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.

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.‍

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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.

Sample of Pocket Immunologist’s Results: 

Summary of Your Drug's Effects‍

Treatment response is strongly associated with a significant and specific expansion of the CD4+ T-cell compartment, particularly terminally differentiated effector memory cells.

1. Potent Increase in CD4+ Effector Memory T Cells

Finding: The most statistically significant change observed in responders is a large increase in CD4+ T Effector Memory RA+ (TEMRA) cells (p=0.0035, +1.73 log2 fold-change).

Interpretation: This is a strong signal that your drug promotes the development of highly differentiated, long-lived CD4+ T cells. An expansion of the TEMRA subset is often linked to a mature, antigen-driven immune response.

2. CD4-Specific, Not CD8-Driven, Response

Finding: The overall CD4+ Non-Treg population is significantly elevated in responders (p=0.0404), while no significant changes are observed across the entire CD8+ T-cell compartment or its memory subsets.

Interpretation: This finding suggests the mechanism of action is preferentially mobilizing the CD4+ 'helper' T-cell lineage. The lack of a parallel CD8+ T-cell response is a key observation that may help pinpoint the drug's specific pathway.

Emerging Story & Next Step:

The data tells a clear story: clinical response is linked to a robust CD4-driven T-cell response. The critical next question is to define the function of these expanded CD4+ TEMRA cells. Are they cytotoxic? Are they producing key pro-inflammatory cytokines? To find out, we recommend investigating functional marker expression on this key cell population.