In vivo cell-cycle profiling in xenograft tumors by quantitative intravital microscopy

Nature Methods

Volumn 12, Issue 6, 2015, Pages 577-585

  Chittajallu, Deepak R ^a   Florian, Stefan ^a   Kohler, Rainer H ^b   Iwamoto, Yoshiko ^b   Orth, James D ^c   Weissleder, Ralph ^a,b   Danuser, Gaudenz ^a,d   Mitchison, Timothy J ^a  

^a HARVARD MEDICAL SCHOOL   (United States)

^b MASSACHUSETTS GENERAL HOSPITAL   (United States)

^c UNIVERSITY OF COLORADO   (United States)

^d UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ERIBULIN; ISPINESIB; PACLITAXEL; RED FLUORESCENT PROTEIN; TRANSCRIPTOME;

ANIMAL CELL; ANIMAL EXPERIMENT; ANIMAL MODEL; ANIMAL TISSUE; ARTICLE; CELL CYCLE G2 PHASE; CELL CYCLE M PHASE; CELL CYCLE PROGRESSION; CELL CYCLE S PHASE; CELL DENSITY; CELL NUCLEUS; CELL STRUCTURE; CONTROLLED STUDY; DRUG CYTOTOXICITY; FEMALE; FIBROSARCOMA CELL LINE; IMAGE ANALYSIS; IN VITRO STUDY; IN VIVO STUDY; INTERPHASE; MICROSCOPY; MITOSIS INHIBITION; MITOSIS SPINDLE; MOUSE; NONHUMAN; NUCLEAR SIZE; PHENOTYPE; PRIORITY JOURNAL; QUANTITATIVE INTRAVITAL MICROSCOPY; SINGLE DRUG DOSE; TUMOR SPHEROID; TUMOR XENOGRAFT; ANIMAL; CELL CYCLE; EXPERIMENTAL NEOPLASM; GENE EXPRESSION REGULATION; IMAGE PROCESSING; METABOLISM; PHYSIOLOGY; PROCEDURES;

MUS;

ANIMALS; CELL CYCLE; GENE EXPRESSION REGULATION, NEOPLASTIC; IMAGE PROCESSING, COMPUTER-ASSISTED; MICE; MICROSCOPY; NEOPLASMS, EXPERIMENTAL; TRANSCRIPTOME;

EID: 84930179384     PISSN: 15487091     EISSN: 15487105     Source Type: Journal    
DOI: 10.1038/nmeth.3363     Document Type: Article

References (46)

1. Baker, B. M. & Chen, C. S. Deconstructing the third dimension: how 3D culture microenvironments alter cellular cues. J. Cell Sci. 125, 3015-3024 (2012).
2. Condeelis, J. & Weissleder, R. In vivo imaging in cancer. Cold Spring Harb. Perspect. Biol. 2, a003848 (2010).
3. Orth, J. D. et al. Analysis of mitosis and antimitotic drug responses in tumors by in vivo microscopy and single-cell pharmacodynamics. Cancer Res. 71, 4608-4616 (2011).
4. Pittet, M. J. & Weissleder, R. Intravital imaging. Cell 147, 983-991 (2011).
5. Thurber, G. M. et al. Single-cell and subcellular pharmacokinetic imaging allows insight into drug action in vivo. Nat. Commun. 4, 1504 (2013).
6. Sakaue-Sawano, A. et al. Visualizing spatiotemporal dynamics of multicellular cell-cycle progression. Cell 132, 487-498 (2008).
7. Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).
8. Held, M. et al. CellCognition: time-resolved phenotype annotation in high-throughput live cell imaging. Nat. Methods 7, 747-754 (2010).
9. Eliceiri, K. W. et al. Biological imaging software tools. Nat. Methods 9, 697-710 (2012).
10. Rasheed, S., Nelson-Rees, W. A., Toth, E. M., Arnstein, P. & Gardner, M. B. Characterization of a newly derived human sarcoma cell line (HT-1080). Cancer 33, 1027-1033 (1974).
11. Sakaue-Sawano, A., Kobayashi, T., Ohtawa, K. & Miyawaki, A. Drug-induced cell cycle modulation leading to cell-cycle arrest, nuclear mis-segregation, or endoreplication. BMC Cell Biol. 12, 2 (2011).
12. Umesh Adiga, P. S. & Chaudhuri, B. B. An efficient method based on watershed and rule-based merging for segmentation of 3-D histo-pathological images. Pattern Recognit. 34, 1449-1458 (2001).
13. Lin, G. et al. A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks. Cytometry A 56A, 23-36 (2003).
14. Wählby, C., Sintorn, I. M., Erlandsson, F., Borgefors, G. & Bengtsson, E. Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections. J. Microsc. 215, 67-76 (2004).
15. Lin, G. et al. Hierarchical, model-based merging of multiple fragments for improved three-dimensional segmentation of nuclei. Cytometry A 63A, 20-33 (2005).
16. Lin, G. et al. A multi-model approach to simultaneous segmentation and classification of heterogeneous populations of cell nuclei in 3D confocal microscope images. Cytometry A 71A, 724-736 (2007).
17. Al-Kofahi, Y., Lassoued, W., Lee, W. & Roysam, B. Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Trans. Biomed. Eng. 57, 841-852 (2010).
18. Vincent, L. Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans. Image Process. 2, 176-201 (1993).
19. Meyer, F. Topographic distance and watershed lines. Signal Process. 38, 113-125 (1994).
20. Amat, F. et al. Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data. Nat. Methods 11, 951-958 (2014).
21. Mitchison, T. J. The proliferation rate paradox in antimitotic chemotherapy. Mol. Biol. Cell 23, 1-6 (2012).
22. Komlodi-Pasztor, E., Sackett, D., Wilkerson, J. & Fojo, T. Mitosis is not a key target of microtubule agents in patient tumors. Nat. Rev. Clin. Oncol. 8, 244-250 (2011).
23. Spencer, S. L. & Sorger, P. K. Measuring and modeling apoptosis in single cells. Cell 144, 926-939 (2011).
24. Jordan, M. A. & Wilson, L. Microtubules as a target for anticancer drugs. Nat. Rev. Cancer 4, 253-265 (2004).
25. Janssen, A., Beerling, E., Medema, R. & van Rheenen, J. Intravital FRET imaging of tumor cell viability and mitosis during chemotherapy. PLoS ONE 8 (2013).
26. Milas, L. et al. Kinetics of mitotic arrest and apoptosis in murine mammary and ovarian tumors treated with taxol. Cancer Chemother. Pharmacol. 35, 297-303 (1995).
27. Zasadil, L. M. et al. Cytotoxicity of paclitaxel in breast cancer is due to chromosome missegregation on multipolar spindles. Sci. Transl. Med. 6, 229ra43 (2014).
28. Gascoigne, K. E. & Taylor, S. S. Cancer cells display profound intra-and interline variation following prolonged exposure to antimitotic drugs. Cancer Cell 14, 111-122 (2008).
29. Shi, J., Orth, J. D. & Mitchison, T. Cell type variation in responses to antimitotic drugs that target microtubules and kinesin-5. Cancer Res. 68, 3269-3276 (2008).
30. Purcell, J. W. et al. Activity of the kinesin spindle protein inhibitor ispinesib (SB-715992) in models of breast cancer. Clin. Cancer Res. 16, 566-576 (2010).
31. Towle, M. J. et al. Broad spectrum preclinical antitumor activity of eribulin (Halaven(R)): optimal effectiveness under intermittent dosing conditions. Anticancer Res. 32, 1611-1619 (2012).
32. Manfredi, M. G. et al. Characterization of alisertib (MLN8237), an investigational small-molecule inhibitor of aurora A kinase using novel in vivo pharmacodynamic assays. Clin. Cancer Res. 17, 7614-7624 (2011).
33. Towle, M. J. et al. Eribulin induces irreversible mitotic blockade: implications of cell-based pharmacodynamics for in vivo efficacy under intermittent dosing conditions. Cancer Res. 71, 496-505 (2011).
34. Mori, T. et al. Retention of paclitaxel in cancer cells for 1 week in vivo and in vitro. Cancer Chemother. Pharmacol. 58, 665-672 (2006).
35. Vanhoefer, U., Cao, S., Harstrick, A., Seeber, S. & Rustum, Y. M. Comparative antitumor efficacy of docetaxel and paclitaxel in nude mice bearing human tumor xenografts that overexpress the multidrug resistance protein (MRP). Ann. Oncol. 8, 1221-1228 (1997).
36. Friedrich, J., Seidel, C., Ebner, R. & Kunz-Schughart, L. A. Spheroid-based drug screen: considerations and practical approach. Nat. Protoc. 4, 309-324 (2009).
37. Tosoni, D., Di Fiore, P. P. & Pece, S. Functional purification of human and mouse mammary stem cells. Methods Mol. Biol. 916, 59-79 (2012).
38. Fan, J. Notes on Poisson distribution-based minimum error thresholding. Pattern Recognit. Lett. 19, 425-431 (1998).
39. Vapnik, V. N. Statistical Learning Theory (Wiley, 1998).
40. Ng, A. Y. & Jordan, M. I. On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes. Adv. Neural Inf. Process. Syst. 14, 841-848 (2002).
41. Ning, J., Zhang, L., Zhang, D. & Wu, C. Interactive image segmentation by maximal similarity based region merging. Pattern Recognit. 43, 445-456 (2010).
42. Breiman, L. Random Forests. Mach. Learn. 45, 5-32 (2001).
43. Otsu, N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62-66 (1979).
44. Klein, S., Staring, M., Murphy, K., Viergever, M. A. & Pluim, J. P. W. Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29, 196-205 (2010).
45. Haralick, R. M., Shanmugam, K. & Dinstein, I. Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3, 610-621 (1973).
46. Hall, M. et al. The WEKA data mining software: an update. SIGKDD Explor. 11, 10-18 (2009).