RT Journal Article
SR Electronic
T1 A Cellular Ground Truth to Develop MRI Signatures in Glioma Models by Correlative Light Sheet Microscopy and Atlas-Based Coregistration
JF The Journal of Neuroscience
JO J. Neurosci.
FD Society for Neuroscience
SP 5574
OP 5587
DO 10.1523/JNEUROSCI.1470-22.2023
VO 43
IS 30
A1 Schregel, Katharina
A1 Heinz, Lennart
A1 Hunger, Jessica
A1 Pan, Chenchen
A1 Bode, Julia
A1 Fischer, Manuel
A1 Sturm, Volker
A1 Venkataramani, Varun
A1 Karimian-Jazi, Kianush
A1 Agardy, Dennis A.
A1 Streibel, Yannik
A1 Zerelles, Roland
A1 Wick, Wolfgang
A1 Heiland, Sabine
A1 Bunse, Theresa
A1 Tews, Björn
A1 Platten, Michael
A1 Winkler, Frank
A1 Bendszus, Martin
A1 Breckwoldt, Michael O.
YR 2023
UL http://www.jneurosci.org/content/43/30/5574.abstract
AB Glioblastoma is the most common malignant primary brain tumor with poor overall survival. Magnetic resonance imaging (MRI) is the main imaging modality for glioblastoma but has inherent shortcomings. The molecular and cellular basis of MR signals is incompletely understood. We established a ground truth-based image analysis platform to coregister MRI and light sheet microscopy (LSM) data to each other and to an anatomic reference atlas for quantification of 20 predefined anatomic subregions. Our pipeline also includes a segmentation and quantification approach for single myeloid cells in entire LSM datasets. This method was applied to three preclinical glioma models in male and female mice (GL261, U87MG, and S24), which exhibit different key features of the human glioma. Multiparametric MR data including T2-weighted sequences, diffusion tensor imaging, T2 and T2* relaxometry were acquired. Following tissue clearing, LSM focused on the analysis of tumor cell density, microvasculature, and innate immune cell infiltration. Correlated analysis revealed differences in quantitative MRI metrics between the tumor-bearing and the contralateral hemisphere. LSM identified tumor subregions that differed in their MRI characteristics, indicating tumor heterogeneity. Interestingly, MRI signatures, defined as unique combinations of different MRI parameters, differed greatly between the models. The direct correlation of MRI and LSM allows an in-depth characterization of preclinical glioma and can be used to decipher the structural, cellular, and, likely, molecular basis of tumoral MRI biomarkers. Our approach may be applied in other preclinical brain tumor or neurologic disease models, and the derived MRI signatures could ultimately inform image interpretation in a clinical setting.SIGNIFICANCE STATEMENT We established a histologic ground truth-based approach for MR image analyses and tested this method in three preclinical glioma models exhibiting different features of glioblastoma. Coregistration of light sheet microscopy to MRI allowed for an evaluation of quantitative MRI data in histologically distinct tumor subregions. Coregistration to a mouse brain atlas enabled a regional comparison of MRI parameters with a histologically informed interpretation of the results. Our approach is transferable to other preclinical models of brain tumors and further neurologic disorders. The method can be used to decipher the structural, cellular, and molecular basis of MRI signal characteristics. Ultimately, information derived from such analyses could strengthen the neuroradiological evaluation of glioblastoma as they enhance the interpretation of MRI data.