Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays

Cell Systems

Volumn 6, Issue 6, 2018, Pages 636-653

  Smith, Kevin ^a,b   Piccinini, Filippo ^c   Balassa, Tamas ^d   Koos, Krisztian ^d   Danka, Tivadar ^d   Azizpour, Hossein ^a,b   Horvath, Peter ^d,e  

^a ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

^b SCIENCE FOR LIFE LABORATORY   (Sweden)

^c DEPARTMENT OF MEDICAL ONCOLOGY   (Italy)

^d INSTITUTE OF EXPERIMENTAL MEDICINE   (Hungary)

^e UNIVERSITY OF HELSINKI   (Finland)

Author keywords

cell classification; drug screening; freely available tools; high content screening; machine learning; microscopy; oncology; phenomics; phenotypic image analysis; single cell analysis

Indexed keywords

ACCURACY; ALGORITHM; HIGH THROUGHPUT SCREENING; HUMAN; IMAGE ANALYSIS; MACHINE LEARNING; MICROSCOPY; MOLECULAR BIOLOGY; MOLECULAR MODEL; PHENOTYPE; PRIORITY JOURNAL; REVIEW; STATISTICAL ANALYSIS; SYSTEMS BIOLOGY; ANIMAL; IMAGE PROCESSING; PROCEDURES; SOFTWARE;

ANIMALS; BIG DATA; HIGH-THROUGHPUT SCREENING ASSAYS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MACHINE LEARNING; MICROSCOPY; PHENOTYPE; SOFTWARE;

EID: 85048445198     PISSN: 24054712     EISSN: 24054720     Source Type: Journal    
DOI: 10.1016/j.cels.2018.06.001     Document Type: Review

References (111)

1. Aha, D.W., Bankert, R.L., A comparative evaluation of sequential feature selection algorithms. Fisher, D., Lenz, H.-J., (eds.) Learning from Data, 1996, Springer, 199–206.
2. Arganda-Carreras, I., Kaynig, V., Rueden, C., Eliceiri, K.W., Schindelin, J., Cardona, A., Sebastian Seung, H., Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33 (2017), 2424–2426.
3. Arbelle, A., Raviv, T.R., Microscopy cell segmentation via adversarial neural networks. arXiv, 2017 1709.05860.
4. Arteta, C., Lempitsky, V., Zak, J., Lu, X., Noble, A., Zisserman, A., HTX: a tool for the exploration and visualization of high-throughput image assays. bioRxiv, 2017, 10.1101/204016.
5. Azizpour, H., Razavian, A.S., Sullivan, J., Maki, A., Carlsson, S., Factors of transferability for a generic convnet representation. IEEE Trans. Pattern Anal. Mach. Intell. 38 (2016), 1790–1802.
6. Banerjee, I., Miyake, Y., Nobs, S.P., Schneider, C., Horvath, P., Kopf, M., Matthias, P., Helenius, A., Yamauchi, Y., Influenza A virus uses the aggresome processing machinery for host cell entry. Science 346 (2014), 473–477.
7. Barry, D.J., Durkin, C.H., Abella, J.V., Way, M., Open source software for quantification of cell migration, protrusions, and fluorescence intensities. J. Cell Biol. 209 (2015), 163–180.
8. Bendall, S.C., Davis, K.L., Amir, E.A.D., Tadmor, M.D., Simonds, E.F., Chen, T.J., Shenfeld, D.K., Nolan, G.P., Pe'er, D., Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157 (2014), 714–725.
9. Bermudez-Chacon, R., and Smith, K. Automatic problem-specific hyperparameter optimization and model selection for supervised machine learning. Technical Report, ETH Zurich, 2015.
10. Berthold, M.R., Cebron, N., Dill, F., Gabriel, T.R., Kötter, T., Meinl, T., Ohl, P., Thiel, K., Wiswedel, B., KNIME-the Konstanz information miner: version 2.0 and beyond. ACM SIGKDD Explorations Newsletter 11 (2009), 26–31.
11. Bickle, M., The beautiful cell: high-content screening in drug discovery. Anal. Bioanal. Chem. 398 (2010), 219–226.
12. Bodzon-Kulakowska, A., Suder, P., Imaging mass spectrometry: instrumentation, applications, and combination with other visualization techniques. Mass Spectrom. Rev. 35 (2016), 147–169.
13. Boutros, M., Heigwer, F., Laufer, C., Microscopy-based high-content screening. Cell 163 (2015), 1314–1325.
14. Brenner, S., The genetics of Caenorhabditis elegans. Genetics 77 (1974), 71–94.
15. Caicedo, J.C., Cooper, S., Heigwer, F., Warchal, S., Qiu, P., Molnar, C., Vasilevich, A.S., Barry, J.D., Bansal, H.S., Kraus, O., et al. Data-analysis strategies for image-based cell profiling. Nat. Methods 14 (2017), 849–863.
16. Carpenter, A.E., Jones, T.R., Lamprecht, M.R., Clarke, C., Kang, I.H., Friman, O., Guertin, D.D., Chang, J.H., Lindquist, R.A., Moffat, J., et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol., 7, 2006, R100.
17. Castelvecchi, D., Can we open the black box of AI?. Nat. News, 538, 2016, 20.
18. Chen, C.L., Mahjoubfar, A., Tai, L.C., Blaby, I.K., Huang, A., Niazi, K.R., Jalali, B., Deep learning in label-free cell classification. Sci. Rep., 6, 2016, 21471.
19. Chia, N.Y., Chan, Y.S., Feng, B., Lu, X., Orlov, Y.L., Moreau, D., Kumar, P., Yang, L., Jiang, J., Lau, M.S., et al. A genome-wide RNAi screen reveals determinants of human embryonic stem cell identity. Nature, 468, 2010, 316.
20. Ching, T., Himmelstein, D.S., Beaulieu-Jones, B.K., Kalinin, A.A., Do, B.T., Way, G.P., Ferrero, E., Agapow, P.M., Zietz, M., Hoffman, M.M., et al. Opportunities and obstacles for deep learning in biology and medicine. bioRxiv, 2018, 10.1101/142760.
21. Cornelissen, F., Cik, M., Gustin, E., Phaedra, a protocol-driven system for analysis and validation of high-content imaging and flow cytometry. J. Biomol. Screen. 17 (2012), 496–506.
22. Dao, D., Fraser, A.N., Hung, J., Ljosa, V., Singh, S., Carpenter, A.E., CellProfiler analyst: interactive data exploration, analysis and classification of large biological image sets. Bioinformatics 32 (2016), 3210–3212.
23. De Chaumont, F., Dallongeville, S., Chenouard, N., Hervé N., Pop, S., Provoost, T., Meas-Yedid, V., Pankajakshan, P., Lecomte, T., Montagner, Y.L., et al. Icy: an open bioimage informatics platform for extended reproducible research. Nat. Methods, 9, 2012, 690.
24. Desbordes, S.C., Placantonakis, D.G., Ciro, A., Socci, N.D., Lee, G., Djaballah, H., Studer, L., High-throughput screening assay for the identification of compounds regulating self-renewal and differentiation in human embryonic stem cells. Cell Stem Cell 2 (2008), 602–612.
25. Dougherty, M.T., Folk, M.J., Zadok, E., Bernstein, H.J., Bernstein, F.C., Eliceiri, K.W., Benger, W., Best, C., Unifying biological image formats with HDF5. Commun. ACM 52 (2009), 42–47.
26. Dürr, O., Sick, B., Single-cell phenotype classification using deep convolutional neural networks. J. Biomol. Screen. 21 (2016), 998–1003.
27. Eliceiri, K.W., Berthold, M.R., Goldberg, I.G., Ibáñez, L., Manjunath, B.S., Martone, M.E., Murphy, R.F., Peng, H., Plant, A.L., Roysam, B., et al. Biological imaging software tools. Nat. Methods 9 (2012), 697–710.
28. Eulenberg, P., Köhler, N., Blasi, T., Filby, A., Carpenter, A.E., Rees, P., Theis, F.J., Wolf, F.A., Reconstructing cell cycle and disease progression using deep learning. Nat. Commun., 8, 2017, 463.
29. Frank, E., Hall, M.A., Witten, I.H., The WEKA Workbench. Data Mining: Practical Machine Learning Tools and Techniques. Fourth Edition, 2016, Morgan Kaufmann Appendix B.
30. Friedman, J., Hastie, T., Tibshirani, R., Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann. Stat. 28 (2000), 337–407.
31. Fusco, L., Lefort, R., Smith, K., Benmansour, F., Gonzalez, G., Barillari, C., Rinn, B., Fleuret, F., Fua, P., Pertz, O., Computer vision profiling of neurite outgrowth dynamics reveals spatiotemporal modularity of Rho GTPase signaling. J. Cell Biol. 212 (2016), 91–111.
32. Giuliano, K., DeBiasio, R., Dunlay, R.T., Gough, A., Volosky, J., Zock, J., Pavlakis, G., Taylor, D.L., High content screening: a new approach to easing key bottlenecks in the drug discovery process. J. Biomol. Screen. 2 (1997), 249–259.
33. Goldsborough, P., Pawlowski, N., Caicedo, J.C., Singh, S., Carpenter, A., CytoGAN: generative modeling of cell images. bioRxiv, 2017, 10.1101/227645.
34. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y. (2014). Generative adversarial nets. Proceeding of the Advances in Neural Information Processing Systems conference (NIPS 2014), pp. 2672–2680.
35. Gustafsdottir, S.M., Ljosa, V., Sokolnicki, K.L., Wilson, J.A., Walpita, D., Kemp, M.M., Seiler, K.P., Carrel, H.A., Golub, T.R., Schreiber, S.L., et al. Multiplex cytological profiling assay to measure diverse cellular states. PLoS One, 8, 2013, e80999.
36. He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR 2016), pp. 770–778.
37. Held, M., Schmitz, M.H., Fischer, B., Walter, T., Neumann, B., Olma, M.H., Peter, M., Ellenberg, J., Gerlich, D.W., CellCognition: time-resolved phenotype annotation in high-throughput live cell imaging. Nat. Methods 7 (2010), 747–754.
38. Horvath, P., Wild, T., Kutay, U., Csucs, G., Machine learning improves the precision and robustness of high-content screens: using nonlinear multiparametric methods to analyze screening results. J. Biomol. Screen. 16 (2011), 1059–1067.
39. Houle, D., Govindaraju, D.R., Omholt, S., Phenomics: the next challenge. Nat. Rev. Genet., 11, 2010, 855.
40. Huber, W., Carey, J.V., Gentleman, R., Anders, S., Carlson, M., Carvalho, S.B., Bravo, C.H., Davis, S., Gatto, L., Girke, T., et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12 (2015), 115–121.
41. Johnson, G.R., Donovan-Maiye, R.M., Maleckar, M.M., Generative modeling with conditional autoencoders: building an integrated cell. arXiv, 2017 1705.00092.
42. Jones, T.R., Kang, I.H., Wheeler, D.B., Lindquist, R.A., Papallo, A., Sabatini, D.M., Golland, P., Carpenter, A.E., CellProfiler analyst: data exploration and analysis software for complex image-based screens. BMC Bioinformatics, 9, 2008, 482.
43. Jones, T.R., Carpenter, A.E., Lamprecht, M.R., Moffat, J., Silver, S., Grenier, J., Root, D., Golland, P., Sabatini, D.M., Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning. Proc. Natl. Acad. Sci.USA 106 (2009), 1826–1831.
44. Kankaanpää P., Paavolainen, L., Tiitta, S., Karjalainen, M., Päivärinne, J., Nieminen, J., Marjomaki, V., Heino, J., White, D.J., BioImageXD: an open, general-purpose and high-throughput image-processing platform. Nat. Methods, 9, 2012, 683.
45. Kamentsky, L., Jones, T.R., Fraser, A., Bray, M.A., Logan, D.J., Madden, K.L., Ljosa, V., Rueden, C., Eliceiri, K.W., Carpenter, A.E., Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software. Bioinformatics 27 (2011), 1179–1180.
46. Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Proceeding of the Advances in Neural Information Processing Systems conference (NIPS 2012), pp. 1097–1105.
47. Kanehisa, M., Goto, S., KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28 (2000), 27–30.
48. Kraus, O.Z., Ba, J.L., Frey, B.J., Classifying and segmenting microscopy images with deep multiple instance learning. Bioinformatics 32 (2016), i52–i59.
49. Kraus, O.Z., Grys, B.T., Ba, J., Chong, Y., Frey, B.J., Boone, C., Andrews, B.J., Automated analysis of high-content microscopy data with deep learning. Mol. Syst. Biol., 13, 2017, 924.
50. Laksameethanasan, D., Tan, R.Z., Toh, G.W.L., Loo, L.H., cellXpress: a fast and user-friendly software platform for profiling cellular phenotypes. BMC Bioinformatics, 14, 2013, S4.
51. Link, W., Oyarzabal, J., Serelde, B.G., Albarran, M.I., Rabal, O., Cebriá A., Alfonso, P., Fominaya, J., Renner, O., Peregrino, S., et al. Chemical interrogation of FOXO3a nuclear translocation identifies potent and selective inhibitors of phosphoinositide 3-kinases. J. Biol. Chem. 284 (2009), 28392–28400.
52. Liu, S., Zheng, W., Wu, K., Lin, Y., Jia, F., Zhang, Y., Wang, Z., Luo, Q., Zhao, Y., Wang, F., Correlated mass spectrometry and confocal microscopy imaging verifies the dual-targeting action of an organoruthenium anticancer complex. Chem. Commun. 53 (2017), 4136–4139.
53. Loo, L.H., Wu, L.F., Altschuler, S.J., Image-based multivariate profiling of drug responses from single cells. Nat. Methods 4 (2007), 445–453.
54. Ljosa, V., Sokolnicki, K.L., Carpenter, A.E., Annotated high-throughput microscopy image sets for validation. Nat. Methods, 9, 2012, 637.
55. Lucchi, A., Márquez-Neila, P., Becker, C., Li, Y., Smith, K., Knott, G., Fua, P., Learning structured models for segmentation of 2-D and 3-D imagery. IEEE Trans. Med. Imaging 34 (2015), 1096–1110.
56. Marée, R., Rollus, L., Stévens, B., Hoyoux, R., Louppe, G., Vandaele, R., Begon, J.M., Kainz, P., Geurts, P., Wehenkel, L., Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinformatics 32 (2016), 1395–1401.
57. Marée, R., Geurts, P., Wehenkel, L., Towards generic image classification using tree-based learning: an extensive empirical study. Pattern Recognit. Lett. 74 (2016), 15–23.
58. Masyuko, R., Lanni, E.J., Sweedler, J.V., Bohn, P.W., Correlated imaging - a grand challenge in chemical analysis. Analyst 138 (2013), 1924–1939.
59. Medical Image Net. (n.d.). http://langlotzlab.stanford.edu/projects/medical-image-net/.
60. Misselwitz, B., Strittmatter, G., Periaswamy, B., Schlumberger, M.C., Rout, S., Horvath, P., Kozak, K., Hardt, W.D., Enhanced CellClassifier: a multi-class classification tool for microscopy images. BMC Bioinformatics, 11, 2010, 30.
61. Murphy, R.F., CellOrganizer: image-derived models of subcellular organization and protein distribution. Methods Cell Biol. 110 (2012), 179–193.
62. Nunez-Iglesias, J., Kennedy, R., Parag, T., Shi, J., Chklovskii, D.B., Machine learning of hierarchical clustering to segment 2D and 3D images. PLoS One, 8, 2013, e71715.
63. Nüsslein-Volhard, C., Wieschaus, E., Mutations affecting segment number and polarity in Drosophila. Nature 287 (1980), 795–801.
64. Ogier, A., Dorval, T., HCS-Analyzer: open source software for high-content screening data correction and analysis. Bioinformatics 28 (2012), 1945–1946.
65. Oquab, M., Bottou, L., Laptev, I., and Sivic, J. (2015). Is object localization for free? Weakly supervised learning with convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 685–694.
66. Orlov, N., Shamir, L., Macura, T., Johnston, J., Eckley, D.M., Goldberg, I.G., WND-CHARM: multi-purpose image classification using compound image transforms. Pattern Recognit. Lett. 29 (2008), 1684–1693.
67. Orvedahl, A., Sumpter, R. Jr., Xiao, G., Ng, A., Zou, Z., Tang, Y., Narimatsu, M., Gilpin, C., Sun, Q., Roth, M., et al. Image-based genome-wide siRNA screen identifies selective autophagy factors. Nature, 480, 2011, 113.
68. Palazzolo, G., Horvath, P., Zenobi-Wong, M., The flavonoid isoquercitrin promotes neurite elongation by reducing RhoA activity. PLoS One, 7, 2012, e49979.
69. Palmer, A., Phapale, P., Chernyavsky, I., Lavigne, R., Fay, D., Tarasov, A., Kovalev, V., Fuchser, J., Nikolenko, S., Pineau, C., et al. FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry. Nat. Methods 14 (2017), 57–60.
70. Pau, G., Fuchs, F., Sklyar, O., Boutros, M., Huber, W., EBImage—an R package for image processing with applications to cellular phenotypes. Bioinformatics 26 (2010), 979–981.
71. Pau, G., Zhang, X., Boutros, M. and Huber, W. (2018). imageHTS: Analysis of high-throughput microscopy-based screens. R package version 1.28.1.
72. Pärnamaa, T., Parts, L., Accurate classification of protein subcellular localization from high-throughput microscopy images using deep learning. G3 (Bethesda) 7 (2017), 1385–1392.
73. Pawlowski, N., Caicedo, J.C., Singh, S., Carpenter, A.E., Storkey, A., Automating morphological profiling with generic deep convolutional networks. bioRxiv, 2016, 10.1101/085118.
74. Pereira, D.A., Williams, J.A., Origin and evolution of high throughput screening. Br. J. Pharmacol. 152 (2007), 53–61.
75. Piccinini, F., Balassa, T., Szkalisity, A., Molnar, C., Paavolainen, L., Kujala, K., Buzas, K., Sarazova, M., Pietiainen, V., Kutay, U., et al. Advanced cell classifier: user-friendly machine-learning-based software for discovering phenotypes in high-content imaging data. Cell Syst. 4 (2017), 651–655.
76. Rämö P., Sacher, R., Snijder, B., Begemann, B., Pelkmans, L., CellClassifier: supervised learning of cellular phenotypes. Bioinformatics 524 (2009), 3028–3030.
77. Rämö P., Drewek, A., Arrieumerlou, C., Beerenwinkel, N., Ben-Tekaya, H., Cardel, B., Dehio, C., Simultaneous analysis of large-scale RNAi screens for pathogen entry. BMC Genomics, 15, 2014, 1162.
78. Rajaram, S., Pavie, B., Wu, L.F., Altschuler, S.J., PhenoRipper: software for rapidly profiling microscopy images. Nat. Methods 9 (2012), 635–637.
79. Razi, M., Tooze, S.A., Correlative light and electron microscopy. Methods Enzymol. 452 (2009), 261–275.
80. Ribeiro, M.T., Singh, S., and Guestrin, C. (2016). Why should I trust you? Explaining the predictions of any classifier. Proceedings of the 22nd International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD), pp. 1135–1144.
81. Rimon, N., Schuldiner, M., Getting the whole picture: combining throughput with content in microscopy. J. Cell Sci. 124 (2011), 3743–3751.
82. Robertson, S., Azizpour, H., Smith, K., Hartman, J., Digital image analysis in breast pathology – from image processing techniques to artificial intelligence. Transl. Res. 194 (2017), 19–35.
83. Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: convolutional networks for biomedical image segmentation. Proceedings of the international conference on medical image computing and computer-assisted intervention (MICCAI 2015), pp. 234–241.
84. Rozenblatt-Rosen, O., Stubbington, M.J., Regev, A., Teichmann, S.A., The human cell atlas: from vision to reality. Nat. News, 550, 2017, 451.
85. Sadanandan, S.K., Ranefall, P., Le Guyader, S., Wählby, C., Automated training of deep convolutional neural networks for cell segmentation. Sci. Rep., 7, 2017, 7860.
86. Sadanandan, S., Karlsson, J., and Wählby, C. (2017b). Spheroid segmentation using multiscale deep adversarial networks. Proceedings of the IEEE international conference on computer vision workshops (ICCV 2017), pp. 36–41.
87. Sailem, H., Arias-Garcia, M., Bakal, C., Zisserman, A., and Rittscher, J. (2017). Discovery of rare phenotypes in cellular images using weakly supervised deep learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), pp. 49–55.
88. Schie, I.W., Huser, T., Methods and applications of Raman microspectroscopy to single-cell analysis. Appl. Spectrosc. 67 (2013), 813–828.
89. Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9 (2012), 676–682.
90. Shamir, L., Delaney, J.D., Orlov, N., Eckley, D.M., Goldberg, I.G., Pattern recognition software and techniques for biological image analysis. PLoS Comput. Biol., 6, 2010, e1000974.
91. Singh, S., Carpenter, A.E., Genovesio, A., Increasing the content of high-content screening: an overview. J. Biomol. Screen. 19 (2014), 640–650.
92. Smith, E., Dent, G., Modern Raman Spectroscopy: A Practical Approach. 2005, John Wiley, 1–210.
93. Smith, K., Horvath, P., Active learning strategies for phenotypic profiling of high-content screens. J. Biomol. Screen. 19 (2014), 685–695.
94. Sommer, C., Gerlich, D.W., Machine learning in cell biology–teaching computers to recognize phenotypes. J. Cell Sci. 126 (2013), 5529–5539.
95. Sommer, C., Straehle, C., Koethe, U., Hamprecht, F.A. (2011). Ilastik: Interactive learning and segmentation toolkit. In 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, pp. 230-233.
96. Sommer, C., Hoefler, R., Samwer, M., Gerlich, D.W., A deep learning and novelty detection framework for rapid phenotyping in high-content screening. bioRxiv, 2017, 10.1091/mbc.e17-05-0333.
97. Taubman, D., Marcellin, M., JPEG2000 Image Compression Fundamentals, Standards and Practice: Image Compression Fundamentals, Standards and Practice. 2012, Springer Science and Business Media, 642.
98. Thomsen, W., Frazer, W.J., Unett, D., Functional assays for screening GPCR targets. Curr. Opin. Biotechnol. 16 (2005), 655–665.
99. Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N.J., Livak, K.J., Mikkelsen, T.S., Rinn, J.L., The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32 (2014), 381–386.
100. Uhlmann, V., Singh, S., Carpenter, A.E., CP-CHARM: segmentation-free image classification made accessible. BMC Bioinformatics, 17, 2016, 51.
101. Usaj, M.M., Styles, E.B., Verster, A.J., Friesen, H., Boone, C., Andrews, B.J., High-content screening for quantitative cell biology. Trends Cell Biol. 26 (2016), 598–611.
102. Venter, J.C., Zhu, X., The sequence of the human genome. Science 291 (2001), 1304–1351.
103. Wagner, M., Single-cell ecophysiology of microbes as revealed by Raman microspectroscopy or secondary ion mass spectrometry imaging. Annu. Rev. Microbiol. 63 (2009), 411–429.
104. Weigert, M., Schmidt, U., Boothe, T., Andreas, M., Dibrov, A., Jain, A., Myers, E.W., Content-aware image restoration: pushing the limits of fluorescence microscopy. bioRxiv, 2017, 10.1101/236463.
105. Wieslander, H. and Forslid, G. (2017). Deep convolutional neural networks for detecting cellular changes due to malignancy. Proceedings of the IEEE International Conference on Computer Vision workshops (ICCV 2017), pp. 82–89.
106. Winsnes, C.F., Sullivan, D.P., Smith, K., Lundberg, E., Multi-label prediction of subcellular localization in confocal images using deep neural networks. Mol. Biol. Cell 27 (2016), 26–39.
107. Yamauchi, Y., Boukari, H., Banerjee, I., Sbalzarini, I.F., Horvath, P., Helenius, A., Histone deacetylase 8 is required for centrosome cohesion and influenza A virus entry. PLoS Pathog., 7, 2011, e1002316.
108. Young, D.W., Bender, A., Hoyt, J., McWhinnie, E., Chirn, G.W., Tao, C.Y., Tallarico, J.A., Labow, M., Jenkins, J.L., Mitchison, T.J., Feng, Y., Integrating high-content screening and ligand-target prediction to identify mechanism of action. Nat. Chem. Biol., 4, 2008, 59.
109. Zanella, F., Lorens, J.B., Link, W., High content screening: seeing is believing. Trends Biotechnol. 28 (2010), 237–245.
110. Zhang, X., Boutros, M., A novel phenotypic dissimilarity method for image-based high-throughput screens. BMC Bioinformatics, 14, 2013, 336.
111. Zhang, H., Xie, X., Fang, C., Yang, Y., Jin, D., Fei, P., High-throughput, high-resolution generated adversarial network microscopy. arXiv, 2018 1801.07330.