Real-Time MRI Guidance for Reproducible Hyperosmolar Opening of the Blood-Brain Barrier in Mice

The blood-brain barrier (BBB) prevents effective delivery of most therapeutic agents to the brain. Intra-arterial (IA) infusion of hyperosmotic mannitol has been widely used to open the BBB and improve parenchymal targeting, but the extent of BBB disruption has varied widely with therapeutic outcomes often being unpredictable. In this work, we show that real-time MRI can enable fine-tuning of the infusion rate to adjust and predict effective and local brain perfusion in mice, and thereby can be allowed for achieving the targeted and localized BBB opening (BBBO). Both the reproducibility and safety are validated by MRI and histology. The reliable and reproducible BBBO we developed in mice will allow cost-effective studies on the biology of the BBB and drug delivery to the brain. In addition, the IA route for BBBO also permits subsequent IA delivery of a specific drug during the same procedure and obtains high targeting efficiency of the therapeutic agent in the targeted tissue, which has great potential for future clinical translation in neuro-oncology, regenerative medicine and other neurological applications

One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

Magnetic resonance imaging (MRI) is a principal radiological modality that provides radiation-free, abundant, and diverse information about the whole human body for medical diagnosis, but suffers from prolonged scan time. The scan time can be significantly reduced through k-space undersampling but the introduced artifacts need to be removed in image reconstruction. Although deep learning (DL) has emerged as a powerful tool for image reconstruction in fast MRI, its potential in multiple imaging scenarios remains largely untapped. This is because not only collecting large-scale and diverse realistic training data is generally costly and privacy-restricted, but also existing DL methods are hard to handle the practically inevitable mismatch between training and target data. Here, we present a Physics-Informed Synthetic data learning framework for Fast MRI, called PISF, which is the first to enable generalizable DL for multi-scenario MRI reconstruction using solely one trained model. For a 2D image, the reconstruction is separated into many 1D basic problems and starts with the 1D data synthesis, to facilitate generalization. We demonstrate that training DL models on synthetic data, integrated with enhanced learning techniques, can achieve comparable or even better in vivo MRI reconstruction compared to models trained on a matched realistic dataset, reducing the demand for real-world MRI data by up to 96%. Moreover, our PISF shows impressive generalizability in multi-vendor multi-center imaging. Its excellent adaptability to patients has been verified through 10 experienced doctors' evaluations. PISF provides a feasible and cost-effective way to markedly boost the widespread usage of DL in various fast MRI applications, while freeing from the intractable ethical and practical considerations of in vivo human data acquisitions.Comment: 22 pages, 9 figures, 1 tabl

CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have not been publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. Manual segmentations of the myocardium and chambers of all the subjects are also provided within the dataset. Scripts of state-of-the-art reconstruction algorithms were also provided as a point of reference. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community. Researchers can access the dataset at https://www.synapse.org/#!Synapse:syn51471091/wiki/.Comment: 14 pages, 8 figure

Dental pulp stem cells : an attractive alternative for cell therapy in ischemic stroke

Ischemic stroke is a major cause of disability and mortality worldwide, but effective restorative treatments are very limited at present. Regenerative medicine research revealed that stem cells are promising therapeutic options. Dental pulp stem cells (DPSCs) are autologously applicable cells that origin from the neural crest and exhibit neuro-ectodermal features next to multilineage differentiation potentials. DPSCs are of increasing interest since they are relatively easy to obtain, exhibit a strong proliferation ability, and can be cryopreserved for a long time without losing their multi-directional differentiation capacity. Besides, use of DPSCs can avoid fundamental problems such as immune rejection, ethical controversy, and teratogenicity. Therefore, DPSCs provide a tempting prospect for stroke treatment

Mesenchymal Stem Cells Do Not Lose Direct Labels Including Iron Oxide Nanoparticles and DFO-89Zr Chelates through Secretion of Extracellular Vesicles

Rapidly ageing populations are beset by tissue wear and damage. Stem cell-based regenerative medicine is considered a solution. Years of research point to two important aspects: (1) the use of cellular imaging to achieve sufficient precision of therapeutic intervention, and the fact that (2) many therapeutic actions are executed through extracellular vesicles (EV), released by stem cells. Therefore, there is an urgent need to interrogate cellular labels in the context of EV release. We studied clinically applicable cellular labels: superparamagnetic iron oxide nanoparticles (SPION), and radionuclide detectable by two main imaging modalities: MRI and PET. We have demonstrated effective stem cell labeling using both labels. Then, we obtained EVs from cell cultures and tested for the presence of cellular labels. We did not find either magnetic or radioactive labels in EVs. Therefore, we report that stem cells do not lose labels in released EVs, which indicates the reliability of stem cell magnetic and radioactive labeling, and that there is no interference of labels with EV content. In conclusion, we observed that direct cellular labeling seems to be an attractive approach to monitoring stem cell delivery, and that, importantly, labels neither locate in EVs nor affect their basic properties

Induction of immunological tolerance to myelinogenic glial-restricted progenitor allografts

The immunological barrier currently precludes the clinical utilization of allogeneic stem cells. Although glial-restricted progenitors have become attractive candidates to treat a wide variety of neurological diseases, their survival in immunocompetent recipients is limited. In this study, we adopted a short-term, systemically applicable co-stimulation blockade-based strategy using CTLA4-Ig and anti-CD154 antibodies to modulate T-cell activation in the context of allogeneic glial-restricted progenitor transplantation. We found that co-stimulation blockade successfully prevented rejection of allogeneic glial-restricted progenitors from immunocompetent mouse brains. The long-term engrafted glial-restricted progenitors myelinated dysmyelinated adult mouse brains within one month. Furthermore, we identified a set of plasma miRNAs whose levels specifically correlated to the dynamic changes of immunoreactivity and as such could serve as biomarkers for graft rejection or tolerance. We put forward a successful strategy to induce alloantigen-specific hyporesponsiveness towards stem cells in the CNS, which will foster effective therapeutic application of allogeneic stem cells

Modeling human pediatric and adult gliomas in immunocompetent mice through costimulatory blockade

Currently, human glioma tumors are mostly modeled in immunodeficient recipients; however, lack of interactions with adaptive immune system is a serious flaw, particularly in the era when immunotherapies dominate treatment strategies. Our group was the first to successfully establish the orthotopic transplantation of human glioblastoma (GBM) in immunocompetent mice by inducing immunological tolerance using a short-term, systemic costimulation blockade strategy (CTLA-4-Ig and MR1). In this study, we further validated the feasibility of this method by modeling pediatric diffuse intrinsic pontine glioma (DIPG) and two types of adult GBM (GBM1, GBM551), in mice with intact immune systems and immunodeficient mice. We found that all three glioma models were successfully established, with distinct difference in tumor growth patterns and morphologies, after orthotopic xenotransplantation in tolerance-induced immunocompetent mice. Long-lasting tolerance that is maintained for up to nearly 200 d in GBM551 confirmed the robustness of this model. Moreover, we found that tumors in immunocompetent mice displayed features more similar to the clinical pathophysiology found in glioma patients, characterized by inflammatory infiltration and strong neovascularization, as compared with tumors in immunodeficient mice. In summary, we have validated the robustness of the costimulatory blockade strategy for tumor modeling and successfully established three human glioma models including the pediatric DIPG whose preclinical study is particularly thwarted by the lack of proper animal models