But by next week or the week after there's going to be some great new technology that we're going to want to use for single cell sequencing, but I'll give you a little bit of the historical context and try to build that bridge between those of you who took the bulk RNA sequencing course. And then some of the lessons there that are going to transfer to single cell and some of those lessons that actually may not transfer to single cell. So the point of the session understand this conceptual shift moving from bulk to single cell become acquainted with all the things that can go wrong and some of the things you have to think about when you're designing a single cell experiment in the lab and how all those design decisions are going to impact what you do with the data when it comes out of the other end of the sequencer. I'm a cancer geomics researcher. So I'm going to show 2 examples of how you can apply single cell sequencing specifically to cancer.  And a multiple myeloma most project, but sort of use those not so much for the cancer biology, but really about some of the techniques that you can use to derive cancer genome variation from single cell data. And right at the end, I have a little like clinical vignette where we use single cell sequencing to try to understand drug response patient treated with a targeted therapy and you can really see clonal selection in action using single cell sequencing. Okay, so the fundamental unit here is single cell, and it's not really a bag of MMS, but it's kind of like that it's a bit of a jumble of different cell types and sizes and colors and labels. Tissues are made of these and where tissues are quite different is they're much more organized in a bag of candy. But the concept is really the same. It's extraordinarily heterogeneous. Some cells, some candies are similar, some are not. And we now actually have the tools to start to dissect how every cell is different. And most absolutely recently, how cells interact spatially within a tissue. I'll try to get to that point towards the end of the talk. Single cell analysis is not new. So I've talked a lot about new technology, but people have been doing single cell genomics in general, basically since the invention of genetics. So I've just shown here, in the upper left, that's actually a caretaker on one of my cells. So the old fashioned way to do single cell work you drop your cell on a glass slide in the right cell cycle. You literally use scissors and glue and you line up all your chromosomes side by side. Now we can do that for hundreds of thousands of cells digitally using next generation sequencing. But the idea is to say the order of the chromosome bands, how many chromosomes do you have? We used to do it one by one, and I can do it on mass. And same thing with RNA seed. There's actually already still clinical tests that are a single probe. In this case, this is a single Epstein bar virus probe.  Now you can put hundreds of programs or look at the entire transcriptome at the single cell level and look at them spatially as well. But the concepts as discovered in singles and original care types still have totally hold like the rules of genetics haven't fundamentally changed just we measure them in a different way, we can measure way more cells than we've been able to in the past. The other revolution is at least in genomics is we're not reliant on averages. So, in your bulk RNA sequencing course you took all the cells you ground them up and you got the top bar graph which is the average of all RNA sequences in a cell, or in a tissue rather. In the bottom now you can have individual cell level measurements of RNA seek, but everything you learn in analyzing data from bulk RNA seek is actually relevant to the single cell as well. You can take a lot of what you've done differential gene expression analysis, spice isoforms all those lessons that you've learned in bulk actually will translate now to the single cells as well. I put this title in quotes because this is the understatement of 2019 in this paper they said there's a wide variety of single cell methods this paper is already four years old, but it was basically a really excellent review of all the many different methods that you can use to measure many different compartments within the cell so you can look at the genome sequence you can look at cytoplasmic RNA you can look at intercellular proteins. And then once you've done these measurements in the cell. In general you can sort of bend these into three broad analytic groups so lineage, how does the cell related to progenitor cells and daughter cells state so for a single cell, what is it. So instead of an existential question. How do you define a cell is it tags is it the expression programs is a protein, probably get into that question multiple times throughout the next couple of days, and trajectory so you're when we do an experiment we capture a single cell at single point in time. What could happen to it cells like it, potentially in light of treatment, and what has happened to cells that gave rise to the point in time that you looked at so some of the most powerful experiments are really those longitudinal methods that you can look at, for example, a most model at multiple different time points and see how that cellular makeup but also sell states actually change over time and there's a lot of bioinformatics methods to sort of operate and see these three broad areas lineage state and trajectory. So I do want to highlight some of the considerations and experimental design parameters that especially as an analyst you really need to know, like what has happened in the lab and ideally, this is a partnership between the wet lab scientist and the dry lab scientists to co developed what is actually going to happen to the tissue or the cells. Before you analyze it it's very painful sometimes kind of inherited data set, and not to have participated in the wet lab design and vice versa it's very tricky for the wet labs scientists to design those experiments without knowing what's going to happen in the dry lab downstream these two aspects of essentially single cell genomics to happen simultaneously. I put this also in quotes because it's also a bit of an understatement it's actually on the y axis is time and then or and the y axis is number of cells the x axis is time, and it really is an exponential scaling of single cell so it used to be a single cell that happened off to then it was in a plate, then there were micro fluid devices in a robotics to let you profile hundreds of plates simultaneously. Almost not all, but a huge fraction of the data presented generate nowadays is using a 10 x genomics platforms is a droplet based method. And then there's other methods that are flow sorting cells and individual wells. So the point of the slides is to show the huge growth and actually continues to ramp upward. But also the many different ways to isolate and do molecular biology on single cells and this paper is a really great job, marching through all these different technologies that ultimately result in very similar data which is gene counts from a single cell. That being said, there are sort of three main steps that are going to influence the type in the nature of the of your data of your data as you receive it. The first is tissue digestion and cell purification. So even just the very act of collecting a cell will change its state. And if you freeze it, buyably freeze it or snap freeze it. That is going to fundamentally change the transcriptional representation in all of those cells. And that's okay, as long as you're collecting and sampling all your tissues in using a common protocol. If you don't want to mix single nuclear data and single cell data, at least not know that that was consciously done as an experimental design consideration. You want to have a single protocol that's really trying to treat all your samples, especially if you're trying to compare them over time. Like your barcoding this is sort of the secret to high throughput sequencing of single cells. If you have a plate, a three or four well plate, a single cell and every well, the way to be able to sequence these is to give each individual well a different barcode. There are lots of different barcoding strategies, you have to know what barcodes are used for your specific experiment. So you can sort of unscramble that I actually done the sequencing. And even a sequencer choice. Certainly a luminous sequencing is the most ubiquitous platform, but there are other longer platforms for example that will give you full length transcripts and the data actually looks quite different than some of these short read technologies and I'll have a few example of those later on. So here are some of the design decisions at these three steps to consider. Do you have bulk profiling data is the plan to do single cell and to combine that with bulk profiling data. Do you are you interested in all cells in a tissue or do you actually only care about a subset you only care about T cells so go through a specific manipulation to isolate T cells flow cytometry be beat selection. So after bulk coding, are you going to do full length transcripts so you can do just the end reads like is done with a 10 x genomics platform. Each of these is has to be a conscious decision as you set up your experiment. And then there's a huge number of bioinformatics platform so I had that lineage those lineage state and trajectory. You have to think carefully and understand those specific algorithms and you'll get some some experience with those this week. So think about why you're choosing a specific algorithm or another, or if you're not sure have a benchmarking plan to have two algorithms or four algorithms, potentially combined or compared against one another that has to be a conscious part of your analytic design strategy. And this paper here really goes through all these three steps in great detail. There's a little more detail in those three specific steps there's lots of ways to isolate single cells so this is just to figure with a few of these. I mentioned plate based methods. There's literally a pipette of pulling individual cells out of the media. There's close cytometry you can sort either a cell population of interest, or just digest the tissue and sort all the cells into a plate. So if you capture micro dissection, you can literally take a laser cut out a single cell, pick it up and sequence it micro fluidics this is essentially encapsulating your cells in an oil droplet. Each cell gets an oil droplet nice sort of march through your micro fluidic device. Open single cell chambers essentially a field with little cell size pits. So the cell comes along it drops in your molecular biology inside there. This is a little esoteric essentially it's magnetic coast with antibodies you can flow a blood sample through and the cells will stick to your post so you can really work with biomedical engineers to get extremely creative. What you want at the end of the day are essentially free floating or isolated cells that aren't stuck to any other so you can actually isolate that cell as a single unit. A protocol we've been using quite a bit in our cancer genomics lab a single nuclei sequencing. So just because you've frozen the cell doesn't mean you can't use it one downside, especially with snap freezing is you actually will lice the cytoplasm. But it turns out actually nuclei are quite hardy so our solution to working with frozen tissues is to do a nuclei preparation so you take a frozen tissue. You can isolate the nuclei but that's now a conscious design decision you've now lost all cytoplasmic transcripts you're going to see that in your data. You also are enriched for unspliced RNA so you're going to have a lot of the introns in that data as well. So that decision in the lab has now really fundamentally changed your data downstream, but it does open up a whole world of biobanked or fresh frozen samples that are quite ubiquitous and relatively easy to get your hands on. So by the nuclei, you kind of treat them like a cell like they can go into the 10x protocol they can be encapsulated in oil droplet. And it has some pros and cons which I've just listed there on the screen, but it does fundamentally change the sequencing reads that you're going to get out at the end of the experiment. And then when working with fresh, never frozen versus fresh frozen tissues is different cells are more susceptible to lysis when you freeze them. So this is an experiment where they took the same tissue, they ice they profiled half of it without freezing it. The other half they isolated the nuclei and then single cell sequencing. And what this shows down here is the cells have now all been clustered into different transcriptional groups and it clustered all the stated together. And you can even see by eye this great big purple cluster, hardly seen at all in the nuclei. And then some of these more minor clusters here are not necessarily seen the cells and you can actually see the difference in frequency so there really is a cell specific freezing artifact so by going down the road of fresh frozen versus nuclei. You can sort of appreciate that your cell fractions are going to be different because different cells are going to essentially come out of freeze or not freeze, not survive the freeze process. If these two methods were totally equal. These frequency bars should be perfectly 5050 and you can tell by either totally not. And that's really a fundamental difference between looking at intact cells, including the cytoplasm versus just looking at the eye. So it's not that one is better than the other. It's just what kind of samples do you have and what's your scientific question. And you're going to hear and probably work with at least one of these different types of data to the most commonly employed RNA sequencing strategies are 10x genomics end reads so this is actually priming off of the three prime and the five prime sequencing the ends of your transcripts. So originally described as drop seat but now it's martialized is this 10x genomics chromium device smart seat two is different you actually put cells in individual wells, and you sequence the entire transcript so it has the benefit you get full length, but the cost is much higher and the scale is much lower. So in this case you get to hundreds to hundreds of thousands of cells. This is sort of limited to what you can manipulate to enter a plate in the lab, but it has this benefit if you really are interested in full length transcripts you want to mutation calling across the whole transcript. If splice site and for spicing information is very important. The seed to method is really where you want to go. If you want to just count transcripts. If you want to look at T and B cell rearrangements or T and B cell repertoire 10x genomics 10 the 10x approach is going to give you the scale number of cells that you're going to need to sort of dissect the cellular populations, especially if you're looking at a large intact tissues. And here's kind of example and I just talked about so smart seat to gives you really nice coverage across an exon the inch on spliced out get another inch on exon spliced out and so on chromium is totally different you just get essentially very one side to data you prime here and all your reads sort of pile up at one or the other end of the transcript. And then differences in scale as well get many many more cells for the same price using the 10x approach. Another method especially for the very very long transcript so the downside with short read sequencing is you're really only going to read 100 to 150 basis transcripts are much much longer they could be roughly 1000 or multiple multiple thousands. The downside with short read sequencing you take your DNA, you fragment into these small pieces and you try to assemble them. You're going to get a lot of overlapping sequences, long read sequencing you just read across the whole fragment in one big shot so really let's you look at some of these very subtle, especially exon usage information and transcript structure information, because you just have a single read that covers the whole thing. There is a cost difference you get many many more short reads using alumina, you get fewer reads using long fewer long reads, but they're long so you can cover the whole transcript so you sort of have these two options. Can I cover this in the both sequencing but I forget why, like why not prime on the internet DNA and then just kind of get the. It's a problem with the sequencer itself so every base you read suffers a slight decline in quality so you start out with perfect quality then you're 99 then you're 98. And by the time you hit 150 it's that quality is actually really falling off the cliff so it's a problem with alumina sequencing and other short read methods specifically. Mana poor will just go until you get the end essentially. And this is sort of an example of what that type of data would look like side by side so if you have this gene, you have all these different spice isoforms short read sequencing you basically have to look for missing data and little bridge that bridge between exons with long you get full fully assembled transcripts. And so that's sort of the introduction to sequencing types and generating data. I then wanted to get into trying to take some of your past knowledge and apply it to single cell and this is especially in the early days this is really quite surprising where I especially would work with a lot of immunologists where a lot of the markers on individual are extremely well worked out. And when we tried to look for those transcripts in our single cell data, they were the expression level is relatively spotty or some of those markers we didn't even see at all on the gene expression level. So this paper described so they did a large mostly T and B cell experiment, and then you sort of score out each of the clusters that are up here in the upper left for individual markers. And this by I, you know some markers are looking pretty good like this cluster here really express CD3, but some of these you can hardly see at all you get this very spotty expression level. And this is really a downside with single cell sequencing since you're reliant on that single cell. If you're a little, if you're a rare if you have a gene that's expressed at a very low level you have these very rare fragments and they don't have to be sampled or amplified or reaction, you're not going to see them so if you have genes are expressed right at the limit of detection of single cell. You're either going to have what I sort of showed up here, you'll sort of have this stochastic detection of those transcripts, or no detection of those transcripts at all. And really where we've had to go for single cell is not necessarily rely on these individual markers but instead move to gene sets that would tag these individual cells as surrogates for those individual are usually originally protein based markers so there's really a difference between the RNA expression and the protein expression. To get around this there's this this protocol invented called site seek. Cellular indexing of transcriptomes and epitopes by sequencing the way this works they sort of co opted the droplet based method, you actually have this antibody with this synthetic bar molecular barcode attached to it so every single cell. If you're accessing your protein of interest will bind this antibody that cell coated with however many antibodies you've tagged all gets encapsulated into the oil droplet. You then burst the cell you sequence the RNA like you normally would, but also all those antibody bound tags also come along for the ride so you know what tags you put into your experiment. You can count those tags and that enables you to count antibody binding events, as well as all the RNA sequencing so this is a very powerful method to start to integrate RNA sequencing with a protein measurement, literally from the same cell and you can see in this experiment. This is the same figure I just showed earlier but now I've added the site seek the protein data, and you can see for some of these are previously quite spotty really wore out using the using the protein based method. So, in this case you can actually integrate these two different data types, literally from the exact same cell and use that for cell identification. I have a question. It's your ability to sort of think of your gene expression levels is a distribution you have some very high, highly expressed genes, most of them are in the middle and some are very expressive very low level. So you're sequencing you're basically grabbing or sampling from that distribution. So you see, basically all the highly expressed genes you see some of the genes expressed in the middle. But if you're grabbing from this distribution, you're actually very unlikely to grab those genes that are expressed at a very very low level. And depending on how many sequencing reads you generate. That's how many sample samples of that population that you have so if you only do. If you have 50,000 reads for a single cell has 50,000 chances to try to grab a rare transcript and sometimes it's actually not enough. So limited detection is a function of the ability to turn the original RNA into CDNA that gets sequenced, but also the number of reads that you happen to generate for a single cell. So there's a perfect limit detection. I couldn't say it's like one in a million or one in 100,000, because it's quite confounded by your cell type, that actual distribution of transcripts in a single cell which is quite different depending on your cell type. But in general the rare transcripts are much harder to find, especially using techniques. It would help to a point. The 10 X sequencing works as it relies on poly A priming and then an extension. But if that poly A on your rare transcript just didn't happen to be primed, didn't have enough primer, or the primer just didn't find the poly A, then you're never going to see it you could sequence it to infinity and you still wouldn't see that transfer. Any other questions yeah. Is it possible to distinguish like highly express broken and low express broken that cannot fire the floor. Yeah, it is quantitative. So because you are counting antibody binding events. It's limited by your ability to find antibodies. So it's different from single cell or from whole transcriptome where you sequence everything. You can find antibody or sites, the panels which are on the order of 200 to 300, maybe 400 antibodies that you could include. Yeah, good question. Anybody else. Right. And then yeah, this is one of my newer slides. Everything we just talked about can now be done in relation to a cells position relative to all other cells, all other cells in a sample. So I talked a lot early on about cell isolation or purifying cells. If you really aren't interested in a single cell population you just want to know what you have in your actual tissue, you can just cut your tissue cut a section and micron section for micron section, put it on to a glass slide. And now there are methods to actually image the RNA in place on a slide so that's what the starry night figure is showing. One of the little tiny dots is a cell, then all the colors are different transcripts so the way this method works is you get a panel of 200 to 400 RNA probes, just like I showed that original virus example, I could do 400 of those probes. And now you can analyze not just the expression of transcripts in a single cell, but you also get this matrix of all cells by all cells, and how far they are away from one another so it's almost like another piece of metadata. And this picture just turns into a huge matrix of how far are you away from other cells, and you can get quite creative to say I'm interested in this cell state, or this cell trajectory and I want to know how that trajectory changes as you're looking inside of a piece of tissue. So spatial marketing looks like this but in bioinformatics it's actually just a huge square table of distances. So don't let these type of figures sort of worry you. It's really all about what's the distance between two cells and it's just another piece of metadata that lets you draw relationships between different cells. And that's what this local neighborhood idea is. I want to know how far my T cell is from a cancer cell, you can now really do that you go through a round of labeling to label your T cells and your cancer cells. And you go through a round of distance analysis now you know what your cells are, what's their distance like as an example. And I guess a plug for Princess Margaret genomic center we just bought one of these so if you want to do single cell we offer our single cell spatial transcriptomics, we offer this as a service. And I'm not going to belabor this suffice to say there's a huge number of bioinformatics tools. I heard at the single cell Atlas someone said there's more tools than users. There's a huge number of you have tools out in the world. You really need to read these papers carefully and work with your colleagues to really figure out what is the right tool for the right, the right jobs this is already a date 2015. There's just been an explosion of tools to do lineage state trajectory. You want a good reality check from your colleagues in the field as to what has worked for them for a question is similar or the same to yours. Okay, so I'm. I'm just going to go right on time. Yeah, so I'm just going to show. Yeah, Gary. So, same, like, 15 tools, kind of solve the same problem. Probably, almost all of them do the same thing. There still are good idea new ideas out there, but a lot of them are done than ideas to. So, I said I'm from cancer background so I'm going to really put all the background I just talked about into the context of two cancer projects that that we worked on in the lab. And really the idea is the complex heterogeneity that is a tumor. So a tumor is dynamic cells come and go there's immune responses. There's different cancer subclones there are mixture different cell types there's cancer cells immune cells. And they change over time so single cell sequencing has totally transformed our approach to trying to understand cancer biology landscapes at a single time point. But also in the context of treatment and trying to understand how cancers develop from the earliest stages and then how do they change once they're treated. So single cell or any second practice, how did cancers, cancers and immune cells change over time so this is sort of a theoretical patients journey tumor burden to serve a surrogate for size or tumor stage. So the earliest of cancer so I'm going to talk about some single cell RNA sequencing of a mouse model to understand the earliest of cancers. So this is a mouse that is going to get myeloma within 70 weeks, you have the opportunity to look at how does that evolution happen. And then I'm going to talk about single cell RNA sequencing while you're on a trial. So once you're treated. What is the effective treatment, why do you relapse and then what what next treatment might be effective based on a single cell profile. So I'm going to talk about the myeloma mouse model first so really starting to watch specifically immune cells evolve at the single cell level as cancer develops over time and there's two papers here that you can read if you want to get to really get into the details. So a little bit on myeloma multiple myeloma actually began it's a it's a cancer B cells cancer the bone marrow. It begins as this benign condition actually it's quite common, especially as you age you get this monoclonal gamophathy of undetermined significance, it then transforms into what's called smoldering myeloma.  The goal in myeloma treatment is to debulk the cancer and get it down to minimal residual disease myeloma is actually not curable so the current standard of care is monitoring a minimal residual disease usually through a bone marrow aspirates you have access to cells from the bone marrow. And if there is a relapse, you want to understand why do they relapse and what is the next treatment that we could use to treat this now relapsed myeloma. So we have a mouse that does exactly this. So it's called the b cap and mick mouse so it is a B in the immunoglobulin locus with the stop codon and essentially you wait around for those B cells to mature until that stop codons reverse and essentially now you have over activation of this mick oncogene. And what happens over time over 70 weeks is you have these no disease mice. At some point that mick gene gets turned on so you get this AMG which is amegas for mice. And then you get multiple myeloma and you can actually take their their femurs and take your bone marrow and look at this specific protein so it's sort of a protein marker that you can see build up over time. So you know when you can actually take these samples for single cell analysis. So we had 12 mice captured at these different time points. So we integrated all 90,000 cells as we did this all with the 10 x genomics platform. We didn't do any cell selection because we didn't know what cells were going to be important. We knew they were going to be cancer cells. We knew they were going to be other cells also in the bone marrow. So our laboratory strategy was comprehensive sequence every cell and then see how they change over time. So step one was to integrate them so we could compare all these all the data over different time points. And you can see all the many different cell types we saw, some of them the same cell type, but their states report trajectories are changing over time. The beauty of single cell data is you don't have to eat the whole cake at once you can look at specific cells you're really interested in for a specific question. So we wanted to know about plasma cells at the very beginning so we're interested in how do normal plasma cells become cancer cells. So we knew from a gene set from a gene set analysis, which of these were from the B cell lineage. So they were all of these so we pulled out informatically pulled out these individual cells and recluster them so we're really looking for different for specific and more high resolution clusters specifically in this plasma cell population. So this little plot here is only the cells. Yeah, only the cells associated with that B cell lineage. And if you call them by mice. There's some mixing of mice and there's some, some clusters that are unique to my so this huge purple pot relations all coming from one mouse and some of these are all add mix. So sort all of these clusters by time point, you can see that normal plasma cell clusters are shared basically by everybody so even when you have late myeloma, you still have a few of these, a few normal plasma cells. There's these curious, not the original B cells but they're not cancer B cells they're kind of there but they're only found in the no disease mice so we tagged these as early disease. There's this AMG cluster these are still B cells, but only in the AMG time point. And then when you get to myeloma, every single cell is different so essentially you see this broadening and differentiation over time you start with a B cell that is healthy and every mouse has it.  All have completely different transcriptional programs for their full blown disease, and it kind of reflects what we see in cancer myeloma is extremely heterogeneous. And you can sort of see everyone starts out with these normal cells that eventually become totally different from each other. So that was sort of our lesson looking at the cancer cells. So saw this trajectory this movement of the of the B cells. And now we're interested in the T cells you want to understand why do these myeloma cells get a foothold and why do they even persist why isn't the mouse's immune system clearing these cancers out. So we pulled out specifically the T cell population and one of the roles is the T cells is cancer surveillance they're supposed to be going around killing all these malignant cells. We actually saw in our control mice and no disease mice. We actually scored all these T cells for actually multiple exhaustion T cell exhaustion signatures we took these from the literature and scored our mice for this exhaustion signature so it's a list of. I forget it's like 20 to 30 genes. So control mice and no disease mice who essentially have not exhausted normal T cells, but as you go through AMG and the full blown myeloma essentially you are losing T cell T cell function, basically your exhaustion score is trying to go up. And if we pulled these cells out, and then validated them using markers for exhausted T cells using flow cytometry. We actually see the same thing so really these be kept a myth mice over time are getting increasingly exhausted and essentially these T cells are not doing the cancer surveillance as they should be allowing the myeloma to fully develop. So the question was okay we have these two models we know myeloma is developing. And we know T cell exhaustion is an issue as a feature of these cancerous bone marrow. Can we combat exhaustion so in this case we had an anti lag three anti PD one antibody so these are therapies that reinvigorate T cells. And that actually showed the ability to delay the onset of myeloma so it didn't completely turn on these T cells forever but essentially push them into a functional state where you could delay the onset of myeloma. And you could see that right at the single cell level by looking in that case at the at the end protein, but also looking at the mouse survival over time. The goal of our single cell experiment one was to understand cancer evolution understand T cell responses, but then that last step, and the plan is right at the beginning of the experiment is that validation what's that validation of your biological observation going to be. In our case it was mouse survival and we were able to show that. The subject is actually when I worked on with Gary's group looking at glioblastoma stem cells and looking at a subclonal analysis of subclonal cancer populations that exist in that in brain tumors. Also published if you really want to get into the details has 40 pages of supplemental methods so if you really want to get into the details it's like a little textbook on how to do single cell work. The idea here was really all based around there's a glioblastoma stem cells the idea you have these cancer stem cells that inhabit the bulk tumor. And the idea is chemotherapy is clearing out the bulk tumor, but the cancer stem cells are resilient to it either because they're not proliferating or they're either just not being selected by chemotherapy. They're the ones that give relapse to the bulk tumor.  You know, the glioblastoma would lose this ability to repopulate the bulk. So that was the hypothesis going in. And that's why we're so interested in single cell because bulk sequencing grinds up all the signal and you can't see the cancer stem cell signal. In this case, we had worked with two sort of two brain experts Peter Dirks and Sam Weiss, they have the ability to take a patient's tumor digest it into free floating single cells. And then each lab has different ways to do this one use flow cytometry to put these into wells, and then you grow up your culture in a single well.  At the end of the day you get the expanded and enriched brain tumor stem cell cultures that we could then use for single cell analysis. So very different from the myeloma experiment myeloma experiment we're grinding up and sequencing every single cell. This is the exact opposite where we knew the cell population we wanted to go for. So we went through all this trouble to isolate the cancer stem cells specifically. So the first question was, are these all genetically and transcriptionally the same. So our brain tumor stem cells, totally homogeneous, or are they totally heterogeneous. So we had 29 patient derived stem cell cultures. I wanted to use single cell sequencing to answer this question. And the answer was it's both. Some of the cultures are perhaps not surprisingly, some of the cultures are clonal so I've sorted all these cultures by the number of sub clones so these ones up here have to what appeared to be two sub clones. It actually turned out, the first sub clone was always a cycling as a population of cells and the second was in a different cell cycle. So, while you see brown and green, they're actually the same cell identity, but they're at different positions in the cell cycle, even though they're supposed to synchronize we always had this little non proliferating population. We always had a proliferating population. So that sort of explained the first row. As you get to the second row you can see three, four and if you get to this really complex is IDH mutant brain tumor you see a huge number of different sub clones. You sort of see this distribution here, but it really was giving us insight into how different these different cultures were. And this may explain the differences that we saw in some of the drug screening experiments that were being done in these cell lines. One of the differences we have for all these sub clones is it being driven by genetics are there additional chromosomes additional mutations that it sort of explained the subclonal structure. So if we took three, three of these samples we had a tumor for treating immune cell we had cancer cells, and we had the glioblastoma line. In this case we use the trick instead of doing pathway analysis using biological pathways we use pathway analysis using chromosomes we took all the chromosomes on chromosome one all the genes rather on chromosome one, and then did a differential gene expression to all the other chromosomes. And what we found was gain of chromosome seven so the two cancer populations here are showing very high up regulation of all genes on chromosome seven. And we knew in these two lines they had a copy number gain had extra chromosomal copies of that specific chromosome, and you don't have that in the normal diploid T cells, and also works for deletions as well so inferred down regulation of this pseudo pathway that actually is all genes on chromosome 10 all showed low expression and that because one of the chromosome and the copies of chromosome 10 was just missing. So you could use a tool that already existed, but throughout the biology and just look at the physical position on the chromosome and you can infer copy number changes, copy number changes across the entire genome. So taking that lesson you can actually apply that to an entire glioblastoma stem cell line so here's one line. We saw four sub clones, and the way to read these each little tiny like one pixel thick line here is a different single cell, and each row is a different chromosome seven every single cell basically had gain of seven only a subset of the cells actually had loss of 10 so every single gene on chromosome 10 in the subset of cells showed low expression that's extremely unlikely. Due to gene regulation or a pathway regulation it's much more likely that that DNA was just wasn't there and that's why those genes are being expressed. And you can go down even lower so it's a little tiny pink cluster as characterized by gain of chromosome 20, and you can really go down the rabbit hole and start to pick out like little sub sub sub sub sub sub clones and basically until you run out of cells but you can build these really beautiful phylogenetic trees of all the relationships purely using a copy number data still derived from RNA. So even though it's a DNA feature, you can infer the DNA feature from RNA expression. Just like in the myeloma mice when you try to cluster all the glioblastoma stem cells at the global level. They never cluster together so there's really something fundamental at the both the DNA level, but also at the transcriptome level. So this was a bit of an issue. In general if you had lines from the same tumors they're similar, they're more similar to each other than they are to other patients. So you almost sort of get that blending like you did in the mice of all the T cells and B cells and all the other non malignant cells. And this is a phenomenon that's been reported across virtually all cancer types. Each patient's tumor really is really is distinct question we want to answer is, is there any common biology so even if every single gene isn't beautifully correlated across patients. Is there a therapeutic vulnerability that you can exploit. So in a study we're looking at developmental at at gradients we're trying to say okay we know they're not different but is there just like in trajectory. Is there a gradient of biology that sort of explains this variation across all glioblastoma stem cells. And there are really two signatures that came to attention, a developmental signature and an injury response signature so developmental here in red so if you're high in development. If you're really low for injury, if you're high for injury, you're low for development. So the model that we extracted out of the single cell data is these this is basically an axis and depending on where your cell went wrong and sort of came out of that gradient. So that's where it defines the biology for that tumor. And you can see your in general, all the cells from a glioblastoma stem cell are heavily development and low and injury, or high and injury and low and development. So there's still some of these in the middle where you sort of get the two gradients kind of crossing. So much this is a better visualization of it where you can really see not trying to put hard thresholds on biology and that was the other like big finding out of this paper was moving away from being overly compartmentalized with these cancer sub types, and instead sort of tolerating some continuity of scoring across all your different cancer models and that's really what this figure is showing here. It's not like there's development or injury to continuous model. We then took what we learned from the purified glioblastoma stem cells and we jump back to the primary tumor so you still want to see can we find the signal, these little tiny cancer stem cells in the overall bulk. And did we did so if you take the injury and response gradients, super clean and the cell lines you get two really nice peaks, the developmental ones and the injury ones. But you look at bulk cells, you can still kind of see that signal there but it's being masked by all the bulk tumor that don't necessarily follow this gradient so the stem cells around the gradient, but the bulk tumor is not they're basically fountaining out these abnormal cells that aren't necessarily perfectly on the gradient where the where the stem cells were some of these tumors more pure than others you can definitely see the signals in there. But it's not something you necessarily would have seen certainly using bulk sequencing, and even using unselected cell sequencing this biology is not necessarily apparent. And this is the final model Gary actually named this the peanut wizard hat because we used to have it oriented this way with the hat going at the back for going up. And the, the peanut is the developmental injury response gradient, and these stem cells are then fountaining out these mature astro site tumor cells so essentially they're pumping out bulk tumor. But this is the problem with combating the bulk tumor, the black part, you still are retaining this the stem cell model and the research now is around. What do we do about these stem cells, how do you pick out cells that are either end or in the middle of this gradient. So this is a single cell project sort of took us into this whole new area of research. And then we validated also using a CRISPR screens as well so this is a work with Stefan on Jay, he said we found this gradient, they have this massive, this massive CRISPR library, and essentially, all the glioblastoma cell lines that run the injury response genes all had there had CRISPR vulnerabilities for injury response genes, and same story on the other end of the gradient those are all the developmental response genes. So really encouraging you to think, not just to your UMAP for your Disney plot but that one extra step what's your validation experiment going to look like are you going to go back in the bulk tumor so you can go do a CRISPR experiment model most model. I'm going to plan that ahead. And I'll just know I'm running out of time here so I just wanted to skip to my last anecdote, which was this patient here. So this is an individual multiple myeloma, just like we did with the mice, we've got a bone marrow aspirate. We did single cells we can sequencing on their bone marrow aspirate, we isolated the T cells and able to pick out definitely an enrichment for exhausted T cells in their population. We also zoomed in specifically on their cancer cells so this big population here is the multiple myeloma cells. And we in this case we actually had two samples we had before treatment and after treatment. So the treatment that they were on was an FGFR3 inhibitor, and actually worked very badly and actually that drugs been taken off the market now because all the patients were progressing. So we want to understand why that why that was. So when we zoomed in on their sequencing reads so here's their here for clusters the top two were only seen pretreatment bottom two were only seen post treatment. And then on their individual sequencing reads, all these cells in cluster two I've shown four of them here, all had this in frame, activating deletion within FGFR3. And in fact, that's the exact cluster that fully melted away. And then this large cluster that has deletion of 17p which is a very aggressive marker of multiple myeloma. This subclone that only had very, very few cells pretreatment essentially filled the niche. So it actually turned out this drug is amazingly specific and highly effective against the subclonal population with this deletion of FGFR3, but it's totally ineffective against this resistant population so it actually turns out it's an excellent drug, but the selection of the patients is a problem in this case they're choosing patients who have these header highly heterogeneous tumors. It worked perfectly in the exact cells that were only visible with single cell sequencing, but it's all the other cells that didn't have this mutation that explained their relapse. So, just to revisit the learning objectives we have to start is a huge number of single cell sequencing technologies you really want to tailor them to your specific scientific question. You can measure the same biology multiple different ways so you can measure biology using protein like with site-seq, you can do that CRISPR screen experiment, you can have a mouse model and then compare it to humans. There's lots of different ways. These are all hammers looking for nails, so you really need to tailor these experimental techniques to your specific question. You can assay multiple different cellular compartments if you're really interested in nuclei, you can isolate just the nuclei, if you want the cell surface protein, you can use site-seq. Think about whether you need to do cell selection, if you're only interested in T cells or do you want to do agnostic sequencing like we did in the Myeloma mouse, and then you really want to be critical of your own data. Data quality, if you've gotten outlier, is it technical or is it really exciting biology? Be critical of your integrations. Your T cells in general should integrate together. It's very rare that cancer cells integrate beautifully together. And then use orthogonal techniques. Use single cell sequencing maybe for discovery, but think ahead to what that validation experiment needs to look like. Is it a clinical outcome? Is it a functional screen? Think all the way through from the tissue all the way through to your final analysis. And I'll leave it there. I listed a huge number of papers. They're all in the slide deck. I'll post off with Gary and I, her name is Shamini. She runs this single cell group that's really focused on spatial transcriptomics. If you'd like to join that group beyond this course, that's our Slack channel link there. You can click that link and it'll link you into that working group. And my email is here too.


Live from the JSA Podcast Studio, presenting Data Movers, showcasing the leaders behind the headlines in the telecom and data center infrastructure industry. Hello, everybody. Welcome to our new podcast series, Data Movers. I'm your host, Jamie Stato-Cutayah, CEO and founder of JSA, along with my fabulous co-host, top feed-a-beat social media influencer, Mr. Evan Christel. Evan, you're looking good today. Thanks so much. Looking forward to our next guest and I see you have a brand new background, so you've moved. I have. I'm officially further south now in California, in Laguna Beach. I can't see your wall behind you, but do you collect art or collectibles or chachkies? I do have a fabulous piece of artwork, but it's in Virginia. Yeah, I do every now and then, something moves your soul and you're like, I need to have it, but very rarely. So, yeah, I feel the same about art. Now, what about digital art? Have you been following this phenomenon of digital art or technically non-fungible tokens where creators could create essentially a coin or a blockchain-based digital piece of art and then sell it out into the marketplace? I mean, that's insanity. So, people are paying millions for this, huh? People are paying millions, tens of millions today and NFT, as they're called, sold for $69 million at Christie's.  The buyer actually burned the original, but kept the digital version, the NFT for himself. So, are we in peak insanity now? Or what's going on, Jamie? I mean, we're all living our digital reality, like reality always, right? I mean, we're here on Zoom. This is like, I feel like it's somehow related to the pandemic, you know? Yeah, I may even have an Evan coin coming out soon. So, we'll talk about that the next episode. In the meantime, I think we have a great guest. Yes. I am very excited to introduce you guys. Of course, you know, we love our guests. We love diving into their background stories, their careers, highs and lows, unique perspectives on the future of our industry. And today, we are very honored to welcome Todd Tweedy. He's the director of sales and marketing for Blackfoot Communications. Todd, welcome. Good morning, Jamie. Good morning, Evan. Yeah, good to see you. You're in the beautiful state of Montana. I see here that you were founded in 1954 at Blackfoot Communications as a local cooperative telephone company. Is that kind of like socialism? Yeah. It's a different business model, if you will, for service providers or telecoms. We've got shareholders. Those are our cooperative members. And we're actually really proud of that heritage. And on more than one occasion, I've seen our CEO, he literally pumps his fist in the air and says, cooperatives are cool. So we're very proud of that heritage. And believe it or not, some of those first cooperative members, they were Montana business owners, Montana ranchers, Montana farmers. And it's gone down through their family. And that actually explains the, Evan's outfit today, right? Range that on. Yes. Yes, I love it. And I love cooperatives, actually. Really shows how important the service that you're providing really serves the community. Makes people rally around it and feel as owners. So it's amazing. So tell us a little bit Todd about yourself and how you got involved in the telecom industry. Sure. A trip down memory lane, lane here. So I've been a Montana resident for about 21 years and I worked for three different telecom companies in that period of time. And the start was, I don't know, somewhat opportunity, opportunistic, maybe somewhat intentional. I had worked for a couple of IT bars and personally found the WAN, the wide area network side of that business. Interesting. And early in my 30s, again, I'm dating myself here, but early in my 30s, I guess I was struck by the entrepreneurial bug and decided to be a partner in a startup ISP company. And it was long enough ago that we were talking 56 K modems and dial up at the time. But really that was my first job, if you will, my first foray into the telecom industry. So you're at the present time deploying fiber optic communications, taking Montana into the 21st, 22nd century. So how did you get there? I mean, what, what have you learned over the years to get you to this, you know, this point in the telecom world? Well, it's interesting to me and in some regard, I feel like it's a little ironic, you know, fiber optics, fiber communication, it's been around for decades. But today it is absolutely the backbone of telecom infrastructure. And, you know, one of our big focuses recently is fiber of the home for those cooperative members. We were talking about just a minute ago, but we also do business with several of the major mobile carriers, the mobile providers in the U.S. And those towers are serviced. They're fed by fiber optic cables. And there's kind of this interesting, you've certainly heard the phrase digital divide. And in some cases that's referring to economic disparity. Well, in the rural West, sometimes that's referring to just the availability of assets and 5G right now is such a buzz phrase or buzz term. And it's very exciting and the capabilities associated with 5G versus 4G and 3G. I mean, exciting times. But those technologies tend to be deployed first in the NFL cities, right? Where the population is the most dense, where you have the most fiber optic assets at your disposal. We get to very rural Montana and there might or might not be fiber available. So when we hear all this buzz about 5G, you know, in certain corners of the rural West, certain corners of Montana, honestly, it's a question mark when those type of services will be available simply because of the infrastructure that may or may not be present. And you know, Todd, as we talk about Blackfoot, we should really mention it to your fabulous reviews, your customer success. You guys are really driven for a culture of excellence, particularly around customer service. What does it take to build a great team? Shoot, I feel like we could have a series, a podcast series on building successful teams. And I really don't think there's a single silver bullet or a single ingredient. I mean, it starts with talented and dedicated professionals. And then I think, you know, starting at the top with the leadership, building a crystal clear understanding of what we're here to do, what's our focus? How do we make money? What are we really good at? And maybe some things that we're not so good at and be willing to walk away from those things that we aren't so good at. And I've heard our CEO, Jason Williams, our CEO talk about, you know, Evan, you commented how long we've been around being founded in 54. Well, he wants us to be around for another six decades. And what we need to be doing from a leadership standpoint, from a strategy standpoint to make sure that happens. So it's really easy to get caught up in tactical things in the here and now. But I think one of the things that's critical to be building a successful team is we always have a vision. Where are we going to be in three years, five years, seven years? And what do we need to do to transform our organization, meet our customer needs, transform our team to be successful in that regard? I'm sure all of us have some folks in our professional career that we would look at as true mentors. And I can remember those, this was a gentleman I worked with years and years ago. You can tell the impact that he had on me because I can still remember to this day some of his quotes. And one of them that I'm so keen of, it was you can't lead where you won't go and you can't teach what you don't know. Which again, from a leadership standpoint, yes, you can surround yourself with talented individuals, but it starts at the top. That vision starts at that top. The dedication starts at that top. And then hopefully you can impart your team members with that vision and that focus and hopefully success is the product of that approach. So, Todd, one of the most interesting aspects of your career right now is on the carrier and services side, working with predominantly rural customers around Montana. That looks like a challenge. Given some of the photos I saw from your customers in a photo contest, give us some good stories there.  Yeah, well, again, a compare and contrast what goes on in Montana to some of the largest metropolitan areas, the NFL cities, where you've got, you know, significantly population density. You've perhaps got a lot of fiber and oh, by the way, you don't have a mountain range in the middle between two cities or a mountain range between two sites where you want line of sight. So the topography can be a challenge in Montana because you don't always have line of sight or line of sight can always be assumed. The lack of population density, of course, there's all kinds of economics of scale that come into play when you're building fiber, deploying networks and, you know, a little while ago we were talking about the difference in population between Southern California and Montana. Southern California has 10x the state of Montana does. So the economics of plowing fiber in some of the rural areas can be challenging. And oftentimes that's where federal subsidies come into play, FCC subsidies. Matter of fact, Blackfoot did just recently participate in the Rural Development Opportunity Fund auction, and we were successful in winning some of those areas. And again, that's an FCC subsidy. So that will actually help expand our network footprint which has advantages to both our residential and our wholesale or carrier services customers. I love this comparison between rural Montana versus other metropolitan areas. And I have a hunch that what you're going to say to this question that I like to often ask our guests, probably might be different due to location, location, location, but what do you see as the top telecom trends that are keeping you guys awake at night or you're keeping a watch on? One was what I touched on a little while ago that 5G is such a buzzword right now, it's an exciting technology it's an exciting time. But I'm a little bit worried in certain corners of the rural west do we really have the infrastructure to support that. When you're talking about connectivity to a cell tower, we have conversations with the providers today that they're talking about we need a 1G backhaul connection. There's talk about 10G and I know this might get a little technical, but some of the small rural providers in Montana their entire core network, their main fiber might only be 10G. So how can they possibly service multiple cell towers if they've got network limitations? So that is one thing that I would like to point out. It's an expensive situation to solve. It takes a lot of money and hopefully the federal government can help us with that. The other one doesn't necessarily keep me awake at night, but it's a trend we're keenly keeping an eye on and embracing and frankly shifting our business model to hopefully meet the marketplace needs and that's been in services to customers, Montana customers, customers across the United States. They're using an MPLS network very secure, very reliable, unfortunately also very expensive. And one of the transitions is to a SD-WAN architecture where I'm using deployment of appliances, deployment of devices and then I go out and procure a couple of what's known as commodity circuits. Just think of everyday broadband circuits. They have a great balance and a great redundancy through the use of these commodity circuits and their studies have been shown that just from a tax saving standpoint, there can be a 40% savings, OPEC savings between an MPLS network and an SD-WAN network. So Jamie, when we think of our enterprise customers and meeting their needs and how their business is trending, that was one we are keenly aware of and we're trying to keep our crosshairs on. And I really can't wait to hear more about the broadband funding and look forward to hearing some great stories on rollout particularly in areas like education and healthcare where there's just a huge gap between bigger cities and suburbs and rural communities. It's one of the pressing things in our country right now, I think. Let me end here with rapid fire questions and feel free to share the first thing that comes to mind. Favorite movie and preferably something based in Montana maybe a cowboy movie, what do you think? No wrong answers then, right Evan? I will be grading these, so yes, don't worry. So favorite Montana movie, River Runs Through It. Oh, a classic. Yes, Robert Redford matter of fact, when that movie was popular, I think that was early 90s. Some of the economic development and tourism businesses and offices in Montana said that movie did more to prop up their business, support their business than any advertising initiatives that they could ever do. Phenomenal, I'm going to re-watch that one. That's definitely a classic. Favorite sport or sports team secretly hoping you'll say hunting and you'll tell us the pheasant story of passion in mind, but favorite sports team hands down the Minnesota Vikings, the NFL. I grew up in Minnesota and my joke is I bleed purple, so big Minnesota Viking fan. Well, there's always next year. Would you say a lot as a Minnesota Viking fan? So, number one spot in the world, you know, where would you like to go now the pandemics as an end in sight? What's on your bucket list? Oh, that one's tough. Greece, Greece. Beaches in Greece look amazing. Yeah, I imagine you don't have beaches in Montana if I know my geography. You have the eighth grade geography, you're right. Ocean's a long ways away. People, the farmers around town, how would they describe you in one word? We have children listening, so please. Passionate. Passionate. I've described myself trying to use more than one word as kind of the poster child for the work hard, play hard. So I've got professional passions. I've got personal passions. Passionate would probably be a good one word description. Awesome. One of our GSA core values, actually, to be passionate about our clients and our industry and our team. I love that one. Good job. Thank you so much, Todd, for joining us. We really got such fabulous insight, not only on Blackfoot communications, your company, but also your leadership, your insights into our industry. We really appreciate it. Thank you. Thanks, Jamie. And if you guys enjoyed watching today's episode of Data Movers, please go ahead and check out jsa.net slash podcast where we have upcoming episodes being released every other week on Wednesday morning. So check us out there. And follow us on Twitter at Jay Scotto and Evan Curstell and look forward to networking. Yep. And as always, guys, networking.


It's the largest fish in unboxing to ever hit YouTube. Twenty, twenty-one, though. Oh my God. You hit my last note. I was partnered with a company before this. You guys know the company, it's Sixth Sense. And I have a few things I would like to say about that. January first, I woke up to a disgusting video online that I'm disappointed in the person who made that video. Yo, it's Poppin', boys and girls, ladies and gents. Welcome back to another episode of Kicking Their Vast TV. This is probably the realest video I've ever dropped on my channel, and we're gonna keep it real with you guys today. It's 2021, it's a new year. As you guys know from the couple of videos that I have dropped, it's a new year, it's a new me, and we're gonna be positive. We're gonna work together as a team this year, and we're gonna kill it. 2020 was a rough year, 2021 is a whole new one, and we're gonna come at it full-fletched, and pound it 100%. So today, I have a few topics I wanna talk about. Before we even get into this world's largest unboxing, which it is, just the largest fishing unboxing to ever hit YouTube, I have a few things I wanna talk about, and I would appreciate if you guys would hear me out just for a moment, because if you don't, you're gonna skip forward in the video and be very confused.  I'm actually sponsored by a new company. A lot of you guys know this company. It is the biggest company in the industry. Hands down, no one can question it. As you guys know, I'm with Luz currently. I've been with Luz the last year and a half, I believe, going on two years. Amazing people over there. I really respect the way that I've been treated. It's a family environment, and I just love working with the guys over there at Luz. With that being said, they're actually joint companies with Strike King, and I can now say that I'm actually sponsored by Strike King. That is going to be my exclusive bait sponsor now, is Strike King, and let me tell you why. So for everybody that watches my videos, you guys know how important my family is, how important this industry is to me, and more importantly, how important the community is, which is you guys watching this video. I've stayed true to myself from the beginning, and every company that I merge myself with and connect myself with, I make it very upfront and very clear that it's more about the relationship more than anything. It's not about the money. It's not about how much product I get. It's about the relationship, and how we're gonna move forward as a team. For everybody that's watched my channel for a while, you guys know me, Daddy Bay and Dan, my mom, we're all on the channel all the time. It's a family environment, and I treat sponsors and companies that I work with in the same way. As we move into the future, I gotta be authentic with myself. More importantly, I gotta be authentic with you guys, and that's the main reason why I'm moving with this company, they have the same values as me, and that's the only way I'm gonna be authentic to myself and be authentic to you guys. I'm very excited about 2021. This is gonna be a big year. This is honestly a very small announcement compared to what we have going on this year.  And I'm just excited to start off 2021 with a bang, bring you guys along with me and create some positive change within this industry. I'm tired of all the drama. You know, all those people out there trying to start drama. It's childish to me. You know, we're not about that over here. We're about all the positivity. We're gonna keep moving forward. So obviously, there's something I have to talk about. I was partnered with a company before this. You guys know the company, it's Six Sense. And I have a few things I would like to say about that. So I actually parted ways with Six Sense back at the beginning of December. Took some time off for the holidays. Now we're back with 2021. We're coming out with the bang. I only wish everybody at Six Sense the best of luck with their business endeavors. Their baits are amazing. You know, I've caught a ton of fish on them. You guys have too. I cannot decredit their baits. They make some awesome baits and they catch a lot of great fish. But the biggest thing with our partnership is, you know, our values just didn't align. And how we're doing business just didn't align. And you know, like I said, guys, I'm here about the relationship. I'm not here about the money. And with that situation, you know, like I said, the values just didn't align. I really want to focus on the community and my vision and not the money. Because at the end of the day, the money doesn't mean anything, guys. A lot of people out there want to chase the money and chase the cloud and they want to chase that. That's completely fine. That's their decision. But that's not authentic to myself and that's not authentic to my future and the direction that I want ahead. Like I said, it's about that relationship. It's not about the money. So let's have a serious conversation now that we're, you know, talking about this. This is something I don't get to do often. So I'm very excited to kind of sit down with you guys and have more of a heart-to-heart conversation. So let's talk about where I'm heading in the future, what I stand for, and where kicking their backs is heading in the future. So you guys saw at the beginning of the year, I dropped a video. It was about, you know, us coming together as a community, us working as hard as we can to be the best person that we can be and being positive individuals. You guys saw in 2020 how negative people are. Not only how negative people are, but how negative the news is, how negative the media, everything coming together. We're not about that here, guys. We're about the positivity. Even me looking online, I see a bunch of negative stuff. January 1st, I woke up to a disgusting video online that I'm disappointed in the person who made that video to push negativity out in 2021 knowing how 2020 was. I'm very disappointed in that person who made that video. You guys know exactly who I'm talking about. I think it's very immature, and I'm not a part of that. I'm a part of positivity, I'm part of bringing us together and putting information like that on the web is not right for me, it's not right for you, it's not right for us as a family. So I don't agree with that. So going into the future, I only wanna be honest, authentic to you guys. I want you guys to also drop some questions down below. I wanna make some more videos like this just talking and being real to you guys because I don't get to express myself enough in what I stand for. I'm all about doing the right thing. Everybody that knows me, I say that all the time, is doing the right thing as simple as it is. You do what's right. Me going and dropping a video on YouTube, talking about somebody and trying to clout chase, in my opinion, isn't the right thing to do. The right thing to do is for us to work together as a team, be positive individuals and build the fishing community rather than separate us. Just being real here, not to try to start drama, not to try to clout chase, that's wrong. We have seen that in other industries. When does that ever work out? It turns people against each other. Guess what the media was doing to the world last year? They were turning everybody against each other, right? I am bringing that in this fishing industry. I wanna be the one to step up and say something about it because nobody else will. So I wanna be real with you guys. That's not what we stand for. That's not what we're ever gonna stand for. And I just wanna get something straight. I wish everybody in this industry, every single person the best of luck this year. Whether you don't like me, whether I don't like you, it doesn't matter. I wish you the best of luck. So going into 2021 with kicking their bass TV, this is a drama-free zone. I don't know what to tell you. I'm not gonna deal with that type of drama. It's not what I'm about. It's not what I'm gonna stand for. And I'm gonna stand for what's right in being a positive person and bringing the community together and that's what we need to do here as a team. Like I said, it's gonna be a great year. It's gonna be phenomenal.  And I can promise you, if you guys work as hard as you can, it's gonna be the best year of y'all's lives as well. Last year was a tough year. And this year is a new year. We're gonna be guns blazing and we're gonna do what's right and we're gonna kick some butt. I'm very excited about it. I'm just more excited to be partnered up with an amazing company that shares the same values as me. And I can stay authentic to myself and authentic to you guys. I think that's like the biggest thing here.  And I know that I'm surrounded by the right people. So if you guys are about positivity, if you guys are about that big bass energy, if you guys are about working together as a team, moving forward, making the world a better place. Listen, you found the perfect community. You found the perfect channel. I want you guys to all drop some comments down below that's positive. Listen, I'm tired about that negative stuff. If you guys have anything negative to say, get off my page. Go unsubscribe, unsubscribe down below. I ain't about it. I ain't about it at all. I'm gonna keep it real with you guys for now on. I ain't about that negative stuff. We're about positivity over here. We're about bringing each other together and moving forward as a unit, moving forward as a team. That's what we're gonna continue to do in 2021, big year. So let's go ahead and hop into this unboxing. This is by far the biggest unboxing of the year. We're about family. We're about love. We're about community, about being together. Let's have some fun, people. What's up, people? 2021, big year. Man, are you ready for this unboxing? I'm tired of talking so serious, man. I don't wanna get out of it. I enjoyed talking serious, but I'm ready to show you guys a ton of tackle. This, corrects me if I'm wrong. I could be wrong, but I believe this is the biggest fish in the box. So how to load the tackle. Yeah, like, put it this way. I couldn't even get it shipped to my house in Savannah. I had to get it shipped here because it was so, so much stuff. Let's go take a look. I'm so excited to show you guys this. I don't know how much I'm gonna show you. I'm telling you, so much tackle. It's just so much. So let's go ahead and hop into this. So this is all the tackle. This is crazy. I'm actually stressed out just looking at all this. I don't know what to do. Like, this is so much tackle. This is so. I'm not knowing. I've never seen this much tackle. I mean, yeah, we had a lot of tackle back there, but this is like. You just took your own tackle shop right here. Dude, this is crazy. Big shout out to Stryking. Big shout out to David at Luz. Big shout out to Rocky. Big shout out to Emily. Thank you guys so much for, you know, obviously keeping that family environment, family relationship, but more importantly just totally hooking me up. This is more than hooking me up. Big shout out to David. Thank you, David. This is incredible. This is just insane.    Like, what you guys gotta understand is open that up so they understand. Like, these are little boxes stacked to the brim. Show them. David, you got room for daddy bandam on the circuit over there, you know what I'm saying? Look at this. So you got three red eyeshads in one box. Like, look at how many- This is just one of the weight. This is the two tap chili crawl. I don't see the weight on there. Do you see the weight? It's probably half ounce. Half ounce on the front. So I don't know what other weights they might have sent you, but that's a good fishing. But like that, isn't that crazy? Like these boxes are actually stacked perfectly. I just started kinda throwing stuff. So what I think we're gonna do is actually unpack everything.  This is literally gonna make everything so unorganized, but who cares, right? We're gonna do it. We're doing it for the YouTube. We're unpacking all of this. Even these, how many packs of crawls? One, two, three, four, five, six. Six packs of crawls in a pack, in a thing. Yep, six each. Yeah, six each, so six, six. All right, well, I'll catch you guys in about- This is your favorite, dude, Rage Crawl. Yeah, that's my favorite, like ever. If you guys go back on videos from like literally six years ago, like not even, dude, like four years ago, that was my favorite bait ever. Rage Crawl on everything. We used to buy these bigger than the six packs. We used to buy them in like the 12 packs. Like there was double, double of this, but we're gonna unpack all this and I'll catch you guys in a moment. So we're trying to figure out what we do with all this. We're gonna take everything out. Like, I just don't know how to even start. I kinda wanna dump, do you think that's okay? I don't know how to- I don't know that you'd have a choice. All right, well, you get over it. I think that's kinda the only way to get in here and do this. Dude, this is crazy. You wanna do this one?  All right, come on, dude. Right. Oh my God. Oh my God. Oh my God, let me see that. Look at this. Oh, this is the biggest top order I've ever seen. Fletcher has one of these. Wow. So that's the biggest top order I've ever seen.  What do we do? I guess, so this is what we're doing. We're gonna open all these boxes. I'm gonna try to get a timeline left going. We're gonna open all these and then dump the baits into that. That's the plan. Oh. Hey, fishing line, fishing line. Look at that. It's a two-ergrade, striking fishing line. That's 20 pounds. This is all 20 pounds. Oh my God. All right, well, let's just get this started. I guess we're gonna figure out how we're gonna break this down. So here's a little update. We have jerk baits and crank baits in this box, the lip laces. And here we have spinner baits, chatter baits, jigs. And here we have top waters, frogs, buzz baits. And then over here we have fishing line. Oh, I don't know what we're doing with that. Still have to unbox all of these in this corner. And then we still haven't even started on the soft plastics.  So it looks as if the hard baits are done.  Terminal tackle. I think we have a lot of boxes on back order that are still coming in. So we'll probably do another unboxing soon, but now we're gonna get to the baits. Probably gonna organize them crawls, worms, swim baits. So let's get down. Well, this has been very, I'm worn out. Are you? And the sad part is I'm actually gonna dump all of this back out for thumbnail. All right, big Steve. I'm worn out. I'm worn out hot. So this is the leftovers for everything we unboxed. And I really got only one thing to say. Now, well, it's time for me to go home. Clean this up for you, Lee. What is this? Yeah. Oh man. So to show you guys, we got crawls. We got jigs and chowder baits. We have worms. We have line and bullet weights. We have frogs in top waters. We have crank baits and lipless crank baits. And we have swim baits and flukes. That is probably the biggest box out of all of them. That's insane. That's a whole lot of swim baits. That's a lot of swim baits. So there's not even that many. I mean, together there's a good bit of flukes. I've actually was trying some of these out before. And these are dope. These are like the elastic material. And you, like a normal fluke, you could probably catch one or two fish on. This one you could probably catch about 10 on. Like actually that pull, here, yeah, pull one out. Have you seen these pull? Like, yeah. Holy Christmas. Yeah. Wow. Yeah. So, so, cause you know how flukes are. You know how they, you know, they're just, like it might last one or two fish. Like this is seriously last. Now if the fish comes in the back and then goes top of top of top of any bite. Now let's go do the fun part. All right. This is what we do for thumbnails. Kiss the target. Yeah baby. Is it going to be enough stuff? Is it going to be enough? Are you kidding me? Are you literally joking me right now? Really? I can't tell it looks like. That's two boxes out of five. You ready for this one? What does it look like? You can go with swim base. I mean. You're almost completely covered. Good. Here's the crawls. Two minutes. Like a big splattered mess. You want more? Yeah. You need more over there? On top of them. When we keep going? I mean. When we dump it right in there. Oh my God. Do you need more over here? You don't need any.  You don't need more box. You can do it right on you. Just do it. Just do it on top of them. We just got to make sure my face is visible. See, see, do it. Don't do it on his face. Don't do it on his face. You can't even pick up the box. Do it for 2021. 2021. You hit my last note. I hate to tell you I forgot to hit record. I'm lying. I don't do that to you. It'll be a long 2020 when mom not here anymore. That's craziness, dude. Thank you guys so much for watching this video. This has been crazy.  I've never seen this much tackle in my life. And just know that this would not be possible without you guys. It would not be in this position to get this offer. It just feels great to be with the best fishing company out there that is just a fact. The biggest fishing brand in the industry. It's just a blessing, guys. Thank you so much for watching. Thank you so much for the support. I love you guys so much. We're keeping it real in 2021. All positivity. And we're gonna keep on rocking. Stay tuned for the next episode. We got some big things. We got so many big things coming. You better watch out what you're doing. I'll catch you guys in the next video. Woo!


There was a hold to commercial activities and vehicular movement as members of civil society organizations in Benin City, the state capital, trooped out to protest the current hardship facing Nigerians. Subsequent to the removal of subsidies, among other issues, protesting across major routes in the state with placards, the protesters appealed to the authorities too as a matter of urgency, see to the end of the suffering currently being experienced. When we were crying and saying that the method and the approach was wrong, Fueh was jack up to 500. As we were battling with 500, Fueh jacked up again to 613 in some location, 700. When we were trying to console ourselves with that, in the midst of this crisis and pain and cries, the first money that was made from subsidy, they decided to share it among themselves. The issue of Fueh 30 days and 800 rapalaceous menjo to question the effect of Fueh 30 is not an issue to Nigerians. I would suggest the President, Mr. President, those money that they are proposed for 8,000 lira to pay for every household, you should use those money pumping into agriculture. How can you allocate 70 billion lira to 3,000 lira 60 plus 1,000 lira 9 members of both chambers? Why the masses? You are talking of 8,000 lira for the masses. So we are saying enough is enough, they should reduce the cost of governance. We are suffering in this country, enough is enough, tell them you are the one that said Nigeria should breathe, allow us to breathe, we refuse to accept that 8,000 lira because when we go to the market, we will not be able to use that money to get even a pot of soup. It's not possible. The Speaker of the Ado State House of Assembly, blessings at Webaku, attempted to calm Frey nerves. We are here for all of you, you are here for all of you, people, people are maddened, you are brothers, you are sisters, the place you are also passing through, we are also passing through it. We are not buying fuel, we are not buying fuel for 1,000 lira, we are buying fuel for 50 crores. I know you found a lot of abiding people, that's why you choose to come here today. We are here for you, we are here to also pay your plight and your pay.


Watch me write a grade 9 GCSE paragraph for English Language Paper 1. Step 1. Start with a point. Step 2. Always embed your evidence. Explain your evidence and mention technique. For, link back to the question. Done! Here's my point. My evidence. Here's my explanation. Here's my link.


There we go. Just ripping this 240 around. Oh, I almost got caught on that transition though. What's up everybody? Welcome back to the channel. I'm EvilRabbit here on ForzaMorris46, back for some more drifting, but this time we're here to test out a subscriber's tuned 240SX. He commented on my last video from ForzaMorris46, talking about his tune, saying he was going to make a tune for me. So we're here to test out a subscriber tune, get sideways, and see what we can do. So we got to get this tune installed. So we're going to load our setup here, and then we're going to search by his name. So if you guys want to check out his tune, make sure you do. I will leave his gamertag in the description box below. All right, so we have a tuned up, we have two tunes actually with a sock engine and a six cylinder skyline engine. I like how it's marked EvilRabbit07, definitely appreciate that. So we're going to download and install this tune here, and we're going to give it a rip and see what it does. All right, so we're going to send this to, I believe, we'll go to Indianapolis. We're going to send it here at Indianapolis, see how this tune does. Like I said, if you guys want to see more ForzaMorris46, don't forget to tap that like button, subscribe to the channel. I do appreciate the support. Having a lot of fun jumping back on a ForzaMorris46, definitely going to be doing some racing and maybe some club racing online. Car sounds so amazing. All right, let's do it. Lesson third. Okay. All right, this thing definitely slides. Okay, you might be onto something here, buddy. I do like the street style tuned car. Minimal power, I do love the sound of that turbo though. Let's kick. There we go. We lost our points, but we're still good. This car slides, definitely enjoying the tune right now. Not a bad tune at all. I'm going to want to try that stock tune, that stock engine tune one if I can find the right gear. There we go. Just ripping this 240 around. Almost got caught on that transition though.  I forgot the sounds in Motorsports 6 sound so amazing. Little clutch kick. I was onto the bank. I'm having shifter issues if you haven't noticed.  Oh geez. Well, we just wrecked the guy's new car. We're going to get another lap here, rip this through. We're going to probably change course, maybe switch up the tune a little bit. Go to the, check out the stock engine tune. What I'm liking is Skyline engine tune. Oh, too early. Too soon. I'm just going to settle that down a little bit. We're good. Nothing happened. Nobody saw that. We're good. Little clutch kick. Yes. All right. Definitely a very well tuned car. If you like stock tuned cars, definitely check out this man's tune. Like I said, I'll leave his gamertag in the description box below. Now, I'm able to get sideways on this thing right now and I haven't been drifting in Motorsports 6 in forever. So if you are a big Motorsports 6 drifter, make sure you check out this stock street tune. You can't deny the sounds though. Too soon. Long e-brake gravel. We're good. Pretty tiled though. It's got pretty good feedback. See?  But it almost has that spin back like the center does. If you enter in right, I was going to let it go there. If you enter in right, it has that spin back like a set of where, you know, how if my transmission was working, yeah, we're just going to go into pits. We're done. Oh, we're definitely done. Let's grab the stock engine tune. 427 horsepower. Let's download and install this tune. Why is that one more expensive? Jeez. Probably because I've never had stock parts. All right. I'm Rock Park. Turbo stock 240. That was my fault. Oh, geez. I feel like I definitely need a lot more practice. I'm 4s of 6 drifting here. Oh, yes. It was looking good there for a second, but then it went to complete crap. Need to fix that chifter. That's on my to-do list for later today. Let's get that fixed. Oh, yeah. Look at that. Look at the feedback feel. Sorry, I was amazed by the fact of the feedback feel of that one right there. Oh, stay out the grass. We're good. Getting the hang of the drifting here again, and I'm actually kind of in love with these tunes right now. I'm going to stay in third gear because I know I'm going to need third gear. Hold it out. Clutch kick. Oh, we saved our points too. Second. Yes. All right, dude. You definitely have some solid tunes. If I can drift your tunes and make it look halfway decent, these are pretty dialed tunes in. Make sure you check out this man's tunes. I'll leave his gamertag in the description box below as well. These are a blast to throw around right now. The low-power kind of street-style tuned cars are a lot of fun. I'm having a lot more fun on lower-powered cars. It gives more of a feel of how it would be if you actually had a car and didn't have crap tons of money. Look at that. Look at that clutch kick. Oh, this car is so much fun right now. If we could find gears, let's still hit that. Oh, my God, I got scared. All right, I need to hit up one more track. Yep, real short. Little real short course.  Definitely having a lot of fun here. I'm fours of six. All right, here we go. A little understeer. The bumps are real though. All right, here we go. Big initiation here, hoping for big things. Just a tap, but we didn't lose our points. How did we not lose our points that time? Clutch kick. Probably should have went down a gear. Just barely powering this thing up there. Yes. Yes.  Here we go. We're getting it now. We're getting the slides in Motorsport 6 after a very horrible last session. Yes. This is money right now. Oh, it's up right there, but I should have been down inside gear actually going uphill. Clutch kick just stored in there. Oh, don't lose that. Like I said, guys, make sure you check out this man's tunes. These tunes are very pretty solid and a lot of fun. Being lower horsepower, throwing the cars weight around, running that inside curb. I was going to say, we haven't hit a cone yet. As I say that, we smash a cone. So like I said, guys, as you know, you guys can follow me on Facebook, Twitch, Twitter and Instagram, all of which are found in the description box below, because we want to see more Motorsport 6. Don't forget to tap the like button, subscribe to the channel, comment down below. Maybe you guys have a tune you want me to try. We'll be more than happy to try all the tunes here on different cars. I promise maybe I won't wreck it as bad as I wreck this one. We're gonna try this one up this section, second gear. Yeah, should have been second gear. I think we're gonna get one more lap here on Rio to end this episode off. If you can't get a good solid lap here, I bought that. Should have clutch kicked it. Good initiation in. Oh, just a tap for this episode here on Porsche Motorsport 6. Like I said, guys, I do appreciate you guys coming out and watching this episode. If you guys like what you see, comment down below. If you guys have a tune you want me to try, comment down below as well. I'm always up to try new tunes. Obviously, I just tried this man's tune and I am in love with this tune right now. Only thing I could say different would be I wish it had body kits on the car, but this is a stock tuned car, so I can appreciate it as I'm just killing this car. But I am glad it's got a roll cage in it because, you know, tandem. So it's the next time, guys. I'd like to thank you guys for watching. I'm Evil Rabbit. I'm gonna slide this out a little more because I'm having way too much fun right now to want to stop. So it's the next time, guys. I'm Evil Rabbit. I'm out.