Integrated 3D light sheet and 2D multiplex imaging for deep histological characterization of somatic mouse glioblastoma.
Overview
- Aix-Marseille University, CNRS, IBDM, Marseille, France
- Miltenyi Biotec B.V. & Co. KG, Bergisch Gladbach, Germany
- Aix-Marseille University, CNRS, INP, Institute Neurophysiopathol, GlioME Team, Marseille, France
- Aix-Marseille University, Réseau Préclinique et Translationnel de Recherche en Neuro-Oncologie, Plateforme PETRA”TECH”, Marseille, France
Abstract
Glioblastoma is a devastating brain cancer for which patient survival has remained largely unchanged for decades, underscoring the need for improved disease modeling and analytical tools. Neural stem cells have been identified as cells of origin of glioblastoma, leading to the development of somatic lineage models. Such models have been deeply characterized by sequencing but systematic histological analyses remain limited. Here, we present a multimodal histological characterization of a somatic glioblastoma mouse model. Using 3D light-sheet imaging, we show that the model is highly reproducible and enables quantitative assessment of tumor growth across cohorts. Through multiplex imaging with MACSima™ Imaging Cyclic Staining, we map the cellular landscape and molecular architecture of the tumors and their environments, and provide a curated resource of mouse-compatible antibodies. Finally, we demonstrate that tissue clearing and light-sheet microscopy can be seamlessly combined with multiplex imaging, enabling spatial proteomic characterization of a 3D pre-defined tumor.
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
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Data and code availability
Data: Data reported in this paper will be shared by the lead contact upon request.
Code: This paper does not report original code.
All other items: Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 3, 28 September 2026
- Authors: added Harold Cremer (0000-0002-8673-5176); removed Harold Cremer
- Funding: added Agence Nationale de la Recherche: ANR-21-CE18-0047, MEGA01, ANR-24-INSB-0005 FBI BIOGEN; Fondation ARC pour la Recherche sur le Cancer; Canceropôle PACA; Canceropôle Provence-Alpes-Côte d’Azur
Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 5 keywords, 62 references, 5 RRIDs.
Cite
This paper
Tiveron, M. C., Coré, N., Bigott, K., Fontana, Y. H., Caccavale, M., Vilvandre, L., El Yassouri, F., Schoppel, V., Rüberg, S., Jungblut, M., Figarella-Branger, D., Tchoghandjian, A., Bosio, A., & Cremer, H. (2026). Integrated 3D light sheet and 2D multiplex imaging for deep histological characterization of somatic mouse glioblastoma. iScience, 29(9), 117476. https://
BibTeX
@article{tiveron2026inte
author = {Tiveron, Marie Catherine and Coré, Nathalie and Bigott, Kevin and Fontana, Yliana Huriaux and Caccavale, Maria and Vilvandre, Léna and El Yassouri, Fabio and Schoppel, Victoria and Rüberg, Silvia and Jungblut, Melanie and Figarella-Branger, Dominique and Tchoghandjian, Aurélie and Bosio, Andreas and Cremer, Harold},
title = {{Integrated 3D light sheet and 2D multiplex imaging for deep histological characterization of somatic mouse glioblastoma}},
journal = {iScience},
year = {2026},
month = sep,
volume = {29},
number = {9},
pages = {117476},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/
url = {https://
pmid = {42741395},
pmcid = {PMC13573706}
}
RIS
TY - JOUR
AU - Tiveron, Marie Catherine
AU - Coré, Nathalie
AU - Bigott, Kevin
AU - Fontana, Yliana Huriaux
AU - Caccavale, Maria
AU - Vilvandre, Léna
AU - El Yassouri, Fabio
AU - Schoppel, Victoria
AU - Rüberg, Silvia
AU - Jungblut, Melanie
AU - Figarella-Branger, Dominique
AU - Tchoghandjian, Aurélie
AU - Bosio, Andreas
AU - Cremer, Harold
TI - Integrated 3D light sheet and 2D multiplex imaging for deep histological characterization of somatic mouse glioblastoma
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/
VL - 29
IS - 9
SP - 117476
SN - 2589-0042
PB - Elsevier
DO - 10.1016/
UR - https://
LA - en
ER -
CSL-JSON
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