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Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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Related Experiment Video

Updated: Jan 11, 2026

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations
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Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

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Allele-specific immune gene quantification and expression analysis in single-cell RNA-seq data.

Ahmad Al Ajami1,2,3, Jonas Schuck1,2,3, Federico Marini4,5

  • 1Goethe University, University Hospital Frankfurt, Neurological Institute (Edinger Institute), Frankfurt/Main 60528, Germany.

NAR Genomics and Bioinformatics
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational method to quantify allele-specific expression of immune genes in single-cell RNA sequencing data. This approach enhances understanding of human immunogenomic diversity and gene expression variations.

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Area of Science:

  • Immunogenomics
  • Computational Biology
  • Single-cell analysis

Background:

  • Immune genes like human leukocyte antigens (HLAs) show high genetic diversity.
  • Understanding allele-specific expression is crucial for immunogenomics.
  • Existing methods may not fully capture immune gene allelic diversity.

Purpose of the Study:

  • To develop a novel computational methodology for allele-specific expression quantification of immune genes.
  • To enable interactive exploration of immune gene expression across multiple annotation layers.
  • To provide insights into human immunogenomic diversity.

Main Methods:

  • Developed a novel R/Bioconductor data structure for handling multi-layered immunogenetic annotation.
  • Applied a computational methodology for allele-specific expression quantification in single-cell RNA sequencing (scRNA-seq) data.
  • Validated the methodology on diverse scRNA-seq datasets.

Main Results:

  • Demonstrated robust performance across different experimental setups.
  • Enabled the study of human leukocyte antigen (HLA) expression loss in tumor cells.
  • Facilitated the discovery of differential HLA allele expression in specific immune cell subtypes.

Conclusions:

  • The novel methodology accurately quantifies allele-specific immune gene expression.
  • This approach offers new insights into human immunogenomic diversity.
  • The tools facilitate interactive exploration of complex immune gene expression patterns.