Analytical and Bioanalytical Chemistry

, Volume 411, Issue 20, pp 5115–5126 | Cite as

Refinement of two-dimensional electrophoresis for vitreous proteome profiling using an artificial neural network

  • Fátima M. Santos
  • Tânia Albuquerque
  • Leonor M. Gaspar
  • João M. L. Dias
  • João P. Castro e Sousa
  • Alberto Paradela
  • Cândida T. Tomaz
  • Luís A. PassarinhaEmail author
Research Paper


Despite technological advances, two-dimensional electrophoresis (2DE) of biological fluids, such as vitreous, remains a major challenge. In this study, artificial neural network was applied to optimize the recovery of vitreous proteins and its detection by 2DE analysis through the combination of several solubilizing agents (CHAPS, Genapol, DTT, IPG buffer), temperature, and total voltage. The highest protein recovery (94.9% ± 4.5) was achieved using 4% (w/v) CHAPS, 0.1% (v/v) Genapol, 20 mM DTT, and 2% (v/v) IPG buffer. Two iterations were required to achieve an optimized response (580 spots) using 4% (w/v) CHAPS, 0.2% (v/v) Genapol, 60 mM DTT, and 0.5% (v/v) IPG buffer at 35 kVh and 25 °C, representing a 2.4-fold improvement over the standard initial conditions of the experimental design. The analysis of depleted vitreous using the optimized protocol resulted in an additional 1.3-fold increment in protein detection over the optimal output, with an average of 761 spots detected in vitreous from different vitreoretinopathies. Our results clearly indicate the importance of combining the appropriate amount of solubilizing agents with a suitable control of the temperature and voltage to obtain high-quality gels. The high-throughput of this model provides an effective starting point for the optimization of 2DE protocols. This experimental design can be adapted to other types of matrices.

Graphical abstract


Artificial neural network Gel-based proteomics Ocular pathologies Two-dimensional gel electrophoresis Vitreous 



Two-dimensional electrophoresis


Artificial neural networks






Isoelectric focusing


Kilovolts hour






Funding information

This project was supported by the University of Beira Interior—Health Sciences Research Centre (CICS). Santos FM received a fellowship (CENTRO-07-ST24-FEDER-002014) and a doctoral fellowship (SFRH/BD/112526/2015) from FCT. Gaspar LM received a fellowship from Novartis Farma-Produtos Farmacêuticos, SA. This work is supported by FEDER funds through the POCI—COMPETE 2020—Operational Programme Competitiveness and Internationalisation in Axis I—Strengthening research, technological development and innovation Project (POCI-01-0145-FEDER-007491) and National Funds by FCT—Foundation for Science and Technology Project (UID/Multi/00709/2013). This work was also supported by the Applied Molecular Biosciences Unit- UCIBIO which is financed by national funds from FCT/MCTES (UID/Multi/04378/2019). CNB-CSIC proteomics lab is a member of Proteored, PRB2-ISCIII and is supported by grant PT13/0001, of the PE I +D+i 2013–2016, funded by ISCIII and FEDER.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving human participants and/or animals

The protocol for sample collection was approved by the Ethics Committee of Leiria-Pombal Hospital, Portugal (Code: CHL-15481). An informed consent from all patients was obtained in agreement with the Declaration of Helsinki.

Supplementary material

216_2019_1887_MOESM1_ESM.pdf (6.8 mb)
ESM 1 (PDF 7014 kb)
216_2019_1887_MOESM2_ESM.xlsx (17 kb)
ESM 2 (XLSX 16 kb)
216_2019_1887_MOESM3_ESM.xls (87 kb)
ESM 3 (XLS 87 kb)
216_2019_1887_MOESM4_ESM.xlsx (147 kb)
ESM 4 (XLSX 146 kb)


  1. 1.
    Semba RD, Enghild JJ, Venkatraman V, Dyrlund TF, Van Eyk JE. The Human Eye Proteome Project: perspectives on an emerging proteome. Proteomics. 2013;13:2500–11.CrossRefGoogle Scholar
  2. 2.
    Jay NL, Gillies M. Proteomic analysis of ophthalmic disease. Clin Exp Ophthalmol. 2012;40:755–63. Scholar
  3. 3.
    Rocha AS, Santos FM, Monteiro JP, Castro-de-Sousa JP, Queiroz JA, Tomaz CT, et al. Trends in proteomic analysis of human vitreous humor samples. Electrophoresis. 2014;35:2495–508. Scholar
  4. 4.
    Monteiro JP, Santos FM, Rocha AS, Castro-de-Sousa JP, Queiroz JA, Passarinha LA, et al. Vitreous humor in the pathologic scope: insights from proteomic approaches. PROTEOMICS Clin Appl. 2015;9:187–202. Scholar
  5. 5.
    Mahajan VB, Skeie JM. Translational vitreous proteomics. Proteomics Clin Appl. 2014;8:204–8. Scholar
  6. 6.
    Sang JK, Kim S, Park J, Hong KL, Kyong SP, Hyeong GY, et al. Differential expression of vitreous proteins in proliferative diabetic retinopathy. Curr Eye Res. 2006;31:231–40. Scholar
  7. 7.
    Nakanishi T, Koyama R, Ikeda T, Shimizu A. Catalogue of soluble proteins in the human vitreous humor: comparison between diabetic retinopathy and macular hole. J Chromatogr B Anal Technol Biomed Life Sci. 2002;776:89–100. Scholar
  8. 8.
    Koss MJ, Hoffmann J, Nguyen N, Pfister M, Mischak H, Mullen W, et al. Proteomics of vitreous humor of patients with exudative age-related macular degeneration. PLoS One. 2014;9:1–11. Scholar
  9. 9.
    Loukovaara S, Nurkkala H, Tamene F, Gucciardo E, Liu X, Repo P, et al. Quantitative proteomics analysis of vitreous humor from diabetic retinopathy patients. J Proteome Res. 2015;14:5131–43. Scholar
  10. 10.
    Santos FM, Gaspar LM, Ciordia S, Rocha AS, Castro e Sousa J, Paradela A, et al. iTRAQ quantitative proteomic analysis of vitreous from patients with retinal detachment. Int J Mol Sci. 2018;19:1157. Scholar
  11. 11.
    Öhman T, Tamene F, Göös H, Loukovaara S, Varjosalo M. Systems pathology analysis identifies neurodegenerative nature of age-related vitreoretinal interface diseases. Aging Cell. 2018;17.
  12. 12.
    Shitama T, Hayashi H, Noge S, Uchio E, Oshima K, Haniu H, et al. Proteome profiling of vitreoretinal diseases by cluster analysis. Proteomics Clin Appl. 2008;2:1265–80. Scholar
  13. 13.
    Yamane K, Minamoto A, Yamashita H, Takamura H, Miyamoto-Myoken Y, Yoshizato K, et al. Proteome analysis of human vitreous proteins. Mol Cell Proteomics. 2003;2:1177–87. Scholar
  14. 14.
    Kim T, Sang JK, Kim K, Kang UB, Lee C, Kyong SP, et al. Profiling of vitreous proteomes from proliferative diabetic retinopathy and nondiabetic patients. Proteomics. 2007;7:4203–15. Scholar
  15. 15.
    Neal RE, Bettelheim FA, Lin C, Winn KC, Garland DL, Zigler JS. Alterations in human vitreous humour following cataract extraction. Exp Eye Res. 2005;80:337–47. Scholar
  16. 16.
    Sugioka K, Saito A, Kusaka S, Kuniyoshi K, Shimomura Y. Identification of vitreous proteins in retinopathy of prematurity. Biochem Biophys Res Commun. 2017;488:483–8. Scholar
  17. 17.
    Gao B-B, Chen X, Timothy N, Aiello LP, Feener EP. Characterization of the vitreous proteome in diabetes without diabetic retinopathy and diabetes with proliferative diabetic retinopathy. J Proteome Res. 2008;7:2516–25. Scholar
  18. 18.
    Gaspar LM, Santos FM, Albuquerque T, Castro-de-Sousa JP, Passarinha LA, Tomaz CT. Proteome analysis of vitreous humor in retinal detachment using two different flow-charts for protein fractionation. J Chromatogr B Anal Technol Biomed Life Sci. 2017;1061–1062:334–41. Scholar
  19. 19.
    Reich M, Dacheva I, Siwy J, Mullen W, Schanstra JP, Choi CY, et al. Proteomics of vitreous in neovascular age-related macular degeneration. Exp Eye Res. 2016;146:107–17. Scholar
  20. 20.
    Rogowska-Wrzesinska A, Le Bihan MC, Thaysen-Andersen M, Roepstorff P. 2D gels still have a niche in proteomics. J Proteome. 2013;88:4–13. Scholar
  21. 21.
    Oliveira BM, Coorssen JR, Martins-de-Souza D. 2DE: the phoenix of proteomics. J Proteome. 2014;104:140–50. Scholar
  22. 22.
    Gauci V, Wright E, Coorssen J. Quantitative proteomics: assessing the spectrum of in-gel protein detection methods. J Chem Biol. 2011;4:3–29. Scholar
  23. 23.
    Magdeldin S, Enany S, Yoshida Y, Xu B, Zhang Y, Zureena Z, et al. Basics and recent advances of two dimensional- polyacrylamide gel electrophoresis. Clin Proteomics. 2014;11:16. Scholar
  24. 24.
    Valente KN, Choe LH, Lenhoff AM, Lee KH. Optimization of protein sample preparation for two-dimensional electrophoresis. Electrophoresis. 2012;33:1947–57. Scholar
  25. 25.
    Chen CPC, Hsu CC, Yeh WL, Lin HC, Hsieh SY, Lin SC, et al. Optimizing human synovial fluid preparation for two-dimensional gel electrophoresis. Proteome Sci. 2011;9.
  26. 26.
    Saraygord-Afshari N, Naderi-Manesh H, Naderi M. Increasing proteome coverage for gel-based human tear proteome maps: towards a more comprehensive profiling. Biomed Chromatogr. 2015;29:1056–67. Scholar
  27. 27.
    Westermeier R. Looking at proteins from two dimensions: a review on five decades of 2D electrophoresis. Arch Physiol Biochem. 2014;120:168–72. Scholar
  28. 28.
    López JL. Two-dimensional electrophoresis in proteome expression analysis. J Chromatogr B Anal Technol Biomed Life Sci. 2007;849:190–202. Scholar
  29. 29.
    Rabilloud T, Lelong C. Two-dimensional gel electrophoresis in proteomics: a tutorial. J Proteome. 2011;74:1829–41. Scholar
  30. 30.
    Guo C-G, Li S, Wang H-Y, Zhang D, Li G-Q, Zhang J, et al. Study on stability mechanism of immobilized pH gradient in isoelectric focusing via the Svensson–Tiselius differential equation and moving reaction boundary. Talanta. 2013;111:20–7. Scholar
  31. 31.
    Cao C-X. Moving chemical reaction boundary and isoelectric focusing I. Conditional equations for Svensson-Tiselius’ differential equation of solute concentration distribution in idealized isoelectric focusing at steady state. 1998.Google Scholar
  32. 32.
    Slibinskas R, Ražanskas R, Zinkevičiute R, Čiplys E. Comparison of first dimension IPG and NEPHGE techniques in two-dimensional gel electrophoresis experiment with cytosolic unfolded protein response in Saccharomyces cerevisiae. Proteome Sci. 2013;11.
  33. 33.
    Hanneken M, Šlais K, König S. pI-Control in comparative fluorescence gel electrophoresis (CoFGE) using amphoteric azo dyes. EuPA Open Proteomics. 2015;8:36–9. Scholar
  34. 34.
    Guo C-G, Shang Z, Yan J, Li S, Li G-Q, Liu R-Z, et al. A tunable isoelectric focusing via moving reaction boundary for two-dimensional gel electrophoresis and proteomics. Talanta. 2015;137:197–203. Scholar
  35. 35.
    Moche M, Albrecht D, Maaß S, Hecker M, Westermeier R, Büttner K. The new horizon in 2D electrophoresis: new technology to increase resolution and sensitivity. Electrophoresis. 2013;34:1510–8. Scholar
  36. 36.
    Hanneken M, König S. Horizontal comparative fluorescence two-dimensional gel electrophoresis for improved spot coordinate detection. Electrophoresis. 2014;35:1118–21. Scholar
  37. 37.
    Khan J, Wei JS, Ringnér M, Saal LH, Ladanyi M, Westermann F, et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med. 2001;7:673–9. Scholar
  38. 38.
    Lancashire LJ, Lemetre C, Ball GR. An introduction to artificial neural networks in bioinformatics - application to complex microarray and mass spectrometry datasets in cancer studies. Brief Bioinform. 2009;10:315–29. Scholar
  39. 39.
    Candiano G, Bruschi M, Musante L, Santucci L, Ghiggeri GM, Carnemolla B, et al. Blue silver: a very sensitive colloidal Coomassie G-250 staining for proteome analysis. Electrophoresis. 2004;25:1327–33. Scholar
  40. 40.
    Patel N, Solanki E, Picciani R, Cavett V, Caldwell-Busby JA, Bhattacharya SK. Strategies to recover proteins from ocular tissues for proteomics. Proteomics. 2008;8:1055–70. Scholar
  41. 41.
    Görg A, Postel W, Friedrich C, Kuick R, Strahler JR, Hanash SM. Temperature-dependent spot positional variability in two-dimensional polypeptide patterns. Electrophoresis. 1991;12:653–8. Scholar
  42. 42.
    Nor NM, Mohamed MS, Loh TC, Foo HL, Rahim RA, Tan JS, et al. Comparative analyses on medium optimization using one-factor-at-a-time , response surface methodology, and artificial neural network for lysine–methionine biosynthesis by Pediococcus pentosaceus RF-1. Biotechnol Biotechnol Equip. 2017;31:935–47. Scholar
  43. 43.
    Pedro AQ, Martins LM, Dias JML, Bonifácio MJ, Queiroz JA, Passarinha LA. An artificial neural network for membrane-bound catechol-O-methyltransferase biosynthesis with Pichia pastoris methanol-induced cultures. Microb Cell Factories. 2015;14:113. Scholar
  44. 44.
    Brown SR, Staff M, Lee R, Love J, Parker DA, Aves SJ, et al. Design of experiments methodology to build a multifactorial statistical model describing the metabolic interactions of alcohol dehydrogenase isozymes in the ethanol biosynthetic pathway of the yeast Saccharomyces cerevisiae. ACS Synth Biol. 2018;7:1676–84. Scholar
  45. 45.
    Padula M, Berry I, O’Rourke M, Raymond B, Santos J, Djordjevic SP. A comprehensive guide for performing sample preparation and top-down protein analysis. Proteomes. 2017;5:11. Scholar
  46. 46.
    Görg A, Boguth G, Obermaier C, Posch A, Weiss W. Two-dimensional polyacrylamide gel electrophoresis with immobilized pH gradients in the first dimension (IPG-Dalt): the state of the art and the controversy of vertical versus horizontal systems. Electrophoresis. 1995;16:1079–86.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Fátima M. Santos
    • 1
    • 2
    • 3
    • 4
  • Tânia Albuquerque
    • 1
  • Leonor M. Gaspar
    • 1
    • 2
  • João M. L. Dias
    • 5
  • João P. Castro e Sousa
    • 1
    • 6
  • Alberto Paradela
    • 7
  • Cândida T. Tomaz
    • 1
    • 2
  • Luís A. Passarinha
    • 1
    • 4
    Email author
  1. 1.CICS-UBI—Health Sciences Research CentreUniversity of Beira InteriorCovilhãPortugal
  2. 2.Chemistry Department, Faculty of SciencesUniversity of Beira InteriorCovilhãPortugal
  3. 3.Laboratory of Pharmacology and Toxicology—UBIMedicalUniversity of Beira InteriorCovilhãPortugal
  4. 4.UCIBIO@REQUIMTE, Departamento de Química, Faculdade de Ciências e TecnologiaUniversidade Nova de LisboaCaparicaPortugal
  5. 5.Cancer Molecular Diagnostics Laboratory, National Institute for Health Research, Biomedical Research CentreUniversity of CambridgeCambridgeUK
  6. 6.Department of OphthalmologyCentro Hospitalar de LeiriaLeiriaPortugal
  7. 7.Unidad de Proteomica, Centro Nacional de BiotecnologíaCSICMadridSpain

Personalised recommendations