Abstract
Migratory cells navigate through crowded 3D microenvironments in vivo. Amoeboid cells, such as immune cells and some cancer cells, are thought to do so by deforming their bodies to squeeze through tight spaces.1 Yet large populations of nearly spherical amoeboid cells migrate2–4 in microenvironments too dense5,6 to move through without extensive shape deformations. How they do so is unknown. We used high-resolution light-sheet microscopy to visualize metastatic melanoma cells in dense environments, finding that cells maintain a round morphology as they migrate and create a path through which to move via bleb-driven mechanical degradation and subsequent macropinocytosis of extracellular matrix components. Proteolytic degradation of the extracellular matrix via matrix metalloproteinases is not required. Membrane blebs are short-lived relative to the timescale of migration, and thus persistence in their polarization is critical for productive ablation of the extracellular matrix. Interactions between small but long-lived cortical adhesions and collagen at the cell front induce PI-3 Kinase signaling that drive bleb enlargement via branched actin polymerization. Large blebs in turn abrade collagen, creating a feedback between extracellular matrix structure, cell morphology, and cell polarization that results in both path generation and persistent cell movement.
Introduction
Cell migration is critical to processes ranging from embryogenesis and wound healing to cancer metastasis.1 When spatially confined, animal and non-animal cells alike exhibit bleb-based motility, a type of amoeboid migration characterized by weak adhesion and minimal proteolytic destruction of the surrounding matrix.7,8 Amoeboid cells can migrate through tight spaces by deforming their body and nucleus, even to the point of nucleus rupture.6,9,10 Studies of invasive cancer cells, particularly of melanoma, have reported that rounded amoeboid cells are enriched at the tumor edge,11 and can move through Matrigel and tumor xenografts.2,12,13 Indeed, the pro-migratory effect of intracellular contractility, which is associated with rounding and surface blebbing, is well known.11,13–17 The pronounced rounded morphology of these cells seems to be at odds with a migration mode that relies on shape deformation. Round, amoeboid cells have been observed to move in tunnels.4 Although under some conditions amoeboid cells secrete matrix metalloproteinases (MMPs),18 amoeboid migration is not usually considered a proteolytic migration mode, and so it has been generally assumed that these tunnels were pre-formed by ‘helper’ cells moving in a mesenchymal manner, a migration mode in which the extracellular matrix (ECM) is degraded via MMPs.4 Despite the notion that amoeboid cells cannot remodel their environment to generate their own paths and thus would be forced to immobility in a very dense microenvironment, blebbing cells in soft extracellular matrices have been observed to physically manipulate fibers, pushing them out of their way19 and tugging on them with adhesions,20 suggesting the possibility of an amoeboid migration mode enabled by matrix remodeling.
To investigate the mechanism of this potentially new path generating migration strategy, we created dense, yet pliable in vivo mimetics by encapsulating cells in fibrous Collagen 1 gels with bulk stiffnesses on the order of 1 kPa (Advanced biomatrix.com).21,22 We imaged cells in 3D with high-resolution light-sheet microscopy at near isotropic ∼350 nm resolution, using specially designed sample chambers that do not interfere with the mechanical properties of the mimetics (Fig. 1a, Movie 1).23,24 Adsorption of collagen to hard surfaces increases collagen stiffness near the surface,25–28 which shifts and eventually diminishes the rounded, blebbing morphotype. To observe migration of these cells without mechanical interference, our chambers enabled cell imaging at greater than 1 mm away from any stiff surfaces.
To determine whether our in vivo mimetics prohibited deformation-based migration through pores, we fluorescently labeled collagen and then measured collagen pore size.24,29,30 Although blebs were small enough to fit inside the pores in the collagen network, the nucleus and cell body of amoeboid melanoma cells were too large to fit through the existing pores (Fig. 1a,b, Movie 2), thus rendering a deformation-based mode of migration unlikely. Long-term time-lapse imaging of melanoma cells confirmed that amoeboid cells were nevertheless able to move through the mimetics while maintaining their largely spherical shape (Fig. 1c, Extended Data Fig. 1a,b).
As a model cell for bleb-based migration through soft crowded environments, we chose metastatic melanoma cells. In vivo, melanoma metastasizes to soft environment such as the brain.31 Consistent with these clinical observations, we noted that melanoma in the soft environment of the zebrafish hindbrain not only have an amoeboid morphology but bleb extensively (Extended Data Fig. 1c). We next tested whether the amoeboid morphology was associated with melanoma metastatic potential within our in vivo mimetic. We imaged populations of primary melanoma cells that were harvested from patients and then passaged in a mouse xenotransplantation system.32,33 Comparing three mechanically distinct collagen microenvironments, including the mimetic, we found that samples with higher metastatic efficiency were enriched in the amoeboid morphology compared to the stretched mesenchymal morphology (Figure 1d, Extended Data Figure 1d,e). Furthermore, using unbiased cell shape motif detection,34 we discovered that a parental melanoma cell line (A375P) had a lower average bleb count than a subpopulation of the cell line that had been enriched for metastatic potential in mouse xenografts (A375M2) (Fig. 1e,f).35 Together, these results establish the significance of our experimental system as a model of metastatic cell migration in soft, ECM-dense tissues.
Migrating amoeboid cells can carve a path without the need for extracellular proteolytic degradation
To determine how blebbing melanoma cells migrate through soft, dense collagen, we imaged them 24 hours after seeding. Over this time frame, many cells had created tunnels (Fig. 1g). We observed a similar tunneling phenomenon with a different melanoma cell line (Extended Data Fig. 2a), as well as with pediatric Ewing sarcoma cells (Extended Data Fig. 2b). Tunnel creation in dense matrices is usually ascribed to matrix metalloproteinase (MMP)-dependent mesenchymal migration.3,36 Thus, our finding of an amoeboid cell morphology associated with a clearly demarcated, cell-generated path seemed paradoxical in view of the current paradigms of cancer cell motility. To begin to solve this puzzle, we applied a broad spectrum MMP inhibitor, GM6001,37,38 and found no effect on the ability of melanoma cells to tunnel (Fig. 1h). The MMP-independence of the migration mode also held when collagen was not first solubilized using pepsin, which removes a collagen crosslinking site potentially rendering the collagen easier to digest.39 Although tunneling is somewhat less frequent in unpepsinized collagen, the pore size is greater as is the extent of directional collagen realignment by the cells, suggesting a reduced need to tunnel (Extended Data Fig. 3). We confirmed that GM6001 inhibited MMP activity by measuring the abundance of an antibody that recognizes the MMP-cleaved collagen site in tunnels (Fig. 1i,j) and by direct measurement of MMP enzymatic activity on a synthetic substrate (Extended Data Fig. 2c). Thus, tunneling is not mediated by the enzymatic activity of MMPs.
Path generation is mediated by bleb-driven ablation of the extracellular matrix (ECM)
To address alternative path generation mechanisms to protease-activity, we analyzed the interactions of cells with collagen in greater detail. Cells inside tunnels were often highly polarized, with many large blebs at the cell front facing the enclosed end of the tunnel (Fig. 2a, Movies 3,4). Measuring the difference in frequencies of protrusive and retractive motion on and off blebs, we found that blebs were on average protrusive and non-blebs retractive (Fig. 2b), suggesting that blebs are responsible for cell protrusion through collagen. We next measured the colocalization of blebs with collagen, finding that collagen was enriched in regions near blebs, but not directly on blebs (Fig. 2c). This is explained by bleb interdigitation into pores in the collagen network, resulting in high collagen fiber density at the base of blebs (Fig. 2d). Then we examined collagen motion, which showed movement of individual collagen fibers at the front of tunneling cells (Fig. 2e,f). Using a 3D optical flow algorithm designed to capture multi-scale motion both near and away from cells (Fig. 2g),40 we compared the collagen speed near blebs with the bleb speed for both protruding and retracting blebs (Fig. 2h). For protrusive blebs, at low bleb speeds collagen speed increased linearly with bleb speed, whereas at high bleb speeds collagen motion plateaued, consistent with bleb interdigitation into collagen pores. For retracting blebs we found that even at high bleb speed, collagen was pulled in concert with the blebs, meaning that retracting blebs pull collagen towards the cell surface. Indeed, at the fronts of highly polarized cells, collagen was often enriched into a shell at the cell periphery alongside extensive internalization of labeled collagen (Fig. 2i,j). Over long periods of time, cells slowly agitated the collagen shell, breaking off fragments of the collagen and pulling them into the cell (Fig. 2k).
To determine the mechanism of collagen internalization, we incubated cells with high molecular weight dextran, finding that it was ingested alongside labeled collagen (Fig. 2l). Internalization of large liquid-phase molecules is indicative of macropinocytosis, since such molecules are excluded from smaller endocytic vesicles.41 Treatment with the sodium hydrogen exchange inhibitor 5-(N-ethyl-N-isopropyl)amiloride (EIPA), an inhibitor of macropinocytosis,42 decreased the number of dextran-labeled vesicles and internalized collagen fragments, with the number of dextran vesicles and collagen fragments highly correlated across cells (Fig. 2m-o). Testing the hypothesis that collagen localization at detected dextran vesicles was random, we found that the distribution of p-values was heavily tilted towards small values, indicating likely collagen enrichment within dextran vesicles (Fig. 2p). Furthermore, we did not observe intracellular vesicles containing fluorescently-labeled clathrin light chain (CLC) that were associated with internalized collagen (Extended Data Fig. 4), indicating that clathrin-mediated endocytosis does not contribute to internalization. Thus, we conclude that macropinocytosis is the dominant form of collagen internalization in this form of path generation for cell migration. The centrality of macropinocytosis to this form of amoeboid cancer cell migration, combined with its known role in enabling nutrient uptake in depleted cancer microenvironments,43 highlights the importance of macropinocytosis to metastasis.
Phosphoinositide 3-kinase (PI3K) establishes bleb polarity in feedback with collagen remodeling
For productive path generation, the slow destruction of dense extracellular matrix at the cell front critically depends on persistence factors that promote the highly polarized and continuous formation of large blebs, which abrade and internalize matrix material in a directed fashion. To measure polarization on the 3D cell surface, we used an approximation of a spherical normal distribution, which has fit parameters that intuitively correspond to the direction of the peak and the peak’s inverse width, here termed the polarization magnitude (Fig. 3a). Using these statistics, we first confirmed that the distribution of large blebs was more polarized than the overall bleb distribution (Fig. 3b). Measuring the directional correlation of large bleb polarization and collagen localization, we next found that large blebs were systematically biased towards areas of high collagen density (Fig. 3c). Hypothesizing that adhesions might couple collagen and bleb localization, we found that paxillin-containing adhesion complexes indeed formed (Fig. 3d, Extended Data Fig. 5) in the direction of the high collagen density at the closed end of the tunnel (Fig. 3e). A canonical cell polarity factor that is organized by nascent adhesions via focal adhesion kinase (FAK) is PI3K.44–46 Similar to the distribution of adhesions, we observed a striking polarization of PI3K near the closed end of the tunnel (Fig. 3f, Movie 5). This was surprising given that fibroblasts exhibiting pseudopodial motility in 3D microenvironments do not seem to require polarized PI3K signaling.47 Moreover, PI3K signaling was more directionally aligned with large blebs than with blebs of all sizes (Fig. 3g), suggesting that PI3K signaling is involved specifically in the polarization of large blebs. Despite their small size, adhesions in the cortical area persisted for several minutes (Fig. 3h), in contrast to the ∼1 min lifetime of similarly-sized nascent adhesions formed in cells on a coverslip (Fig. 3i,j).48 The localization and persistence of cortical adhesions at the front of tunneling cells may enable the recruitment of PI3K to the cell front.
To test this hypothesis, we acutely inhibited FAK signaling using a small molecule inhibitor of FAK-kinase activity. FAK inhibition resulted in a decrease in bleb volume, even though bleb polarization and number were unaffected (Fig. 3k). PI3K polarization and mean cell surface motion were also decreased (Fig. 3k, Extended Data Fig. 6). Measuring the full-width half-maxima of the FAK inhibition response times, we found that PI3K polarization fell first, followed by bleb volume and then cell surface motion. This led us to conclude that PI3K polarity is upstream of large bleb formation at the cell front. Indeed, stratification of blebs by volume revealed that large blebs in particular were enriched for high PI3K signaling and also associated with increased collagen motion (Extended Data Fig. 7).
To determine if the relationship between PI3K and bleb size was causative, we used photoactivation to increase PI3K signaling locally in blebbing cells, resulting in a striking increase in proximal but not distal bleb size (Fig. 3l, Extended Data Fig. 8, Movie 6). We also pharmacologically inhibited PI3K signaling by acute addition of a low dose of an inhibitor specific for PI3Kα. In the region of former high PI3K activity, PI3K biosensor intensity and bleb size rapidly decreased, even though de novo bleb formation was not inhibited (Extended Data Fig. 9, Movie 7). Aggregating over multiple cells, we found that both PI3K polarization and bleb size were decreased by PI3K inhibition, whereas the number of blebs and bleb polarization were not affected (Fig. 3m). Altogether, these results indicate that PI3K is responsible for generating large blebs but does not govern the frequency or location of bleb initiation.
PI3K enlarges blebs by enhancing branched actin polymerization
The fact that PI3K activity was responsive to FAK manipulation left open the possibility of a mechanical feedback from large blebs to PI3K, which would also explain the observed persistence in polarization of blebs and signaling. To test if the presence of large blebs affected PI3K activity, we reduced cell blebbing via addition of wheat germ agglutinin (WGA), which binds to sialic acid and N-acetylglucosaminyl residues on the extracellular surface of the cell membrane, thereby increasing membrane stiffness.49,50 We found that decreasing bleb abundance via WGA decreased the polarity and strength of PI3K signaling (Fig. 4a, Extended Data Fig. 10), supporting the notion of a sustained feedback between blebbing and local PI3K activity.
We therefore sought to uncover the mechanism that couples PI3K activity and bleb size. Bleb growth is thought to be driven by pressure-based cytoplasmic flows51 with actin filament formation only at very late growth stages to reform the actin cortex.52 In contrast, PI3K is usually associated with actin-driven protrusion.53 Thus, the involvement of PI3K in bleb expansion seemed paradoxical. We noted, however, that PI3K and actin were directionally correlated (Fig. 4b,c) and that regions of the cell with higher PI3K activity protruded faster (Fig. 4d). Indeed, in agreement with previous findings,51 we found that blebs that ultimately reached a larger size did so by growing faster (Fig. 4e). Inhibition of PI3K activity dramatically decreased the growth rate and final bleb size (Fig. 4e), confirming that fast bleb growth and large size are due to PI3K signaling.
Given the known association of PI3K with actin-based migration, we wondered if actin polymerization could be playing a role in bleb growth despite previous reports otherwise. We found that the F-tractin construct, which localizes to filamentous actin,54 was absent during bleb expansion and localized only to the bleb cortex, as previously reported.52 However, expressing low levels of HALO-tagged actin showed that actin is present during bleb expansion (Extended Data Fig. 11a). Calculating the ratio of actin to F-tractin revealed that actin was enriched in blebs relative to filamentous actin (Extended Data Fig. 11b), raising the possibility that nascent actin filaments formed in blebs, but are not recognized by the F-tractin probe. To further explore this possibility, we used a TIRF microscope with a high numerical aperture (NA) objective to image actin in the growing blebs of HeLa cells gene-edited to express a copy of the beta actin gene fused to GFP. We found that actin filaments are indeed formed in the bleb during expansion (Fig. 4f, Movie 8). Furthermore, stratification of blebs by their final size revealed that large blebs contained a significantly higher concentration of actin than small blebs, in particular later in their life (Fig. 4g).
The putative link between PI3K signaling and actin polymerization may rest on the Rac1 – WAVE – Arp2/3 pathway,44,55 which promotes lamellipodia expansion. To test the involvement of this pathway also in bleb expansion, we first blocked Arp2/3 activity by the small molecule inhibitor CK666,56,57 which decreased bleb volume but did not alter bleb number (Fig. 4h). This is consistent with previous findings that both genetic and acute inhibition of the Arp2/3 complex reduces bleb size.58 Coupled with previous findings that CK666 treatment does not decrease intracellular pressure59 and thus is not expected to globally decrease the driving force for bleb expansion, our results suggest that active actin polymerization contributes to bleb growth, especially in large blebs. To add to this conclusion, we employed a photoactivatable Rac1 construct,60 which allowed us to increase Rac1 activity locally and acutely, avoiding the more global and pleiotropic effects Arp2/3 inhibition may exert. Local photoactivation resulted in a dramatic, reproducible increase in local bleb size that was immediately reversible upon light cessation (Fig. 4i,j, Movie 9). Our results thus show that the mechanism by which PI3K localization promotes bleb growth at the front of tunneling cells is via Rac1-mediated actin polymerization.
Discussion
Our data uncover a mode of cell migration that is effective in dense, soft environments. We refer to this mode as worrying, which means wearing down or tearing repeatedly, like a dog worrying a bone, a meaning that predates the more figurative use as a term related to anxiety.61 In the context of cell migration worrying denotes that the core element of the mechanism is the sustained agitation and tearing of the extracellular matrix at the cell front by persistently polarized and dynamic cell surface blebs. The persistence is mounted by a mechanochemical feedback between actin-enforced large bleb formation, matrix ablation, adhesion signaling, and PI3K/Rac1 triggered activation of actin filament assembly inside the bleb. The discovery of this self-reinforcing machinery depended on the development of 3D imaging assays to capture cell dynamic behaviors without mechanical interference from the microscope optics, computer vision to extract the relations between cell blebbing and signaling, and optogenetic approaches to acutely interfere with the feedback loop.
Our custom-built technology enabled the study of migration in dense, yet soft environments. Not only are such tissues common in vivo, with melanoma in particular known to prefer soft environments,62 but many tissues throughout the body are likely mechanical composites with pockets of mechanically soft microenvironments.63,64 It is well established that mesenchymal migration, which is especially common in stiff environments, is facilitated by ECM remodeling leading to tunnel generation and directional fiber alignment.65 From the recent discovery that amoeboid immune cells build specialized actin structures to push fibers out of the way,66 combined with the results of this study, we now conclude that all major modes of migration are remodeling modes, in which the environment is at least transiently reorganized. Hence, alongside the core processes of cell migration established by Abercrombie67 decades ago, i.e. of protrusion, adhesion and contraction, we add environmental remodeling as the fourth process. Moving forward, the use of advanced technologies to dissect migration in ever more complex environments will be critical to enhancing our understanding of the mechanisms of environmental remodeling and its integration with the other core processes.
Our results additionally underscore how much remains to be understood about blebs. Blebs are known to play a role in diverse processes beyond cancer cell migration, including apoptosis, cytokinesis, cell spreading, and virus entry.68 Although the role of blebs as spatial compartments has been previously understood, we show here as an additional function for blebs the pushing and pulling of the environment in order to remodel it. In so doing, we found that bleb expansion is locally controlled by PI3K signaling and mediated by branched actin polymerization. This raises the possibility that the branched actin machinery governing bleb expansion in cell migration, may also be critical to other processes involving blebs, for example organelle segregation into blebs in apoptosis, demanding the need for future studies to understand how cellular control of blebbing enables critical cell functions.
Competing Interests
The authors declare no competing interests.
Data Availability
The data that support the findings of this study are available from the corresponding authors upon reasonable request.
Code Availability
Much of the code used in this study was associated with previously published methods papers and is available at https://github.com/DanuserLab. All remaining code will be made available at that repository upon publication.
Methods
Cell culture and reagents
MV3 cells were obtained from Peter Friedl (MD Anderson Cancer Center, Houston TX). A375 (ATCC® CRL-1619) and A375MA2 (ATCC® CRL-3223) cells were acquired from ATCC. SKNMC Ewing sarcoma cells were obtained from the Whitehurst lab at UT Southwestern. MV3, A375, and SKNMC cells were cultured in DMEM (Gibco) supplemented with 10% fetal bovine serum (FBS; ThermoFisher) at 37 °C and 5% CO2.
Populations of primary melanoma cells were created from tumors grown in murine xenograft models as described previously,1 and provided as a gift by the Laboratory of Sean Morrison (UT Southwestern Medical Center, Dallas, TX). Briefly, cells were suspended in Leibovitz’s L-15 Medium (ThermoFisher) containing mg/ml bovine serum albumin, 1% penicillin/streptomycin, 10 mM HEPES and 25% high protein Matrigel (product 354248; BD Biosciences). Subcutaneous injections of human melanoma cells were performed in the flank of NOD.CB17-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) mice (Jackson Laboratory). These experiments were performed according to protocols approved by the animal use committees at the University of Texas Southwestern Medical Center (protocol 2011-0118). After surgical removal, tumors were mechanically dissociated and subjected to enzymatic digestion for 20 min with 200 U ml−1 collagenase IV (Worthington), 5 mM CaCl2, and 50 U ml−1 DNase at 37oC. Cells were filtered through a 40 μm cell strainer to break up cell aggregates and washed through the strainer to remove cells from large tissue pieces. The cells were then cultured in medium containing the Melanocyte Growth Kit (ATCC PCS-200-042) and Dermal Cell Basal Medium (ATCC PCS-200-030).
Inhibitors
FAK inhibitor 14 was purchased from Tocris (3414). PI3K alpha inhibitor IV was purchased from Santa Cruz (sc-222170). CK666 was purchased from Millipore Sigma (SML0006). The ¾-collagen antibody was purchased from Adipogen (AG-25T-011). NEIPA was purchased from Sigma (A3085). WGA was purchased from VWR (101098-084). 70 kDa Fluorescein isothiocyanate–dextran was purchased from Sigma (46945). GM6001 was purchased from Sigma (CC1010).
MMP activity assay
We used the MMP activity assay kit by Abcam (ab112146) according to their instructions in order to test the efficiency of MMP inhibitors. MV3 melanoma cells were cultured in a 24 well plate with (a) media with no cells, (b) cells treated with vehicle (DMSO) control, and (c) cells treated with GM6001 for 24h. Two positive controls of recombinant human MMP1 and MMP8 from RnD systems (901-MP & 908-MP) were used for the assay. The control MMPs were dissolved in assay buffer and a 2mM AMPA working solution was prepared with assay buffer. The MMP and the test samples were mixed 1:1 vol/vol with the AMPA working solution and incubated for 1h at 37 °C. The MMP green substrate working solution was prepared in assay buffer and then mixed 1:1 vol/vol in the black walled 96 well plate and further incubated for 1h. The samples were then read on a Biotek, Synergy H1 hybrid plate reader at Ex/Em = 490/525 nm.
Recombinant DNA Constructs
The GFP-AktPH construct was obtained from the laboratory of Jason Haugh (North Carolina State University, Raleigh NC)2 and cloned into the pLVX-IRES-puro vector (Clontech). The GFP-tractin construct was a gift from Dyche Mullins (Addgene plasmid # 58473; http://n2t.net/addgene:58473; RRID:Addgene_58473)3 and was cloned into the pLVX-IRES-puro vector (Clontech). Paxillin-pEGFP was a gift from Rick Horwitz (Addgene plasmid # 15233; http://n2t.net/addgene:15233; RRID:Addgene_15233)4. mRuby2-CLC was a gift from the laboratory of Sandra Schmid (UT Southwestern Medical Center). Cells expressing lentiviral vectors were created by following the manufacturer’s instructions for virus preparation and cell infection (Clontech). Cells were selected for expression by treatment with puromycin, G418, or by fluorescence activated cell sorting.
The photoactivatable PI3K construct (Idevall-Hagren et al., 2012) was created by cloning mCherry-CRY2-iSH2 (Addgene Plasmid #66839) into the pLVX-neo vector (Clontech). The CIBN-CAAX plasmid was obtained from Addgene (Plasmid #79574) and cloned into the pLVX-puro vector. Cells expressing both the mCherry-CRY2-iSH2 and the CIBN-CAAX constructs were selected by treatment with 10 mg/mL puromycin and fluorescence activated cell sorting. It is critical for the two part cry2 photoactivation system that cells express sufficient concentration of the CIBN-CAAX construct or the cry2 construct will aggregate in the cytosol instead of being recruited to the membrane. Thus, the optimal ratio of CIBN:cry2 is greater than one; cells expressing insufficient CIBN-CAAX will not respond to light. We also noted through the course of our experiments that cells will stop expressing one or both of these constructs if not kept constantly under selective pressure. Such a loss of expression will result in non-responsive cells. The PA-Rac1 construct was obtained from Yi I. Wu (University of Connecticut Health Center, Farmington, CT).
Overexpression of fluorescently tagged monomeric actin can perturb cell cytoskeletal dynamics. To avoid this artifact while imaging tagged actin, we expressed HALO-tagged actin under the control of a truncated CMV promotor, which results in lower expression of tagged actin than the full length promoter. The original actin construct features an 18 amino acid linker between mNeonGreen and actin in a pLVX-shRNA2 vector and was obtained from Allele Biotech. We truncated the CMV promoter, and replaced the mNeonGreen fluorophore with the HALO tag sequence. The sequence of the CMV100 promoter region is as follows, with the CMV sequence highlighted and the start codon in bold:
AGTTATTAATAGTAATCAATTACGGGGTCATTAGTTCATAGCCCATATATGGAGTTCCGCGTTACATAAC TTACGGTAAATGGCCCGCCTGGCTGACCGCCGCTAGCGCTAACTAGTGCCACCATG
Phase-contrast imaging
Live-cell phase-contrast imaging was performed on a Nikon Ti microscope equipped with an environmental chamber held at 37°C and 5% CO2 and imaged with 20x magnification.
Cells on top of gels
Collagen slabs were made from rat tail collagen Type 1 (Corning; 354249) at a final concentration of 3 mg/mL, created by mixing with the appropriate volume of 10x PBS and water and neutralized with 1N NaOH. A total of 200 μL of collagen solution was added to the glass bottom portion of a gamma irradiated 35 mm glass bottom culture dish (MatTek P35G-0-20-C). The dish was then placed in an incubator at 37°C for 15 minutes to allow for polymerization.
Cells were seeded on top of the collagen slab at a final cell count of 5000 cells in 400 uL of medium per dish. The dish was then placed in a 37°C incubator for 4 hours. Following incubation, 1 mL of medium was gently added to the dish. The medium was gently stirred to suspend debris and unattached cells. The medium was then drawn off and gently replaced with 2 mL of fresh medium.
Cells embedded in 3D collagen
Collagen gels were created by mixing bovine collagen I (Advanced Biomatrix 5005 and 5026) with concentrated phosphate buffered saline (PBS) and water for a final concentration of 2 mg/mL collagen. This collagen solution was then brought to pH 7 with 1N NaOH and mixed with cells just prior to incubation at 37°C to induce collagen polymerization. Cells were suspended using trypsin/EDTA (Gibco), centrifuged to remove media, and then mixed with collagen just prior to incubation at 37°C to initiate collagen polymerization. To image collagen fibers, a small amount of collagen was conjugated directly to AlexaFluor 568 dye and mixed with the collagen sample just prior to polymerization. FITC-conjugated collagen was purchased from Sigma (C4361).
3D confocal imaging
The cell/collagen mixture described in the previous section was added to Nunc Lab-Tek II Chambered Coverglass samples holders with a No. 1.5 borosilicate glass bottom (Thermo Scientific). Cells were fixed with paraformaldehyde and stained with Hoechst and FITC-phalloidin. Images were acquired on a Zeiss LSM 880 using a Plan-Apochromat 63x/1.4 Oil objective.
Classification of cell morphology
For Figure 1d, we classified the fraction of cells as “rounded” as follows. For cells embedded in 3D collagen, we labeled as rounded cells with extensive blebbing as well as round cells with few protrusions of any sort. For cells placed on top of collagen and imaged using phase contrast microscopy, we manually scored each cell as either rounded or stretched, with the rounded morphology indicating the amoeboid phenotype.
Zebrafish injection and imaging
B16F10 melanoma cells expressing Lifeact-eGFP were injected into the hindbrain ventricle of 2 days post-fertilization wildtype zebrafish larvae using previously described protocols.5 Briefly, B16F10 melanoma cells were suspended in HBSS. 25-50 cancer cells were transplanted into the hindbrain ventricle of anesthetized larvae. Injected zebrafish larvae were incubated at 31°C with 0.2 mM PTU to prevent pigment formation. Live-cell in vivo imaging was performed using a Zeiss spinning disc microscope with a QuantEM EMCCD camera.
High NA TIRF microscopy
Human cervical adenocarcinoma cells HeLa-Kyoto with TALEN-edited ActB fused with GFP (Cellectis, France) were maintained in DMEM/F12 supplemented with 10% FBS (Invitrogen) at 37°C and 5% CO2, and imaged using CO2-independent medium (Invitrogen) supplemented with 10% FBS. Cells were confined by a PDMS stamp in a non-adhesive, PLL-g-PEG (0.5mg/ml) coated chamber of ∼3μm height, as described previously,6 and imaged 20 minutes after initiating the confinement to observe actin dynamics within blebs. The high NA TIRF consisted of a standard setup equipped with a 473nm laser 500mW (Laserquantum), an objective TIRF NA=1.49 (Olympus), and a camera (Andor Zyla 4.2). A single notch filter was used in the emission light path to block the laser line at 473 nm (Chroma). Acquisition was controlled by the Andor SOLIS software.
3D cell tracking from phase-contrast movies
Cells were embedded in 2.0 mg/mL pepsinized bovine collagen in Nunc Lab-Tek II Chambered Coverglass samples holders as described above. Live-cell phase-contrast imaging was performed on a Nikon Ti microscope as described above. Cells were outlined manually using ImageJ, and position and shape data were exported for analysis using Matlab. Cell shape was calculated using roundness, given by 4*area/(π*major_axis^2), and cells were classified as either round (roundness > 0.8) or stretched (roundness < 0.8). Autocorrelation was calculated using the Matlab function xcorr. Cell velocity was calculated from cell centroid positions.
3D light-sheet imaging
3D samples were imaged using either an axially-swept light-sheet microscope7 or a meSPIM microscope,8 both of which provide nearly isotropic, diffraction-limited 3D images. Samples were imaged in phenol red free DMEM containing 25mM HEPES (ThermoFisher) with 10% FBS and antibiotic-antimycotic (Gibco), held at 37°C during imaging. Images were collected using sCMOS cameras (Orca Flash4.0 v2, Hamamatsu) and microscopes were operated using custom Labview software. All software was developed using a 64-bit version of LabView 2016 equipped with the LabView Run-Time Engine, Vision Development Module, Vision Run-Time Module and all appropriate device drivers, including NI-RIO Drivers (National Instruments). Software communicated with the camera via the DCAM-API for the Active Silicon Firebird frame-grabber and delivered a series of deterministic TTL triggers with a field programmable gate array (PCIe 7852R, National Instruments). These triggers included analog outputs for control of mirror galvanometers, piezoelectric actuators, laser modulation and blanking, camera fire and external trigger. All images were saved in the OME-TIFF format (https://docs.openmicroscopy.org/ome-model/5.6.3/ome-tiff/). Some of the core functions and routines in the microscope control software are licensed under a material transfer agreement from Howard Hughes Medical Institute, Janelia Research Campus.
3D cell image analysis
3D light-sheet images of cells were first deconvolved using the Richardson-Lucy algorithm built-in to Matlab (Mathworks). To reduce deconvolution artifacts, images were apodized, as previously described.8 Following deconvolution, we used our previously published u-shape3D analysis framework.9 to segment cells, detect blebs, map fluorescence intensity to the cell surface, measure surface motion, and calculate polarization statistics. Briefly, images of cells were segmented to create a cell surface represented as a 3D triangle mesh. We used u-shape3D’s twoLevelSurface segmentation mode, which combines a blurred image of the cell interior with an automatically thresholded image of the cell surface. Blebs were detected by decomposing the surface into convex patches, and using a machine learning algorithm to classify the patches as a bleb or not a bleb. For each patch classified as a bleb, the bleb neck was defined as the boundary between that patch and neighboring patches. Distance from a bleb neck was calculated at every face on the mesh as the geodesic distance to the closest bleb neck. To determine the fluorescence intensity at each mesh face, we used the raw, non-deconvolved, fluorescence image. At each mesh face, a kd-tree was used to identify the cell-interior voxels within a sampling radius of 1 or 2 μm of the mesh face. Before averaging the intensity values in these voxels, the intensity values were depth-normalized to correct for surface-curvature dependent artifacts.10 The u-shape3D software, as well as the trained machine learning models used here, are available with the previously published manuscript.9
Polarization statistics were calculated by mapping data defined on the cell surface to a sphere, and fitting the mapped data to a 3D von Mises distribution, which is akin to a spherical normal distribution. We calculated bleb polarization by representing each bleb by the location on the bleb surface farthest from the bleb neck, with distances measured on the cell surface. Additionally, since the adhesion images had substantial fluorescence background, to measure adhesion polarization, we bandpass filtered the raw images via a difference of Gaussians procedure, selecting for objects between 1 and 6 pixels in radius.
3D collagen image analysis
To enhance linear image features, such as collagen fibers, the 3D collagen images were processed with a steerable filter of width 2 pixels, as previously described.8 To emphasize collagen fiber location, some figure panels, as indicated in the figure legends, show steerable-filter enhanced collagen. Other collagen images, especially those related to endocytosis, were neither filtered nor deconvolved to avoid the creation of artifacts. Collagen polarization near the cell surface was measured after mapping image intensity values from steerable-filtered images onto the cell surface. Following steerable filtering and automatic thresholding, the nematic order parameter of collagen networks was calculated as described previously8, except that the average fiber directionality in each 3D image was used as the reference direction. The fiber directionality was calculated at each voxel via a steerable filter. Collagen pore size analysis was also performed as described previously.11 Images were filtered and then thresholded at 2.5 times the intensity threshold calculated by Otsu’s algorithm.12 To measure pore sizes, for each image, we first fitted the largest possible sphere into the collagen pores. We then iteratively fitted the next largest sphere into the pores space minus the volume of previously fitted spheres until no remaining spheres above a size threshold would fit. We defined the distribution of collagen pore sizes as the distribution of fitted sphere diameters. Collagen motion was measured using a previously published 3D optical flow algorithm.13 This algorithm combines a matching framework for large displacements across frames with a variational framework for small displacements. We mapped the magnitude of the collagen motion calculated via optical flow onto the cell surface using the framework for mapping fluorescence intensity onto mesh faces described above. To create panel 3H, we separated cell surface motion, aggregated across multiple cells, into bins by magnitude and found the mean collagen motion magnitude associated with each bin. The collagen sample moves during imaging, and although the average collagen motion in each frame was subtracted from the measured collagen velocities, residual local motions contribute to create a non-zero background collagen motion.
3D dextran assay image analysis
To measure the uptake of collagen fragments alongside 70 kDa dextran, we first segmented the cell using the dextran channel. To do so, we inverted each 3D image, subtracted the median intensity, normalized by the 99th intensity percentile, subtracted the image background, thresholded, morphologically dilated by 1 pixel, morphologically eroded by 8 pixels, filled holes, and finally selected the largest image component. Since the cell is morphologically eroded to a greater extent than it is dilated, the cell segmentation is effectively shrunk, reducing the effect of segmentation errors on later analysis. To detect dots of endocytosed collagen and dextran, we employed a previously published multiscale stochastic filter.14 For this filter, we used scales of 1.5 to 4 pixels, an α = 0.01, and detected dots only inside the segmented cell. The p-value distribution shown in Fig. 2o results from testing, for each cell, the hypothesis that collagen fluorescence intensity is greater at the location of detected dextran dots than elsewhere in the cell. To calculate the p value for each cell, we randomly picked n collagen intensity values within the cell 100,000 times, where n is the number of detected dextran dots, and calculated the probability that the mean of the randomly picked values was greater than the mean of the collagen intensity values at the true detected dextran dots.
Photoactivation
Photoactivation of subcellular regions was performed using a 488 nm laser at 10% power via the FRAP module of a Zeiss LSM780 outfitted with temperature and CO2 control. Cells for the PI3K optogenetics were treated with 200uM of PI3K inhibitor IV just prior to photo activation. To assess bleb size change in phase contrast movies, we analyzed multiple blebs within the stimulated region by manually outlining individual blebs at their largest size using ImageJ. Bleb size was measured prior to activation and during activation in the same sub-region of the cell.
Visualization and Statistics
3D surface renderings were made in ChimeraX.15 Colored triangle meshes representing the cell surface were imported into ChimeraX from u-shape3D as Collada dae files, as previously described.9 To render collagen, steerable-filtered images were opened directly in ChimeraX and thresholded. To create the rendering of adhesions shown in Figure 3d, the raw paxillin images were bandpassed, admitting objects between 0.5 and 3 pixels in radius, and then median filtered.
Figure 2h has histograms with varied bin sizes. To avoid the existence of bins with very little data, each bin in this panel contains a decile of data. Furthermore, to ease visual interpretation, the time series data in Figure 3k were smoothed using a moving average filter with a span of 5 frames.
All statistical comparisons shown in figures were calculated using a one-sided or two-sided t-test with α = 0.05. Error bars in figures show either 95% confidence intervals or the standard error of the mean, as stated in the figure legends. Number of cells and/or number of different experiments analyzed are given in the figure legends.
Extended Data Figures and Legends
Movie Legends
Movie 1. Collagen movement in soft collagen. Single optical slice of a light sheet fluorescence time-lapse series showing motion of collagen labeled with Alexafluor 568 in the proximity of a single melanoma cell (unlabeled).
Movie 2. Collagen network surrounding a melanoma cell. Movie animates stepping through single slices of a light sheet fluorescence image of collagen labeled with Alexafluor 568 along with a single melanoma cell showing local surface curvature mapped to the surface of the cell.
Movie 3. A cell tunneling through collagen. Movie shows a time-lapse sequence of a single slice of a light sheet microscope image of a melanoma cell expressing GFP-AktPH (green) and collagen (red).
Movie 4. Animation showing blebs identified by u-shape 3D. Movie shows rotation of a 3D rendering of the surface of a single melanoma cell, imaged using light sheet microscopy. Blebs identified by u-shape 3D are shown in different colors.
Movie 5. PI3K polarity in a migrating cell. Movie shows a time-lapse sequence of the surface of light sheet microscope images of a melanoma cell expressing GFP-AktPH. The local intensity of GFP-AktPH is projected onto the surface of the cell, according to the colormap shown in Extended Data Figure 7a.
Movie 6. Photoactivation of PI3K in a blebbing cell. Time-lapse sequence of a single optical section of a confocal microscope acquisition of an MV3 cell expressing GFP-AktPH. Photoactivation of Cry2-iSH2/CIBN-CAAX occurred via activation of the FRAP module in the location and time shown by the green circle.
Acknowledgments
We thank Allan Zhang for manually tracking the cells imaged via phase-contrast microscopy, Ugur Eskiocak, Elena Piskounova, Arin Aurora, and Sean Morrison for providing primary melanoma cells. We thank Stefan Wieser and Verena Ruprecht for use of their custom high NA TIRF microscope and Philippe Roudot for advice using the optical flow algorithm. We also thank Alba Diz-Muñoz and Jörg Renkawitz for kindly commenting on the manuscript. Cell and collagen rendering was performed with UCSF ChimeraX, developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco, with support from National Institutes of Health R01-GM129325 and the Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases. This work was supported by the following grants: K25CA204526 to ESW, K99GM123221 to MKD, INSERM ITMO Cancer Plan Cancer 2015-2020 to MP, Fondation ARC no. DOC20190508743 PhD fellowship to WMG-A, NIH R33CA235254 and R35GM133522 to RF, Cancer Prevention Research Institute of Texas grant RR160057 to RF, and R35-GM136428 to GD.