Evidence map›Paper›PMID 41745380›Full record

ReviewJournal of personalized medicine2026

Single-Cell Multi-Omics Profiling of Human Septal Myectomy Tissue: Toward Precision Medicine in Obstructive Hypertrophic Cardiomyopathy.

Quynh Nguyen, Jeremy Parker, Amrit Singh, Ying Wang, Jamil Bashir, Zachary Laksman

Abstract readReview
In one paragraph

Review in Journal of personalized medicine, 2026. 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

6 authors.

Quynh NguyenDivision of Cardiac Surgery, Department of Surgery, University of British Columbia, Vancouver, BC V6Z 1Y6, Canada.ORCID 0000-0003-1330-9026
Jeremy ParkerSchool of Biomedical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
Amrit SinghCentre for Heart Lung Innovation, University of British Columbia, Vancouver, BC V6Z 1Y6, Canada.ORCID 0000-0002-7475-1646
Ying WangCentre for Heart Lung Innovation, University of British Columbia, Vancouver, BC V6Z 1Y6, Canada.ORCID 0000-0002-1444-5778
Jamil BashirDivision of Cardiac Surgery, Department of Surgery, University of British Columbia, Vancouver, BC V6Z 1Y6, Canada.
Zachary LaksmanSchool of Biomedical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hypertrophic cardiomyopathy (HCM) is an inherited cardiac disorder most commonly caused by pathogenic variants in sarcomeric genes, yet many patients remain genotype-negative and the mechanisms linking genetic alterations to disease pathology are not fully understood. Traditional bulk analyses have provided limited insight into the cellular and molecular changes that drive disease progression. Recent advances in single-cell and spatial multi-omics technologies now allow detailed characterization of cell type-specific transcriptional programs, signaling pathways, and tissue remodeling within the human myocardium. These approaches have begun to redefine HCM as a complex, multicellular disease rather than a purely sarcomeric disorder. This review summarizes current single-cell and spatial transcriptomic studies of human septal myectomy tissue, outlines their major findings and limitations, and discusses how these data may inform the development of precision medicine strategies in obstructive HCM.

Indexed as

Hypertrophic cardiomyopathymulti-omicsseptal myectomysingle-cell RNA sequencingsingle-nucleus RNA sequencingspatial transcriptomics

Identifiers

PMID41745380
PMCPMC12942523

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.