| Literature DB >> 20556632 |
Sylvia E Le Dévédec1, Kuan Yan, Hans de Bont, Veerander Ghotra, Hoa Truong, Erik H Danen, Fons Verbeek, Bob van de Water.
Abstract
Cell migration is essential in a number of processes, including wound healing, angiogenesis and cancer metastasis. Especially, invasion of cancer cells in the surrounding tissue is a crucial step that requires increased cell motility. Cell migration is a well-orchestrated process that involves the continuous formation and disassembly of matrix adhesions. Those structural anchor points interact with the extra-cellular matrix and also participate in adhesion-dependent signalling. Although these processes are essential for cancer metastasis, little is known about the molecular mechanisms that regulate adhesion dynamics during tumour cell migration. In this review, we provide an overview of recent advanced imaging strategies together with quantitative image analysis that can be implemented to understand the dynamics of matrix adhesions and its molecular components in relation to tumour cell migration. This dynamic cell imaging together with multiparametric image analysis will help in understanding the molecular mechanisms that define cancer cell migration.Entities:
Mesh:
Year: 2010 PMID: 20556632 PMCID: PMC2933849 DOI: 10.1007/s00018-010-0419-2
Source DB: PubMed Journal: Cell Mol Life Sci ISSN: 1420-682X Impact factor: 9.261
Imaging techniques to study protein dynamics and interactions in adhesion
| Technology | Biology | What is next | References |
|---|---|---|---|
| Photoactivation | Measure high resolution diffusion, trafficking and stability of protein
| For in vivo imaging, to track in the long term photoconverted cells and study protein dynamics | [ |
| FRAP or FLIP | Measure
| Comined FRAP and/or FLIP with TIRF and/or FRET | [ |
| FRAP–FLIP | Measure
| Comined FRAP-FLIP with TIRF and/or FRET | Le Dévédec et al., unpublished data |
| FCS, ICS, RICS | Determine rates of diffusion, degree of aggregation, number of fluorescent entities and flow velocities (mainly used in solution)
| In living cells to study protein distribution, dynamics and interactions at high time and spatial resolution | [ |
| FSM | Movement of structure, assembly dynamics, and subunit turnover
| [ | |
| FRET | Protein–protein interaction and protein activity
| Combined with FRAP and/or TIRF and in vivo | [ |
Modelling distinct modes of tumour migration
| Environment | Models | Microscopy | Obtained information | References |
|---|---|---|---|---|
| In vitro/2D | Matrix coating | Wide-field, confocal | Insights into the organisation of molecular machineries underlying cell adhesion and migration | [ |
| Patterned | ||||
| In vitro/3D | Matrigel | Wide-field, confocal, confocal reflection microscopy, SHG | Distinguish aspects of cell movement/invasion (collective/individual; mesenchymal/amoeboid). Visualise interactions cell. ECM (in particular, collagen fibers I) | [ |
| On/in collagen gel | ||||
| In vivo | Zebrafish | Confocal, confocal reflection, Multiphoton (SHG and FLIM) | Aspects of cell movement in the primary environment. Visualise interaction between tumour cells and tumour environment (ECM, host cells and blood vessels). Visualise intravasation event when blood vessels are counterstained | [ |
| Mouse | ||||
| Rat |
Challenges and potential solutions to increase throughput of imaging techniques
| Challenge | Type | Potential solutions |
|---|---|---|
| Sample preparation | 2D | Development of predictive screening assays (→ use of primary or embryonic stem cells (ESCs) instead of easy to culture tumour cell-lines but genetically aberrant) |
| 3D | Development and validation of relevant 3D scaffolds (→ characterise ECM of patient tumour material) | |
| Improve 3D cell culture techniques for automated liquid handling robotics (→ collaboration between academia and pharmaceutical companies) | ||
| In vivo | Automated microinjection of tumour cells in ZF (→ automatic microinjector based on pattern recognition) | |
| Automated filling of the microwells plates with ZF preferably all similarly orientated (→ make use of adapted mould) | ||
| Automated image acquisition | 2D | Autofocus combined with z-scans for 3D imaging (→ image-based or reflection-based autofocusing) |
| Pre-optimisation of the acquisition settings (→ autoexposure algorithm to adjust integration time of detectors) | ||
| Automated object (cells of matrix adhesion) localisation (→ autoexposure algorithm to adjust integration time of detectors) | ||
| Intelligent microscope [ | ||
| 3D/in vivo | Higher throughput kinetic imaging microscopes suitable for automated 3D invasion studies (→ see commercially available kinetic imaging systems such as Incucyte or Cell-IQ [ | |
| Higher throughput kinetic imaging microscopes suitable for FRET, FRAP or FCS (→ in the future, intelligent microscope that recognises the object to be visualised) | ||
| Data handling | 2D | Storing terabytes of data (→ storage area network (SAN) which has multiterabyte to tens of terabytes capacity; commonly, data on the SAN are backed up on tape as well) |
| 3D/in vivo | ||
| Data management (→ development of databases retrieved [ | ||
| Image analysis | 2D | Image segmentation (→ depending on imaging quality, choose between region-based, edge-based or region-growing method) |
| 3D/in vivo | ||
| Multiparametric image analysis (→ phenotypic profiling which involves computer vision methods) | ||
| Object tracking (→ high time resolution for imaging; adapted tracking algorithms for 3D imaging [ | ||
| Data mining and modeling | 2D | Screening reproducibility and estimators (→ quality standards, e.g. coefficient of variations (CVs) and zscores should not exceed 5% and should be higher than 0.5, respectively) |
| 3D/in vivo | Significant behaviour changes detection | |
| Automated classification (→ supervised machine learning) | ||
| Development of computational models |
Fig. 1Matrix adhesions diversity and composition. a Schematic view of the three classes of matrix adhesions found in adherent cells in vitro. b Image analysis of matrix adhesions. Confocal picture of epithelial cell stained for Hoechst (blue), P-Tyr (green) and F-actin (red) (a); scale bar 10 μm. Confocal picture of focal adhesions only (b). Matrix adhesions segmentation (c) and clustering according to size (d). Distribution of the matrix adhesions according to their size (e) and clustering according to matrix adhesions intensity and length (f). c Matrix adhesions differ in size and shape according to their environment: in 2D rigid versus soft and in 3D; scale bar 10 μm
Fig. 2Imaging and analysis of single cell migration. a Epifluorescent imaging and analysis of migrating MTLn3 cells ectopically expressing GFP. Epifluorescent pictures (a) are waterline based segmented (b) and cells are consequently tracked (c); scale bar 50 μm. b Individual cell tracks of MTLn3 stimulated (a) or not by EGF (b) and clustering analysis of both treatments based on directionality, extension and velocity (c)
Fig. 3Imaging adhesions by confocal, wide-field and TIRF microscopy. a Z-scan series of the same renal epithelial LLC-PK1 cell overexpressing the reporter construct GFP-dSH2 performed with confocal (a), wide-field (b) and TIRF microscopy (c); scale bar 20 μm. Note the advantage of TIRF microscopy for visualising matrix adhesions. b Analysis of matrix adhesions dynamics with TIRF microscopy. Time lapse of a migrating MTLn3 cell expressing GFP-paxillin and overlay of the different frames to illustrate the focal adhesion turnover; scale bar 10 μm. Note the fast turnover of matrix adhesions in these cells. c Multiparametric analysis of matrix adhesion dynamics. a Matrix adhesion segmentation, b tracking of individual matrix adhesions, c plot of all individual matrix adhesion trajectories and lifetime, d example of possible plot of different features (FA size, elongation and intensity) of an individual matrix adhesion over the time. The different features are normalised so that the data distribution is scaled to 1 and the average of all features are shifted to zero. A FA size of −2 indicates that the FA size in this frame is smaller than its average size by 2 in the normalised feature space
Fig. 4Studying dynamics of matrix adhesion associated proteins by FRAP analysis. a Time lapse of a typical spot-bleaching experiment. A region of interest within a focal adhesion is defined, bleached with a high power laser intensity and subsequently followed over the time until fluorescence intensity reached a steady state. Fluorescence recovery over the time is plotted. Scale bar 1 μm. b Combined FLIP–FRAP experiment is performed over the whole cell which allows analysis of several adhesions in the same time. Average loss in fluorescence and recovery of fluorescence are plotted over the time. Scale bar 10 μm
Fig. 5Imaging adhesion and cell migration in 3D culture system in vitro. a Phase contrast (scale bar 100 μm) and confocal pictures (scale bar 50 μm) of tubulogenesis assays conducted with LLC-PK1 cells overexpressing either GFP alone (a) or GFP-dSH2 (b) in Matrigel-collagen gels. b Time lapse series (of 17 h) of 4T1 mouse mammary carcinoma cells control (a) and paxillin knockdown (b) invading 3D collagen gels (made with the help of H. Truong). Scale bar 200 μm. c Detailed time lapse serie of one migrating 4T1 control cell. Scale bar 50 μm
Fig. 6Imaging tumour cell migration in vivo. a Migration and cell mass formation of human tumour cells injected into the yolk sac of zebrafish embryos (Pictures obtained from V. Gothra, S. He, BE Snaar-Jagalska, and EHJ Danen). a Phase contrast overview picture of the yolk sac of zebrafish embryos. b An example of spreading of 4T1 breast tumour cells (red) in transgenic zebrafish embryos expressing GFP under an endothelial promotor. Cells invaded, migrated and formed distant micrometastases, which are indicated with arrows. Scale bar 1 mm. c Two examples of zebrafishes without angiogenesis (i) and with angiogenesis formed through the tumour cell mass formed (ii). Scale bar 200 μm. b Rat mammary carcinoma MTLn3 cells in orthotopic mammary tumours move show high motility in vivo with an amoeboid. a Multiphoton microscopy shows tumour mass (green) and extra cellular matrix visualised by second harmonic generation (blue). Scale bar 100 μm. b Time-lapse images of MTLn3 carcinoma cells as they extend protrusions along ECM fibres (arrowheads). Images shown are at 5-min intervals
Fig. 7Steps for high content systems microscopy approach to understand cancer metastasis. Overview of imaging techniques that allow the phenotypic profiling of proteomics and cellomics (fixed multicolour and time-lapse) and the understanding at systems level (FRET, FRAP and FCS). To enhance our understanding of cancer metastasis, the high-throughput fluorescence microscopy should be applied in 2D, 3D and finally in vivo. a Sample preparation including cell transfection, exposure or immunostaining is nowadays conducted in multi-well dishes using robotics. b Automated image acquisition of fixed or living cells is done using automated microscopes (see Table 1, Available High Content Screening (HCS) instrumentation in [157]). Images can be acquired using different fluorescence microscopy techniques (e.g. fixed multicolour, time-lapse, FRET, FRAP, FCS. c Image data storage requires specialised software and hardware for data handling. d Automated image analysis which needs to be adapted or developed for each assay and is currently a challenge in HTS field. e Another big challenge in the field is the data mining and modelling which requires different disciplines such as statistics and bioinformatics (see Table 2 and available HCS informatics tools in [157]) (adapted from [142])