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

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

Background

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.

Methods

A human-induced pluripotent stem cell line (hiPSCs-L1) was generated constitutively expressing luciferase. Luciferase-expressing hiPSCs were differentiated into islet organoids. The islet organoids were transplanted into the scapular brown adipose tissue (BAT) of nonobese diabetic/severe combined immunodeficiency disease (NOD/SCID) mice as the BAT group and under the left kidney capsule (KC) of NOD/SCID mice as a control group, respectively. Bioluminescence imaging (BLI) of the organoid grafts was performed on days 1, 7, 14, 28, 35, 42, 49, 56, and 63 posttransplantation.

Results

BLI signals were detected in all recipients, including both the BAT and control groups. The BLI signal gradually decreased in both BAT and KC groups. However, the graft BLI signal intensity under the left KC decreased substantially faster than that of the BAT. Furthermore, our data show that islet organoids transplanted into streptozotocin-induced diabetic mice restored normoglycemia. Positron emission tomography/MRI verified that the islet organoids were transplanted at the intended location in these diabetic mice. Immunofluorescence staining revealed the presence of functional organoid grafts, as confirmed by insulin and glucagon staining.

Conclusions

Our results demonstrate that BAT is a potentially desirable site for islet organoid transplantation for T1D therapy.

The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review

BACKGROUND

Stem cell research, particularly in the domain of induced pluripotent stem cell (iPSC) technology, has shown significant progress. The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in refining iPSC classification, monitoring cell functionality, and conducting genetic analysis. These enhancements are broadening the applications of iPSC technology in disease modelling, drug screening, and regenerative medicine. This review aims to explore the role of AI in the advancement of iPSC research.

METHODS

In December 2023, data were collected from three electronic databases (PubMed, Web of Science, and Science Direct) to investigate the application of AI technology in iPSC processing.

RESULTS

This systematic scoping review encompassed 79 studies that met the inclusion criteria. The number of research studies in this area has increased over time, with the United States emerging as a leading contributor in this field. AI technologies have been diversely applied in iPSC technology, encompassing the classification of cell types, assessment of disease-specific phenotypes in iPSC-derived cells, and the facilitation of drug screening using iPSC. The precision of AI methodologies has improved significantly in recent years, creating a foundation for future advancements in iPSC-based technologies.

CONCLUSIONS

Our review offers insights into the role of AI in regenerative and personalized medicine, highlighting both challenges and opportunities. Although still in its early stages, AI technologies show significant promise in advancing our understanding of disease progression and development, paving the way for future clinical applications.

Bioengineering toolkits for potentiating organoid therapeutics

Organoids are three-dimensional, multicellular constructs that recapitulate the structural and functional features of specific organs. Because of these characteristics, organoids have been widely applied in biomedical research in recent decades. Remarkable advancements in organoid technology have positioned them as promising candidates for regenerative medicine. However, current organoids still have limitations, such as the absence of internal vasculature, limited functionality, and a small size that is not commensurate with that of actual organs. These limitations hinder their survival and regenerative effects after transplantation. Another significant concern is the reliance on mouse tumor-derived matrix in organoid culture, which is unsuitable for clinical translation due to its tumor origin and safety issues. Therefore, our aim is to describe engineering strategies and alternative biocompatible materials that can facilitate the practical applications of organoids in regenerative medicine. Furthermore, we highlight meaningful progress in organoid transplantation, with a particular emphasis on the functional restoration of various organs.

Magnetic Particle Imaging: From Tracer Design to Biomedical Applications in Vasculature Abnormality

Magnetic particle imaging (MPI) is an emerging non-invasive tomographic technique based on the response of magnetic nanoparticles (MNPs) to oscillating drive fields at the center of a static magnetic gradient. In contrast to magnetic resonance imaging (MRI), which is driven by uniform magnetic fields and projects the anatomic information of the subjects, MPI directly tracks and quantifies MNPs in vivo without background signals. Moreover, it does not require radioactive tracers and has no limitations on imaging depth. This article first introduces the basic principles of MPI and important features of MNPs for imaging sensitivity, spatial resolution, and targeted biodistribution. The latest research aiming to optimize the performance of MPI tracers is reviewed based on their material composition, physical properties, and surface modifications. While the unique advantages of MPI have led to a series of promising biomedical applications, recent development of MPI in investigating vascular abnormalities in cardiovascular and cerebrovascular systems, and cancer are also discussed. Finally, recent progress and challenges in the clinical translation of MPI are discussed to provide possible directions for future research and development.

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

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.

Spectroscopic techniques for monitoring stem cell and organoid proliferation in 3D environments for therapeutic development

Spectroscopic techniques for monitoring stem cell and organoid proliferation have gained significant attention in therapeutic development. Spectroscopic techniques such as fluorescence, Raman spectroscopy, and infrared spectroscopy offer noninvasive and real-time monitoring of biochemical and biophysical changes that occur during stem cell and organoid proliferation. These techniques provide valuable insight into the underlying mechanisms of action of potential therapeutic agents, allowing for improved drug discovery and screening. This review highlights the importance of spectroscopic monitoring of stem cell and organoid proliferation and its potential impact on therapeutic development. Furthermore, this review discusses recent advances in spectroscopic techniques and their applications in stem cell and organoid research. Overall, this review emphasizes the importance of spectroscopic techniques as valuable tools for studying stem cell and organoid proliferation and their potential to revolutionize therapeutic development in the future.

Progress in magnetic particle imaging signal and iron quantification methods in vivo - application to long circulating SPIONs

The strengths of Magnetic Particle Imaging (MPI) lay in its sensitivity, quantitative nature, and lack of signal attenuation for Superparamagnetic Iron Oxide Nanoparticles (SPION). These advantages make MPI a powerful tool for the non-invasive monitoring of tracer behaviour over time. With more MPI studies emerging, a standardized method for determining the boundaries of a region of interest (ROI) and iron quantification is crucial. The current approaches are inconsistent, making it challenging to compare studies, hindering MPI progression. Here we showcase three different ROI selection methods for the quantification of iron in vivo and ex vivo. Healthy mice were intravenously administered a long circulating tracer, never before applied in MPI, and the ROI methods were tested for their ability to accurately quantify the total signal present, in addition to the accumulation of the tracer in individual organs. We discuss how the quantified iron amount can be vastly altered based on the choice of ROI, the importance of the standard curve and the challenges associated with each method. Lastly, the user variability and accuracy of each method was compared by 3 independent users to ensure their consistency and lack of bias.

Machine learning assisted-nanomedicine using magnetic nanoparticles for central nervous system diseases

Magnetic nanoparticles possess unique properties distinct from other types of nanoparticles developed for biomedical applications. Their unique magnetic properties and multifunctionalities are especially beneficial for central nervous system (CNS) disease therapy and diagnostics, as well as targeted and personalized applications using image-guided therapy and theranostics. This review discusses the recent development of magnetic nanoparticles for CNS applications, including Alzheimer's disease, Parkinson's disease, epilepsy, multiple sclerosis, and drug addiction. Machine learning (ML) methods are increasingly applied towards the processing, optimization and development of nanomaterials. By using data-driven approach, ML has the potential to bridge the gap between basic research and clinical research. We review ML approaches used within the various stages of nanomedicine development, from nanoparticle synthesis and characterization to performance prediction and disease diagnosis.

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.

Sensitive and quantitative in vivo analysis of PD-L1 using magnetic particle imaging and imaging-guided immunotherapy

PURPOSE

The programmed cell death protein-1 (PD-1) and programmed cell death ligand-1 (PD-L1) expression correlate with the immunotherapeutic response rate. The sensitive and non-invasive imaging of immune checkpoint biomarkers is favorable for the accurate detection and characterization, image-guided immunotherapy in cancer precision medicine. Magnetic particle imaging (MPI), as a novel and emerging imaging modality, possesses the advantages of high sensitivity, no image depth limitation, positive contrast, and absence of radiation. Hence, in this study, we performed the pioneer investigation of monitoring PD-L1 expression using MPI and the MPI-guided immunotherapy.

METHODS

We developed anti-PD-L1 antibody (aPDL1)-conjugated magnetic fluorescent hybrid nanoparticles (MFNPs-aPDL1) and utilized MPI in combination with fluorescence imaging (FMI) to dynamically monitor and quantify PD-L1 expression in various tumors with different PD-L1 expression levels. The ex vivo real-time polymerase chain reaction (qPCR), western blotting, and immunofluorescence staining analysis were further performed to validate the in vivo imaging observation. Moreover, the MPI was further performed for the guidance of immunotherapy.

RESULTS

Our data showed that PD-L1 expression can be specifically and sensitively monitored and quantified using MPI and FMI imaging methods, which were validated by ex vivo qPCR and western blotting analysis. In addition, MPI-guided PD-L1 immunotherapy can enhance the effectiveness of cancer immunotherapy.

CONCLUSION

To our best knowledge, this is the pioneer study to utilize MPI in combination with a newly developed MFNPs-aPDL1 imaging probe to dynamically visualize and quantify PD-L1 expression in tumor microenvironment. This imaging strategy may facilitate the clinical optimization of immunotherapy management.

Improved Quantitative Analysis Method for Magnetic Particle Imaging Based on Deblurring and Region Scalable Fitting

PURPOSE

Magnetic particle imaging (MPI) is a technique for imaging magnetic particle concentration distribution. It has the advantages of high sensitivity, no signal attenuation with depth, and no ionizing radiation. Although MPI has been widely used in the biomedical field, accurate image analysis has been challenging due to its anisotropic point spread function (PSF). The purpose of this study is to propose an MPI image restoring and segmentation method to facilitate a more precise quantitative evaluation of the magnetic particle imaging in vivo.

PROCEDURES

We proposed a DeRSF method that combined deblurring and region scalable fitting (RSF) to determine the imaging tracer distribution. Then a uniform erosion and scaling criterion was established based on simulation experiments to correct the segmentation results, which was further validated on phantom imaging. Finally, we imaged the MPI tracer at gradient concentrations to establish the calibration curve between the MPI signal and iron mass for iron quantification in phantom and in vivo imaging.

RESULTS

The phantom imaging experiments showed that our method achieved improved segmentation performance. The mean value of the dice coefficients for segmentation was up to 0.86, demonstrating that our method can accurately map and quantify the distribution of the tracer. Moreover, the iron quantification on both phantom and in vivo mouse imaging was realized with the minimal error of 5.50%, by our established calibration curve.

CONCLUSIONS

Our proposed DeRSF method was successfully used for improved MPI quantitative analysis. More importantly, this method also showed accurate quantitative results on images with different shapes and tracer concentrations in both phantom and in vivo data, which laid the foundation for the biomedical study of MPI.

In Vivo Bioluminescence for the Detection of the Fate of Pancreatic Islet Organoids Post-transplantation

Pancreatic islet transplantation is a promising cell replacement treatment for patients afflicted with type 1 diabetes (T1D), which is an autoimmune disease resulting in the destruction of insulin-producing islet β-cells. However, the shortage of donor pancreatic islets significantly hampers the widespread application of this strategy as routine therapy. Pluripotent stem cell-derived insulin-producing islet organoids constitute a promising alternative β-cell source for T1D patients. Early after transplantation, it is critical to know the fate of transplanted islet organoids, but determining their survival remains a significant technical challenge. Bioluminescence imaging (BLI) is an optical molecular imaging technique that detects the survival of living cells using light emitted from luciferase-expressing bioreporter cells. Through BLI, the post-transplantation fate of islet organoids can be evaluated over time in a noninvasive fashion with minimal intervention, thus making BLI an ideal tool to determine the success of the transplant and improving cell replacement therapy approaches for T1D.

Germline stem cells in human

The germline cells are essential for the propagation of human beings, thus essential for the survival of mankind. The germline stem cells, as a unique cell type, generate various states of germ stem cells and then differentiate into specialized cells, spermatozoa and ova, for producing offspring, while self-renew to generate more stem cells. Abnormal development of germline stem cells often causes severe diseases in humans, including infertility and cancer. Primordial germ cells (PGCs) first emerge during early embryonic development, migrate into the gentile ridge, and then join in the formation of gonads. In males, they differentiate into spermatogonial stem cells, which give rise to spermatozoa via meiosis from the onset of puberty, while in females, the female germline stem cells (FGSCs) retain stemness in the ovary and initiate meiosis to generate oocytes. Primordial germ cell-like cells (PGCLCs) can be induced in vitro from embryonic stem cells or induced pluripotent stem cells. In this review, we focus on current advances in these embryonic and adult germline stem cells, and the induced PGCLCs in humans, provide an overview of molecular mechanisms underlying the development and differentiation of the germline stem cells and outline their physiological functions, pathological implications, and clinical applications.

Deep learning for improving the spatial resolution of magnetic particle imaging

Objective.Magnetic particle imaging (MPI) is a new medical, non-destructive, imaging method for visualizing the spatial distribution of superparamagnetic iron oxide nanoparticles. In MPI, spatial resolution is an important indicator of efficiency; traditional techniques for improving the spatial resolution may result in higher costs, lower sensitivity, or reduced contrast.Approach.Therefore, we propose a deep-learning approach to improve the spatial resolution of MPI by fusing a dual-sampling convolutional neural network (FDS-MPI). An end-to-end model is established to generate high-spatial-resolution images from low-spatial-resolution images, avoiding the aforementioned shortcomings.Main results.We evaluate the performance of the proposed FDS-MPI model through simulation and phantom experiments. The results demonstrate that the FDS-MPI model can improve the spatial resolution by a factor of two.Significance.This significant improvement in MPI could facilitate the preclinical application of medical imaging modalities in the future.

Advanced Magnetic Resonance Imaging (MRI) Techniques: Technical Principles and Applications in Nanomedicine

Simple Summary

Magnetic Resonance Imaging (MRI) is a consolidated imaging tool for the multiparametric assessment of tissues in various pathologies from degenerative and inflammatory diseases to cancer. In recent years, the continuous technological evolution of the equipment has led to the development of sequences that provide not only anatomical but also functional and metabolic information. In addition, there is a growing and emerging field of research in clinical applications using MRI to exploit the diagnostic and therapeutic capabilities of nanocompounds. This review illustrates the application of the most advanced magnetic resonance techniques in the field of nanomedicine.

Abstract

In the last decades, nanotechnology has been used in a wide range of biomedical applications, both diagnostic and therapeutic. In this scenario, imaging techniques represent a fundamental tool to obtain information about the properties of nanoconstructs and their interactions with the biological environment in preclinical and clinical settings. This paper reviews the state of the art of the application of magnetic resonance imaging in the field of nanomedicine, as well as the use of nanoparticles as diagnostic and therapeutic tools, especially in cancer, including the characteristics that hinder the use of nanoparticles in clinical practice.