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ImmunoPheno: A Computational Framework for Data-Driven Design and Analysis of Immunophenotyping Experiments.
Lincoln Wu1, Minh A Nguyen1,2, Zhangliang Yang1,3
1Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Biorxiv : the Preprint Server for Biology
|February 12, 2026
Summary
ImmunoPheno is a new computational platform that improves immunophenotyping accuracy and reproducibility. It automates antibody panel design and cell identification, enabling better characterization of immune cells.
Area of Science:
- Single-cell biology
- Computational biology
- Immunology
Background:
- Immunophenotyping is crucial for understanding cellular composition and immune infiltration.
- Current methods face limitations in accuracy and reproducibility due to manual processes and heuristic panel design.
Purpose of the Study:
- To introduce ImmunoPheno, an open-source platform for optimizing immunophenotyping.
- To leverage single-cell data for automated antibody panel design, gating strategies, and cell annotation.
Main Methods:
- Repurposing large-scale single-cell proteo-transcriptomic data.
- Developing an automated pipeline for antibody panel and gating strategy design.
- Creating a harmonized reference dataset (HICAR) of immune cell populations.
Main Results:
- ImmunoPheno successfully designed minimal antibody panels for rare cell populations (MAIT cells, pDCs), validated experimentally.
- Achieved accurate cell identity annotation across diverse cytometry datasets and technologies, including spatial platforms.
- Established HICAR, a comprehensive resource with 390 antibodies and 93 human immune cell types.
Conclusions:
- ImmunoPheno enhances the accuracy, reproducibility, and resolution of immunophenotyping.
- The platform offers a scalable framework for experimental design and data analysis.
- ImmunoPheno complements expert knowledge and supports continuous improvement in the field.
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