0:43
Thank you, Matt, and thanks everyone for your time.
0:45
So I'm Yuan, today I'll be sharing with you a groundbreaking technology called Stereo-seq.
0:57
So today I'll be introducing you to this technology called Stereo-seq and how it can accelerate multi omics discovery studies.
1:08
See, I'm going to skip the introduction because I know everyone here knows spatial information matters and spatial transcriptomics, maps and pairs the transcriptional data ways the positional data to map gene expression -back to its original locations within given tissue cells.
1:31
And spatial transcriptomics as well as spatial proteomics is a key factor that we can use to create Atlas project, which is an effort to map and characterise individual, single individual cell types within tissues to understand the disease and the normal biologies.
1:58
And if we focus on spatial transcriptomics for a minute and this technology has been primarily grouped into two categories, one is imaging based, the other is sequencing based, mostly NGS sequencing based and for the imaging based it uses in vivo detection probes that you target the gene of your interest.
2:24
So it is a very high gene on specificity and it can reach a sub cellular resolution.
2:31
However, because you need to detect the probes for the detection regions, therefore it requires prior knowledge and that takes time and also a lot of effort to optimise your panels.
2:44
Therefore it is you know very low throughput and you don't get, the whole picture of the biological questions you want to ask.
2:58
On the other hand, a sequencing-based technology, it can capture whole transcriptomics and at high throughput.
3:07
However before Stereo-seq those technology has relatively low resolution and the sensitivities. If we look at under the hood at how biochemistry works for the sequencing based and spatial tools from the market, they are again two major strategies.
3:27
One is using Poly T probes which can help you to capture total MRI.
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However, in this strategy you are still missing for example long coding RNA or other RNA's that don't have poly A tails.
3:42
And so this is still missing, some of the critical information you may want.
3:49
And another strategy mainstream product is that they are using this probe based method that uncovers the so-called entire transcriptomics regions.
4:02
And this also has its cons because it again requires prior knowledge and you need to design probes for that.
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So it's still not suitable for a very good discovery tools.
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So we set up to develop better spatial discovery tools with three main factors in mind.
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First, we want to still keep the throughput high.
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We want to capture the whole transcriptomics, and we can pool libraries together to get a multiple inputs simultaneously.
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And we wanted this technology to be species agnostic.
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So we don't need the prior knowledge to design anything.
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Everybody can immediately use it.
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And then we want to keep the resolution as single cell resolution.
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If you look at the chart here that is a size chart of average cell sizes, you can see most of the cells ranging from less than one Micron to slightly bigger than 10 Microns.
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So we really want to have some technologies that have those important features.
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Then like around 5 or 6 years ago, the STOmics that developed this technology, we call it the Stereo-seq and spatially enhanced the resolution omics sequencing and this technology is being so Complete Genomics is a company based in California and they are the exclusive distributor of STOmics in American regions.
5:46
And so our technology is utilising the sequencing chip, which is a silicon-based chip as you can see here.
5:59
I'm going to go into the sizes later.
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And this is a patterned array with millions of billions capturing spots patterned on a single chip.
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Each capturing spot is in 220 nanometres in diameter and the centre to centre distance the distance between two spots is only 500 manometers.
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If we zoom in a section that mount on our chip, as you can see here, we also take nuclei imaging into our workflow and then you can see within this nuclei the dark spots here.
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Within that nuclei individual dark spots is a capturing spot and you can see within a single nuclei there are hundreds of capturing probes and on each capturing spots it's coded with hundreds to thousands of individual capturing probes.
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And on individual probes we have the CID barcode which will provide X&Y information that helps you to map the online transcripts to its original physical location.
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And all the probes from the same spot share the same CID barcode.
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However, every single probe on those spot, they have unique molecular identifier UMI which help you to count the RNA transcripts numbers.
7:34
And then we have two strategies to capture RNA, one is just like everybody else.
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Some of them will be launching this strategy which is Poly A based so that can help us to capture total mRNA unbiasedly and species agnostic.
8:05
So this can help us to capture total RNA.
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So in addition to coding RNA, we can also capture noncoding RNA like microbial RNA.
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Any RNA information that is important to your research studies.
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And then after we capture the, you know the only from our chip and then we do in situ reverse transcription and synthesise DNA from this chip.
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And then we have in house developed algorithm that will help us to map the transcripts back to its original location and create a map.
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So sometimes 3D maps like this.
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So like I mentioned that our technology supports nuclei staining, but we also support the several other different staining methods.
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And then the nuclei staining will help us to do the cellbin analysis because you can infiltrate the cell boundary based on nuclei standing.
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Alternatively, you can also choose H&E staining on the same section, which will provide pathology or histology information that can help you to get a more accurate region specific transcriptional data.
9:36
Antibody staining can also be incorporated into this process on the same section.
9:41
So that will help us to couple the protein information along with the RNA profiling. Here is an example of CD45R and CD4 proteins standings on the mouse testis.
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And this is a co-localisation of the proteins with microgenes.
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So what I just mentioned that we can take different staining method, for example, if you are using nuclei staining or membrane protein staining our pipeline can merge those images together and provide either cellbin analysis or just physical squarebin analysis result.
10:30
And then we can offer multiomics information in addition just to transcriptomics information.
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This is an in house developed visualisation tool and it is also compatible with the third party cell segmentation method.
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There are 17 segmentation methods.
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And for each single cell, each cell type or tissue type, you need to optimise and find the best method for yourself.
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And our platform is compatible with effort like that.
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And then another great advantage which no other platform can offer is that as I mentioned we developed this technology on sequencing chips.
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For now our largest sequencing chip is 13 centimetres by 13 centimetres and nobody is using that right now.
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But we do have commercially available sizes from 0.5 to 0.5 all the way to two centimetre by three centimetre, which can fit a lot of organ or tissue needs.
11:43
And we also can customise chips like this is a six centimetres by six centimetres.
11:53
So what I just introduce you this high resolution and large field of view and which can also provide multiomics information.
12:03
So I'm going to go into a little bit, you know, going through a few examples showing you the applications in different fields, for example, neuro-oncology, ageing and development.
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So my first example is the world’s first 3D single cell Atlas of Macaque cortex.
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So this research group, what they did is that they took 161 section from three Macaque brains and they are using our large chip design centimetre by 5 centimetre this big.
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So they use hundreds of those large chips to map Macaque cortex and based on the 261 cell types identified, they actually created the most comprehensive Macaque cortical taxonomy.
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And then they because again, our technology is a great discovery tool.
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So they also did the cross-species analysis and then they identified, you know, primate specific cell types enriched in this so-called layer 4 and research works have been ongoing for human brain as well globally.
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So this is from, you know, researchers from China, but they're also like European groups and even US groups using our large chip design for studies like this.
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So this is a science cover paper published in 2022, and it studied the axolotl telencephalon studies at the normal development stage versus a regeneration study course examination.
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So what they did is that they damaged half of the brain axolotl and then they look at the time course of 60 days for this study and compare with the control half brain.
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They identified very interesting findings that a subtype of glial cells they actually were activated by the injury and substitute for the lost neurons.
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So this cannot be done if you don't have spatial information and they can actually identify where those subtype of cells originated.
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And they also find that this regeneration process partially recapitulates the normal development process and the similar studies are ongoing for the lever regeneration by some of our US customers.
14:53
This is a very interesting paper to understand the tumour mechanism and potentially figure out a new treatment for liver cancers.
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So what this paper talks about it or found is that discover is that there is a 500 micron area they called the tumour invasive zone.
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They detected a strong immunosuppression as well as metabolic reprogramming.
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And there's also a subgroup of severe damaged hepatocytes which have overexpression of sero-ameyloid A1 and A2 collectively to as SAs and in the clinical association studies, they have 5 cohort studies with more than 400 patients with primary and secondary liver cancers.
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And then they identified that patients with over expression of SAs in these hepatocytes within this tumour invasive zone had the worst prognosis.
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So this cannot be identified.
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Even with single cell you wouldn't get the spatial information.
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So only spatial can tell you on this type of information.
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And then they went on to this in vivo study and they knocked down SAs in mice and in their disease model.
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And then they found that mice with downregulated SAs had actually decreased the macrophage accumulation and delayed the tumour growth as shown here.
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So we also have users trying our technology in clinical trials.
16:42
So the answer they're asking is what is the mode of action and the resistance mechanisms?
16:49
And they take, you know, samples from patients, you know, before treatment and after treatment and follow up.
16:56
And there are some interesting findings and I'm going to skip that because there's still ongoing studies and it is confidential.
17:05
So what I just mentioned is the works that have been done on our Poly T design chips.
17:13
So now I'm going to switch gears to this Poly or random design chips, which is we call it Omni.
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Omni is a Latin word for all.
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And we have this Omni case that is compatible with FPPE examples.
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So first the capture properties as you can see here, we grab the data from public database and then this is a three prime Poly A captured, you know, method and you have very nice three prime expression profiling.
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However, this is our Omni products.
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You can evenly capture the entire gene but is using random design.
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And then another comparison is that when you look at so our technology is in purple and the target approach you can capture the protein coding region very nicely.
18:07
However, you are missing a lot of non-coding RNA information like noncoding small RNA or micro RNA or small non coding only.
18:17
We can also examine host microbiome interactions in spatial context. Shown here is a mouse lung infected with TB and it's again a time course study and you can examine the Mtb spatial localization during this time course.
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And then you can also look at its relationship with, for example, PCR related gene and identify this negative correlation, which totally makes sense.
18:48
This is a paper from our collaborator and from MD Anderson and Sammy and Samuel and they are sitting in the audience.
18:59
So what they are doing is that they take different sections from the same patient sample.
19:04
They propose to combine histology information, metabolic information, protein information as well as information you can get from Stereo-seq, which is the whole transcriptomics along with microbe information.
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And then they are using this 3D module proposal trying to understand the tumour microenvironment in ovarian cancer.
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So this is a partial list of validated the sample types for our Omni kits.
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And the list is growing, every day.
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And again, it's species agnostic.
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So we can do human, we can do mouse, we can do non-human.
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For example, some plants, pigs, dogs.
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And we are offering this technology as an open platform.
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We invite innovations.
20:06
For example, this group from Cornell, they did in situ Polyadenylation prior to RNA capture using our Poly T probe design chip.
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So therefore all the RNAs are already labelled with Poly A and then they can capture the entire RNA just using our Poly T design chips.
20:29
And they actually profile the RNA transcriptomics information as well as microbiome because it's in testing data sample type and they can get very interesting microbiome profiling information on different genesis.
20:47
So this is a workflow of our Stereo-seq.
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We offer kits that you can perform, it's instrument free.
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You can directly take our chip or combine with our own reagents, doing regular molecular biologies and stuff in your lab.
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And then after NGS libraries being made, you can do sequencing on complete genomics instrument.
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And then we have a publically available primary analysis pipeline as well as the StereoMap.
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I just showed you the visualisation tool and this is the complete solution or offering from Complete Genomics, the transcriptomics kit I just mentioned Poly T design, Omni random design which is compatible with FFPE.
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And we have large chip design.
21:40
You can do H&E staining, you can do immunoflorescence.
21:44
What I didn't mention is that you can also so combine CITE-seq which provides high Plex of protein information for antibody staining.
21:52
Because of the limitation of fluorescent signal you can only do like 2 to 5 proteins but for CITE-seq you can do protein panels.
22:00
We tested the total CITE-seq from biology and which provides more than 100 protein information, in addition to transcriptome mix information and CG have different sequencing platform and which is very cost effective.
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For example, the highest throughput T7 it can reach the price of $150.00 per genome.
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WGS whole genome transcription.
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We also this go optical which is a microscope fast speed which can scan field of view up to 7 centimetres by 10 centimetres within 15 minutes.
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If you want to ask it for example, 1 centimetre by 1 centimetre, it only takes you know several minutes.
22:48
So speed is very critical for spatial, you don’t wont to wait for a long time and the RNA isdegraded.
23:04
Thank you for your attention.