Back

Engineering an Enzymatically Active Granular Matrix for On-Chip Modeling of Bone-Like Mineralization

Sanaei, F.; Zandieh, D.; Hofman, D.; Joziasse, L. S.; van den Beucken, J. J. J. P.; Leeuwenburgh, S. C. G.; Diba, M.

2026-07-13 bioengineering
10.64898/2026.07.12.737035 bioRxiv
Show abstract

Controlled biomineralization is central to engineering physiologically relevant hard-tissue models, yet achieving spatially organized, three-dimensional (3D) mineral deposition in microfluidic on-chip systems remains challenging. While cell-based bone-on-chip platforms offer biological complexity, they intrinsically couple mineral initiation to confounding factors such as matrix remodeling and paracrine signaling, obscuring the earliest biochemical drivers of nucleation. Drawing inspiration from bottom-up synthetic biology, we engineered an enzymatically active granular matrix that recapitulates a key osteogenic function within a perfusable 3D microenvironment. Alkaline phosphatase (ALP), the key driver of native bone formation, was covalently conjugated to poly(ethylene glycol)-based (PEG) microgels via thiol-ene photochemistry, retaining over 90% enzymatic activity after 48 h. These monodisperse microgels were assembled into a jammed, perfusable matrix within an on-chip chamber, enabling independent control over enzyme loading and substrate delivery. The system supported rapid in situ mineralization (24-48 h), yielding a carbonated, calcium-deficient, apatite-like phase characteristic of early-stage bone mineral. We demonstrate that the spatial 3D localization of enzymatic activity to discrete microscale compartments, coupled with interstitial perfusion, enables localized and near-physiological mineral formation. This mechanistically defined, acellular platform provides a programmable foundation for investigating ALP-driven 3D mineralization and establishes a modular route toward hybrid biosynthetic models of (patho)physiological tissue mineralization.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Advanced Functional Materials
46 papers in training set
Top 0.1%
15.2%
2
Advanced Healthcare Materials
85 papers in training set
Top 0.1%
13.0%
3
Advanced Materials
56 papers in training set
Top 0.1%
12.0%
4
Advanced Science
286 papers in training set
Top 0.2%
12.0%
50% of probability mass above
5
Nature Communications
5641 papers in training set
Top 18%
9.9%
6
Science Advances
1243 papers in training set
Top 2%
6.8%
7
Nature Materials
28 papers in training set
Top 0.1%
3.2%
8
ACS Nano
113 papers in training set
Top 0.7%
3.2%
9
Proceedings of the National Academy of Sciences
2444 papers in training set
Top 21%
2.5%
10
Nature Nanotechnology
32 papers in training set
Top 0.3%
2.0%
11
Nature Chemical Biology
119 papers in training set
Top 1%
1.7%
12
Small
78 papers in training set
Top 0.9%
1.7%
13
Nature Chemistry
42 papers in training set
Top 0.7%
1.3%
14
Nano Letters
71 papers in training set
Top 1%
1.0%
15
Biomacromolecules
29 papers in training set
Top 0.4%
1.0%
16
Biomaterials
84 papers in training set
Top 1%
0.9%
17
Biofabrication
36 papers in training set
Top 0.6%
0.9%
18
ACS Applied Materials & Interfaces
39 papers in training set
Top 0.9%
0.9%
19
ACS Synthetic Biology
287 papers in training set
Top 3%
0.6%
20
Scientific Reports
3612 papers in training set
Top 78%
0.6%
21
ACS Central Science
71 papers in training set
Top 2%
0.6%
22
Journal of the American Chemical Society
217 papers in training set
Top 3%
0.6%