Computational Tools for Quantitative Analysis of Cell Growth Patterns and Morphogenesis in Actively Developing Plant Stem Cell Niches

  • Anirban Chakraborty
  • Ram Kishor Yadav
  • Min Liu
  • Moses Tataw
  • Katya Mkrtchyan
  • Amit Roy Chowdhury
  • G. Venugopala Reddy
Part of the Methods in Molecular Biology book series (MIMB, volume 876)


Pattern formation in developmental fields involves precise spatial arrangement of different cell types in a dynamic landscape wherein cells exhibit a variety of behaviors, such as cell division, cell expansion, and cell migration [Reddy (Curr Opin Plant Biol 11:88–931, 2008) and Meyerowitz (Cell 88:299–3082, 2007)]. The information is exchanged between multiple cell layers through cell–cell communication processes to regulate gene expression and cell behaviors in specifying distinct cell types. Therefore, a quantitative and dynamic understanding of the spatial and temporal organization of gene expression and cell behavioral patterns within multilayered and actively growing developmental fields is crucial to model the process of development. The quantification of spatiotemporal dynamics of cell behaviors requires computational tools in image analysis, statistical modeling, pattern recognition, machine learning, and dynamical system identification. Here, we give a brief account of recently developed methods in analyzing both local and global growth patterns in Arabidopsis shoot apical meristems. The computational toolkit can be used to gain new insights into causal relationships among cell growth, cell division, changes in gene expression patterns, and organ development by analyzing various mutants that affect these processes. This may allow us to develop function space models that capture variations in several growth parameters both at local/single-cell level and at global/organ level. In the long run, this may enable clustering of molecular pathways that mediate distinct cell behaviors.

Key words

Arabidopsis Shoot apical meristem Cell segmentation Cell tracking Self-renewal Differentiation Function space model 



We thank the microscopy core facility of center for plant cell biology (CEPCEB) and institute of integrative genome biology (IIGB), University of California, Riverside. This work is funded by National Science Foundation grants (IOS-0820842 to GVR and IIS-0712253 to ARC).


  1. 1.
    Reddy GV (2008) Live-imaging stem-cell homeostasis in the Arabidopsis shoot apex. Curr Opin Plant Biol 11:88–93PubMedCrossRefGoogle Scholar
  2. 2.
    Meyerowitz EM (2007) Genetic control review of cell division patterns in developing plants. Cell 88:299–308CrossRefGoogle Scholar
  3. 3.
    Steeves TA, Sussex IM (1989) Patterns in plant development. Cambridge University Press, New YorkCrossRefGoogle Scholar
  4. 4.
    Barton MK (2010) Twenty years on: the inner workings of the shoot apical meristem, developmental dynamo. Dev Biol 341:95–113PubMedCrossRefGoogle Scholar
  5. 5.
    Reddy GV, Heisler MG, Ehrhardt DW, Meyerowitz EM (2004) Real-time lineage analysis reveals oriented cell divisions associated with morphogenesis at the shoot apex of Arabidopsis thaliana. Development 131:4225–4237PubMedCrossRefGoogle Scholar
  6. 6.
    Grandjean O, Vernoux T, Laufs P, Belcram K, Mizukami Y, Traas J (2003) In Vivo Analysis of Cell Division. Cell Growth, and Differentiation at the Shoot Apical Meristem in Arabidopsis, Plant cell 16:74–87Google Scholar
  7. 7.
    Heisler MG, Ohno C, Das P, Sieber P, Reddy GV, Long JA, Meyerowitz EM (2005) Auxin transport dynamics and gene expression patterns during primordium development in the Arabidopsis Inflorescence Meristem. Curr Biol 15:1899–1911PubMedCrossRefGoogle Scholar
  8. 8.
    Reddy GV, Meyerowitz EM (2005) Stem-cell homeostasis and growth dynamics can be uncoupled in the Arabidopsis shoot apex. Science 310:663–667PubMedCrossRefGoogle Scholar
  9. 9.
    Yadav RK, Girke T, Pasala S, Xie M, Reddy GV (2009) Gene expression map of the Arabidopsis shoot apical meristem stem cell niche. Proc Natl Acad Sci USA 106:4941–4946PubMedCrossRefGoogle Scholar
  10. 10.
    Brady SM et al (2007) A high-resolution root spatiotemporal map reveals dominant expression patterns. Science 318:801–806PubMedCrossRefGoogle Scholar
  11. 11.
    Reddy GV, Roy-Chowdhury A (2009) Live-imaging and image processing of shoot apical meristems of Arabidopsis thaliana. Methods Mol Biol 553:305–316PubMedCrossRefGoogle Scholar
  12. 12.
    Viola P, Wells WM III (1997) Alignment by maximization of mutual information. Int J Comput 24:137–154CrossRefGoogle Scholar
  13. 13.
    Najman L, Schmitt M (1994) Watershed of a continuous function. Signal Process 38:99–112CrossRefGoogle Scholar
  14. 14.
    Chan T, Vese L (2001) Active contours without edges. IEEE Trans Image Process 10:266–277PubMedCrossRefGoogle Scholar
  15. 15.
    Nath S, Palaniappan K, Bunyak F (2006) Cell segmentation using coupled level sets and graph-vertex coloring. Lect Notes Comput Sci (MICCAI) 4190:101–108CrossRefGoogle Scholar
  16. 16.
    Gelas A, Mosaliganti K, Gouaillard A, Souhait L, Noche R, Obholzer N, Megason SG (2009) Variational level-set with Gaussian shape model for cell segmentation. In: IEEE International Conference on Image Processing, p 1089–1092Google Scholar
  17. 17.
    Zimmer C, Labruyere E, Meas-Yedid V, Guillen N, Olivo-Marin J-C (2002) Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: A tool for cell-based drug testing. IEEE Trans Med Imaging 21:1212–1221PubMedCrossRefGoogle Scholar
  18. 18.
    Dzyubachyk O, Niessen WJ, Meijering E (2007) A variational model for level-set based cell tracking in time-lapse fluorescence microscopy images. IEEE International Symposium on Biomedical Imaging: From Nano to Macro.Google Scholar
  19. 19.
    Ray N, Acton ST (2002) Active contours for cell tracking. In: Proceedings of the fifth IEEE southwest symposium on image analysis and interpretation, p 7–9Google Scholar
  20. 20.
    Mukherjee DP, Ray N, Acton ST (2001) Level set analysis for leukocyte detection and tracking. IEEE Trans Image Process 13:562–672CrossRefGoogle Scholar
  21. 21.
    Gor V, Elowitz M, Bacarian T, Mjolsness E (2005) Tracking cell signals in fluorescent images. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern RecognitionGoogle Scholar
  22. 22.
    Rangarajan A, Chui H, Bookstein FL (2005) The Softassign procrustes matching algorithm. Information Process Med Imaging 29–42Google Scholar
  23. 23.
    Dufour A, Shinin V, Tajbakhsh S, Guillen-Aghion N, Olivo-Marin JC, Zimmer C (2005) Segmentation and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces. IEEE Trans Image Process 14:1396–1410PubMedCrossRefGoogle Scholar
  24. 24.
    Padfield D, Rittscher J, Thomas N, Roysam B (2009) Spatiotemporal cell cycle phase analysis using level sets and fast marching methods. Medical Image Analysis 13(1):143–155Google Scholar
  25. 25.
    Debeir O, Van Ham P, Kiss R, Decaestecker C (2005) Tracking of migrating cells under phase-contrast video microscopy with combined mean-shift processes. IEEE Trans Med Imaging 24:697–711PubMedCrossRefGoogle Scholar
  26. 26.
    Li K, Miller ED, Weiss LE, Campbell PG, Kanade T. (2006) Online tracking of migrating and proliferating cells imaged with phase- contrast microscopy. In: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop, p 65–72Google Scholar
  27. 27.
    Kanade T, Li K (2005) Tracking of migrating and proliferating cells in phase-contrast microscopy imagery for tissue engineering. In: Proceedings of the First International Workshop on Computer Vision for Biomedical Image Applications (CVBIA), p 24Google Scholar
  28. 28.
    Fernandez R, Das P, Mirabet V, Moscardi E, Traas J, Verdeil JL, Malandain G, Godin C (2010) Imaging plant growth in 4D: robust tissue reconstruction and lineaging at cell resolution. Nature Methods 7:547–553PubMedCrossRefGoogle Scholar
  29. 29.
    Liu M, Yadav RK, Roy-Chowdhury A, Reddy GV. Automated tracking of stem cell lineages of arabidopsis shoot apex using local graph matching. Plant Journal Oxford, UK: Blackwell Publishing Ltd, 62(1):135–147Google Scholar
  30. 30.
    Nakahari T, Murakami M, Yoshida H, Miyamoto M, Sohma Y, Imai Y (1990) Decrease in rat submandibular acinar cell volume during ach stimulation. Am J Physiol 258:878–886Google Scholar
  31. 31.
    Farinas J, Kneen M, Moore M, Verkman A (1997) Plasma membrane water permeability of cultured cells and epithelia measured by light microscopy with spatial filtering. J Gen Physiol 110:283–296PubMedCrossRefGoogle Scholar
  32. 32.
    Kawahara K, Onodera M, Fukuda Y (1994) A simple method for continuous measurement of cell height during a volume change in a single a6 cell. Jpn J Physiol 44:411–419PubMedCrossRefGoogle Scholar
  33. 33.
    Chakraborty A, Liu M, Mkrtchyan K, Reddy GV, Roy-Chowdhury A (2010) Cell volume estimation from a sparse collection of noisy confocal image slices. In: Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing, p 183–189Google Scholar
  34. 34.
    Tataw O, Liu M, Yadav R, Reddy V, Roy-Chowdhury A (2010) Pattern analysis of stem cell growth dynamics in the shoot apex of Arabidopsis. In: IEEE International Conference on Image Processing, p 3617–3620Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Anirban Chakraborty
    • 1
  • Ram Kishor Yadav
    • 2
  • Min Liu
    • 1
  • Moses Tataw
    • 1
  • Katya Mkrtchyan
    • 1
  • Amit Roy Chowdhury
    • 1
  • G. Venugopala Reddy
    • 2
  1. 1.Department of Electrical EngineeringUniversity of CaliforniaRiversideUSA
  2. 2.Department of Botany and Plant Sciences, and Center for Plant Cell Biology, Institute of Integrative Genome BiologyUniversity of CaliforniaRiversideUSA

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