Evidence map›Paper›PMID 35821232›Full record

ArticleNature communications2022

Self-assembly of tessellated tissue sheets by expansion and collision.

Matthew A Heinrich, Ricard Alert, Abraham E Wolf, Andrej Košmrlj, Daniel J Cohen

Abstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

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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

5 authors.

Matthew A Heinrich *Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ, 08544, USA.
Ricard Alert *Princeton Center for Theoretical Science, Princeton University, Princeton, NJ, 08544, USA.ORCID http://orcid.org/0000-0002-1885-9177
Abraham E Wolf *Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, 08544, USA.ORCID http://orcid.org/0000-0002-1135-6497
Andrej KošmrljDepartment of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ, 08544, USA. andrej@princeton.edu.ORCID http://orcid.org/0000-0001-6137-9200
Daniel J CohenDepartment of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ, 08544, USA. danielcohen@princeton.edu.

Funding

Understanding and controlling collective cell behaviorsR35GM133574 · NIGMS · PRINCETON UNIVERSITY · PI Daniel Cohen · 2019 to 2026
$3.2M
NIGMS NIH HHS R35 GM133574U.S. Department of Health & Human Services | National Institutes of Health (NIH) 1R35 GM133574-01
6 · The paper itself

Abstract

Tissues do not exist in isolation-they interact with other tissues within and across organs. While cell-cell interactions have been intensely investigated, less is known about tissue-tissue interactions. Here, we studied collisions between monolayer tissues with different geometries, cell densities, and cell types. First, we determine rules for tissue shape changes during binary collisions and describe complex cell migration at tri-tissue boundaries. Next, we propose that genetically identical tissues displace each other based on pressure gradients, which are directly linked to gradients in cell density. We present a physical model of tissue interactions that allows us to estimate the bulk modulus of the tissues from collision dynamics. Finally, we introduce TissEllate, a design tool for self-assembling complex tessellations from arrays of many tissues, and we use cell sheet engineering techniques to transfer these composite tissues like cellular films. Overall, our work provides insight into the mechanics of tissue collisions, harnessing them to engineer tissue composites as designable living materials.

Identifiers

PMID35821232
PMCPMC9276766

What OpenQuestion holds

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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.