Imaging of Endometriotic Lesions Using cRGD-MN Probe in a Mouse Model of Endometriosis.

Approximately 10% of women suffer from endometriosis during their reproductive years. This disease is a chronic debilitating condition whose etiology for lesion implantation and survival heavily relies on adhesion and angiogenic factors. Currently, there are no clinically approved agents for its detection. In this study, we evaluated cRGD-peptide-conjugated nanoparticles (RGD-Cy5.5-MN) to detect lesions using magnetic resonance imaging (MRI) in a mouse model of endometriosis. We utilized a luciferase-expressing murine suture model of endometriosis. Imaging was performed before and after 24 h following the intravenous injection of RGD-Cy5.5-MN or control nanoparticles (Cy5.5-MN). Next, we performed biodistribution of RGD-Cy5.5-MN and correlative fluorescence microscopy of lesions stained for CD34. Tissue iron content was determined using inductively coupled plasma optical emission spectrometry (ICP-OES). Our results demonstrated that targeting endometriotic lesions with RGD-Cy5.5-MN resulted in a significantly higher delta T2* upon its accumulation compared to Cy5.5-MN. ICP-OES showed significantly higher iron content in the lesions of the animals in the experimental group compared to the lesions of the animals in the control group. Histology showed colocalization of Cy5.5 signal from RGD-Cy5.5-MN with CD34 in the lesions pointing to the targeted nature of the probe. This work offers initial proof-of-concept for targeting angiogenesis in endometriosis which can be useful for potential clinical diagnostic and therapeutic approaches for treating this disease.

Deep learning-enabled quantification of simultaneous PET/MRI for cell transplantation monitoring.

Current methods of in vivo imaging islet cell transplants for diabetes using magnetic resonance imaging (MRI) are limited by their low sensitivity. Simultaneous positron emission tomography (PET)/MRI has greater sensitivity and ability to visualize cell metabolism. However, this dual-modality tool currently faces two major challenges for monitoring cells. Primarily, the dynamic conditions of PET such as signal decay and spatiotemporal change in radioactivity prevent accurate quantification of the transplanted cell number. In addition, selection bias from different radiologists renders human error in segmentation. This calls for the development of artificial intelligence algorithms for the automated analysis of PET/MRI of cell transplantations. Here, we combined K-means++ for segmentation with a convolutional neural network to predict radioactivity in cell-transplanted mouse models. This study provides a tool combining machine learning with a deep learning algorithm for monitoring islet cell transplantation through PET/MRI. It also unlocks a dynamic approach to automated segmentation and quantification of radioactivity in PET/MRI.

Detection of Endometriosis Lesions Using Gd-Based Collagen I Targeting Probe in Murine Models of Endometriosis.

PURPOSE: Endometriosis is a chronic condition characterized by high fibrotic content and affecting about 10% of women during their reproductive years. Yet, no clinically approved agents are available for non-invasive endometriosis detection. The purpose of this study was to investigate the utility of a gadolinium-based collagen type I targeting probe (EP-3533) to non-invasively detect endometriotic lesions using magnetic resonance imaging (MRI). Previously, this probe has been used for detection and staging of fibrotic lesions in the liver, lung, heart, and cancer. In this study we evaluate the potential of EP-3533 for detecting endometriosis in two murine models and compare it with a non-binding isomer (EP-3612). PROCEDURES: For imaging, we utilized two GFP-expressing murine models of endometriosis (suture model and injection model) injected intravenously with EP3533 or EP-33612. Mice were imaged before and after bolus injection of the probes. The dynamic signal enhancement of MR T1 FLASH images was analyzed, normalized, and quantified, and the relative location of lesions was validated through ex vivo fluorescence imaging. Subsequently, the harvested lesions were stained for collagen, and their gadolinium content was quantified by inductively coupled plasma optical emission spectrometry (ICP-OES). RESULTS: We showed that EP-3533 probe increased the signal intensity in T1-weighted images of endometriotic lesions in both models of endometriosis. Such enhancement was not detected in the muscles of the same groups or in endometriotic lesions of mice injected with EP-3612 probe. Consequentially, control tissues had significantly lower gadolinium content, compared to the lesions in experimental groups. Probe accumulation was similar in endometriotic lesions of either model. CONCLUSIONS: This study provides evidence for feasibility of targeting collagen type I in the endometriotic lesions using EP3533 probe. Our future work includes investigation of the utility of this probe for therapeutic delivery in endometriosis to inhibit signaling pathways that cause the disease.

Synthesis of siRNA-Conjugated Dextran-Coated Iron Oxide Nanoparticles for Islet Protection During Transplantation and Noninvasive Imaging.

Pancreatic islet transplantation (Tx) has a lifesaving potential for type 1 diabetes (T1D) patients. Islet damage during and after transplantation is one of the major reasons hampering its wide clinical application. Inability to monitor transplanted islets also severely limits our understanding of mechanisms regarding declining graft function after transplantation. Our team has proposed to use magnetic nanoparticles conjugated to siRNA (MN-siRNA) to label islets prior to transplantation with two goals in mind: to protect them from damage by silencing harmful genes and to monitor them after transplantation using noninvasive magnetic resonance imaging (MRI). This manuscript provides a step-by-step protocol for the synthesis and characterization of MN-siRNA probes.

3D in vivo Magnetic Particle Imaging of Human Stem Cell-Derived Islet Organoid Transplantation Using a Machine Learning Algorithm.

Stem cell-derived islet organoids constitute a promising treatment of type 1 diabetes. A major hurdle in the field is the lack of appropriate in vivo method to determine graft outcome. Here, we investigate the feasibility of in vivo tracking of transplanted stem cell-derived islet organoids using magnetic particle imaging (MPI) in a mouse model. Human induced pluripotent stem cells-L1 were differentiated to islet organoids and labeled with superparamagnetic iron oxide nanoparticles. The phantoms comprising of different numbers of labeled islet organoids were imaged using an MPI system. Labeled islet organoids were transplanted into NOD/scid mice under the left kidney capsule and were then scanned using 3D MPI at 1, 7, and 28 days post transplantation. Quantitative assessment of the islet organoids was performed using the K-means++ algorithm analysis of 3D MPI. The left kidney was collected and processed for immunofluorescence staining of C-peptide and dextran. Islet organoids expressed islet cell markers including insulin and glucagon. Image analysis of labeled islet organoids phantoms revealed a direct linear correlation between the iron content and the number of islet organoids. The K-means++ algorithm showed that during the course of the study the signal from labeled islet organoids under the left kidney capsule decreased. Immunofluorescence staining of the kidney sections showed the presence of islet organoid grafts as confirmed by double staining for dextran and C-peptide. This study demonstrates that MPI with machine learning algorithm analysis can monitor islet organoids grafts labeled with super-paramagnetic iron oxide nanoparticles and provide quantitative information of their presence in vivo.

Crocin and Metformin suppress metastatic breast cancer progression via VEGF and MMP9 downregulations: in vitro and in vivo studies.

Metastatic breast cancer remains a serious health concern and numerous investigations recommended medicinal plants as a complementary therapy. Crocin is one of the known anticancer bio-component. Recently, the inhibitory effect of metformin has been studied on the various aspects of cancer. However, no study reported their combination effects on metastatic breast cancer. In the present study, we have assessed their anti-metastatic effects on in vitro and in vivo breast cancer models. Using MTT assay, scratch, and adhesion tests, we have evaluated the cytotoxic, anti-invasive and anti-adhesion effects of crocin and metformin on 4T1 cell line, respectively. Their protective effects and MMP9 as well as VEGF protein expression levels (Western blotting) investigated in the 4T1 murine breast cancer model. Our results showed that both crocin and metformin reduced cell viability, delayed scratch healing and inhibited the cell adhesion, in vitro. While crocin alone restored the mice's weight reduction, crocin, metformin, and their combination significantly reduced the tumor volume size and enhanced animal survival rate in murine breast cancer model, responses that were associated with VEGF and MMP9 down-regulation. These findings suggest that a combination of crocin and metformin could serve as a novel therapeutic approach to enhance the effectiveness of metastatic breast cancer therapy.

Artificial Intelligence Analysis of Magnetic Particle Imaging for Islet Transplantation in a Mouse Model.

PURPOSE: Current approaches to quantification of magnetic particle imaging (MPI) for cell-based therapy are thwarted by the lack of reliable, standardized methods of segmenting the signal from background in images. This calls for the development of artificial intelligence (AI) systems for MPI analysis. PROCEDURES: We utilize a canonical algorithm in the domain of unsupervised machine learning, known as K-means++, to segment the regions of interest (ROI) of images and perform iron quantification analysis using a standard curve model. We generated in vitro, in vivo, and ex vivo data using islets and mouse models and applied the AI algorithm to gain insight into segmentation and iron prediction on these MPI data. In vitro models included imaging the VivoTrax-labeled islets in varying numbers. In vivo mouse models were generated through transplantation of increasing numbers of the labeled islets under the kidney capsule of mice. Ex vivo data were obtained from the MPI images of excised kidney grafts. RESULTS: The K-means++ algorithms segmented the ROI of in vitro phantoms with minimal noise. A linear correlation between the islet numbers and the increasing prediction of total iron value (TIV) in the islets was observed. Segmentation results of the ROI of the in vivo MPI scans showed that with increasing number of transplanted islets, the signal intensity increased with linear trend. Upon segmenting the ROI of ex vivo data, a linear trend was observed in which increasing intensity of the ROI yielded increasing TIV of the islets. Through statistical evaluation of the algorithm performance via intraclass correlation coefficient validation, we observed excellent performance of K-means++-based model on segmentation and quantification analysis of MPI data. CONCLUSIONS: We have demonstrated the ability of the K-means++-based model to provide a standardized method of segmentation and quantification of MPI scans in an islet transplantation mouse model.

Magnetic Particle Imaging: Current Applications in Biomedical Research.

Magnetic particle imaging (MPI) is a new imaging modality with the potential for high-resolution imaging while retaining the noninvasive nature of other current modalities such as magnetic resonance imaging (MRI) and positron emission tomography (PET). It is able to track location and quantities of special superparamagnetic iron oxide nanoparticles without tracing any background signal. MPI utilizes the unique, intrinsic aspects of the nanoparticles: how they react in the presence of the magnetic field, and the subsequent turning off of the field. The current group of nanoparticles that are used in MPI are usually commercially available for MRI. Special MPI tracers are in development by many groups that utilize an iron-oxide core encompassed by various coatings. These tracers would solve the current obstacles by altering the size and material of the nanoparticles to what is required by MPI. In this review, the theory behind and the development of these tracers are discussed. In addition, applications such as cell tracking, oncology imaging, neuroimaging, and vascular imaging, among others, stemming from the implementation of MPI into the standard are discussed. Level of Evidence: 5 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2020;51:1659-1668.

uMUC1-Targeting Magnetic Resonance Imaging of Therapeutic Response in an Orthotropic Mouse Model of Colon Cancer.

PURPOSE: Noninvasive assessment of chemotherapeutic response in colon cancer would tremendously aid in therapeutic intervention of cancer patients and improve outcomes. The aim of the study was to evaluate the feasibility of a noninvasive assessment of chemotherapeutic response by magnetic resonance imaging utilizing underglycosylated mucin 1 (uMUC1) tumor antigen as a biomarker of therapeutic response in a colon cancer mouse model. PROCEDURES: The study was performed by applying molecular imaging approach based on targeting uMUC1 with specific dual-modality imaging probe (MN-EPPT). The probe consisted of dextran-coated iron oxide nanoparticles conjugated to the near infrared fluorescent dye Cy5.5 and to a uMUC1-specific peptide (EPPT) and was used for magnetic resonance imaging (MRI) and fluorescence optical imaging. An orthotopic murine model of colon cancer expressing human uMUC1 peptide (MC38 MUC1) was created along with the control model devoid of the antigen (MC38 neo). Animals received chemotherapy with 5-fluorouracil (5-FU) followed by MN-EPPT-enhanced MR and optical imaging. RESULTS: In vivo imaging of animals with uMUC1 expressing tumors after 5-FU therapy showed that the average deltaT2 was reduced by 7.27 ms (p = 0.045) compared with animals in control groups indicating lower accumulation of MN-EPPT caused by uMUC1 downregulation. In vivo optical imaging, biodistribution, and fluorescence microscopy confirmed the MRI findings. Interestingly, we found that the group of animals that did not respond to chemotherapy ("progressive disease" per RECIST) showed higher accumulation of MN-EPPT compared to the group of responders ("stable disease") consistent with proliferating tumor cells and increased antigen availability. CONCLUSIONS: We believe that in application to over 50 % of human cancers expressing uMUC1, our results could provide insight into overall assessment of therapeutic response based on its expression as defined by non-invasive MN-EPPT-enhanced MRI.