10 Tips for Quantifying Immunohistochemistry Staining
The scientific community is devoting a significant effort towards developing increasingly complex in vitro assays to facilitate high resolution imaging. While this is possible in vivo, highly-specialized training and expensive equipment are required.
In vitro high-resolution imaging makes it possible to directly observe the role of different cell types in the onset of diseases or in drug responses. This is becoming a particularly powerful tool, now that studies have shown that the cellular microenvironment can greatly impact the development of diseases such as cancer1, and the success of therapeutic treatments such as chemotherapy.2
Generally, powerful phenotypic information can be extracted from cell images. In addition, one might want to obtain genotypic data to correlate phenotype and genotype. For this, immunohistochemistry (IHC) has typically been the tool of choice.
IHC consists of staining fixed cells with a fluorescent antibody to detect the presence of a specific biomarker. Note that IHC uses fixed cells and is therefore limited to studying samples at a fixed time point. Alternatively, one can use live-fluorescent staining which can be powerful but at the same time, the staining can influence the viability and behavior of cells in unknown ways.
As powerful as IHC is, it is also often unclear how to extract information from the resulting images.
Here we expose 10 tips which should help you design a strategy for analyzing your IHC images.
These general tips are intended to help you design your analysis, and can be applied to your image analysis software of choice.
Garbage in, garbage out
There is only so much you can do with an image post-processing.
If the images happen to be of bad quality or if the experimental design was not well-prepared, the analysis will inevitably be flawed.
The first recommendation is to be highly selective with the images you choose for analysis, because you could waste a lot of time optimizing an analysis which is doomed because of the poor quality of the images.
Do not wait to acquire images from numerous experiments to refine your analysis
As soon as you have obtained IHC images from your first set of experiments, take a look at them so you can quickly identify any potential problems with your staining, and confirm that your current IHC protocol enables you to answer the question of interest.
This ensures you can quickly adapt your protocol ahead of your next set of experiments and minimizes the risk of wasting time and energy on staining samples inadequately.
Quality controls are vital
With IHC you will not answer your question in absolute terms.
In other words, staining can be subject to many factors during the experiments which are inherent to the procedure or operator. This means the only way to properly answer a scientific question with IHC is to compare your results to control samples that have gone through the same process.
You also need to use controls to demonstrate that your stain actually works. For example, you need to use isotype controls for your first and second antibody, to check that the signal is not caused by non-specific interactions between the antibody and the cells. Negative and positive controls are necessary for validating the specificity of your staining.
Specifically, you should use cells that you know do not express the biomarker of interest, and prove that your biomarker is not detected in these negative controls.
On the other hand, you should also stain cells which you know should express the marker, and demonstrate that you can see the biomarker in the positive control.
IHC can help you help you detect the presence of a biomarker
Determining whether a biomarker of interest is expressed in your sample is a basic question that can be answered with IHC.
A biomarker is said to be “expressed” if you can detect color pixels which correspond to the fluorescent tag you used in your IHC protocol.
For example, if you used a 488 nm conjugated antibody, the presence of green pixels would show you where the biomarker is expressed. It goes without saying that you want to make sure nothing else in your sample is green, otherwise you cannot distinguish the signal specific to your biomarker.
Of note, you can often find background fluorescence that is not specific to your biomarker – this can be quantified on your negative control samples that are not supposed to express any of the biomarker.
You need to decide whether it is minimal enough that you can ignore it, or whether it is important enough that you should subtract this background fluorescence to your signal, or decide on a threshold below which you consider your fluorescence to be nonspecific.
Make sure that the noise-to-signal ratio is small though, otherwise you might just be observing noise!
Has the amount of marker changed between condition A and B?
Once you know your marker is expressed in your sample, you can start exploring its relative expression in different conditions.
In this case you have several options: you can either compare the intensity, or the extent of staining between your samples.
In other words, compare the histograms of the signal to see whether:
- there are more pixels expressing any amount of the marker, or:
- a higher signal intensity is being expressed, that is the same number of pixels that have higher intensities.
Both changes can point to a higher expression of your biomarker over the entire sample.
Do more cells express the biomarkers?
So far we have discussed analyzing pixels over the entire image. However, more meaningful information can be extracted by studying individual cells.
If you see a global change in the signal over the entire image, does it mean more cells express the biomarker or the same number of cells express more of it?
To answer these questions, segment the edges of cells so you can consider them on an individual basis.
Once that is done, you can quantify how many cells contain pixels of the color of interest: cells that overlap with the signal can be considered to express the biomarker.
Again, it is very important to decide on a background threshold below which you consider the signal being just noise, using your negative control.
You will see that co-localizing the signal to cells might be much more informative than averaging the signal over the entire cell population in your images, as cells can be very heterogeneous.
Is more biomarker expressed per cell?
In the interest of resolving the cellular heterogeneity, you could also quantify the intensity of the signal per cells and not just its presence. This is different than answering whether more cells express the biomarker: this is testing whether cells express more or less of the signal.
Again, you would need to segment your cells and quantify the total intensity of the signal per cells. This could reveal important information about specific cells within your samples that are sensitive to the change in conditions while others may not.
Where in the cell is it expressed?
You might also want to pinpoint the intracellular location of the biomarker. For example, some intracellular proteins might be translocated from the nucleus into the cytoplasm of the cell.
To do so, co-localize the staining of the biomarker of interest with the nucleus, which can be stained with DAPI.
By quantifying the change in the percentage of overlap between the nucleus stain and the biomarker stain, you can test whether the protein of interest has changed its intracellular localization, from the nucleus into the cytoplasm.
Establish biomarker distribution
Surface receptors are crucial to many biological processes and are commonly stained with IHC. In addition to testing for their presence, it can be informative to look at staining distribution around the cell membrane, and observe whether it changes under certain circumstances.
To do this, you could calculate an intensity profile over the circumference of the cell, and observe whether the distribution of receptors changes between conditions. This could be particularly relevant for surface receptors involved in tight junctions between cells.
Relation to microenvironment
Analyzing different colors can improve your analysis. For example, fluorescently-labeled cells can be visualized in a color different than that of the biomarker of interest.
This enables the study of the spatial relationship between specific cell types and the biomarker of interest.
You could determine whether physical contact or proximity between different cell types changes the expression of a biomarker in a given cell. This opens the door to the study of the microenvironmental effect on the expression of given biomarkers which can be rather powerful and relevant, especially in the context of the development of complex in vitro models that contain numerous types of cells.
- Joyce, J. a & Pollard, J. W. Microenvironmental regulation of metastasis. Nat. Rev. Cancer 9, 239–52 (2009).
- Hughes, R. et al. Perivascular M2 Macrophages Stimulate Tumor Relapse after Chemotherapy. Cancer Res. (2015). doi:10.1158/0008-5472.CAN-14-3587