This protocol provides a Fiji-based, user-friendly methodology along with straightforward instructions explaining how to reliably analyze actomyosin behavior in individual cells and curved epithelial tissues. No programming skills are required to follow the tutorial; all steps are performed in a semi-interactive manner using the graphical user interface of Fiji and associated plugins.
Drosophila immature eggs are called egg chambers, and their structure resembles primitive organs that undergo morphological changes from a round to an ellipsoid shape during development. This developmental process is called oogenesis and is crucial to generating functional mature eggs to secure the next fly generation. For these reasons, egg chambers have served as an ideal and relevant model to understand animal organ development.
Several in vitro culturing protocols have been developed, but there are several disadvantages to these protocols. One involves the application of various covers that exert an artificial pressure on the imaged egg chambers in order to immobilize them and to increase the imaged acquisition plane of the circumferential surface of the analyzed egg chambers. Such an approach may negatively influence the behavior of the thin actomyosin machinery that generates the power to rotate egg chambers around their longer axis.
Thus, to overcome this limitation, we culture Drosophila egg chambers freely in the media in order to reliably analyze actomyosin machinery along the circumference of egg chambers. In the first part of the protocol, we provide a manual detailing how to analyze the actomyosin machinery in a limited acquisition plane at the local cellular scale (up to 15 cells). In the second part of the protocol, we provide users with a new Fiji-based plugin that allows the simple extraction of a defined thin layer of the egg chambers’ circumferential surface. The following protocol then describes how to analyze actomyosin signals at the tissue scale (>50 cells). Finally, we pinpoint the limitations of these approaches at both the local cellular and tissue scales and discuss its potential future development and possible applications.
The continual development of novel imaging and software technologies with applications in the life sciences has provided an enormous impact on understanding the basic principles of life. One of the main challenges is the reliable visualization of developmental processes in combination with their live imaging in various tissues. Tissues are parts of organs and bodies and, as such, the majority are not easily accessible for imaging. Therefore, protocols that allow their dissection and in vitro culturing have been developed in order to visualize biological events that sufficiently reflect the in vivo situation within a living body.
Over the past decades, the culturing and live imaging of Drosophila egg chambers, acinar-like structures resembling primitive organs, has contributed immensely to the understanding of the basic principles of primitive organ development1,2,3. Currently, there are several culturing protocols available, and their usage depends on acquisition time, cell type to be imaged, and their accessibility (e.g., inner germline vs. outer somatic line)4.
A common feature in all these culturing protocols is the need for the immobilization of analyzed egg chambers that display a high contractile activity in liquid media. The contractile activity of egg chambers is caused mainly by the muscle sheet that covers a long string of connected egg chambers5,6,7. Therefore, to achieve proper immobilization of young egg chambers, various approaches have been developed, involving covering egg chambers with coverslips6,8,9 or flexible blankets4,10 or embedding them in low-melting-point agarose3,11. These approaches are popular as they also allow the imaging of a larger visual plane due to the subtle flattening of the circumferential surface of the egg chambers.
However, recently, it has been shown that young egg chambers (stage 1-8) rotate around their anterior-posterior axis6 and that this tissue motion is powered by a fine actomyosin network close to the circumferential surface of these young egg chambers12. Therefore, artificial alteration of the cellular surface caused by a subtle flattening of this tissue may have a negative impact on the behavior of the force-generating actomyosin machinery. The counterpoint is that if the egg chamber tissue is not flattened, microscopic imaging of proteins at the circumferential surface of egg chambers becomes even more limited by the decreased size of the acquisition plane.
Therefore, we have combined protocols from Prasad et al.9 and the lab of Celeste Berg4,10 and further modified them so that no coverslip/flexible blanket/agarose is used in the developed method. Drosophila egg chambers are freely cultured in media and the protocol presented here applies only inverted microscopy. There are two parts to the protocol. The first part is focused on the analysis of actomyosin signals at the local cellular scale (up to 15 cells) within egg chambers. In the second part, we focus on overcoming the limitations associated with a small acquisition plane caused by the free culturing of egg chambers. In this regard, we have developed a novel Fiji-based computational method with a semi-interactive graphical user interface that selectively extracts and unfolds defined layers of a circumferential tissue surface. This is followed by a protocol that describes how to analyze actomyosin at the tissue scale (i.e., >50 cells). As the selective extraction of a defined thin layer of curved epithelial tissues has not been easily possible using a classical z-stack projection (Figure 1), this easy-to-use method serves as an important prerequisite to comprehensively understand the behavior of a thin (<1 µm) actomyosin network at the tissue scale in Drosophila egg chambers.
In addition, to facilitate the protocol, we provide example time-lapse movies (TLMs) and sample files of fluorescently tagged nonmuscle conventional Myosin II behavior (see Supplementary File 3). Myosin II is a motor protein and represents the active contractile part of the actomyosin machinery. In order to image Myosin II, we use Drosophila transgenic lines that contain a modified regulatory light chain of Myosin II called MRLC::GFP (see Table of Materials for details)12,13. In order to visualize cell membranes, the protocol is based on commercial dyes (see Table of Materials). This protocol is suitable not only for the analysis of small subcellular MRLC::GFP signals12 but also for any similar-sized subcellular particles around ±300 µM, such as those observed with Life-Act::GFP12,14.
Although both these protocols are presented using in vitro cultured Drosophila egg chambers, the acquisition of actomyosin signals can also be performed using other tissues upon the optimization of the culturing media and depending on the availability of either fluorescently tagged proteins with corresponding commercial dyes or, for example, mRNA microinjections to obtain transient gene expression profiles. Similarly, the Fiji-based protocol for the extraction of a thin layer from a circumferential surface can be applied more generally to ellipsoid and organ-like tissues.
NOTE: The following protocol provides instructions on how to analyze actomyosin at the local cellular and the tissue scale in Drosophila egg chambers. The local-scale approach allows users to analyze detailed actomyosin behavior in up to 15 cells per egg chamber and requires the acquisition of TLMs for a short period of time (5–10 min) by using high-speed imaging and an inverted confocal microscope. In contrast, the tissue scale provides users with actomyosin information in 50–100 cells and requires the acquisition of TLMs for a long period of time (≥30 min) by using low-speed imaging and an inverted spinning disc microscope (see Figure 2 and recommended parameters at each scale in Table 1). The decision at which scale to analyze actomyosin signals entirely depends on the user’s scientific question. Accompanied test TLMs should help to make this decision.
1. Local Cellular Scale (LCS)
NOTE: To dissect and image in vitro cultured Drosophila egg chambers, follow the protocol described in Supplementary File 1. To analyze acquired TLMs, continue with the following protocol. Links to accompanying test files of TLMs are provided in the Supplementary File 3.
2. Tissue Scale
NOTE: To dissect and image in vitro cultured Drosophila egg chambers, follow the protocol in Supplementary File 2. To analyze acquired TLMs, continue with the following protocol below. Accompanied test files of TLMs are placed in the Supplementary File 3.
This protocol enables scientists to investigate the behavior of actomyosin networks in epithelial tissues. This is only possible when a detailed analysis of actomyosin behavior at the local cellular scale (a few cells) is combined with a similar analysis at the tissue scale (many cells). However, epithelial tissues are often curved and the extraction of a thin layer of these tissues was previously not easily possible, as shown in Drosophila egg chambers (Figure 1). The protocol presented here provides users with simple instructions describing how to analyze actomyosin behavior at both these scales (Figure 2).
The first part of this protocol focuses on instructions regarding the analysis of actomyosin pulses using the Surface manager plugin (Figure 3). It also describes how to manually analyze individual actomyosin behavior at the local cellular scale (Figure 4). The second part of this protocol explains how to extract a thin layer of epithelial tissue using the Ellipsoid Surface Projection plugin (Figure 5 and Figure 6). Only then is it possible to analyze actomyosin pulses at the tissue scale using the Surface manager plugin (Figure 7). Representative results and a comparison of actomyosin behavior in rotating control and static fat2 mutant Drosophila egg chambers at the local cellular and tissue scale is shown in Figure 8 and in corresponding Movie 1, Movie 2, Movie 3, Movie 4, Movie 5, and Movie 6 (for details, see Figure 8).
Figure 1: Selective extraction of a thin layer of a curved tissue surface. (A) In order to obtain sufficient information regarding actomyosin networks in circumferentially curved epithelia, it is necessary to acquire a deep z-stack (pink) over the majority of a visible tissue as shown for the curved follicle epithelium (grey) of Drosophila egg chambers. (A’) However, a simple projection of such a z-stack leads to the mixing of whole actomyosin networks in follicle cells. Myosin II visualized with MRLC::GFP (green) shows the strongly expressing inner (apical) region of follicle cells in such a z-projection and almost no information from the basal (outer) side, where Myosin II displays a strong planar cell polarity phenotype, but its signal intensity is low12. To avoid such mixing as shown in panel A’, we have developed a user-friendly, Fiji-based plugin called Ellipsoid Surface Projection in BigDataViewer18 that allows (B) the selective extraction of a defined, thin layer (pink) of actomyosin from a curved epithelium (B’) as shown for Myosin II (green) in a selected extraction of the basal (outer) side of the follicle epithelium. Compare the difference in signal of Myosin II (MRLC::GFP) in panels A’ and B’. Note the planar polarized pattern of Myosin II in panel B’. The cell outlines are in red. The anterior side is on the left. Scale bar = 50 µm. Please click here to view a larger version of this figure.
Figure 2: Planar polarized actomyosin network at the local cellular and tissue scales. The imaging of actomyosin at different scales allows the analysis of different numbers of epithelial cells, namely (A) up to 15 cells at the local cellular scale and (B) 50-100 cells at the tissue scale in the follicle epithelium of cultured Drosophila egg chambers. Nonmuscle Myosin II is visualized with MRLC::GFP (green) and the genotype of the used transgenic line is specified in the Table of Materials. The cell outlines are in magenta. The anterior side is on the left. Scale bars = 5 µm (A); 50 µm (B). Please click here to view a larger version of this figure.
Figure 3: An example of the analysis of Myosin II pulses in Surface manager at the local cellular scale. (A) A particle stack is loaded into Surface manager. Note that all identified cells in the TLM appear in the Surface manager window. It is important to delete all unwanted or incomplete cells throughout a TLM. (B) Statistics obtained on Myosin II (MRLC::GFP) for selected cells are shown in the window called Average grey value Slice by Slice in Surface manager. MRLC::GFP is shown in green whilst cellular membranes are in red. The anterior side is on the left. Please click here to view a larger version of this figure.
Figure 4: An example of the manual analysis of individual Myosin II signals at the local cellular scale. (A) A selected 30 s-long submovie is placed next to (B) its time projection. Note that upon the time projection, Myosin II (MRLC::GFP) shows longer trajectory lines (see panel B) within individual cells than in an individual time frame (see panel A). Individual lines in cells should be analyzed for their angular direction relative to the anterior-posterior axis of the egg chambers. (C) Note that Fiji does not measure 0°–360° but only 0°–180° for signals pointing up and 0°–179° for signals pointing down. Be aware that 0° is atypically placed on the right of an analyzed image. (D) Based on this information, lines that move with (up in pink) or against (down in yellow) the direction of the epithelial rotation in a particular egg chamber should be binned to identify whether symmetry breaking of analyzed signals is present within a cell and then for multiple cells in the analyzed tissue. MRLC::GFP is shown in green whilst cellular membranes are in red. The anterior side is on the left. Please click here to view a larger version of this figure.
Figure 5: An example of generating an ellipsoid fit at the tissue scale. In order to fit an ellipsoid onto an egg chamber using the Ellipsoid Surface Projection plugin in BigDataViewer, it is required to define (A) a bounding box that includes the majority of the scanned tissue. Next, it is important (B) to identify signal dots, which are a prerequisite for an optimal ellipsoid fit generation. (C) When the ellipsoid fit is not optimal and does not nicely surround an egg chamber, this results in (D) poor tissue layer extraction. See the extraction and later projection results in Figure 6. Please click here to view a larger version of this figure.
Figure 6: Comparison of an incorrect and optimal ellipsoid fit and corresponding surface projections. (A) When the ellipsoid is not fitted properly, (B) the final surface projection will not provide complete signal data in the thin layer of the analyzed tissue. (C) However, when fitted optimally, it guarantees an equal signal detection of a thin layer in the tissue. (D) Note that the optimal surface projection contains several thin layers as a surface-projected z-stack over time, which can be separated subsequently as shown in Figure 8. Myosin II signals (MRLC::GFP, white) and membrane signals (white) are initially merged before the projection (panels A and C), but upon the surface projection itself, these channels are separated (see projections of Myosin II in panels B and D). Please click here to view a larger version of this figure.
Figure 7: An example of the analysis of Myosin II pulses in Surface manager at the tissue scale. (A) A particle stack is loaded into Surface manager. Note that all identified cells in the TLM appear in the Surface manager window. It is important to delete all unwanted or incomplete cells throughout a TLM. (B) Statistics obtained on Myosin II (MRLC::GFP) pulses (mean or median) for selected cells over time are shown in the window called Average grey value Slice by Slice in Surface manager. Note that stronger Myosin II dots may influence the final measure of intrinsic Myosin II intensity. This results from the issue that these Myosin II dots may appear to migrate beyond the cell outline where there is an incorrect cell outline definition in the generated cell mask. MRLC::GFP is shown in green whilst cellular membranes are in red. The anterior side is on the top. Please click here to view a larger version of this figure.
Figure 8: Representative results of a Myosin II network in the Drosophila follicle epithelium. Representative examples of dynamic Myosin II (MRLC::GFP) behavior (A and B) at the local cellular scale and (C–F) at the tissue scale for control and fat2 mutant Drosophila egg chambers. See Table of Materials for detailed information on used genotypes. Note that MRLC::GFP signals (green) move perpendicular to the anterior-posterior (AP) axis of control egg chambers. This polarity is lost in fat2 mutant egg chambers and leads to anisotropic Myosin II pulses/oscillations12. Upon manual analysis of small MRLC::GFP signals (~300 µm) and the quantification of their angular directional movement as described in the protocol, symmetry breaking of MRLC::GFP signals can be observed with a preference against the direction of the epithelial rotation in an analyzed egg chamber12 (Figure 4). Corresponding movies are: panel A = Movie 1, panel B = Movie 2, panel C = Movie 3, panel D = Movie 4, panel E = Movie 5, and panel F = Movie 6. Cell outlines are shown in magenta. The anterior side is on the left. Scale bars = 5 µm (A and B) and 50 µm (C–F). Please click here to view a larger version of this figure.
Recommended parameters for short-time high-speed imaging at the local cellular scale: | |||
i. | Tissue of interest freely placed in the culturing medium (i.e. no cover slips, flexible blankets, etc.) | ||
ii. | 63x water objective with numerical aperture >= 1.3 | ||
iii. | Inverted confocal microscope | ||
iv. | Single plane | ||
v. | 6-12 s time intervals | ||
vi. | 5-10 mins TLMs | ||
vii. | Data storage requirement per TLM up to 100MB | ||
Recommended parameters for long-time low-speed imaging at the tissue scale: | |||
i. | Tissue of interest freely placed in the culturing medium (i.e. no cover slips, flexible blankets, etc.) | ||
ii. | 40x water objectives and numerical aperture >= 1.3 | ||
iii. | Inverted spinning disc microscope | ||
iv. | z-stacks to acquire ca. half of an egg chamber | ||
v. | 60 s time intervals | ||
vi. | At least 30 mins TLMs | ||
vii. | Data storage requirement ca. 1GB |
Table 1: Recommended imaging parameters.
Movie 1: Representative dynamic behavior of Myosin II (MRLC::GFP) at the cellular scale in a control Drosophila egg chamber. Note that MRLC::GFP (green) prefers to move perpendicular to the AP axis of the egg chambers. Cell outlines are in magenta. Basal view. The anterior side is on the left. Scale bar = 5 µm. Please click here to view this video. Right-click to download.
Movie 2: Representative dynamic behavior of Myosin II (MRLC::GFP) at the cellular scale in a fat2 mutant Drosophila egg chamber. Note that MRLC::GFP pulses (green) and is no longer planar aligned perpendicular to the AP axis of static egg chambers. Cell outlines are in magenta. Basal view. The anterior side is on the left. Scale bar = 5 µm. Please click here to view this video. Right-click to download.
Movie 3: Representative dynamic behavior of basal Myosin II (MRLC::GFP) at the tissue scale in a control Drosophila egg chamber. Note that only a thin basal (outer) MRLC::GFP layer is extracted from almost half of the follicle epithelium of an egg chamber. MRLC::GFP is in green and cell outlines are in magenta. Notice the difference in the level of detail of the obtained MRLC::GFP signal behavior here (representing the tissue scale) as compared to Movie 1 (representing the cellular scale). The anterior side is on the left. Scale bar = 50 µm. Please click here to view this video. Right-click to download.
Movie 4: Representative dynamic behavior of basal Myosin II (MRLC::GFP) at the tissue scale in a fat2 mutant Drosophila egg chamber. Note that MRLC::GFP (green) pulses strongly at the basal side of almost half of the follicle epithelium of a fat2 mutant egg chamber and fails to generate the synchronized force required to promote epithelial rotation. Cell outlines are in magenta. The anterior side is on the left. Scale bar = 50 µm. Please click here to view this video. Right-click to download.
Movie 5: Representative dynamic behavior of apical Myosin II (MRLC::GFP) at the tissue scale in a control Drosophila egg chamber. Only a thin MRLC::GFP layer is extracted from the apical (inner) side of almost half of the follicle epithelium of an egg chamber. Note that MRLC::GFP (in green) shows different dynamic behavior here at the apical side as compared to the basal side of the follicle epithelium (as shown in Movie 3). Cell outlines are in magenta. The anterior side is on the left. Scale bar = 50 µm. Please click here to view this video. Right-click to download.
Movie 6: Representative dynamic behavior of apical Myosin II (MRLC::GFP) at the tissue scale in a fat2 mutant Drosophila egg chamber. Altered dynamic behavior of MRLC::GFP (green) extracted from a thin apical region of almost half of the follicle epithelium of a static fat2 mutant egg chamber. Cell outlines are in magenta. The anterior side is on the left. Scale bar = 50 µm. Please click here to view this video. Right-click to download.
Critical steps and troubleshooting for the dissection and culturing of egg chambers
If too many flies are placed into a small vial, the fly food can turn muddy after 2–3 days due to extensive amounts of feeding larvae and adult flies getting trapped in the fly food. In such a case, flip the rest of these flies into a new vial with fresh food and downsize their number. In particular, exclude females that were stuck in the food.
The Schneider mix (SM) should be prepared in advance and can be stored at 4 °C for ~14 days. Be aware that an older mix may contain crystals that can damage the surface of egg chambers. Always mix the SM with freshly added insulin and allow it sufficient time to reach room temperature. This protects egg chambers against cold shock, which can have a negative impact on the growth of microtubules and the planar alignment of the cytoskeleton (as seen in the developing Drosophila wing19).
Egg chambers and selected ovarioles are very fragile and, due to their small size, may float in the SMI (SM with insulin). It is recommended to let them spontaneously sink in the SMI. They also often stick to the dissection forceps/cactus tool. In such a case, let them release themselves from dissection devices by gently moving them in the SMI. Avoid squeezing and touching them directly at all times. If required, exclude these egg chambers/ovarioles from the further protocol.
As a dissection stereoscope is not reliable for the identification of damaged egg chambers/ovarioles, egg chambers/ovarioles should be checked using a CellMask or FM 4-64 dye under a confocal/spinning disk microscope. Damaged egg chambers show extreme coloring as compared to an undamaged egg chamber tissue background. Never acquire a TLM with strong dye patches.
Critical steps and troubleshooting for the in vitro live imaging of egg chambers
If freely placed egg chambers in the SMI still move, check again under the confocal microscope to see whether there are still any overlooked remnants of muscle sheet and debris floating in the SMI. Remove them and try again. Of the egg chambers, 90% should be stable and immobile during subsequent imaging.
To make sure that a selected egg chamber/ovariole is stable, use high-speed imaging (6 s intervals) for 1 min. Unstable egg chambers would move by this point. However, it is recommended to watch the whole time-lapse recording to be able to gently correct a potential unexpected movement of the imaged egg chamber. This can be done by moving the microscopic stage/table, which holds the Petri dish with the cultured egg chambers/ovarioles, to the original position so that the egg chamber of the interest is again in the imaged, focused window.
Stop imaging if a sudden and unexpected movement of the imaged egg chamber appears. Check the cushioning of the microscope table, and avoid walking around near the microscope during the acquisition time as this may result in the disruption of the image acquisition due to the vibrations caused.
If the cell membrane dye is not visible after 30 min and the used laser line is correct, add more of the dye and increase its concentration for the next acquisition.
If the actomyosin signals appear to be blurred, check the NA of the objective used. An NA lower than 1.3 will decrease the imaging quality. Additionally, make sure that used immersion oil has been applied correctly to the water 63x objective. Add or replace it if necessary.
If the egg chambers shrink and the observed cell membranes deform, check whether the lid is properly closed. If the lid is missing or not properly closed, the egg chambers can dry out due to SMI evaporation over the acquisition time.
If the rotation of the egg chambers slows down or stops, decrease the laser power. If a hole appears in the egg chamber, it has been burned by the laser. Decrease the laser power.
If the actomyosin signals bleach after 2 min during TLM acquisition, decrease the laser power and increase the signal amplification in the microscope software.
Once a TLM of the follicle epithelium of one egg chamber has been acquired, it is recommended to avoid imaging again in the same tissue region of this egg chamber. However, as egg chambers rotate around their AP axis, it is possible to repeat the acquisition of a TLM using this undamaged egg chamber after circa 30 min. While imaging the follicle epithelium in one egg chamber, other egg chambers will not be bleached/damaged even if they are located in the same ovariole or in another ovariole in the SMI.
If all these requirements are met, the percentage of successfully imaged egg chambers should be circa 90%–100% for stages 6–8, circa 50%–60% for stages 3–5, and circa 20%–30% for stages 1–-2. Failure is mainly due to the movement of egg chambers or damage to them during their dissection/manipulation.
Critical steps and troubleshooting for data processing
During mask generation using the provided script in Fiji, it can happen that there are a lot of generated cell outlines that do not reflect the actual cell membranes in a TLM. This is often caused by high background noise, especially when dyes to stain cell membranes are used. In such a case, to avoid the tedious correction of undesired cell outlines in the generated mask, run the segmentation again and set the parameters to the best fit. This can be done by adjusting the Estimated noise tolerance parameter.
When loading one of the ParticleStack.tif files containing cell outlines into Surface manager, the loading time scales linearly with the number of cell outlines and can take several minutes. If the loading is disrupted or incorrect, repeat it. Make sure that the uploading window is in focus and no other program is being used.
Sometimes a cell outline needs to be corrected in some frames; in such a case, use the Brush button. Draw the correct cell outline in one particular frame by dragging the mouse around the cell membrane. Move to a different frame of the TLM, and then, return to the time frame with the correction: the incorrect cell outline should now be replaced. Then, go again to the next frame to correct that one. The Brush tool will now switch to erase mode. If necessary, correct the outlines in the next time frames by pushing the existing cell outline and then pressing the +Add button to create a new cell outline. Delete the old S number from the Surface manager window and rename the new cell outline by pressing the Rename button if required.
If an entire important cell is missing throughout a TLM, create a new mask outline by pressing Unselect > Polygon. Create a new cell outline in the first frame of the TLM by clicking along the cell membrane. Then, go to the last frame of the TLM and do the same for the selected cell. By running the movie in time, the cell outlines will be interpolated. Correct with the Brush tool if necessary. Once finished, press the +Add button and rename the cell outline by pressing the Rename button. The more points/clicks to create a new cell outline, the better interpolation works.
Besides epithelial rotation, TLMs are sometimes affected by unwanted movement. Therefore, it is recommended to correct for such tissue drift in these TLMs. By doing so, TLMs are also corrected for the epithelial rotation of egg chambers, and cell membranes become rigid. This makes the manual analysis of actomyosin signals easier. However, such an approach does not allow scientists to distinguish whether the observed actomyosin signals move or are static relative to the cell membrane. If a distinction between static and active movements of actomyosin signals is the goal of such an analysis, no tissue drift correction should be applied. We found that the majority of actomyosin signals actively move relative to the cell membrane and only a minor portion of them appear static.
Critical steps and troubleshooting for the analysis of subcellular actomyosin signals
If the statistics generated contain outliers, ensure that any extremely low or high values with respect to the whole data set are truly reflecting the behavior in the cell and are not artifacts. This can be done by identifying the cell containing the outliers and checking for cell outline quality at the time when the low/high value was measured. Often, such extreme values result from defective cell outlines that incorrectly measure part of another cell. This may be particularly apparent when large blobs interfere with the cell outline of a neighboring cell. In such a case, it is crucial to correct the cell outlines and repeat the measurement.
To make the quantification easier, analyze the signals in one particular cell surface and continue one by one until all cells are analyzed in a submovie. The results of manual analysis of subcellular actomyosin signals may not show symmetry breaking in one cell and one particular egg chamber. We experienced that the actomyosin does not clearly break symmetry relative to the tissue movement in <10% of the rotating egg chambers (stages 6–8). This percentage is increased around stage 412. We also found that there is no difference whether 5 min or 10 min are analyzed in the identification of the preferred direction of actomyosin signal movement in an egg chamber12.
Critical steps and troubleshooting for the selective extraction of actomyosin signals from curved tissues
The wrong size of blobs (identified signals) will result in no blobs and the program will freeze in the next step. In this case, force-quit Fiji and newly restart the plugin Ellipsoid Surface Projection. Do the same when the program does not react after pressing Compute. This is often an indication that unsuitable parameters have been chosen.
If there are too many blobs and/if they are concentrated toward one side of the analyzed egg chamber, this may impact the designed ellipsoid and not provide the correct fit for the egg chamber. Go back to the blob identification and try to arrange their size in the combination with the x-, y-, and z-axis selection of the egg chamber. A failure to generate a good ellipsoid fit also often occurs when unsuitable cut-off distance settings are used.
The projections obtained may sometimes look misfocused, or perhaps the obtained z plane actually moves between cell layers near the surface of the egg chamber. This is usually a sign of a poorly fitting ellipsoid where one region of the ellipsoid is set too far from the egg chamber. Try to fit the ellipsoid so that it maintains the same distance from the egg chamber circumference.
Limitations of the method and novel approaches
This free culturing of egg chambers omits the subtle flattening of an egg chamber’s surface. To this end, this method has its advantages and disadvantages. When using confocal microscopy, it provides users with high-resolution and high-speed imaging that reliably uncovers actomyosin behavior in cells at the circumference of egg chambers for a short period of time (5–10 min). However, by doing so, it limits the size of a single plane that can be imaged with confocal microscopy over time. In general, it is possible to image up to ~15 cells per egg chamber at stages 6-8 but only about two cells in egg chambers at stages 1–5. Therefore, we have defined this method here as being suitable only for the local cellular scale.
To overcome these size limits that are caused by the limited size of a single confocal plane, we have developed an alternative Fiji-based approach called Ellipsoid Surface Projection, for use at the tissue scale. This combines spinning disc microscopy with a semi-interactive surface extraction of egg chambers at the tissue scale. In this way, actomyosin signals can be obtained at the same time for more than 50–-100 cells from one analyzed egg chamber. It is important to note that this approach generates very large datasets of the acquired data (~giga bytes), has a lower resolution (it uses a 40x vs. a 63x objective), and also provides a slower imaging speed (it takes ~60 s to scan through half of the tissue of an egg chamber [stages 6–8] and, as such, it is 10x slower than the limited confocal plane method).
Compared to other existing software for extracting layered projections from parametrized surfaces, the plugin developed for this protocol is focused on ease-of-use and interactive visual feedback at every step of the process. Other tools, such as the MATLAB-based ImSaNE20, used by Chen et al.21, focus on handling a wide variety of parametrized and nonparametrized surface models and various projection methods. For example, ImSaNE requires data to be preprocessed and aligned in a particular way and partially requires external tools in intermediate steps. In contrast, the plugin presented here handles any 3D/4D/5D image (sequence) that can be opened in Fiji without external preprocessing. While ImSaNE is highly configurable (programmable) by editing MATLAB scripts, we provide a minimal set of options in one workflow that is tailored to the specific problem discussed here. Each step of the interactive workflow provides results that can be immediately visually inspected and adjusted if necessary.
The decision as to which of these imaging approaches, the local cellular or tissue scale, is the best for a particular experiment, depends purely on the scientific question to be answered (i.e., higher vs. lower resolution; short vs. long acquisition time). A good compromise between these two scales could be to combine both approaches, thus gaining the imaging of actomyosin signals at the semi-tissue scale (i.e., 20–30 cells). This requires a spinning disc microscope, a water 63x lens with NA ≥1.3, and established z-stack settings for 20–30 cells of an egg chamber. This much shallower z-stack (in contrast to the z-stack required for the acquisition of one half of an egg chamber) allows faster scanning of under 60 s. The time gained here can be used either for repeated z-stack acquisition to speed up the imaging or for a sample recovery (time interleaves) between individual z-stacks. With the latter, a longer acquisition time (>30 min) of TLMs of rotating egg chambers can be achieved. This semi-tissue approach guarantees a sufficient resolution for actomyosin signals and the imaging of more cells at the same time over longer time periods.
Future applications and vision
Both described methods (at the local cellular and tissue scale) provide a simple and low-cost approach (excluding microscope devices) with limited side effects on the actomyosin network and can be implemented and easily adopted for other dissected animal tissues. The only prerequisite here is an existing culturing protocol in a Petri dish for a curved tissue of interest, available transgenes, and markers or labeling methods.
In combination with light-sheet fluorescence microscopy22, it is also possible to image actomyosin machinery in toto (i.e., image the complete outer circumferential surface of Drosophila egg chambers simultaneously and then subsequently unfold using the plugin Ellipsoid Surface Extraction). However, there are a few limitations that need to be resolved in terms of suitability for high-speed imaging of actomyosin signals, namely 1) the embedding of egg chambers in a low-point melting agarose or their sticking to a capillary during imaging; ii) a low numerical aperture of used water lenses that do not provide sufficient actomyosin signal resolution; iii) the additional time needed to acquire several angles of egg chambers, which prevents high-speed imaging.
To this end, it is foreseeable that, with the refinement of microscope parameters such as the speed to scan through the epithelial tissue and the improvement of used microscopic lenses, the detailed analysis of actomyosin machinery will, in the future, enable promising high-resolution results to be obtained at the tissue and in toto scale over long time periods.
The authors have nothing to disclose.
The authors are very thankful to Miriam Osterfield for sharing her advice on the in vitro life imaging of egg chambers using an approach adopted from the Celeste Berg laboratory4. We also thank to Sebastian Tosi for the Fiji script that enables cell segmentation.
Schneider Medium | Gibco | 21720-024 | [+]-Glutamine |
Penicillin/Streptomycin | Gibco | 15140-122 | [+] 10.000 Units/ml Penicillin [+] 10.000 µg/ml Streptomycin |
Fetal Bovine Serum | Sigma | F135 | Heat inactivated |
Insulin Solution Human | Sigma | I9278 | |
50 ml Falcon tubes | Eppendorf | sterile | |
Millex-GV filter | Millipore | SLGV033NS | 33 mm |
Glass Bottom Microwell Dishes | MatTek Corporation | P35G-1.5-14-C | 35 mm Petri dish, 14 mm Microwell, No. 1.5 cover glass |
Forceps | A. Dumont & Fils | #55 | |
CellMask Deep Red (cell membrane dye) | Invitrogen | C10046 | |
FM4-64 (cell membrane dye) | ThermoFisher/Invitrogen | T13320 | |
Depression dissection slide | Fisher Scientific | 12-560B | |
Microscopic equipment | |||
Steromicroscope | e.g. Zeiss | Stemi SV 6 | |
Inverted confocal microscope | e.g. Zeiss/Olympus | ||
Spinning disc microscope | e.g. Zeiss/Andor | ||
Microscopic glass/cover glass | e.g. Thermo Scientific/Menzel Glas | ||
Cactus tool | |||
Wironit needle holder | Hammacher | 9160020 | |
Cactus spine | Echinocactus grusonii/barrel cactus | ||
Drosophila stocks used in the manuscript | |||
AX3/AX3; sqh-MRLC::GFP/sqh-MRLC::GFP | For detail see Rauzi et al.: Planar polarized actomyosin contractile flows control epithelial junction remodeling. Nature 2010, Vol. 468, pg. 1110-1115. | ||
AX3/AX3; sqh-MRLC::GFP/sqh-MRLC::GFP; fat258D/fat103C | For detail see Viktorinova et al.: Epithelial rotation is preceded by planar symmetry breaking of actomyosin and protects epithelial tissue from cell deformations. PLoS Genetics 2017, Vol. 13, Issue 11, pg. e1007107. |