T cells are considered the immunological memory of the body. Many infections, diseases, vaccinations and therapies therefore leave signatures in the T-cell receptor (TCR) repertoire. These signatures can be used to analyse a patient’s immunological history or monitor immune responses. Similarly, if these signatures point towards the presence of pre-existing immunity, it can be exploited to predict how a patient will respond towards a therapy or vaccine.
ImmuneWatch DETECT
Analyse and predict T-cell responses
ImmuneWatch DETECT is designed to turn these TCR signatures into clinically actionable data
What is the T-cell receptor repertoire?
Next-Generation Sequencing (NGS) has caused many scientific disciplines to move from laborious cell experiments to high-throughput sequencing. Examples from this shift can be found in the genomics or microbiome research fields. Fortunately, this revolution has also made it into the field of immunology. Multiple single-cell or bulk-based sequencing approaches have been developed in the recent years that allow us to profile the TCR repertoire.
Transform TCR Data into Actionable Insights
Standard TCR repertoire analysis techniques give insights in the dynamics and size of the T-cell receptor (TCR) repertoire. They can reveal what kind of TCRs are clonally expanded and what the total diversity of the repertoire is. However, these analyses lack the most crucial step to make the data useful in any clinical setting: annotation of the pathogens (epitopes/antigens) that each T-cell receptor recognises. At ImmuneWatch we are maintaining a large database and have developed machine learning tools that can offer these annotations.
Why use ImmuneWatch DETECT?
Unlock Epitope-specific T-Cell Response Insights
Do you want to better understand the T-cell response to a vaccine or therapy, for example in the context of a clinical trial? ImmuneWatch can help you profile the T-cell receptor (TCR) repertoire. Start from blood or tissue samples, sequence the TCR repertoire and use our AI-driven technology to annotate the epitopes recognized by the TCRs. These insights, will give you a better understanding of the cellular immune response towards your vaccine or therapy.
Predict Unwanted Immune Responses with Pre-existing Immunity Data
Are you looking to profile pre-existing immunity to avoid unwanted immune responses? ImmuneWatch can take a deep dive into an individual’s TCR repertoire which will generate insights into their immunological history. This information can then be used to predict how this individual will react towards a certain therapy. Use cases include predicting seroconversion after vaccination or predict whether there will be strong immune responses against AAV-based gene therapies.
Expert analysis of TCR repertoires
Do you have TCR repertoire data available and having a hard time to extract relevant knowledge? We have have extensive experience in analysing these type of datasets and are happy to help out!
Real-World Applications of ImmuneWatch DETECT
Using ImmuneWatch DETECT to Monitor and Predict the Immune Response
In this eLife paper, we analysed the immune response towards a Hepatitis B vaccination program using the TCR repertoire. Because of the machine learning algorithms used, they were able to elegantly profile the TCRs that were specifically recognising the vaccine antigens and show how their composition was altered because of the vaccination.
In addition, they asked themselves: “Can we use the TCR repertoire to predict the serostatus prior to vaccination?”. The answer was yes, as they found that individuals with a high fraction of pre-existing TCRs that recognise epitopes of the vaccine’s antigen, showed a better seroconversoin status that individuals without pre-existing immunity.
Tracking T-Cell Responses to Experimental Cancer Vaccines
Experimental cancer vaccines typically contain a set of (neo-)epitopes. In this use case, we used TCR sequencing and our machine-learning algorithms to find out which of these epitopes were successfully causing a T-cell response. In this case, we found that TCRs recognising Epitope Y were stimulated, while TCRs recognising Epitope X stayed at the same abundance.
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