Non-enzymatic Crosslinking
Non-enzymatic Crosslinking - Related Videos
03:47
Crosslinking Neuronal Surface Proteins in the Mouse Brain using a Chemical Crosslinking Assay
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2025
This video demonstrates the crosslinking of neuronal surface proteins using Bis-sulfosuccinimidyl suberate or BS3. Isolated and chilled mouse brain is sectioned coronally. The desired region is extracted from the sections, minced, and treated with BS3. BS3 crosslinks surface proteins like GABA receptors, leaving internal proteins unaltered. The reaction is quenched with glycine before processing the samples for further analysis.
11:15
Nanosponge Tunability in Size and Crosslinking Density
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Cited by 1 •
2017
This article describes a process for tuning the size and crosslinking density of covalently crosslinked nanoparticles from linear polyesters containing pendant functionality. By tailoring synthesis parameters (polymer molecular weight, pendant functionality incorporation, and crosslinker equivalents), a desired nanoparticle size and crosslinking density can be achieved for drug delivery applications.
09:06
Preparation of DNA-crosslinked Polyacrylamide Hydrogels
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Cited by 5 •
2014
Our laboratory has developed DNA-crosslinked polyacrylamide hydrogels, a dynamic hydrogel system, to better understand the effects of modulating tissue stiffness on cell function. Here, we provide schematics, descriptions, and protocols to prepare these hydrogels.
09:58
Light-driven Enzymatic Decarboxylation
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Cited by 6 •
2016
We describe a protocol for the light-catalyzed generation of hydrogen peroxide — a cofactor for oxidative transformations.
11:04
NiO Nanoflowers for Non-Enzymatic Amperometric Detection of Glucose
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2025
The primary objective of this article is to help researchers and scientists understand the process of glucose sensing using electrochemistry and to optimize parameters to maximize the glucose sensing response. The process of fabricating a glucose sensor is described in detail with guidelines for obtaining its best chronoamperometric response.
FAQs
At the molecular level, reactive carbonyl groups can react with amino groups on proteins, producing covalent links between biomolecules. Because these links connect neighboring molecular structures, they can create a more stable network rather than a collection of independent molecules. This chemistry provides a mechanistic basis for examining how matrix organization changes in biological systems.
Unlike enzyme-dependent crosslinking, this approach relies on chemical reactivity between available groups or on an added chemical crosslinker. In biomaterials, the crosslinker connects adjacent polymer chains; in biological systems, carbonyl and amino groups can participate. This distinction lets researchers investigate matrix-like structures without assigning the bond-forming step to an enzyme.
Defined reaction conditions influence how effectively adjacent molecules or polymer chains become linked and, therefore, the organization of the resulting material. In cancer research, that organization matters because extracellular-matrix structure and tissue stiffness are examined as variables affecting tumor-cell adhesion, migration, invasion, and response to therapy.
A basic workflow begins by selecting biomolecules or polymer chains that contain reactive groups or can be connected by a chemical crosslinker. The components are brought together under defined conditions, allowing covalent connections to form. The resulting structure can then serve as a controlled model of tissue organization.
It can be used to build disease models and biomaterials that reproduce selected biochemical and mechanical features of tumor tissues. By varying the resulting matrix organization or stiffness, researchers can examine consequences for tumor-cell adhesion, migration, invasion, and therapy response. These models connect material design with cancer-related cell behavior.
Results should be considered in relation to both biochemical organization and mechanical stiffness, rather than as effects of crosslinking alone. A model that more closely reproduces tumor-tissue features can help relate matrix conditions to changes in adhesion, migration, invasion, or therapy response. This framing supports comparisons among disease models and biomaterial designs.