Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-24T05:42:08.590Z Has data issue: false hasContentIssue false
  • English
  • Français

Robot-Guided Atomic Force Microscopy for Mechano-Visual Phenotyping of Cancer Specimens

Published online by Cambridge University Press:  07 September 2015

Wenjin Chen ,
Zachary Brandes ,
Rajarshi Roy ,
Marina Chekmareva ,
Hardik J. Pandya ,
Jaydev P. Desai  and
David J. Foran
Wenjin Chen*
Affiliation:
Center for Biomedical Imaging & Informatics, Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, 195 Little Albany Street, New Brunswick, NJ 08901, USA Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, One RWJ Place, New Brunswick, NJ 08901, USA
Zachary Brandes
Affiliation:
Department of Mechanical Engineering, Maryland Robotics Center, Institute for Systems Research, University of Maryland, Glenn L. Martin Hall, College Park, MD 20742, USA
Rajarshi Roy
Affiliation:
Department of Mechanical Engineering, Vanderbilt University, Room 409, 2400 Highland Avenue, Nashville, TN 37205, USA
Marina Chekmareva
Affiliation:
Center for Biomedical Imaging & Informatics, Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, 195 Little Albany Street, New Brunswick, NJ 08901, USA
Hardik J. Pandya
Affiliation:
Department of Mechanical Engineering, Maryland Robotics Center, Institute for Systems Research, University of Maryland, Glenn L. Martin Hall, College Park, MD 20742, USA
Jaydev P. Desai
Affiliation:
Department of Mechanical Engineering, Maryland Robotics Center, Institute for Systems Research, University of Maryland, Glenn L. Martin Hall, College Park, MD 20742, USA
David J. Foran
Affiliation:
Center for Biomedical Imaging & Informatics, Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, 195 Little Albany Street, New Brunswick, NJ 08901, USA Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, One RWJ Place, New Brunswick, NJ 08901, USA
*
*Corresponding author. [email protected]
Get access

Abstract

Atomic force microscopy (AFM) and other forms of scanning probe microscopy have been successfully used to assess biomechanical and bioelectrical characteristics of individual cells. When extending such approaches to heterogeneous tissue, there exists the added challenge of traversing the tissue while directing the probe to the exact location of the targeted biological components under study. Such maneuvers are extremely challenging owing to the relatively small field of view, limited availability of reliable visual cues, and lack of context. In this study we designed a system that leverages the visual topology of the serial tissue sections of interest to help guide robotic control of the AFM stage to provide the requisite navigational support. The process begins by mapping the whole-slide image of a stained specimen with a well-matched, consecutive section of unstained section of tissue in a piecewise fashion. The morphological characteristics and localization of any biomarkers in the stained section can be used to position the AFM probe in the unstained tissue at regions of interest where the AFM measurements are acquired. This general approach can be utilized in various forms of microscopy for navigation assistance in tissue specimens.

Keywords

atomic force microscopyrobotic microscopywhole-slide imagingvirtual microscopyserial sections registration
Type
Biological Applications
Information
Microscopy and Microanalysis , Volume 21 , Issue 5 , October 2015 , pp. 1224 - 1235
Copyright
© Microscopy Society of America 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alessandrini, A. & Facci, P. (2005). AFM: A versatile tool in biophysics. Meas Sci Technol 16(6), R65–R92.CrossRefGoogle Scholar
Apostolopoulos, J., Davenport, P. & Tipping, P.G. (1996). Interleukin-8 production by macrophages from atheromatous plaques. Arterioscler Thromb Vasc Biol 16(8), 1007–1012.CrossRefGoogle ScholarPubMed
Barbareschi, M., Pecciarini, L., Cangi, M.G., Macri, E., Rizzo, A., Viale, G. & Doglioni, C. (2001). p63, a p53 homologue, is a selective nuclear marker of myoepithelial cells of the human breast. Am J Surg Pathol 25(8), 1054–1060.CrossRefGoogle ScholarPubMed
Batistatou, A., Stefanou, D., Arkoumani, E. & Agnantis, N.J. (2003). The usefulness of p63 as a marker of breast myoepithelial cells. In Vivo 17(6), 573–576.Google ScholarPubMed
Binnig, G., Quate, C.F. & Gerber, C. (1986). Atomic force microscope. Phys Rev Lett 56(9), 930–934.CrossRefGoogle ScholarPubMed
Borovec, J., Kybic, J., Busta, M., Ortiz-de-Solórzano, C. & Munoz-Barrutia, A. (2013). Registration of multiple stained histological sections. In 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI), IEEE, San Francisco, CA, USA, April 7–11, 2013, pp. 1034–1037.CrossRefGoogle Scholar
Braumann, U.D., Kuska, J.P., Einenkel, J., Horn, L.C., Loffler, M. & Hockel, M. (2005). Three-dimensional reconstruction and quantification of cervical carcinoma invasion fronts from histological serial sections. IEEE Trans Med Imaging 24(10), 1286–1307.CrossRefGoogle ScholarPubMed
Chen, W. & Foran, D.J. (2007). A computer-assisted microscopy system for automated image analysis of pathology specimens and tissue microarrays. In Image Analysis in Medical Microscopy and Pathology, Hai-Shan Wu & Andrew J. Einstein (Ed.), pp. 123–152. New York, NY: Research Signpost.Google Scholar
Cooper, L., Sertel, O., Kong, J., Lozanski, G., Huang, K. & Gurcan, M. (2009). Feature-based registration of histopathology images with different stains: An application for computerized follicular lymphoma prognosis. Comput Methods Programs Biomed 96(3), 182–192.CrossRefGoogle ScholarPubMed
Darling, E.M., Zauscher, S., Block, J.A. & Guilak, F. (2007). A thin-layer model for viscoelastic, stress-relaxation testing of cells using atomic force microscopy: Do cell properties reflect metastatic potential? Biophys J 92(5), 1784–1791.CrossRefGoogle ScholarPubMed
Dimitriadis, E.K., Horkay, F., Maresca, J., Kachar, B. & Chadwick, R.S. (2002). Determination of elastic moduli of thin layers of soft material using the atomic force microscope. Biophys J 82(5), 2798–2810.CrossRefGoogle ScholarPubMed
Domke, J. & Radmacher, M. (1998). Measuring the elastic properties of thin polymer films with the atomic force microscope. Langmuir 14(12), 3320–3325.CrossRefGoogle Scholar
Goode, A., Gilbert, B., Harkes, J., Jukic, D. & Satyanarayanan, M. (2013). OpenSlide: A vendor-neutral software foundation for digital pathology. J Pathol Inform 4, 27.CrossRefGoogle ScholarPubMed
Haga, H., Sasaki, S., Kawabata, K., Ito, E., Ushiki, T. & Sambongi, T. (2000). Elasticity mapping of living fibroblasts by AFM and immunofluorescence observation of the cytoskeleton. Ultramicroscopy 82(1–4), 253–258.CrossRefGoogle ScholarPubMed
Johnson, K.L. (1982). One hundred years of hertz contact. Proc Inst Mech Eng [H] 196, 363–378.CrossRefGoogle Scholar
Kallioniemi, O.-P., Wagner, U., Kononen, J. & Sauter, G. (2001). Tissue microarray technology for high-throughput molecular profiling of cancer. Hum Mol Genet 10(7), 657–662.CrossRefGoogle ScholarPubMed
Kuska, J.-P., Braumann, U.-D., Scherf, N., Loffler, M., Einenkel, J., Hockel, M., Horn, L.-C., Wentzensen, N. & von Knebel Doeberitz, M. (2006). Image registration of differently stained histological sections. In 2006 IEEE International Conference on Image Processing, IEEE, Atlanta, GA, USA, October 8–11, 2006, pp. 333–336.CrossRefGoogle Scholar
Lal, R. & John, S.A. (1994). Biological applications of atomic force microscopy. Am J Physiol Cell Physiol 266(1), C1–C21.CrossRefGoogle ScholarPubMed
Li, Q.S., Lee, G.Y.H., Ong, C.N. & Lim, C.T. (2008). AFM indentation study of breast cancer cells. Biochem Biophys Res Commun 374, 609–613.CrossRefGoogle ScholarPubMed
Lippolis, G., Edsjö, A., Helczynski, L., Bjartell, A. & Overgaard, N.C. (2013). Automatic registration of multi-modal microscopy images for integrative analysis of prostate tissue sections. BMC Cancer 13(1), 408.CrossRefGoogle ScholarPubMed
Mahaffy, R., Shih, C., MacKintosh, F. & Käs, J. (2000). Scanning probe-based frequency-dependent microrheology of polymer gels and biological cells. Phys Rev Lett 85(4), 880–883.CrossRefGoogle ScholarPubMed
Maitra, A., Adsay, N.V., Argani, P., Iacobuzio-Donahue, C., De Marzo, A., Cameron, J.L., Yeo, C.J. & Hruban, R.H. (2003). Multicomponent analysis of the pancreatic adenocarcinoma progression model using a pancreatic intraepithelial neoplasia tissue microarray. Mod Pathol 16(9), 902–912.CrossRefGoogle ScholarPubMed
Moreno-Flores, S. & Toca-Herrera, J.L. (2012). Hybridizing Surface Probe Microscopies: Toward a Full Description of the Meso-and Nanoworlds . Boca Raton, FL: CRC Press.CrossRefGoogle Scholar
Moses, R.L., Flint, P.W., Paik, C., Zinreich, S.J. & Cummings, C.W. (1995). Three‐dimensional reconstruction of the feline larynx with serial histologic sections. Laryngoscope 105(2), 164–168.CrossRefGoogle ScholarPubMed
Murayama, K., Meeker, R.B., Murayama, S. & Greenwood, R.S. (1993). Developmental expression of vasopressin in the human hypothalamus: double-labeling with in situ hybridization and immunocytochemistry. Pediatric research 33(2), 152–158.CrossRefGoogle ScholarPubMed
Pandya, H.J., Kim, H.T., Roy, R., Chen, W., Cong, L., Zhong, H., Foran, D.J. & Desai, J.P. (2014). Towards an automated MEMS-based characterization of benign and cancerous breast tissue using bioimpedance measurements. Sens Actuators B Chem 199, 259–268.CrossRefGoogle ScholarPubMed
Pandya, H.J., Roy, R., Chen, W., Chekmareva, M.A., Foran, D.J. & Desai, J.P. (2015). Accurate characterization of benign and cancerous breast tissues: Aspecific patient studies using piezoresistive microcantilevers. Biosens Bioelectron 63, 414–424.CrossRefGoogle ScholarPubMed
Pavlakis, K., Zoubouli, C., Liakakos, T., Messini, I., Keramopoullos, A., Athanassiadou, S., Kafousi, M. & Stathopoulos, E. (2006). Myoepithelial cell cocktail (p63+SMA) for the evaluation of sclerosing breast lesions. Breast 15(6), 705–712.CrossRefGoogle Scholar
Plodinec, M., Loparic, M., Monnier, C.A., Obermann, E.C., Zanetti-Dallenbach, R., Oertle, P., Hyotyla, J.T., Aebi, U., Bentires-Alj, M., Lim, R.Y. & Schoenenberger, C.A. (2012). The nanomechanical signature of breast cancer. Nat Nanotechnol 7(11), 757–765.CrossRefGoogle ScholarPubMed
Roberts, N., Magee, D., Song, Y., Brabazon, K., Shires, M., Crellin, D., Orsi, N.M., Quirke, R., Quirke, P. & Treanor, D. (2012). Toward routine use of 3D histopathology as a research tool. Am J Pathol 180(5), 1835–1842.CrossRefGoogle ScholarPubMed
Roy, R. (2014). Mechanical characterization of normal and cancerous breast tissue specimens using atomic force microscopy. PhD Thesis. University of Maryland, College Park, MD.Google Scholar
Roy, R., Chen, W., Cong, L., Goodell, L.A., Foran, D.J. & Desai, J.P. (2013). A semi-automated positioning system for contact-mode atomic force microscopy (AFM). IEEE Trans Autom Sci Eng 10(2), 462–465.CrossRefGoogle ScholarPubMed
Roy, R., Chen, W., Goodell, L.A., Hu, J., Foran, D.J. & Desai, J.P. (2010 a). Microarray-facilitated mechanical characterization of breast tissue pathology samples using contact-mode atomic force microscopy (AFM). In 2010 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), IEEE, Tokyo, Japan, September 26–29, 2010, pp. 710–715.CrossRefGoogle Scholar
Roy, R., Chen, W., Hu, J., Goodell, L.A., Foran, D.J. & Desai, J.P. (2010 b). Tissue microarray facilitated mechanical characterization of cancerous breast tissue using atomic force microscopy. Arch Pathol Lab Med 134, 939.Google Scholar
Roy, R. & Desai, J.P. (2014). Determination of mechanical properties of spatially heterogeneous breast tissue specimens using contact mode atomic force microscopy (AFM). Ann Biomed Eng 42(9), 1806–1822.CrossRefGoogle ScholarPubMed
Song, Y., Treanor, D., Bulpitt, A.J. & Magee, D.R. (2013). 3D reconstruction of multiple stained histology images. J Pathol Inform 4(Suppl), 7.CrossRefGoogle ScholarPubMed
Tomas, D. & Krušlin, B. (2004). The potential value of (Myo) fibroblastic stromal reaction in the diagnosis of prostatic adenocarcinoma. Prostate 61(4), 324–331.CrossRefGoogle ScholarPubMed
Tsujino, T., Seshimo, I., Yamamoto, H., Ngan, C.Y., Ezumi, K., Takemasa, I., Ikeda, M., Sekimoto, M., Matsuura, N. & Monden, M. (2007). Stromal myofibroblasts predict disease recurrence for colorectal cancer. Clin Cancer Res 13(7), 2082–2090.CrossRefGoogle ScholarPubMed
Wang, J., Wan, Z., Liu, W., Li, L., Ren, L., Wang, X., Sun, P., Ren, L., Zhao, H. & Tu, Q. (2009). Atomic force microscope study of tumor cell membranes following treatment with anti-cancer drugs. Biosens Bioelectron 25(4), 721–727.CrossRefGoogle ScholarPubMed
Wu, X., Liu, H., Liu, J., Haley, K.N., Treadway, J.A., Larson, J.P., Ge, N., Peale, F. & Bruchez, M.P. (2003). Immunofluorescent labeling of cancer marker Her2 and other cellular targets with semiconductor quantum dots. Nat Biotechnol 21(1), 41–46.CrossRefGoogle ScholarPubMed