Sign in
GenomicsScientific Data
Peer-reviewed journal article

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

Faizan Hasan, Rachel D. EdgariD, Jawairia AtifiD, Diana NakibiD, Cornelia E. ThoeniiD, Amanda RicciutoiD, Blayne Amir SayediD, Ian D. McGilvrayiD, Gary D. BaderiD, Sonya A. MacParlandiD

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{micro}m 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{micro}m 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.

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISHildebrandt '21Kamel '25Yakubovsky '26Ding '24Watson '25Andrews '21
72% semantic matchGenomics
Franziska Hildebrandt, Alma Andersson, Sami Saarenpää+10 more·Nature Communications·Dec 2, 2021· 195 citations