's relatively rapid.
• 51:16But for me as a radiation oncologist,
• 51:19I don't feel like I need to run in
• 51:22and get a sample in the first hour.
• 51:25There are a lot of things that happen
• 51:272448 hours later that take that long
• 51:30to start to manifest and be able to be
• 51:34discernible even if you did seamless
• 51:36offstaining in the affected tissue.
• 51:43It seems to be quite rapid,
• 52:01so I think there's a myth that
• 52:04every vesicle a cell relieves,
• 52:07shoots out and heads straight
• 52:09for the bloodstream and.
• 52:11Circulates and then whatever
• 52:15the kidneys, clear some.
• 52:16There lots of urine studies which look at
• 52:20vascular populations deliver clear some.
• 52:22I suspect that the clearance is dependent
• 52:25on the surface markers like selectins
• 52:28and organ specific distributions,
• 52:32but
• 52:34I wish I knew who first said this,
• 52:36but I I've heard it said that.
• 52:41The blood is sort of our ocean within.
• 52:45So in the context of organisms evolving
• 52:50through mammals and vertebrates to have a
• 52:54circulating system that those circulating
• 52:58systems strikingly reflect the oceans and
• 53:02those salinity conditions. Other things
• 53:07there's a researcher at MIT.
• 53:11Who? Sally Chisholm,
• 53:14who discovered when she was a
• 53:17postdoc Prochlorococcus bacteria,
• 53:19which are responsible for some
• 53:22ridiculous amount of the world's
• 53:24CO2 metabolism in the oceans.
• 53:26Like when you fly over in some areas are kind
• 53:28of green and some areas are kind of blue.
• 53:30It's different. Prochloroccus.
• 53:32They shed vesicles and there's a
• 53:37thought that's part of how they.
• 53:40Communicate and cross regulate.
• 53:42But I think in our compact systems,
• 53:46there's probably a great deal of
• 53:48vesicle release that impacts the
• 53:51local tumor microenvironment and
• 53:53is not necessarily part of what
• 53:56processes out and which which
• 53:59stay and which get processed out.
• 54:01We talked a little bit about it at dinner.
• 54:04We just we have to find better ways of
• 54:06studying the extracellular spaces I think.
• 54:24So I was hoping I could find
• 54:26unique sorts of things,
• 54:27but that's not what I'm finding.
• 54:29I'm finding patterns among classes of cells
• 54:33as opposed to unique this versus that.
• 54:37And I I think you know, if you think
• 54:39about anatomic pathology and how you
• 54:41take a chunk of tissue and you look at it,
• 54:43so PSMA, that's pretty indicative of a
• 54:47prostate cancer cell in a certain state.
• 54:50If you took a chunk of prostate tissue out,
• 54:52if you took a piece of my perotid,
• 54:55you'd also see high levels of PSMA.
• 54:58So PSMA is not really a good
• 55:03prostate cancer necessarily marker.
• 55:07So I I actually think the the
• 55:13best classifying markers will will
• 55:15probably not be exactly the same
• 55:17as those which have been defined
• 55:20so far in intact tissue contexts.
• 55:24All
• 55:29right, I put everybody to sleep.
• 55:32Thank you, everybody.