Automatic quantification of mitochondrial fragmentation from two-photon microscope images of mouse brain tissue

Journal of Microscopy

Volumn 260, Issue 3, 2015, Pages 338-351

  Lihavainen, Eero ^a   Kislin, M ^b   Toptunov, D ^c   Khiroug, L ^b   Ribeiro, A S ^a  

^a TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

^b UNIVERSITY OF HELSINKI   (Finland)

^c Neurotar Ltd   (Finland)

Author keywords

Beta regression; Image analysis; Intravital imaging; Mitochondrial fragmentation; Mitochondrial morphology; Two photon microscopy

Indexed keywords

IMAGE ENHANCEMENT; MAMMALS; MORPHOLOGY; NEURONS; PHOTONS; PLANTS (BOTANY); TISSUE;

AUTOMATIC QUANTIFICATION; BETA REGRESSION; IMAGE-ANALYSIS; INTRAVITAL IMAGING; MICROSCOPE IMAGES; MITOCHONDRIAL FRAGMENTATION; MITOCHONDRIAL MORPHOLOGY; MITOCHONDRIAS; TWO PHOTON; TWO PHOTON MICROSCOPY;

MITOCHONDRIA;

CYAN FLUORESCENT PROTEIN; ENHANCED CYAN FLUORESCENT PROTEIN; GREEN FLUORESCENT PROTEIN;

ANIMAL TISSUE; ARTICLE; BRAIN TISSUE; CONTROLLED STUDY; HEART ARREST; MICROSCOPE IMAGE; MITOCHONDRION; MOUSE; NOISE; NONHUMAN; PIA MATER; PRIORITY JOURNAL; PROMOTER REGION; SUPPORT VECTOR MACHINE; TWO PHOTON MICROSCOPE IMAGE; ANIMAL; BRAIN; EVALUATION STUDY; GENE FUSION; GENETICS; IMAGE PROCESSING; LABORATORY AUTOMATION; MULTIPHOTON MICROSCOPY; NERVE CELL; PATHOLOGY; PROCEDURES; STAINING; TRANSGENIC MOUSE;

ANIMALS; ARTIFICIAL GENE FUSION; AUTOMATION, LABORATORY; BRAIN; GREEN FLUORESCENT PROTEINS; HEART ARREST; IMAGE PROCESSING, COMPUTER-ASSISTED; MICE, TRANSGENIC; MICROSCOPY, FLUORESCENCE, MULTIPHOTON; MITOCHONDRIA; NEURONS; PATHOLOGY; STAINING AND LABELING;

EID: 84955202465     PISSN: 00222720     EISSN: 13652818     Source Type: Journal    
DOI: 10.1111/jmi.12301     Document Type: Article

References (35)

1. Bilsland, L.G., Sahai, E., Kelly, G., Golding, M., Greensmith, L. & Schiavo, G. (2010) Deficits in axonal transport precede ALS symptoms in vivo. Proc. Natl. Acad. Sci. U. S. A. 107, 20523-20528.
2. Brady, N.R., Hamacher-Brady, A. & Gottlieb, R.A. (2006) Proapoptotic BCL-2 family members and mitochondrial dysfunction during ischemia/reperfusion injury, a study employing cardiac HL-1 cells and GFP biosensors. Biochim. Biophys. Acta. 1757, 667-678.
3. Chen, S., Tran, S., Sigler, A. & Murphy, T.H. (2011) Automated and quantitative image analysis of ischemic dendritic blebbing using in vivo 2-photon microscopy data. J. Neurosci. Methods. 195, 222-231.
4. Cortes, C. & Vapnik, V. (1995) Support-Vector Networks. Mach. Learn. 20, 273-297.
5. Eleyan, A., Özkaramanli, H. & Demirel, H. (2008) Complex wavelet transform-based face recognition. EURASIP J. Adv. Signal Process. 2008, 1-14. doi:10.1155/2008/185281.
6. Farrand, L., Kim, J.Y., Im-Aram, A., Suh, J.-Y., Lee, H.J. & Tsang, B.K. (2013) An improved quantitative approach for the assessment of mitochondrial fragmentation in chemoresistant ovarian cancer cells. PLoS One. 8, e74008.
7. Ferrari, S. & Cribari-Neto, F. (2004) Beta regression for modelling rates and proportions. J. Appl. Stat. 31, 799-815.
8. Friberg, H., Rundgren, M., Westhall, E., Nielsen, N. & Cronberg, T. (2013) Continuous evaluation of neurological prognosis after cardiac arrest. Acta Anaesthesiol. Scand. 57, 6-15.
9. Hall, A.M., Rhodes, G.J., Sandoval, R.M., Corridon, P.R. & Molitoris, B.A. (2013) In vivo multiphoton imaging of mitochondrial structure and function during acute kidney injury. Kidney Int. 83, 72-83.
10. Haralick, R.M., Shanmugam, K. & Dinstein, I. (1973) Textural features for image classification. IEEE Trans. Syst. Man. Cybern. B. Cybern. SMC-3, 610-621.
11. Hastie, T., Tibshirani, R. & Friedman, J. (2009) The Elements of Statistical Learning, Springer Series in Statistics. Springer New York, New York, NY. doi:10.1007/b94608
12. Holschneider, M., Kronland-Martinet, R., Morlet, J., Tchamitchian, P. (1990) A real-time algorithm for signal analysis with the help of the wavelet transform. Wavelets, Time-Frequency Methods and Phase Space, Inverse Problems and Theoretical Imaging (ed. by J.-M. Combes, A. Grossmann & P. Tchamitchian ), pp. 286-297. Berlin Heidelberg, Springer.
13. Holtmaat, A., Bonhoeffer, T., Chow, D.K. et al. (2009). Long-term, high-resolution imaging in the mouse neocortex through a chronic cranial window. Nat. Protoc. 4, 1128-1144.
14. Knott, A. & Perkins, G. (2008). Mitochondrial fragmentation in neurodegeneration. Nat. Rev. Neurosci. 9, 505-518.
15. Kucharz, K., Wieloch, T. & Toresson, H. (2011). Rapid fragmentation of the endoplasmic reticulum in cortical neurons of the mouse brain in situ following cardiac arrest. J. Cereb. Blood Flow Metab. 31, 1663-1667.
16. Li, Z., Okamoto, K.I., Hayashi, Y. & Sheng, M. (2004). The importance of dendritic mitochondria in the morphogenesis and plasticity of spines and synapses. Cell. 119, 873-887.
17. Lihavainen, E., Mäkelä, J., Spelbrink, J.N. & Ribeiro, A.S. (2012). Mytoe: automatic analysis of mitochondrial dynamics. Bioinformatics. 28, 1050-1051.
18. Lin, M.T. & Beal, M.F. (2006). Mitochondrial dysfunction and oxidative stress in neurodegenerative diseases. Nature. 443, 787-795.
19. Lin, Y., Lin, C., Tsai, Y. & Ku, T. (2010). A spectral graph theoretic approach to quantification and calibration of collective morphological differences in cell images. Bioinformatics. 26, i29-i37.
20. Liu, R.R. & Murphy, T.H. (2009). Reversible cyclosporin A-sensitive mitochondrial depolarization occurs within minutes of stroke onset in mouse somatosensory cortex in vivo: a two-photon imaging study. J. Biol. Chem. 284, 36109-17.
21. MATLAB and Image Processing Toolbox Release 2012b. (2012) The MathWorks, Inc., Natick, Massachusetts, United States.
22. Mattson, M.P., Gleichmann, M., Cheng, A. (2008). Mitochondria in neuroplasticity and neurological disorders. Neuron. 60, 748-766.
23. McDowall, L.M. & Dampney, R.A.L. (2006). Calculation of threshold and saturation points of sigmoidal baroreflex function curves. Am. J. Physiol. Heart Circ. Physiol. 291, H2003-H2007.
24. Misgeld, T., Kerschensteiner, M., Bareyre, F.M., Burgess, R.W., Lichtman, J.W. (2007). Imaging axonal transport of mitochondria in vivo. Nat. Methods 4, 559-561.
25. Nikić, I., Merkler, D., Sorbara, C., et al. (2011). A reversible form of axon damage in experimental autoimmune encephalomyelitis and multiple sclerosis. Nat. Med. 17, 495-499.
26. Peng, J.-Y. et al. (2011). Automatic morphological subtyping reveals new roles of caspases in mitochondrial dynamics. PLoS Comput. Biol. 7. e1002212.
27. Picard, M., Shirihai, O.S., Gentil, B.J. & Burelle, Y. (2013). Mitochondrial morphology transitions and functions: implications for retrograde signaling? Am. J. Physiol. Regul. Integr. Comp. Physiol. 304, R393-R406.
28. Popp, D., Yamamoto, A. & Maéda, Y. (2007). Crowded surfaces change annealing dynamics of actin filaments. J. Mol. Biol. 368, 365-374.
29. Ricardo, M. & Carlos, P. (2008). A craniotomy surgery procedure for chronic brain imaging. J. Vis. Exp. doi:10.3791/680.
30. Schon, E.A. & Przedborski, S. (2011). Mitochondria: The next (neurode)generation. Neuron 70, 1033-1053.
31. Selesnick, I.W., Baraniuk, R.G. & Kingsbury, N.G. (2005). The dual-tree complex wavelet transform. Signal Process. Mag. 22, 123-151.
32. Shitara, H., Shimanuki, M., Hayashi, J.-I. & Yonekawa, H. (2010). Global imaging of mitochondrial morphology in tissues using transgenic mice expressing mitochondrially targeted enhanced green fluorescent protein. Exp. Anim. 59, 99-103.
33. Smithson, M., Verkuilen, J. (2006). A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol. Methods. 11, 54-71.
34. Zou, H. & Hastie, T. (2005). Regularization and variable selection via the elastic net. J.R. Stat. Soc. Ser. B (Stat. Methodol.). 67, 301-320.
35. Zujovic, J., Pappas, T. & Neuhoff, D. (2013). Structural texture similarity metrics for image analysis and retrieval. IEEE Trans. Image Process. 22, 2545-2558.