Machine learning based methodology to identify cell shape phenotypes associated with microenvironmental cues

Biomaterials

Volumn 104, Issue , 2016, Pages 104-118

  Chen, Desu ^a   Sarkar, Sumona ^c   Candia, Julián ^b,d,e   Florczyk, Stephen J ^c   Bodhak, Subhadip ^c   Driscoll, Meghan K ^b   Simon, Carl G ^c   Dunkers, Joy P ^c   Losert, Wolfgang ^b  

^a UNIVERSITY OF MARYLAND   (United States)

^b University of Maryland   (United States)

^c NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY   (United States)

^d UNIVERSITY OF MARYLAND SCHOOL OF MEDICINE   (United States)

^e NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

Cell morphology; Fibrous substrates; Machine learning; Stem cell; Supercell

Indexed keywords

ARTIFICIAL INTELLIGENCE; CELL CULTURE; CELL PROLIFERATION; CELLS; LEARNING SYSTEMS; MORPHOLOGY; STEM CELLS;

CELL MORPHOLOGY; CELL-MATERIAL INTERACTION; GENERALIZATION CAPABILITY; HETEROGENEOUS POPULATIONS; HUMAN BONE MARROW STROMAL CELLS; POTENTIAL INDICATORS; SELECTION PROCEDURES; SUPER CELL;

CYTOLOGY;

BIOMATERIAL;

ARTICLE; BONE MARROW STROMA CELL; CELL HETEROGENEITY; CELL POPULATION; CELL SHAPE; CELL STRUCTURE; CLASSIFICATION ALGORITHM; CONFOCAL MICROSCOPY; CONTROLLED STUDY; HUMAN; HUMAN CELL; IMAGE PROCESSING; MACHINE LEARNING; METHODOLOGY; MICROENVIRONMENT; PHENOTYPE; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE; AUTOMATED PATTERN RECOGNITION; CELL CULTURE; CELL SIZE; COMPUTER ASSISTED DIAGNOSIS; CYTOLOGY; MECHANOTRANSDUCTION; MESENCHYMAL STROMA CELL; MICROSCOPY; PHYSIOLOGY; PROCEDURES; TUMOR MICROENVIRONMENT;

CELL SIZE; CELLS, CULTURED; CELLULAR MICROENVIRONMENT; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MACHINE LEARNING; MECHANOTRANSDUCTION, CELLULAR; MESENCHYMAL STROMAL CELLS; MICROSCOPY; PATTERN RECOGNITION, AUTOMATED; PHENOTYPE;

EID: 84978701864     PISSN: 01429612     EISSN: 18785905     Source Type: Journal    
DOI: 10.1016/j.biomaterials.2016.06.040     Document Type: Article

References (57)

1. [1] Tee, S.Y., Fu, J., Chen, C.S., Janmey, P.A., Cell shape and substrate rigidity both regulate cell stiffness. Biophys. J. 100 (2011), L25–L27.
2. [2] Docheva, D., Padula, D., Popov, C., Mutschler, W., Clausen-Schaumann, H., Schieker, M., Researching into the cellular shape, volume and elasticity of mesenchymal stem cells, osteoblasts and osteosarcoma cells by atomic force microscopy. J. Cell. Mol. Med. 12 (2008), 537–552.
3. [3] Chen, C.S., Alonso, J.L., Ostuni, E., Whitesides, G.M., Ingber, D.E., Cell shape provides global control of focal adhesion assembly. Biochem. Biophys. Res. Commun. 307 (2003), 355–361.
4. [4] Bhadriraju, K., Yang, M., Alom Ruiz, S., Pirone, D., Tan, J., Chen, C.S., Activation of ROCK by RhoA is regulated by cell adhesion, shape, and cytoskeletal tension. Exp. Cell Res. 313 (2007), 3616–3623.
5. [5] McBeath, R., Pirone, D.M., Nelson, C.M., Bhadriraju, K., Chen, C.S., Cell shape, cytoskeletal tension, and RhoA regulate stem cell lineage commitment. Dev. Cell 6 (2004), 483–495.
6. [6] Kilian, K.A., Bugarija, B., Lahn, B.T., Mrksich, M., Geometric cues for directing the differentiation of mesenchymal stem cells. Proc. Natl. Acad. Sci. U. S. A. 107 (2010), 4872–4877.
7. [7] Guilak, F., Cohen, D.M., Estes, B.T., Gimble, J.M., Liedtke, W., Chen, C.S., Control of stem cell fate by physical interactions with the extracellular matrix. Cell Stem Cell 5 (2009), 17–26.
8. [8] Marklein, R.A., Lo Surdo, J.L., Bellayr, I.H., Godil, S.A., Puri, R.K., Bauer, S.R., High content imaging of early morphological signatures predicts long term mineralization capacity of human mesenchymal stem cells upon osteogenic induction. Stem Cells 34 (2016), 935–947.
9. [9] Treiser, M.D., Yang, E.H., Gordonov, S., Cohen, D.M., Androulakis, I.P., Kohn, J., et al. Cytoskeleton-based forecasting of stem cell lineage fates. Proc. Natl. Acad. Sci. U. S. A. 107 (2010), 610–615.
10. [10] Unadkat, H.V., Groen, N., Doorn, J., Fischer, B., Barradas, A.M., Hulsman, M., et al. High content imaging in the screening of biomaterial-induced MSC behavior. Biomaterials 34 (2013), 1498–1505.
11. [11] Matsuoka, F., Takeuchi, I., Agata, H., Kagami, H., Shiono, H., Kiyota, Y., et al. Morphology-based prediction of osteogenic differentiation potential of human mesenchymal stem cells. PLoS One, 8, 2013, e55082.
12. [12] Matsuoka, F., Takeuchi, I., Agata, H., Kagami, H., Shiono, H., Kiyota, Y., et al. Characterization of time-course morphological features for efficient prediction of osteogenic potential in human mesenchymal stem cells. Biotechnol. Bioeng. 111 (2014), 1430–1439.
13. [13] Farooque, T.M., Camp, C.H. Jr., Tison, C.K., Kumar, G., Parekh, S.H., Simon, C.G. Jr., Measuring stem cell dimensionality in tissue scaffolds. Biomaterials 35 (2014), 2558–2567.
14. [14] Sasaki, H., Takeuchi, I., Okada, M., Sawada, R., Kanie, K., Kiyota, Y., et al. Label-free morphology-based prediction of multiple differentiation potentials of human mesenchymal stem cells for early evaluation of intact cells. PLoS One, 9, 2014, e93952.
15. [15] Driscoll, M.K., Albanese, J.L., Xiong, Z.M., Mailman, M., Losert, W., Cao, K., Automated image analysis of nuclear shape: what can we learn from a prematurely aged cell?. Aging (Albany NY) 4 (2012), 119–132.
16. [16] Mogilner, A., Keren, K., The shape of motile cells. Curr. Biol. 19 (2009), R762–R771.
17. [17] Weiger, M.C., Ahmed, S., Welf, E.S., Haugh, J.M., Directional persistence of cell migration coincides with stability of asymmetric intracellular signaling. Biophys. J. 98 (2010), 67–75.
18. [18] Driscoll, M.K., McCann, C., Kopace, R., Homan, T., Fourkas, J.T., Parent, C., et al. Cell shape dynamics: from waves to migration. PLoS Comput. Biol., 8, 2012, e1002392.
19. [19] Matrone, M.A., Whipple, R.A., Balzer, E.M., Martin, S.S., Microtentacles tip the balance of cytoskeletal forces in circulating tumor cells. Cancer Res. 70 (2010), 7737–7741.
20. [20] Downing, T.L., Soto, J., Morez, C., Houssin, T., Fritz, A., Yuan, F., et al. Biophysical regulation of epigenetic state and cell reprogramming. Nat. Mater. 12 (2013), 1154–1162.
21. [21] Kumar, G., Tison, C.K., Chatterjee, K., Pine, P.S., McDaniel, J.H., Salit, M.L., et al. The determination of stem cell fate by 3D scaffold structures through the control of cell shape. Biomaterials 32 (2011), 9188–9196.
22. [22] Kumar, G., Waters, M.S., Farooque, T.M., Young, M.F., Simon, C.G. Jr., Freeform fabricated scaffolds with roughened struts that enhance both stem cell proliferation and differentiation by controlling cell shape. Biomaterials 33 (2012), 4022–4030.
23. [23] Ahn, E.H., Kim, Y., Kshitiz, An, S.S., Afzal, J., Lee, S., et al. Spatial control of adult stem cell fate using nanotopographic cues. Biomaterials 35 (2014), 2401–2410.
24. [24] Thakar, R.G., Cheng, Q., Patel, S., Chu, J., Nasir, M., Liepmann, D., et al. Cell-shape regulation of smooth muscle cell proliferation. Biophys. J. 96 (2009), 3423–3432.
25. [25] Nuzzo, R., Scientific method: statistical errors. Nature 506 (2014), 150–152.
26. [26] Johnson, V.E., Revised standards for statistical evidence. Proc. Natl. Acad. Sci. U. S. A. 110 (2013), 19313–19317.
27. [27] Pearson, K., LIII. On lines and planes of closest fit to systems of points in space. Philos. Mag. Ser. 6:2 (1901), 559–572.
28. [28] Pollard, S.M., Yoshikawa, K., Clarke, I.D., Danovi, D., Stricker, S., Russell, R., et al. Glioma stem cell lines expanded in adherent culture have tumor-specific phenotypes and are suitable for chemical and genetic Screens. Cell Stem Cell 4 (2009), 568–580.
29. [29] Kohonen, T., Self-organizing Maps. third ed., 2001, Springer-Verlag Berlin Heidelberg, New York.
30. [30] Li, W.J., Tuli, R., Huang, X., Laquerriere, P., Tuan, R.S., Multilineage differentiation of human mesenchymal stem cells in a three-dimensional nanofibrous scaffold. Biomaterials 26 (2005), 5158–5166.
31. [31] Xin, X., Hussain, M., Mao, J.J., Continuing differentiation of human mesenchymal stem cells and induced chondrogenic and osteogenic lineages in electrospun PLGA nanofiber scaffold. Biomaterials 28 (2007), 316–325.
32. [32] Hu, J., Feng, K., Liu, X., Ma, P.X., Chondrogenic and osteogenic differentiations of human bone marrow-derived mesenchymal stem cells on a nanofibrous scaffold with designed pore network. Biomaterials 30 (2009), 5061–5067.
33. [33] Ruckh, T.T., Kumar, K., Kipper, M.J., Popat, K.C., Osteogenic differentiation of bone marrow stromal cells on poly(ε-caprolactone) nanofiber scaffolds. Acta Biomater. 6 (2010), 2949–2959.
34. [34] Cristianini, N., Shawe-Taylor, J., An Introduction to Support Vector Machines: and Other Kernel-based Learning Methods. 2000, Cambridge University Press, Cambridge; New York.
35. [35] Witten, I.H., Frank, E., Hall, M.A., Chapter 4-Algorithms: the basic methods. Hall, M.A., Frank, E., Witten, I.H., (eds.) Data Mining: Practical Machine Learning Tools and Techniques, third ed., 2011, Morgan Kaufmann, Boston, 85–145.
36. [36] Candia, J., Maunu, R., Driscoll, M., Biancotto, A., Dagur, P., McCoy, J.P. Jr., et al. From cellular characteristics to disease diagnosis: uncovering phenotypes with supercells. PLoS Comput. Biol., 9, 2013, e1003215.
37. [37] Candia, J., Banavar, J.R., Losert, W., Understanding health and disease with multidimensional single-cell methods. J. Phys. Condens. Matter, 26, 2014, 073102.
38. [38] Lützen, H., Gesing, T.M., Hartwig, A., Nucleation as a new concept for morphology adjustment of crystalline thermosetting epoxy polymers. React. Funct. Polym. 73 (2013), 1038–1045.
39. [39] Bitar, M., Benini, F., Brose, C., Friederici, V., Imgrund, P., Bruinink, A., Evaluation of early stage human bone marrow stromal proliferation, cell migration and osteogenic differentiation on mu-MIM structured stainless steel surfaces. J. Mater. Sci. Mater. Med. 24 (2013), 1285–1292.
40. [40] Faia-Torres, A.B., Guimond-Lischer, S., Rottmar, M., Charnley, M., Goren, T., Maniura-Weber, K., et al. Differential regulation of osteogenic differentiation of stem cells on surface roughness gradients. Biomaterials 35 (2014), 9023–9032.
41. [41] Xu, C., Prince, J.L., Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7 (1998), 359–369.
42. [42] Sethian, J.A., Level set methods: evolving interfaces in geometry, fluid mechanics. Computer Vision, and Materials Science, 1996, Cambridge University Press, Cambridge.
43. [43] Tarca, A.L., Carey, V.J., X.-w. Chen, R. Romero, S. Drăghici, Machine learning and tts applications to biology. PLoS Comput. Biol., 3, 2007, e116.
44. [44] Altschuler, S.J., Wu, L.F., Cellular Heterogeneity: do differences make a difference?. Cell 141 (2010), 559–563.
45. [45] Gareth, J., Witten, D., Hastie, T., Tibshirani, R., An Introduction to Statistical Learning: with Applications in R. 2013, Springer-Verlag Berlin Heidelberg, New York.
46. [46] Shamir, L., Delaney, J.D., Orlov, N., Eckley, D.M., Goldberg, I.G., Pattern recognition software and techniques for biological image analysis. PLoS Comput. Biol., 6, 2010, e1000974.
47. [47] Guyon, I., Feature Extraction Foundations and Applications, Studies in Fuzziness and Soft Computing. 2006, Springer-Verlag Berlin Heidelberg.
48. [48] Xie, Z.X., Hu, Q.H., Yu, D.-R., Improved feature selection algorithm based on SVM and correlation. Wang, J., Yi, Z., Zurada, J., Lu, B.L., Yin, H., (eds.) Advances in Neural Networks - ISNN 2006, 2006, Springer-Verlag Berlin Heidelberg, 1373–1380.
49. [49] Jirapech-Umpai, T., Aitken, S., Feature selection and classification for microarray data analysis: evolutionary methods for identifying predictive genes. BMC Bioinforma., 6, 2005, 148.
50. [50] Downs, T., Gates, K.E., Masters, A., Exact simplification of support vector solutions. J. Mach. Learn. Res. 2 (2002), 293–297.
51. [51] Xia, X.L., Lyu, M., Lok, T.M., Huang, G.-B., Methods of decreasing the number of support vectors via k-mean clustering. Huang, D.S., Zhang, X.P., Huang, G.B., (eds.) Advances in Intelligent Computing, 2005, Springer-Verlag Berlin Heidelberg, 717–726.
52. [52] Chen, W.C., Wu, P.H., Phillip, J.M., Khatau, S.B., Choi, J.M., Dallas, M.R., et al. Functional interplay between cell cycle and cell phenotypes. Integr. Biol. quant. Biosci. nano macro 5 (2013), 523–534, 10.1039/c1032ib20246h.
53. [53] Ho, A.D., Wagner, W., Franke, W., Heterogeneity of mesenchymal stromal cell preparations. Cytotherapy 10 (2008), 320–330.
54. [54] Meyers, J., Craig, J., Odde, D.J., Potential for control of signaling pathways via cell size and shape. Curr. Biol. 16 (2006), 1685–1693.
55. [55] James, J., Goluch, E.D., Hu, H., Liu, C., Mrksich, M., Subcellular curvature at the perimeter of micropatterned cells influences lamellipodial distribution and cell polarity. Cell Motil. Cytoskelet. 65 (2008), 841–852.
56. [56] Arnsdorf, E.J., Tummala, P., Kwon, R.Y., Jacobs, C.R., Mechanically induced osteogenic differentiation–the role of RhoA, ROCKII and cytoskeletal dynamics. J. Cell Sci. 122 (2009), 546–553.
57. [57] Fu, L., Tang, T., Miao, Y., Zhang, S., Qu, Z., Dai, K., Stimulation of osteogenic differentiation and inhibition of adipogenic differentiation in bone marrow stromal cells by alendronate via ERK and JNK activation. Bone 43 (2008), 40–47.