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Displacement correlations between a single mesenchymal-like cell and its nucleus effectively link subcellular activities and motility in cell migration analysis

View Article: PubMed Central - PubMed

ABSTRACT

Cell migration is an essential process in organism development and physiological maintenance. Although current methods permit accurate comparisons of the effects of molecular manipulations and drug applications on cell motility, effects of alterations in subcellular activities on motility cannot be fully elucidated from those methods. Here, we develop a strategy termed cell-nuclear (CN) correlation to parameterize represented dynamic subcellular activities and to quantify their contributions in mesenchymal-like migration. Based on the biophysical meaning of the CN correlation, we propose a cell migration potential index (CMPI) to measure cell motility. When the effectiveness of CMPI was evaluated with respect to one of the most popular cell migration analysis methods, Persistent Random Walk, we found that the cell motility estimates among six cell lines used in this study were highly consistent between these two approaches. Further evaluations indicated that CMPI can be determined using a shorter time period and smaller cell sample size, and it possesses excellent reliability and applicability, even in the presence of a wide range of noise, as might be generated from individual imaging acquisition systems. The novel approach outlined here introduces a robust strategy through an analysis of subcellular locomotion activities for single cell migration assessment.

No MeSH data available.


One to three-minute time intervals can describe the robust CMPI utilizing various image acquisition systems.(a)CMPI of 6 different cell types calculated at 1, 2, 3, 5, or 10 min time intervals are compared against the corresponding diffusion coefficients, μ. (b) Random Gaussian noise-implemented CMPI values are plotted against the standard deviation of the Gaussian noise (ranging from 0–1.0 μm). The initial data were obtained from NIH 3T3 fibroblasts at 1-min time intervals. Inset: The distribution of system noise was determined from the centroid positions of a fixed cell. (c) The fitting results (R2) between noise-implemented CMPI and corresponding diffusion coefficients, μ, of 6 cell types were plotted against the standard deviation of introduced noise (ranging from 0–1.0 μm). CMPI were calculated using data extracted at 1-, 2-, 3-, 5-, and 10-min intervals from 1-hour movies of the 6 different cell types.
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f6: One to three-minute time intervals can describe the robust CMPI utilizing various image acquisition systems.(a)CMPI of 6 different cell types calculated at 1, 2, 3, 5, or 10 min time intervals are compared against the corresponding diffusion coefficients, μ. (b) Random Gaussian noise-implemented CMPI values are plotted against the standard deviation of the Gaussian noise (ranging from 0–1.0 μm). The initial data were obtained from NIH 3T3 fibroblasts at 1-min time intervals. Inset: The distribution of system noise was determined from the centroid positions of a fixed cell. (c) The fitting results (R2) between noise-implemented CMPI and corresponding diffusion coefficients, μ, of 6 cell types were plotted against the standard deviation of introduced noise (ranging from 0–1.0 μm). CMPI were calculated using data extracted at 1-, 2-, 3-, 5-, and 10-min intervals from 1-hour movies of the 6 different cell types.

Mentions: To determine the optimal time intervals at which to record the CMPI, the CMPI of the 6 mesenchymal-like cell types were calculated at different time intervals, τ = 1, 2, 3, 5, and 10 min, from 50 cells during a one-hour monitoring time. The resultant CMPI were linearly fitted to the corresponding PRW-derived diffusion coefficients (τ = 1 min) to determine the time interval at which the derived CMPI most closely correlated with the motility trends of different cells acquired via the PRW model. The results showed that the most consistent trends between the CMPI and their corresponding diffusion coefficients (with an R2 value ≥ 0.95) occurred at time intervals of 1 to 3 minutes (Fig. 6a).


Displacement correlations between a single mesenchymal-like cell and its nucleus effectively link subcellular activities and motility in cell migration analysis
One to three-minute time intervals can describe the robust CMPI utilizing various image acquisition systems.(a)CMPI of 6 different cell types calculated at 1, 2, 3, 5, or 10 min time intervals are compared against the corresponding diffusion coefficients, μ. (b) Random Gaussian noise-implemented CMPI values are plotted against the standard deviation of the Gaussian noise (ranging from 0–1.0 μm). The initial data were obtained from NIH 3T3 fibroblasts at 1-min time intervals. Inset: The distribution of system noise was determined from the centroid positions of a fixed cell. (c) The fitting results (R2) between noise-implemented CMPI and corresponding diffusion coefficients, μ, of 6 cell types were plotted against the standard deviation of introduced noise (ranging from 0–1.0 μm). CMPI were calculated using data extracted at 1-, 2-, 3-, 5-, and 10-min intervals from 1-hour movies of the 6 different cell types.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037420&req=5

f6: One to three-minute time intervals can describe the robust CMPI utilizing various image acquisition systems.(a)CMPI of 6 different cell types calculated at 1, 2, 3, 5, or 10 min time intervals are compared against the corresponding diffusion coefficients, μ. (b) Random Gaussian noise-implemented CMPI values are plotted against the standard deviation of the Gaussian noise (ranging from 0–1.0 μm). The initial data were obtained from NIH 3T3 fibroblasts at 1-min time intervals. Inset: The distribution of system noise was determined from the centroid positions of a fixed cell. (c) The fitting results (R2) between noise-implemented CMPI and corresponding diffusion coefficients, μ, of 6 cell types were plotted against the standard deviation of introduced noise (ranging from 0–1.0 μm). CMPI were calculated using data extracted at 1-, 2-, 3-, 5-, and 10-min intervals from 1-hour movies of the 6 different cell types.
Mentions: To determine the optimal time intervals at which to record the CMPI, the CMPI of the 6 mesenchymal-like cell types were calculated at different time intervals, τ = 1, 2, 3, 5, and 10 min, from 50 cells during a one-hour monitoring time. The resultant CMPI were linearly fitted to the corresponding PRW-derived diffusion coefficients (τ = 1 min) to determine the time interval at which the derived CMPI most closely correlated with the motility trends of different cells acquired via the PRW model. The results showed that the most consistent trends between the CMPI and their corresponding diffusion coefficients (with an R2 value ≥ 0.95) occurred at time intervals of 1 to 3 minutes (Fig. 6a).

View Article: PubMed Central - PubMed

ABSTRACT

Cell migration is an essential process in organism development and physiological maintenance. Although current methods permit accurate comparisons of the effects of molecular manipulations and drug applications on cell motility, effects of alterations in subcellular activities on motility cannot be fully elucidated from those methods. Here, we develop a strategy termed cell-nuclear (CN) correlation to parameterize represented dynamic subcellular activities and to quantify their contributions in mesenchymal-like migration. Based on the biophysical meaning of the CN correlation, we propose a cell migration potential index (CMPI) to measure cell motility. When the effectiveness of CMPI was evaluated with respect to one of the most popular cell migration analysis methods, Persistent Random Walk, we found that the cell motility estimates among six cell lines used in this study were highly consistent between these two approaches. Further evaluations indicated that CMPI can be determined using a shorter time period and smaller cell sample size, and it possesses excellent reliability and applicability, even in the presence of a wide range of noise, as might be generated from individual imaging acquisition systems. The novel approach outlined here introduces a robust strategy through an analysis of subcellular locomotion activities for single cell migration assessment.

No MeSH data available.