Designing T-Cell Activation and Exhaustion Assays for Autoimmune Programs: Expert Insights

T-cell activation and exhaustion are increasingly being evaluated in autoimmune programs as biomarkers of disease activity and therapeutic response. As therapies aim to fine-tune immune responses rather than broadly suppress them, accurately measuring these T-cell states becomes critical.

But generating meaningful activation and exhaustion data requires careful consideration. Marker selection, assay design, and therapeutic mechanism can all influence how these states are detected and interpreted.

To explore how to approach measurement in practice, we spoke with David Possamaï, Principal Development Scientist at CellCarta. In this Q&A, he shares insights into the challenges of measuring T-cell activation and exhaustion, key considerations for marker selection, and how therapeutic mechanism of action influences assay design.

Why is measuring T-cell activation and exhaustion challenging in autoimmune programs?

One of the biggest challenges is ensuring that what works during assay development translates into clinical samples, because the difference can be dramatic.

In the development and validation phases, activation and exhaustion markers are often easy to detect and appear robust because of appropriate cell populations expressing the makers of interest are used. Once the assay is applied to patient samples, however, marker expression can look very different. T cells span multiple differentiation states and simultaneously express activation and inhibitory “exhaustion” receptors, while autoreactive T cells are often present at very low frequencies in blood and are often sequestered in tissues.

Ensuring that activation and exhaustion markers can be detected at comparable sensitivity, and interpreted confidently, in clinical samples can be the most difficult part of the process.

What’s the best way to approach marker selection when measuring T-cell activation and exhaustion?

The most effective approach to marker selection is to start with what’s already well established in the field and relevant to your therapeutic area.

In practice, marker selection is guided by what’s been published and recognized in human studies over time. Activation and exhaustion markers that have been consistently used are typically the safest place to begin because their biology and limitations are broadly known. These are markers the field understands, so there’s a familiarity with how to interpret them in clinical studies.

In clinical settings, the goal is generally not about introducing novel markers, but to work with established ones and then evaluate how they are modulated in the context of your therapy.

Using well‑recognized markers allows results to be interpreted consistently across studies and compared with historical data. However, additional or exploratory markers can be layered in to refine and complement interpretation.

What is the best way to determine whether a marker will perform reliably in a real disease context?

Markers are typically validated using surrogate or controlled matrices, which are necessary for assay development but does not fully predict clinical performance. The most reliable way to confirm whether a marker is truly appropriate for a specific indication is to test it directly in disease-state samples.

Running a proof-of-concept experiment in patient material allows you to assess whether the marker is detectable and behaves as anticipated in the real biological context. This step provides confidence that observations made during development will translate into clinical samples.

Admittedly, this is not always straightforward. Access to disease-specific samples can be limited, time-consuming, and costly, and as a results proof‑of‑concept testing is often omitted in practice.

In autoimmune diseases, this challenge is amplified because many cells of interest are tissue‑resident (for example, in central nervous system, synovium, skin, or gut). Accessing these compartments is frequently invasive, ethically constrained, or not feasible.

However, when marker performance is likely to be central to interpretation or downstream decision-making, planning early to secure representative patient samples can make a significant difference by reducing risk and helping to avoid delays later in development.

Why can certain markers, such as TOX, be difficult to use reliably?

First, most activation and exhaustion markers are not specific to a single T cell state and can be expressed across multiple cell types and differentiation states. As a result, markers such as TOX must be analyzed in parallel with other markers (lineage, activation or exhaustion) to place their expression in the appropriate biological context and support robust phenotypic definitions.

In addition, some markers are more technically challenging to measure reliably, and TOX is a good example. As an intracellular transcription factor, TOX requires fixation and permeabilization steps, adding complexity relative to surface staining. Intracellular staining generally carries a higher background, which can complicate data interpretation.

TOX expression can be very low. In some cases, additional optimization is required to achieve detectable signal, and available reagents don’t always perform optimally and consistently across sample types. Together, these factors can limit resolution in a flow cytometry and make it difficult to distinguish true biological signal from technical noise.

Another challenge is that many activation and exhaustion markers, including TOX, do not exhibit a clear negative-positive (bimodal) distribution. Instead, expression often appears as a continuum, resulting in “smear” rather than cleanly separable population. When that occurs, defining threshold for positivity becomes challenging and it can be difficult to determine whether observed shifts are biologically meaningful or due to background variation.

Careful gate placement is therefore critical for these markers. Defining positivity often requires the use of internal reference populations within the same sample, such as another cell type or subset expected to be negative or low to anchor the gate and control for background. Without such internal controls, gate placement can become subjective.

Ultimately, a marker becomes unreliable when its resolution is insufficient to support confident interpretation, particularly in clinical or longitudinal studies. For markers such as TOX, it’s essential to balance biological relevance against the technical demands and limitations of detecting them robustly in disease contexts.

How should mechanism of action (MoA) inform the way activation and exhaustion are measured?

MoA directly determines what can be detected and how it should be measured when assessing T cell activation and exhaustion. Understanding the MoA is essential for designing assays that are interpretable in clinical samples. For example, if a therapy directly targets a receptor such as LAG-3, and LAG-3 expression is also of interest, the drug itself may block, mask or alter the detection antibody binding. In such cases, the assay design must account for potential epitope competition, receptor occupancy, or internalization.

MoA also influences more practical aspects of panel design in flow cytometry. For instance, if a therapy is expected to upregulate the expression of a marker such as CD25, that marker may become very highly expressed following treatment. Anticipating this shift is important for fluorochrome assignment: using the brightest fluorochrome for markers that are expected to become highly expressed can reduce dynamic range and compromise resolution of other, dimmer markers. This issue can become more pronounced when transitioning from controlled validation samples to heterogenous clinical samples.

More broadly, understanding how a therapy is expected to modulate T cell biology  informs antibody clone selection, fluorochrome assignment, gating strategy and overall panel configuration. Without this context, assay development would be far less targeted and may fail when applied to patient samples.

In short, the more comprehensive the understanding of the therapy’s MoA and its expected downstream biological effects, the more effectively we can design a robust assay that performs reliably and effectively measures activation and exhaustion in real clinical settings.

What is the key thing teams should keep in mind when designing studies to measure T-cell activation and exhaustion in autoimmune diseases?

Context matters.

A clear understanding of the therapy, its mechanism of action, and the expected biological effects is essential when designing studies to measure T-cell activation and exhaustion in autoimmune diseases. This context informs critical assay decisions, including antibody clone selection, fluorochrome assignment, gating strategy and overall panel design.

Incorporating this information as early as possible in assay development is particularly important. The clearer the understanding of how a therapy is expected to modulate T cell biology, the more deliberately and effectively the assay can be designed to perform reliably in heterogeneous clinical samples to generate high quality, interpretable data.

Want to find out how CellCarta can support your autoimmune program? Explore our immunology capabilities