Single cell RNA sequencing overview | ScRNA seq vs Bulk seq | chemistry of ScRNA seq |Bio Techniques

in this video we'll be talking about single cell RNA sequencing which is a transcriptomic method that has revolutionized recent biology so basically single cell RNA sequencing is a method where it analyze gene expression profile of individual cells within a heterogeneous cell population so here you can see a heterogeneous cell mixture which would be running through a microfluidic device where it would be combined with barcoded beads and since these barcoded beads are uniquely combined with one cell type when these cells would be lized it would be completely known that the RNA that came out of the

cell come from which particular cell because one type of cell would get one type of bead so basically the barcode would give us then information about the transcript coming from a particular cell type later on it would be sequenced and the data would be visualized in this video we are going to talk about the entire procedure step by step so stay tuned till the end let's put things in context imagine you are looking at a tissue and there are multiple cells that constituent the tissue but the cells are not homogeneous it hardly happens in real

life instead the cells are heterogeneous and imagine you're looking for let's say four genes and four genes might not be expressed in a similar fashion in all these cell type there could be heterogenity in the gene expression altoe there could be dynamics of gene expression which could which could be different between all these cells color coded differently so these kind of intrinsic variability of the gene expression or unique profile of the gene expression can be explored using the technique like single cell RNA sequencing before we delve into details we should understand that this technique evolved

gradually initially qpcr was one of the important technique to look at many genes at the time later on when micro came it was one of the state-of-the-art technique in the field which was eventually replaced by the bulk RNA sequencing and these days single cell RNA sequencing has taken over from 2009 and over last decade single cell RNA sequencing has been used extensively in the field of biology neuroscience and many more other fields so imagine you have a tissue so you want to like take out the cells and look at the gene expression profile in a

bulk RNA sequencing now many things can go wrong in this case because this the assumption that a tissue would be homogeneous is completely wrong because tissue is actually heterogeneous in a real life scenario so imagine a single cell versus a bulk scenario in the Single Cell sequencing individual cells and their heterogenity in the gene expression profile can be visualized whereas in the bulk RNA sequencing that information is kind of masked imagine there is a gene which is upregulated in one cell type let's say in this blue cell and now that is down regulated in the

red cell in the same magnitude so in a bulk RNA sequencing you are not able to characterize this phenomena because those are two different direction of changes and the magnitude is same so it kind of cancels out so these sort of problem s are always experienced in bulk RNA sequencing which is overcomed or circumvented using the Single Cell RNA approach so in this video let's talk about more about this but you can broadly understand single cell RNA sequencing to be a fruit CED where you can enjoy and understand individual texture of these fruits you know

which fruit you are eating but imagine you are eating a smoothie you would only able to understand the dominant tests right you would not be able to understand the test of individ ual components of that smoothie and that's what exactly true for bulk RNA sequencing versus single cell RNA sequencing so let's talk about the procedure in step by step so there is a extensive and exhaustive wet lab procedure followed by a dry lab analysis Paradigm we'll talk about both first we'll start with a tissue which has obviously heterogeneous cell types but it's in a tissue

format so it has to be dissociated with the help of some sort of enzyme like Trine or pepen pepen is very frequently used to dissociate tissue and form this single cell suspension this single cell suspension is the starting point of the Single Cell RNA sequencing so in a tube you take the cells incubate that with Trine wait for some time and then pass those uh suspension after pip it up and down in a fax tube there are mesh in the fax tube which allow allow the passage of individual cells if the cells are clumped they

would not pass through this SE ultimately you would end up with single cell suspension step two is to count the cells using hemocytometer so you can put a little bit alicot of cells and try to count the cells using the hemocytometer to know how much cells are there in the suspension and what is the viability status then there is a specific chip onto which the cells are loaded along with oil and the gel beads so these gem beads are important beads so basically the they have specific barcode attachment to it we'll talk about it later

but let us talk about these overall assembly you can see the gem beads are in the panel number two and in the row number one there is cell plus enzyme so basically one can clearly imagine that um what would happen here the gem bead would flow through the microfluidic device that that would combine with the cells which are also associated with enzymes and since from the well three the oil is also flowing all these components would gel weld together to form a bead it's a imulsion that is formed right because basically you have oil you

have watery substances where the cell was suspended now it forms a phas separated entity which is known as the J bead it has a bead it has cell and it has enzymes and you can imagine this is not only uniform event there are many many cell types and there are many many gem bits which have which would be combined to form many of these single cell gem emulsions so basically gem imulsion contains these four components single cells reverse transcript is enzymes and reagents then gel beads and basically then um oil so these are the components

so they create create a reaction vessel as if these are individual small PCR tubes and in a moment it would be clear why these are important now basically this entire process is happening in this particular small equipment tabletop equipment but it's basically allowing the flow through these microfluidic device and combining these gy proper gem formation is the first and foremost criteria of a successful uh single cell RNA sequencing experiment now you can understand this is the step where you just put the gem beads you have to Vortex the gem beads and then incorporate into the

um chip eventually this gem bead would have this kind of hairy appearances on the surface so if you look at and zoom into one of them you would find each of these hairs have primers 10x cell barcode unique molecular identifier and a poly DT attachment this would be useful to capture mRNA which would come from the cells anyway this is basically the 10x barcode of gem bead then basically you put the Single Cell suspension in a different well and it would be combined in this micr fluidic device and now in the gem beat you would

have in one particular Emulsion you would have one type of gem bead let's say this green color bead with the red color cell and let's see why this is important and formation of the Emulsion is really critical so now if we zoom into each of these Emulsion we would be able to see that there are specific cells right and eventually we would lice these cells when we lice these cells there would be rnas which come out now each of these rnas would be captured by the oligodt part of these gel now once it is captured

on these bead like this particular scenario each of these Emulsion has the enzyme so inside this reaction vessel which is this Emulsion uh drop you can see a cdna would be formed from the MRNA right and this is really important this cdna is labeled with the specific cell bar code that means even if you mix it up with other samples you would know that where it came from so basically this process is exactly like attaching a barcode to anything later on just by reading the barcode you can understand where it came from so inside these

mini reaction vessels which are these emulsions one can convert the um RNA into cdna just to stabilize them and the sequences that have same barcode came from the same cell this is the key principle and essence of the Single Cell RNA sequencing right now imagine this sequence all these mRNA and the cdnas are now mixed even if they are mixed just by looking at their BARC code one can understand that where does these Cell come from in this example you can see these red barcoded uh cdna came from this particular cell anyway so common cell

barcode means they have a common cell of origin so obviously just by looking at the barcode one can understand that what was the origin cell type it came from and then people ask okay what was that cell type so now we have the bead and be help us to create this Emulsion and that imulsion acts like a small reaction vessel which separates each transcript of each cell and that's the essence of the Single Cell RNA sequencing data now this step is known as the library preparation where there are cdnf fragments incorporated into a specific orientation

so first it would be fragmented with enzymatic fragmentation ends would be repaired and uh liation of the ends would be performed followed by a cleanup and priming step specific sequencing barcodes would be attached to it and now this particular thing is ready to be loaded onto a sequencer now the step is to look at the library QC in this step one looks for the overall fragment size and the fragment should has a kind of uniform distribution around 500 base pairs that's a good Library QC so overall what we looked at so far is a workflow

where it starts with single cell suspension followed by combining that those single cells with the gem bead to form Emulsion this is a critical step the gem Emulsion formation is a very critical step in the step three we looked at how we basically barcoded these uh individual cdna with that in Emulsion RT reaction after this step three that means Emulsion formation eventually the Emulsion would be broken cleaned up the oil would be taken away and the cdna would be cleaned up to form a libr the sequence Library would be then U checked for its quality

control and eventually if it passed the QC then it would be loaded onto a sequencer so from the sequencer what we would get is basically fragment of sequences each fragment of these sequences are known as reads we would have thousands and millions of these sequences so sequence fragment would be later aligned to a reference genome using specific software and and in this software provided by many companies provide detailed quality control parameters like you see here I'm not going to go into the NR details of this in this video but anyway it's another QC parameter it

would tell you how many cells was captured in the uh uh process and all all these kind of information would be provided here now what we got out of this is a count Matrix that means in one side you have the cells in another side you have the genes it's kind of like an IND dimensional data so IND dimensional data has to be reduced in terms of dimensionality to visualize it better one such dimensionality reduction algorithm is umap or manifold reduction so here we can see and visualize this data just to put it in a

very Layman's perspective looking at this data you just have to see each colored dots each colored dots represent one particular set cell for example these red colored dot is one type of cell and this particular dot is very different from these orange or blue colored cell types anyway there are many other ways of visualizing the data one can plot heat maps of cells versus genes or log to fold change of all the genes that are differentially upregulated or down regulated one can look for enrichment of Pathways Etc so basically this is how basic um map

look like there are different different different clusters and the way the algorithm defined cluster is based on transcriptional similarity versus dissimilarity that means this particular in this particular case you can see the green cluster was very different from the red cluster because they are also different in context of transcription or the transcript they have so one way of understanding which transcript is upregulated in which cluster one can look at a marker Gene in a feature plot here it's kind of like a heat map you can have that overall landscape it's like a geographical map and

then it talks about this particular Gene that ms4 A1 is highly expressed in these green cluster and it is not expressed in other clusters one can also look at and visualize the same information in format of a violin plot one can even ask that okay among all these eight clusters what are the top genes that make them different and plot all the all of that in format of a heat heat map one can also use another representation called Bubble plot where each of these bubble represent the percentage of cell that Express this Gene and the

color represent the expression levels one can also create trajectory that means like a differentiation trajectory can be analyzed from these uh single cell RNA sequencing data so lot of analysis can be done and it's a Too Short video to explain all of these but one of the important example that I can give you is that one can look at a mutant versus a control scenario or a control versus a treated drug treated scenario in this case one can look at the transcriptor of the mutant versus control and compare them each other so one can look

at a particular Gene and ask whether that Gene is upregulated or down regulated one can visualize that using violin plot one going look at a plethora of genes I mean a set of genes and try to understand what is the change of their levels how many cell change that level using a bubble plot one can look at the pathways that has changed based on the change in these genes so all these information can be retrieved from the Single Cell RNA sequencing data so it's basically you need to understand how to analyze and mind information from

this data but also it's important to notice that there are many limitations of single cell RNA sequencing there are technical variabilities there are detection sensitivity issues there are some sort of bias when cells are stress and cost and uh analysis pipelines these are also a problem in a current scenario but despite of all these limitations this particular technology has literally revolutionized the field of transcriptomics so I hope you got a quick refresher and quick concept about this technique um and in subsequent videos we'll talk about this technique and data analysis in bit more details so

see you in next video d