B-Cell Monitoring in Autoimmune Programs: Expert Insights | CellCarta

June 30, 2026

Sasha Silva-Barrios

B-cell depletion therapies (BCDTs) can offer autoimmune patients sustained disease control that was previously difficult to achieve, with therapies such as rituximab able to meaningfully alter the course of diseases such as rheumatoid arthritis and multiple sclerosis.1,2 But the B-cell monitoring that underpins the development of these therapies can be complex. Detecting B-cells and their subsets at the very low levels encountered during depletion and reconstitution places significant demands on assay sensitivity, and sustaining reliable measurement across studies spanning months brings operational challenges.

We sat down with Sasha Silva-Barrios, Principal Development Scientist at CellCarta, who leads assay development and validation for clinical immunology programs, to explore how to approach B-cell monitoring in practice.

In this Q&A, she shares her insights on the challenges of measuring B-cell depletion and reconstitution, how to think about assay strategy, and what it takes to sustain reliable measurement across a long-term study.

What are the key challenges in measuring B-cell depletion and reconstitution in autoimmune studies?

There are a few interconnected challenges, mostly stemming from the nature of B-cells themselves.

B-cells make up roughly 10–15% of peripheral blood lymphocytes under normal conditions, already a small fraction, then you’re trying to detect specific subsets during depletion and reconstitution when their levels are even lower. At those counts, small variations in how data is acquired or gated can have a disproportionate impact on results, and there’s a risk of misinterpreting what the data actually shows.

A second challenge is that blood-based measurement only tells part of the story. B-cells also reside in immune organs such as the lymph nodes and spleen, and assays performed on whole blood cannot capture what’s happening in those compartments. A patient could appear fully depleted in circulation while still carrying a meaningful B-cell burden in tissue, an important interpretive limitation that must be acknowledged in the data.

What role does assay sensitivity play in generating reliable B-cell depletion data, and how can you ensure adequate sensitivity?

Sensitivity is the most critical property of any monitoring assay in this context. Decisions around dosing, retreatment timing, and patient safety all depend on confidence in what the assay is showing at extremely low counts.

To ensure adequate sensitivity, you have to establish defined lower limits of detection (LOD) and quantification (LLOQ), which are study-specific and determined by the therapy’s mechanism of action. Sample volume also needs to be determined on a study-by-study basis. Because B-cells are scarce, sufficient cell input is essential for statistically confident detection, and the volume required depends on the minimum number of events needed to achieve that.

Antibody quality is another factor that directly affects sensitivity. Detection antibodies must be highly specific to the relevant epitopes, and recognition can vary between clones from different suppliers, affecting both sensitivity and reliability. This is especially important in CAR-T programs, where the CAR construct targets the same epitopes as the detection antibodies. Some clones may compete directly with the therapeutic, producing false negatives, so detection antibodies need to be developed and validated in parallel with the CAR construct to ensure sufficient sensitivity.

Why is B-cell subset profiling important, and how does it inform interpretation of reconstitution?

Total B-cell counts tell you whether depletion is occurring, but to understand how the immune system is actually recovering, you need to look at what subsets are coming back and in what proportions.

The return of naïve B-cells is generally a good sign, suggesting reconstitution is proceeding in a healthy direction. The return of memory B-cells or the presence of plasma cells, on the other hand, can be an early sign of relapse, which may warrant further investigation.

As mentioned, the catch is that resolving these subsets reliably is technically demanding. You’re already working with a small circulating population, and each subset is a smaller fraction within that. Standard assays often don’t have the resolution to go beyond total CD19⁺ cells, which is why high-sensitivity panels tend to be necessary for subset-level data that you can actually act on.

Which assays are suited to B-cell monitoring, and how do you determine which is best for your application?

Flow cytometry is the gold standard for B-cell monitoring in BCDT programs. It allows direct measurement of circulating B-cell numbers and subset-level phenotyping from a single sample, providing insight into both the degree of depletion and the quality of immune reconstitution.

Programs most commonly use TBNK assays, but they can lack the sensitivity needed to detect the very low residual B-cell populations during deep depletion, so high-sensitivity panels that resolve clinically relevant B-cell subsets are usually necessary where deeper interrogation is needed.

Beyond flow cytometry, there are complementary approaches you can consider depending on your study needs. ELISA can be used to measure autoantibodies and serum immunoglobulin levels, which gives you an indication of what might be happening at the tissue level, even when circulating B-cells appear depleted. ELISpot is useful for detecting active antibody-secreting cells to confirm patient B-cell depletion. And for programs where tissue-resident B-cell populations are a particular concern, immuno-PET can help map B-cell presence in the spleen and lymph nodes.

Beyond refining your assay strategy, what are the key considerations for ensuring reliable B-cell monitoring across a long-term study?

You need to cover all three phases, pre-treatment, active depletion, and reconstitution, and within those, capture the key biological events at the right time points. In practice, that typically means sampling around every one to two weeks during the active phases, though the right frequency depends on the therapy and the disease.

Sample volume requirements are a key consideration that needs to be built into the study design from the outset. Because B-cells are scarce, you need sufficient cell input to achieve confident detection, and those volumes need to be collected repeatedly over what may be many months, which can be a burden on patients. Understanding those requirements early and designing the sampling plan around them can help ensure this doesn’t cause issues and delay later in the study.

Then there’s stability. In multi-site programs, samples collected at dispersed sites need to remain viable across the transit window to the analysis lab. Sites in remote locations, or where courier infrastructure is unreliable, may simply fall outside validated stability windows. Working with a laboratory that has established logistics infrastructure and experience managing limited-stability samples across global sites can take a lot of that operational risk off the table, but either way, site selection needs to be factored in early to avoid disruption later in the study.

What would you say is the most important thing to keep in mind when designing a B-cell monitoring strategy?

It may sound simple, but understanding your end goal. The therapy you’re targeting, the disease state you’re working in, and the biological questions you need to answer should drive every decision, including the sensitivity your assay needs to achieve, the subsets you prioritize, and how you structure your sampling plan.

Without that clarity upfront, you risk building a monitoring strategy that generates unreliable data you can’t fully act on. Get that foundation right, and everything else follows from it.

Looking to build a reliable B-cell monitoring strategy for your autoimmune program? Get in touch with CellCarta’s immunology experts to discuss your study needs

 

About the author:

author photo

Sasha Silva-Barrios is a Principal Development Scientist at CellCarta. She leads assay development and validation for clinical immunology programs, where she uses her extensive experience to design assays that generate high-quality data, enabling sponsors to move their therapies through to the next stages of clinical development. She holds a PhD in Immunology, where she focused on understanding the role of B cells in infection and the innate immune response. 

References 

  1. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1454747/full?utm  
  2. https://www.nature.com/articles/d42859‑018‑00030‑8#:~:text=Traditionally%2C%20T%20cell%2Dmediated%20neuroinflammation,recently%20been%20tested%20in%20MS.  

B-Cell Monitoring in Autoimmune Programs: Expert Insights | CellCarta

June 30, 2026

Sasha Silva-Barrios

B-cell depletion therapies (BCDTs) can offer autoimmune patients sustained disease control that was previously difficult to achieve, with therapies such as rituximab able to meaningfully alter the course of diseases such as rheumatoid arthritis and multiple sclerosis.1,2 But the B-cell monitoring that underpins the development of these therapies can be complex. Detecting B-cells and their subsets at the very low levels encountered during depletion and reconstitution places significant demands on assay sensitivity, and sustaining reliable measurement across studies spanning months brings operational challenges.

We sat down with Sasha Silva-Barrios, Principal Development Scientist at CellCarta, who leads assay development and validation for clinical immunology programs, to explore how to approach B-cell monitoring in practice.

In this Q&A, she shares her insights on the challenges of measuring B-cell depletion and reconstitution, how to think about assay strategy, and what it takes to sustain reliable measurement across a long-term study.

What are the key challenges in measuring B-cell depletion and reconstitution in autoimmune studies?

There are a few interconnected challenges, mostly stemming from the nature of B-cells themselves.

B-cells make up roughly 10–15% of peripheral blood lymphocytes under normal conditions, already a small fraction, then you’re trying to detect specific subsets during depletion and reconstitution when their levels are even lower. At those counts, small variations in how data is acquired or gated can have a disproportionate impact on results, and there’s a risk of misinterpreting what the data actually shows.

A second challenge is that blood-based measurement only tells part of the story. B-cells also reside in immune organs such as the lymph nodes and spleen, and assays performed on whole blood cannot capture what’s happening in those compartments. A patient could appear fully depleted in circulation while still carrying a meaningful B-cell burden in tissue, an important interpretive limitation that must be acknowledged in the data.

What role does assay sensitivity play in generating reliable B-cell depletion data, and how can you ensure adequate sensitivity?

Sensitivity is the most critical property of any monitoring assay in this context. Decisions around dosing, retreatment timing, and patient safety all depend on confidence in what the assay is showing at extremely low counts.

To ensure adequate sensitivity, you have to establish defined lower limits of detection (LOD) and quantification (LLOQ), which are study-specific and determined by the therapy’s mechanism of action. Sample volume also needs to be determined on a study-by-study basis. Because B-cells are scarce, sufficient cell input is essential for statistically confident detection, and the volume required depends on the minimum number of events needed to achieve that.

Antibody quality is another factor that directly affects sensitivity. Detection antibodies must be highly specific to the relevant epitopes, and recognition can vary between clones from different suppliers, affecting both sensitivity and reliability. This is especially important in CAR-T programs, where the CAR construct targets the same epitopes as the detection antibodies. Some clones may compete directly with the therapeutic, producing false negatives, so detection antibodies need to be developed and validated in parallel with the CAR construct to ensure sufficient sensitivity.

Why is B-cell subset profiling important, and how does it inform interpretation of reconstitution?

Total B-cell counts tell you whether depletion is occurring, but to understand how the immune system is actually recovering, you need to look at what subsets are coming back and in what proportions.

The return of naïve B-cells is generally a good sign, suggesting reconstitution is proceeding in a healthy direction. The return of memory B-cells or the presence of plasma cells, on the other hand, can be an early sign of relapse, which may warrant further investigation.

As mentioned, the catch is that resolving these subsets reliably is technically demanding. You’re already working with a small circulating population, and each subset is a smaller fraction within that. Standard assays often don’t have the resolution to go beyond total CD19⁺ cells, which is why high-sensitivity panels tend to be necessary for subset-level data that you can actually act on.

Which assays are suited to B-cell monitoring, and how do you determine which is best for your application?

Flow cytometry is the gold standard for B-cell monitoring in BCDT programs. It allows direct measurement of circulating B-cell numbers and subset-level phenotyping from a single sample, providing insight into both the degree of depletion and the quality of immune reconstitution.

Programs most commonly use TBNK assays, but they can lack the sensitivity needed to detect the very low residual B-cell populations during deep depletion, so high-sensitivity panels that resolve clinically relevant B-cell subsets are usually necessary where deeper interrogation is needed.

Beyond flow cytometry, there are complementary approaches you can consider depending on your study needs. ELISA can be used to measure autoantibodies and serum immunoglobulin levels, which gives you an indication of what might be happening at the tissue level, even when circulating B-cells appear depleted. ELISpot is useful for detecting active antibody-secreting cells to confirm patient B-cell depletion. And for programs where tissue-resident B-cell populations are a particular concern, immuno-PET can help map B-cell presence in the spleen and lymph nodes.

Beyond refining your assay strategy, what are the key considerations for ensuring reliable B-cell monitoring across a long-term study?

You need to cover all three phases, pre-treatment, active depletion, and reconstitution, and within those, capture the key biological events at the right time points. In practice, that typically means sampling around every one to two weeks during the active phases, though the right frequency depends on the therapy and the disease.

Sample volume requirements are a key consideration that needs to be built into the study design from the outset. Because B-cells are scarce, you need sufficient cell input to achieve confident detection, and those volumes need to be collected repeatedly over what may be many months, which can be a burden on patients. Understanding those requirements early and designing the sampling plan around them can help ensure this doesn’t cause issues and delay later in the study.

Then there’s stability. In multi-site programs, samples collected at dispersed sites need to remain viable across the transit window to the analysis lab. Sites in remote locations, or where courier infrastructure is unreliable, may simply fall outside validated stability windows. Working with a laboratory that has established logistics infrastructure and experience managing limited-stability samples across global sites can take a lot of that operational risk off the table, but either way, site selection needs to be factored in early to avoid disruption later in the study.

What would you say is the most important thing to keep in mind when designing a B-cell monitoring strategy?

It may sound simple, but understanding your end goal. The therapy you’re targeting, the disease state you’re working in, and the biological questions you need to answer should drive every decision, including the sensitivity your assay needs to achieve, the subsets you prioritize, and how you structure your sampling plan.

Without that clarity upfront, you risk building a monitoring strategy that generates unreliable data you can’t fully act on. Get that foundation right, and everything else follows from it.

Looking to build a reliable B-cell monitoring strategy for your autoimmune program? Get in touch with CellCarta’s immunology experts to discuss your study needs

 

About the author:

author photo

Sasha Silva-Barrios is a Principal Development Scientist at CellCarta. She leads assay development and validation for clinical immunology programs, where she uses her extensive experience to design assays that generate high-quality data, enabling sponsors to move their therapies through to the next stages of clinical development. She holds a PhD in Immunology, where she focused on understanding the role of B cells in infection and the innate immune response. 

References 

  1. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1454747/full?utm  
  2. https://www.nature.com/articles/d42859‑018‑00030‑8#:~:text=Traditionally%2C%20T%20cell%2Dmediated%20neuroinflammation,recently%20been%20tested%20in%20MS.  

CAR Kinetics in Autoimmune Studies: Expert Insights on Designing Effective Monitoring Strategies | CellCarta

June 30, 2026

Laïla-Aïcha Hanafi

CAR-based therapies, originally developed for oncology, are now advancing into autoimmune diseases with promising early clinical results.1 In this setting, the biological context differs, and so does the way CAR kinetics are evaluated and understood.

To explore what this means in practice, we sat down with Laïla-Aïcha Hanafi, Director Global Assay Development here at CellCarta. With a background in immuno-oncology and translational biomarkers for cell therapies, Laïla works closely with sponsors to design and execute CAR kinetic strategies across programs.

In this Q&A, she shares how immunologists approach CAR kinetic monitoring in autoimmune disease and what sponsors should consider when designing their strategy.

How does CAR kinetic monitoring in autoimmune diseases differ from oncology?

It may sound obvious, but the differences in monitoring strategies really come down to the differing goals of CAR therapies in oncology versus autoimmune diseases. In oncology, the aim is to achieve strong CAR expansion and maintain those cells over time to sustain tumor control. Whereas in autoimmune disease, the focus is on eliminating diseased B cells, allowing the immune system to reset, and then letting CAR levels decline. From a monitoring perspective, that difference translates into two key considerations.

First, the timeline. In oncology, monitoring can extend for six months to a year or more because persistence is part of the intended outcome. In autoimmune programs, the most informative window is often much earlier, typically around the first one to two weeks post-infusion, when CAR expansion peaks. After that, the focus shifts to confirming contraction and monitoring immune cell recovery rather than tracking long-term maintenance.

Second, assay sensitivity. In autoimmune disease, starting doses are lower; as a result, CAR expansion may be harder to detect than in oncology. Detecting and quantifying those lower levels reliably requires highly sensitive, well-optimized assays.

What key factors should be considered when choosing an assay for CAR kinetic monitoring in autoimmune diseases?

Assay selection in autoimmune CAR programs largely comes down to sensitivity, specificity, and what type of information you need to generate.

In practice, two main assay families are used to monitor CAR kinetics: flow cytometry and PCR-based approaches. Digital PCR can provide highly sensitive, quantitative detection of the CAR construct and can be run in batches. It gives a clear numerical readout of CAR signal in the sample, but it does not indicate which cells are expressing the CAR.

Flow cytometry, on the other hand, allows direct detection of CAR-expressing cells and makes it possible to identify which cell types have been transfected. This is important in certain contexts, such as in vivo CAR approaches, where the CAR construct may not be restricted to a single predefined cell population. Flow cytometry also enables phenotyping alongside enumeration, providing additional biological insight.

In autoimmune programs, that biological context is particularly relevant, as sponsors need to understand how CAR levels relate to downstream immune effects, especially B-cell depletion and recovery.

In most cases, it’s best to use both approaches. PCR offers sensitivity and quantitative measurement of the construct, while flow cytometry provides cell-level resolution and biological context.

How should immunologists use CAR enumeration and absolute counts to interpret CAR kinetics in autoimmune disease?

Enumeration is the starting point for understanding CAR kinetics—it shows whether CAR cells are present and how their levels change over time. But interpreting those numbers meaningfully requires looking at absolute counts rather than percentages alone.

Absolute counts allow teams to construct the full kinetic curve: how quickly the CAR cells expand, how high they peak, and how long they remain detectable. That information helps in understanding dose, exposure, and how the CAR levels relate to B-cell reduction.

The early expansion phase, typically around day 7–14 post-infusion, is where absolute counts really add value, allowing the assessment of peak levels and overall exposure. Later in the timeline, when CAR levels are much lower, small numerical differences become less meaningful. At that stage, interpretation focuses more on whether CAR cells are still detectable rather than on detailed quantitative comparisons.

What are the biggest interpretation challenges in autoimmune CAR monitoring?

As mentioned, one of the main challenges is sensitivity. Because CAR expansion may be lower in autoimmune programs, detecting small populations reliably can be difficult. Increasing assay input or optimizing the assay design may be necessary to capture low-level signals.

Specificity is just as important, particularly when using flow cytometry. Background signal or non-specific binding can obscure low-level CAR detection. Addressing this may require increasing assay input to improve sensitivity, refining gating strategies, or adding additional markers, such as negative selection or “dump” channels, to reduce background.

Finally, as CAR levels decline, deeper phenotyping becomes more difficult. To ensure detailed biological insight can be extracted, it is better to conduct subpopulation analysis during the expansion peak, when there are sufficient events to analyze.

Overall, what would you say are the key things sponsors should keep in mind when designing CAR kinetic monitoring strategies for autoimmune programs?

In autoimmune programs, timing and sensitivity are key. Plan monitoring around the most informative window (days 7–14 post-infusion), make sure assays are sensitive enough for lower signals, and think beyond simple detection.

At the same time, it’s important not to look at CAR levels in isolation. In autoimmune disease, what ultimately matters is how those kinetics translate into biological effect. Monitoring B-cell depletion and recovery, including changes in specific subsets, helps connect CAR exposure to immune reset.

In the end, good CAR kinetic monitoring in autoimmune programs is about seeing the full picture, not just whether CAR cells are present, but how the kinetic profile aligns with downstream immune changes and the therapeutic goal.

Supporting CAR Kinetic Monitoring in Autoimmune Programs

To support robust CAR kinetic monitoring from early expansion through immune reconstitution, CellCarta provides integrated assay solutions tailored to autoimmune programs.

  • Digital PCR: custom and off-the-shelf assays for sensitive, quantitative detection of CAR constructs.
  • Flow cytometry: custom CAR detection panels for enumeration and phenotyping, and spectral flow cytometry for deeper immune profiling.
  • Advanced immune profiling: CyTOF for high-dimensional phenotyping and single-cell genomics for deeper biological characterization.
  • Ready-to-deploy assays for pharmacodynamic monitoring, including: B-cell aplasia and recovery, memory B-cell phenotyping, TBNK CD20 panels, and absolute B-cell enumeration.

Explore how CellCarta’s immunology platforms can help you generate high-quality CAR kinetic and immune monitoring data for your autoimmune programs

 

About the author:

author photo

Laïla-Aïcha Hanafi is the Director of Global Assay Development at CellCarta. She supplemented her PhD in immuno-oncology with post-doctoral studies in translational biomarkers for cell therapies at the Fred Hutchinson Cancer Center. Laïla has combined scientific knowledge and operational efficiency to address biomarker needs in clinical trial and prioritizing high-quality data to move therapies to the next stage of clinical deployment. 

 

 

Reference

  1. https://pmc.ncbi.nlm.nih.gov/articles/PMC12488630/pdf/fimmu-16-1613622.pdf

CAR Kinetics in Autoimmune Studies: Expert Insights on Designing Effective Monitoring Strategies | CellCarta

June 30, 2026

Laïla-Aïcha Hanafi

CAR-based therapies, originally developed for oncology, are now advancing into autoimmune diseases with promising early clinical results.1 In this setting, the biological context differs, and so does the way CAR kinetics are evaluated and understood.

To explore what this means in practice, we sat down with Laïla-Aïcha Hanafi, Director Global Assay Development here at CellCarta. With a background in immuno-oncology and translational biomarkers for cell therapies, Laïla works closely with sponsors to design and execute CAR kinetic strategies across programs.

In this Q&A, she shares how immunologists approach CAR kinetic monitoring in autoimmune disease and what sponsors should consider when designing their strategy.

How does CAR kinetic monitoring in autoimmune diseases differ from oncology?

It may sound obvious, but the differences in monitoring strategies really come down to the differing goals of CAR therapies in oncology versus autoimmune diseases. In oncology, the aim is to achieve strong CAR expansion and maintain those cells over time to sustain tumor control. Whereas in autoimmune disease, the focus is on eliminating diseased B cells, allowing the immune system to reset, and then letting CAR levels decline. From a monitoring perspective, that difference translates into two key considerations.

First, the timeline. In oncology, monitoring can extend for six months to a year or more because persistence is part of the intended outcome. In autoimmune programs, the most informative window is often much earlier, typically around the first one to two weeks post-infusion, when CAR expansion peaks. After that, the focus shifts to confirming contraction and monitoring immune cell recovery rather than tracking long-term maintenance.

Second, assay sensitivity. In autoimmune disease, starting doses are lower; as a result, CAR expansion may be harder to detect than in oncology. Detecting and quantifying those lower levels reliably requires highly sensitive, well-optimized assays.

What key factors should be considered when choosing an assay for CAR kinetic monitoring in autoimmune diseases?

Assay selection in autoimmune CAR programs largely comes down to sensitivity, specificity, and what type of information you need to generate.

In practice, two main assay families are used to monitor CAR kinetics: flow cytometry and PCR-based approaches. Digital PCR can provide highly sensitive, quantitative detection of the CAR construct and can be run in batches. It gives a clear numerical readout of CAR signal in the sample, but it does not indicate which cells are expressing the CAR.

Flow cytometry, on the other hand, allows direct detection of CAR-expressing cells and makes it possible to identify which cell types have been transfected. This is important in certain contexts, such as in vivo CAR approaches, where the CAR construct may not be restricted to a single predefined cell population. Flow cytometry also enables phenotyping alongside enumeration, providing additional biological insight.

In autoimmune programs, that biological context is particularly relevant, as sponsors need to understand how CAR levels relate to downstream immune effects, especially B-cell depletion and recovery.

In most cases, it’s best to use both approaches. PCR offers sensitivity and quantitative measurement of the construct, while flow cytometry provides cell-level resolution and biological context.

How should immunologists use CAR enumeration and absolute counts to interpret CAR kinetics in autoimmune disease?

Enumeration is the starting point for understanding CAR kinetics—it shows whether CAR cells are present and how their levels change over time. But interpreting those numbers meaningfully requires looking at absolute counts rather than percentages alone.

Absolute counts allow teams to construct the full kinetic curve: how quickly the CAR cells expand, how high they peak, and how long they remain detectable. That information helps in understanding dose, exposure, and how the CAR levels relate to B-cell reduction.

The early expansion phase, typically around day 7–14 post-infusion, is where absolute counts really add value, allowing the assessment of peak levels and overall exposure. Later in the timeline, when CAR levels are much lower, small numerical differences become less meaningful. At that stage, interpretation focuses more on whether CAR cells are still detectable rather than on detailed quantitative comparisons.

What are the biggest interpretation challenges in autoimmune CAR monitoring?

As mentioned, one of the main challenges is sensitivity. Because CAR expansion may be lower in autoimmune programs, detecting small populations reliably can be difficult. Increasing assay input or optimizing the assay design may be necessary to capture low-level signals.

Specificity is just as important, particularly when using flow cytometry. Background signal or non-specific binding can obscure low-level CAR detection. Addressing this may require increasing assay input to improve sensitivity, refining gating strategies, or adding additional markers, such as negative selection or “dump” channels, to reduce background.

Finally, as CAR levels decline, deeper phenotyping becomes more difficult. To ensure detailed biological insight can be extracted, it is better to conduct subpopulation analysis during the expansion peak, when there are sufficient events to analyze.

Overall, what would you say are the key things sponsors should keep in mind when designing CAR kinetic monitoring strategies for autoimmune programs?

In autoimmune programs, timing and sensitivity are key. Plan monitoring around the most informative window (days 7–14 post-infusion), make sure assays are sensitive enough for lower signals, and think beyond simple detection.

At the same time, it’s important not to look at CAR levels in isolation. In autoimmune disease, what ultimately matters is how those kinetics translate into biological effect. Monitoring B-cell depletion and recovery, including changes in specific subsets, helps connect CAR exposure to immune reset.

In the end, good CAR kinetic monitoring in autoimmune programs is about seeing the full picture, not just whether CAR cells are present, but how the kinetic profile aligns with downstream immune changes and the therapeutic goal.

Supporting CAR Kinetic Monitoring in Autoimmune Programs

To support robust CAR kinetic monitoring from early expansion through immune reconstitution, CellCarta provides integrated assay solutions tailored to autoimmune programs.

  • Digital PCR: custom and off-the-shelf assays for sensitive, quantitative detection of CAR constructs.
  • Flow cytometry: custom CAR detection panels for enumeration and phenotyping, and spectral flow cytometry for deeper immune profiling.
  • Advanced immune profiling: CyTOF for high-dimensional phenotyping and single-cell genomics for deeper biological characterization.
  • Ready-to-deploy assays for pharmacodynamic monitoring, including: B-cell aplasia and recovery, memory B-cell phenotyping, TBNK CD20 panels, and absolute B-cell enumeration.

Explore how CellCarta’s immunology platforms can help you generate high-quality CAR kinetic and immune monitoring data for your autoimmune programs

 

About the author:

author photo

Laïla-Aïcha Hanafi is the Director of Global Assay Development at CellCarta. She supplemented her PhD in immuno-oncology with post-doctoral studies in translational biomarkers for cell therapies at the Fred Hutchinson Cancer Center. Laïla has combined scientific knowledge and operational efficiency to address biomarker needs in clinical trial and prioritizing high-quality data to move therapies to the next stage of clinical deployment. 

 

 

Reference

  1. https://pmc.ncbi.nlm.nih.gov/articles/PMC12488630/pdf/fimmu-16-1613622.pdf

Cytokine Profiling in Autoimmune Programs: Expert Insights on Assay Selection and Getting Data You Can Trust | CellCarta

June 30, 2026

Mirzo Kanoatov

Cytokine profiling can be an immensely valuable tool for understanding immune mechanisms and treatment response in autoimmune drug development. But translating that potential into reliable, interpretable data can be challenging.

Key targets such as IL-2 and IL-6 circulate at very low levels, assay platforms differ widely in what they can detect and at what scale, and the right approach depends on where a sponsor is in their therapeutic development journey. Knowing which technology is right for your target, your sample type, and your stage of development is what separates useful data from missed signals.

To explore what that looks like in practice, we spoke with Mirzo Kanoatov, Scientific Team leader at CellCarta. In this Q&A, he walks through how cytokine profiling is applied in autoimmune drug development and what sponsors should consider when building their strategy.

Where does cytokine profiling fit in an autoimmune development program?

Cytokine profiling can play a role at almost every stage of an autoimmune program, though what you are looking for, and how you use the data, changes as development progresses.

In early clinical phases, it provides insight into the temporal dynamics of immune cell activation, such as how quickly different cytokines rise and fall, which pathways are engaging, and whether there are early signs of adverse immune activity. These kinds of readouts, particularly when combined with complementary assays such as cell enumeration and phenotyping, can also inform decisions around optimal dosing, supplementing data from the maximum tolerable dose approach. As a program advances, these profiling data sets can guide better strategies for patient stratification, while continuing to support safety and efficacy assessments.

What role does cytokine profiling play in interpreting complex immune events, such as immune overactivation or cytokine release syndrome?

When an unexpected immune event occurs, the key question is where in the immune system it came from. Cytokine profiling is a very useful tool for distinguishing between the different sources.

Innate immune triggers tend to be reflected in rapid elevation of cytokines such as IL-6, TNF-α, and IL-1β. Adaptive immune activity, on the other hand, is more typically associated with elevation of IFN-γ and IL-2, which reflect T cell activation and expansion. By characterizing this cytokine signature across time points, researchers can piece together the sequence of immune activation that led to the event.

Retrospective analysis of samples using multiplex cytokine panels can help distinguish whether events are driven by innate or adaptive immune cascades, supporting assessment of whether they are related to the investigational therapy or reflect an unrelated event. Such information can inform risk management in subsequent cohorts and guide the design of future programs.

How do you ensure you’re using the right assay for your trial? In what situations would a high-plex or custom cytokine panel be particularly useful in autoimmune trials?

High-plex panels are most valuable when the biological landscape is not yet fully defined. In early-stage autoimmune studies, you can’t predict with confidence which immune pathways your therapy will engage. A broad, high-plex approach, such as Olink PEA technology, allows hundreds of proteins to be measured simultaneously, letting the data surface relevant pathways. That exploratory view is useful for hypothesis generation and for identifying which cytokines will be most informative in later studies.

Custom panels add further flexibility. Where standard commercial options do not include all the analytes of interest, or where prior research points to a specific combination of cytokines relevant to the mechanism being studied, a custom panel allows the study to be designed precisely around the scientific question.

As a program matures and the most informative cytokines become established, the approach typically narrows. More targeted, quantitative platforms, such as MSD, offer the reproducibility and dynamic range needed for formal analytical validation, supporting the regulatory requirements of late-phase studies.

That progression, from broad early exploration to focused late-stage measurement, is the most rational way to build a cytokine profiling strategy across a development program.

Platform Best for Strengths Main limitations
Olink Broad discovery, low sample volume, pathway level profiling Very high multiplexing, small input volume, good specificity, useful for pattern discovery Usually relative quantification, less direct absolute concentration reporting, can miss some very low abundance analytes depending on panel/matrix
ELISA Validation of a few cytokines High specificity, familiar, often easier for absolute quantification Low plex, more sample volume per analyte, inefficient for broad profiling
MSD Sensitive multiplexing, low abundance cytokines, translational work Strong sensitivity, good dynamic range, good for absolute quantification, useful for biomarker work Lower plex than Olink, higher cost/complexity than ELISA
Ella Fast, automated single or low plex quantification Easy workflow, strong reproducibility, good for targeted validation Not a discovery platform; limited plex compared with Olink/MSD

Table 1: Overview of cytokine profiling platforms and their key strengths and limitations

Overall, what would you say are the key things sponsors should keep in mind when designing a cytokine profiling strategy for autoimmune trials?

Get the sensitivity right first. It sounds basic, but this can be where profiling strategies most often fall short. For example, cytokines such as IL-2 and IL-4 circulate at very low levels in peripheral blood. For these targets, standard ELISA platforms are often insufficient. Electrochemiluminescent platforms such as MSD, and specifically S-PLEX MSD assays, can extend the sensitivity floor significantly, making it possible to reliably quantify targets that would otherwise fall below the limit of detection. Getting that right requires platform expertise and careful assay optimization.

Sample type is another critical factor. Different matrices—blood, serum, plasma, urine, cerebrospinal fluid, saliva, tissue, stimulated and unstimulated cells—carry different background levels and interfering substances that affect assay performance. Technical and interpretive challenges vary substantially between sample types, so having access to validated workflows across all of these matrices means sponsors are not constrained by their sample collection strategy.

After that, let your stage of development guide your platform choice, and think carefully about your sample type from the outset. Careful consideration of target sensitivity, development stage, and matrix are what a well-designed cytokine profiling strategy is built on.

Comprehensive cytokine profiling for your autoimmune programs

CellCarta offers integrated cytokine profiling solutions, backed by decades of expertise, across platforms and sample types, tailored to the stage and goals of your program.

  • MSD & S-PLEX MSD: ultra-sensitive, multiplex electrochemiluminescent detection for low-abundance targets including IL-2 and IL-6.
  • Olink PEA Technology: high-plex protein profiling, including custom panels, for broad exploratory studies.
  • Ella™: automated, high-throughput ELISA multiplexing for focused panels at scale.
  • Broad sample type expertise: validated workflows across blood, serum, plasma, urine, CSF, saliva, tissue, and stimulated/unstimulated cells.

Explore how CellCarta’s immunology platforms can help you generate high-quality cytokine profiling data for your autoimmune programs

About the author:

author photo

Mirzo Kanoatov is a Scientific Group Leader at CellCarta, where he works with sponsors, scientists, and data analysts to ensure that each project’s bioanalytical testing needs are met. His team also ensures that the analytical design, assay design, and platform selection generate the appropriate data to support the regulatory requirements of sponsors’ programs. He supplemented his PhD in bioanalytical chemistry with a post-doctoral fellowship , where he focused on translating novel biomarker assay platforms from exploratory research methods into robust tools suitable for clinical application. 

Cytokine Profiling in Autoimmune Programs: Expert Insights on Assay Selection and Getting Data You Can Trust | CellCarta

June 30, 2026

Mirzo Kanoatov

Cytokine profiling can be an immensely valuable tool for understanding immune mechanisms and treatment response in autoimmune drug development. But translating that potential into reliable, interpretable data can be challenging.

Key targets such as IL-2 and IL-6 circulate at very low levels, assay platforms differ widely in what they can detect and at what scale, and the right approach depends on where a sponsor is in their therapeutic development journey. Knowing which technology is right for your target, your sample type, and your stage of development is what separates useful data from missed signals.

To explore what that looks like in practice, we spoke with Mirzo Kanoatov, Scientific Team leader at CellCarta. In this Q&A, he walks through how cytokine profiling is applied in autoimmune drug development and what sponsors should consider when building their strategy.

Where does cytokine profiling fit in an autoimmune development program?

Cytokine profiling can play a role at almost every stage of an autoimmune program, though what you are looking for, and how you use the data, changes as development progresses.

In early clinical phases, it provides insight into the temporal dynamics of immune cell activation, such as how quickly different cytokines rise and fall, which pathways are engaging, and whether there are early signs of adverse immune activity. These kinds of readouts, particularly when combined with complementary assays such as cell enumeration and phenotyping, can also inform decisions around optimal dosing, supplementing data from the maximum tolerable dose approach. As a program advances, these profiling data sets can guide better strategies for patient stratification, while continuing to support safety and efficacy assessments.

What role does cytokine profiling play in interpreting complex immune events, such as immune overactivation or cytokine release syndrome?

When an unexpected immune event occurs, the key question is where in the immune system it came from. Cytokine profiling is a very useful tool for distinguishing between the different sources.

Innate immune triggers tend to be reflected in rapid elevation of cytokines such as IL-6, TNF-α, and IL-1β. Adaptive immune activity, on the other hand, is more typically associated with elevation of IFN-γ and IL-2, which reflect T cell activation and expansion. By characterizing this cytokine signature across time points, researchers can piece together the sequence of immune activation that led to the event.

Retrospective analysis of samples using multiplex cytokine panels can help distinguish whether events are driven by innate or adaptive immune cascades, supporting assessment of whether they are related to the investigational therapy or reflect an unrelated event. Such information can inform risk management in subsequent cohorts and guide the design of future programs.

How do you ensure you’re using the right assay for your trial? In what situations would a high-plex or custom cytokine panel be particularly useful in autoimmune trials?

High-plex panels are most valuable when the biological landscape is not yet fully defined. In early-stage autoimmune studies, you can’t predict with confidence which immune pathways your therapy will engage. A broad, high-plex approach, such as Olink PEA technology, allows hundreds of proteins to be measured simultaneously, letting the data surface relevant pathways. That exploratory view is useful for hypothesis generation and for identifying which cytokines will be most informative in later studies.

Custom panels add further flexibility. Where standard commercial options do not include all the analytes of interest, or where prior research points to a specific combination of cytokines relevant to the mechanism being studied, a custom panel allows the study to be designed precisely around the scientific question.

As a program matures and the most informative cytokines become established, the approach typically narrows. More targeted, quantitative platforms, such as MSD, offer the reproducibility and dynamic range needed for formal analytical validation, supporting the regulatory requirements of late-phase studies.

That progression, from broad early exploration to focused late-stage measurement, is the most rational way to build a cytokine profiling strategy across a development program.

Platform Best for Strengths Main limitations
Olink Broad discovery, low sample volume, pathway level profiling Very high multiplexing, small input volume, good specificity, useful for pattern discovery Usually relative quantification, less direct absolute concentration reporting, can miss some very low abundance analytes depending on panel/matrix
ELISA Validation of a few cytokines High specificity, familiar, often easier for absolute quantification Low plex, more sample volume per analyte, inefficient for broad profiling
MSD Sensitive multiplexing, low abundance cytokines, translational work Strong sensitivity, good dynamic range, good for absolute quantification, useful for biomarker work Lower plex than Olink, higher cost/complexity than ELISA
Ella Fast, automated single or low plex quantification Easy workflow, strong reproducibility, good for targeted validation Not a discovery platform; limited plex compared with Olink/MSD

Table 1: Overview of cytokine profiling platforms and their key strengths and limitations

Overall, what would you say are the key things sponsors should keep in mind when designing a cytokine profiling strategy for autoimmune trials?

Get the sensitivity right first. It sounds basic, but this can be where profiling strategies most often fall short. For example, cytokines such as IL-2 and IL-4 circulate at very low levels in peripheral blood. For these targets, standard ELISA platforms are often insufficient. Electrochemiluminescent platforms such as MSD, and specifically S-PLEX MSD assays, can extend the sensitivity floor significantly, making it possible to reliably quantify targets that would otherwise fall below the limit of detection. Getting that right requires platform expertise and careful assay optimization.

Sample type is another critical factor. Different matrices—blood, serum, plasma, urine, cerebrospinal fluid, saliva, tissue, stimulated and unstimulated cells—carry different background levels and interfering substances that affect assay performance. Technical and interpretive challenges vary substantially between sample types, so having access to validated workflows across all of these matrices means sponsors are not constrained by their sample collection strategy.

After that, let your stage of development guide your platform choice, and think carefully about your sample type from the outset. Careful consideration of target sensitivity, development stage, and matrix are what a well-designed cytokine profiling strategy is built on.

Comprehensive cytokine profiling for your autoimmune programs

CellCarta offers integrated cytokine profiling solutions, backed by decades of expertise, across platforms and sample types, tailored to the stage and goals of your program.

  • MSD & S-PLEX MSD: ultra-sensitive, multiplex electrochemiluminescent detection for low-abundance targets including IL-2 and IL-6.
  • Olink PEA Technology: high-plex protein profiling, including custom panels, for broad exploratory studies.
  • Ella™: automated, high-throughput ELISA multiplexing for focused panels at scale.
  • Broad sample type expertise: validated workflows across blood, serum, plasma, urine, CSF, saliva, tissue, and stimulated/unstimulated cells.

Explore how CellCarta’s immunology platforms can help you generate high-quality cytokine profiling data for your autoimmune programs

About the author:

author photo

Mirzo Kanoatov is a Scientific Group Leader at CellCarta, where he works with sponsors, scientists, and data analysts to ensure that each project’s bioanalytical testing needs are met. His team also ensures that the analytical design, assay design, and platform selection generate the appropriate data to support the regulatory requirements of sponsors’ programs. He supplemented his PhD in bioanalytical chemistry with a post-doctoral fellowship , where he focused on translating novel biomarker assay platforms from exploratory research methods into robust tools suitable for clinical application. 

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

June 30, 2026

David Possamaï

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.

 

About the author:

author photo

David Possamaï is a Principal Development Scientist at CellCarta. He leads assay development and validation for clinical immunology programs where he uses his extensive assay development experience to design thoughtful, informative assays to generate high-quality data that enables therapies to move to the next stage of clinical development. He holds a PhD in Biomedical Sciences, where he focused on understanding antigen presentation in B cells, as well as characterizing activated B cells.  

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

June 30, 2026

David Possamaï

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.

 

About the author:

author photo

David Possamaï is a Principal Development Scientist at CellCarta. He leads assay development and validation for clinical immunology programs where he uses his extensive assay development experience to design thoughtful, informative assays to generate high-quality data that enables therapies to move to the next stage of clinical development. He holds a PhD in Biomedical Sciences, where he focused on understanding antigen presentation in B cells, as well as characterizing activated B cells.  

CellCarta Lake Forest | Rescuing a Pilot Study

April 14, 2026

Patrick Reese

Even the most carefully designed early-stage programs can encounter challenges that threaten to disrupt progress. When they do, resolving the issue quickly is essential to keeping research on track.

That was the case during a Lunaphore COMET-based multiplex immunofluorescence pilot study conducted at CellCarta’s California, Lake Forest laboratory. Following initiation of the project, it was discovered that the originally sourced samples did not meet the study’s unique requirements, placing the continuation of the pilot in doubt.

This case study examines how coordinated scientific and operational expertise helped restore continuity and preserve study momentum.

The Study: Developing a COMET-Based Multiplex Panel

CellCarta was engaged by a big pharma company to provide multiplex immunofluorescence testing and data analysis, and to evaluate the capabilities of the Lunaphore COMET platform.

To support this objective, the Lake Forest team initiated development of an 8-plex multiplex immunofluorescence (mIF) assay on the Lunaphore COMET platform, incorporating key immune markers: FoxP3, CD8, CD4, CD20, CD3, CD56, CD68, and Ki67.

In parallel with wet lab assay optimization, digital image analysis workflows were developed using HALO AI HighPlex FL, including AI-driven nuclear segmentation to support accurate cell phenotyping and quantification.

Assay development and algorithm optimization were progressing as planned when an unexpected complication emerged.

The Challenge: An Unexpected Compliance Obstacle

Immediately after study initiation, the client informed CellCarta that their procured samples had been collected under IRB-waived consent rather than full informed consent.

As the study required samples that met defined informed consent criteria, the existing materials were no longer suitable for use. With assay development and algorithm optimization already underway, the stakes were high: cancellation would not only have halted the pilot but delayed the customer’s broader technology and vendor assessment program — pushing back critical decisions about their future multiplex immunofluorescence strategy.

The Response: End-to-End Collaboration

To preserve the pilot, the CellCarta Lake Forest team moved quickly to identify an alternative path forward. Replacement tissue samples meeting the study’s informed consent requirements were sourced through a preferred vendor within one week — but the speed of procurement was only part of the challenge.

Because study activities had already begun, the sample swap triggered a coordinated restart across assay development, pathology, digital pathology, procurement, and project management functions to realign the study materials and maintain continuity.  Despite the steep operational hurdles involved, the customer experienced no delays and no degradation to the study.

Maintaining Progress Through Unexpected Disruption

Scientific innovation depends on momentum. When unforeseen challenges arise, biopharma companies need more than scientific expertise from their CRO partners; they need flexibility and coordinated problem-solving to keep studies moving forward.

The Lake Forest team’s integrated approach to assay development, digital pathology, and sample sourcing ensured that this COMET-based pilot could continue as intended. For sponsors navigating complex spatial biology studies, that responsiveness matters.

Working on an early-stage program?

Connect with CellCarta’s Lake Forest team to discuss how we can support your next milestone.

About CellCarta’s California, Lake Forest Laboratory

For over 20 years, CellCarta’s CLIA and CAP-accredited Lake Forest laboratory has supported pharmaceutical and biotechnology sponsors across preclinical, discovery, and clinical development programs. Key capabilities include:

  • Multiplex immunohistochemistry and immunofluorescence expertise
  • Integrated digital pathology and spatial analysis workflows
  • In-house tissue biobank access
  • Rapid turnaround times designed for early-stage decision-making

Since its opening, the Lake Forest team has worked with over 230 sponsors on over 2,400 unique projects. Operating independently and capable of participating in multi-site joint studies, Lake Forest delivers the speed and flexibility required to advance early-stage programs with confidence.

 

About the Author:

author photo

Patrick Reese has served as Vice President, Operations for CellCarta US Histopathology Services since April of 2022. In his role, Patrick also serves as site head for the Lake Forest, California and Naperville, Illinois facilities.

Patrick has spent over 16 years in histopathology laboratory leadership across numerous regions of the United States in a variety of technical, operational leadership, consulting and business roles. He has designed and established pathology laboratories in numerous US States including North Carolina, Georgia, California, Ohio and Hawaii.

CellCarta Lake Forest | Rescuing a Pilot Study

April 14, 2026

Patrick Reese

Even the most carefully designed early-stage programs can encounter challenges that threaten to disrupt progress. When they do, resolving the issue quickly is essential to keeping research on track.

That was the case during a Lunaphore COMET-based multiplex immunofluorescence pilot study conducted at CellCarta’s California, Lake Forest laboratory. Following initiation of the project, it was discovered that the originally sourced samples did not meet the study’s unique requirements, placing the continuation of the pilot in doubt.

This case study examines how coordinated scientific and operational expertise helped restore continuity and preserve study momentum.

The Study: Developing a COMET-Based Multiplex Panel

CellCarta was engaged by a big pharma company to provide multiplex immunofluorescence testing and data analysis, and to evaluate the capabilities of the Lunaphore COMET platform.

To support this objective, the Lake Forest team initiated development of an 8-plex multiplex immunofluorescence (mIF) assay on the Lunaphore COMET platform, incorporating key immune markers: FoxP3, CD8, CD4, CD20, CD3, CD56, CD68, and Ki67.

In parallel with wet lab assay optimization, digital image analysis workflows were developed using HALO AI HighPlex FL, including AI-driven nuclear segmentation to support accurate cell phenotyping and quantification.

Assay development and algorithm optimization were progressing as planned when an unexpected complication emerged.

The Challenge: An Unexpected Compliance Obstacle

Immediately after study initiation, the client informed CellCarta that their procured samples had been collected under IRB-waived consent rather than full informed consent.

As the study required samples that met defined informed consent criteria, the existing materials were no longer suitable for use. With assay development and algorithm optimization already underway, the stakes were high: cancellation would not only have halted the pilot but delayed the customer’s broader technology and vendor assessment program — pushing back critical decisions about their future multiplex immunofluorescence strategy.

The Response: End-to-End Collaboration

To preserve the pilot, the CellCarta Lake Forest team moved quickly to identify an alternative path forward. Replacement tissue samples meeting the study’s informed consent requirements were sourced through a preferred vendor within one week — but the speed of procurement was only part of the challenge.

Because study activities had already begun, the sample swap triggered a coordinated restart across assay development, pathology, digital pathology, procurement, and project management functions to realign the study materials and maintain continuity.  Despite the steep operational hurdles involved, the customer experienced no delays and no degradation to the study.

Maintaining Progress Through Unexpected Disruption

Scientific innovation depends on momentum. When unforeseen challenges arise, biopharma companies need more than scientific expertise from their CRO partners; they need flexibility and coordinated problem-solving to keep studies moving forward.

The Lake Forest team’s integrated approach to assay development, digital pathology, and sample sourcing ensured that this COMET-based pilot could continue as intended. For sponsors navigating complex spatial biology studies, that responsiveness matters.

Working on an early-stage program?

Connect with CellCarta’s Lake Forest team to discuss how we can support your next milestone.

About CellCarta’s California, Lake Forest Laboratory

For over 20 years, CellCarta’s CLIA and CAP-accredited Lake Forest laboratory has supported pharmaceutical and biotechnology sponsors across preclinical, discovery, and clinical development programs. Key capabilities include:

  • Multiplex immunohistochemistry and immunofluorescence expertise
  • Integrated digital pathology and spatial analysis workflows
  • In-house tissue biobank access
  • Rapid turnaround times designed for early-stage decision-making

Since its opening, the Lake Forest team has worked with over 230 sponsors on over 2,400 unique projects. Operating independently and capable of participating in multi-site joint studies, Lake Forest delivers the speed and flexibility required to advance early-stage programs with confidence.

 

About the Author:

author photo

Patrick Reese has served as Vice President, Operations for CellCarta US Histopathology Services since April of 2022. In his role, Patrick also serves as site head for the Lake Forest, California and Naperville, Illinois facilities.

Patrick has spent over 16 years in histopathology laboratory leadership across numerous regions of the United States in a variety of technical, operational leadership, consulting and business roles. He has designed and established pathology laboratories in numerous US States including North Carolina, Georgia, California, Ohio and Hawaii.