Back

Single-cell mechanical analysis reveals viscoelastic similarities between normal and neoplastic brain cells

Onwudiwe, K.; Najera, J.; Holen, L.; Burchett, A. A.; Rodriguez, D.; Zarodniuk, M.; Siri, S.; Datta, M.

2023-09-24 bioengineering
10.1101/2023.09.23.559055 bioRxiv
Show abstract

Understanding cancer cell mechanics allows for the identification of novel disease mechanisms, diagnostic biomarkers, and targeted therapies. In this study, we utilized our previously established fluid shear stress assay to investigate and compare the viscoelastic properties of normal immortalized human astrocytes (IHAs) and invasive human glioblastoma (GBM) cells when subjected to physiological levels of shear stress that are present in the brain microenvironment. We used a parallel-flow microfluidic shear system and a camera-coupled optical microscope to expose single cells to fluid shear stress and monitor the resulting deformation in real-time, respectively. From the video-rate imaging, we fed cell deformation information from digital image correlation into a three-parameter generalized Maxwell model to quantify the nuclear and cytoplasmic viscoelastic properties of single cells. We further quantified actin cytoskeleton density and alignment in IHAs and GBM cells via immunofluorescence microscopy and image analysis techniques. Results from our study show that contrary to the behavior of many extracranial cells, normal and cancerous brain cells do not exhibit significant differences in their viscoelastic behavior. Moreover, we also found that the viscoelastic properties of the nucleus and cytoplasm as well as the actin cytoskeletal densities of both brain cell types are similar. Our work suggests that malignant GBM cells exhibit unique mechanical behaviors not seen in other cancer cell types. These results warrant future study to elucidate the distinct biophysical characteristics of the brain and reveal novel mechanical attributes of GBM and other primary brain tumors.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
Scientific Reports
3102 papers in training set
Top 7%
9.9%
2
APL Bioengineering
18 papers in training set
Top 0.1%
8.3%
3
Journal of Biomechanics
57 papers in training set
Top 0.1%
7.1%
4
PLOS ONE
4510 papers in training set
Top 29%
6.2%
5
Cellular and Molecular Bioengineering
21 papers in training set
Top 0.1%
4.8%
6
Biomechanics and Modeling in Mechanobiology
25 papers in training set
Top 0.2%
4.2%
7
Journal of The Royal Society Interface
189 papers in training set
Top 1%
3.5%
8
Annals of Biomedical Engineering
34 papers in training set
Top 0.3%
3.5%
9
Frontiers in Physics
20 papers in training set
Top 0.1%
3.0%
50% of probability mass above
10
Biophysical Journal
545 papers in training set
Top 2%
2.6%
11
Frontiers in Bioengineering and Biotechnology
88 papers in training set
Top 0.8%
2.6%
12
Journal of the Mechanical Behavior of Biomedical Materials
22 papers in training set
Top 0.1%
2.1%
13
Biotechnology and Bioengineering
49 papers in training set
Top 0.3%
2.0%
14
Advanced Science
249 papers in training set
Top 9%
2.0%
15
Acta Biomaterialia
85 papers in training set
Top 0.4%
2.0%
16
Physics of Fluids
13 papers in training set
Top 0.1%
1.8%
17
ACS Omega
90 papers in training set
Top 2%
1.5%
18
Soft Matter
50 papers in training set
Top 0.3%
1.3%
19
Bioengineering & Translational Medicine
21 papers in training set
Top 0.5%
1.3%
20
Computers in Biology and Medicine
120 papers in training set
Top 3%
1.3%
21
PLOS Computational Biology
1633 papers in training set
Top 20%
1.2%
22
Integrative Biology
13 papers in training set
Top 0.1%
1.2%
23
ACS Biomaterials Science & Engineering
37 papers in training set
Top 0.9%
0.9%
24
Nano Letters
63 papers in training set
Top 2%
0.9%
25
Bioengineering
24 papers in training set
Top 1%
0.7%
26
Biomaterials Science
21 papers in training set
Top 0.6%
0.7%
27
Lab on a Chip
88 papers in training set
Top 1%
0.7%
28
Advanced Healthcare Materials
71 papers in training set
Top 2%
0.7%
29
International Journal of Molecular Sciences
453 papers in training set
Top 17%
0.7%
30
International Journal for Numerical Methods in Biomedical Engineering
12 papers in training set
Top 0.3%
0.7%