Photobiomodulation therapy can change actin filaments of 3T3 mouse fibroblast

Abstract

The purpose of this study was to investigate the effects that photobiomodulation therapy might produce in cells, in particular, related to their structure. Thus, this paper presents the results of morphological changes in fibroblasts following low-intensity light illumination. Mouse fibroblasts were grown on glass coverslips on either 4 kPa or 16 kPa gels, to mimic normal tissue conditions. Cells were photo-irradiated with laser light at either 625 nm or 808 nm (total energies ranging from 34 to 47 J). Cells were fixed at 5 min, 1 h, or 24 h after photo-irradiation, stained for both actin filaments and the cell nucleus, and imaged by confocal microscopy. A non-light exposed group was also imaged. A detailed analysis of the images demonstrated that the total polymerized actin and number of actin filaments decrease, while the nucleus area increases in treated cells shortly after photo-irradiation, regardless of substrate and wavelength. This experiment indicated that photobiomodulation therapy could change the morphological properties of cells and affect their cytoskeleton. Further investigations are required to determine the specific mechanisms involved and how this phenomenon is related to the photobiomodulation therapy mechanisms of action.

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Funding

This study was funded by Coordination for the Improvement of Higher Education Personnel, CAPES (Proc. No. BEX 3481/14-0), and Brazilian National Council for Scientific and Technological Development, CNPq, Brazilian funding agencies. Additional support was provided by the Ontario Ministry of Health and Long-Term Care through operational funding of the Princess Margaret Cancer Centre.

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Correspondence to Ana Carolina de Magalhães.

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Appendix. Data analysis details

Appendix. Data analysis details

Raw data analysis

With the primary data extracted from the images, several quantitative studies were performed. At first, the number of cells in each image was determined, resulting in the histograms shown in Fig. 6 for the control and PBM-treated groups. The majority of images has between three and nine cells; however, there were some images with more than 13 cells.

Fig. 6
figure6

Histogram of the number of cells per image, for the control group (a) and photobiomodulation-treated group (b). Less than 10% of images have more than ten cells, for both groups

As the distributions of the cell density per image are similar, subsequent investigations considered only the different number of cells per image, and the four variables, CAr (cell area), NAr (nuclear area), NuF (number of filaments), and ToA (total actin), for further analysis. For the control group, the four parameters are shown as a function of cells per image in Fig. 7. The dependencies are approximately linear with cell number. However, a small deviation from the linearity is observed for all the investigated variables in the case of a high cell density. The same behaviour is also noted in the PBM group.

Fig. 7
figure7

Raw data for all variables, nuclear area NAr (a), cell area CAr (b), number of filaments NuF (c), and total actin ToA (d), for the control group. The large symbols represent the average for each number of cells. The behaviour of each of the variables with the number of cells is approximately linear. However, a small deviation from the linearity is perceived at high cell densities. The 837 images of the control groups (non-photobiomodulation) contributed to these data

A linear function Y(N) = a N (where Y is the analysed variable—NAr, CAr, NuF, or ToA; N is the number of cells in the image; and a is the slope) would be adequate only for images with low cell densities. However, in order to adjust for the deviation from linearity at high cell densities, a fit required a small quadratic term as shown in Eq. A.1, where γ was determined from the global relation between Y and N for all data. The resulting γ coefficients are small: 0.0095, 0.0180, 0.0088, and 0.0057, for NAr, CAr, NuF, and ToA, respectively.

$$ Y(N)=a\ N\ \left(1-\gamma N\right) $$
(A.1)

The uncertainty, σN, for each Y(N) variable (NAr, CAr, NuF, or ToA) was determined as a function of the image’s cell density. Considering the statistical variation in Y due to each cell as σ1, in an image with N cells, the uncertainty of Y(N) can be estimated by standard uncertainty propagation, leading to \( {\sigma}_N={\sigma}_1\ \sqrt{N} \), where σN is the uncertainty for that number of cells. In the fittings, the uncertainty in each variable for each cell, σ1, was found from the overall variance of the data around the fitted functions, assuming the hypothesis that the χ2 is equal to the degrees of freedom, or that \( {\chi}_R^2=1 \). This allows to estimate σ1 and the uncertainty of the fitted parameters with a large number of degrees of freedom (~ 1660).

p values of the statistical tests

For Tables 8, 9, 11, 13, and 15, one asterisk symbol indicates that the coefficients are different from zero, according to the Z test and p < 0.05, and two asterisks indicate p < 0.01. For Tables 10, 12, 14, and 16, one asterisk symbol indicates that the coefficients are different from each other, according to the Z test and p < 0.05, and two asterisks indicate p < 0.01.

Table 8 Results of the p values for the Z test, for the comparison of β with zero, for the global analysis
Table 9 Results of the p values for the Z test, for the comparison of β with zero, as a function of the wavelength
Table 10 Results of the p values for the Z test, for the comparison of the β coefficients between them, as a function of the wavelength
Table 11 Results of the p values for the Z test, for the comparison of β with zero, as a function of the gel stiffness
Table 12 Results of the p values for the Z test, for the comparison of the β coefficients between them, as a function of the gel stiffness
Table 13 Results of the p values for the Z test, for the comparison of β with zero, as a function of the time post-PBM treatment
Table 14 Results of the p values of the Z test, for the comparison of the β coefficients between them, as a function of the time post-PBM treatment
Table 15 Results of the p values for the Z test, for the comparison of β with zero, for the simultaneous influence of all parameters
Table 16 Results of the p values of the Z test, for the comparison of β of each time post-PBM treatment, between them, for the simultaneous influence of all parameters

Alternative graphic analysis of the PBM treatment effect

The same differences presented in the paper can also be qualitatively verified, with graphs of the relationship between pairs of variables, regardless of the cell number, as shown in the scatter plots of Fig. 8. Indeed, the highest difference between the control and PBM-treated groups appears when the variables plotted have variations in opposite directions, which means β positive for one variable and negative for the other. Figure 8a shows an example of this case with the number of filaments as a function of the nuclear area indicating that the PBM treatment leads to a reduction in the number of filaments in images with a similar nuclear area. On the other hand, if the plotted variables have variations in the same direction (β with the same signal), it is not possible to see differences between control and treated groups, as shown in Fig. 8b, for the total actin and number of filaments. This analysis method has the advantage to be insensitive to the hypotheses expressed in Eq. A.1. While this alternative method is unable to explain the cause of the differences or lack thereof, it is an indication of the former analysis’ robustness.

Fig. 8
figure8

Examples of graphs of the relationship between pairs of variables, black symbols represent the control samples and grey symbols the photobiomodulation-treated samples. Number of filaments NuF versus nuclear area NAr (a) is a case that has the β coefficients with different signals (NAr has a positive β and the NuF has a negative β), and total actin versus number of filaments (b) is a case that the β coefficients have the same signal for both the ToA and NuF (both have negative β coefficents). The lines connecting the points are a visual guide only. The error bars indicate the standard error of the mean (SEM)

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de Magalhães, A.C., Guimarães-Filho, Z., Yoshimura, E.M. et al. Photobiomodulation therapy can change actin filaments of 3T3 mouse fibroblast. Lasers Med Sci 35, 585–597 (2020). https://doi.org/10.1007/s10103-019-02852-y

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Keywords

  • Photobiomodulation therapy
  • Fibroblasts
  • Actin filaments
  • Low-level light therapy