Small Volume Microrheology to Evaluate Viscoelastic Properties of Nucleic Acid-Based Supra-Assemblies.

Microrheology Nucleic acids Particle tracking Supra-assemblies Viscoelastic properties

Journal

Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969

Informations de publication

Date de publication:
2023
Historique:
pmc-release: 01 01 2024
medline: 14 8 2023
pubmed: 13 8 2023
entrez: 12 8 2023
Statut: ppublish

Résumé

Particle tracking (PT) microrheology is a passive microrheological approach that characterizes material properties of soft matter. Multicomponent materials with the ability to create extensive crosslinking, such as supra-assemblies, may exhibit a complex interplay of viscous and elastic properties with a substantial contribution of liquid phase still diffusing through the system. Microrheology analyzes the motion of microscopic beads immersed in a sample, making it possible to evaluate the rheological properties of biological supra-assemblies. This method requires only a small volume of the sample and a relatively simple, inexpensive experimental setup. The objective of this chapter is to describe the experimental procedures for the observation of particle motion, calibration of an optical setup for particle tracking, preparation of imaging chambers, and the use of image analysis software for particle tracking in viscoelastic nucleic acid-based supra-assemblies.

Identifiants

pubmed: 37572280
doi: 10.1007/978-1-0716-3417-2_11
pmc: PMC10482311
mid: NIHMS1925781
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

179-189

Subventions

Organisme : NIGMS NIH HHS
ID : R35 GM139587
Pays : United States

Informations de copyright

© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Références

J Struct Biol. 2005 Aug;151(2):182-95
pubmed: 16043363
ACS Appl Mater Interfaces. 2021 Aug 25;13(33):39030-39041
pubmed: 34402305

Auteurs

Akhilesh Kumar Gupta (AK)

Department of Physics, University of Nebraska Omaha, Omaha, NE, USA.

Joel Petersen (J)

Department of Physics, University of Nebraska Omaha, Omaha, NE, USA.

Elizabeth Skelly (E)

Nanoscale Science Program, Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC, USA.

Kirill A Afonin (KA)

Nanoscale Science Program, Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC, USA.

Alexey V Krasnoslobodtsev (AV)

Department of Physics, University of Nebraska Omaha, Omaha, NE, USA. akrasnos@unomaha.edu.

Articles similaires

Selecting optimal software code descriptors-The case of Java.

Yegor Bugayenko, Zamira Kholmatova, Artem Kruglov et al.
1.00
Software Algorithms Programming Languages

Exploring blood-brain barrier passage using atomic weighted vector and machine learning.

Yoan Martínez-López, Paulina Phoobane, Yanaima Jauriga et al.
1.00
Blood-Brain Barrier Machine Learning Humans Support Vector Machine Software
1.00
Humans Magnetic Resonance Imaging Brain Infant, Newborn Infant, Premature
Cephalometry Humans Anatomic Landmarks Software Internet

Classifications MeSH