Evidence map›Paper›PMID 41702598›Full record

ReviewImmunoHorizons2026

Macrophages in human atherosclerotic plaques in the era of single-cell and spatial transcriptomics.

Adil Ijaz, Adil Rasheed, Marco Orecchioni

Abstract readReview
In one paragraph

Review in ImmunoHorizons, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. 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

3 authors.

Adil IjazImmunology Center of Georgia, Augusta University, Augusta, Georgia, USA.
Adil RasheedImmunology Center of Georgia, Augusta University, Augusta, Georgia, USA.ORCID 0000-0002-3204-6674
Marco OrecchioniImmunology Center of Georgia, Augusta University, Augusta, Georgia, USA.ORCID 0000-0002-3602-8510

Funding

Augusta University #941152
6 · The paper itself

Abstract

Macrophages are central players of inflammation, lipid metabolism, and remodeling in atherosclerotic plaques. Historically simplified into "M1" and "M2" polarization states, their biology has been fundamentally redefined by single-cell and spatial transcriptomic technologies. Over the past decade, these approaches have identified multiple macrophage subsets within human atheromas, each driven by distinct metabolic and cytokine signatures and occupying discrete spatial niches. Human single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and multimodal omic profiling collectively demonstrate that macrophage subsets extend far beyond fixed polarization states to engage their long-recognized functions in the atheroma, including inflammation, lipid handling and repair. These findings now link macrophage identity to microenvironmental cues, vascular location, and disease stage. Importantly, these data demonstrate that these macrophages do not exist in mutually exclusive states and can transition between these subtypes in response to these aforementioned factors. Here we synthesize these advances, focusing on human data describing macrophage diversity, spatial organization, and metabolic function, and discuss how this knowledge is reshaping mechanistic models of atherosclerosis and the potential therapeutic targeting of macrophage-mediated pathology.

Indexed as

AtherosclerosisMacrophagesPlaque, AtheroscleroticAnimalsHumansLipid MetabolismSingle-Cell AnalysisSingle-Cell Gene Expression AnalysisSpatial TranscriptomicsTranscriptomeatherosclerosisfoam cellmacrophagessingle-cell RNA sequencingspatial transcriptomics

Identifiers

PMID41702598
PMCPMC13003318

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.