Collections / A cellular hierarchy in melanoma uncouples growth and metastasis

A cellular hierarchy in melanoma uncouples growth and metastasis

Although melanoma is notorious for its high degree of heterogeneity and plasticity, the origin and magnitude of cell state diversity remains poorly understood. Equally, it is not known whether melanoma growth and metastatic dissemination are supported by overlapping or distinct melanoma subpopulations. By combining mouse genetics, unbiased lineage tracing and quantitative modelling, single-cell and spatial transcriptomics, we provide evidence of a hierarchical model of tumour growth that mirrors the cellular and molecular logic underlying embryonic neural crest cell fate specification and differentiation. Our findings indicate that tumorigenic competence is associated with a spatially localized perivascular niche environment, a phenotype acquired through a NOTCH3-dependent intercellular communication pathway established by endothelial cells. Consistent with a model in which only a fraction of melanoma cells is fated to fuel growth, temporal single-cell tracing of a population of melanoma cells harbouring a mesenchymal-like state revealed that these cells do not contribute to primary tumour growth but, instead, constitutes a pool of metastatic-initiating cells that can switch cell identity while disseminating to secondary organs. Our data provide a spatially and temporally resolved map of the diversity and trajectories of cancer cell states within the evolving melanoma ecosystem and suggest that the ability to support growth and metastasis are limited to distinct pools of melanoma cells. The observation that these phenotypic competencies can be dynamically acquired upon exposure to specific niche signals warrant the development of therapeutic strategies that interfere with the cancer cell reprogramming activity of such microenvironmental cues.

Datasets2
Size~492 MB
AssayVisium
Samplemouse · skin of body
KindOther
Flag a data issue
PMID:36131018 ↗
Python · sddb client
from sddb import Catalog

cohort = Catalog().collection("geo-gse207592")
cohort.members        # the 2 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/")
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-gse207592")
coll.ordered_artifacts.to_dataframe()   # the 2 members, ordered

# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]
Shell
# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse207592 --dest data/

Licenses: 2 NCBI GEO policy

Open in Vitessce
Visium
Mouse skin of body
GSM6319698
mouse skin of body melanoma 248 MB 4,992 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissueskin of body
Conditionmelanoma
Size248 MB
Spots4,992
silver H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse skin of body
GSM6319696
mouse skin of body melanoma 244 MB 4,992 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissueskin of body
Conditionmelanoma
Size244 MB
Spots4,992
silver H&E NCBI GEO policy Open in Vitessce

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No linked publication found — please cite the data source.