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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.

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Summary
This summary is machine-generated.

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.

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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.