Evidence map›Paper›PMID 24901766›Full record

ArticlePLoS computational biology2014

A novel cell traction force microscopy to study multi-cellular system.

Xin Tang, Alireza Tofangchi, Sandeep V Anand, Taher A Saif

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
2.7field-weighted citation impact, top 10% of its field
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

33 citing papers in PubMed, 71 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Deep learning reveals how cells pull, buckle, and navigate fibrous environments.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  6. Article
  7. Review
  8. Article
  9. Field Guide to Traction Force Microscopy.Cellular and molecular bioengineering · 2024
    Review
  10. Review
  11. YAP at the Crossroads of Biomechanics and Drug Resistance in Human Cancer.International journal of molecular sciences · 2023
    Review
  12. Article
  13. Mechanobiological Analysis of Nanoparticle Toxicity.Nanomaterials (Basel, Switzerland) · 2023
    Review
  14. Functional Interrogation of CaMethods in molecular biology (Clifton, N.J.) · 2023
    Article
  15. Article
  16. Review
  17. Article
  18. Article
  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 at 1 institution in 1 country.

Xin TangDepartment of Mechanical Science and Engineering (MechSE), College of Engineering, University of Illinois at Urbana-Champaign (UIUC), Urbana, Illinois, United States of America.
Alireza TofangchiDepartment of Mechanical Science and Engineering (MechSE), College of Engineering, University of Illinois at Urbana-Champaign (UIUC), Urbana, Illinois, United States of America.
Sandeep V AnandDepartment of Mechanical Science and Engineering (MechSE), College of Engineering, University of Illinois at Urbana-Champaign (UIUC), Urbana, Illinois, United States of America.
Taher A SaifDepartment of Mechanical Science and Engineering (MechSE), College of Engineering, University of Illinois at Urbana-Champaign (UIUC), Urbana, Illinois, United States of America; Micro and Nanotechnology Laboratory (MNTL), University of Illinois at Urbana-Champaign (UIUC), Urbana, Illinois, United States of America.
University of Illinois Urbana-Champaign · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traction forces exerted by adherent cells on their microenvironment can mediate many critical cellular functions. Accurate quantification of these forces is essential for mechanistic understanding of mechanotransduction. However, most existing methods of quantifying cellular forces are limited to single cells in isolation, whereas most physiological processes are inherently multi-cellular in nature where cell-cell and cell-microenvironment interactions determine the emergent properties of cell clusters. In the present study, a robust finite-element-method-based cell traction force microscopy technique is developed to estimate the traction forces produced by multiple isolated cells as well as cell clusters on soft substrates. The method accounts for the finite thickness of the substrate. Hence, cell cluster size can be larger than substrate thickness. The method allows computing the traction field from the substrate displacements within the cells' and clusters' boundaries. The displacement data outside these boundaries are not necessary. The utility of the method is demonstrated by computing the traction generated by multiple monkey kidney fibroblasts (MKF) and human colon cancerous (HCT-8) cells in close proximity, as well as by large clusters. It is found that cells act as individual contractile groups within clusters for generating traction. There may be multiple of such groups in the cluster, or the entire cluster may behave a single group. Individual cells do not form dipoles, but serve as a conduit of force (transmission lines) over long distances in the cluster. The cell-cell force can be either tensile or compressive depending on the cell-microenvironment interactions.

Indexed as

Models, BiologicalAnimalsBiophysical PhenomenaCell AdhesionCell Line, TumorCells, CulturedCellular MicroenvironmentCompressive StrengthComputational BiologyFinite Element AnalysisHumansMechanotransduction, CellularMicroscopyTensile Strength

Identifiers

PMID24901766
PMCPMC4046928
OpenAlexW2035393702

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.