A novel retinal ganglion cell quantification tool based on deep learning.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
12 01 2021
12 01 2021
Historique:
received:
31
07
2020
accepted:
15
12
2020
entrez:
13
1
2021
pubmed:
14
1
2021
medline:
11
8
2021
Statut:
epublish
Résumé
Glaucoma is a disease associated with the loss of retinal ganglion cells (RGCs), and remains one of the primary causes of blindness worldwide. Major research efforts are presently directed towards the understanding of disease pathogenesis and the development of new therapies, with the help of rodent models as an important preclinical research tool. The ultimate goal is reaching neuroprotection of the RGCs, which requires a tool to reliably quantify RGC survival. Hence, we demonstrate a novel deep learning pipeline that enables fully automated RGC quantification in the entire murine retina. This software, called RGCode (Retinal Ganglion Cell quantification based On DEep learning), provides a user-friendly interface that requires the input of RBPMS-immunostained flatmounts and returns the total RGC count, retinal area and density, together with output images showing the computed counts and isodensity maps. The counting model was trained on RBPMS-stained healthy and glaucomatous retinas, obtained from mice subjected to microbead-induced ocular hypertension and optic nerve crush injury paradigms. RGCode demonstrates excellent performance in RGC quantification as compared to manual counts. Furthermore, we convincingly show that RGCode has potential for wider application, by retraining the model with a minimal set of training data to count FluoroGold-traced RGCs.
Identifiants
pubmed: 33436866
doi: 10.1038/s41598-020-80308-y
pii: 10.1038/s41598-020-80308-y
pmc: PMC7804414
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
702Références
Mead, B. & Tomarev, S. Evaluating retinal ganglion cell loss and dysfunction. Exp. Eye Res. 151, 96–106 (2016).
pubmed: 27523467
pmcid: 5045805
doi: 10.1016/j.exer.2016.08.006
Guo, L. & Cordeiro, M. F. Assessment of neuroprotection in the retina with DARC. Prog. Brain Res. 173, 437–450 (2008).
pubmed: 18929126
pmcid: 2603274
doi: 10.1016/S0079-6123(08)01130-8
Köbbert, C. et al. Current concepts in neuroanatomical tracing. Prog. Neurobiol. 62, 327–351 (2000).
pubmed: 10856608
doi: 10.1016/S0301-0082(00)00019-8
Abdel-Majid, R. M., Archibald, M. L., Tremblay, F. & Baldridge, W. H. Tracer coupling of neurons in the rat retina inner nuclear layer labeled by Fluorogold. Brain Res. 1063, 114–120 (2005).
pubmed: 16263096
doi: 10.1016/j.brainres.2005.09.046
Peinado-Ramon, P., Salvador, M., Villegas-Perez, M. P. & Vidal-Sanz, M. Effects of axotomy and intraocular administration of NT-4, NT-3, and brain-derived neurotrophic factor on the survival of adult rat retinal ganglion cells. A quantitative in vivo study. Invest. Ophthalmol. Vis. Sci. 37, 489–500 (1996).
pubmed: 8595949
Nadal-Nicolás, F. M. et al. Brn3a as a marker of retinal ganglion cells: qualitative and quantitative time course studies in naive and optic nerve-injured retinas. Invest. Ophthalmol. Vis. Sci. 50, 3860–3868 (2009).
pubmed: 19264888
doi: 10.1167/iovs.08-3267
Nadal-Nicolas, F. M. et al. Whole number, distribution and co-expression of Brn3 transcription factors in retinal ganglion cells of adult albino and pigmented rats. PLoS ONE 7, e49830 (2012).
pubmed: 23166779
pmcid: 3500320
doi: 10.1371/journal.pone.0049830
Rodriguez, A. R., de Sevilla Müller, L. P. & Brecha, N. C. The RNA binding protein RBPMS is a selective marker of ganglion cells in the mammalian retina. J. Comp. Neurol.522, 1411–1443 (2014).
Tran, N. M. et al. Single-cell profiles of retinal ganglion cells differing in resilience to injury reveal neuroprotective genes. Neuron 104, 1039–1055 (2019).
pubmed: 31784286
pmcid: 6923571
doi: 10.1016/j.neuron.2019.11.006
Rheaume, B. A. et al. Single cell transcriptome profiling of retinal ganglion cells identifies cellular subtypes. Nat. Commun. 9, 2759 (2018).
pubmed: 30018341
pmcid: 6050223
doi: 10.1038/s41467-018-05134-3
Kwong, J. M., Caprioli, J. & Piri, N. RNA binding protein with multiple splicing: a new marker for retinal ganglion cells. Invest. Ophthalmol. Vis. Sci. 51, 1052–1058 (2010).
pubmed: 19737887
pmcid: 3979483
doi: 10.1167/iovs.09-4098
Baden, T. et al. The functional diversity of retinal ganglion cells in the mouse. Nature 529, 345–350 (2016).
pubmed: 26735013
pmcid: 4724341
doi: 10.1038/nature16468
Bae, J. A. et al. Digital museum of retinal ganglion cells with dense anatomy and physiology. Cell 173, 1293–1306 (2018).
pubmed: 29775596
pmcid: 6556895
doi: 10.1016/j.cell.2018.04.040
Zhao, D. et al. Characterization of the circumlimbal suture model of chronic iop elevation in mice and assessment of changes in gene expression of stretch sensitive channels. Front. Neurosci. 11, 41 (2017).
pubmed: 28239332
pmcid: 5301305
doi: 10.3389/fnins.2017.00041
Liu, R., Wang, Y., Pu, M. & Gao, J. Effect of alpha lipoic acid on retinal ganglion cell survival in an optic nerve crush model. Mol. Vis. 22, 1122–1136 (2016).
pubmed: 27703307
pmcid: 5040455
Wang, W. et al. Programmed cell death-1 is expressed in large retinal ganglion cells and is upregulated after optic nerve crush. Exp. Eye Res. 140, 1–9 (2015).
pubmed: 26277582
pmcid: 5420326
doi: 10.1016/j.exer.2015.08.008
Guymer, C. et al. Software for quantifying and batch processing images of brn3a and RBPMS immunolabelled retinal ganglion cells in retinal wholemounts. Transl. Vis. Sci. Technol. 9, 28 (2020).
pubmed: 32821525
pmcid: 7409150
doi: 10.1167/tvst.9.6.28
Moen, E. et al. Deep learning for cellular image analysis. Nat. Methods 16, 1233–1246 (2019).
pubmed: 31133758
doi: 10.1038/s41592-019-0403-1
pmcid: 8759575
Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-assisted Intervention, 234–241 (Springer, 2015).
De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342–1350 (2018).
pubmed: 30104768
doi: 10.1038/s41591-018-0107-6
Ritch, M. D. et al. Axonet: a deep learning-based tool to count retinal ganglion cell axons. Sci. Rep. 10, 8034 (2020).
pubmed: 32415269
pmcid: 7228952
doi: 10.1038/s41598-020-64898-1
Falk, T. et al. U-Net: deep learning for cell counting, detection, and morphometry. Nat. Methods 16, 67–70 (2019).
pubmed: 30559429
doi: 10.1038/s41592-018-0261-2
Shermin, T., Murshed, M., Lu, G. & Teng, S. W. Transfer learning using classification layer features of CNN. arXiv:1811.07459 (2018).
Hofbauer, A. & Dräger, U. C. Depth segregation of retinal ganglion cells projecting to mouse superior colliculus. J. Comp. Neurol. 234, 465–474 (1985).
pubmed: 3988995
doi: 10.1002/cne.902340405
Petros, T. J., Rebsam, A. & Mason, C. A. Retinal axon growth at the optic chiasm: to cross or not to cross. Annu. Rev. Neurosci. 31, 295–315 (2008).
pubmed: 18558857
doi: 10.1146/annurev.neuro.31.060407.125609
Campbell, C. G., Ting, D. S., Keane, P. A. & Foster, P. J. The potential application of artificial intelligence for diagnosis and management of glaucoma in adults. Br. Med. Bull. 134, 21–33 (2020).
pubmed: 32518944
doi: 10.1093/bmb/ldaa012
Balyen, L. & Peto, T. Promising artificial intelligence-machine learning-deep learning algorithms in ophthalmology. Asia Pac. J. Ophthal. 8, 264–272 (2019).
Zheng, C., Johnson, T. V., Garg, A. & Boland, M. V. Artificial intelligence in glaucoma. Curr. Opin. Ophthalmol. 30, 97–103 (2019).
pubmed: 30562242
doi: 10.1097/ICU.0000000000000552
Martin, K. R. et al. Use of machine learning on contact lens sensor-derived parameters for the diagnosis of primary open-angle glaucoma. Am. J. Ophthalmol. 194, 46–53 (2018).
pubmed: 30053471
doi: 10.1016/j.ajo.2018.07.005
Normando, E. M. et al. A CNN-aided method to predict glaucoma progression using DARC (detection of apoptosing retinal cells). Expert Rev. Mol. Diagn.1–12 (2020).
Li, W. et al. Automatic anterior chamber angle measurement for ultrasound biomicroscopy using deep learning. J. Glaucoma 29, 81–85 (2020).
pubmed: 31790065
doi: 10.1097/IJG.0000000000001411
Xu, B. Y. et al. Deep learning classifiers for automated detection of gonioscopic angle closure based on anterior segment OCT images. Am. J. Ophthalmol. 208, 273–280 (2019).
pubmed: 31445003
pmcid: 6888901
doi: 10.1016/j.ajo.2019.08.004
Hedberg-Buenz, A. et al. Quantitative measurement of retinal ganglion cell populations via histology-based random forest classification. Exp. Eye Res. 146, 370–385 (2016).
pubmed: 26474494
doi: 10.1016/j.exer.2015.09.011
Alahmari, S. S. et al. Automated cell counts on tissue sections by deep learning and unbiased stereology. J. Chem. Neuroanat. 96, 94–101 (2019).
pubmed: 30594529
doi: 10.1016/j.jchemneu.2018.12.010
Phoulady, H. A., Goldgof, D., Hall, L. O. & Mouton, P. R. Automatic ground truth for deep learning stereology of immunostained neurons and microglia in mouse neocortex. J. Chem. Neuroanat. 98, 1–7 (2019).
doi: 10.1016/j.jchemneu.2019.02.006
Pham, B. et al. Cell counting and segmentation of immunohistochemical images in the spinal cord: Comparing deep learning and traditional approaches. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 842–845 (IEEE, 2018).
Penttinen, A.-M. et al. Implementation of deep neural networks to count dopamine neurons in substantia nigra. Eur. J. Neurosci. 48, 2354–2361 (2018).
pubmed: 30144349
pmcid: 6585833
doi: 10.1111/ejn.14129
Kyriazis, A. D. et al. An end-to-end system for automatic characterization of iba1 immunopositive microglia in whole slide imaging. Neuroinformatics 17, 373–389 (2019).
pubmed: 30406865
doi: 10.1007/s12021-018-9405-x
Suleymanova, I. et al. A deep convolutional neural network approach for astrocyte detection. Sci. Rep. 8, 1–7 (2018).
doi: 10.1038/s41598-018-31284-x
Dibas, A., Millar, C., Al-Farra, A. & Yorio, T. Neuroprotective effects of psalmotoxin-1, an acid-sensing ion channel (ASIC) inhibitor, in ischemia reperfusion in mouse eyes. Curr. Eye Res. 43, 921–933 (2018).
pubmed: 29595330
doi: 10.1080/02713683.2018.1454478
Stankowska, D. L. et al. Hybrid compound SA-2 is neuroprotective in animal models of retinal ganglion cell death. Invest. Ophthalmol. Vis. Sci. 60, 3064–3073 (2019).
pubmed: 31348824
doi: 10.1167/iovs.18-25999
Cone, F. E., Gelman, S. E., Son, J. L., Pease, M. E. & Quigley, H. A. Differential susceptibility to experimental glaucoma among 3 mouse strains using bead and viscoelastic injection. Exp. Eye Res. 91, 415–424 (2010).
pubmed: 20599961
pmcid: 2954410
doi: 10.1016/j.exer.2010.06.018
Frankfort, B. J. et al. Elevated intraocular pressure causes inner retinal dysfunction before cell loss in a mouse model of experimental glaucoma. Invest. Ophthalmol. Vis. Sci. 54, 762–770 (2013).
pubmed: 23221072
pmcid: 3562118
doi: 10.1167/iovs.12-10581
Mukai, R. et al. Mouse model of ocular hypertension with retinal ganglion cell degeneration. PLoS ONE 14, e0208713 (2019).
pubmed: 30640920
pmcid: 6331128
doi: 10.1371/journal.pone.0208713
Blandford, S. N. et al. Retinal characterization of the Thy1-GCaMP3 transgenic mouse line after optic nerve transection. Invest. Ophthalmol. Vis. Sci. 60, 183–191 (2019).
pubmed: 30640971
doi: 10.1167/iovs.18-25861
Omodaka, K. et al. Neuroprotective effect against axonal damage-induced retinal ganglion cell death in apolipoprotein e-deficient mice through the suppression of kainate receptor signaling. Brain Res. 1586, 203–212 (2014).
pubmed: 25160129
doi: 10.1016/j.brainres.2014.08.053
Yamamoto, K. et al. The novel Rho kinase (ROCK) inhibitor K-115: a new candidate drug for neuroprotective treatment in glaucoma. Invest. Ophthalmol. Vis. Sci. 55, 7126–7136 (2014).
pubmed: 25277230
doi: 10.1167/iovs.13-13842
Ryu, M. et al. Critical role of calpain in axonal damage-induced retinal ganglion cell death. J. Neurosci. 90, 802–815 (2012).
Tsuda, S. et al. Real-time imaging of RGC death with a cell-impermeable nucleic acid dyeing compound after optic nerve crush in a murine model. Exp. Eye Res. 146, 179–188 (2016).
pubmed: 27013099
doi: 10.1016/j.exer.2016.03.017
Xia, X. et al. Protection of pattern electroretinogram and retinal ganglion cells by oncostatin m after optic nerve injury. PLoS ONE 9, e108524 (2014).
pubmed: 25243471
pmcid: 4171539
doi: 10.1371/journal.pone.0108524
Himori, N. et al. Critical neuroprotective roles of heme oxygenase-1 induction against axonal injury-induced retinal ganglion cell death. J. Neurosci. 92, 1134–1142 (2014).
De Groef, L. et al. Differential visual system organization and susceptibility to experimental models of optic neuropathies in three commonly used mouse strains. Exp. Eye Res. 145, 235–247 (2016).
pubmed: 26791081
doi: 10.1016/j.exer.2016.01.006
Dräger, U. & Olsen, J. F. Ganglion cell distribution in the retina of the mouse. Invest. Ophthalmol. Vis. Sci. 20, 285–293 (1981).
pubmed: 6162818
Jeon, C.-J., Strettoi, E. & Masland, R. H. The major cell populations of the mouse retina. J. Neurosci. 18, 8936–8946 (1998).
pubmed: 9786999
pmcid: 6793518
doi: 10.1523/JNEUROSCI.18-21-08936.1998
Salinas-Navarro, M. et al. Retinal ganglion cell population in adult albino and pigmented mice: a computerized analysis of the entire population and its spatial distribution. Vis. Res. 49, 637–647 (2009).
pubmed: 19948111
doi: 10.1016/j.visres.2009.01.010
Jakobs, T. C., Libby, R. T., Ben, Y., John, S. W. & Masland, R. H. Retinal ganglion cell degeneration is topological but not cell type specific in DBA/2J mice. J. Cell Biol. 171, 313–325 (2005).
pubmed: 16247030
pmcid: 2171185
doi: 10.1083/jcb.200506099
Geeraerts, E. et al. A freely available semi-automated method for quantifying retinal ganglion cells in entire retinal flatmounts. Exp. Eye Res. 147, 105–113 (2016).
pubmed: 27107795
doi: 10.1016/j.exer.2016.04.010
Guo, C. et al. A murine glaucoma model induced by rapid in vivo photopolymerization of hyaluronic acid glycidyl methacrylate. PLoS ONE 13, e0196529 (2018).
pubmed: 29949582
pmcid: 6021085
doi: 10.1371/journal.pone.0196529
Geeraerts, E. et al. Optogenetic stimulation of the superior colliculus confers retinal neuroprotection in a mouse glaucoma model. J. Neurosci. 39, 2313–2325 (2019).
pubmed: 30655352
pmcid: 6433760
doi: 10.1523/JNEUROSCI.0872-18.2018
Valiente-Soriano, F. J. et al. Effects of ocular hypertension in the visual system of pigmented mice. PLoS ONE 10, e0121134 (2015).
pubmed: 25811653
pmcid: 4374934
doi: 10.1371/journal.pone.0121134
Schlamp, C. L., Li, Y., Dietz, J. A., Janssen, K. T. & Nickells, R. W. Progressive ganglion cell loss and optic nerve degeneration in DBA/2J mice is variable and asymmetric. BMC Neurosci. 7, 66 (2006).
pubmed: 17018142
pmcid: 1621073
doi: 10.1186/1471-2202-7-66
Soto, I. et al. Retinal ganglion cells downregulate gene expression and lose their axons within the optic nerve head in a mouse glaucoma model. J. Neurosci. 28, 548–561 (2008).
pubmed: 18184797
pmcid: 6670511
doi: 10.1523/JNEUROSCI.3714-07.2008
DellaSantina, L., Inman, D. M., Lupien, C. B., Horner, P. J. & Wong, R. O. Differential progression of structural and functional alterations in distinct retinal ganglion cell types in a mouse model of glaucoma. J. Neurosci. 33, 17444–17457 (2013).
doi: 10.1523/JNEUROSCI.5461-12.2013
Galindo-Romero, C. et al. Axotomy-induced retinal ganglion cell death in adult mice: quantitative and topographic time course analyses. Exp. Eye Res. 92, 377–387 (2011).
pubmed: 21354138
doi: 10.1016/j.exer.2011.02.008
Pérez de Lara, M. . J. et al. Assessment of inner retina dysfunction and progressive ganglion cell loss in a mouse model of glaucoma. Exp. Eye Res. 122, 40–49 (2014).
pubmed: 24631335
doi: 10.1016/j.exer.2014.02.022
Ito, Y. A., Belforte, N., Vargas, J. L. C. & Di Polo, A. A magnetic microbead occlusion model to induce ocular hypertension-dependent glaucoma in mice. J. Vis. Exp. 109, e53731 (2016).
Nadal-Nicolás, F. M., Salinas-Navarro, M., Vidal-Sanz, M. & Agudo-Barriuso, M. Two methods to trace retinal ganglion cells with fluorogold: from the intact optic nerve or by stereotactic injection into the optic tract. Exp. Eye Res. 131, 12–19 (2015).
pubmed: 25482219
doi: 10.1016/j.exer.2014.12.005
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
pubmed: 22743772
doi: 10.1038/nmeth.2019
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2019).