Visualization of Actin Polymerization in Invasive Structures of Macrophages and Carcinoma Cells Using Photoconvertible β-Actin – Dendra2 Fusion Proteins

Actin polymerization controls a range of cellular processes, from intracellular trafficking to cell motility and invasion. Generation and elongation of free barbed ends defines the regions of actively polymerizing actin in cells and, consequently, is of importance in the understanding of the mechanisms through which actin dynamics are regulated. Herein we present a method that does not involve cell permeabilization and provides direct visualization of growing barbed ends using photoswitchable β-actin - Dendra2 constructs expressed in murine macrophage and rat mammary adenocarcinoma cell lines. The method exploits the ability of photoconverted (red) G-actin species to become incorporated into pre-existing (green) actin filaments, visualized in two distinct wavelengths using TIRF microscopy. In growing actin filaments, photoconverted (red) monomers are added to the barbed end while only green monomers are recycled from the pointed end. We demonstrate that incorporation of actin into intact podosomes of macrophages occurs constitutively and is amenable to inhibition by cytochalasin D indicating barbed end incorporation. Additionally, actin polymerization does not occur in quiescent invadopodial precursors of carcinoma cells suggesting that the filaments are capped and following epidermal growth factor stimulation actin incorporation occurs in a single but extended peak. Finally, we show that Dendra2 fused to either the N- or the C-terminus of β-actin profoundly affects its localization and incorporation in distinct F-actin structures in carcinoma cells, thus influencing the ability of monomers to be photoconverted. These data support the use of photoswitchable actin-Dendra2 constructs as powerful tools in the visualization of free barbed ends in living cells

Dendra2 Photoswitching through the Mammary Imaging Window

In the last decade, intravital microscopy of breast tumors in mice and rats at single-cell resolution1-4 has resulted in important insights into mechanisms of metastatic behavior such as migration, invasion and intravasation of tumor cells5, 6, angiogenesis3 and immune cells response7-9. We have recently reported a technique to image orthotopic mammary carcinomas over multiple intravital imaging sessions in living mice10. For this, we have developed a Mammary Imaging Window (MIW) and optimized imaging parameters for Dendra211 photoswitching and imaging in vivo. Here, we describe the protocol for the manufacturing of MIW, insertion of the MIW on top of a tumor and imaging of the Dendra2- labeled tumor cells using a custom built imaging box. This protocol can be used to image the metastatic behavior of tumor cells in distinct microenvironments in tumors and allows for long term imaging of blood vessels, tumor cells and host cells

Multiparametric Classification Links Tumor Microenvironments with Tumor Cell Phenotype

<div><p>While it has been established that a number of microenvironment components can affect the likelihood of metastasis, the link between microenvironment and tumor cell phenotypes is poorly understood. Here we have examined microenvironment control over two different tumor cell motility phenotypes required for metastasis. By high-resolution multiphoton microscopy of mammary carcinoma in mice, we detected two phenotypes of motile tumor cells, different in locomotion speed. Only slower tumor cells exhibited protrusions with molecular, morphological, and functional characteristics associated with invadopodia. Each region in the primary tumor exhibited either fast- or slow-locomotion. To understand how the tumor microenvironment controls invadopodium formation and tumor cell locomotion, we systematically analyzed components of the microenvironment previously associated with cell invasion and migration. No single microenvironmental property was able to predict the locations of tumor cell phenotypes in the tumor if used in isolation or combined linearly. To solve this, we utilized the support vector machine (SVM) algorithm to classify phenotypes in a nonlinear fashion. This approach identified conditions that promoted either motility phenotype. We then demonstrated that varying one of the conditions may change tumor cell behavior only in a context-dependent manner. In addition, to establish the link between phenotypes and cell fates, we photoconverted and monitored the fate of tumor cells in different microenvironments, finding that only tumor cells in the invadopodium-rich microenvironments degraded extracellular matrix (ECM) and disseminated. The number of invadopodia positively correlated with degradation, while the inhibiting metalloproteases eliminated degradation and lung metastasis, consistent with a direct link among invadopodia, ECM degradation, and metastasis. We have detected and characterized two phenotypes of motile tumor cells <i>in vivo</i>, which occurred in spatially distinct microenvironments of primary tumors. We show how machine-learning analysis can classify heterogeneous microenvironments <i>in vivo</i> to enable prediction of motility phenotypes and tumor cell fate. The ability to predict the locations of tumor cell behavior leading to metastasis in breast cancer models may lead towards understanding the heterogeneity of response to treatment.</p></div

Photoconversion-enabled fate mapping links invadopodia with tumor cell dissemination and metastasis.

<p>MDA-MB-231-Dendra2 tumors were photoconverted from green to red in the areas shown and representative images are shown for the 0 and 24 times after photoconversion. (A) In microenvironments with invadopodia, photoconverted (red) cells disappeared from the field of view and their numbers are reduced, suggesting intravasation. Photoconverted cell counts are shown on the bottom left. Scale bar: 40 µm. (B) In microenvironments of fast-locomotion, photoconverted (red) cells dispersed throughout the field of view without a decrease in number. (C) Relative number of photoconverted cells measured in microenvironments where either invadopodia (red bars) or fast-locomotion (blue bars) were present at 0 h, or in animals treated daily with GM6001 (grey bars). Measurements were normalized to 0 h and corrected for cell division (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001995#s4" target="_blank">Materials and Methods</a>). The number of photoconverted cells shows a negative trend only in fields that contain invadopodia (<i>p</i> = 1.4×10−², based on linear mixed effects model). Bars are means ± SEM. (D) Negative correlation was found between the relative number of invadopodia at 0 h and the number of photoconverted cells remaining in the field at 24 h (R² = 0.45, <i>p</i> = 5.4×10⁻⁶). (E) The number of invadopodia in the field of view significantly decreases (paired <i>t</i>-test, <i>p</i><10⁻⁴) in animals treated with GM6001. Invadopodia were monitored in matched fields of view and each field is represented by different color. For (C–E), measurements were done on 37 fields in ten animals. (F) Photoconverted (red) tumor cells were traced to lungs at 5 days post-photoconversion, where some metastases were a mix of tumor cells in red (arrived at the lung at 0–5 days), orange or yellow (photoconverted cells that divided and synthesized additional green Dendra2), and green (arrival time cannot be traced). (G) The number of lung metastases containing red cells counted 5 days post-photoconversion (control) is significantly affected by BAPN or GM6001 treatment panel i. Red metastases are also largely diminished in Tks5 KD tumors compared to Tks5 CTRL (panel ii), **<i>p</i><0.01, ***<i>p</i><0.001 based on paired <i>t</i>-tests; based on contingency analyses, χ²>20 for all treatments. Average values represent sum of lung metastases across 1 cm² (200 fields of view) in <i>n</i> = 3 animals per condition are shown along with ±SEM.</p

Mammary breast carcinoma exhibits two motility phenotypes.

<p>(A) Locomotion of MDA-MB-231-Dendra2 tumor cells (green) is analyzed in each section of 4-D stack from 0′–30′, while simultaneously visualizing collagen fibers (purple), blood vessels (red), and macrophages (white). Scale bar: 60 µm. See <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001995#pbio.1001995.s013" target="_blank">Movie S1a</a>. (B) Cell tracks over the 30′ period (30′–0′ = Δ30) are overlayed in blue showing fast locomotion in a number of tumor cells. (C) Zoom-in into an individual tumor cell (yellow outline) at 0′, 15′, and 30′ and the cell track over 30′ period (blue overlay). Scale bars: 10 µm. (D) Example of a field of view where fast locomotion was not detected. Scale bar: 60 µm. See Movie 1b. (E) Zoom-in into two different tumor cells shows that they have small protrusions (white arrowheads). Still frames at 0′, 15′, and 30′ are shown. Bottom panels show motility over 30′ (Δ30, red), overlaid on image at 0′. Also see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001995#pbio.1001995.s014" target="_blank">Movie S2</a>. Scale bars: 10 µm. (F) The two phenotypes of motility can be distinguished by comparing cell velocities of cell front, measured as average distance per hour. Tumor cells exhibit either small protrusions associated with slow-locomotion (red bars, velocity range 2–15 µm/h, red line is the mean, 8 µm/h) or fast-locomotion (blue bars, velocity range 22–250 µm/h, blue line is the mean, 69 µm/h). Note the difference in the bin range. For legibility, interval of every second bar is listed. (G) Comparisons of cell tracks over 300 min. Measurements were done at 3′, 9′, 15′, 21′, 30′, 60′, and 300′; time is plotted on a log scale. Each time point represents mean ± SEM. Measurements were done using 78 tumor cells in <i>n</i> = 7 animals.</p

Small protrusions associated with slow-locomoting cells are directed at ECM and blood vessels.

<p>(A) Range of directions of cell motility in relation to closest blood vessel (red strip) or collagen fiber (purple line). Protrusions on slow-locomoting cells are commonly directed towards blood vessels and surrounding collagen fibers while the direction of fast-locomoting cells is along collagen fibers and independent of blood vessels. (B) Fast-locomotion (blue dots) is independent on the blood vessel size. In contrast, the ability of distant tumor cells to form small protrusions is dependent on the blood vessel diameter. Tumor cells can form small protrusions either close or far from macrovessels, but only adjacent to microvessels (red shaded triangle illustrates this trend). While cells with small protrusions show a significant positive trend in variance (R² = 0.29, <i>p</i> = 1.7×10⁻⁸), distance of fast-locomoting cells' from the blood vessel is independent on the blood vessel diameter (R² = 0.07, <i>p</i> = 1.5×10⁻³). See <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001995#s4" target="_blank">Materials and Methods</a> for details. (C) Small protrusions associated with slow-locomotion (red) and fast-locomoting cells (blue) occur in similar percent of tumor cells within respective field of view (measured over 30′). (D) Numbers of small protrusions present on each of the fast- or slow- locomoting cells. Fast-locomoting cells do not exhibit small protrusions. Measurements were based on 1,000 cells in <i>n</i> = 4 animals and plotted on log scale. (E) Injection with MMP-inhibitor GM6001 (arrow) does not affect fast-locomotion (blue) but eliminates small protrusions (red). Measurements are based on 15 time-lapse movies in <i>n</i> = 3 animals, (***<i>p</i><0.001, based on paired <i>t</i>-tests, bars represent means ± SEM).</p

Small protrusions on slow-locomoting cells have the defining characteristics of invadopodia.

<p>(A) <i>In vivo</i> images of MDA-MB-231-cortactin-GFP cells show enrichment of cortactin (green) at the tip of small protrusions (left and middle panels) adjacent to collagen fibers (purple) or blood vessels (red) and, based on SVM parameter classification, located in microenvironments amenable for slow-locomotion. In the fast-locomoting cells (right panel), cortactin-GFP fluorescence is uniformly distributed (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001995#pbio.1001995.s016" target="_blank">Movie S4</a>). Lower panels are line-scans of cortactin-GFP fluorescence along the yellow lines. Scale bars: 10 µm. (B) MMPSense 680 can be observed in the blood vessel lumen immediately after injection. After extravasation (approximately 3 h), MMPSense 680 (cyan) colocalizes with the small protrusion (left), but is not present around the fast-locomoting cell (right). Lower panels are line-scans of cortactin-GFP (black) and MMPSense 680 (cyan) along the yellow lines, and show colocalization of cortactin and MMPSense 680 in the small protrusion. Also see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001995#pbio.1001995.s005" target="_blank">Figure S5</a> and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001995#pbio.1001995.s017" target="_blank">Movie S5</a>. Scale bars: 10 µm. (C) Comparison of MMPSense 680 accumulation (detected at 24 h) in fields of view where either slow-locomotion (“Slow” bar) or fast-locomotion (“Fast”) were detected at 0 h, orin fields of view where slow-locomotion was detected at 0 h prior to treatment with GM6001 (panel i). Accumulation of MMPSense 680 was also measured in Tks5 CTRL tumors and knockdown tumors Tks5 KD1 and Tks5 KD2 (panel ii). Same fields of view were located at 0 h and 24 h using fate-mapping. See <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001995#s4" target="_blank">Materials and Methods</a> and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001995#pbio.1001995.s006" target="_blank">Figure S6</a>. We observed approximately 3-fold higher relative MMPSense 680 signal in fields of view with slow-locomotion compared to fields of view with fast locomotion or treated with GM6001. Similar reduction in MMPSense 680 signal is observed in Tks5 knockdown tumors. Each bar represents mean ± SEM based on ≥6 regions in <i>n</i>≥3 animals. **<i>p</i><0.01, ***<i>p</i><0.001, using homoscedastic t-tests. (<b>D</b>) We observed significant positive correlation (R² = 0.44, <i>p</i> = 6×10⁻⁵) between the number of small protrusions at 0 h and MMPSense 680 fluorescence measured at 24 h (cyan triangles), but not when animals were treated with GM6001 (black triangles; R² = 0.14, <i>p</i> = 0.06).</p

ECM modulation changes the frequency of motility phenotypes and may induce phenotype switching.

<p>(A–C) The number of invadopodia monitored in matched fields of view remains stable over 2 days. Each field of view is represented by different line color. (B) Invadopodium number is significantly increased by 1 g/kg/day L-ribose treatment. (C) Invadopodium number is diminished by 6 mg/kg/day BAPN treatment. Inset: some fields of view start exhibiting fast-locomotion phenotype when invadopodia are diminished. Such transition correlates with loosening of the ECM. (D) Quantification of raw data from (A–C). Normalized numbers of invadopodia show no significant change in control areas 0–48 h (purple bars). A significant increase follows L-ribose treatment (green bars, <i>p</i><10⁻³) and a significant decrease follows BAPN treatment (red bars, <i>p</i><10⁻⁴) based on paired <i>t</i>-tests. Average values along with ±SEM are shown. (E) Normalized collagen fiber density, measured by second harmonic in the same areas as (D), follows the same trends as invadopodia numbers. While control areas show minimal collagen remodeling over 48 h, treatment with L-ribose (green bars) increases cross-linking and fiber bundling, resulting in fiber density increase, while BAPN treatment (red bars) reduces cross-linking and results in lower fiber density at 48 h. Also See <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001995#pbio.1001995.s008" target="_blank">Figure S8</a>. (F) Numbers of fast locomoting cells show no significant change in control areas 0–48 h (purple bars), while L-ribose treatment (green bars) induces significant reduction of fast locomotion. In addition, BAPN treatment (red bars) induces reduction of fast locomotion at 48 h. Average values along with ±SEM are shown, paired <i>t</i>-tests were done for comparison, *<i>p</i><0.05, **<i>p</i><0.01.</p

Tumor microenvironment complexity requires a non-linear SVM classification to predict locations of motility phenotypes.

<p>(A) Fast-locomotion (blue squares) or small protrusions associated with slow-locomotion (red squares) were scored in 184 fields of view collected in ten animals. These two phenotypes were mutually exclusive in 181/184 fields of view (blue and red squares). (B) Workflow of the intravital systems microscopy approach: The mammary imaging window is surgically implanted on top of the tumor and multicolor/4-D stacks (512 µm×512 µm×100 µm×30′) are collected in one to six random locations, with maximum 3 h of imaging. 3-D time lapses of motile tumor cells (green channel) are separated into fast-or slow-locomotion on the basis of cell track size. Multicolor 3-D stacks (at a single time point) are separated into four channels and microenvironment parameters were extracted. Next, statistical analyses are done to link motility phenotypes with microenvironment parameters. (C) A 3-D projection with top three performing parameters from 5-parameter SVM classification of tumor microenvironments. Circle sizes reflect the number of motile cells detected in a field of view. Shaded areas illustrate 3-D “phase space” of two microenvironment classes. Misclassifications (green circles) occur at borderline conditions. Also see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001995#pbio.1001995.s001" target="_blank">Figure S1</a> and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001995#pbio.1001995.s015" target="_blank">Movie S3</a>.</p