Evidence map›Paper›PMID 29150767›Full record

ArticleAnnals of biomedical engineering2018

Cell Migration in 1D and 2D Nanofiber Microenvironments.

Horacio M Estabridis, Aniket Jana, Amrinder Nain, David J Odde

Abstract read
In one paragraph

Article in Annals of biomedical engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Mesenchymal cell migration on one-dimensional micropatterns.Frontiers in cell and developmental biology · 2024
    Review
  8. Article
  9. Multiscale models of integrins and cellular adhesions.Current opinion in structural biology · 2023
    Review
  10. Article
  11. Article
  12. Quantification of cell migration: metrics selection to model application.Frontiers in cell and developmental biology · 2023
    Review
  13. Article
  14. Review
  15. Nanofiber curvature with Rho GTPase activity increases mouse embryonic fibroblast random migration velocity.Integrative biology : quantitative biosciences from nano to macro · 2021
    Article
  16. Article
  17. Design Challenges in Polymeric Scaffolds for Tissue Engineering.Frontiers in bioengineering and biotechnology · 2021
    Review
  18. On the Interaction between 1D Materials and Living Cells.Journal of functional biomaterials · 2020
    Review
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Horacio M EstabridisDepartment of Biomedical Engineering, University of Minnesota, 312 Church St. SE, 7-132 Nils-Hasselmo Hall, Minneapolis, MN, 55455, USA.
Aniket JanaDepartment of Mechanical Engineering, Virginia Tech, Blacksburg, VA, 24061, USA.
Amrinder NainDepartment of Mechanical Engineering, Virginia Tech, Blacksburg, VA, 24061, USA.
David J OddeDepartment of Biomedical Engineering, University of Minnesota, 312 Church St. SE, 7-132 Nils-Hasselmo Hall, Minneapolis, MN, 55455, USA. oddex002@umn.edu.

Funding

Project 2: Cell Migration in Mechanically Complex MicroenvironmentsU54CA210190 · NCI · UNIVERSITY OF MINNESOTA · PI LARGAESPADA, DAVID ANDREW, ODDE, DAVID J. · 2016 to 2020
$10.0M
Modeling and microsystems approach to glioma invasionR01CA172986 · NCI · UNIVERSITY OF MINNESOTA · PI ODDE, DAVID J., ROSENFELD, STEVEN S · 2013 to 2017
$2.3M
NIH HHS R01-CA-172986NIH HHS U54-CA-210190
6 · The paper itself

Abstract

Understanding how cells migrate in fibrous environments is important in wound healing, immune function, and cancer progression. A key question is how fiber orientation and network geometry influence cell movement. Here we describe a quantitative, modeling-based approach toward identifying the mechanisms by which cells migrate in fibrous geometries having well controlled orientation. Specifically, U251 glioblastoma cells were seeded onto non-electrospinning Spinneret based tunable engineering parameters fiber substrates that consist of networks of suspended 400 nm diameter nanofibers. Cells were classified based on the local fiber geometry and cell migration dynamics observed by light microscopy. Cells were found in three distinct geometries: adhering two a single fiber, adhering to two parallel fibers, and adhering to a network of orthogonal fibers. Cells adhering to a single fiber or two parallel fibers can only move in one dimension along the fiber axis, whereas cells on a network of orthogonal fibers can move in two dimensions. We found that cells move faster and more persistently in 1D geometries than in 2D, with cell migration being faster on parallel fibers than on single fibers. To explain these behaviors mechanistically, we simulated cell migration in the three different geometries using a motor-clutch based model for cell traction forces. Using nearly identical parameter sets for each of the three cases, we found that the simulated cells naturally replicated the reduced migration in 2D relative to 1D geometries. In addition, the modestly faster 1D migration on parallel fibers relative to single fibers was captured using a correspondingly modest increase in the number of clutches to reflect increased surface area of adhesion on parallel fibers. Overall, the integrated modeling and experimental analysis shows that cell migration in response to varying fibrous geometries can be explained by a simple mechanical readout of geometry via a motor-clutch mechanism.

Indexed as

Cell MovementTumor MicroenvironmentCell AdhesionCell Line, TumorGlioblastomaHumansNanofibersDisplacementGlioblastomaMigrationParameterizationPersistent random walkPositionRandom walkSimulation

Identifiers

PMID29150767
PMCPMC5809563

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.