Evidence map›Paper›PMID 31815156›Full record

ArticleBio-protocol2019

High Throughput Traction Force Microscopy for Multicellular Islands on Combinatorial Microarrays.

Ian C Berg, Gregory H Underhill

Open access · hybridAbstract read
In one paragraph

Article in Bio-protocol, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.2field-weighted citation impact, top 46% 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

6 citing papers in PubMed, 5 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Ian C BergDepartment of Bioengineering, University of Illinois Urbana Champaign, Champaign, IL, USA.
Gregory H UnderhillDepartment of Bioengineering, University of Illinois Urbana Champaign, Champaign, IL, USA.
University of Illinois Urbana-Champaign · US

Funding

Tissue microenvironment (TIMe) training programT32EB019944 · NIBIB · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI BHARGAVA, ROHIT, GASKINS, REX · 2016 to 2025
$1.9M
NIBIB NIH HHS T32 EB019944
6 · The paper itself

Abstract

The composition and mechanical properties of the cellular microenvironment along with the resulting distribution of cellular devolved forces can affect cellular function and behavior. Traction Force Microscopy (TFM) provides a method to measure the forces applied to a surface by adherent cells. Numerous TFM systems have been described in literature. Broadly, these involve culturing cells on a flexible substrate with embedded fluorescent markers which are imaged before and after relaxion of cell forces. From these images, a displacement field is calculated, and from the displacement field, a traction field. Here we describe a TFM system using polyacrylamide substrates and a microarray spotter to fabricate arrays of multicellular islands on various combinations of extra cellular matrix (ECM) proteins or other biomolecules. A microscope with an automated stage is used to image each of the cellular islands before and after lysing cells with a detergent. These images are analyzed in a semi-automated fashion using a series of MATLAB scripts which produce the displacement and traction fields, and summary data. By combining microarrays with a semi-automated implementation of TFM analysis, this protocol enables evaluation of the impact of substrate stiffness, matrix composition, and tissue geometry on cellular mechanical behavior in high throughput.

Indexed as

Cellular forcesMechanobiologyMicroarrayMicroenvironmentMulticellular islandSubstrate stiffnessTFMTraction force microscopy

Identifiers

PMID31815156
PMCPMC6897381
OpenAlexW2987166582

What OpenQuestion holds

Textmetadata
LicenceTDM
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.