Elucidating Local Confinement in Crowded Polymer Solutions Within Giant Unilamellar Vesicles (GUVs) Through Single Particle Tracking Toward Deeper Understanding of Cells.

IF 10.7 2区 材料科学 Q1 CHEMISTRY, PHYSICAL
Yingming Zhao, Shubin Li, Yanhao Liu, Chao Li, Yongshuo Ren, Xiangxiang Zhang, Zhao Wang, Mingdong Dong, Xiaojun Han
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Abstract

In a crowded environment, macromolecules occupy a significant proportion volume of cells to repulse other molecules in H2O-rich phase domains. These H2O-rich phase domains have been found to significantly influence material transportation and biochemical reactions. However, the accurate quantification of the size of these domains remains a challenge. Here, formulas are set up to calculate the anomalous diffusion exponent (α), the concentration threshold (cp), and the radius of the H2O-rich phase domain (r0) to characterize the crowded solutions. Fitting coefficient (R2) of the r0 fitted curves are 0.9989 for PEG-8k Da and 0.9901 for PEG-20k Da, respectively, which confirms the formulas to be suitable for quantifying the crowding degree. The values of α, r0, and cp of three different cell lysates is are calculated using these formulas. The r0 values of the cytosol from eukaryotic cells are 1.22 µm for HEK-293T and 1.46 µm for S. Cerevisiae, respectively, which are smaller than that (2.13 µm) from prokaryotic cells (E. coli). This may be due to the more complex components, with higher molecular weight but lower concentration in the eukaryotic cells. This method for quantifying the H2O-rich phase in a crowded solution helps to have a deeper understanding of the biochemical mechanism inside cells.

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来源期刊
Small Methods
Small Methods Materials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍: Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.
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