Rapid RNA analysis at a single cell level: a PLAYR's story

We've talked about single-cell RNA sequencing and how looking at single cells gives us a way to see through the noise of cellular processes. If we want to know how specific cells respond to cellular signals or diseases, it can sometimes be difficult if they are surrounded by other cell types that don't respond. It's hard to parse out what is real and what could be random signals. If we could look directly at precise populations of cells and their individual responses, we could more accurately target drugs or find vulnerable populations of cells.

Recently, a lab from Stanford University developed a technique that allows for high-throughput analysis of RNA expression in sub-populations of cells. It takes advantage of a new biological tool called mass cytometry. Mass cytometry is similar to the more well-known flow cytometry which involves using fluorescent antibodies that recognize cell surface molecules to distinguish cells types. For example, if you have a homogenous mixture of blood cells like you do in your circulating peripheral blood, like B-cells, t-cells, myeloid cells, etc., you can use fluorescent antibodies that tag surface molecules like B220 on B-cells, or Mac-1 on myeloid cells. In this way, you know all of the cells expressing, say, a green B220 are B-cells, and all of the myeloid cells express a red Mac-1. By using flow cytometry, you can analyze the fluorescence of each single cell to categorize and sort them. Mass cytometry is different in that it doesn't use fluorescent markers, but uses metals as markers. Different metals react differently to electric fields, which the machine can measure to determine which marker is being used. The concept is very similar to flow cytometry, although mass cytometry allows you to visualize about 2 times as many markers as flow cytometry because it doesn't involve distinguishing between similar colors. These machines are capable of analyzing over a thousand cells per second. It is an extremely useful way to analyze bulk populations of cells based on their cell surface markers, allowing you to parse out different cell types. Building off of this technique, the researchers from Stanford developed a way to analyze cells using mass and flow cytometry based off of their mRNA transcripts - something that had not been done before.

Their technique PLAYR (for Proximity Ligation Assay for RNA) enlists 2 small DNA probes that recognize adjacent regions of the target mRNA sequence of interest (shown in step 1 and 2 in the figure to the right). These probes have 1 region designed to recognize the mRNA, and another side designed to hybridize to two other sequences called backbone and insert (shown in step 3). The backbone and insert are ligated together (shown in step 4) and then copied many times using a special enzyme called phi29 that starts rolling circle amplification using a single strand of circular DNA (shown in step 5). This allows them to create many copies of the backbone and insert for every mRNA they are looking for. Finally, they add a fluorescent or metal tagged DNA probe that recognizes the amplified sequence (shown in step 6). The cells that express the mRNA of interest will now shine a specific color, or contain a specific metal because of the DNA probe. The probes they use to recognize the mRNA shown in step 2 are very specific. This means there is low background, so you know the signal you see is real. You can also use different pairs of probes that target the same mRNA, ensuring the signal is robust and precise. The PLAYR technique can be done in conjunction with the cell surface markers we talked about before, which allows you to confirm mRNA expression and quantity in specific cell types.

By using this technique, the researchers we able to distinguish different cell types based off of their cell surface proteins, and then confirmed their findings by looking at each cell type's gene expression. Shown below is a map of the cells they looked at clustered by their surface protein expression. You can see they were able to distinguish the different cell types pretty clearly, although this isn't very novel. In the image below to the right, what they were able to do is show how each cell type expresses a given mRNA and the protein the mRNA codes for. Each transcript shows robust cell-type specificity - meaning each mRNA or protein appears to only be expressed (indicated here as more yellow) in the subset of cells that should express them. This data act as a good control, showing that the mRNA labeling works just as well as the more established protein protocols. To take this one step further, the researchers wanted to do more than confirm their technique. They wanted to show that PLAYR could be used to look at the timelines of multiple gene expression profiles in specific populations of cells, something that couldn't be done efficiently before PLAYR.

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In order to do this, they looked at cytokine levels at different time points after inducing an inflammation response. They looked at the same peripheral blood cells that we saw in the figure above, although this time with more depth - that is, at more subtypes of cells. If you looked at peripheral blood cells during inflammation, you would see a cytokine response, the appropriate signals during inflammation. However,

you wouldn't be able to decipher exactly what each cell type was doing. It would also be difficult to get an idea of time course of the response by just looking at protein. By using this technique, not only can you separate the different cell types, but you can also see accurate measurements of specific RNA levels. Shown to the right are 2 different cell types out of the 8 they could separate and the levels of 8 different cytokines over the course of 6 hours. By looking at cytokine RNA levels instead of cytoplasmic cytokine proteins levels, you get a more accurate snapshot of the molecular programming going on in the cell. They see that the cytokine response is limited almost exclusively to CD14 expressing monocytes, with very faint expression in CD16 expressing monocytes. The other 6 populations they looked at showed nearly no expression (data not shown). Using PLAYR with mass cytometry paints a precise picture of transcription at a single cell level. RNA levels are a more accurate way of measuring cellular responses as they are the first to step and have shorter lifespans than proteins, In this way, you to see levels increase and decrease with far more precision. Altogether, using mass cytometry and PLAYR technology allows for the quantification of over 40 proteins and mRNAs at the same time in single cells. You can analyze the nuanced changes and differences of gene expression in specific cell types. Hopefully this will make the identification of cell-type specific responses and drug targeting a far more straightforward process.

References:

Bendall, S.C., Nolan, G.P., Roederer, M., Chattopadhyay, P.K. A deep profiler's guide to cytometry. Trends in Immunology 33, 323-332 (2012).

Frei, A.P., Bava, F.A., et al. Highly multiplexed simultaneous detection of RNAs and proteins in single cells. Nature methods 13, 269-275 (2016).

Image credits:

All images are augmented from the Frei & Bava et al. paper cited above.