ArticleiScience2026
Integrated 3D light sheet and 2D multiplex imaging for deep histological characterization of somatic mouse glioblastoma.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Protocol for 3D-guided sectioning and deep cell phenotyping via light sheet imaging and 2D spatial multiplexing.STAR protocols · 2026Article
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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