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Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 21, Iss. 21 — Oct. 21, 2013
  • pp: 25346–25355
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Image-inspired 3D multiphoton excited fabrication of extracellular matrix structures by modulated raster scanning

Visar Ajeti, Chi-Hsiang Lien, Shean-Jen Chen, Ping-Jung Su, Jayne M. Squirrell, Katharine H. Molinarolo, Gary E. Lyons, Kevin W. Eliceiri, Brenda M. Ogle, and Paul J. Campagnola  »View Author Affiliations


Optics Express, Vol. 21, Issue 21, pp. 25346-25355 (2013)
http://dx.doi.org/10.1364/OE.21.025346


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Abstract

Multiphoton excited photochemistry is a powerful 3D fabrication tool that produces sub-micron feature sizes. Here we exploit the freeform nature of the process to create models of the extracellular matrix (ECM) of several tissues, where the design blueprint is derived directly from high resolution optical microscopy images (e.g. fluorescence and Second Harmonic Generation). To achieve this goal, we implemented a new form of instrument control, termed modulated raster scanning, where rapid laser shuttering (10 MHz) is used to directly map the greyscale image data to the resulting protein concentration in the fabricated scaffold. Fidelity in terms of area coverage and relative concentration relative to the image data is ~95%. We compare the results to an STL approach, and find the new scheme provides significantly improved performance. We suggest the method will enable a variety of cell-matrix studies in cancer biology and also provide insight into generating scaffolds for tissue engineering.

© 2013 Optical Society of America

1. Introduction

The native extracellular matrix (ECM) has intrinsic 3D complexity with size features over length scales of ~100 nm in diameter to several microns in length. In addition to providing structural support, the ECM directs cell shape, differentiation, migration, proliferation, as well as new tissue synthesis [1

1. R. E. Burgeson, “Basement Membranes,” in Dermatology in General Medicine, T. B. Fitzpatrick, A. Z. Eisen, K. Wolff, I. M. Freedberg, and K. F. Austen, eds. (McGraw-Hill, 1987), pp. 288–303.

] by presenting a complex set of topographic, mechanical and biochemical cues to cells. It is now clear that in order to properly perform these functions cells recognize 3D spatial and biochemical domains of the ECM at the nano/microscale [2

2. N. J. Sniadecki, R. A. Desai, S. A. Ruiz, and C. S. Chen, “Nanotechnology for cell-substrate interactions,” Ann. Biomed. Eng. 34(1), 59–74 (2006). [CrossRef] [PubMed]

5

5. J. L. Charest, L. E. Bryant, A. J. Garcia, and W. P. King, “Hot embossing for micropatterned cell substrates,” Biomaterials 25(19), 4767–4775 (2004). [CrossRef] [PubMed]

]. The regulation of these events has implications for proper functioning of normal tissues but also for dynamics in diseased states [6

6. T. R. Cox and J. T. Erler, “Remodeling and homeostasis of the extracellular matrix: implications for fibrotic diseases and cancer,” Dis. Model. Mech. 4(2), 165–178 (2011). [CrossRef] [PubMed]

]. It is also becoming better appreciated that the ECM is altered in essentially all epithelial cancers and this remodeling continues through disease progression [7

7. P. Lu, V. M. Weaver, and Z. Werb, “The extracellular matrix: a dynamic niche in cancer progression,” J. Cell Biol. 196(4), 395–406 (2012). [CrossRef] [PubMed]

, 8

8. P. Friedl and S. Alexander, “Cancer invasion and the microenvironment: plasticity and reciprocity,” Cell 147(5), 992–1009 (2011). [CrossRef] [PubMed]

]. Thus an improved understanding of the ECM in terms of structural organization and cell-matrix interactions could lead to the development of more efficacious treatments. For example, cell adhesion and migration become mis-regulated during ovarian and other cancers [9

9. L. A. Liotta and W. G. Stetler-Stevenson, “Tumor invasion and metastasis: an imbalance of positive and negative regulation,” Cancer Res. 51(18Suppl), 5054s–5059s (1991). [PubMed]

], and better insight into these processes could lead to targeted therapeutics. The ECM also influences differentiation of stem cells during development and ECM cues have been effective in initiating differentiation in vitro [10

10. J. A. Santiago, R. Pogemiller, and B. M. Ogle, “Heterogeneous differentiation of human mesenchymal stem cells in response to extended culture in extracellular matrices,” Tissue Eng. Part A 15(12), 3911–3922 (2009). [CrossRef] [PubMed]

]. Creating biomimetic models of the ECM would afford such better investigations of the cancer biology and also provide insight into optimizing scaffolds for tissue regeneration/repair.

2. Methods

2.1 Materials

Fabrication solutions containing Bovine Serum Albumin (BSA, Sigma, St. Louis, MO) and Rose Bengal (Sigma) were prepared at 100 mg/mL and 1mM concentrations respectively. Fibronectin (FN; 1mg/mL, Millipore, Billerica, MA) was mixed with BSA and Rose Bengal at 1% v/v. A BSA monolayer adsorbed to the surface of silanized microscope slide serves as the non-specific background upon which the proteins are crosslinked.

2.2 Photochemistry

2.3 Optical setup and instrument control

The purpose built multiphoton fabrication instrument has been described in detail previously [16

16. M. Sridhar, S. Basu, V. L. Scranton, and P. J. Campagnola, “Construction of a laser scanning microscope for multiphoton excited optical fabrication,” Rev. Sci. Instrum. 74(7), 3474–3477 (2003). [CrossRef]

] and the new salient features are shown in Fig. 1
Fig. 1 Optical Configuration for the purpose built fabrication system, incorporating separate EOMs for power control and rapid shuttering and an FPGA for optimized control of the fabrication process.
. The ti:sapphire laser is coupled to a upright microscope stand (Axioskop 2, Zeiss, Thornewood, NY) and scanning is performed through a combination of laser scanning galvos (Cambridge Technolgoies, Bedford, MA) and a motorized stage (x-y-z, Ludl Electronic Products Ltd, Hawthorne, NY)) under LabVIEW control with a field programmable gate array (FPGA) board (Virtex-II PCI-7831R, National Instruments, Austin, TX) functioning as a DAQ [16

16. M. Sridhar, S. Basu, V. L. Scranton, and P. J. Campagnola, “Construction of a laser scanning microscope for multiphoton excited optical fabrication,” Rev. Sci. Instrum. 74(7), 3474–3477 (2003). [CrossRef]

]. The laser power entering the optical train is controlled through a 10 KHz electro-optic modulator (EOM, Conoptics, Danbuty, CT) and the laser is rapidly shuttered by a second, higher speed EOM (maximum 100 MHz, Conoptics). This instrument affords much greater flexibility in terms of scaffold size and complexity than could be achieved with a commercial laser scanning microscope. Parameters such as power, scanning area, the scan rate of galvos, the repetition of scanning pattern (#scans/layer) are set within the graphical user interface (GUI). The TPEF from entrapped residual photoactivator serves as the online fabrication diagnostic of crosslinking and is also read by the FPGA. The microscope is equipped with phase contrast and two-photon fluorescence imaging capabilities for characterization (e.g. immunofluorescence and quality control). We have shown previously that the minimum feature sizes for crosslinked protein structures correspond to the two-photon excited point spread function (PSF). For example, using 0.75 NA and 780 nm two-photon excitation, the lateral and axial “resolution” is about 600 nm and 1.8 microns, respectively [16

16. M. Sridhar, S. Basu, V. L. Scranton, and P. J. Campagnola, “Construction of a laser scanning microscope for multiphoton excited optical fabrication,” Rev. Sci. Instrum. 74(7), 3474–3477 (2003). [CrossRef]

]. Sub-resolution features have been obtained using MPE polymerization of polymers due to a chemical nonlinearity in the free radical kinetics [23

23. S. Maruo and J. T. Fourkas, “Recent progress in multiphoton microfabrication,” Laser Photon Rev 2(1-2), 100–111 (2008). [CrossRef]

]. However, this is not operative in protein crosslinking.

An FPGA was incorporated in the fabrication system to exploit parallelism of command executions (80 MHz clock rate) and to avoid bottlenecks in communications between the CPU and hardware through four of the First-In First-Out (FIFO) channels. The first two FIFO channels relay information from the main LabVIEW program to the FPGA to control the galvo mirrors and fast EOM shutter, while the other two record information from the PMT to create a live image of the fabrication making the communication between CPU and hardware near real-time. The source code of the instrument control software is freely available at: http://campagnola.molbio.wisc.edu/.

2.4 Modulated raster scanning

2.5 Image inspired fabrication

Figure 2
Fig. 2 Flowchart showing the steps required for the fabrication process.
shows the flow chart of the steps used in fabrication process to generate 3D structures using modulated raster scanning. We begin with 3D 8-bit image files (.bmp or .tif) that were acquired with proper sampling with respect to the Nyquist criterion. This is crucial for accurate representation of the tissue as well as providing optimal 3D structural integrity of the corresponding fabricated scaffold. This is also important so that the resolution (i.e. feature sizes) in the fabricated construct properly matches that of the original image. 12 bit images can also be down-converted to 8 bit for this purpose. Then we the images are processed either for the modulate raster scanning approach or using the previously described STL model and scanning approach [17

17. L. P. Cunningham, M. P. Veilleux, and P. J. Campagnola, “Freeform multiphoton excited microfabrication for biological applications using a rapid prototyping CAD-based approach,” Opt. Express 14(19), 8613–8621 (2006). [CrossRef] [PubMed]

], which we use below for comparison (see Fig. 4).

The images can then further processed (e.g. with ImageJ) to generate patterns to optimally produce the desired structure, as this is not always possible due to overlapping fibers in the ECM that make it difficult to discern clear, discrete patterns. This processing includes combinations of filters (e.g. background subtraction, noise removal, despeckle, threshold, etc.) and enhancement (e.g. edge detection, Gaussian Blur and determination of the Eigenvalues of the Hessian matrix to uniquely identify fibers) [37

37. C. Lorenz, I. C. Carlsen, T. M. Buzug, C. Fassnacht, and J. Weese, “A multi-scale line filter with automatic scale selection based on the Hessian matrix for medical image segmentation,” Lect. Notes Comput. Sci. 1252, 152–163 (1997). [CrossRef]

]. As an example, Fig. 3
Fig. 3 Example of image processing used in scaffold design. Far left is the original SHG image of the collagen in the stroma from an ovarian tissue malignancy. The middle image results from a threshold to remove the background noise. The far right image is obtained by calculating the Eigenvectors of the Hessian matrix of the SHG image to better accentuate fiber structures. Scale bar = 40 microns.
shows an original SHG image (single optical section) from a human ovarian malignancy as well as the same image processed with a binary threshold and also that resulting from the calculated Eigenvectors of the Hessian matrix. The final image patterns are then converted to 8-bit .bmp files and fed to the LabVIEW program for fabrication. The program also generates an MPE fluorescence image from the residual fluorescence of the photoactivator to give an assessment of the fabrication quality of the pattern. The structure is then created one section at a time based on the same respective optical section in the 3D image stack, with the same step sizes as used in the properly sampled image acquisition. The two-photon image is then acquired with the same objective and field size. By contrast, the STL method requires creating a 3D model from the original image data, then creating single sections for hatching, and features can be lost in the translation between these formats.

3. Results

We first demonstrate the capabilities of the modulated raster scanning approach by recreating a single section of a developing mouse heart (postnatal day 2). Figure 4(a)
Fig. 4 Single optical section of Col IV immunofluorescence and resulting fabricated structures. (a) Original immunofluorescence image,(b) immunofluorescence of BSA/FN fabricated structure created through modulated raster scanning, (c) two color overlap of (a) and (b), where white indicates high overlap, and (d) two-color overlap of (a) and structure created through a STL model, where the contrast was from Rose Bengal fluorescence. The green shows the regions which were not reproduced in the fabrication process. Scale bar = 30 microns.
shows the original image of the mouse heart immunostained for collagen IV. This pattern was directly fed to the fabrication microscope without further processing to generate the structure shown in Fig. 4(b). For demonstration purposes, the protein solution was a mixture of BSA with 1% FN, as we have shown previously it is more efficient to include BSA for increased structural support as it has much greater solubility than matrix proteins (e.g. 50 vs. 1 mg/mL). Moreover, due to pH2 considerations FN is easier to work with than Col IV, although we have developed different photochemistries for the latter [19

19. J. D. Pitts, A. R. Howell, R. Taboada, I. Banerjee, J. Wang, S. L. Goodman, and P. J. Campagnola, “New photoactivators for multiphoton excited three-dimensional submicron cross-linking of proteins: bovine serum albumin and type 1 collagen,” Photochem. Photobiol. 76(2), 135–144 (2002). [CrossRef] [PubMed]

]. The two color overlap of the original image (green) and that of the fabricated construct (purple) is shown in Fig. 4(c). Here the white indicates a high degree of spatial overlap and few green regions are seen, indicating that few topographic regions were not reproduced. As a quantitative comparison, we can calculate the fidelity, which we define both in terms of spatial overlap between the model of the image data and the fabricated construct and also by the match between the respective gray scale intensities. This is performed by pixel by pixel co-localization tests using Fiji (ImageJ). This analysis showed that this structure matches the original image with 96% fidelity both in spatial localization and intensity. Thus the fabricated structure preserved most of the microarchitecture of the original image with the resulting relative protein concentration corresponding to relative intensities of the original pattern. To show the comparison between the new approach and the STL-hatching method, the raw image data was converted to an STL model and hatched in AutoCad (Autodesk, San Rafael, CA) to define the galvo step size and fabricated, and the two-color overlap with the image data is shown in Fig. 4(d). Of note are the green regions, showing that this fabricated structure lacks more of the features present in the original image than that produced by modulated raster scanning. Moreover, the hatching approach is essentially binary and the resulting scaffold contains uniform protein concentration across the field of view. Using the same co-localization tests as in Fig. 4(c), the STL generated structure had fidelity to original pattern of approximately 75%, and was much less than that using the modulated raster scanning approach.

We have also used modulated raster scanning to fabricate a 3D scaffold from a mixture of BSA and FN where the design was derived directly from a 3D confocal immunofluorescence (FN) image stack of the left ventricle of mouse postnatal day 2. The image stack, (taken near and around a blood vessel), was comprised of 22 optical sections, taken 1 micron apart and the rendering is shown in Fig. 5(a)
Fig. 5 3D renderings of confocal image (a) derived from FN in mouse left ventricle and resulting fabricated structure created through modulated raster scanning (b). The large feature indicated by the arrow is a blood vessel. The contrast was FN immunofluorescence in both images. Scale bar = 30 microns.
. Here we applied a threshold to the image data in preparation of fabrication, which used the same number of slices and volume (129 x129 x 21 microns) and the 3D rendering of the FN immunofluorescence in the fabricated structure is shown in Fig. 5(b). If we compare both pixel coverage and intensity between the thresholded image data and fabricated structure, fidelity of at least 95% was achieved. We note that some subtle changes in intensity and therefore structural features were lost when the threshold was applied.

As another demonstration application for this technology, we show the feasibility of the fabrication technique to create 3D structures based on SHG image data of a human ovarian cancer. It has proven difficult to completely reproduce the original SHG images due to densely packed overlapping fibers of differing length. As an alternative, we utilized the Hessian eigenvalue approach (see Fig. 3(c)) to create a 3D model, as it captures the predominant fibers. The result of the 3D rendering of the raw data and fabricated construct (resulting from intrinsic fluorescence of entrapped Rose Bengal) are shown in Fig. 6(a)
Fig. 6 3D renderings of SHG image (a)) and fabricated structure (b) from a human ovarian cancer. Scale bar = 50 microns. (c) SEM of the fabricated structure taken at 600X magnification.
and 6(b), respectively. The structure was created at 0.75 NA, with an axial step size of 1 micron, which was the same as in the original image stack. The structure was comprised of the full 100 microns of thickness as in the original image stack however we were not able to image the entire axial extent due to a strong secondary filter effect of the entrapped Rose Bengal and instead show the top 50 microns. Good fidelity (~95%) is achieved, in terms of feature sizes (and area covered) and pattern intensity. The scanning electron micrograph (SEM) in Fig. 6(c) shows the fibrillar network in cross section throughout the 3D volume of the structure.

4. Discussion

While other nanofabrication technologies, e.g. photolithographies, can afford superior resolution or minimum feature sizes, these methods do not have freeform capabilities nor can produce crosslinked protein structures nor are compatible with aqueous environments. In addition, it is difficult to create spatially varying features, and almost impossible to do so with multiple components, as these techniques are designed to create replicates of the same structure. While conventional 3D printing via stereolithography is a freeform method, the minimum feature sizes are 50-100 microns, and the materials (e.g. plastics) have limited biocompatibility. In sum, these well-established methods do not readily afford full recapitulation of the complex native ECM microenvironment in a controlled and reproducible manner and MPE fabrication with flexible instrument control can fill this missing gap in technology.

5. Conclusions

Acknowledgments

PJC gratefully acknowledges support from NSF CBET-1057766. PJC and BMO gratefully acknowledge support from the American Heart Association IRG5570039. We thank Mr. Jorge Lara for assistance in creating figures. VA gratefully acknowledges support under NSF EAPSI-1210133.

References and links

1.

R. E. Burgeson, “Basement Membranes,” in Dermatology in General Medicine, T. B. Fitzpatrick, A. Z. Eisen, K. Wolff, I. M. Freedberg, and K. F. Austen, eds. (McGraw-Hill, 1987), pp. 288–303.

2.

N. J. Sniadecki, R. A. Desai, S. A. Ruiz, and C. S. Chen, “Nanotechnology for cell-substrate interactions,” Ann. Biomed. Eng. 34(1), 59–74 (2006). [CrossRef] [PubMed]

3.

S. Wang, C. Wong Po Foo, A. Warrier, M. M. Poo, S. C. Heilshorn, and X. Zhang, “Gradient lithography of engineered proteins to fabricate 2D and 3D cell culture microenvironments,” Biomed. Microdevices 11(5), 1127–1134 (2009). [CrossRef] [PubMed]

4.

X. Jiang, D. A. Bruzewicz, A. P. Wong, M. Piel, and G. M. Whitesides, “Directing cell migration with asymmetric micropatterns,” Proc. Natl. Acad. Sci. U.S.A. 102(4), 975–978 (2005). [CrossRef] [PubMed]

5.

J. L. Charest, L. E. Bryant, A. J. Garcia, and W. P. King, “Hot embossing for micropatterned cell substrates,” Biomaterials 25(19), 4767–4775 (2004). [CrossRef] [PubMed]

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T. R. Cox and J. T. Erler, “Remodeling and homeostasis of the extracellular matrix: implications for fibrotic diseases and cancer,” Dis. Model. Mech. 4(2), 165–178 (2011). [CrossRef] [PubMed]

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P. Lu, V. M. Weaver, and Z. Werb, “The extracellular matrix: a dynamic niche in cancer progression,” J. Cell Biol. 196(4), 395–406 (2012). [CrossRef] [PubMed]

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P. Friedl and S. Alexander, “Cancer invasion and the microenvironment: plasticity and reciprocity,” Cell 147(5), 992–1009 (2011). [CrossRef] [PubMed]

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10.

J. A. Santiago, R. Pogemiller, and B. M. Ogle, “Heterogeneous differentiation of human mesenchymal stem cells in response to extended culture in extracellular matrices,” Tissue Eng. Part A 15(12), 3911–3922 (2009). [CrossRef] [PubMed]

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D. Qin, Y. Xia, and G. M. Whitesides, “Soft lithography for micro- and nanoscale patterning,” Nat. Protoc. 5(3), 491–502 (2010). [CrossRef] [PubMed]

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J. J. Rice, M. M. Martino, L. De Laporte, F. Tortelli, P. S. Briquez, and J. A. Hubbell, “Engineering the regenerative microenvironment with biomaterials,” Adv Healthc Mater 2(1), 57–71 (2013). [CrossRef] [PubMed]

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C. A. DeForest and K. S. Anseth, “Advances in bioactive hydrogels to probe and direct cell fate,” Annu Rev Chem Biomol Eng 3(1), 421–444 (2012). [CrossRef] [PubMed]

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J. J. Moon, J. E. Saik, R. A. Poché, J. E. Leslie-Barbick, S. H. Lee, A. A. Smith, M. E. Dickinson, and J. L. West, “Biomimetic hydrogels with pro-angiogenic properties,” Biomaterials 31(14), 3840–3847 (2010). [CrossRef] [PubMed]

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J. D. Pitts, P. J. Campagnola, G. A. Epling, and S. L. Goodman, “Reaction efficiencies for sub-micron multi-photon freeform fabrications of proteins and polymers with applications in sustained release,” Macromolecules 33, 1514–1523 (2000). [CrossRef]

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17.

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S. Basu, L. P. Cunningham, G. D. Pins, K. A. Bush, R. Taboada, A. R. Howell, J. Wang, and P. J. Campagnola, “Multiphoton Excited Fabrication of Collagen Matrixes Cross-linked by a Modified Benzophenone Dimer: Bioactivity and Enzymatic Degradation,” Biomacromolecules 6(3), 1465–1474 (2005). [CrossRef] [PubMed]

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37.

C. Lorenz, I. C. Carlsen, T. M. Buzug, C. Fassnacht, and J. Weese, “A multi-scale line filter with automatic scale selection based on the Hessian matrix for medical image segmentation,” Lect. Notes Comput. Sci. 1252, 152–163 (1997). [CrossRef]

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OCIS Codes
(120.4610) Instrumentation, measurement, and metrology : Optical fabrication
(180.6900) Microscopy : Three-dimensional microscopy
(190.4180) Nonlinear optics : Multiphoton processes
(220.4000) Optical design and fabrication : Microstructure fabrication
(350.3450) Other areas of optics : Laser-induced chemistry

ToC Category:
Laser Microfabrication

History
Original Manuscript: July 29, 2013
Revised Manuscript: September 19, 2013
Manuscript Accepted: September 22, 2013
Published: October 17, 2013

Citation
Visar Ajeti, Chi-Hsiang Lien, Shean-Jen Chen, Ping-Jung Su, Jayne M. Squirrell, Katharine H. Molinarolo, Gary E. Lyons, Kevin W. Eliceiri, Brenda M. Ogle, and Paul J. Campagnola, "Image-inspired 3D multiphoton excited fabrication of extracellular matrix structures by modulated raster scanning," Opt. Express 21, 25346-25355 (2013)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-21-21-25346


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References

  1. R. E. Burgeson, “Basement Membranes,” in Dermatology in General Medicine, T. B. Fitzpatrick, A. Z. Eisen, K. Wolff, I. M. Freedberg, and K. F. Austen, eds. (McGraw-Hill, 1987), pp. 288–303.
  2. N. J. Sniadecki, R. A. Desai, S. A. Ruiz, and C. S. Chen, “Nanotechnology for cell-substrate interactions,” Ann. Biomed. Eng.34(1), 59–74 (2006). [CrossRef] [PubMed]
  3. S. Wang, C. Wong Po Foo, A. Warrier, M. M. Poo, S. C. Heilshorn, and X. Zhang, “Gradient lithography of engineered proteins to fabricate 2D and 3D cell culture microenvironments,” Biomed. Microdevices11(5), 1127–1134 (2009). [CrossRef] [PubMed]
  4. X. Jiang, D. A. Bruzewicz, A. P. Wong, M. Piel, and G. M. Whitesides, “Directing cell migration with asymmetric micropatterns,” Proc. Natl. Acad. Sci. U.S.A.102(4), 975–978 (2005). [CrossRef] [PubMed]
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