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Prediction of HLA genotypes from single-cell transcriptome data.

Benjamin D Solomon1, Hong Zheng2,3, Laura W Dillon4

  • 1Department of Pediatrics, Stanford University, Palo Alto, CA, United States.

Frontiers in Immunology
|May 14, 2023
PubMed
Summary
This summary is machine-generated.

Predicting human leukocyte antigen (HLA) genotypes from single-cell RNA sequencing data is now feasible. Combining multiple tools achieved 86% accuracy, enabling better allele-specific expression analysis for immune studies.

Keywords:
HLA genotypeHLA typing algorithmallele specific expressionhuman leukocyte antigen (HLA)major histocompatibility (MHC)next-generation sequencing data (NGS)single-cell sequencing (scRNA-seq)

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Area of Science:

  • Immunogenomics
  • Computational Biology
  • Molecular Biology

Background:

  • The human leukocyte antigen (HLA) locus is crucial for immune function, impacting transplant compatibility and disease associations.
  • Allele-specific expression (ASE) of HLA genes is known, but requires precise genotyping.
  • Predicting HLA genotypes from single-cell RNA sequencing (scRNA-seq) data remains an underexplored area.

Purpose of the Study:

  • To evaluate the feasibility of predicting HLA genotypes directly from scRNA-seq data.
  • To assess and improve computational HLA genotyping tools for single-cell applications.
  • To enable accurate ASE quantification using scRNA-seq derived HLA genotypes.

Main Methods:

  • Comparison of multiple computational HLA genotyping tools against gold-standard molecular genotyping using human scRNA-seq data.
  • Development of a composite model combining predictions from several genotyping tools.
  • Creation of a model to predict copy number for the *HLA-DRB345* genes.
  • Analysis of genotype prediction accuracy based on read depth and reproducibility.
  • Metanalytic approach to correlate ASE ratios from computational genotypes with gold-standard genotypes.

Main Results:

  • The highest 2-field accuracy for HLA genotyping from scRNA-seq reached 76% with arcasHLA, improving to 86% with a composite model.
  • A novel model achieved high accuracy (AUC 0.93) for predicting *HLA-DRB345* copy number.
  • Genotyping accuracy demonstrated a positive correlation with read depth and was reproducible.
  • HLA genotypes derived from PHLAT and OptiType showed high correlation (R² = 0.8 and 0.94) with gold-standard genotyping for ASE ratios.

Conclusions:

  • Computational HLA genotyping from scRNA-seq data is feasible and can achieve high accuracy.
  • A composite genotyping approach and specific copy number models enhance precision.
  • Accurate HLA genotyping from scRNA-seq facilitates robust allele-specific expression analysis in immunological research.