Glioma-Induced Alterations in Neuronal Activity and Neurovascular Coupling during Disease Progression

Abstract

Diffusely infiltrating gliomas are known to cause alterations in cortical function, vascular disruption, and seizures. These neurological complications present major clinical challenges, yet their underlying mechanisms and causal relationships to disease progression are poorly characterized. Here, we follow glioma progression in awake Thy1-GCaMP6f mice using in vivo wide-field optical mapping to monitor alterations in both neuronal activity and functional hemodynamics. The bilateral synchrony of spontaneous neuronal activity gradually decreases in glioma-infiltrated cortical regions, while neurovascular coupling becomes progressively disrupted compared to uninvolved cortex. Over time, mice develop diverse patterns of high amplitude discharges and eventually generalized seizures that appear to originate at the tumors' infiltrative margins. Interictal and seizure events exhibit positive neurovascular coupling in uninfiltrated cortex; however, glioma-infiltrated regions exhibit disrupted hemodynamic responses driving seizure-evoked hypoxia. These results reveal a landscape of complex physiological interactions occurring during glioma progression and present new opportunities for exploring novel biomarkers and therapeutic targets.

Keywords: glioma; interictal discharges; neurovascular coupling; seizures; wide field optical mapping.

Figure 1.. Immunohistochemical Analysis Reveals Diffuse Infiltration of the Cortex and Progressive Disruption of the Vasculature within the Highly Cellular Core of the Tumor

(A) Coronal sections obtained from glioma-bearing mice at 22 and 28 days post injection (DPI) show the distribution of glioma cells (HA; red), GCaMP6f+ neurons (green), and IB4-labeled vessels (magenta). Stitched images show a single confocal plane. Bar, 1,000 μm. (B) 3D rendering of GCaMP6f+ neurons (green) surrounded by infiltrating glioma cells (red) (80 × 80 × 15 μm XYZ volume). (C) Infiltrating glioma cells (red) migrate along blood vessels (arrowheads), with AQP4 (white) remaining closely associated with IB4+ vessels (magenta). Maximum intensity projections of confocal stacks. Bar, 50 μm. (D) Representative fields of glioma at the indicated time points and locations. AQP4 association with IB4+ vessels is intact in the contralateral (uninfiltrated) cortex and at the infiltrative margins of the tumor, but is disrupted in the tumor core. Bar, 100 μm. (E) Contralateral cortex and tumor core of a 22- and a 28-DPI tumor stained with CD34. Maximum intensity projections of confocal stack. Bar, 100 μm. Graphs (mean ± standard deviation) show fold change of CD34 signal intensity and area occupied in tumor versus contralateral cortex. C, contralateral cortex; T, tumor core. Two-tailed, unpaired t tests were used to calculate significance.

Figure 2.. Correlation of GCaMP6f and Hemodynamic Signals between Tumor and Non-Tumor Regions during Tumor Progression (Mouse 2)

(A) Images of raw fluorescence signal showing progressive loss of GCaMP6f fluorescence with no corresponding loss of green reflectance signal. Magenta asterisks indicate glioma injection site. Bar, 2 mm. (B) Maps of correlation to a seed region, outlined in black, for GCaMP6f and hemodynamic data (HbT). (C) Time courses taken from representative runs for bilaterally symmetric tumor and non-tumor regions of interest (ROIs) indicated by squares in (A). (D) Top: k-means clustered regions used to choose frontal cortex (dark red diagonal hatch line fill) and visual cortex (dark blue) regions for correlation analysis. Center: bar plots of both GCaMP6f and hemodynamic correlation between bilateral frontal (gray with diagonal hatch line fill) and visual regions (gray fill) as tumor progresses, shown for one representative animal. Bottom: slopes of regression lines for GCaMP6f and hemodynamic correlation trends for one representative animal, and across all animals (error bars are standard error across animals). Significance across all animals was calculated using paired, two-sample, two-tailed t tests between non-tumor and tumor regions for each channel at significance of *p < 0.05 and **p < 0.005. Equivalent data for mice 1 and 3 is shown in Figure S3.

Figure 3.. Disruption of the Neurovascular Response to Whisker Stimulus during Tumor Progression

(A) ROIs chosen using k-means clustering to correspond approximately to motor, forepaw, hindpaw, and whisker regions (1–4) overlaid on raw GCaMP images from between 11 and 32 DPI (mouse 2). Insets show GCaMP- and HbT-averaged whisker response maps to right tactile whisker stimuli, averaged after exclusion of trials in which the mouse ran (see STAR Methods). (B) Averaged neural and hemodynamic responses in the ROIs indicated in (A) for each DPI (mouse 2). Note differing amplitude scales for each ROI. Shaded error bounds show SEM. (C) Hemodynamic response function (HRF) deconvolution results for each ROI and DPI (mouse 2). Here, all plots are shown on the same y axis scale, in most cases demonstrating a consistent relationship between %GCaMP signal change and micron ΔHb changes, despite the more variable amplitudes of the raw responses in (B). Notable exceptions are seen in tumor-infiltrated regions, indicated by black arrows. The numbers in color indicate the correlation coefficient between the original data and the HRF convolution fit. (D) Summary metric calculated as the integral of the first 1.8 s of the HRF (relative to t = 0) across days for each mouse (columns) for ROIs in the unaffected whisker regions (inset image, regions a and c). (E) Results for frontal ROIs (regions c and d), with “d” corresponding to the tumor in each case. A progressive trend of increasing [HbR] and decreasing [HbO] within the tumor HRF is seen in all mice (arrows). See Figures S5 and S6 for further results in mice 1 and 3.

Figure 4.. Spatial Variation of Initiation and Propagation of Interictal Events (Mouse 1)

(A) Raw fluorescence image marked with ROIs. (B) ΔF/F image time-series of 3 epochs from a 180-s WFOM recording (without external stimulus) containing 13 spatially distinct interictal events (collected at DPI 35). Magenta arrows indicate areas of interictal event initiation. (C) Time-courses of GCaMP6f fluorescence, HbO, HbR, and HbT extracted from the 5 ROIs in (A). The lower amplitude and altered hemodynamic responses in the tumor region (i) are clearly visible, as well as differing relative amplitudes of the IIEs in each region for each event, reflecting local or global spread. (D) Contour plots showing the onset time to 20% of the maximum for each of the 13 events (a–m) denoted in (C). Dark red regions correspond to t = 0 initiation locations (see STAR Methods). (E) Frequency of events for each mouse in each imaging session (all identified events, divided by the total duration of imaging). There is a significant increase in relative frequency of events over time (R = 0.3282, p value = 0.036177).

Figure 5.. Region-Specific Neurovascular Coupling during Interictal Events (Mouse 2)

(A) Raw fluorescence image marked with ROIs. Bar, 2 mm. (B) Time series of a single representative interictal event (34 DPI) within in the left posterior and right anterior (tumor) regions. (C) Image frames corresponding to the event in (B) (timing of frames indicated by gray lines in B for GCaMP6f and dashed green lines for hemodynamics). (D) Temporally aligned time series for all interictal events across all sessions (all days) in mouse 2; for each ROI in (A), thicker line shows average. (E) Peak amplitudes of GCaMP6f and hemodynamic responses (left) and time to half-peak of the hemodynamic response (right), for mouse 2 (top row) and across all animals (bottom row). Error bars show standard deviation across trials or across animals, respectively. Significance across all animals was calculated using paired, two-sample, two-tailed t tests between non-tumor and tumor regions for each channel at significance of *p < 0.05 and **p < 0.005. (F) The overall average response for a non-tumor versus tumor ROI for mouse 2, with vertical bars denoting peak times of each response (note differences in the different Y axes).

Figure 6.. Neuronal and Hemodynamic Activity during a Generalized Seizure (Mouse 2, 34 DPI)

(A) Raw fluorescence image with non-tumor (blue) and tumor (orange) ROIs indicated. Bar, 2 mm. (B) Time series of a generalized seizure, shown for both GCaMP6f and hemodynamics and averaged across non-tumor and tumor regions. (C) Spatial patterns of GCaMP6f and hemodynamics shown for frame times indicated by dashed gray lines in (B) during seizure event. Full seizure shown in Video S4.

Figure 7.. Comparing Seizure Activity across the Infiltrative Margin and Model-Based Prediction of the Seizure-Related Hemodynamic Response (Mouse 2)

(A) Raw fluorescence image marked with ROIs. Bar, 2 mm. (B) Time series of seizure activity for both GCaMP6f and hemodynamics in three regions indicated in (A). Vertical axes are consistent between plots. (C and D) Time series of seizure activity for both GCaMP6f and hemodynamics for non-tumor (i) and tumor (ii) ROIs indicated in (A). Green dashed lines in (Ci) and (Cii) show predicted change in HbT using convolution of the GCaMP signal with HRFs shown in (Di) and (Dii), derived from the four initial interictal events (indicated with asterisks) for the non-tumor and tumor regions, respectively. Vertical axes are scaled differently to show detail. The measured HbT response in non-tumor regions (black arrow) is significantly lower than predicted by the intense neural seizure activity, suggesting saturation of the hemodynamic response. In the tumor region, the predicted impaired HbT is similar to the measured HbT response (purple arrow), suggesting that the response seen results from active tumor-evoked constriction of vessels within the tumor.