Five immunocompetent C57BL/6-cBrd/cBrd/Cr (albino C57BL/6) mice were injected with GL261-luc2 cells, a cell line sharing characteristics of human glioblastoma multiforme (GBM). The mice were imaged using magnetic resonance (MR) at five separate time points to characterize growth and development of the tumor. After 25 days, the final tumor volumes of the mice varied from 12 mm³ to 62 mm³, even though mice were inoculated from the same tumor cell line under carefully controlled conditions. We generated hypotheses to explore large variances in final tumor size and tested them with our simple reaction-diffusion model in both a 3-dimensional (3D) finite difference method and a 2-dimensional (2D) level set method. The parameters obtained from a best-fit procedure, designed to yield simulated tumors as close as possible to the observed ones, vary by an order of magnitude between the three mice analyzed in detail. These differences may reflect morphological and biological variability in tumor growth, as well as errors in the mathematical model, perhaps from an oversimplification of the tumor dynamics or nonidentifiability of parameters. Our results generate parameters that match other experimental in vitro and in vivo measurements. Additionally, we calculate wave speed, which matches with other rat and human measurements.

Atypical brainstem modulation of pain might contribute to changes in sensory processing typical of migraine. The study objective was to investigate whether migraine is associated with brainstem structural alterations that correlate with this altered pain processing. MRI T1-weighted images of 55 migraine patients and 58 healthy controls were used to: (1) create deformable mesh models of the brainstem that allow for shape analyses; (2) calculate volumes of the midbrain, pons, medulla and the superior cerebellar peduncles; (3) interrogate correlations between regional brainstem volumes, cutaneous heat pain thresholds, and allodynia symptoms. Migraineurs had smaller midbrain volumes (healthy controls = 61.28 mm³, SD = 5.89; migraineurs = 58.80 mm³, SD = 6.64; p = 0.038), and significant (p < 0.05) inward deformations in the ventral midbrain and pons, and outward deformations in the lateral medulla and dorsolateral pons relative to healthy controls. Migraineurs had a negative correlation between ASC-12 allodynia symptom severity with midbrain volume (r = − 0.32; p = 0.019) and a positive correlation between cutaneous heat pain thresholds with medulla (r = 0.337; p = 0.012) and cerebellar peduncle volumes (r = 0.435; p = 0.001). Migraineurs with greater symptoms of allodynia have smaller midbrain volumes and migraineurs with lower heat pain thresholds have smaller medulla and cerebellar peduncles. The brainstem likely plays a role in altered sensory processing in migraine and brainstem structure might reflect severity of allodynia and hypersensitivity to pain in migraine.

Single-cell studies of phenotypic heterogeneity reveal more information about pathogenic processes than conventional bulk-cell analysis methods. By enabling high-resolution structural and functional imaging, a single-cell three-dimensional (3D) imaging system can be used to study basic biological processes and to diagnose diseases such as cancer at an early stage. One mechanism that such systems apply to accomplish 3D imaging is rotation of a single cell about a fixed axis. However, many cell rotation mechanisms require intricate and tedious microfabrication, or fail to provide a suitable environment for living cells. To address these and related challenges, we applied numerical simulation methods to design new microfluidic chambers capable of generating fluidic microvortices to rotate suspended cells. We then compared several microfluidic chip designs experimentally in terms of: (1) their ability to rotate biological cells in a stable and precise manner; and (2) their suitability, from a geometric standpoint, for microscopic cell imaging. We selected a design that incorporates a trapezoidal side chamber connected to a main flow channel because it provided well-controlled circulation and met imaging requirements. Micro particle-image velocimetry (micro-PIV) was used to provide a detailed characterization of flows in the new design. Simulated and experimental results demonstrate that a trapezoidal side chamber represents a viable option for accomplishing controlled single cell rotation. Further, agreement between experimental and simulated results confirms that numerical simulation is an effective method for chamber design.

Background: Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM.

Methods: We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set.

Results: We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients).

Conclusion: Multi-parametric MRI and texture analysis can help characterize and visualize GBM’s spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.