DBiT-seq for High-Spatial-Resolution Multi-Omics Profiling

Transcript

• 00:00<v Rong>Everyone, thank you,</v>
• 00:01from the Cancer Center leadership for giving me
• 00:03this opportunity to share my latest work.
• 00:07I have been working my entire research career,
• 00:12for almost 15 years, on cancer.
• 00:15But the presentation I'm giving today,
• 00:19it's not much about cancer
• 00:21and not much about the single cell analysis
• 00:23I have been working on for almost 10 years.
• 00:26This is something we haven't published,
• 00:27it just came out in my lab.
• 00:29I'm happy to hear feedback from you guys.
• 00:32So, I think that largely the anomaly
• 00:37in the omics area recently is,
• 00:40people can do single cell omics
• 00:43and multi-omics to understand tumor heterogenetics,
• 00:49but you really don't have the spatial information anymore.
• 00:53So the spatial omics kind of came out, or emerged,
• 00:57to address this challenge.
• 00:59Over the past couple years, I think largely,
• 01:01you'll see many different technologies,
• 01:04but largely, they are all based on just FISH.
• 01:07The more specific and more precise FISH,
• 01:10being a single molecule level FISH.
• 01:13So the shortcomings here, using FISH is,
• 01:18it's difficult, even my lab work and technology,
• 01:22I just cannot do it.
• 01:23This requires very advanced imaging technology,
• 01:27single-molecule fluorescence.
• 01:29You need to image over some time
• 01:32for a very sort of high volume
• 01:34and genome-scale data you want to collect from one sample,
• 01:38you probably need to image over days, repeatedly,
• 01:42to get this sort of large number of genes
• 01:46analyzed on the same sample.
• 01:49And also, that's not a sort of unbiased genome-scale,
• 01:53you really need to know the sequence you want to analyze.
• 01:58And also, so far, I think no one else talks about
• 02:02spatial omics and another terminology
• 02:05people use in this field is this spatial transcriptomics.
• 02:10It's not so obvious,
• 02:12how you can extend to other omics measurements using FISH.
• 02:18So I think the latest breakthrough
• 02:21came out actually this year,
• 02:24the two papers published, I think one
• 02:26just came out last week in Nature Methods,
• 02:29Another paper a couple of months ago in Science,
• 02:34to really use the power of Next Generation Sequencing
• 02:39for spatial omics mapping,
• 02:41or spatial transcriptome mapping.
• 02:43So an approach they took actually is quite similar.
• 02:48So they create sort of a barcoded surface
• 02:51using the packed beads.
• 02:54So whoever working in this space
• 02:56probably know no matter text genomics on
• 03:01the DropSeq technology, you need a DNA barcoder beads.
• 03:06So each bead has this thing, the DNA barcode,
• 03:09to really tell you which messenger is from which cell,
• 03:13or whether or not they are from the same cell.
• 03:16They're basically packing the beads
• 03:17on a monolayer on a glass slide.
• 03:20And they need to decode the beads,
• 03:21they need to know which bead has what sequence.
• 03:26So this decoding process was done
• 03:28by either SOLiD sequencing, or again,
• 03:30very much like FISH, you do repeated cell hybridization
• 03:34and imaging to decode the beads.
• 03:36That is a very tedious process as well.
• 03:39But afterwards, you get
• 03:42sort of a freshly micro-sectioned tissue sample
• 03:46and you place it on top and you lyse the tissue section
• 03:49and hopefully, the messenger is released from the cells
• 03:54in the proximity of the specific bead.
• 03:58It should be captured only by that bead,
• 04:00but I don't think the lateral sort of diffusion
• 04:03can be really avoided.
• 04:06But at least they saw a pretty good preferential capture
• 04:09of the messengers from the adjacent cells.
• 04:14I think this technology published or released in Science,
• 04:17demonstrate you can do 10 micron resolution
• 04:21spatial mapping of mRNA transcriptome by sequencing.
• 04:26And this paper came out last week
• 04:29demonstrating you can actually use even smaller beads,
• 04:31like two micron beads, to further sort of reduce
• 04:36the pixel size and increase the resolution.
• 04:38But two microns really (mumbles),
• 04:41the data analysis becomes even more complicated.
• 04:44And it turns out there have to be multiple beads
• 04:50to get a quality image.
• 04:54So interesting, when we visited their data,
• 04:57we found although they can see sort of anatomic or
• 05:01histological structure of different cells in a tissue,
• 05:05but it is almost impossible to visualize individual genes
• 05:08because the number of genes they can detect per pixel
• 05:10is extremely sparse, about like 100, 200 genes per spot.
• 05:16If you tried to image on individual genes
• 05:19across on pixel's entire tissue,
• 05:22the data totally is sort of not that meaningful at all.
• 05:26So what we can do is fundamentally different,
• 05:30I'm not about to say too much in the technical details,
• 05:34but this is totally different.
• 05:36We don't use beads and we just need
• 05:38a bunch of reagents with this device.
• 05:41And although we have been working
• 05:44on microfluids for years,
• 05:45but I don't like complicate microfluids like you guys.
• 05:50So this device, basically, you just place PDMS
• 05:53on top of your tissue and your clamp it, that's it.
• 05:56That's everything you need to do
• 05:58to deal with the microfluids.
• 06:00Afterwards, you just pipette your reagent to the host.
• 06:04So in the data, the validation data we have shown
• 06:07is we use sort of pan-messenger RNA FISH
• 06:11to visualize the individual tissue pixels
• 06:14we eventually are able to sequence
• 06:18with the spatial resolution.
• 06:21So we found we can get a very nice 10 micron pixel,
• 06:24as shown here if you zoom in.
• 06:26And then also interestingly,
• 06:29we saw sort of in the tissues
• 06:31after we process with our barcoding strategy,
• 06:34our barcoding approach, show some topological features.
• 06:38Even under optical microscope
• 06:41you can see where your individual pixels
• 06:44are located on the tissue.
• 06:45And worth noting, so this is sort of exactly the same tissue
• 06:50we're gonna take for sequencing,
• 06:53rather than the previous methods
• 06:56that always have to compare to an adjacent tissue.
• 07:00They are not able to get any good image
• 07:02from the same tissue at all.
• 07:04Also, the tissue sample we analyzed,
• 07:08they are just a formaldehyde-fixed tissue sample
• 07:12on a glass slide.
• 07:13So if you have a freezer of those samples banked
• 07:16in your freezer, we can look at those samples as well.
• 07:21We don't have to use sort of frozen tissue block
• 07:24and a fresh section to put on our slide.
• 07:31So we did some quantitative analysis
• 07:33of how many cells we can get per pixel,
• 07:37using this DAPI staining.
• 07:39And also, we were also concerned whether or not
• 07:44each pixel is distinct molecular barcode,
• 07:47we can put on or some sort of diffusion between the pixel
• 07:52that might cause cross contamination.
• 07:54We quantified a diffusion distance,
• 07:56we found it using the fluorophores basically.
• 07:59So we found the diffusion distance
• 08:00is actually just one micro meter,
• 08:04which suggests we can potentially
• 08:07further reduce the pixel size and increase the resolution
• 08:12to about like two micron using our technology.
• 08:17So the feature size matched
• 08:21the sort of the microfluid design very well.
• 08:24And the number of cells we can get
• 08:27in the 10 micron pixel size device is about 1.7 cells,
• 08:32we're really getting close
• 08:34to single cell level spatial omics.
• 08:38As I kinda alluded a little bit earlier,
• 08:42so the qualitative data, very important.
• 08:44So we compared our data to the Slide-seq data
• 08:48published earlier this year.
• 08:50So for the number of genes they can detect per pixel,
• 08:52about the size, 10 micron
• 08:54and then the number of genes we detected
• 08:57by using our technology.
• 08:58So really all that (mumbles) increase,
• 09:01in terms of how many genes,
• 09:03how many transcripts we can detect.
• 09:05About two years, three years ago,
• 09:07similar technology, sort of barcoded surface,
• 09:11basically capture of messenger RNAs
• 09:13for spatial transcriptome mapping
• 09:15was published in Science 2016.
• 09:19But that was very low spatial resolution,
• 09:21about 150 micron, but in that data,
• 09:24when you look at how many genes they can detect,
• 09:26that's about the same as what we can do.
• 09:29But the resolution is much, much lower.
• 09:32Or if you calculated sort of an area per pixel,
• 09:35it's 100 times larger than what we have.
• 09:39So I was very excited about this sort of data quality,
• 09:43which really enabled on the following slides,
• 09:46we can really visualize individual genes
• 09:48rather than using extremely sophisticated informatics
• 09:52to identify genes just to visualize
• 09:57the different cells types.
• 09:59We can actually interrogate every single genes
• 10:02across the entire tissue map.
• 10:06So when we first start with this,
• 10:11I'm extremely excited about
• 10:13tumor micro environment feature.
• 10:14But we decide to pick something
• 10:16that's well characterized,
• 10:18people know what cell types are there.
• 10:20So we used mouse embryo
• 10:22in the earlier stage of organogenesis, it's about 10 days.
• 10:27We were able to map out, actually, I wanna talk about
• 10:30a messenger RNA, actually, we can do also
• 10:33about 22 types of protein simultaneously mapped out
• 10:38using the same barcoding strategy,
• 10:41microfluid barcoding strategy.
• 10:42Showing here, is sort of pan-messenger RNA,
• 10:45but done by sequencing.
• 10:46So you can see actually the intensity
• 10:48of the total signal of the messenger
• 10:52does reflect (mumbles) in the tissue on the embryo slides.
• 10:58And here, this average signal of over 22 proteins
• 11:02we're able to look at as a panel.
• 11:06That doesn't really correlate that very well,
• 11:09but I think that makes sense,
• 11:10because you're not looking at it globally on all proteins,
• 11:14but the sub panel, it really depends
• 11:16on what proteins you put in your panel.
• 11:19Then we did a cluster analysis.
• 11:21When we look at single cells, we used tSNE,
• 11:25but here, it does make sense you have to use tSNE
• 11:27because you know exactly where the spatial location
• 11:31of every single pixel is.
• 11:33But the computational algorithm for clustering
• 11:37is identical, so, but after clustering,
• 11:39we just put it back on the tissue histological.
• 11:42The spatial map, we see sort of
• 11:48about eight clusters over here.
• 11:50And they pretty much match the anatomic annotation
• 11:55we got from the eMouseAtlas.
• 11:58And more interestingly, I think in the eMouseAtlas
• 12:00you're now able to kind of resolve
• 12:03a wide stripe the tissue here,
• 12:06but we saw a very distinct stripe of sort of cell type.
• 12:10We're still unclear what those cells are,
• 12:15but probably associated with the mouse
• 12:18sort of major aorta around the area.
• 12:24As I mentioned, we are able to visualize individual genes
• 12:27or individual proteins at a very high quality
• 12:31across the entire tissue section.
• 12:35Showing here a couple of genes and couple of proteins.
• 12:39And overall, I think the protein signal way higher,
• 12:43it's not a big surprise, this is because you measure
• 12:46only like 22 rather than genome scale.
• 12:49But when you compare, you see consistence,
• 12:52you see concordance and also discordance
• 12:54between the gene and proteins
• 12:56people have seen over and over.
• 12:58And very interestingly, when we look at EpCAM,
• 13:03it's a very nice concordance
• 13:06between the protein and messenger RNA
• 13:08in the EpCAM expression right here.
• 13:11And this one, I think, this is a microvascular tissue,
• 13:16microvascular tissue already developed
• 13:18in mouse embryo at this stage all over the whole body,
• 13:22we can see they are expressed everywhere,
• 13:25but we don't see a distinct structure at this resolution,
• 13:28because this resolution is about 50 micron, not 10 micron.
• 13:32I will get down to the high resolution data later.
• 13:35And then we did a sort of validation
• 13:37to compare our data to immunofluorescence staining
• 13:41for several selected genes.
• 13:43And this vasculature, again, you see extensive everywhere.
• 13:47You see EpCAM exactly the same pattern
• 13:50as we saw using sequencing.
• 13:52So just a couple of those locations
• 13:55showing the expression of the EpCAM.
• 14:00And another validation is
• 14:01we've done the sequencing data
• 14:03and the paper published earlier this year
• 14:05by Jason Du, from the University of Washington,
• 14:07they used single cell sequencing to map out
• 14:10several mouse embryos over different stages.
• 14:13And then you can basically do a tissue,
• 14:17a sort of sample tSNE, or sample UMap,
• 14:20this is not a single cell UMAP, but a sample UMap.
• 14:23So we found a four sample sequence
• 14:25actually mapped very well to this
• 14:28sort of differential or developmental trajectory.
• 14:32So in here, from their data, this is sort of the E9.5
• 14:38and that this is E10.5 and we are right in the middle.
• 14:42Those are kind of a little bit later stages
• 14:46of the developmental mouse embryos.
• 14:52And then we used a little bit higher resolution
• 14:55to look at the embryonic brain.
• 14:58This is about the entire brain
• 15:01and a little bit other tissues in the head and the neck.
• 15:04And also, this one, we didn't know what that is,
• 15:09but after data analysis, we found that actually
• 15:11it's a piece of the heart.
• 15:13And what we see from the protein
• 15:16and from the messenger RNA is,
• 15:18again, the messenger RNA atlas
• 15:20does reflect in the tissue histology very well.
• 15:23And the protein now, is much higher resolution
• 15:25of 25 micron, you do see some sort of correlation
• 15:28between tissue histology and protein expression atlas,
• 15:33but not as so distinct compared to the messenger RNA.
• 15:39So we were able to visualize
• 15:41individual proteins essentially,
• 15:42here are four of them, I think are very interesting.
• 15:46Again, EPCAM, this is a very high resolution,
• 15:49you can see very tight clusters of EpCAM expression
• 15:54in specific tissue regions right here and here
• 15:56and there's two or three or four.
• 15:58And the microvasculature, we can see the microvasculature
• 16:02by sequencing very well.
• 16:04And when you go to look on the tissue histology,
• 16:07or maybe I'm not pathology by training,
• 16:09I just cannot identify where the microvasculature
• 16:13are located based on the tissue histology.
• 16:16And the two other proteins, very interesting as well.
• 16:19This MAdCAM, we found it is a highly enriched
• 16:22in part of the forebrain, but not entire forebrain.
• 16:26And we see in CD63 it's widely implicated
• 16:29in the early stage mouse development.
• 16:32It's kinda anti-correlated with MAdCAM in other areas,
• 16:37so we kinda put them together,
• 16:38you can see their relative correlation each other.
• 16:44So, again, this technology where we want to validate
• 16:47to make sure what we saw using sequencing
• 16:51does match immunofluorescence staining.
• 16:54So this is from sequencing, this is from sequencing,
• 16:57this is about microvasculature, this is EpCAM,
• 17:01this immuno staining, you'll se almost a perfect match.
• 17:03I was very surprised, this is really a perfect match
• 17:09of distinct clusters right here, a little bit right here
• 17:11from immuno staining and we can pick up.
• 17:14It's only a few, so one single pixel layer thickness
• 17:19we can pick up very well.
• 17:21And so now here, you can see
• 17:22those microvascular network using immuno staining,
• 17:27which was also observed in our sequencing map atlas.
• 17:33So I got an interested in,
• 17:34this particular protein called MAdCAM and asked my poster
• 17:38to do some differential gene expression sort of.
• 17:41But the MAdCAM transcripts, it's difficult to see
• 17:45the sort of spatially distinct expression,
• 17:48but in the protein data, you can it see very well.
• 17:50Then we decided to use our sort of
• 17:53high quality spatial protein data
• 17:55to guide the differential gene expression
• 17:57across the entire transcriptome
• 17:59for different tissue reagents.
• 18:01So in this case, we're looking at MAdCAM-positive
• 18:03and a MAdCAM-negative and mapped out the top ranked genes
• 18:07for MAdCAM-positive region.
• 18:08This is still ongoing, since I'm still in the stages
• 18:12of learning developmental pathology,
• 18:14but what we can see some interesting features.
• 18:17But in the negative region, clearly,
• 18:19so this is the heart, turns out, this is kind of heart,
• 18:22kind of microtube associated proteins.
• 18:25And this is interesting thing,
• 18:27we don't really see this protein showed up extensively
• 18:30in the brain, but some how look like in this local area.
• 18:35And I have no idea what that is,
• 18:37but later we figure out that's actually the eye, here.
• 18:41And then we decided to do even higher resolution,
• 18:44which is a 10 micron resolution mapping
• 18:46of a particular region of the brain.
• 18:49And again, we had no idea where to map now,
• 18:53we just randomly placed our device on top
• 18:56and then mapped out this region.
• 18:58And the red color actually real data,
• 19:00this basically just pan-messenger RNA data.
• 19:03You can see the signal relatively uniformed
• 19:05and not perfect, but that's totally okay,
• 19:07just like when we do single cellular sequencing,
• 19:09we always do normalizations.
• 19:10Then that gives you, as long as your sequencing quality,
• 19:13sequencing data quality, number of genes you can read out
• 19:17(mumbles) genes, you can always do normalization
• 19:20and compare across different pixels.
• 19:23And as I told you, actually, we can see in the same tissue
• 19:27sort of after the barcoding and before the sequencing,
• 19:31we can even just under optical microscope,
• 19:34we can see individual pixels over here.
• 19:36And then when my poster showed me this image,
• 19:40it's okay, you got a key wide fiber over there
• 19:42very likely, because we saw this
• 19:45when we used microfluids before.
• 19:48And I thought that's unfortunate
• 19:50but anyhow, let's go ahead
• 19:51and process the sequencing data.
• 19:54But turns out that's not a key wide fiber
• 19:56that's really a very thin layer,
• 19:58actually it's a single cell layer of melanocytes
• 20:02lining a round the eye field.
• 20:05At this stage, the eye field actually,
• 20:07it's a very, very early stage only,
• 20:09called the eye vesicle an even no optical caps,
• 20:13it's the optical vesicle.
• 20:15So we can see, very distinctly, a group of genes
• 20:19strongly enriched inside the eye
• 20:22and also lining around the eye, optical vesicle.
• 20:27And then when we put them together,
• 20:31a little bit more structures you can see.
• 20:33For example in Pax6 enriched pretty much
• 20:36in an entire eye field
• 20:38but also in this region is optical nerve fiber.
• 20:43But here this protein, only expressed in the eye,
• 20:47but also other tissue type but not so much optical fiber.
• 20:51You can see this very well at a very high resolution,
• 20:54it's really about a single cell resolution.
• 20:57So, okay, when you look at it carefully,
• 20:59you see some yellow spots over here.
• 21:01That means the Pax6 and the Pmel are actually co-expressed
• 21:05in those kinda melanoblast cells but this one is not.
• 21:09The Six6 is not expressed,
• 21:11only within the eye, optical vesicle.
• 21:15If you further zoom in, you can see
• 21:18the sort of gene expression within the vesicle
• 21:21and also individual pixels, every little square here.
• 21:24So we can overlay the tissue image
• 21:26and the transcriptome data.
• 21:29So we noticed one gene which
• 21:33is strongly enriched right here,
• 21:36very strongly differential expression spatially.
• 21:39We're all curious what this gene does.
• 21:42We did sort of,
• 21:46this time they're still global, gene differential analysis.
• 21:49We saw only top ranked genes and these two showed up.
• 21:55But we found their functioning on a top ranked pathways,
• 22:01to some degree, okay, except those ones,
• 22:04to some degree, are mutually exclusive.
• 22:06And then later we realized
• 22:10but that has never been observed before,
• 22:12I don't have sort of last year's data to support.
• 22:15But it seems like those cells
• 22:21sort of characterized by this particular gene,
• 22:23later on are gonna determine the development of the lens.
• 22:27And those cells, even at this stage,
• 22:30you don't see any morphological difference,
• 22:32they already predetermined to develop
• 22:35the retina and the photo receptor cells.
• 22:39And then we were able to basically
• 22:41just put out those pictures obviously
• 22:43and compare it to those to perform
• 22:45a differential gene expression analysis.
• 22:47And another surprise, now this gene just showed up
• 22:51extremely differentially expressed.
• 22:54But we see many other genes that were very interesting.
• 22:58We still try to look into the details.
• 23:01So they are kinda enriched on the left side.
• 23:04Eventually, very likely,
• 23:07they will contribute to the photo receptor cell development.
• 23:13Okay, so even though we're able
• 23:15to visualize individual genes,
• 23:17we don't have to use the gene cell enrichment
• 23:19to identify different tissue types,
• 23:21but we had a challenge in particular
• 23:24in this kind of eye field region,
• 23:27due to our lack of knowledge in mouse embryonic development.
• 23:31But it'll be great if some computational pipeline
• 23:35can automatically identify different features,
• 23:37tissue features.
• 23:38That's what we demonstrate as well.
• 23:41So using this automatic automated
• 23:44feature identification pipeline,
• 23:47we were able to identify actually 20 different features
• 23:49in this very small region of the brain
• 23:52around the eye field.
• 23:54I just will show you some of those,
• 23:58you can see not just the eye, actually you can see
• 24:00very already development of the ear
• 24:03based on the sort of gene expression,
• 24:07but histologically, you cannot see any difference at all.
• 24:12But we also look at entire mouse embryo the E10.
• 24:18We're able to identify about 20 different features.
• 24:21But we're asking, so if at later stage
• 24:25many other organs begin to develop,
• 24:26whether or not this pipeline can identify many more
• 24:29tissue features or tissue subtypes.
• 24:33That turns out that that's right.
• 24:36And using E12, we're now able to cover entire embryo
• 24:39actually just the lower part of the body,
• 24:43we identify about 40 different features already.
• 24:47So this is a very high resolution as well.
• 24:51Okay, I'm gonna just summarize
• 24:54back to my sort of, ,the main interest in cancer.
• 24:59So I believe this enabling platform,
• 25:02we demonstrate can do protein and the transcripts.
• 25:05But actually, in my lab, another post I'm working on,
• 25:08so spatial, high spatial resolution epigenomics.
• 25:11I believe we can do high res,
• 25:13high spatial resolution ATAC,
• 25:15high spatial resolution CHIP-seq.
• 25:17And the application is extremely broad
• 25:19and the cancer is put right in the middle
• 25:21because that's really my main focus.
• 25:25I will like to thank people in my lab who work on this
• 25:28and thank you for your attention.