Back

Mapping 3D cellular mechanical activity with matrix-embedded DNA force-history probes

Peng, Y.-H.; Lettinga, M.; Taubenberger, A.; Krieg, E.

2026-08-06 bioengineering
10.64898/2026.08.05.742976 bioRxiv
Show abstract

Cells continuously integrate mechanical cues from the surrounding extracellular matrix to control fundamental biological processes such as proliferation and migration. Yet, studying the mechanical activity of cells in 3D over time is challenging, low-throughput, and requires specialized equipment. Here, we introduce DNA-based force-history probes, which convert transient pico-Newton forces into cumulative fluorescent signals within a mechanically adjustable DNA-crosslinked cell culture matrix. The probes provide control over signal lifetimes, allowing stress patterns to be recorded over minutes to days with tunable temporal memory. We use this system to visualize the mechanical activity of breast cancer spheroids and map the trajectories of migrating cancer cells. By combining different fluorophores and DNA-encoded signal lifetimes, we produce dual-color probes that associate temporal information to mechanical events. Overall, force-history probes provide an endpoint-readable record of mechanical cell-matrix activity, offering new opportunities for studying cell function in physiology and disease.

Matching journals

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

1
Nature Communications
○ 5641 papers in training set
Top 12%
15.1%
Similar papers in this journal
2
Advanced Science
○ 286 papers in training set
Top 0.2%
12.7%
Similar papers in this journal
3
Nature Materials
○ 28 papers in training set
Top 0.1%
9.7%
Similar papers in this journal
4
Science Advances
● 1243 papers in training set
Top 2%
8.9%
Similar papers in this journal
5
Nano Letters
● 71 papers in training set
Top 0.1%
6.7%
Similar papers in this journal
50% of probability mass above
6
Nature Biotechnology
○ 172 papers in training set
Top 0.9%
4.3%
7
ACS Nano
● 113 papers in training set
Top 0.5%
4.3%
8
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 18%
2.8%
9
Small
○ 78 papers in training set
Top 0.5%
2.8%
10
Nature Nanotechnology
○ 32 papers in training set
Top 0.2%
2.4%
11
Nature Methods
○ 385 papers in training set
Top 3%
2.4%
12
Advanced Materials
○ 56 papers in training set
Top 0.5%
2.4%
13
Advanced Functional Materials
○ 46 papers in training set
Top 0.6%
1.9%
14
Cell Systems
○ 201 papers in training set
Top 3%
1.5%
15
Communications Biology
○ 993 papers in training set
Top 18%
1.4%
16
Angewandte Chemie International Edition
○ 93 papers in training set
Top 1%
1.1%
17
Scientific Reports
○ 3612 papers in training set
Top 66%
1.1%
18
eLife
● 5828 papers in training set
Top 61%
1.1%
19
Cell Reports Methods
○ 165 papers in training set
Top 3%
1.0%
20
Nature Chemical Biology
○ 119 papers in training set
Top 3%
0.9%
21
Small Methods
○ 29 papers in training set
Top 0.6%
0.9%
22
Cell
○ 431 papers in training set
Top 10%
0.8%
23
Journal of the American Chemical Society
● 217 papers in training set
Top 3%
0.8%
24
Journal of Cell Biology
● 392 papers in training set
Top 4%
0.6%
25
ACS Sensors
● 49 papers in training set
Top 0.8%
0.6%
26
Nucleic Acids Research
◐ 1281 papers in training set
Top 14%
0.6%
27
Science Translational Medicine
● 127 papers in training set
Top 4%
0.6%
28
Lab on a Chip
● 96 papers in training set
Top 1%
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.