Assessing micrometastases as a target for nanoparticles using 3D microscopy and machine learning. Kingston, B. R., Syed, A. M., Ngai, J., Sindhwani, S., & Chan, W. C. W. PNAS, 116(30):14937–14946, July, 2019. ISBN: 9781907646119 Publisher: National Academy of Sciences Section: PNAS Plus![Assessing micrometastases as a target for nanoparticles using 3D microscopy and machine learning [link]](https://bibbase.org/img/filetypes/link.svg "link")
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Paper doi abstract bibtex 2 downloads Metastasis of solid tumors is a key determinant of cancer patient survival. Targeting micrometastases using nanoparticles could offer a way to stop metastatic tumor growth before it causes excessive patient morbidity. However, nanoparticle delivery to micrometastases is difficult to investigate because micrometastases are small in size and lie deep within tissues. Here, we developed an imaging and image analysis workflow to analyze nanoparticle–cell interactions in metastatic tumors. This technique combines tissue clearing and 3D microscopy with machine learning-based image analysis to assess the physiology of micrometastases with single-cell resolution and quantify the delivery of nanoparticles within them. We show that nanoparticles access a higher proportion of cells in micrometastases (50% nanoparticle-positive cells) compared with primary tumors (17% nanoparticle-positive cells) because they reside close to blood vessels and require a small diffusion distance to reach all tumor cells. Furthermore, the high-throughput nature of our image analysis workflow allowed us to profile the physiology and nanoparticle delivery of 1,301 micrometastases. This enabled us to use machine learning-based modeling to predict nanoparticle delivery to individual micrometastases based on their physiology. Our imaging method allows researchers to measure nanoparticle delivery to micrometastases and highlights an opportunity to target micrometastases with nanoparticles. The development of models to predict nanoparticle delivery based on micrometastasis physiology could enable personalized treatments based on the specific physiology of a patient’s micrometastases.
@article{kingston_assessing_2019,
title = {Assessing micrometastases as a target for nanoparticles using {3D} microscopy and machine learning},
volume = {116},
copyright = {© 2019 . https://www-pnas-org.myaccess.library.utoronto.ca/site/aboutpnas/licenses.xhtmlPublished under the PNAS license.},
issn = {0027-8424, 1091-6490},
url = {http://www.pnas.org/content/116/30/14937},
doi = {10.1073/pnas.1907646116},
abstract = {Metastasis of solid tumors is a key determinant of cancer patient survival. Targeting micrometastases using nanoparticles could offer a way to stop metastatic tumor growth before it causes excessive patient morbidity. However, nanoparticle delivery to micrometastases is difficult to investigate because micrometastases are small in size and lie deep within tissues. Here, we developed an imaging and image analysis workflow to analyze nanoparticle–cell interactions in metastatic tumors. This technique combines tissue clearing and 3D microscopy with machine learning-based image analysis to assess the physiology of micrometastases with single-cell resolution and quantify the delivery of nanoparticles within them. We show that nanoparticles access a higher proportion of cells in micrometastases (50\% nanoparticle-positive cells) compared with primary tumors (17\% nanoparticle-positive cells) because they reside close to blood vessels and require a small diffusion distance to reach all tumor cells. Furthermore, the high-throughput nature of our image analysis workflow allowed us to profile the physiology and nanoparticle delivery of 1,301 micrometastases. This enabled us to use machine learning-based modeling to predict nanoparticle delivery to individual micrometastases based on their physiology. Our imaging method allows researchers to measure nanoparticle delivery to micrometastases and highlights an opportunity to target micrometastases with nanoparticles. The development of models to predict nanoparticle delivery based on micrometastasis physiology could enable personalized treatments based on the specific physiology of a patient’s micrometastases.},
language = {en},
number = {30},
urldate = {2021-11-06},
journal = {PNAS},
author = {Kingston, Benjamin R. and Syed, Abdullah Muhammad and Ngai, Jessica and Sindhwani, Shrey and Chan, Warren C. W.},
month = jul,
year = {2019},
pmid = {31285340},
note = {ISBN: 9781907646119
Publisher: National Academy of Sciences
Section: PNAS Plus},
keywords = {3D microscopy, image analysis, machine learning, metastasis, nanoparticles},
pages = {14937--14946},
file = {Full Text PDF:files/1844/Kingston et al. - 2019 - Assessing micrometastases as a target for nanopart.pdf:application/pdf;Snapshot:files/1846/14937.html:text/html},
url_Paper = {https://inbs.med.utoronto.ca/wp-content/uploads/2022/01/KINGST1-1.pdf}
}
Downloads: 2
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