Back

Stepwise Stiffening/Softening of and Cell Recovery from Reversibly Formulated Hydrogel Double Networks

Kopyeva, I.; Goldner, E. C.; Hoye, J. W.; Yang, S.; Regier, M. C.; Vera, K. R.; Bretherton, R. C.; DeForest, C. A.

2024-04-08 bioengineering
10.1101/2024.04.04.588191 bioRxiv
Show abstract

Biomechanical contributions of the ECM underpin cell growth and proliferation, differentiation, signal transduction, and other fate decisions. As such, biomaterials whose mechanics can be spatiotemporally altered - particularly in a reversible manner - are extremely valuable for studying these mechanobiological phenomena. Herein, we introduce a poly(ethylene glycol) (PEG)-based hydrogel model consisting of two interpenetrating step-growth networks that are independently formed via largely orthogonal bioorthogonal chemistries and sequentially degraded with distinct bacterial transpeptidases, affording reversibly tunable stiffness ranges that span healthy and diseased soft tissues (e.g., 500 Pa - 6 kPa) alongside terminal cell recovery for pooled and/or single-cell analysis in a near "biologically invisible" manner. Spatiotemporal control of gelation within the primary supporting network was achieved via mask-based and two-photon lithography; these stiffened patterned regions could be subsequently returned to the original soft state following sortase-based secondary network degradation. Using this approach, we investigated the effects of 4D-triggered network mechanical changes on human mesenchymal stem cell (hMSC) morphology and Hippo signaling, as well as Caco-2 colorectal cancer cell mechanomemory at the global transcriptome level via RNAseq. We expect this platform to be of broad utility for studying and directing mechanobiological phenomena, patterned cell fate, as well as disease resolution in softer matrices. TOC DescriptionBiomaterials that can dynamically change stiffnesses are essential in further understanding the role of extracellular matrix mechanics. Using independently formulated and subsequently degradable interpenetrating hydrogel networks, we reversibly and spatiotemporally trigger stiffening/softening of cell-laden matrices. Terminal cell recovery for pooled and/or single-cell analysis is permitted in a near "biologically invisible" manner. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=172 SRC="FIGDIR/small/588191v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@d89309org.highwire.dtl.DTLVardef@9d6dc0org.highwire.dtl.DTLVardef@19065e6org.highwire.dtl.DTLVardef@1120aec_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
Advanced Materials
53 papers in training set
Top 0.1%
40.3%
2
Advanced Functional Materials
41 papers in training set
Top 0.3%
8.6%
3
Advanced Science
249 papers in training set
Top 2%
7.0%
50% of probability mass above
4
Advanced Healthcare Materials
71 papers in training set
Top 0.4%
5.0%
5
Nature Communications
4913 papers in training set
Top 34%
4.4%
6
Nature Materials
21 papers in training set
Top 0.2%
3.8%
7
Small
70 papers in training set
Top 0.1%
3.7%
8
Nature Nanotechnology
30 papers in training set
Top 0.4%
2.8%
9
ACS Applied Materials & Interfaces
39 papers in training set
Top 0.4%
1.9%
10
Nano Letters
63 papers in training set
Top 1%
1.8%
11
Advanced Materials Technologies
27 papers in training set
Top 0.3%
1.7%
12
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 32%
1.7%
13
Science Advances
1098 papers in training set
Top 20%
1.5%
14
Journal of the American Chemical Society
199 papers in training set
Top 3%
1.4%
15
ACS Nano
99 papers in training set
Top 3%
1.3%
16
Nature Biotechnology
147 papers in training set
Top 5%
1.3%
17
Angewandte Chemie International Edition
81 papers in training set
Top 3%
1.0%
18
Biofabrication
32 papers in training set
Top 0.6%
0.9%
19
Acta Biomaterialia
85 papers in training set
Top 0.8%
0.8%
20
Nature Biomedical Engineering
42 papers in training set
Top 2%
0.7%
21
Cell Systems
167 papers in training set
Top 13%
0.7%
22
Biomaterials
78 papers in training set
Top 2%
0.5%