Multimodal imaging of the dynamic brain tumor microenvironment during glioblastoma progression and in response to treatment

Abstract

Tumors evolve in a dynamic communication with their native tissue environment and recruited immune cells. The diverse components of the tumor microenvironment (TME) can critically regulate tumor progression and therapeutic response. In turn, anticancer treatments may alter the composition and functions of the TME. To investigate this continuous dialog in the context of primary brain cancers, we developed a multimodal longitudinal imaging strategy. We combined macroscopical magnetic resonance imaging with subcellular resolution two-photon intravital microscopy, and leveraged the power of single-cell analysis tools to gain insights into the ongoing interactions between different components of the TME and cancer cells. Our experiments revealed that the migratory behavior of tumor-associated macrophages is different in genetically distinct glioblastomas, and in response to macrophage-targeted therapy. These results underscore the importance of studying cancer longitudinally in an in vivo setting, to reveal complex and dynamic alterations in the TME during disease progression and therapeutic intervention.

Keywords: Cancer; Medical imaging; Microenvironment; Physics magnetic resonance imaging.

Graphical abstract

Figure 1

Multimodal IVM-MRI imaging to visualize dynamics of the brain TME over multiple weeks (A) Schematic showing the experimental design used to investigate TME dynamics during tumor progression and in response to BLZ945 treatment in Ink4a/Arf-deficient and p53-deficient GBMs. Mice were imaged weekly to assess tumor growth by MRI and to monitor TME dynamics by IVM. The dashed gray line indicates a representative tumor growth curve of a GBM-bearing mouse. (B) In order to obtain visual access to the brain tumor and perform multimodal IVM-MRI, a CIW was surgically implanted in the mouse skull at the site of tumor induction (left). The incorporation of metal-free components enabled the concomitant use of MRI (right). The dashed orange circle indicates the position of the CIW on the top of the tumor (green). (C) Representative example of longitudinal multimodal imaging of the same mouse by MRI and IVM before and after treatment with the CSF-1R inhibitor BLZ945 (treatment initiated at day 0). The upper MRI images show the anatomical location of the tumor (green line) and the projection of the CIW position (dashed orange circle). The lower panels show matching two-photon microscopy tile scans visualizing the dynamics of GFP-positive cancer cells (green) and brain-resident TAMs (red) through the CIW (dashed orange circle). The blue squares indicate an example of the location of two FOVs that were used to record time-lapses throughout the duration of the longitudinal IVM-MRI imaging experiment. Scale bars: 400 μm. Brain silhouette in (B) was sourced from https://scidraw.io/. See also Figures S1 and S2.

Figure 2

TME characterization of lineage-tracing mouse models (A) Representative two-photon microscopy images of the CX3CR1 lineage-tracing model showing brain-resident TAMs (red), dextran-Pacific Blue-labeled vessels (white), SHG imaging signal (cyan), and GFP-expressing cancer cells (green). Scale bars: 150 μm. (B) The FLT3 lineage-tracing model allowed the detection of Flt3⁺ GFP-expressing immune cells (yellow), ectopic tdTomato mainly expressed in vessel-like structures (violet), and SHG imaging signal (cyan). Scale bars: 150 μm. (C and D) Flow cytometry analysis showed an equivalent abundance of the major immune cell types in GBM-bearing mice with and without a CIW (paired Wilcoxon test, no statistically significant differences were observed). Each dot represents one tumor. (E) Endpoint volume of the tumors (measured by MRI) that were characterized by flow cytometry in (C) and (D) (Mann-Whitney test, no statistically significant difference was observed). Each dot represents one tumor. Data in (C), (D), and (E) are represented as mean ± SD. See also Table S1.

Figure 3

The dura mater, but not the brain parenchyma, shows SHG imaging signal (A and B) Representative two-photon microscopy images of the dura mater dissected from GBM-bearing (A) CX3CR1 lineage-tracing mice and (B) FLT3 lineage-tracing mice showing the second harmonic generation (SHG) signal. Scale bars: 100 μm. (C) 425-μm thick tumor-bearing brain slices without dura mater (left, tumor area outlined by the dashed green line) imaged by two-photon microscopy (right) showed GFP-positive cancer cells and the adjacent healthy brain parenchyma, but no SHG signal. Scale bar: 100 μm. See also Table S1.

Figure 4

Analysis of a large IVM dataset reveals temporal dynamics of brain-resident TAMs during the treatment of GBM (A) Tumor volume curves from weekly MRI of Ink4a/Arf- or p53-deficient GBM-bearing mice. Two key phases were observed in both models: tumor growth in the 3 weeks preceding BLZ945 treatment (start = timepoint 0), and tumor regression in the 3 weeks post-treatment initiation. (B) Analysis pipeline of IVM data collected from Ink4a/Arf- or p53- deficient GBM-bearing mice (CX3CR1 lineage-tracing model) that were intravitally imaged at different stages of tumor progression or therapeutic response. After segmentation of the different components of the GBM TME in each of the 30-min time-lapse experiments (step 1 and 2), brain-resident TAMs were tracked (step 3). Of note, migration of cancer cells was not observed in the total duration of the time-lapse. Data were extracted from 80 movies that were collected from 5 Ink4a/Arf-deficient GBMs and from 3 p53-deficient GBMs, measuring in total 76,036 brain-resident TAMs, and the SingleCellExperiment Bioconductor package was used to store, organize, and analyze the data (step 4). Scale bars: 100 μm. (C) UMAP visualization of brain-resident TAM clusters from this analysis pipeline. (D) Violin plots show the migration speed of brain-resident TAMs in each of the clusters (the cluster numbers correspond to the IDs shown in (C)). (E) Percentage of migrating brain-resident TAMs in Ink4a/Arf- or p53-deficient GBMs at each of the stages of tumor progression (timepoints −3, −2, −1, and 0) and in response to BLZ945 (timepoints 1, 2, and 3) (Student’s t-test, ∗ indicates p < 0.05). Each dot represents one FOV. Data are represented as mean ± SD. See also Figures S3, S4, and S5, and Videos S1, S2, S3, S4, and S5.