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Parallelized immunomagnetic nanopore sorting: modeling, scaling, and optimization of surface marker specific isolation of extracellular vesicles from complex media

The isolation of specific subpopulations of extracellular vesicles (EVs) based on their expression of surface markers poses a significant challenge due to their nanoscale size (< 800 nm), their heterogeneous surface marker expression, and the vast number of background EVs present in clinical spec...

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Autores principales: Lin, Andrew A., Shen, Hanfei, Spychalski, Griffin, Carpenter, Erica L., Issadore, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Journal Experts 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10246262/
https://www.ncbi.nlm.nih.gov/pubmed/37292737
http://dx.doi.org/10.21203/rs.3.rs-2913647/v1
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author Lin, Andrew A.
Shen, Hanfei
Spychalski, Griffin
Carpenter, Erica L.
Issadore, David
author_facet Lin, Andrew A.
Shen, Hanfei
Spychalski, Griffin
Carpenter, Erica L.
Issadore, David
author_sort Lin, Andrew A.
collection PubMed
description The isolation of specific subpopulations of extracellular vesicles (EVs) based on their expression of surface markers poses a significant challenge due to their nanoscale size (< 800 nm), their heterogeneous surface marker expression, and the vast number of background EVs present in clinical specimens (10(10)-10(12) EVs/mL in blood). Highly parallelized nanomagnetic sorting using track etched magnetic nanopore (TENPO) chips has achieved precise immunospecific sorting with high throughput and resilience to clogging. However, there has not yet been a systematic study of the design parameters that control the trade-offs in throughput, target EV recovery, and specificity in this approach. We combine finite-element simulation and experimental characterization of TENPO chips to elucidate design rules to isolate EV subpopulations from blood. We demonstrate the utility of this approach by increasing specificity > 10x relative to prior published designs without sacrificing recovery of the target EVs by selecting pore diameter, number of membranes placed in series, and flow rate. We compare TENPO-isolated EVs to those of gold-standard methods of EV isolation and demonstrate its utility for wide application and modularity by targeting subpopulations of EVs from multiple models of disease including lung cancer, pancreatic cancer, and liver cancer.
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spelling pubmed-102462622023-06-08 Parallelized immunomagnetic nanopore sorting: modeling, scaling, and optimization of surface marker specific isolation of extracellular vesicles from complex media Lin, Andrew A. Shen, Hanfei Spychalski, Griffin Carpenter, Erica L. Issadore, David Res Sq Article The isolation of specific subpopulations of extracellular vesicles (EVs) based on their expression of surface markers poses a significant challenge due to their nanoscale size (< 800 nm), their heterogeneous surface marker expression, and the vast number of background EVs present in clinical specimens (10(10)-10(12) EVs/mL in blood). Highly parallelized nanomagnetic sorting using track etched magnetic nanopore (TENPO) chips has achieved precise immunospecific sorting with high throughput and resilience to clogging. However, there has not yet been a systematic study of the design parameters that control the trade-offs in throughput, target EV recovery, and specificity in this approach. We combine finite-element simulation and experimental characterization of TENPO chips to elucidate design rules to isolate EV subpopulations from blood. We demonstrate the utility of this approach by increasing specificity > 10x relative to prior published designs without sacrificing recovery of the target EVs by selecting pore diameter, number of membranes placed in series, and flow rate. We compare TENPO-isolated EVs to those of gold-standard methods of EV isolation and demonstrate its utility for wide application and modularity by targeting subpopulations of EVs from multiple models of disease including lung cancer, pancreatic cancer, and liver cancer. American Journal Experts 2023-05-16 /pmc/articles/PMC10246262/ /pubmed/37292737 http://dx.doi.org/10.21203/rs.3.rs-2913647/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. https://creativecommons.org/licenses/by/4.0/License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License (https://creativecommons.org/licenses/by/4.0/)
spellingShingle Article
Lin, Andrew A.
Shen, Hanfei
Spychalski, Griffin
Carpenter, Erica L.
Issadore, David
Parallelized immunomagnetic nanopore sorting: modeling, scaling, and optimization of surface marker specific isolation of extracellular vesicles from complex media
title Parallelized immunomagnetic nanopore sorting: modeling, scaling, and optimization of surface marker specific isolation of extracellular vesicles from complex media
title_full Parallelized immunomagnetic nanopore sorting: modeling, scaling, and optimization of surface marker specific isolation of extracellular vesicles from complex media
title_fullStr Parallelized immunomagnetic nanopore sorting: modeling, scaling, and optimization of surface marker specific isolation of extracellular vesicles from complex media
title_full_unstemmed Parallelized immunomagnetic nanopore sorting: modeling, scaling, and optimization of surface marker specific isolation of extracellular vesicles from complex media
title_short Parallelized immunomagnetic nanopore sorting: modeling, scaling, and optimization of surface marker specific isolation of extracellular vesicles from complex media
title_sort parallelized immunomagnetic nanopore sorting: modeling, scaling, and optimization of surface marker specific isolation of extracellular vesicles from complex media
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10246262/
https://www.ncbi.nlm.nih.gov/pubmed/37292737
http://dx.doi.org/10.21203/rs.3.rs-2913647/v1
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