Evidence map›Paper›PMID 41229397›Full record

ArticleNAR genomics and bioinformatics2025

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

Ahmad Al Ajami, Jonas Schuck, Federico Marini, Katharina Imkeller

Abstract read
In one paragraph

Article in NAR genomics and bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ahmad Al AjamiGoethe University, University Hospital Frankfurt, Neurological Institute (Edinger Institute), Frankfurt/Main 60528, Germany.ORCID https://orcid.org/0009-0006-5615-7447
Jonas SchuckGoethe University, University Hospital Frankfurt, Neurological Institute (Edinger Institute), Frankfurt/Main 60528, Germany.ORCID https://orcid.org/0009-0003-5705-4579
Federico MariniUniversity Medical Center Mainz, Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Mainz 55118, Germany.ORCID https://orcid.org/0000-0003-3252-7758
Katharina ImkellerGoethe University, University Hospital Frankfurt, Neurological Institute (Edinger Institute), Frankfurt/Main 60528, Germany.ORCID https://orcid.org/0000-0002-5177-0852

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immune molecules such as human leukocyte antigens (HLAs) or killer Ig-like receptors are encoded in the most genetically diverse loci of the human genome. Many of these immune genes exhibit remarkable allelic diversity across populations. Here, we present a novel computational methodology for allele-specific expression quantification of immune genes in single-cell RNA sequencing (scRNA-seq) data. Our quantification method features a novel R/Bioconductor data structure that can handle expression data across multiple immunogenetic annotation layers and enables interactive exploration of immune gene expression. We validate our methodology on multiple scRNA-seq datasets to demonstrate its performance across different experimental setups. We illustrate how these new tools allow us to study loss of HLA expression in tumor cells and to discover differential HLA allele expression in specific immune cell subtypes. By capturing such allele-specific expression patterns and their variation, our methodology offers novel insights into human immunogenomic diversity.

Indexed as

AllelesHLA AntigensRNA-SeqSingle-Cell AnalysisGene Expression ProfilingHumansSequence Analysis, RNASingle-Cell Gene Expression AnalysisHLA Antigens

Identifiers

PMID41229397
PMCPMC12604667

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.