Evidence map›Paper›PMID 42342734›Full record

ArticleScientific data2026

High-resolution spatial transcriptomics of adult and pediatric human liver with Visium HD.

Faizan Hasan, Rachel D Edgar, Jawairia Atif, Diana Nakib, Cornelia Thoeni, Amanda Ricciuto, Blayne A Sayed, Ian McGilvray, Gary D Bader, Sonya A MacParland

Abstract readDataset
In one paragraph

Article in Scientific data, 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

10 authors.

Faizan Hasan *Ajmera Transplant Centre, Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada.
Rachel D Edgar *Ajmera Transplant Centre, Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada. r.edgar@utoronto.ca.
Jawairia AtifAjmera Transplant Centre, Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada.
Diana NakibAjmera Transplant Centre, Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada.
Cornelia ThoeniDepartment of Pathology and Lab Medicine, Nova Scotia Health Authority, Halifax, Nova Scotia, Canada.
Amanda RicciutoDivision of Gastroenterology, Hepatology & Nutrition, Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada.
Blayne A SayedAjmera Transplant Centre, Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada.
Ian McGilvrayAjmera Transplant Centre, Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada.
Gary D BaderDepartment of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
Sonya A MacParlandAjmera Transplant Centre, Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada. s.macparland@utoronto.ca.

Funding

Canadian Institutes of Health Research (CIHR) NHC-142832New Frontiers in Research Fund (NFRF) NFRFT-2020-00787
6 · The paper itself

Abstract

The liver is composed of diverse cell populations that coordinate essential metabolic and immune functions. Single-cell transcriptomics has advanced characterization of liver cellular composition, but dissociation of tissue to single-cells can introduce biases through the enrichment or depletion of cell types. Spatial transcriptomics is a complementary approach to avoid inherent bias for cell populations and to add important spatial context. The Visium HD spatial transcriptomics technology from 10X Genomics enables high-resolution spatial mapping of gene expression in tissue samples with a bin width of 2 µm enabling quantification of transcripts at a sub-cellular resolution. We applied Visium HD to three healthy human liver donor samples, from two adult and one pediatric donor. We identified cell types by clustering 8 µm bins and integration with single-cell reference maps. Differential expression analyses identified spatially distinct gene expression resulting in development of a high-resolution map of the liver. This resource provides cell-level and spatially-resolved insights into the cellular and anatomical heterogeneity of the liver to serve as a resource for researchers to identify disease-specific spatial signatures and novel therapeutic targets.

Indexed as

LiverSpatial TranscriptomicsTranscriptomeAdultChildGene Expression ProfilingHumansSingle-Cell Gene Expression Analysis

Identifiers

PMID42342734
PMCPMC13658214

What OpenQuestion holds

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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.