Back

StemBond hydrogels optimise the mechanical microenvironment for embryonic stem cells

Labouesse, C.; Agley, C. C.; Tan, B. X.; Hofer, M.; Winkel, A.; Stirparo, G. G.; Stuart, H. T.; Verstreken, C. M.; Mansfield, W.; Bertone, P.; Franze, K.; Silva, J. C. R.; Chalut, K. J.

2019-09-13 cell biology
10.1101/768762 bioRxiv
Show abstract

Studies of mechanical signalling are typically performed by comparing cells cultured on soft and stiff hydrogel-based substrates. However, it is challenging to independently and robustly control both substrate stiffness and tethering of extracellular matrix (ECM) to substrates, making ECM tethering a potentially confounding variable in mechanical signalling investigations. Moreover, poor ECM tethering can lead to weak cell attachment. To address this, we developed StemBond hydrogels, a hydrogel formulation in which ECM tethering is stable and can be varied independently of stiffness. We show that soft StemBond hydrogels provide an optimal format for culturing embryonic stem (ES) cells. We find that soft StemBond substrates improve the homogeneity of ES cell populations, boost their self-renewal, and increase the efficiency of cellular reprogramming. Our findings underline how soft microenvironments impact mechanosensitive signalling pathways regulating self-renewal and differentiation, indicating that optimising the complete mechanical microenvironment will offer greater control over stem cell fate specification.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Stem Cell Reports
○ 130 papers in training set
Top 0.1%
23.2%
Similar papers in this journal
2
Nature Communications
○ 5641 papers in training set
Top 22%
7.3%
Similar papers in this journal
3
Advanced Science
○ 286 papers in training set
Top 0.6%
7.3%
Similar papers in this journal
4
Cell Stem Cell
○ 62 papers in training set
Top 0.2%
6.8%
Similar papers in this journal
5
eLife
● 5828 papers in training set
Top 21%
5.6%
Similar papers in this journal
50% of probability mass above
6
iScience
○ 1154 papers in training set
Top 4%
4.1%
7
Development
● 497 papers in training set
Top 2%
3.3%
8
Biomaterials
○ 84 papers in training set
Top 0.6%
3.3%
9
Cell Reports
○ 1498 papers in training set
Top 13%
3.1%
10
Nature Materials
○ 28 papers in training set
Top 0.2%
2.5%
11
Developmental Cell
○ 196 papers in training set
Top 2%
2.4%
12
Cell Systems
○ 201 papers in training set
Top 2%
2.1%
13
Scientific Reports
○ 3612 papers in training set
Top 48%
2.0%
14
Communications Biology
○ 993 papers in training set
Top 14%
1.7%
15
Science Advances
● 1243 papers in training set
Top 20%
1.7%
16
Cell Reports Methods
○ 165 papers in training set
Top 2%
1.5%
17
Journal of Cell Science
● 393 papers in training set
Top 3%
1.1%
18
EMBO Reports
○ 263 papers in training set
Top 5%
1.1%
19
Advanced Functional Materials
○ 46 papers in training set
Top 0.8%
1.1%
20
Advanced Materials
○ 56 papers in training set
Top 0.8%
1.1%
21
Stem Cells
◐ 31 papers in training set
Top 0.5%
1.1%
22
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 34%
1.1%
23
Frontiers in Cell and Developmental Biology
○ 233 papers in training set
Top 3%
1.1%
24
The EMBO Journal
○ 309 papers in training set
Top 5%
1.0%
25
Life Science Alliance
● 285 papers in training set
Top 7%
0.9%
26
Advanced Healthcare Materials
○ 85 papers in training set
Top 1%
0.6%
27
PLOS Biology
● 486 papers in training set
Top 13%
0.6%
28
npj Regenerative Medicine
○ 24 papers in training set
Top 0.8%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.