Dataset Information

Simultaneous spatiotemporal tracking and oxygen sensing of transient implants in vivo using hot-spot MRI and machine learning.

ABSTRACT: A varying oxygen environment is known to affect cellular function in disease as well as activity of various therapeutics. For transient structures, whether they are unconstrained therapeutic transplants, migrating cells during tumor metastasis, or cell populations induced by an immunological response, the role of oxygen in their fate and function is known to be pivotal albeit not well understood in vivo. To address such a challenge in the case of generation of a bioartificial pancreas, we have combined fluorine magnetic resonance imaging and unsupervised machine learning to monitor over time the spatial arrangement and the oxygen content of implants encapsulating pancreatic islets that are unconstrained in the intraperitoneal (IP) space of healthy and diabetic mice. Statistically significant trends in the postimplantation temporal dependence of oxygen content between aggregates of 0.5-mm or 1.5-mm alginate microcapsules were identified in vivo by looking at their dispersity as well as arrangement in clusters of different size and estimating oxygen content on a pixel-by-pixel basis from thousands of 2D images. Ultimately, we found that this dependence is stronger for decreased implant capsule size consistent with their tendency to also induce a larger immunological response. Beyond the bioartificial pancreas, this work provides a framework for the simultaneous spatiotemporal tracking and oxygen sensing of other cell populations and biomaterials that change over time to better understand and improve therapeutic design across diverse applications such as cellular transplant therapy, treatments preventing metastatic formation, and modulators for improving immunologic response, for all of which oxygen is a major mechanistic component.

SUBMITTER: Spanoudaki V

PROVIDER: S-EPMC6421446 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Simultaneous spatiotemporal tracking and oxygen sensing of transient implants in vivo using hot-spot MRI and machine learning.

Spanoudaki Virginia V Doloff Joshua C JC Huang Wei W Norcross Samuel R SR Farah Shady S Langer Robert R Anderson Daniel G DG

Proceedings of the National Academy of Sciences of the United States of America 20190226 11

A varying oxygen environment is known to affect cellular function in disease as well as activity of various therapeutics. For transient structures, whether they are unconstrained therapeutic transplants, migrating cells during tumor metastasis, or cell populations induced by an immunological response, the role of oxygen in their fate and function is known to be pivotal albeit not well understood in vivo. To address such a challenge in the case of generation of a bioartificial pancreas, we have c ...[more]

PMID: 30808810

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Project description:Background Cartilage repair outcomes of matrix-associated stem cell implants (MASIs) in patients have been highly variable. Conventional MRI cannot help distinguish between grafts that will and grafts that will not repair the underlying cartilage defect until many months after the repair. Purpose To determine if ferumoxytol nanoparticle labeling could be used to depict successful or failed MASIs compared with conventional MRI in a large-animal model. Materials and Methods Between January 2016 and December 2017, 10 Göttingen minipigs (n = 5 male; n = 5 female; mean age, 6 months ± 5.1; age range, 4-20 months) received implants of unlabeled (n = 12) or ferumoxytol-labeled (n = 20) viable and apoptotic MASIs in cartilage defects of the distal femur. All MASIs were serially imaged with MRI on a 3.0-T imaging unit at week 1 and weeks 2, 4, 8, 12, and 24, with calculation of T2 relaxation times. Cartilage regeneration outcomes were assessed by using the MR observation of cartilage repair tissue (MOCART) score (scale, 0-100), the Pineda score, and histopathologic quantification of collagen 2 production in the cartilage defect. Findings were compared by using the unpaired Wilcoxon rank sum test, a linear regression model, the Fisher exact test, and Pearson correlation. Results Ferumoxytol-labeled MASIs showed significant T2 shortening (22.2 msec ± 3.2 vs 27.9 msec ± 1.8; P < .001) and no difference in cartilage repair outcomes compared with unlabeled control MASIs (P > .05). At week 2 after implantation, ferumoxytol-labeled apoptotic MASIs showed a loss of iron signal and higher T2 relaxation times compared with ferumoxytol-labeled viable MASIs (26.6 msec ± 4.9 vs 20.8 msec ± 5.3; P = .001). Standard MRI showed incomplete cartilage defect repair of apoptotic MASIs at 24 weeks. Iron signal loss at 2 weeks correlated with incomplete cartilage repair, diagnosed at histopathologic examination at 12-24 weeks. Conclusion Ferumoxytol nanoparticle labeling can accelerate the diagnosis of successful and failed matrix-associated stem cell implants at MRI in a large-animal model. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Sneag and Potter in this issue.

Dataset Information

Simultaneous spatiotemporal tracking and oxygen sensing of transient implants in vivo using hot-spot MRI and machine learning.

Publications

Simultaneous spatiotemporal tracking and oxygen sensing of transient implants in vivo using hot-spot MRI and machine learning.

Similar Datasets

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