How to Interpret Cytometry Results Using Pocket Immunologist, an AI-driven assistant

Overview

Pocket Immunologist is an AI assistant that explains what specific immune cell types mean in the context of your cytometry data.

Use it to:

• Understand your drug’s mechanism of action.
• Identify immune responses driving: 
  
  • Patient outcomes (response, non-response, adverse events) 
  • Dose changes (dose escalation) 
  • Changes before, during and after treatment 
• Save time on manual data review.

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Quick Start: How to Use Pocket Immunologist

Step 1 – Navigate to “Immune Changes”
In the left-hand sidebar, click Immune Changes.

Step 2 – Select Parameters

• Endpoint – Choose grouping to compare (timepoint, dose, age, sex, treatment line, treatment response, subject).
• Show Cells As % Of – Select the reference population (T cells, B cells, NK cells, non-granulocytes).
• Cell Type(s) – Pick the populations to include in your analysis.

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Step 3 – Run Pocket Immunologist
Click Pocket Immunologist in the upper-right corner of the panel.

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Note: If the button is greyed out, no p-values are available for your current selection.

What You Can Compare

You can run Pocket Immunologist by dose, timepoint, or treatment response to assess:

• Population frequencies (% of total cells)
• Functional subset frequencies (% of parent)
• Marker expression levels

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‍Tips for Refining Your Analysis

• Filter – Focus on specific subsets (e.g., post-dose Day 14 in Responders).
• Group By – Compare baseline vs Day 28 across Responders and Non-responders.

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Sample of Pocket Immunologist’s Results: 

Summary of Your Drug's Effects
Clinical response is strongly associated with the expansion of highly differentiated CD4+ T effector memory cells, alongside a concurrent rise in regulatory T cells.‍

1. Response Linked to CD4+ Effector T Cell Expansion

• Finding: Responders show a highly significant increase in CD4+ T Effector Memory RA+ (TEMRA) cells (log2 fold-change = 2.24, p=0.0004) and CD4+ T Effector Memory (TEM) cells (log2 fold-change = 1.53, p=0.0043).
• Interpretation: This is a strong signal that your drug's efficacy is driven by promoting a powerful, terminally-differentiated CD4+ T cell response. These cells are critical for orchestrating the overall immune attack.

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2. Concurrent Rise in Suppressive T Cells

• Finding: Regulatory T cells (Tregs) are also significantly increased in Responders (log2 fold-change = 1.27, p=0.0159).
• Interpretation: This suggests a potential negative feedback mechanism is also activated. While this may control immune-related toxicity, a high Treg to effector ratio could eventually limit the durability of the response.

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Emerging Story & Next Step:‍

The data reveals a potent but counter-regulated CD4+ T cell response driving clinical benefit, with a notable absence of a CD8+ T cell signature. The key question is the balance between these opposing forces. We recommend you directly visualize the ratio of effector to regulatory T cells.

What could you do next with these AI insights?

‍Create a chart that compares the proportion of effector T cells (immune-activating) to regulatory T cells (immune-suppressing). This ratio helps you assess whether the immune response is dominated by activation or suppression, and how that balance shifts over time or between groups.