3.1 3DSIM of MDVs and dynamic mitochondrial tubules

The optimized acquisition conditions for high-speed and lowest possible laser illumination intensity—while still achieving good 3DSIM reconstruction—enabled capturing videos of the delicate and fast-moving OMP25-TM positive MDVs in H9c2 cells at volumetric super-resolution for up to 100 time-points. A part of such a time-lapse is shown in Figure 1, with the large top panel showing an overview of the volumetric super-resolution image. The full time-lapse (maximum intensity z-projected 3DSIM images) is provided in Movie S1. The highest volumetric imaging speed achieved was 1.5 s for image volumes of  μm3. Higher imaging speeds could be achieved for even smaller volumes, but then greatly reducing the likelihood of capturing interesting biological events in a subcellular context.

In addition to an abundance of MDVs, several rapidly extending and retracting tubules derived from the mitochondrial outer membrane were observed in cells cultivated under both GLU and GAL conditions. An example of a rapidly extending and retracting tubule over a time span of about a minute is shown and indicated by arrows in the lower part of Figure 1. At its longest, this tubule extended to about 5 μm away from the mitochondrion from which it formed, and with a width of about 120 nm (FWHM of the tubule in the maxPrj 3DSIM image), corresponding to the lateral SIM resolution (GFP channel). The actual width of the tubule could be much narrower, but that is not measurable using this imaging technique. For the retracting tubule pointed out by the arrow in magenta color (106.5–108 s), the retraction appears to be occurring at a speed of several micrometers per second.

Such dynamic mitochondrial tubulation has previously been detected by SIM imaging in normal rat kidney cells (NRK) as well as in several other cell lines [14]. Recently, dynamic mitochondrial tubules were studied in COS-7 cell-line where they were shown to mediate transportation of mitochondrial DNA between different mitochondria [15]. However, mitochondrial tubulation has previously not been demonstrated in H9c2 cardiomyoblasts. Interestingly, mitochondrial tubules have similarities to nanotunnels formed between mitochondria in cardiomyocytes [16]. In Movies S2 and (magnified view) S3, some of the tubules fuse with other mitochondria to form a temporary membrane bridge between two different mitochondria. A tubule can then subsequently detach from the originating mitochondria and remain attached to the recipient mitochondria. Interestingly, also the budding of a fragment was detected from a retracting tubule. A magnified view of this phenomenon is provided in Movie S4. Our measurements of the speed of these tubules are within the range of velocities measured for the tubules in COS-7 cells [17].

Next, we present a simple but effective semi-automatic analysis pipeline to quantify and compare the number of MDVs in cultured H9c2 cells under different conditions.

3.2 Quantification of vesicles in live and fixed cells

Two different strategies were employed to quantify and compare the number of MDVs between H9c2 cells cultivated under either GLU or GAL conditions: Either live-cell imaging using small imaging volumes (840.5 μm3) or fixed-cell conditions using six times larger volumes (5043 μm3). The two different strategies entail different advantages and limitations. For the live-cell imaging strategy, we ensure that no vesicles are lost or destroyed during fixation. On the other hand, due to the unwanted effect of motion artifacts in the SIM images, only small volumes were acquired, reducing throughput for statistical inferences. For the fixed-cell case, we risk no motion-related artifacts such that larger volumes and better statistics (per imaging time) can be inferred. Also, the nature of fixed samples allows for an easier comparison of different conditions without risking effects from possible time delays between and during imaging experiments. On the downside, the process of fixation can alter the biology we wish to study, and it can be hard to assess how this compares to the live-cell condition.

Two different protocols for chemical preservation of the cells were tested: either fixation using 4% PFA in PBS or 4% PFA + 0.2% GA in PBS. The results are shown in Figure 2, where a comparison with mitochondria in living cells is used as reference (Figure 1A). As shown in Figure 2B, using PFA only for fixation led to unsatisfactory preservation of the mitochondrial morphology at SIM resolution. Trying to segment MDVs from these images led to a high portion of false positives, where fragmented mitochondria—seemingly part of a continuous structure before chemical fixation—would be counted as many additional MDVs not actually present. At conventional optical resolution (like the deconvolved image example in Figure 2C), this effect is much harder to notice and may be unimportant for some types of quantification of mitochondria. However, the analysis of MDVs—which are smaller than the resolution limit—requires the use of super-resolution techniques like 3DSIM for accurate quantification, together with a more potent fixation method than PFA alone. Preservation using both PFA and GA (Figure 2D) gave satisfactory results, resembling the live-cell imaging results even at SIM resolution (compare panels A and D of Figure 2) and showing MDVs unlikely to appear from fixation-induced mitochondrial fragmentation. A different challenge concerning GA fixed samples is the added background signal from GA autofluorescence. We noted additional artifacts in the SIM images likely resulting from this origin.

Comparison of live and fixed mitochondrial morphologies. The regions indicated in the upper panels A–D are displayed magnified below. Fixation using paraformaldehyde (PFA) only (panels B and C) causes fragmentation and disruption of the mitochondrial network as observed for living samples (panel A) hindering reliable MDV quantification. This effect is difficult to notice at conventional resolution (deconvolved image, panel C). Applying a fixation solution additionally containing glutaraldehyde (GA) preserves the live mitochondrial morphology much better and can be used for quantifying MDVs in fixed samples (panel D). The images are maxPrj 3DSIM images of H9c2 cells from the normal cultivation condition (GLU). Scale bars: 1 μm

To overcome the challenges of MDV quantification in the presence of both SIM reconstruction artifacts (often of similar size and intensity level as the MDVs) and comparatively large and bright mitochondria, we employed TWS for MDV segmentation prior to quantification.

The pixelwise TWS classifier was trained using supervised learning on two classes: Background and Mitochondria. The annotated regions used for training the classifier together with the rest of the particle quantification workflow are shown in Figure 3.

Segmentation of mitochondria for MDV quantification was done in ImageJ/Fiji using TWS. First, a maximum intensity z-projected (maxPrj) 3DSIM image was annotated with regions from the two classes background (including SIM artifacts) and mitochondria. Then the classifier was trained and applied to the remaining data of both live and fixed cells from both the GAL and GLU growth conditions. A probability map was chosen as output and a threshold of 0.80 was chosen as a binary mask for the mitochondria class. This was chosen after visual inspection and comparison with the SIM images to yield a satisfactory segmentation. The MDVs were measured from the binary images using Analyze Particles in ImageJ, excluding particles with a circularity <0.7 or not falling within the area range 0.0032 to 0.30 μm2

To reduce the impact of SIM artifacts, lower axial resolution and to facilitate the assessment of classifier performance, only maxPrj 3DSIM images were used both for training and MDV quantification. The main loss of information from this simplification is that the MDVs located directly above or below the mitochondria are not counted. Since the images are 10–20 times larger laterally than axially, and the volume thickness only constitute about five resolution units laterally (for 2 μm stacks) versus 170 resolution units in either lateral dimension (for 20.2 μm images), we considered this a valid simplification only improving the accuracy of the particular analysis.

The training conducted on a fixed GLU cell (with visible SIM artifacts relegated to the background class) showed good transfer learning to both GAL and live-cell images (of smaller size). The manually assessed segmentation results were found satisfactory for all except two cases (discarded from the MDV quantification) of the fixed-cell images that appeared particularly challenging due to two reasons: Two different cells with distinct mitochondria morphology and brightness in the same image. Dominating SIM reconstruction artifacts obscuring the actual cellular details. Both of these fail cases are displayed in Figure S1.

The MDV quantification results are summarized in Table 1. Both the live- and fixed-cell imaging strategies yielded a higher vesicle number for cells under the GAL condition than under the GLU condition.

TABLE 1. The number of MDVs measured per image, area and volume for GLU and GAL samples under both fixed and live imaging conditions Sample condition GLU GAL Live Fixed Live Fixed MDV/image 60 ± 26 127 ± 48 70 ± 31 223 ± 59 MDV/μm2 0.14 0.076 0.17 0.13 MDV/μm3 0.071 0.025 0.083 0.044

Quantification of MDVs in H9c2 cells has previously been conducted using confocal images of fixed cells with immunofluorescent staining of TOMM20 and PDH mitochondria markers after galactose adaptation [11] or during normal (glucose) growth conditions [18]. The numbers of MDVs per cell obtained in these studies (10–30 MDVs per cell) are significantly lower than the numbers obtained here (the numbers per image cover less than one cell). Furthermore, a comparison of the number of MDVs in H9c2 cells under these different growth conditions was not investigated earlier.

The high standard deviations in Table 1 indicate that the particular state of individual cells also has a large impact on the number of MDVs, beyond growth conditions. Although the fixed images are of six times larger volumes (four times larger area), the number of MDVs was only 2.1 and 3.2 times higher (for GAL and GLU, respectively). The reason for this is can be a combination of several factors: The inclusion of sparser parts of the samples with few or no MDVs, that is, especially the nuclear region and volumes completely outside of the cells. The effect of z-projection is larger in the case of fixed samples as larger z-stacks were used for these. In effect, a larger portion of the vesicles could be invisible straight above or below brighter mitochondria. Vesicles could be lost or destroyed during fixation and the subsequent sample washing steps. Especially when considering volumetric cell densities of MDVs, the smaller image volumes (with carefully chosen volumetric boundaries) are likely to provide a more accurate estimate of the actual vesicle density inside cells, as the non-cell containing sample parts can be more accurately excluded. The cell boundaries could alternatively be determined with help of a membrane marker. However, due to the added cellular stress, experimental and analytical complexity associated with this membrane labeling, we instead used the distribution of mitochondria as a rough guide for cell boundaries.