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Basophil Activation Test for Allergy Diagnosis
Published on: May 31, 2021
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Label-free, machine-learning assisted impedance assessment of basophil activation
Sungu Kim1, Leon Hannes Kloker2, Wonjun Lee1
1Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA.
Sensors and Actuators. B, Chemical
|November 27, 2025
Summary
Label-free impedance flow cytometry (IFC) combined with machine learning accurately predicts basophil activation for food allergy diagnosis. This innovative approach offers a simpler, more accessible alternative to conventional methods, improving diagnostic capabilities.
Area of Science:
- Immunology
- Biotechnology
- Medical Diagnostics
Background:
- Food allergies are rising, but current diagnostic tests are often inaccurate or unsafe.
- The basophil activation test (BAT) shows promise but faces clinical adoption hurdles due to logistical issues and reliance on complex flow cytometry analysis (FCA).
Purpose of the Study:
- To evaluate label-free impedance flow cytometry (IFC) combined with machine learning for predicting basophil activation status.
- To offer a simpler, more accessible alternative to conventional BAT methods.
Main Methods:
- Human basophils from 15 donors were stimulated with allergens.
- Impedance measurements were taken at six frequencies using IFC.
- IFC data was correlated with activation markers measured by FCA and analyzed using machine learning models.
Main Results:
- IFC metrics showed a strong correlation (Pearson correlation coefficient up to 0.89) with FCA-measured activation levels.
- Machine learning models accurately classified positive/negative BAT results with a 96% true positive rate and 88% true negative rate.
Conclusions:
- Label-free IFC coupled with machine learning is a viable and accurate method for assessing basophil activation.
- This technology has the potential to simplify and broaden the clinical adoption of the BAT for food allergy diagnosis.
