Characterization of microparticles of iron oxide for magnetic resonance imaging.

Microparticles of iron oxide (MPIOs) are increasingly used for contrast generation in magnetic resonance imaging (MRI). In particular, Dynabeads® MyOne™ Tosylactivated MPIOs have enabled sensitive and targeted molecular imaging, e.g., to detect vascular inflammation. For the first time we measured the relaxivities as well as the molar susceptibility χM of these MPIOs at 7 T in agarose gels. They are r1 = 0.69 ± 0.03 s-1/mM, r2 = 220 ± 6 s-1/mM, r2* = 679 ± 14 s-1/mM, and χM = 0.66 ± 0.05 ppm/mM, when expressed with respect to the iron concentration. These material parameters are essential to optimize MRI protocols and progress toward quantitative imaging. To address the heterogeneous nature of the MPIO distributions over the size of a typical MRI voxel, we coupled the MPIOs to a fluorophore to create a bimodal phantom that can be imaged by both Light Sheet microscopy and MRI. In this phantom, the MPIOs produced contrast similar to that found in vivo . The submicron resolution of Light Sheet microscopy images provided a precise measurement of the MPIO spatial distribution in phantoms also imaged by MRI. MPIO aggregates occupying less than one MRI voxel were responsible for alterations in R2* and magnetic susceptibility χ across several MRI voxels. In these cases, the sum of R2* or χ over the affected MRI volume correlated better with the microscopically determined number of MPIOs. These findings were confirmed with simulations performed in the static dephasing regime. The microscopically determined MPIO distribution was also entered directly into the simulation framework, indicating that the bimodal phantom is a useful tool to test theoretical models against experimental measurements.

Pharmacological Modulation of Blood-Brain Barrier Permeability by Kinin Analogs in Normal and Pathologic Conditions.

The blood-brain barrier (BBB) is a major obstacle to the development of effective diagnostics and therapeutics for brain cancers and other central nervous system diseases. Peptide agonist analogs of kinin B1 and B2 receptors, acting as BBB permeabilizers, have been utilized to overcome this barrier. The purpose of the study was to provide new insights for the potential utility of kinin analogs as brain drug delivery adjuvants. In vivo imaging studies were conducted in various animal models (primary/secondary brain cancers, late radiation-induced brain injury) to quantify BBB permeability in response to kinin agonist administrations. Results showed that kinin B1 (B1R) and B2 receptors (B2R) agonists increase the BBB penetration of chemotherapeutic doxorubicin to glioma sites, with additive effects when applied in combination. B2R agonist also enabled extravasation of high-molecular-weight fluorescent dextrans (155 kDa and 2 MDa) in brains of normal mice. Moreover, a systemic single dose of B2R agonist did not increase the incidence of metastatic brain tumors originating from circulating breast cancer cells. Lastly, B2R agonist promoted the selective delivery of co-injected diagnostic MRI agent Magnevist in irradiated brain areas, depicting increased vascular B2R expression. Altogether, our findings suggest additional evidence for using kinin analogs to facilitate specific access of drugs to the brain.

Cerebrovascular inflammation promotes the formation of brain metastases.

Brain metastases are the most prevalent intracranial malignancy. Patient outcome is poor and treatment options are limited. Hence, new avenues must be explored to identify potential therapeutic targets. Inflammation is a known critical component of cancer progression. Intratumoral inflammation drives progression and leads to the release of circulating tumor cells (CTCs). Inflammation at distant sites promotes adhesion of CTCs to the activated endothelium and then initiates the formation of metastases. These interactions mostly involve cell adhesion molecules expressed by activated endothelial cells. For example, the vascular cell adhesion molecule-1 (VCAM-1) is known to promote transendothelial migration of cancer cells in different organs. However, it is unclear whether a similar mechanism occurs within the specialized environment of the brain. Our objective was therefore to use molecular imaging to assess the potential role of VCAM-1 in promoting the entry of CTCs into the brain. First, magnetic resonance imaging (MRI) and histological analyses revealed that cerebrovascular inflammation induced by intracranial injection of lipopolysaccharide significantly increased the expression of VCAM-1 in the Balb/c mouse brain. Next, intracardiac injection of 4T1 mammary carcinoma cancer cells in animals with cerebrovascular inflammation yielded a higher brain metastasis burden than in the control animals. Finally, blocking VCAM-1 prior to 4T1 cells injection prevented this increased metastatic burden. Here, we demonstrated that by contributing to CTCs adhesion to the activated cerebrovascular endothelium, VCAM-1 improves the capacity of CTCs to form metastatic foci in the brain.

Automated detection and quantification of breast cancer brain metastases in an animal model using democratized machine learning tools.

Advances in digital whole-slide imaging and machine learning (ML) provide new opportunities for automated examination and quantification of histopathological slides to support pathologists and biologists. However, implementation of ML tools often requires advanced skills in computer science that may not be immediately available in the traditional wet-lab environment. Here, we propose a simple and accessible workflow to automate detection and quantification of brain epithelial metastases on digitized histological slides. We leverage 100 Hematoxylin & Eosin (H&E)-stained whole slide images (WSIs) from 25 Balb/c mice with various level of brain metastatic tumor burden. A supervised training of the Trainable Weka Segmentation (TWS) from Fiji was achieved from annotated WSIs. Upon comparison with manually drawn regions, it is apparent that the algorithm learned to identify and segment cancer cell-specific nuclei and normal brain tissue. Our approach resulted in a robust and highly concordant correlation between automated metastases quantification of brain metastases and manual human assessment (R2 = 0.8783; P < 0.0001). This simple approach is amenable to other similar analyses, including that of human tissues. Widespread adoption of these tools aims to democratize ML and improve precision in traditionally qualitative tasks in histopathology-based research.