Subscribe

Subscribe to our Newsletter and get informed about new publication regulary and special discounts for subscribers!

ILNS > Volume 54 > Morphology and Phenotype of Peripheral...
Morphology and Phenotype of Peripheral Erythrocytes of Fish: A Rapid Screening of Images by Using Software
< Back to Volume

Morphology and Phenotype of Peripheral Erythrocytes of Fish: A Rapid Screening of Images by Using Software

Full Text PDF

Abstract:

Many information of biological study as stained cells analysis under microscope cannot be obtained rich information like detail morphology, shape, size, proper intensity etc. but image analysis software can easily be detected all these parameters within short duration. The cells types can be yeast cells to mammalian cells. An attempt has been made to detect cellular abnormalities from an image of metronidazole (MTZ) treated compared to control images of peripheral erythrocytes of fish by using non-commercial, open-source, CellProfiler (CP) image analysis software (Ver. 2.1.0). The comparative results were obtained after analysis the software. In conclusion, this image based screening of Giemsa stained fish erythrocytes can be a suitable tool in biological research for primary toxicity prediction at DNA level alongwith cellular phenotypes. Moreover, still suggestions are needed in relation to accuracy of present analysis for Giemsa stained fish erythrocytes because previous works have been carried out images of cells with fluorescence dye.

Info:

Periodical:
International Letters of Natural Sciences (Volume 54)
Pages:
27-41
DOI:
https://doi.org/10.18052/www.scipress.com/ILNS.54.27
Citation:
S. N. Talapatra et al., "Morphology and Phenotype of Peripheral Erythrocytes of Fish: A Rapid Screening of Images by Using Software", International Letters of Natural Sciences, Vol. 54, pp. 27-41, 2016
Online since:
May 2016
Authors:
Soumendra Nath Talapatra, Priyadarshini Mitra, Snehasikta Swarnakar
Keywords:
CellProfiler Software, Erythrocytes Image, Image Analysis Software, Image Automation, Morphological Alteration, Peripheral Fish Erythrocytes, Phenotype
Export:
RIS, BibTeX, Text
Distribution:
Open Access

Creative Commons License
This work is licensed under a
Creative Commons Attribution 4.0 International License

References:

[1] J. T. Isaacs, W.B. Isaacs, W.F.J. Feitz, J. Scheres, Establishment and characterization of seven dunning rat prostatic cancer cell lines and their use in developing methods for predicting metastatic abilities of prostatic cancers, Prostate 9 (1986).

DOI: https://doi.org/10.1002/pros.2990090306

[2] K.A. Giuliano, R.L. DeBiasio, R.T. Dunlay, A. Gough, J.M. Volosky, J. Zock, G.N. Pavlakis, D.L. Taylor, High-content screening: a new approach to easing key bottlenecks in the drug discovery process, J. Biomol. Screen. 2 (1997) 249-259.

DOI: https://doi.org/10.1177/108705719700200410

[3] B.M. Rothen-Rutishauser, S. Schurch, B. Haenni, N. Kapp, P. Gehr, Interaction of fine particles and nanoparticles with red blood cells visualized with advanced microscopic techniques, Environ. Sci. Technol. 40 (2006) 4353-4359.

DOI: https://doi.org/10.1021/es0522635

[4] K. Tomankova, H. Kolarova, M. Vujtek, H. Zapletalova, Study of cancer cells used atomic force microscopy. Modern Research and Educational Topics in Microscopy. A. Méndez-Vilas, and J. Díaz, (Eds. ). Formatex (2007) pp.23-28.

[5] C. Wahlby, I.M. Sintorn, F. Erlandsson, G. Borgefors, E. Bengtsson, Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections, J. Microsc. 215 (2004) 67-76.

DOI: https://doi.org/10.1111/j.0022-2720.2004.01338.x

[6] A.E. Carpenter, T.R. Jones, M.R. Lamprecht, C. Clarke, I.H. Kang, O. Friman, D.A. Guertin, J.H. Chang, R.A. Lindquist, J. Moffat, P. Golland, D.M. Sabatini, CellProfiler: image analysis software for identifying and quantifying cell phenotypes, Genome Biol. 7 (2006).

DOI: https://doi.org/10.1186/gb-2006-7-10-r100

[7] M. R. Lamprecht, D.M. Sabatini, A.E. Carpenter, CellProfiler: free, versatile software for automated biological image analysis, Biotechniques 42 (2007) 71-75.

DOI: https://doi.org/10.2144/000112257

[8] T.R. Jones, A.E. Carpenter, M.R. Lamprecht, J. Moffat, S.J. Silver, J.K. Grenier, B. Adam, A.D. Castoreno, U.S. Eggert, D.E. Root, P. Golland, D.M. Sabatini, Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning, Proc. Nat. Acad. Sci. USA 106 (6) (2009).

DOI: https://doi.org/10.1073/pnas.0808843106

[9] V. Ljosa, A.E. Carpenter, Introduction to the quantitative analysis of two-dimensional fluorescence microscopy images for cell-based screening, PLoS Comput. Biol. 5 (12) (2009) e1000603. doi: 10. 1371/journal. pcbi. 1000603.

DOI: https://doi.org/10.1371/journal.pcbi.1000603

[10] L. Kamentsky, T.R. Jones, A. Fraser, M. -A. Bray, D.J. Logan, K.L. Madden, V. Ljosa, C. Rueden, K.W. Eliceiri, A.E. Carpenter, Improved structure, function, and compatibility for CellProfiler: Modular high-throughput image analysis software, Bioinformatics 27 (2011).

DOI: https://doi.org/10.1093/bioinformatics/btr095

[11] M. -A. Bray, M.S. Vokes, A.E. Carpenter, Using CellProfiler for automatic identification and measurement of biological objects in images, Curr. Protoc. Mol. Biol. 109 (2015) 14. 17. 1-14. 17. 13 doi: 10. 1002/0471142727. mb1417s109.

DOI: https://doi.org/10.1002/0471142727.mb1417s109

[12] M.V. Boland, M.K. Markey, R.F. Murphy, Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images, Cytometry 33 (1998) 366-375.

DOI: https://doi.org/10.1002/(sici)1097-0320(19981101)33:3<366::aid-cyto12>3.3.co;2-a

[13] M.V. Boland, R.F. Murphy, A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells, Bioinformatics 17 (2001) 1213-1223.

DOI: https://doi.org/10.1093/bioinformatics/17.12.1213

[14] A.A. Kiger, B. Baum, S. Jones, M.R. Jones, A. Coulson, C. Echeverri, N. Perrimon, A functional genomic analysis of cell morphology using RNA interference, J. Biol. 2 (4) (2003) 27.

[15] M. Tanaka, B. Bateman, D. Rauh, E. Vaisberg, S. Ramachandani, C. Zhang, K.C. Hansen, A.L. Burlingame, J.K. Trautman, K.M. Shokat, C.L. Adams, An unbiased cell morphology-based screen for new, biologically active small molecules, PLoS Biol. 3 (5) (2005).

DOI: https://doi.org/10.1371/journal.pbio.0030128

[16] J. Moffat, D.A. Grueneberg, X. Yang, S.Y. Kim, A.M. Kloepfer, G. Hinkle, B. Piqani, T. M. Eisenhaure, B. Luo, J.K. Grenier, A.E. Carpenter, S.Y. Foo, S.A. Stewart, B.R. Stockwell, N. Hacohen, W.C. Hahn, E.S. Lander, D.M. Sabatini, D.E. Root, A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen, Cell 124 (2006).

DOI: https://doi.org/10.1016/j.cell.2006.01.040

[17] B. Neumann, M. Held, U. Liebel, H. Erfle, P. Rogers, R. Pepperkok, J. Ellenberg, High-throughput RNAi screening by time-lapse imaging of live human cells, Nat. Methods 3 (2006) 385-390.

DOI: https://doi.org/10.1038/nmeth876

[18] C.L. Adams, V. Kutsyy, D.A. Coleman, G. Cong, A.M. Crompton, A. Elias, D. R. Oestreicher, J.K. Trautman, E. Vaisberg, Compound classification using image-based cellular phenotypes, Methods Enzymol. 414 (2006) 440-468.

DOI: https://doi.org/10.1016/s0076-6879(06)14024-0

[19] X. Chen, R.F. Murphy, Automated interpretation of protein subcellular location patterns, " Int. Rev. Cytol. 249 (2006) 193-227.

[20] N. Orlov, J. Johnston, T. Macura, L. Shamir, I. Goldberg, Computer vision for microscopy applications. Vision Systems: Segmentation and Pattern Recognition, eds. G. Obinata, and A. Dutta, I-Tech, Vienna, (2007) pp.221-242.

DOI: https://doi.org/10.5772/4962

[21] C. Lin, W. Mak, P. Hong, K. Sepp, N. Perrimon, Intelligent interfaces for mining large-scale RNAi-HCS image databases. IEEE 7th International Conference on Bioinformatics and Biomedical Engineering, IEEE, Washington DC (2007).

DOI: https://doi.org/10.1109/bibe.2007.4375742

[22] L.H. Loo, L.F. Wu, S.J. Altschuler, Image-based multivariate profiling of drug responses from single cells, Nat. Methods 4 (2007) 445-453.

[23] D.W. Young, A. Bender, J. Hoyt, E. McWhinnie, G-W. Chirn, C.Y. Tao, J.A. Tallarico, M. Labow, J.L. Jenkins, T.J. Mitchison, Y. Feng, Integrating high-content screening and ligand-target prediction to identify mechanism of action, Nat. Chem. Biol. 4 (2008).

DOI: https://doi.org/10.1038/nchembio.2007.53

[24] J. Wang, X. Zhou, P.L. Bradley, S-F. Chang, N. Perrimon, S.T.C. Wong, Cellular phenotype recognition for high-content RNA interference genome-wide screening, J. Biomol. Screen 13 (2008) 29-39.

DOI: https://doi.org/10.1177/1087057107311223

[25] C. Bakal, J. Aach, G. Church, N. Perrimon, Quantitative morphological signatures define local signaling networks regulating cell morphology, Science 316 (2007) 1753-1756.

DOI: https://doi.org/10.1126/science.1140324

[26] S.N. Talapatra, S. Dasgupta, G. Guha, M. Auddy, A. Mukhopadhyay, Therapeutic efficacies of Coriandrum sativum aqueous extract against metronidazole-induced genotoxicity in Channa punctatus peripheral erythrocytes, Food Chem. Toxicol. 48 (12) (2010).

DOI: https://doi.org/10.1016/j.fct.2010.09.021

[27] K. Rodenacker, E.A. Bengtsson, Feature set for cytometry on digitized microscopic images, Anal. Cell. Pathol. 25 (2003) 1-36.

[28] R.M. Haralick, K. Shanmuga, I. Dinstein, Textural features for image classification, Ieee T Syst Man Cyb SMC3 (1973) 610-621.

DOI: https://doi.org/10.1109/tsmc.1973.4309314

[29] D. Gabor, Theory of communication, J. Institute Electrical Engineers 93 (1946) 429-441.

[30] M.R. Turner, Texture discrimination by Gabor functions, Biol. Cybern. 55 (1986) 71-82.

[31] D. Zhang, G. Lu, Improving retrieval performance of zernike moment descriptor on affined shapes, IEEE Int. Conf. on Multimedia and Expo 1 (2002) 205-208.

DOI: https://doi.org/10.1109/icme.2002.1035754

[32] D.B. Wheeler, S.N. Bailey, D.A. Guertin, A.E. Carpenter, C.O. Higgins, D.M. Sabatini, RNAi living-cell microarrays for loss-of-function screens in Drosophila melanogaster cells, Nat. Methods 1 (2004) 127-132.

DOI: https://doi.org/10.1038/nmeth711

[33] S.N. Bailey, S.M. Ali, A.E. Carpenter, C.O. Higgins, D.M. Sabatini, Microarrays of lentiviruses for gene function screens in immortalized and primary cells, Nat. Methods 3 (2006) 117-122.

DOI: https://doi.org/10.1038/nmeth848

[34] L.E. Cowen, A.E. Carpenter, O. Matangkasombut, G.R. Fink, S. Lindquist, Genetic architecture of Hsp90-dependent drug resistance, Eukaryot. Cell 5 (12) (2006) 2184-2188.

DOI: https://doi.org/10.1128/ec.00274-06

[35] A.E. Baltus, D.B. Menke, Y.C. Hu, M.L. Goodheart, A.E. Carpenter, D.G. de Rooij, D.C. Page, In germ cells of mouse embryonic ovaries, the decision to enter meiosis precedes premeiotic DNA replication, Nat. Genet. 38 (12) (2006) 1430-1434.

DOI: https://doi.org/10.1038/ng1919

[36] A. Sigal, R. Milo, A. Cohen, N. Geva-Zatorsky, Y. Klein, I. Alaluf, N. Swerdlin, N. Perzov, T. Danon, Y. Liron, T. Raveh, A.E. Carpenter, G. Lahav, U. Alon, Dynamic proteomics in individual human cells uncovers widespread cell-cycle dependence of nuclear proteins, Nat. Methods 3 (2006).

DOI: https://doi.org/10.1038/nmeth892

[37] A. Khotanzad, Y.H. Hong, Invariant image recognition by zernike moments, IEEE, 12 (5) (1990) 489-497.

[38] T. Suk, J. Flusser, B. Zitova, Moments and moment invariants in pattern recognition, Wiley and Sons Ltd (2009).

[39] M. Vorobyov, Shape classification using zernike moments, iCamp at University of California Irvine August 5 (2011).

Show More Hide