Collections / A skeletal muscle atlas shows neuromuscular junction adaptations to growth and atrophy

A skeletal muscle atlas shows neuromuscular junction adaptations to growth and atrophy

The molecular mechanisms underlying the opposing states of muscle atrophy (such as with disuse or in aging) and hypertrophy (such as with increased physical activity) are still not well-defined. Progress has been slowed by challenges in characterizing the niche heterogeneity of skeletal muscle (whereby myofibers are the most abundant) and in obtaining well-characterized samples. Here we describe: i) changes in muscle cell populations that are undergoing atrophy or hypertrophy, with complex, opposing cell transcriptional responses during each process; and ii) a remarkable remodeling of the neuromuscular junction (NMJ) domain, whereby we identify emerging new subpopulations of synaptic myonuclei (SynM) and terminal Schwann cells (tSCs) that are specifically adapted to each growth/atrophy condition. High-resolution 3D imaging and spatial transcriptomics reveal these structural and molecular adaptations at the tissue level. Critically, these changes also occur in the NMJ of both denervated and exercised human muscle. Our study lays the ground for identifying novel targets for medical, pharmacological and lifestyle interventions in aging and disease.

Datasets6

Size~106 MB

AssayVisium

Samplemouse · plantaris

KindComparison

Flag a data issue

PMID:42019489 ↗

Download the whole collection

Client Lamin CLI

Python · sddb client

from sddb import Catalog

cohort = Catalog().collection("geo-gse294765")
cohort.members        # the 6 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-gse294765")
coll.ordered_artifacts.to_dataframe()   # the 6 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-gse294765 --dest data/

Install (Python 3.12+) Docs →

Licenses: 6 NCBI GEO policy

Member datasets · 6

Visium

Mouse plantaris

GSM8919244

mouse plantaris 15.0 MB 1,068 spots bronze H&E NCBI GEO policy

TechnologyVisium

Organismmouse

Tissueplantaris

Size15.0 MB

Spots1,068

Magnificationother · 3.497 µm/px

bronze H&E NCBI GEO policy

Visium

Mouse plantaris

GSM8919242

mouse plantaris 16.1 MB 1,030 spots bronze H&E NCBI GEO policy

TechnologyVisium

Organismmouse

Tissueplantaris

Size16.1 MB

Spots1,030

Magnificationother · 3.498 µm/px

bronze H&E NCBI GEO policy

Visium

Mouse plantaris

GSM8919245

mouse plantaris 17.4 MB 1,286 spots bronze H&E NCBI GEO policy

TechnologyVisium

Organismmouse

Tissueplantaris

Size17.4 MB

Spots1,286

Magnificationother · 3.496 µm/px

bronze H&E NCBI GEO policy

Visium

Mouse plantaris

GSM8919247

mouse plantaris 19.4 MB 1,169 spots bronze H&E NCBI GEO policy

TechnologyVisium

Organismmouse

Tissueplantaris

Size19.4 MB

Spots1,169

Magnificationother · 3.504 µm/px

bronze H&E NCBI GEO policy

Visium

Mouse plantaris

GSM8919246

mouse plantaris 22.9 MB 1,311 spots bronze H&E NCBI GEO policy

TechnologyVisium

Organismmouse

Tissueplantaris

Size22.9 MB

Spots1,311

bronze H&E NCBI GEO policy

Visium

Mouse plantaris

GSM8919243

mouse plantaris 15.5 MB 1,062 spots bronze H&E NCBI GEO policy

TechnologyVisium

Organismmouse

Tissueplantaris

Size15.5 MB

Spots1,062

Magnificationother · 3.496 µm/px

bronze H&E NCBI GEO policy

Cite this collection

doi.org/10.1016/j.devcel.2026.03.010 ↗

BibTeX

@article{Campanario_2026,
  title = {A skeletal muscle atlas shows neuromuscular junction adaptations to growth and atrophy},
  volume = {61},
  ISSN = {1534-5807},
  url = {http://dx.doi.org/10.1016/j.devcel.2026.03.010},
  DOI = {10.1016/j.devcel.2026.03.010},
  number = {5},
  journal = {Developmental Cell},
  publisher = {Elsevier BV},
  author = {Campanario, Silvia and Grima-Terrén, Mercedes and Rommelfanger, Megan and Dell’ Orso, Stefania and Feng, Xuesong and Cisneros, Andrés and Ramírez-Pardo, Ignacio and Calls-Cobos, Aina and Yang, Benjamin A. and Ko, Kyung Dae and García-Domínguez, Esther and Pena-Couso, Laura and Chou, Grace and Zheng, Yuewen and Hah, Nasun and Randazzo, Davide and Pérez-Garcia, Alberto and Markowitz, Tovah E. and Milisenda, Jose and Pinal-Fernandez, Iago and Mammen, Andrew and Gómez-Cabrera, Mari Carmen and Serrano, Antonio L. and Perdiguero, Eusebio and Sartorelli, Vittorio and Isern, Joan and Muñoz-Cánoves, Pura},
  year = {2026},
  month = May,
  pages = {1028–1043.e9}
}