Collections / Single-cell and spatial transcriptomics characterisation of the immunological landscape in the healthy and PSC human liver

Single-cell and spatial transcriptomics characterisation of the immunological landscape in the healthy and PSC human liver

Primary sclerosing cholangitis (PSC) is a rare liver disease characterized by chronic inflammation and irreparable damage to the bile ducts. Due to a limited understanding of the underlying pathogenesis of disease, there remains a paucity of treatment options. As such, we sequenced healthy and diseased livers to compare the activity, interactions, and localization of immune and non-immune cells. This revealed that outside PSC scar regions, hepatocytes are transitioning to bile duct cells, whereas within the scars, there is an accumulation of immune cells. Of these cells, macrophages that typically contribute to tissue repair were enriched in immunoregulatory genes and were less responsive to stimulation. These cells are likely involved in maintaining hepatic inflammation and could be targeted in novel therapeutic development.

Datasets4

Size~149 MB

AssayVisium

Samplehuman · liver

KindOther

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PMID:38199298 ↗

Download the whole collection

Client Lamin CLI

Python · sddb client

from sddb import Catalog

cohort = Catalog().collection("geo-gse240429")
cohort.members        # the 4 datasets as a table

print(cohort.licenses())              # check the terms before you download

sdatas = cohort.to_dict()             # {uid: SpatialData}, cached locally
# or keep the .zarr files in a folder of your choice:
# sdatas = cohort.to_dict(dest="data/")

Install (Python 3.12+) Docs →

Python · lamindb

Needs pip install lamindb and network access to the Lamin database, which is limited during the alpha. If it fails, use the Client tab.

import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
coll = db.Collection.get(key="geo-gse240429")
coll.ordered_artifacts.to_dataframe()   # the 4 members, ordered

# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]

Install (Python 3.12+) Docs →

Shell

# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse240429 --dest data/

Install (Python 3.12+) Docs →

Licenses: 4 NCBI GEO policy

Member datasets · 4

Visium

Human liver

GSM7697870

human liver 46.2 MB 4,992 spots bronze H&E NCBI GEO policy

TechnologyVisium

Organismhuman

Tissueliver

Size46.2 MB

Spots4,992

bronze H&E NCBI GEO policy

Visium

Human liver

GSM7697871

human liver 34.2 MB 4,992 spots bronze H&E NCBI GEO policy

TechnologyVisium

Organismhuman

Tissueliver

Size34.2 MB

Spots4,992

bronze H&E NCBI GEO policy

Visium

Human liver

GSM7697868

human liver 37.3 MB 4,992 spots bronze H&E NCBI GEO policy

TechnologyVisium

Organismhuman

Tissueliver

Size37.3 MB

Spots4,992

bronze H&E NCBI GEO policy

Visium

Human liver

GSM7697869

human liver 31.7 MB 4,992 spots bronze NCBI GEO policy

TechnologyVisium

Organismhuman

Tissueliver

Size31.7 MB

Spots4,992

bronze NCBI GEO policy

Cite this collection

BibTeX

@article{Andrews_2024,
  title = {Single-cell, single-nucleus, and spatial transcriptomics characterization of the immunological landscape in the healthy and PSC human liver},
  author = {Andrews, TS and Nakib, D and Perciani, CT and Ma, XZ and Liu, L and Winter, E and Camat, D and Chung, SW and Lumanto, P and Manuel, J and Mangroo, S and Hansen, B and Arpinder, B and Thoeni, C and Sayed, B and Feld, J and Gehring, A and Gulamhusein, A and Hirschfield, GM and Ricciuto, A and Bader, GD and McGilvray, ID and MacParland, S},
  journal = {Journal of hepatology},
  year = {2024},
  volume = {80},
  number = {5},
  pages = {730-743},
  pmid = {38199298}
}