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Germinal Center Texture Entropy as Possible Indicator of Humoral Immune Response: Immunophysiology Viewpoint

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Abstract

Purpose

In this article, we present the results indicating that spleen germinal center (GC) texture entropy determined by gray-level co-occurrence matrix (GLCM) method is related to humoral immune response.

Procedures

Spleen tissue was obtained from eight outbred male short-haired guinea pigs previously immunized by sheep red blood cells (SRBC). A total of 312 images from 39 germinal centers (156 GC light zone images and 156 GC dark zone images) were acquired and analyzed by GLCM method. Angular second moment, contrast, correlation, entropy, and inverse difference moment were calculated for each image. Humoral immune response to SRBC was measured using T cell-dependent antibody response (TDAR) assay.

Results

Statistically highly significant negative correlation was detected between light zone entropy and the number of TDAR plaque-forming cells (r s = −0.86, p < 0.01). The entropy decreased as the plaque-forming cells increased and vice versa. A statistically significant negative correlation was also detected between dark zone entropy values and the number of plaque-forming cells (r s = −0.69, p < 0.05).

Conclusions

Germinal center texture entropy may be a powerful indicator of humoral immune response. This study is one of the first to point out the potential scientific value of GLCM image texture analysis in lymphoid tissue cytoarchitecture evaluation. Lymphoid tissue texture analysis could become an important and affordable addition to the conventional immunophysiology techniques.

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References

  1. Gibbs P, Turnbull LW (2003) Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med 50:92–98

    Article  PubMed  Google Scholar 

  2. García G, Maiora J, Tapia A, De Blas M (2011) Evaluation of texture for classification of abdominal aortic aneurysm after endovascular repair. J Digit Imaging. doi:10.1007/s10278-011-9417-7

  3. Vince DG, Dixon KJ, Cothren RM, Cornhill JF (2000) Comparison of texture analysis methods for the characterization of coronary plaques in intravascular ultrasound images. Comput Med Imaging Graph 24:221–229

    Article  PubMed  CAS  Google Scholar 

  4. Chen EL, Chung P-C, Chen CL, Tsa HM, Chang CI (1998) An automatic diagnostic system for CT liver image classification. IEEE Trans Biomed Eng 45:783–794

    Article  PubMed  CAS  Google Scholar 

  5. Shamir L, Wolkow CA, Goldberg IG (2009) Quantitative measurement of aging using image texture entropy. Bioinformatics 25:3060–3063

    Article  PubMed  CAS  Google Scholar 

  6. Karaçali B, Tözeren A (2007) Automated detection of regions of interest for tissue microarray experiments: an image texture analysis. BMC Med Imag 7:2

    Article  Google Scholar 

  7. Pagano C, Calcagno A, Giacomelli L et al (2004) Molecular and morphometric description of adipose tissue during weight changes: a quantitative tool for assessment of tissue texture. Int J Mol Med 14:897–902

    PubMed  Google Scholar 

  8. Losa GA, Castelli C (2005) Nuclear patterns of human breast cancer cells during apoptosis: characterisation by fractal dimension and co-occurrence matrix statistics. Cell Tissue Res 322:257–267

    Article  PubMed  Google Scholar 

  9. Yogesan K, Jørgensen T, Albregtsen F, Tveter KJ, Danielsen HE (1996) Entropy-based texture analysis of chromatin structure in advanced prostate cancer. Cytometry 24:268–276

    Article  PubMed  CAS  Google Scholar 

  10. Haralick R, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC 3:610–621

    Article  Google Scholar 

  11. Li X, Pai A, Blumenkrantz G et al (2009) Spatial distribution and relationship of T1rho and T2 relaxation times in knee cartilage with osteoarthritis. Magn Reson Med 61:1310–1318

    Article  PubMed  Google Scholar 

  12. Gupta S, Gupta R, Singh S, Gupta K, Kudesia M (2010) Nuclear morphometry and texture analysis of B-cell non-Hodgkin lymphoma: utility in subclassification on cytosmears. Diagn Cytopathol 38:94–103

    PubMed  Google Scholar 

  13. Bordería AV, Hartmann BM, Fernandez-Sesma A, Moran TM, Sealfon SC (2008) Antiviral-activated dendritic cells: a paracrine-induced response state. J Immunol 181:6872–6881

    PubMed  Google Scholar 

  14. Pantić VS, Pantić SM (1992) Opposite actions of alpha-adrenergic vs beta-adrenergic influences on humoral immune response in guinea pigs. Ann N Y Acad Sci 650:165–169

    Article  PubMed  Google Scholar 

  15. White KL, Musgrove DI, Brown RD (2009) The sheep erythrocyte T-dependent antibody response (TDAR). Springer protocols 598:173–184

    Google Scholar 

  16. Ladics GS (2007) Primary immune response to sheep red blood cells (SRBC) as the conventional T-cell dependent antibody response (TDAR) test. J Immunotoxicol 4:149–152

    Article  PubMed  Google Scholar 

  17. Cabrera JE (2007) Texture analyzer for image J v 0.4. National Institutes of Health (NIH). http://rsbweb.nih.gov/ij/plugins/texture.html. Accessed 26 Aug 2011

  18. Hassan HH, Goussev S (2011) Texture analysis of high resolution aeromagnetic data to identify geological features in the Horn River Basin, NE British Columbia; Recovery–2011 CSPG CSEG CWLS Convention, Calgary, AB, Canada

  19. Albregtsen F (1995) Statistical texture measures computed from gray level coocurrence matrices, monograph, image processing laboratory Department of Informatics University of Oslo, 1995 http://www.ifi.uio.no/in384/info/glcm.ps. Accessed 26 Aug 2011

  20. Allen CD, Okada T, Cyster JG (2007) Germinal-center organization and cellular dynamics. Immunity 27:190–202

    Article  PubMed  CAS  Google Scholar 

  21. Cyster JG (2010) Shining a light on germinal center B cells. Cell 143:503–505

    Article  PubMed  CAS  Google Scholar 

  22. Carvalho LJ, Ferreira-da-Cruz MF, Daniel-Ribeiro CT, Pelajo-Machado M, Lenzi HL (2007) Germinal center architecture disturbance during Plasmodium berghei ANKA infection in CBA mice. Malar J 6:59

    Article  PubMed  CAS  Google Scholar 

  23. Zhang J, Tong L, Wang L, Li N (2008) Texture analysis of multiple sclerosis: a comparative study. Magn Reson Imag 26:1160–1166

    Article  Google Scholar 

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Acknowledgments

The authors are grateful to The Ministry of Education and Science, Republic of Serbia, Research Projects: oi-175059 and iii-41027. The authors also acknowledge the work of Julio E. Cabrera (National Institutes of Health, USA) and Toby C. Cornish (Johns Hopkins University, MD, USA) for integration of GLCM analysis into the National Institutes of Health “ImageJ” software.

Conflict of Interest

The authors report no conflicts of interest regarding this work.

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Correspondence to Igor Pantic.

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Pantic, I., Pantic, S. Germinal Center Texture Entropy as Possible Indicator of Humoral Immune Response: Immunophysiology Viewpoint. Mol Imaging Biol 14, 534–540 (2012). https://doi.org/10.1007/s11307-011-0531-1

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