Ultra-thin and ultra-porous nanofiber networks as a basement-membrane mimic. Graybill, P. M., Jacobs, E. J. t., Jana, A., Agashe, A., Nain, A. S., & Davalos, R. V. Lab Chip, 23(20):4565-4578, 2023. 1473-0189 Graybill, Philip M Orcid: 0000-0002-2057-7478 Jacobs, Edward J 4th Orcid: 0000-0002-6130-2411 Jana, Aniket Agashe, Atharva Nain, Amrinder S Davalos, Rafael V Orcid: 0000-0003-1503-9509 P01 CA207206/CA/NCI NIH HHS/United States R44 TR003968/TR/NCATS NIH HHS/United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. England 2023/09/29 Lab Chip. 2023 Oct 10;23(20):4565-4578. doi: 10.1039/d3lc00304c.doi abstract bibtex Current basement membrane (BM) mimics used for modeling endothelial and epithelial barriers in vitro do not faithfully recapitulate key in vivo physiological properties such as BM thickness, porosity, stiffness, and fibrous composition. Here, we use networks of precisely arranged nanofibers to form ultra-thin (∼3 μm thick) and ultra-porous (∼90%) BM mimics for blood-brain barrier modeling. We show that these nanofiber networks enable close contact between endothelial monolayers and pericytes across the membrane, which are known to regulate barrier tightness. Cytoskeletal staining and transendothelial electrical resistance (TEER) measurements reveal barrier formation on nanofiber membranes integrated within microfluidic devices and transwell inserts. Further, significantly higher TEER values indicate a biological benefit for co-cultures formed on the ultra-thin nanofiber membranes. Our BM mimic overcomes critical technological challenges in forming co-cultures that are in proximity and facilitate cell-cell contact, while still being constrained to their respective sides. We anticipate that our nanofiber networks will find applications in drug discovery, cell migration, and barrier dysfunction studies.
@article{RN88,
author = {Graybill, P. M. and Jacobs, E. J. th and Jana, A. and Agashe, A. and Nain, A. S. and Davalos, R. V.},
title = {Ultra-thin and ultra-porous nanofiber networks as a basement-membrane mimic},
journal = {Lab Chip},
volume = {23},
number = {20},
pages = {4565-4578},
note = {1473-0189
Graybill, Philip M
Orcid: 0000-0002-2057-7478
Jacobs, Edward J 4th
Orcid: 0000-0002-6130-2411
Jana, Aniket
Agashe, Atharva
Nain, Amrinder S
Davalos, Rafael V
Orcid: 0000-0003-1503-9509
P01 CA207206/CA/NCI NIH HHS/United States
R44 TR003968/TR/NCATS NIH HHS/United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
England
2023/09/29
Lab Chip. 2023 Oct 10;23(20):4565-4578. doi: 10.1039/d3lc00304c.},
abstract = {Current basement membrane (BM) mimics used for modeling endothelial and epithelial barriers in vitro do not faithfully recapitulate key in vivo physiological properties such as BM thickness, porosity, stiffness, and fibrous composition. Here, we use networks of precisely arranged nanofibers to form ultra-thin (∼3 μm thick) and ultra-porous (∼90%) BM mimics for blood-brain barrier modeling. We show that these nanofiber networks enable close contact between endothelial monolayers and pericytes across the membrane, which are known to regulate barrier tightness. Cytoskeletal staining and transendothelial electrical resistance (TEER) measurements reveal barrier formation on nanofiber membranes integrated within microfluidic devices and transwell inserts. Further, significantly higher TEER values indicate a biological benefit for co-cultures formed on the ultra-thin nanofiber membranes. Our BM mimic overcomes critical technological challenges in forming co-cultures that are in proximity and facilitate cell-cell contact, while still being constrained to their respective sides. We anticipate that our nanofiber networks will find applications in drug discovery, cell migration, and barrier dysfunction studies.},
keywords = {*Nanofibers
Porosity
Blood-Brain Barrier/physiology
Coculture Techniques
Basement Membrane},
ISSN = {1473-0197 (Print)
1473-0189},
DOI = {10.1039/d3lc00304c},
year = {2023},
type = {Journal Article}
}
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