Abstract

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 co-register MRI and light sheet microscopy (LSM) data to each other and to an anatomical reference atlas for quantification of 20 predefined anatomical 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 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 neurological disease models and derived MRI signatures could ultimately inform image interpretation in a clinical setting.

SIGNIFICANCE STATEMENT:

We established a histological ground-truth based approach for MR image analyses and tested this method in three preclinical glioma models exhibiting different features of glioblastoma. Co-registration of light sheet microscopy to MRI allowed for an evaluation of quantitative MRI data in histologically distinct tumor subregions. Co-registration 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 neurological 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.