image-guided-vivo-tracking-transplanted-distal-lung-epithelial

Image-guided <em>In Vivo</em> Tracking of Transplanted Distal Lung Epithelial Progenitor Cells for Pulmonary Fibrosis Using Magnetic Particle Imaging

Pulmonary fibrosis (PF) is a progressive and chronic lung disease characterized by repeated alveolar epithelial injury that leads to excessive ...

Michigan State University

Diabetes is a chronic condition which affects the glucose metabolism in the body. In lieu of any clinical "cure," the condition is managed through the administration of pharmacological aids, insulin supplements, diet restrictions, exercise, and the like. The conventional clinical prescriptions are limited by their life-long dependency and diminished potency, which in turn hinder the patient's recovery. This necessitated an alteration in approach and has instigated several investigations into other strategies. As Type 1 diabetes (T1D) is known to be an autoimmune disorder, targeting the immune system in activation and/or suppression has shown promise in reducing beta cell loss and improving insulin levels in response to hyperglycemia. Another strategy currently being explored is the use of nanoparticles in the delivery of immunomodulators, insulin, or engineered vaccines to endogenous immune cells. Nanoparticle-assisted targeting of immune cells holds substantial potential for enhanced patient care within T1D clinical settings. Herein, we summarize the knowledge of etiology, clinical scenarios, and the current state of nanoparticle-based immunotherapeutic approaches for Type 1 diabetes. We also discuss the feasibility of translating this approach to clinical practice.

Nanotechnology in Immunotherapy for Type 1 Diabetes: Promising Innovations and Future Advances.

Type 1 diabetes (T1D) is a chronic autoimmune disorder which affects the insulin-producing beta cells in the pancreas. A variety of strategies, namely, insulin replacement therapy, engineered vaccines, immunomodulators, etc., have been explored to correct this condition. Recent studies have attributed the development of T1D to the anomalous expression of microRNAs in the pancreatic islets. Here, we describe the protocol for the development of a theranostic approach to modify the expression of aberrant miRNAs. The MRI-based nanodrug consists of superparamagnetic iron oxide nanoparticles conjugated to microRNA-targeting oligonucleotides that can promote proliferation of pancreatic beta cells in a mouse model of T1D. This theranostic approach can successfully serve as a potential therapeutic approach for the targeted treatment of T1D with minimal side effects.

miRNA Theranostic Nanoparticles Promote Pancreatic Beta Cell Proliferation in Type 1 Diabetes Model.

Ovarian tissue cryopreservation has been successfully applied worldwide for fertility preservation. Correctly selecting the ovarian tissue with high follicle loading for freezing and reimplantation increases the likelihood of restoring ovarian function, but it is a challenging process. In this work, we explore the use of three-dimensional spectral-domain optical coherence tomography (SD-OCT) to identify different follicular stages, especially primary follicles, compare the identifications with H&E images, and measure the size and age-related follicular density distribution differences in mice ovaries. We use the thickness of the layers of granulosa cells to differentiate primordial and primary follicles from secondary follicles. The measured dimensions and age-related follicular distribution agree well with histological images and physiological aging. Finally, we apply attenuation coefficient map analyses to significantly improve the image contrast and the contrast-to-noise ratio (p < 0.001), facilitating follicle identification and quantification. We conclude that SD-OCT is a promising method to noninvasively evaluate ovarian follicles.

Three-dimensional imaging and quantification of mouse ovarian follicles via optical coherence tomography.

Current methods of  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 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.

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

Ovarian tissue cryopreservation has been successfully applied worldwide for fertility preservation. Correctly selecting the ovarian tissue with high follicle loading for freezing and reimplantation increases the likelihood of restoring ovarian function, but it is a challenging process. In this work, we explore the use of three-dimensional spectral-domain optical coherence tomography (SD-OCT) to identify different follicular stages, compare the identifications with H&E images, and measure the size and age-related follicular density distribution differences in mice ovaries. We use the thickness of the layers of granulosa cells to differentiate primordial and primary follicles from secondary follicles. The measured dimensions and age-related follicular distribution agree well with histological images and physiological aging. Finally, we apply attenuation coefficient map analyses to significantly improve the image contrast and the contrast-to-noise ratio (p < 0.001), facilitating follicle identification and quantification. We conclude that SD-OCT is a promising method to noninvasively evaluate ovarian follicles for ovarian tissue cryopreservation.

Caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has shown extensive lung manifestations in vulnerable individuals, putting lung imaging and monitoring at the forefront of early detection and treatment. Magnetic particle imaging (MPI) is an imaging modality, which can bring excellent contrast, sensitivity, and signal-to-noise ratios to lung imaging for the development of new theranostic approaches for respiratory diseases. Advances in MPI tracers would offer additional improvements and increase the potential for clinical translation of MPI. Here, a high-performance nanotracer based on shape anisotropy of magnetic nanoparticles is developed and its use in MPI imaging of the lung is demonstrated. Shape anisotropy proves to be a critical parameter for increasing signal intensity and resolution and exceeding those properties of conventional spherical nanoparticles. The 0D nanoparticles exhibit a 2-fold increase, while the 1D nanorods have a > 5-fold increase in signal intensity when compared to VivoTrax. Newly designed 1D nanorods displayed high signal intensities and excellent resolution in lung images. A spatiotemporal lung imaging study in mice revealed that this tracer offers new opportunities for monitoring disease and guiding intervention.

Shape Anisotropy-Governed High-Performance Nanomagnetosol for In Vivo Magnetic Particle Imaging of Lungs.

In recent years, magnetic particle imaging (MPI) has emerged as a promising imaging technique depicting high sensitivity and spatial resolution. It originated in the early 2000s where it proposed a new approach to challenge the low spatial resolution achieved by using relaxometry in order to measure the magnetic fields. MPI presents 2D and 3D images with high temporal resolution, non-ionizing radiation, and optimal visual contrast due to its lack of background tissue signal. Traditionally, the images were reconstructed by the conversion of signal from the induced voltage by generating system matrix and X-space based methods. Because image reconstruction and analyses play an integral role in obtaining precise information from MPI signals, newer artificial intelligence-based methods are continuously being researched and developed upon. In this work, we summarize and review the significance and employment of machine learning and deep learning models for applications with MPI and the potential they hold for the future. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.

Machine Learning and Deep Learning Applications in Magnetic Particle Imaging.

Transplantation of human-induced pluripotent stem cell (hiPSC)-derived islet organoids is a promising cell replacement therapy for type 1 diabetes (T1D). It is important to improve the efficacy of islet organoids transplantation by identifying new transplantation sites with high vascularization and sufficient accommodation to support graft survival with a high capacity for oxygen delivery.

Brown Adipose Tissue as a Unique Niche for Islet Organoid Transplantation: Insights From In Vivo Imaging.

The integration of Positron emission tomography (PET) with Magnetic resonance imaging (MRI) combines the functional imaging capabilities of PET with the high-resolution anatomical detail of MRI, creating a synergistic platform for advanced diagnostic imaging and image-guided therapies. Central to the success of this dual-modality system is the development of specialized PET/MRI dual-modality probes, particularly those capable of simultaneous functionality, which present significant technical challenges in synthesis and applications. This review explores the advancements in PET/MRI probe development, summarizes the current applications, and highlights the critical challenges in translating PET/MRI probes from experimental research to clinical applications, offering insights into the future direction of this transformative imaging technology. EVIDENCE LEVEL: 5. TECHNICAL EFFICACY: Stage 1.

Article	Year
Nanotechnology in Immunotherapy for Type 1 Diabetes: Promising Innovations and Future Advances. Pharmaceutics\| PubMed ID: 35336018	2022
miRNA Theranostic Nanoparticles Promote Pancreatic Beta Cell Proliferation in Type 1 Diabetes Model. Methods in molecular biology (Clifton, N.J.)\| PubMed ID: 36507996	2023
Three-dimensional imaging and quantification of mouse ovarian follicles via optical coherence tomography. bioRxiv : the preprint server for biology\| PubMed ID: 36747698	2023
Deep learning-enabled quantification of simultaneous PET/MRI for cell transplantation monitoring. iScience\| PubMed ID: 37416468	2023
Three-dimensional imaging and quantification of mouse ovarian follicles via optical coherence tomography. Biomedical optics express\| PubMed ID: 37497517	2023
Shape Anisotropy-Governed High-Performance Nanomagnetosol for In Vivo Magnetic Particle Imaging of Lungs. Small (Weinheim an der Bergstrasse, Germany)\| PubMed ID: 37735143	2024
Machine Learning and Deep Learning Applications in Magnetic Particle Imaging. Journal of magnetic resonance imaging : JMRI\| PubMed ID: 38358090	2025
Brown Adipose Tissue as a Unique Niche for Islet Organoid Transplantation: Insights From In Vivo Imaging. Transplantation direct\| PubMed ID: 38881741	2024
The Development and Challenges of PET/MRI Dual-Modality Imaging Probes-An Update. Journal of magnetic resonance imaging : JMRI\| PubMed ID: 40192185	2025

Saumya Nigam

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