Perspectives in Mouse Immunophenotyping

In our continuing saga of publishing the opinions of leading scientists working in the cutting-edge areas of flow cytometry, we present this month an interview with Dr. Herve Luche, Scientific Director of the Immuno-phenotyping module at the Centre for Immunophenomics – CIPHE (Inserm-CNRS-AMU) in Marseille, France. Dr. Luche and his team are interested in the characterization of genes and their role in the immune response. Dr. Luche is also involved with the DYADEM project, which aims to gain insights into the markers expressed in the immune system components of mice and their functions in the immune response. Flow cytometry instruments, reagents and advanced data analysis tools developed by BD are an important part of Dr. Luche’s work.

1. Can you begin by describing the research activities in your institute?

I work at the Centre for ImmunoPHEnomics – CIPHE (Inserm-CNRS-AMU) in Marseille, Luminy, and the objective of this institute is to try to characterize the function of a given gene in the immune response. We are mostly researching the mouse model, which is the main model used in studying fundamental immunology. The main aim of our institute is to establish a catalogue of the function of genes with a focus on the immune system. We have tools to generate precise knock-in and knockout models and analyze them in a standardized high-throughput mode. To achieve this, the institute is divided into four big units. The first one is a huge animal house in SOPF condition. Another unit does the generation of mouse models and the unit I am heading is involved in the analysis of these mutants. The fourth unit deals with the immune-challenging of the mutants with pathogens in a BSL-3 environment. Because the immune system is not an integrated system like the muscle or the brain, the major tool we use to analyze these mutants is cytometry. From the beginning, we have been developing standardized assays, a bit like what people are doing in the clinic, and transferring that to fundamental research. The reason why I am keen to have this interview with you is that we have used the different methods and tools that BD has been introducing during the BD Horizon™ Tours to build panels. We have been trying to be rational in setting up the instruments, designing panels and analyzing data using several metrics that we learned over the years from BD Bioscience educational programs or interactions with BD scientists.

2. Can you tell us something about the DYADEM project?

DYADEM* stands for DYnamics of Antigen Density Expression in the Mouse. This project aims to gain insight into the number of markers expressed in different leukocytes in mice of different immune statuses. Why this? There are several objectives. One is obvious, as we need this information on an instrument independent scale (AbC) to build panels rationally. We all know, thanks to the BD Horizon™ Tour, that the amount of marker expression is very important when you design a panel. The major problem here is that with the number of colors available now, you either must depend on your initial biological knowledge or get information from the internet, which is never comprehensive. This is one of the major hurdles in designing panels, so you need a kind of resource that can tell you which markers are highly or slightly expressed relative to others, and in which subset. This is what we are trying to build with DYADEM. We have introduced standardized challenges to the immune system because a lot of markers are not expressed at basal levels in mice. One ‘innate’ trigger with poly-I: C induces a lot of changes, particularly in the myeloid and innate compartments of the immune system. We have seen a lot of co-stimulation markers upregulated in this condition. Another trigger is more of an adaptive one, where we inject the mice with anti-CD3 antibodies to activate T-cells. We do two injections of anti-CD3 on two consecutive days. Thanks to this challenge, T-cells get activated then exhausted and upregulate the expression of PD1, Tim3, and other immunomodulatory receptors. This is interesting for the design of panels that would work in flow cytometry, in both steady-state and inflammatory conditions, for people studying tumor immunology, vaccine studies or models of infectious diseases.

We will also be able to gain insight into the biology of the mice by understanding the dynamics of expression of all these markers as we will be able to compare them under different conditions and establish profiles of cell activation. The quantification work is performed on 18 cell subsets at the same time using a 17-colors panel and so is very comprehensive. It is only one organ but we dissect all the major components of the immune system.

3. Going forward, what impact do you think the DYADEM project would have on immunology and flow cytometry?

We have spent a lot of time designing this assay. We used the BD QuantiBRITE™ for the quantification, but we have worked on many details on standardization and validation of the assay. So we know exactly how it works and what its limitations are. In addition, we are using unsupervised methods of analysis and not conventional, supervised methods. This choice has been made so that we can compare nicely, the results from steady-state and inflammatory conditions which is a lot harder to do if you have a fixed gating strategy. The idea is that we deliver a robust resource that is available for people wishing to build multi-parameter panels. One can also imagine comparing the results of this extensive profiling to see how it relates to the human dataset that is already available. Seeing what is overlapping and what is different between humans and mice will be useful for fundamental immunology and translational research.

4. You have spoken about unsupervised analysis methods. What kind of tools did you use for this?

Because we use the same approach in other projects, we are using a scaffold algorithm¹ that was published by the Nolan lab and developed by Pier Federico Gherardini, who is working at the Parker Institute now. The idea of this tool is to cluster your FCS files, so you get clusters of your initial data. But what always takes time with the other analysis pipelines is the meta-clustering part. Once you have your clusters, you try to label them based on available knowledge, which can be very time-consuming, depending on the level of details you are interested in. Let us say, I want the effector memory CD4 or CD8 and I have many markers. I must be sure that I am not mixing different clusters that do not fulfill the phenotypic definition that I am after. The advantage of the scaffold is that it uses a gated defined reference population and will map your clusters that have been derived by the unsupervised approach onto this gated population. This way you can build a reference landscape that is based on your knowledge but as an algorithm makes it, you don’t make mistakes in mapping your clusters to the reference population. The other advantage is that when you are studying the inflammatory status, you may study the same populations but there could be changes in the expression profile of certain markers. This has been, in our hands, very robust and we have been able to ensure that a given set of clusters correspond to a given cell population across inflammatory conditions in an operator-independent manner. We are through 2/3rd of the DYADEM project now and we have been able to analyze 3000 data points in this way.

5. Our next question is about BD. Why did you choose BD and how has BD helped in your research?

There is the instrumentation, which is top quality and allows you to do things that instruments by other companies didn’t propose initially. Now you have more competition but initially, that was the key. And then, there is a major drive at BD towards developing new dyes, which will increase the number of parameters we can analyze simultaneously. Finally, there is an interest at BD in educating people. The expertise of people within BD guiding others in high-content flow cytometry, which is not easy from the start, has been key. We have been using this since the beginning of CIPHE in 2012. Applications settings, CS&T, the importance of stain indices that Alan Stall, Bob Balderas, and others at BD have been saying for many years have been put into practice by regional teams with the BD Horizon™ Tour. You know that if you have an advanced question you would get a sharp reply from BD, which may not be the case with other companies.

6. What future innovations would you like to see in flow cytometry? What are the burning questions that remain to be answered?

The reason why we use the mouse model is that the immune system is a very plastic system that changes a lot. There are a lot of different cell types, metabolic states, and functions that get acquired over time. When you want to understand this complexity, it helps if you have a product-linked relationship that helps you do a trajectory analysis based on your data. Now, it is achievable with the high content data we can acquire, with the BD FACSymphony A5™ Instrument, for example. Still, one of the issues is the relationship between protein and transcript. We all mostly only look at proteins in flow cytometry. There are techniques available to look at phospho- epitopes, and transcripts but the number of pheno-traits you can follow at the same time is a bit low. So, we need to increase the number of dimensions we can study simultaneously. BD^® AbSeq Antibodies, which for me is a new kind of cytometry not requiring a flow cytometer, is something we really should go into. With this technique, we don’t just rely on the operating transcript but look at proteins and transcripts in an integrated manner at the single-cell level. In addition, this technique should stop us from discovering new cell types that are only other immune states of known cell types. The maps we have of the immune system should get more precise. It’s a major segment of development for BD and I think it is the right approach as we need to integrate transcriptome and proteome at the single-cell level.

As for the burning questions, for me, they are going to be concerning immuno-oncology. Why a given immune-modulator has so much impact on T-cells, how we can improve treatment and what is the next target to aim with immunotherapy or other means. It will help scientists to find new cures for cancer. I think flow cytometry and innovations should go into this as it is for the good of all of us. Cancer is a very prominent disease with the number of cases increasing every year. It will be very good if we can find ways to make the immune system fight cancer more efficiently and limit autoimmune adverse effects, which are being observed with current therapies.

7. You had asked about the types of antibodies that are or will be available for the BD^® AbSeq Antibodies. Of course, you are interested in mouse antibodies?

Yes, don’t forget the mouse people. I know that these experiments are expensive. You need lots of new reagents and there is a lot of validation going on. You need to build up this portfolio. Please don’t forget the mouse. Something I have always wanted to do is to be able to look at the transcriptome in a targeted manner, not necessarily a whole-cell transcriptome assay. You will have an issue with the depth of the transcriptome that you’ll get with the single-cell technologies now. Targeting would involve having a set of genes you want to see and being able to look at lots of proteins, possibly the clonotype of your T-cells, at the same time. In a pre-clinical setting, while testing the efficacy of a new drug, one might be able to detect variations in the clonotypic composition of T-cells in the tumor and see at the transcriptional level and protein level that some of them become functionally more active. These combined layers of analysis should help understand the efficacy of a candidate treatment. This is what our partners are after and therefore, this is something we need to address.

The other thing is that once you are used to the advanced analysis methods in flow cytometry, the transition to these single-cell technologies will be easier. This is something that people could be afraid of and they may need the support of bioinformaticians. With your latest acquisition of FlowJo™ Software and the dynamic team there, I think people could handle these themselves. So people should not be afraid of jumping into this field.

8. Do you plan to use FlowJo™ Software in future studies?

With FlowJo™ Software there was an issue with version 10 on PCs. So we stopped using it. We have started using it again because issues have been fixed and there are now all these new plugins with R scripts that are talking to the FlowJo™ Software interface. Although I am very fortunate to have one bioinformatician on my team, I have also twelve biologists generating data. One bio-informatician is not enough for all the analyses we run. So, there is a need to make people autonomous and more engaged in their advanced analysis and FlowJo™ Software is a really good tool for that.