What is single-cell RNA sequencing (scRNA-seq)?

DNA and RNA are both essential nucleic acids, but they play different roles in our cells. DNA serves as the primary blueprint for genetic information, while RNA acts as a messenger carrying instructions from DNA to help assemble proteins.

Gene expression is the process your body uses to read the instruction in your DNA and turn them into the proteins your body needs. When it comes to studying gene expression, there are two main types of RNA sequencing, bulk RNA sequencing, and single cell RNA sequencing. In this HealthTree University lesson, we will dive into the exciting world of single cell RNA sequencing and explain how it's transforming myeloma research by offering deeper insights into the disease at a cellular level.

What is the difference between DNA and RNA?

Every cell has the same DNA content, and these DNA then becomes RNA and RNA expression if what leads expression to genes in each single cells. So every cells, if you think about it, are cells of the heart, the cell of the skin have a different set of genes and RNA sequencing is what helps us understand which genes are actually expressed in each cell type.

What is the difference between bulk RNA sequencing and single cell RNA sequencing?

The bulk is basically what the whole technology be for single cells. You take all the cells together, you just extract the RNA from all the cells without selecting. Of course, we select the plasma cells, but you just extract all the RNA and you just send me the RNA all together. So basically you get one number for each gene only. So that patient that sample has one number in a single cell you take 1000 cells. So you have 1000 numbers.

So that's the kind of like granularity like think about like an Excel spreadsheet. You have one patient, one line for a gene and a single cell, one patient hundreds of life. And each line is a cell. So that's kind of like allow you to really understand with more granularity what's going on.

Bulk RNA sequencing and single cell RNA sequencing can be understood using a fruit analogy. Imagine you want to know what fruits are in a fruit salad. Bulk RNA sequencing is like putting the entire fruit salad into a blender and making a smoothie. When you taste the smoothie, you can tell the overall flavor, but you can't tell how many pieces of each fruit were there or which fruits were combined in the same spoonful. You lose the individuality of each fruit.

In contrast, single cell RNA sequencing is like examining each piece of fruit in the salad one by one. You can count how many strawberries, blueberries, and kiwi slices there are, notice differences between them and even detect rare fruits that would have been hidden in the smoothie. Similarly, bulk RNA sequencing gives an average gene expression signal from all cells mixed together, while single cell RNA sequencing reveals the unique gene activity of each individual cell, uncovering diversity that bulk methods can mask.

What are the advantages of using single cell RNA sequencing?

We do know that myeloma is a very heterogeneous disease. So, not all the myeloma cells are alike. They're different. Myeloma cells within the same patient express different things. So when we used to do bulk sequencing, so sequencing everything together, we used to miss a lot of the genetic abnormalities that exist there. Because of that we were later we would later on discover that that was a mechanism of resistance to the therapies that they were getting.

So what we would discover later on is, yeah, they have a good response initially, but then they progressed and that those clones of plasma cells that had a genetic abnormality that we did not detect earlier are present now, like they grow much, much more. And now we see them more clearly. But we missed them before.

With single cell sequencing, we're able to take each cell that we take, let's say a million cells from the patient. And we were able to look at every single cell and see all the genetic abnormalities, all the RNA, all the DNA, all the proteins that expresses at the cellular level. And this way, I think we'll be able to miss less clones and it would definitely help us pick the best treatments for patients.

We now have techniques that are getting more and more sensitive, even to the point where even in our own laboratory, we can take a myeloma biopsy and we can analyze the genetic characteristics of one cell. Which means if I can analyze the characteristics of one cell, I can analyze the characteristics of 100 different cells. Now I can look at each individual cell and ask, is there a difference in the genetic characteristics from one cell to another in the tumor. And we find those.

And in fact there are some examples where there are characteristics in a small subpopulation of cells that we've identified by the single cell approaches that are hallmarks for cells that are ultimately destined to relapse and become the drug resistant population that comes out.

So one of the things that's happened in oncology in general is that our ability to define the individual characteristics of every individual disease, as well as the individual cells within a tumor in one individual, the technologies have become more and more sensitive so that we have much better capability of really doing a very minute job of defining genetic characteristics of the disease.

How is single cell RNA sequencing being used to characterize normal cells?

And finally which I believe is the most, you know, useful approach platform or, you know, area where we can apply single cell RNA is it can characterize normal cells. So why we cannot characterize normal cell in bulk or in sequencing like the one that have been doing for 20 years or in gene expression array in the last, you know, in the prior that gets the reason is that to define a signal, you need all the cells to behave in the same way.

So that's why it works very well when you take the tumor cells, because more or less all tumor cells share a certain genotype or set the profile. They are different subclones, but overall the picture is more or less homogeneous. When you go to the normal cells, each cell is different from one another, so that's why you cannot get anything out of it.

With the bulk sequencing, it's very hard to really get okay with what the T cell do. Well, what the monocytes or the NK or all this subpopulation. So that's why it's been very difficult to do immune studies on with bulk sequencing.

So the viability of single cell you can actually say oh this cell is a NK. This cell is another NK. This cell is a T lymphocytes. And I can compare these T lymphocytes of this patient with another patient and see oh this patient has a more aggressive disease. The T cell were less effective. And the patient did not respond to CAR-T while these other patients had a very active immune environment, very clean and not exhausted. The tumor was not as aggressive as the other. And they respond very well and they are still with remission after five years.

So this type of integration of immune cells into our, you know, pool of large big data we're generating on the tumor cells is actually, I think, the real advantage of single cell RNA.

What are Uniform Manifold Approximation and Projections or UMAPs?

So this basically is just like a way to represent the data where you see, as I said, each cell has its own profile because it's single cell. But the T cell tends to be similar to one another. Of course there are differences between different subtypes.

So when you say okay, using a statistical tool, you just think about like a black box. You just put the data in and the tool generate this data. Say this group of cells are together, they are similar. And then you go there and you say, oh, these cells all express the CD3 or CD4. So these are like T cells.

And then you get the lymphocytes. Oh these like B cells you know C20 or did you see the other 38 is plasma cells. So those bubbles those like clouds are actually type of cells. That's why we color them with different colors. These are T cell, these are NK or you can call also with patients like for turmors.

When you present the tumor UMAPs these bubble for tumor cells. Because usually we split tumor and immune to not create too much confusion. And cloudy picture. All these clouds are basically patients and each patient has own clouds usually. That's also underlying how each patient is very different. One from another. And these single cell analyzes are really showing how really it's each patient in its own history.

Can single cell technologies be used to compare cancer cells to normal cells and identify the differences?

Once you have all this pool of data, you can basically say, okay, I want to know how a plasma cells looks like in patients, in people that are not patient. They're just like healthy individuals 60 years old. And then I compare the same plasma cells to patients 16 years old, multiple myeloma. And I look at the differences and I you know, you will spot a lot of interesting data.

How can single cell RNA sequencing be used in myeloma research?

For instance, for myeloma cells, we know it's a clonal disease of a barren plasma cells. But like the clone might change. So even though they come from the same cell like mother cells, when they grow, they can change a little bit and like single cell sequencing allows us to understand that and see which trajectory they are getting.

And that's real for myeloma is all three of four. When we are studying, we'll talk a little bit more about the bone marrow microenvironment. But when we are looking at the bone marrow microenvironment is really interesting to actually look at single cell RNA seq of the different population.

Is single-cell RNA sequencing used in the clinic, or is it mainly for research?

So RNA sequencing look at RNA. So gene expression is being used in myeloma mainly on the research sites for years. There are classifications that allows us based on the specific genes expressed to tell us the type of myeloma and if the myeloma can behave less or more aggressively.

But this is now really used in the clinical setting yet.

To better understand the laboratory technologies used to analyze genetic abnormalities in myeloma and assess your risk status, watch the other video lessons in HealthTree University Cytogenetics testing in myeloma course.