Integrated 3D light sheet and 2D multiplex imaging for deep histological characterization of somatic mouse glioblastoma
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 TM 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.
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BibTeX
@article{tiveron2026integrated,
title = {{Integrated 3D light sheet and 2D multiplex imaging for deep histological characterization of somatic mouse glioblastoma}},
author = {Marie Catherine Tiveron and Nathalie Coré and Kevin Bigott and Yliana Huriaux Fontana and Maria Caccavale and Léna Vilvandre and Fabio El Yassouri and Victoria Schoppel and Silvia Rüberg and Melanie Jungblut and Dominique Figarella-Branger and Aurélie Tchoghandjian and Andreas Bosio and Harold Cremer},
journal = {iScience},
year = {2026},
month = sep,
doi = {10.1016/j.isci.2026.117476},
url = {https://hal.science/hal-05760627v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Integrated 3D light sheet and 2D multiplex imaging for deep histological characterization of somatic mouse glioblastoma
AU - Marie Catherine Tiveron
AU - Nathalie Coré
AU - Kevin Bigott
AU - Yliana Huriaux Fontana
AU - Maria Caccavale
AU - Léna Vilvandre
AU - Fabio El Yassouri
AU - Victoria Schoppel
AU - Silvia Rüberg
AU - Melanie Jungblut
AU - Dominique Figarella-Branger
AU - Aurélie Tchoghandjian
AU - Andreas Bosio
AU - Harold Cremer
PY - 2026
DA - 2026/09/08
JO - iScience
DO - 10.1016/j.isci.2026.117476
UR - https://hal.science/hal-05760627v1
AB - 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 TM 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.
SN - 2589-0042
ER -