Previous data demonstrates that Fascin can inhibit the activity of Myosin in vitro (Elkhatib et al., 2014). Fascin functions in both the nurse cells and the border cells to promote on-time border cell migration (Lamb et al., 2020). Additionally, during border cell migration Myosin generates forces in the nurse cells that push upon the border cells, causing the border cells to activate Myosin and stiffen (Aranjuez et al., 2016), suggesting that the nurse cells control the stiffness of the border cell cluster. Based on these observations, we hypothesized that Fascin may regulate Myosin activity in the Drosophila follicle, specifically the nurse cells, to promote border cell migration.

To test this hypothesis, we assessed if Fascin limits Myosin activity in the Drosophila follicle. Myosin is activated via phosphorylation on its regulatory light chain subunit (MRLC). To assess changes in Myosin activation in the follicle, we stained follicles using an antibody against phosphorylated MRLC (pMRLC); wild-type and fascin-null follicles were stained in the same tube to account for staining variability. We observe a striking increase in active MRLC along both the nurse cell and border cell membranes of fascin-null follicles (Figure 2B compared to A, blue arrows and B’ compared to A’, orange arrows). We quantified levels of active MRLC by measuring the relative fluorescence intensity of pMRLC on the nurse cell and border cell membranes (Figure 2C–D; for example quantifications see Figure 2—figure supplement 1A,B). Briefly, for the nurse cell quantifications, 3 line segments per follicle were drawn across nurse cell-nurse cell membranes and the fluorescence intensity peak for pMRLC was normalized to phalloidin intensity at the same point; phalloidin intensity is not affected by loss of Fascin (Figure 2—figure supplement 1C). Concurrent border cell staining ensured we did not measure across a border cell cluster protrusion. The three values were then averaged for a single image (for example quantifications see Figure 2—figure supplement 1A,B). For border cell intensity, the border cell cluster was traced using the phalloidin or border cell stain and the mean fluorescence intensity for pMRLC was measured and normalized to the mean fluorescence intensity of pMRLC of the same shape in the nurse cell cytoplasm (for example quantifications see Figure 2—figure supplement 1A). We used the nurse cell cytoplasm pMRLC stain as the background for the border cell measurement because there is no available antibody that works against MRLC. Importantly, nurse cell cytoplasmic pMRLC intensity (Figure 2—figure supplement 1D) and Myosin heavy chain (Drosophila Zipper) protein levels are not significantly different between wild-type and fascin-null follicles (Figure 2—figure supplement 1E,F). For increased clarity, throughout the entire manuscript, all graphs quantifying MRLC activity on the nurse cell membranes are shown using blue circles and MRLC activity on the border cell cluster are shown using orange circles. We find that there is a significant increase in active MRLC intensity on the fascin-null nurse cell membranes compared to wild-type follicles (Figure 2C, p < 0.0001). Additionally, active MRLC is also significantly increased on the border cell cluster when Fascin is lost (Figure 2D, p < 0.0001). Further, we assessed the spatial distribution of active MRLC on the nurse cell membranes surrounding the border cell cluster. In both wild-type and fascin mutant follicles, we observe active Myosin enriched on nurse cell membranes in front, behind, and on the sides of the migrating cluster (Figure 2A and B). While the intensity of pMRLC staining is higher in the fascin mutants indicating higher Myosin activation, there does not seem to be a change in the spatial distribution of active Myosin on the border cell cluster.

We also quantified changes in active MRLC puncta number and length on the border cell cluster (Figure 2E and F; see Methods for quantification details). Briefly, the puncta on each border cell cluster were manually counted and the maximum length of each punctum was measured from maximum projections of 2–4 confocal slices using ImageJ software. Loss of Fascin increases puncta number but decreases puncta length (Figure 2E and F, p < 0.001). Together these results demonstrate that Fascin limits Myosin activation in the Drosophila S9 follicle on both the nurse cell membranes and the border cell cluster, providing the first evidence that Fascin regulates Myosin activity in vivo.

We next wanted to determine how Fascin influences Myosin dynamics during border cell migration. In addition to the level of activation, the localization and dynamics of Myosin influence invasive migration (Vicente-Manzanares et al., 2009; Majumder et al., 2012; Aguilar-Cuenca et al., 2014; Aranjuez et al., 2016). Indeed, during border cell migration, dynamic cycles of Myosin activation and inactivation at the cluster membrane are essential for proper migration (Aranjuez et al., 2016). We visualized Myosin dynamics on the border cell cluster using a C-terminally GFP-tagged MRLC (MRLC-GFP; Drosophila Spaghetti Squash, Sqh), under the control of its endogenous promoter. Previous data demonstrates that MRLC-GFP is highly expressed on the border cell cluster during migration and accumulates in transient puncta on the cluster; these puncta depend on Myosin activation, suggesting they are sites of active Myosin (Majumder et al., 2012). Using live imaging, we find in control follicles, MRLC-GFP puncta appear and disappear rapidly on the border cell cluster (Figure 2G–G’’’, Video 1). However, in the fascin-null follicles, the MRLC-GFP puncta dynamics are much slower (Figure 2H–H’’’, Video 2). We quantified this change in MRLC-GFP dynamics by measuring puncta lifetime on the cluster (Figure 2I). The control follicles display an average puncta lifetime of 70.2 s, while in fascin-null follicles the average puncta lifetime is 151.8 s (Figure 2I, p < 0.0001). These results suggest that Fascin limits Myosin dynamics on the migrating border cell cluster.

As increased Myosin activity increases actomyosin contractility and cell stiffness, we next wanted to directly measure the stiffness of fascin-null follicles. Substrate stiffness is thought to be a driving regulator of cell migration and migrating cell stiffness (Di Martino et al., 2016; Gasparski et al., 2017; Oakes, 2018; Ren et al., 2021), therefore we aimed to directly quantify nurse cell stiffness. AFM is a standard method to directly measure mechanical properties of biological tissues (Kreplak, 2016). AFM can be used to quantify the elastic modulus, which is a measurement of how easily an elastic material is deformed when a known amount of force is applied (Kreplak, 2016). A high elastic modulus value corresponds to a stiff tissue. We used AFM nanoindentation technique to quantify the stiffness of fascin-null and wild-type nurse cells (Crest et al., 2017; Chen et al., 2019). The nurse cells are the substrate for the border cells and their stiffness regulates border cell migration and cluster stiffness (Aranjuez et al., 2016). Notably, during S9, the nurse cells are surrounded by a layer of stretch follicle cells and a basement membrane that envelopes the entire follicle (Figure 3A). Previous measurements on Drosophila follicles using AFM established that there is significant difference in stiffness between the basement membrane and the underlying nurse cells (Crest et al., 2017; Chen et al., 2019). These different tissues stiffnesses can be separated by using different indentation ranges to indent the AFM probe into just the basement membrane or to indent deeper into the nurse cells (Figure 3B; Chlasta et al., 2017). Thus, by using two indentation ranges to fit the mechanical response, we can quantify the distinct stiffness of the basement membrane versus that of the underlying nurse cells (Chlasta et al., 2017).

We used AFM and the Hertzian elastic contact model to calculate the stiffness of wild-type and fascin-null S9 follicles (Figure 3C). For increased clarity, throughout the entire manuscript all graphs quantifying stiffness by AFM are represented using green circles. For an indentation range of 0–100 nm, which probes the basement membrane, wild-type follicles have an average stiffness of 24.2 kPa and fascin-null follicles have a similar average stiffness of 26.5 kPa (Figure 3D, p > 0.05). However, for an indentation range of 310–550 nm, which probes the nurse cell stiffness, wild-type follicles have an average stiffness of 10.1 kPa while fascin-null follicles have a significantly increased average stiffness of 25.9 kPa (Figure 3D, p < 0.0001). Thus, the stiffness of the fascin-null nurse cells is >2 x higher than wild-type nurse cells. Together these results demonstrate that loss of Fascin increases the stiffness of the nurse cells in S9 Drosophila follicles.

As increased stiffness of the nurse cells or border cells inhibits border cell migration (Aranjuez et al., 2016), we hypothesized that the increased Myosin activity in fascin-null follicles contributes to the previously characterized border cell migration delays (Lamb et al., 2020). To address this hypothesis, we first used a pharmacological inhibitor of Myosin and assessed the effect on border cell migration. Follicles were incubated for 2 hr in either control media or 200 µM of Y-27632, a Rho inhibitor previously used to reduce Myosin activity in Drosophila follicles (He et al., 2010). This inhibitor reduces activated Myosin levels on both the nurse cells and border cells (Figure 4—figure supplement 1A,B). We then employed our previously developed method to quantify delays in border cell migration during S9, which takes the ratio of the distance the border cells have migrated from the anterior end of the follicle to the distance of the outer follicle cells from the anterior end of the follicle (see schematic Figure 4A; Lamb et al., 2020). We call this value the migration index; for increase clarity, throughout the entire manuscript all migration indexes data are shown in magenta. A migration index of approximately one indicates on-time migration during S9, while a value less that one indicates delayed migration and a value greater than one indicates an accelerated migration. As we previously established, loss of Fascin significant delays migration (Figure 1C, Lamb et al., 2020). Here, we find that inhibiting Myosin activity with Y-27632 in fascin-null follicles restores on-time border cell migration compared to the fascin-null control (Figure 4C–D and G, migration index 1.1 compared to 0.78) and is not significantly different from the wild-type control (Figure 4B and G, migration index 1.1 compared to 0.95).

As loss of Fascin increases Myosin activity on both the nurse cells and the border cells, we next sought to determine whether increased Myosin activity on the nurse cells and/or border cells is responsible for delays in border cell migration. We used the UAS/GAL4 system to express an RNAi against MRLC (Drosophila Sqh) to knockdown Myosin in fascin-null mutants in different cell types (see schematic of cell specific knockdown in Figure 6A) – the germline (matα GAL4), somatic (c355 GAL4), or border cells (c306 GAL4). Unfortunately, knockdown of Myosin in the somatic (c355 GAL4) or border cells (c306 GAL4) was lethal, however knockdown of Myosin in the germline (matα GAL4) was viable. Germline knockdown of MRLC in fascin mutants significantly decreased active Myosin levels on the nurse cells compared to fascin-null controls (Figure 4—figure supplement 1C). However, it fails to restore normal levels of active Myosin on the border cell cluster, as Myosin activation remains significantly increased compared to the wild-type control and is not signficantly different than the fascin-null control (Figure 4—figure supplement 1D). We next assessed whether altering Myosin activity within the nurse cells can restore on-time border cell migration in fascin-null mutants using the migration index quantification (see schematic, Figure 4A). Germline knockdown of MRLC in fascin mutant follicles rescues border cell migration (Figure 4E, F and H, migration index 0.91 compared to 0.65). Together these results suggest that Fascin is required to limit Myosin activity within the nurse cells to promote on-time border cell migration.

Previous data demonstrated that in vitro Fascin can limit Myosin activation; however, the mechanism of how Fascin regulates Myosin activity is unknown (Elkhatib et al., 2014). It was hypothesized that Fascin’s ability to tightly bundle F-actin precludes Myosin from being able to bind to actin filaments and generate force (Elkhatib et al., 2014). Phosphorylation of Fascin at serine 52 (S52, mammalian S39) inhibits its F-actin bundling function (Yamakita et al., 1996; Ono et al., 1997). If Fascin’s bundling activity is required to limit Myosin activation, we would predict that global expression (actin 5 c GAL4) of phosphomimetic Fascin (S52E) in fascin-null mutants would fail to suppress the increased active Myosin. As a control, we find that global expression of wild-type Fascin (GFP-Fascin) in fascin mutant follicles significantly reduces active MRLC enrichment on both the nurse cell membranes (Figure 5) and border cell cluster (Figure 5B, orange arrow and E). Conversely, when the phosphomimetic form of Fascin (GFP-Fascin S52E) is expressed in fascin-null mutants, we observe high levels of active Myosin on both the nurse cell membranes (Figure 5C, blue arrows and D) and border cell cluster (Figure 5C, orange arrows and E) that are not significantly different than the fascin mutant control (Figure 5A, D and E). These data support the model that Fascin limits Myosin activity by bundling F-actin and precluding Myosin’s ability to bind to actin filaments.

As we found that tight regulation of Myosin activity by Fascin is critical for on-time border cell migration (Figure 4), and expression of phosphomimetic Fascin (S52E) in fascin mutant follicles fails to restore normal levels of Myosin activity (Figure 5B–E), we expected it would also fail to fully rescue the delays in border cell migration. We previously found global expression (actin 5 c GAL4) of wild-type Fascin in fascin mutant follicles restores on-time border cell migration (Lamb et al., 2020). As expected, when we quantify the migration index (described in Figure 4A) for fascin mutant follicles with global expression of phosphomimetic Fascin (S52E), we find it only partially rescues delays in border cell migration (Figure 5—figure supplement 1C, migration index 0.90 compared to 0.80). Together these data indicate Fascin functions in other ways besides bundling F-actin and limiting Myosin activity to promote on-time border cell migration.

Previous evidence demonstrated that the stiffness of the nurse cells regulates border cell cluster stiffness as indicated by active Myosin levels and on-time border cell migration (Aranjuez et al., 2016). Since Fascin is required in both the nurse cells and the border cells to promote on-time border cell migration (Lamb et al., 2020), we wanted to determine which cells Fascin acts in to regulate Myosin activation. To test this, we used the UAS/GAL4 system to express a Fascin RNAi construct to knockdown Fascin in specific cell types (Figure 6A) – the germline (matα GAL4), somatic (c355 GAL4), or border cells (c306 GAL4) – and analyzed how loss of Fascin in these different cells affects Myosin activation throughout the follicle. We have previously validated the use of UAS/GAL4 system to knockdown Fascin in these cell types (Lamb et al., 2020).

Based on the literature, we hypothesized knockdown of Fascin in the germline would increase Myosin activation in both the nurse cells and border cells, while knockdown of Fascin in the border cells would only increase Myosin activation in the border cells. We observe, as expected, knockdown of Fascin in the germline results in a significant increase in pMRLC (active MRLC) enrichment on the nurse cell membranes (Figure 6C, blue arrows and F, p < 0.0001). However, knockdown down of Fascin in the germline unexpectedly fails to alter active MRLC enrichment on the border cell cluster (Figure 6C, orange arrow and G, p > 0.05). We next knocked down Fascin in all the somatic cells or just the border cells and anticipated that this would lead to a significant increase in active MRLC on the border cell cluster but not the nurse cells. As expected, we observe a significant increase of active MRLC on the border cell cluster when Fascin is knocked down in the border cells (Figure 6D, E and G, orange arrows, p < 0.0001). Surprisingly, knockdown of Fascin in the somatic or just the border cells also significantly increased active MRLC enrichment on the nurse cells (Figure 6D, E and F, blue arrows, p < 0.0001). These data surprisingly suggest that knockdown of Fascin in the border cells increases border cell stiffness and this, in turn, induces the stiffening of their substrate, the nurse cells.

Further, we used AFM to directly assess the changes in nurse cell stiffness of our cell specific Fascin knockdowns. Germline knockdown of Fascin results in nurse cells that are 1.5 X stiffer than their GAL4 control (Figure 6H, p < 0.0001), while border cell knockdown results in nurse cells that are 1.8 X stiffer than their GAL4 control (Figure 6H, p < 0.0001). Together these data demonstrate the unexpected finding that Fascin acts the border cell cluster to regulate the stiffness of the surrounding nurse cell substrate (Figure 8).

Our RNAi experiments indicate that Fascin acts primarily in the border cells to control Myosin activation and nurse cell stiffness. If this is true, then restoring Fascin expression in only the somatic cells of a fascin mutant follicle, including the border cells, should restore normal Myosin activation in both the border cells and the nurse cells, and normal nurse cell stiffness. Indeed, we find that expressing GFP-Fascin in the somatic cells of fascin-null follicles significantly reduces active MRLC enrichment on both the nurse cell and border cell membranes compared to the fascin-null control (Figure 7A–E, p < 0.0001). Further, restoring Fascin expression in the somatic cells of fascin-null follicles significantly reduced the stiffness of the nurse cells compared to the fascin-null control (Figure 7, 19.4 kPa compared to 38.9 kPa, p < 0.0001). Together our data indicate that Fascin acts in the border cells to regulate the stiffness of both the border cell cluster and its substrate, the nurse cells.

Given the surprising nature of our findings, we next wanted to determine if the border cell regulation of nurse cell stiffness is specific to Fascin or if it is a general principle. To test this idea, we expressed a constitutively active form of Rok (Rok-CAT) in the border cells (c306 GAL4). Rok is one of the kinases that phosphorylates MRLC to activate Myosin. Thus, expressing constitutively active Rok will increase activation of Myosin, which, in turn, will increase cortical tension and therefore the stiffness of the border cells. We find that expression of constitutively active Rok in the border cells significantly increases active MRLC enrichment on both the nurse cell membranes (Figure 7—figure supplement 1B compared to 1 A, blue arrows, and C, p < 0.0001) and the border cell cluster (Figure 7—figure supplement 1B compared to 1 A, orange arrows and D, p < 0.001). These data suggest that the nurse cells, in general, respond to changes in stiffness of the border cells by altering their own cellular stiffness (Figure 8). This non-autonomous regulation of substrate stiffness by the migratory border cells is an unexpected finding.

Fly stocks were maintained on cornmeal/agar/yeast food at 21 °C, except where noted. Before immunofluorescence staining and live imaging, flies were fed wet yeast paste daily for 2–4 days. Unless otherwise noted, yw was used as the wild-type control. The following stocks were obtained from the Bloomington Stock Center (Bloomington, IN): matα GAL4 (third chromosome), c355 GAL4, c306 GAL4, actin5C GAL4, UASp-RNAi-Fascin (TRiP.HMS02450), UASp-Sqh-RNAi (TRiP.HMS00437), and UASp-Rok-CAT. The fTRG sqh stock was obtained from the Vienna Drosophila Resource Center. The fascin^sn28 line was a generous gift form Jennifer Zanet (Université de Toulouse, Toulouse, France Zanet et al., 2012), the oskar GAL4 line (second chromosome) was a generous gift from Anne Ephrussi (European Molecular Biology Laboratory, Heidelber, Germany Telley et al., 2012), and the UASp-GFP-Fascin and UASp-GFP-Fascin-S52E lines were a generous gift from Francois Payre (Université de Toulouse, Toulouse, France Zanet et al., 2009). For germline expression during S9, either matα GAL4 or oskar GAL4 were utilized interchangeably. Expression of UASp-RNAi-Fascin was achieved by crossing to matα GAL4, c355 GAL4, and c306 GAL4, maintaining crosses at 25 °C and progeny at 29 °C for 3 days. Expression of UASp-Sqh-RNAi was achieved by crossing to oskar GAL4, maintaining crosses at 25 °C and progeny at 29 °C for 3 days. The sn28, c355 GAL4 flies were generated previously (Lamb et al., 2020). Expression of UASp-GFP-Fascin or UASp-GFP-Fascin-S52E was achieved by crossing to actin5C GAL4, crosses were maintained at 25 °C and progeny at 29 °C for 2 days. The specific genotypes for each experiment are listed in Table 1.

Whole-mount Drosophila ovary samples (approximately five flies per experiment) were dissected into Grace’s insect media (Lonza, Walkersville, MD) and fixed for 10 min at room temperature in 4 % paraformaldehyde in Grace’s insect media. Briefly, samples were blocked using antibody wash (1 X phosphate-buffered saline, 0.1 % Triton X-100, and 0.1 % bovine serum albumin) six times for 10 min each. Primary antibodies were diluted with antibody wash and incubated overnight at 4 °C. The following primary antibodies were obtained from the Developmental Studies Hybridoma Bank (DSHB) developed under the auspices of the National Institute of Child Health and Human Development and maintained by the Department of Biology, University of Iowa (Iowa City, IA): mouse anti-Hts 1:50 (1B1, Lipshitz, HD Zaccai and Lipshitz, 1996), mouse anti-FasIII 1:50 (7G10, Goodman, C Patel et al., 1987); mouse anti-Fascin 1:20 (sn7c, Cooley, L Cant et al., 1994). Additionally, the following primary antibody was used: rabbit anti-GFP 1:2000 (pre-absorbed on yw ovaries at 1:20 and used at 1:100; Torrey Pines Biolabs, Inc, Secaucus, NJ). After six washes in Triton antibody wash (10 min each), secondary antibodies were incubated overnight at 4 °C or for ~4 hr at room temperature. The following secondary antibodies were used at 1:500: AlexaFluor (AF)488::goat anti-mouse, AF568::goat anti-mouse, AF488::goat anti-rabbit, AF568::goat anti-rabbit (Thermo Fischer Scientific). AF647-, or AF568-conjugated phalloidin (Thermo Fischer Scientific) was included with primary and secondary antibodies at a concentration of 1:250. After six washes in antibody wash (10 minutes each), 4’,6-diamidino-2-phenylindole (DAPI, 5 mg/ml) staining was performed at a concentration of 1:5,000 in 1 X PBS for 10 minutes at room temperature. Ovaries were mounted in 1 mg/ml phenylenediamine in 50 % glycerol, pH 9 (Platt and Michael, 1983). All experiments were performed a minimum of three independent times.

Active-MRLC staining was performed using a modified protocol provided by Jocelyn McDonald (Majumder et al., 2012; Aranjuez et al., 2016). Briefly, ovaries were fixed for 20 min at room temperature in 8 % paraformaldehyde in 1 X phosphate-buffered saline (PBS) and 0.5 % Triton X-100. Samples were blocked by incubating in Triton antibody wash (1XPBS, 0.5 % Triton X-100, and 5 % bovine serum albumin) for 30 min. Primary antibodies were incubated for 48 hr at 4 °C. The rabbit anti-pMRLC (S19; Cell Signaling, Davers, MA) was diluted 1:100 in Triton antibody wash. Anti-Fascin (sn7c, 1:20) was sometimes added to the primary antibody solution to differentiate between wild-type and fascin-null follicles in the same sample or to confirm Fascin RNAi knockdown. In other cases, anti-Hts (1B1, 1:50) and anti-FasIII (7G10, 1:50) were added to the primary antibody solution to allow for visualization of the border cell cluster boundaries. After six washes in Triton antibody wash (10 min each), the secondary antibodies were diluted 1:500 in Triton antibody wash and incubated overnight at 4 °C. Alexa Fluor 647–phalloidin (Invitrogen, Life Technologies, Grand Island, NY) was included with both primary and secondary antibodies at a concentration of 1:250; this allowed for visualization of the border cell cluster boundaries. Samples were washed six times in Triton antibody wash (10 min each) and the stained with DAPI and mounted as described above.

Microscope images of fixed Drosophila follicles were obtained using LAS AS SPE Core software on a Leica TCS SPE mounted on a Leica DM2500 using an ACS APO 20 x/0.60 IMM CORR -/D objective (Leica Microsystems, Buffalo Grove, IL) or using Zen software on a Zeiss 700 LSM mounted on an Axio Observer.Z1 using a Plan‐Apochromat 20 x/0.8 working distance (WD) = 0.55 M27 or a EC-Plan-Neo-Fluar 40 x/1.3 oil objective (Carl Zeiss Microscopy, Thornwood, NY). Maximum projections (two to four confocal slices), merged images, rotations, and cropping were performed using ImageJ software (Abramoff et al., 2004). S9 follicles were identified during fixed imaging by the size of the follicle (~150–250 μm), the position and morphology of the outer follicle cells, and presence of a border cell cluster. The beginning of S10 was defined as when the anterior most outer follicle cells reached the nurse cell-oocyte boundary and flattened.

Quantification of the migration index of border cell migration was performed as described previously (Fox et al., 2020; Lamb et al., 2020). Briefly, measurements of S9 follicles were performed on confocal image stacks of follicles stained with anti-Hts and anti-FasIII or phalloidin. Measurements of migration distances were obtained from maximum projections of 2–4 confocal slices of deidentified 20 x confocal images using ImageJ software (Abramoff et al., 2004). Briefly, a line segment was drawn from the anterior end of the follicle to the front or posterior of the border cell cluster and the distance in microns measured, this was defined as the distance of border cell migration. Additionally, a line segment was drawn from the anterior end of the follicle to the anterior end of the main-body follicle cells and the distance measured, this was defined as the distance of the outer follicle cells. Lastly, the entire follicle length was measured along the anterior-posterior axis. The migration index was calculated in Excel (Microsoft, Redmond, WA) by dividing the border cell distance by the follicle cell distance. Cluster length was determined by measuring the distance from the front to the rear of the border cell cluster (detached cells were not included). Data was compiled, graphs generated, and statistical analysis performed using Prism (GraphPad Software).

Fluorescence intensity analyses were performed on maximum projections of 3 confocal slices of 40 x confocal images using ImageJ software. Concurrent Fascin or border cell staining (Hts and FasIII) was used to define the boundaries of the border cell cluster. For nurse cell intensity, three line segments per follicle were drawn across nurse cell-nurse cell membranes on maximum projections of 2–3 confocal slices of follicles stained for pMRLC and phalloidin. The fluorescent intensity peak for pMRLC was determined for each line and normalized to phalloidin intensity at the same point. These three values were then averaged for a single image. Averages were then normalized to the wild-type average for each experiment due to experimental variability (for example quantification see Figure 2—figure supplement 1). For border cell intensity, the border cell cluster was traced using the phalloidin stain and the mean fluorescence intensity for pMRLC was measured for this shape and this was then normalized to the mean fluorescence intensity of pMRLC of the same shape in the nurse cell cytoplasm (for example quantification see Figure 2—figure supplement 1). For the puncta number and length, puncta on the border cell cluster were manually counted and length measured from a maximum projection image using ImageJ software. Data was compiled, graphs generated, and statistical analysis performed using Prism (GraphPad Software).

Whole ovaries were dissected from flies fed wet yeast paste for 2–3 days and maintained at 25 °C until the last 16–24 hr when they were moved to 29 °C. Genotypes used for live imaging were sn28/FM7; sqh-GFP and sn28/sn28; sqh-GFP. Ovaries were dissected in Stage 9 (S9) medium (Prasad and Montell, 2007): Schneider’s medium (Life Technologies), 0.6 x penicillin/streptomycin (Life Technologies), 0.2 mg/ml insulin (Sigma-Aldrich, St. Louis, MO), and 15 % fetal bovine serum (Atlanta Biologicals, Flowery Branch, GA). S9 follicles were hand dissected and embedded in 1.25 % low-melt agarose (IBI Scientific, Peosta, IA) made with S9 media on a coverslip-bottom dish (MatTek, Ashland, MA). Just prior to live imaging, fresh S9 media was added to coverslip-bottom dish. Live imaging was performed with Zen software on a Zeiss 700 LSM mounted on an Axio Observer.Z1 using a Plan‐Apochromat 20 x/0.8 working distance (WD) = 0.55 M27 (Carl Zeiss Microscopy, Thornwood, NY) with a 2 x zoom. Images were acquired every 30 seconds for at least 1 hour for Sqh-GFP flies. Maximum projections (2–5 confocal slices), merge images, rotations, and cropping were performed using ImageJ software (Abramoff et al., 2004) To aid in visualization live imaging videos were brightened by 50 % in Photoshop (Adobe, San Jose, CA).

Quantifications of live imaging videos were performed in ImageJ (Abramoff et al., 2004) using maximum projection of 2–5 confocal slices from time-lapse videos of border cell migration. For MRLC-GFP live imaging, puncta lifetime was defined by the amount of time elapsed from when a punctum first appeared to when it disappeared completely. Data were compiled, graphs generated, and statistical analysis performed using Prism (GraphPad Software).

Whole ovaries were dissected from flies fed wet yeast paste for 2–3 days. Ovaries were dissected in S9 medium (Prasad and Montell, 2007), as described above. S9 follicles were hand isolated and mounted on poly-D-lysine coated 35 mm round glass coverslips. Force spectroscopy data were collected using a molecular force probe 3D (Asylum research) AFM in a liquid cell. AFM force spectroscopy was performed in a buffered solution within 1–2 hr after submersion. A new silicon nitride AFM probe (Bruker, DNP-10) was used for every experiment with a nominal spring constant of 0.12 N/m and a half cone angle of 20 degrees. Actual spring constant was calibrated using the built-in thermal noise method prior to measurement collection in each experiment. S9 follicles were located using the top view video camera and AFM force versus indentation data were collected on the middle of the follicle. The force data were recorded with a 0.6–1.2 μm/s tip approach velocity and a maximum force ranging from 1 to 5 nN. For each genotype, two to three follicles were probed per experiment for three independent experiments; a total of six to nine follicles were probed per genotype. In each region, five to eight different positions with 2–10 μm separations were probed. For each position, 3–10 multiple repeated force curves were recorded. Two stiffness values of follicles were determined by fitting the approach data of two separate tip depth force-indentation curves to the rearranged form of the Hertzian elastic contact model (Heinrich, 1882). These two force-indentation ranges were selected to measure the stiffness of the basement membrane (20–100 nm) and underlying nurse cells (310–550 nm) and are similar to previous studies (Chlasta et al., 2017; Crest et al., 2017; Chen et al., 2019). Poisson’s ratios of 0.5 and 0.25 were assumed for the follicles and AFM probe, respectively. The data analysis was carried out as in our previously reported work (Kruger et al., 2019; Bell et al., 2020; Kruger et al., 2020; McGowan et al., 2020).

Flies were fed wet yeast paste for 2–3 days and maintained at room temperature. Ovaries of wild-type (yw) or fascin mutant (fascin^sn28/sn28) flies were then dissected in S9 medium (Prasad and Montell, 2007), as described above. Ovarioles were teased apart and then were incubated at room temperature for 2 hr in either control media (S9 media + vehicle (DMSO)) or 200 µM of Y-27632. After 2 hr, ovaries were rinsed three times with S9 media and then fixed and stained following the pMRLC staining protocol described above.

Approximately 100 S9 follicles were dissected in room temperature Grace’s insect media (Lonza, Walkersville, MD, USA or Thermo Fisher Scientific, Waltham, MA) and transferred to a 1.5 mL microcentrifuge tube containing 50 µL of Grace’s media. Grace’s media was removed and replaced with 50 μL 1 x PBS, 50 μL 2 X SDS Sample Buffer was added and the tissue lysed by grinding with a plastic pestle. Ten μL of sample were loaded per lane on either 8 % or 10 % SDS-PAGE gels. Western blots were performed using standard methods. The membranes were cut prior to incubation with primary antibodies to allow for two proteins to be simultaneously assessed. The following primary antibodies were used: rat α−Vasa Spradling, A.C.; obtained from the Developmental Studies Hybridoma Bank (DSHB), 1:100 and rabbit α-Zipper (Karess, R.; Institut Jacques Monod, Paris, France; Wheatley et al., 1995), 1:10,000. For the Vasa primary antibody incubations, the antibody was diluted in 5 % non-fat milk in 1 x Tris-buffered saline and 0.1 % Tween-20. For the Zipper primary antibody incubations, the antibody was diluted in 5 % Bovine Serum Albumin in 1 x Tris-buffered saline and 0.1 % Tween-20. The following secondary antibodies were used: Peroxidase-AffiniPure Goat Anti-Rat IgG (H + L), 1:5000 and Peroxidase-AffiniPure Goat Anti-Rabbit IgG (H + L), 1:10,000 (Jackson ImmunoResearch Laboratories, West Grove, PA, USA). Blots were developed with SuperSignal West Pico or Femto Chemiluminescent Substrate (Thermo Scientific, Waltham, MA, USA) and imaged using the Amersham Imager 600 (GE Healthcare Life Sciences, Chicago, IL). Bands were quantified using densitometry analysis in ImageJ (Abramoff et al., 2004). Zipper levels were assessed using four independent, biological samples per genotype, and statistical significance was determined using a two-sample t-test with unequal variance in Excel (Microsoft, Redmond, WA, USA).