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Endocrine, Metabolic & Immune Disorders - Drug Targets

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ISSN (Print): 1871-5303
ISSN (Online): 2212-3873

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Research Article

Evaluation of Image Classification for Quantifying Mitochondrial Morphology Using Deep Learning

Author(s): Kaori Tsutsumi, Keima Tokunaga, Shun Saito, Tatsuya Sasase and Hiroyuki Sugimori*

Volume 23, Issue 2, 2023

Published on: 22 August, 2022

Page: [214 - 221] Pages: 8

DOI: 10.2174/1871530322666220701093644

Price: $65

Abstract

Background: Mitochondrial morphology reversibly changes between fission and fusion. As these changes (mitochondrial dynamics) reflect the cellular condition, they are one of the simplest indicators of cell state and predictors of cell fate. However, it is currently difficult to classify them using a simple and objective method.

Objective: The present study aimed to evaluate mitochondrial morphology using Deep Learning (DL) technique.

Methods: Mitochondrial images stained by MitoTracker were acquired from HeLa and MC3T3-E1 cells using fluorescent microscopy and visually classified into four groups based on fission or fusion. The intra- and inter-rater reliabilities for visual classification were excellent [(ICC(1,3), 0.961 for rater 1; and 0.981 for rater 2) and ICC(1,3), respectively]. The images were divided into test and train images, and a 50-layer ResNet CNN architecture (ResNet-50) using MATLAB software was used to train the images. The datasets were trained five times based on five-fold cross-validation.

Result: The mean of the overall accuracy for classifying mitochondrial morphology was 0.73±0.10 in HeLa. For the classification of mixed images containing two types of cell lines, the overall accuracy using mixed images of both cell lines for training was higher (0.74±0.01) than that using different cell lines for training.

Conclusion: We developed a classifier to categorize mitochondrial morphology using DL.

Keywords: Mitochondrial morphology, mitochondrial dynamics, fission, fusion, deep learning, ResNet.

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Graphical Abstract

[1]
Senft, D.; Ronai, Z.A. Regulators of mitochondrial dynamics in cancer. Curr. Opin. Cell Biol., 2016, 39, 43-52.
[http://dx.doi.org/10.1016/j.ceb.2016.02.001] [PMID: 26896558]
[2]
Mishra, P.; Chan, D.C. Metabolic regulation of mitochondrial dynamics. J. Cell Biol., 2016, 212(4), 379-387.
[http://dx.doi.org/10.1083/jcb.201511036] [PMID: 26858267]
[3]
Di Nottia, M.; Verrigni, D.; Torraco, A.; Rizza, T.; Bertini, E.; Carrozzo, R. Mitochondrial dynamics: Molecular mechanisms, related primary mitochondrial disorders and therapeutic ap-proaches. Genes (Basel), 2021, 12(2), 1-24.
[http://dx.doi.org/10.3390/genes12020247] [PMID: 33578638]
[4]
Chen, H.; Chan, D.C. Emerging functions of mammalian mitochondrial fusion and fission. Hum. Mol. Genet., 2005, 14 Spec No. 2(Suppl. 2), R283-R289.
[http://dx.doi.org/10.1093/hmg/ddi270] [PMID: 16244327]
[5]
Reddy, P.H.; Reddy, T.P.; Manczak, M.; Calkins, M.J.; Shirendeb, U.; Mao, P. Dynamin-related protein 1 and mitochondrial fragmentation in neurodegenerative diseases. Brain Res. Brain Res. Rev., 2011, 67(1-2), 103-118.
[http://dx.doi.org/10.1016/j.brainresrev.2010.11.004] [PMID: 21145355]
[6]
Smirnova, E.; Griparic, L.; Shurland, D-L.; van der Bliek, A.M. Dynamin-related protein Drp1 is required for mitochondrial division in mammalian cells. Mol. Biol. Cell, 2001, 12(8), 2245-2256.
[http://dx.doi.org/10.1091/mbc.12.8.2245] [PMID: 11514614]
[7]
Ni, H.M.; Williams, J.A.; Ding, W.X. Mitochondrial dynamics and mitochondrial quality control. Redox Biol., 2015, 4, 6-13.
[http://dx.doi.org/10.1016/j.redox.2014.11.006] [PMID: 25479550]
[8]
Manczak, M.; Kandimalla, R.; Yin, X.; Reddy, P.H. Mitochondrial division inhibitor 1 reduces dynamin-related protein 1 and mi-tochondrial fission activity. Hum. Mol. Genet., 2019, 28(2), 177-199.
[http://dx.doi.org/10.1093/hmg/ddy335] [PMID: 30239719]
[9]
Losón, O.C.; Song, Z.; Chen, H.; Chan, D.C. Fis1, Mff, MiD49, and MiD51 mediate Drp1 recruitment in mitochondrial fission. Mol. Biol. Cell, 2013, 24(5), 659-667.
[http://dx.doi.org/10.1091/mbc.e12-10-0721] [PMID: 23283981]
[10]
Lee, H.; Smith, S.B.; Sheu, S.S.; Yoon, Y. The short variant of optic atrophy 1 (OPA1) improves cell survival under oxidative stress. J. Biol. Chem., 2020, 295(19), 6543-6560.
[http://dx.doi.org/10.1074/jbc.RA119.010983] [PMID: 32245890]
[11]
Jang, S.; Javadov, S. OPA1 regulates respiratory supercomplexes assembly: The role of mitochondrial swelling. Mitochondrion, 2020, 51, 30-39.
[http://dx.doi.org/10.1016/j.mito.2019.11.006] [PMID: 31870826]
[12]
Palikaras, K.; Lionaki, E.; Tavernarakis, N. Mechanisms of mi-tophagy in cellular homeostasis, physiology and pathology. Nat. Cell Biol., 2018, 20(9), 1013-1022.
[http://dx.doi.org/10.1038/s41556-018-0176-2] [PMID: 30154567]
[13]
Bertholet, A.M.; Delerue, T.; Millet, A.M.; Moulis, M.F.; David, C.; Daloyau, M.; Arnauné-Pelloquin, L.; Davezac, N.; Mils, V.; Miquel, M.C.; Rojo, M.; Belenguer, P. Mitochondrial fusion/fission dynamics in neurodegeneration and neuronal plasticity. Neurobiol. Dis., 2016, 90, 3-19.
[http://dx.doi.org/10.1016/j.nbd.2015.10.011] [PMID: 26494254]
[14]
Chan, D.C. Fusion and fission: Interlinked processes critical for mitochondrial health. Annu. Rev. Genet., 2012, 46, 265-287.
[http://dx.doi.org/10.1146/annurev-genet-110410-132529 ] [PMID: 22934639]
[15]
Ishihara, N.; Nomura, M.; Jofuku, A.; Kato, H.; Suzuki, S.O.; Masuda, K.; Otera, H.; Nakanishi, Y.; Nonaka, I.; Goto, Y.; Taguchi, N.; Morinaga, H.; Maeda, M.; Takayanagi, R.; Yokota, S.; Mihara, K. Mitochondrial fission factor Drp1 is essential for embryonic development and synapse formation in mice. Nat. Cell Biol., 2009, 11(8), 958-966.
[http://dx.doi.org/10.1038/ncb1907] [PMID: 19578372]
[16]
Pradeepkiran, J.A.; Reddy, P.H. Defective mitophagy in Alzheimer’s disease. Ageing Res. Rev., 2020, 64, 101191.
[http://dx.doi.org/10.1016/j.arr.2020.101191] [PMID: 33022416]
[17]
Manczak, M.; Calkins, M.J.; Reddy, P.H. Impaired mitochondrial dynamics and abnormal interaction of amyloid beta with mitochondrial protein Drp1 in neurons from patients with Alzheimer’s disease: Implications for neuronal damage. Hum. Mol. Genet., 2011, 20(13), 2495-2509.
[http://dx.doi.org/10.1093/hmg/ddr139] [PMID: 21459773]
[18]
Kandimalla, R.; Reddy, P.H. Multiple faces of dynamin-related protein 1 and its role in Alzheimer’s disease pathogenesis. Biochim. Biophys. Acta, 2016, 1862(4), 814-828.
[http://dx.doi.org/10.1016/j.bbadis.2015.12.018] [PMID: 26708942]
[19]
Skeie, J.M.; Nishimura, D.Y.; Wang, C.L.; Schmidt, G.A.; Al-drich, B.T.; Greiner, M.A. Mitophagy: An emerging target in ocu-lar pathology. Invest. Ophthalmol. Vis. Sci., 2021, 62(3), 22.
[http://dx.doi.org/10.1167/iovs.62.3.22] [PMID: 33724294]
[20]
Wong, Y.C.; Ysselstein, D.; Krainc, D. Mitochondria-lysosome contacts regulate mitochondrial fission via RAB7 GTP hydrolysis. Nature, 2018, 554(7692), 382-386.
[http://dx.doi.org/10.1038/nature25486] [PMID: 29364868]
[21]
Lenaers, G.; Hamel, C.; Delettre, C.; Amati-Bonneau, P.; Procaccio, V.; Bonneau, D.; Reynier, P.; Milea, D. Dominant optic atrophy. Orphanet J. Rare Dis., 2012, 7(1), 46.
[http://dx.doi.org/10.1186/1750-1172-7-46] [PMID: 22776096]
[22]
Detmer, S.A.; Chan, D.C. Complementation between mouse Mfn1 and Mfn2 protects mitochondrial fusion defects caused by CMT2A disease mutations. J. Cell Biol., 2007, 176(4), 405-414.
[http://dx.doi.org/10.1083/jcb.200611080] [PMID: 17296794]
[23]
Heudorf, U.; Steul, K.; Gottschalk, R. Sars-Cov-2 in children - insights and conclusions from the mandatory reporting data in Frankfurt am Main, Germany, March-July 2020. GMS Hyg. Infect. Control, 2020, 15, Doc24.
[PMID: 33214989]
[24]
Durand, M.J.; Ait-Aissa, K.; Levchenko, V.; Staruschenko, A.; Gutterman, D.D.; Beyer, A.M. Visualization and quantification of mitochondrial structure in the endothelium of intact arteries. Cardiovasc. Res., 2019, 115(10), 1546-1556.
[http://dx.doi.org/10.1093/cvr/cvy294] [PMID: 30476208]
[25]
Koopman, H.; Verkaart, W.J.; Visch, S.; van der Westhuizen, F.H.; Murphy, M.P. P J van den Heuvel, L.W.; M Smeitink, J.A.; G M Willems, P.H.; Koopman, H. Inhibition of complex I of the electron transport chain causes O2-mediated mitochondrial outgrowth. Am. J. Physiol. Cell Physiol., 2005, 288, 1440-1450.
[http://dx.doi.org/10.1152/ajpcell.00607.2004]
[26]
Leonard, A.P.; Cameron, R.B.; Speiser, J.L.; Wolf, B.J.; Peterson, Y.K.; Schnellmann, R.G.; Beeson, C.C.; Rohrer, B. Quantitative analysis of mitochondrial morphology and membrane potential in living cells using high-content imaging, machine learning, and morphological binning. Biochim. Biophys. Acta, 2015, 1853(2), 348-360.
[http://dx.doi.org/10.1016/j.bbamcr.2014.11.002] [PMID: 25447550]
[27]
Fogo, G.M.; Anzell, A.R.; Maheras, K.J.; Raghunayakula, S.; Wider, J.M.; Emaus, K.J.; Bryson, T.D.; Bukowski, M.J.; Neumar, R.W.; Przyklenk, K.; Sanderson, T.H. Machine learning-based classification of mitochondrial morphology in primary neurons and brain. Sci. Rep., 2021, 11(1), 5133.
[http://dx.doi.org/10.1038/s41598-021-84528-8] [PMID: 33664336]
[28]
Valente, A.J.; Maddalena, L.A.; Robb, E.L.; Moradi, F.; Stuart, J.A. A simple ImageJ macro tool for analyzing mitochondrial network morphology in mammalian cell culture. Acta Histochem., 2017, 119(3), 315-326.
[http://dx.doi.org/10.1016/j.acthis.2017.03.001] [PMID: 28314612]
[29]
Dagda, R.K.; Cherra, S.J., III; Kulich, S.M.; Tandon, A.; Park, D.; Chu, C.T. Loss of PINK1 function promotes mitophagy through effects on oxidative stress and mitochondrial fission. J. Biol. Chem., 2009, 284(20), 13843-13855.
[http://dx.doi.org/10.1074/jbc.M808515200] [PMID: 19279012]
[30]
Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; Rabinovich, A. Going Deeper with Convolutions, Boston, MA, USA, 7-12 June 2015; 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015, pp. 1-9.
[http://dx.doi.org/10.1109/CVPR.2015.7298594]
[31]
He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition, Las Vegas, NV, USA, 27-30 June 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778.
[http://dx.doi.org/10.1109/CVPR.2016.90]
[32]
Sugimori, H. Classification of computed tomography images in different slice positions using deep learning. J. Healthc. Eng., 2018, 2018, 1753480.
[http://dx.doi.org/10.1155/2018/1753480] [PMID: 30123439]
[33]
Park, H.S.; Jeon, K.; Cho, Y.J.; Kim, S.W.; Lee, S.B.; Choi, G.; Lee, S.; Choi, Y.H.; Cheon, J.E.; Kim, W.S.; Ryu, Y.J.; Hwang, J.Y. Diagnostic performance of a new convolutional neural net-work algorithm for detecting developmental dysplasia of the hip on anteroposterior radiographs. Korean J. Radiol., 2021, 22(4), 612-623.
[http://dx.doi.org/10.3348/kjr.2020.0051] [PMID: 33289354]
[34]
Iqbal, M.S.; Luo, B.; Mehmood, R.; Alrige, M.A.; Alharbey, A.R. Mitochondrial organelle movement classification (fission and fusion) via convolutional neural network approach. IEEE Access, 2019, 7, 86570-86577.
[http://dx.doi.org/10.1109/ACCESS.2019.2925041]
[35]
Yamamori, T.; Ike, S.; Bo, T.; Sasagawa, T.; Sakai, Y.; Suzuki, M.; Yamamoto, K.; Nagane, M.; Yasui, H.; Inanami, O. Inhibition of the mitochondrial fission protein dynamin-related protein 1 (DRP1) impairs mitochondrial fission and mitotic catastrophe after x-irradiation. Mol. Biol. Cell, 2015, 26(25), 4607-4617.
[http://dx.doi.org/10.1091/mbc.E15-03-0181] [PMID: 26466676]
[36]
Koo, T.K.; Li, M.Y. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med., 2016, 15(2), 155-163.
[http://dx.doi.org/10.1016/j.jcm.2016.02.012] [PMID: 27330520]
[37]
Mikołajczyk, A.; Grochowski, M. Data Augmentation for Improving Deep Learning in Image Classification Problem, Poland, 9-12 May 2018; International Interdisciplinary PhD Workshop (IIPhDW); , 2018; pp. 1177-122.
[http://dx.doi.org/10.1109/IIPHDW.2018.8388338]
[38]
Sugimori, H.; Hamaguchi, H.; Fujiwara, T.; Ishizaka, K. Classifi-cation of type of brain magnetic resonance images with deep learning technique. Magn. Reson. Imaging, 2021, 77, 180-185.
[http://dx.doi.org/10.1016/j.mri.2020.12.017] [PMID: 33359426]

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