Evidence map›Paper›PMID 29677521›Full record

ArticleBiomaterials2018

Investigating the interplay between substrate stiffness and ligand chemistry in directing mesenchymal stem cell differentiation within 3D macro-porous substrates.

Matthew G Haugh, Ted J Vaughan, Christopher M Madl, Rosanne M Raftery, Laoise M McNamara, Fergal J O'Brien, Sarah C Heilshorn

Abstract read
In one paragraph

Article in Biomaterials, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing 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

32 citing papers in PubMed.

  1. Article
  2. Mechanobiological Dynamics-Inspired Mechanomodulatory Biomaterials.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  3. Article
  4. The Rise of Mechanobiology for Advanced Cell Engineering and Manufacturing.Advanced materials (Deerfield Beach, Fla.) · 2025
    Review
  5. Engineered Protein Hydrogels as Biomimetic Cellular Scaffolds.Advanced materials (Deerfield Beach, Fla.) · 2024
    Review
  6. Article
  7. Article
  8. Article
  9. Segmenting mechanically heterogeneous domains via unsupervised learning.Biomechanics and modeling in mechanobiology · 2024
    Article
  10. Review
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. 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

7 authors.

Matthew G HaughDepartment of Materials Science and Engineering, Stanford University, Stanford, CA, USA; Tissue Engineering Research Group, Department of Anatomy, Royal College of Surgeons in Ireland, Dublin, Ireland.
Ted J VaughanDepartment of Biomedical Engineering, National University of Ireland, Galway, Ireland.
Christopher M MadlDepartment of Bioengineering, Stanford University, Stanford, CA, USA.
Rosanne M RafteryTissue Engineering Research Group, Department of Anatomy, Royal College of Surgeons in Ireland, Dublin, Ireland; Trinity Centre for Bioengineering, Trinity College Dublin (TCD), Dublin, Ireland; Advanced Materials and Bioengineering Research (AMBER) Centre, RCSI & TCD, Dublin, Ireland.
Laoise M McNamaraDepartment of Biomedical Engineering, National University of Ireland, Galway, Ireland.
Fergal J O'BrienTissue Engineering Research Group, Department of Anatomy, Royal College of Surgeons in Ireland, Dublin, Ireland; Trinity Centre for Bioengineering, Trinity College Dublin (TCD), Dublin, Ireland; Advanced Materials and Bioengineering Research (AMBER) Centre, RCSI & TCD, Dublin, Ireland.
Sarah C HeilshornDepartment of Materials Science and Engineering, Stanford University, Stanford, CA, USA. Electronic address: heilshorn@stanford.edu.

Funding

Stanford/UNC Biomimetic U19 Research CenterU19AI116484 · NIAID · STANFORD UNIVERSITY · PI AMIEVA, MANUEL R, KUO, CALVIN J · 2015 to 2025
$13.5M
NIAID NIH HHS U19 AI116484
6 · The paper itself

Abstract

Dimensionality can have a profound impact on stiffness-mediated differentiation of mesenchymal stem cells (MSCs). However, while we have begun to understand cellular response when encapsulated within 3D substrates, the behavior of cells within macro-porous substrates is relatively underexplored. The goal of this study was to determine the influence of macro-porous topographies on stiffness-mediated differentiation of MSCs. We developed macro-porous recombinant elastin-like protein (ELP) substrates that allow independent control of mechanical properties and ligand chemistry. We then used computational modeling to probe the impact of pore topography on the mechanical stimulus that cells are exposed to within these substrates, and finally we investigated stiffness induced biases towards adipogenic and osteogenic differentiation of MSCs within macro-porous substrates. Computational modeling revealed that there is significant heterogeneity in the mechanical stimuli that cells are exposed to within porous substrates and that this heterogeneity is predominantly due to the wide range of possible cellular orientations within the pores. Surprisingly, MSCs grown within 3D porous substrates respond to increasing substrate stiffness by up-regulating both osteogenesis and adipogenesis. These results demonstrate that within porous substrates the behavior of MSCs diverges from previously observed responses to substrate stiffness, emphasizing the importance of topography as a determinant of cellular behavior.

Indexed as

Cell DifferentiationAlkaline PhosphataseAmino Acid SequenceDNAElastinHumansLigandsMesenchymal Stem CellsModels, MolecularPorositySubstrate SpecificityTriglyceridesAlkaline PhosphataseDNAElastinLigandsTriglyceridesCell-matrix interactionsDifferentiationMacro-porosityMechanotransductionMesenchymal stem cellsSubstrate stiffnessTissue engineering

Identifiers

PMID29677521
PMCPMC5997298

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Registered trials

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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.