Figure 1.
Shape and network analysis of synthetic objects in 2D and 3D.
(A) The image (upper panel) shows the binary (black and white) 2D test image with synthetic objects. The lower panel shows the vectorized skeletons produced as part of the network analysis. Object shape and network properties were analyzed in two separate operations. (B) A 3D test z-stack was made by combining three copies of the 2D test image, flanked by an empty (black) image on the top and bottom. The upper panel shows 3D volumetric models made by iso-surface rendering (voxel size x = y = z = 1), and the lower panel display the 3D vectorized skeletons. Shape (C) and network (D) data of descriptors with a correspondent meaning in 2D and 3D are shown. The descriptor variables are further explained in Table 1.
Table 1.
Parameters of mitochondrial morphology.
Figure 2.
Image optimization for mitochondrial segmentation in 3D z-stacks.
(A) The images show all sections of an unprocessed (“RAW”) z-stack of a HUVECexpressing mitoGFP. (B) The histogram shows the cumulative pixel fluorescence intensity in the individual z-stack sections (RAW). The highest intensity column (section 8) is highlighted in white. (C) The effect of 3D blind deconvolution (“Deconv”) on the S/N ratio. Fluorescence intensity was measured across the indicated line (upper panel) before and after 2, 4, 6, 8 or 10 deconvolution cycles. The figure shows z-stack section 8 (upper panel) and the associated line intensity diagrams (middle panel) derived after 0 (“RAW”), 4 (“4*Deconv”) and 10 (“10*Deconv”) deconvolution cycles. The line intensity peaks and valleys identify mitochondrial objects/filaments and background, respectively. The intensities of five selected peak and background pixels (numbered 1–5, middle panel), before and after 2–10 deconvolution cycles are displayed in the column diagram (bottom panel). (D) Usefulness of Fast Fourier Transform (FFT) filtering following deconvolution. The images (upper panel) show z-stack section 8 after FFT filtering using frequency domain area of interest (AOI) radius setting 10, 5 and 2 (including Hi-Pass filtering). The intensity profiles across the same line (see above) are shown (middle panel), and the selected peak/background pixel intensities are plotted in the column diagram (bottom panel) for comparison with the non-FFT filtered image (n, identical to “10*Deconv” in (C)).
Figure 3.
Evaluation of FFT filtering for improving mitochondrial segmentation in z-stacks.
A sample z-stack was acquired from a HUVEC expressing mitoGFP (same as in Fig. 2). (A) The large image shows the highest intensity z-stack section (section 8) after 3D blind deconvolution, and a selected region of interest (ROI) is indicated. The smaller images are magnifications of the ROI before (“Deconv”) and after FFT filtering (including Hi-Pass filtering) with spectrum AOI radius set to 2, 5 or 10, as indicated (e.g. “FFT10”). Each FFT filtered ROI-version was binarized (BIN) by selecting the 20% brightest pixels (the corresponding grey tone threshold values are shown in parenthesis). (B) 3D volume models of the z-stack ROI were generated and analyzed before and after FFT processing (spectrum AOI radius = 2, 5 or 10). The result after FFT filtering with AOI radius = 5 (“FFT5”) is shown together with the non-FFT processed version (“Deconv”). Shape and network analysis was performed employing the same threshold values as in (A). (C) Quantitative data from shape and network analysis in (B). Descriptor variables are explained in Table 1.
Figure 4.
2D and semi-3D analysis of mitochondrial shape and network properties in HUVEC z-stack.
A sample z-stack was acquired from a HUVEC expressing mitoGFP, and a region of interest (ROI) was selected (identical to the sample z-stack and the ROI shown in Fig. 3). (A) The uppermost row shows the ROI sections from the unprocessed z-stack (“RAW”). Further, the ROI was analyzed after spatial filtering (“Spatially filtered”; blue panels), as previously established for 2D mitochondrial analysis [27], or after 3D blind deconvolution as described in the current article (“Deconvolution”; orange panels). 2D projections were made by averaging the three sections of highest intensity (“Avg7–9”), and by creating a maximum intensity composite (MIC) of the three highest intensity sections (“MIC7–9”) or the entire z-stack (“MICall”). These are shown in the right hand panels. Shape analysis (“SHAPE”; yellow panels) was performed after binarization and size filtering. The binarization threshold was fixed to include the 20% brightest pixels in the highest intensity section (section 8). For the 2D projections, the threshold was set to include the 35% brightest pixels in the Avg7–9 and MIC7–9 versions, and 40% for the MICall version. Network analysis (“NETWORK”; pink panels) was performed after skeletonization and vectorization, using the same intensity thresholds as for binarization. (B) Section intensity profiles of the unprocessed (“RAW”), spatially filtered and deconvolution processed z-stack ROIs, and the corresponding 2D projections. The resulting quantitative data of mitochondrial shape (C) and network (D) parameters are shown (see Table 1 for explanations).
Figure 5.
Threshold setting for mitochondrial segmentation in a HUVEC z-stack.
A sample z-stack was acquired from a HUVEC expressing mitoGFP, and a region of interest (ROI) was selected (identical to the sample z-stack and the ROI as shown in Fig. 3). (A) The images show maximum intensity composites (MIC) of the entire unprocessed sample z-stack (“RAW”) and a magnification of the ROI. The figure also displays the comparative strategy to evaluate effects of spatial filtering (“Spatial filtering”; blue panels) and deconvolution (“Deconvolution”; orange panels) for the purpose of 3D mitochondrial segmentation and analysis. (B) 3D models of the ROI were generated after spatial filtering, as previously established for 2D mitochondrial analysis [27], and after deconvolution as described in the current paper. The percentages reflect the segmentation thresholds (grey tone values) defining the 10%–35% (as indicated) brightest pixels in the highest intensity section (section 8). The smaller 2D images indicate the effects of threshold setting (MICs created from the processed z-stacks). 3D volume and network models are shown for three of the studied intensity thresholds (10%, 20% and 35%). (C) The diagrams show the quantified data from (B). Descriptor variables are explained in Table 1.
Figure 6.
Comparative 2D/3D mitochondrial analysis to detect effects of metabolic stress in HUVECs.
Mitochondrial morphology was studied in normal and metabolically stressed (250 nM rotenone, 3 days) HUVECs expressing mitoGFP. (A) The figure shows a schematic overview over the procedure established to analyze mitochondrial shape and network properties in 2D and 3D. The boxes represent image outcome, and the arrows indicate mathematical operations. “RAW”, unprocessed z-stack; “3D BD”, z-stack 3D blind deconvolution (10 cycles); “2D BIN”, binarized 2D image (intensity threshold); “2D Network”, skeleton based on the 2D image; “3D Surface”, iso-suface model (intensity threshold); “3D Network”, skeletonized 3D model. The intensity thresholds were determined to give the best reflection of the source image. (B) The images are maximum intensity composites (MICs) of the processed z-stacks from an untreated HUVEC (“Normal (CTR)”) and a ROT-treated stressed HUVEC (“Stressed (ROT)”). The z-stacks were processed by deconvolution and a contrast stretch (as described in the current article). Five different regions of interest (ROIs; numbered 1–5 in the images) were selected in each cell. (C) The histograms show the intensity profile of the two z-stacks. The highest intensity sections are shown as white columns. (D) 2D analysis was performed on the highest intensity frame, and the z-stack MIC. The panels show shape and network representations of one ROI selected from each cell. (E) 3D shape and network models were generated and analyzed from each of the ROIs in both cells. Here, the 3D models of one ROI in each cell type are shown.
Figure 7.
Shape and network analysis of normal vs. stressed HUVEC mitochondria.
The images from the study in Fig. 6 were analyzed with respect to 2/3D “SHAPE” (A) and “NETWORK” parameters (B). The positions of the different ROIs are shown in Fig. 6B. The data resulting from 2D analysis of the highest intensity frame (“2D single sect.”) and the z-stack MIC (“2D MIC”) where compared with the 3D data (“3D volume”). The descriptor variables are explained in Table 1. *p<0.05 compared to CTR, Student’s t-test, two-tailed.
Figure 8.
Integrative network/shape analysis of normal vs. stressed HUVEC mitochondria.
Based on the analysis performed in Fig. 6 and Fig. 7, integrative “NETWORK/SHAPE” indexes were calculated as described in Table 1.