Evidence map›Paper›PMID 41641929›Full record

ReviewJournal of medical virology2026

Epstein-Barr Virus Infection at Single-Cell Resolution.

Elliott D SoRelle

Abstract readReview
In one paragraph

Review in Journal of medical virology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
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

1 author.

Elliott D SoRelleDepartment of Microbiology & Immunology, University of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0000-0002-3362-1028

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Eric R. Fearon · 1988 to 2026
$178.2M
Resolving viral oncoprotein control of terminal cell fates to limit Epstein-Barr virus-driven lymphoproliferationK22CA288946 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Elliott Daniel SoRelle · 2025 to 2026
$362k
National Institutes of Health (NIH) National Cancer Institute (NCI) K22 1K22CA288946NCI NIH HHS K22 CA288946NCI NIH HHS P30 CA046592University of Michigan Department of Microbiology and Immunology and the University of Michigan Rogel Cancer Center P30CA046592
6 · The paper itself

Abstract

Epstein-Barr virus (EBV) infection has been studied at single-cell resolution for six decades and counting. Such investigations can reveal virus-host interactions and their dependence on viral strain, cellular niche, infection program, immune response regulation, and time. Understanding these factors is paramount to treating EBV-associated cancers and autoimmune diseases. This review examines the state of the field in EBV single-cell and spatial-omics spanning experimental models and clinical samples. Topics of primary interest include the growing adoption and emerging biological themes from single-cell assays and analyses, the shift from characterization toward functional single-cell studies, and strategies to maximize clinically relevant insights from dense single-cell and spatial datasets. Ancillary topics include the historical evolution of the single-cell EBV field and end-to-end single-cell sequencing workflows. Special attention is given to open questions in molecular mechanisms of EBV pathogenesis and how they might be resolved by future studies utilizing single-cell techniques.

Indexed as

Epstein-Barr Virus InfectionsHerpesvirus 4, HumanHost-Pathogen InteractionsSingle-Cell AnalysisAnimalsHumansautoimmunityB cell lymphomaEBVinfectious mononucleosismultiple sclerosisnasopharyngeal carcinomaNK/T cell lymphomascATAC‐seqscRNA‐seqspatial‐omics

Identifiers

PMID41641929
PMCPMC12875166

What OpenQuestion holds

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LicenceCC BY
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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.