Imaging-in-flow: Digital holographic microscopy as a novel tool to detect and classify nanoplanktonic organisms

Limnology and Oceanography: Methods

Volumn 12, Issue NOV, 2014, Pages 757-775

  Zetsche, Eva Maria ^a   El Mallahi, Ahmed ^b   Dubois, Frank ^b   Yourassowsky, Catherine ^b   Kromkamp, Jacco C ^c   Meysman, Filip J R ^a,c  

^a VRIJE UNIVERSITEIT BRUSSEL   (Belgium)

^b UNIVERSITÉ LIBRE DE BRUXELLES   (Belgium)

^c ROYAL NETHERLANDS INSTITUTE FOR SEA RESEARCH   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84921666267     PISSN: None     EISSN: 15415856     Source Type: Journal    
DOI: 10.4319/lom.2014.12.757     Document Type: Article

References (77)

1. Anderson, D. M., A. D. Cembella, and G. M. Hallegraeff. 2012. Progress in understanding harmful algal blooms: paradigm shifts and new technologies for research, monitoring, and management. Annu. Rev. Mar. Sci. 4:143-176 [doi:10. 1146/ annurev-marine-120308-081121].
2. Bandyopadhyay, A., and others. 2011. Novel metabolic attributes of the genus cyanothece, comprising a group of unicellular nitrogen-fixing cyanobacteria. mBio 2:1-10.
3. Benfield, M. C., and others. 2007. RAPID-Research on automated plankton identification. Oceanography 20:172-187 [doi:10. 5670/ oceanog. 2007. 63].
4. Blaschko, M. B., and others. 2005. Automatic in situ identification of plankton., p. 79-86. In Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05). IEEE Computer Society Washington, DC, USA ©2005.
5. Bochdansky, A. B., M. H. Jericho, and G. J. Herndl. 2013. Development and deployment of a point-source digital inline holographic microscope for the study of plankton and particles to a depth of 6000 m. Limnol. Oceanogr. Methods 11:28-40 [doi:10. 4319/ lom. 2013. 11. 28].
6. Boistel, R., J. Swoger, U. Kržič, V. Fernandez, B. Gillet, and E. G. Reynaud. 2011. The future of three-dimensional microscopic imaging in marine biology. Mar. Ecol. 32:438-452 [doi:10. 1111/ j. 1439-0485. 2011. 00442. x].
7. Chang, C.-C., and C.-J. Lin. 2011. LIBSVM: a library for support vector machines. ACM TIST 2:27.
8. Collier, J. L. 2000. Flow cytometry and the single cell in phycology. J. Phycol. 36:628-644 [doi:10. 1046/ j. 1529-8817. 2000. 99215. x].
9. Cortes, C., and V. Vapnik. 1995. Support-vector networks. Mach. Learn. 20:273-297 [doi:10. 1007/ BF00994018].
10. Culverhouse, P. F. 2007. Human and machine factors in algae monitoring performance. Ecol. Inform. 2:361-366 [doi:10. 1016/ j. ecoinf. 2007. 07. 001].
11. Culverhouse, P. F., R. Williams, B. Reguera, V. Herry, and S. González-Gil. 2003. Do experts make mistakes? A comparison of human and machine indentification of dinoflagellates. Mar. Ecol. Prog. Ser. 247:17-25 [doi:10. 3354/ meps247017].
12. Culverhouse, P. F., and others. 2006. Automatic image analysis of plankton: future perspectives. Mar. Ecol. Prog. Ser. 312:297-309 [doi:10. 3354/ meps312297].
13. Davis, C. S., Q. Hu, S. M. Gallager, X. Tang, and C. J. Ashjian. 2004. Real-time observation of taxa-specific plankton distributions: an optical sampling method. Mar. Ecol. Prog. Ser. 284:77-96 [doi:10. 3354/ meps284077].
14. De Philippis, R., C. Faraloni, C. Sili, and M. Vincenzini. 2005. Populations of exopolysaccharide-producing cyanobacteria and diatoms in the mucilaginous benthic aggregates of the Tyrrhenian Sea (Tuscan Archipelago). Sci. Total Environ. 353:360-368 [doi:10. 1016/ j. scitotenv. 2005. 09. 078].
15. Descy, J., and others. 2006. Climate variability as recorded in Lake Tanganyika (CLIMLAKE): Final report. Brussels: Belgian science policy.
16. Devijver, P. A., and J. Kittler. 1982. Pattern recognition: A statistical approach. Prentice/Hall International Englewood Cliffs, NJ.
17. Dubelaar, G. B. J., and P. L. Gerritzen. 2000. CytoBuoy: a step forward towards using flow cytometry in operational oceanography. Sci. Mar. 64:255-265.
18. Dubois, F., L. Joannes, and J.-C. Legros. 1999. Improved threedimensional imaging with a digital holography microscope with a source of partial spatial coherence. Appl. Opt. 38:7085-7094 [doi:10. 1364/ AO. 38. 007085].
19. Dubois, F., M.-L. Novella Requena, C. Minetti, O. Monnom, and E. Istasse. 2004. Partial spatial coherence effects in digital holographic microscopy with a laser source. Appl. Opt. 43:1131-1139 [doi:10. 1364/ AO. 43. 001131].
20. Dubois, F., N. Callens, C. Yourassowsky, M. Hoyos, P. Kurowski, and O. Monnom. 2006a. Digital holographic microscopy with reduced spatial coherence for three-dimensional particle flow analysis. Appl. Opt. 45:864-871 [doi:10. 1364/ AO. 45. 000864].
21. Dubois, F., C. Schockaert, N. Callens, and C. Yourassowsky. 2006b. Focus plane detection criteria in digital holography microscopy by amplitude analysis. Opt. Express 14:5895-5908 [doi:10. 1364/ OE. 14. 005895].
22. Dubois, F., and others. 2006c. Digital holographic microscopy for the three-dimensional dynamic analysis of in vitro cancer cell migration. J. Biomed. Opt. 11:054032-054035.
23. El Mallahi, A. 2013. Automated 3D object analysis by digital holographic microscopy. PhD. Université Libre de Bruxelles.
24. El Mallahi, A., and F. Dubois. 2011. Dependency and precision of the refocusing criterion based on amplitude analysis in digital holographic microscopy. Opt. Express 19:6684-6698 [doi:10. 1364/ OE. 19. 006684].
25. El Mallahi, A., A. Detavernier, C. Yourassowsky, and F. Dubois. 2012. Automated 3D detection and classification of Giardia lamblia cysts using digital holographic microscopy with partially coherent source, p. 84291D-84291. In Proc. SPIE 8429, Optical Modelling and Design II. SPIE-The International Society for Optical Engineering.
26. El Mallahi, A., and F. Dubois. 2013. Separation of overlapped particles in digital holographic microscopy. Opt. Express 21:6466 [doi:10. 1364/ OE. 21. 006466].
27. El Mallahi, A., C. Minetti, and F. Dubois. 2013. Automated threedimensional detection and classification of living organisms using digital holographic microscopy with partial spatial coherent source: application to the monitoring of drinking water resources. Appl. Opt. 52:A68-A80 [doi:10. 1364/ AO. 52. 000A68].
28. Embleton, K., C. Gibson, and S. Heaney. 2003. Automated counting of phytoplankton by pattern recognition: a comparison with a manual counting method. J. Plankton Res. 25:669-681 [doi:10. 1093/ plankt/ 25. 6. 669].
29. Gabor, D. 1948. A new microscopic principle. Nature 161:777-778 [doi:10. 1038/ 161777a0].
30. Gonzalez, R. C., and R. E. Woods. 2008. Digital image processing, 3rd ed. Pearson International Edition.
31. Graham, G. W., and W. a. M. Nimmo-Smith. 2010. The application of holography to the analysis of size and settling velocity of suspended cohesive sediments. Limnol. Oceanogr. Methods 8:1-15 [doi:10. 4319/ lom. 2010. 8. 1].
32. Hu, Q., and C. Davis. 2005. Automatic plankton image recognition with co-occurrence matrices and support vector machine. Mar. Ecol. Prog. Ser. 295:21-31 [doi:10. 3354/ meps295021].
33. Hu, Q., and C. Davis. 2006. Accurate automatic quantification of taxa-specific plankton abundance using dual classification with correction. Mar. Ecol. Prog. Ser. 306:51-61 [doi:10. 3354/ meps306051].
34. Jakobsen, H. H., and J. Carstensen. 2011. FlowCAM: Sizing cells and understanding the impact of size distributions on biovolume of planktonic community structure. Aquat. Microb. Ecol. 65:75-87 [doi:10. 3354/ ame01539].
35. Jalba, A. C., M. H. F. Wilkinson, and J.B.T.M. Roerdink. 2004. Automatic segmentation of diatom images for classification. Microsc. Res. Tech. 65:72-85 [doi:10. 1002/ jemt. 20111].
36. Katz, J., and J. Sheng. 2010. Applications of holography in fluid mechanics and particle dynamics. Annu. Rev. Fluid Mech. 42:531-555 [doi:10. 1146/ annurev-fluid-121108-145508].
37. Kemper, B., and G. Von Bally. 2008. Digital holographic microscopy for live cell applications and technical inspection. Appl. Opt. 47:A52-A61 [doi:10. 1364/ AO. 47. 000A52].
38. Knox, C. 1966. Holographic microscopy as a technique for recording dynamic microscopic subjects. Science 153:989-990 [doi:10. 1126/ science. 153. 3739. 989].
39. Kremer, B. 2006. Mat-forming coccoid cyanobacteria from early Silurian marine deposits of Sudetes, Poland. Acta Palaeontol. Pol. 51:143.
40. Kromkamp, J., and M. Limbeek. 1993. Effect of short-term variation in irradiance on light harvesting and photosynthesis of the marine diatom Skeletonema costatum: a laboratory study simulating vertical mixing. J. Gen. Microbiol. 139:2277-2284 [doi:10. 1099/ 00221287-139-9-2277].
41. Kromkamp, J., and R. M. Forster. 2003. The use of variable fluorescence measurements in aquatic ecosystems: differences between multiple and single turnover measuring protocols and suggested terminology. Eur. J. Phycol. 38:103-112 [doi:10. 1080/ 0967026031000094094].
42. Leckey, C. A., and M. K. Hinders. 2012. Viscous effects in the acoustic manipulation of algae for biofuel production. J. Appl. Phycol. 24:145-156 [doi:10. 1007/ s10811-011-9662-7].
43. Lee, C. L., and Y. Lee. 2009. Cyanobacterial bio-indicator survey for two main rivers in Taitung Taiwan. Environ. Ecolog. Proceedings 2:1-26.
44. Macleod, N., M. Benfield, and P. Culverhouse. 2010. Time to automate identification. Nature 467:154-155 [doi:10. 1038/ 467154a].
45. Malkassian, A., D. Nerini, M. A. Van Dijk, M. Thyssen, C. Mante, and G. Gregori. 2011. Functional analysis and classification of phytoplankton based on data from an automated flow cytometer. Cytom. Part A 79A:263-275 [doi:10. 1002/ cyto. a. 21035].
46. Mansoor, H., M. Sorayya, S. Aishah, and M. a. A. Mosleh. 2011. Automatic recognition system for some cyanobacteria using image processing techniques and ANN approach, p. 73-78. In 2011 International Conference on Environmental and Computer Science. IPCBEE. IACSIT Press.
47. Maxwell, K., and G. N. Johnson. 2000. Chlorophyll fluorescence- a practical guide. J. Exp. Bot. 51:659-668 [doi:10. 1093/ jexbot/ 51. 345. 659].
48. Mosteller, F., and J. Tukey. 1968. Data analysis, including statistics. In G. Lindzey and E. Aronson [eds.], Revised handbook of social psychology. Addison-Wesley.
49. Olson, R. J., and H. M. Sosik. 2007. A submersible imaging-inflow instrument to analyze nano-and microplankton: Imaging FlowCytobot. Limnol. Oceanogr. Methods 5:195-203 [doi:10. 4319/ lom. 2007. 5. 195].
50. Pavillon, N., and others. 2010. Cell morphology and intracellular ionic homeostasis explored with a multimodal approach combining epifluorescence and digital holographic microscopy. J. Biophotonics 3:432-436 [doi:10. 1002/ jbio. 201000018].
51. Pereira, G., and N. Ebecken. 2011. Combining in situ flow cytometry and artificial neural networks for aquatic systems monitoring. Expert Syst. Appl. 38:9626-9632 [doi:10. 1016/ j. eswa. 2011. 01. 140].
52. Pfitsch, D. W., E. Malkiel, Y. Ronzhes, S. R. King, J. Sheng, and J. Katz. 2005. Development of a free-drifting submersible digital holographic imaging system, p. 690-696. In OCEANS, 2005. Proceedings of MTS/IEEE. IEEE. Washington DC.
53. Pomati, F., J. Jokela, M. Simona, M. Veronesi, and B. W. Ibelings. 2011. An automated platform for phytoplankton ecology and aquatic ecosystem monitoring. Environ. Sci. Technol. 45:9658-9665 [doi:10. 1021/ es201934n].
54. Poulton, N. J., and J. L. Martin. 2010. 8 Imaging flow cytometry for quantitative phytoplankton analysis-FlowCAM, p. 47-54. In B. Karlson, C. Cusack, and E. Bresnan [eds.], Microscopic and molecular methods for quantitative phytoplankton analysis. Intergovernmental Oceanographic Commission of UNESCO.
55. Ralph, P. J., and R. Gademann. 2005. Rapid light curves: a powerful tool to assess photosynthetic activity. Aquat. Bot. 82:222-237 [doi:10. 1016/ j. aquabot. 2005. 02. 006].
56. Ritchie, R. J. 2006. Consistent sets of spectrophotometric chlorophyll equations for acetone, methanol and ethanol solvents. Photosynth. Res. 89:27-41 [doi:10. 1007/ s11120-006-9065-9].
57. Ruiz, G. M., T. K. Rawlings, F. C. Dobbs, L. A. Drake, T. Mullady, A. Huq, and R. R. Colwell. 2000. Global spread of microorganisms by ships. Nature 408:49-50 [doi:10. 1038/ 35040695].
58. Rutten, T. P. A., B. Sandee, and A. R. T. Hofman. 2005. Phytoplankton monitoring by high performance flow cytometry: A successful approach? Cytom. Part A 64A:16-26 [doi:10. 1002/ cyto. a. 20106].
59. Sadeghi, R., R. Zarkami, K. Sabetraftar, and P. Van Damme. 2012. Use of support vector machines (SVMs) to predict distribution of an invasive water fern Azolla filiculoides (Lam.) in Anzali wetland, southern Caspian Sea, Iran. Ecol. Modell. 244:117-126 [doi:10. 1016/ j. ecolmodel. 2012. 06. 029].
60. Schaap, A., T. Rohrlack, and Y. Bellouard. 2012. Optical classification of algae species with a glass lab-on-a-chip. Lab Chip 12:1527-1532 [doi:10. 1039/ c2lc21091f].
61. Schulze, K., U. M. Tillich, T. Dandekar, and M. Frohme. 2013. PlanktoVision-an automated analysis system for the identification of phytoplankton. BMC Bioinformatics 14:115 [doi:10. 1186/ 1471-2105-14-115].
62. Sezgin, M., and B. Sankur. 2004. Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13:146-168 [doi:10. 1117/ 1. 1631315].
63. Sieracki, C. K., M. E. Sieracki, and C. S. Yentsch. 1998. An imaging-in-flow system for automated analysis of marine microplankton. Mar. Ecol. Prog. Ser. 168:285-296 [doi:10. 3354/ meps168285].
64. Sieracki, C. K., and others. 2010. Optical plankton imaging and analysis systems for ocean observation, p. 21-25. In J. Hall, D. E. Harrison, and D. Stammer [eds.], Proceedings of OceanObs'09: Sustained Ocean Observations and Information for Society. ESA Publication WPP-306.
65. Silsbe, G. M., and J. C. Kromkamp. 2012. Modeling the irradiance dependency of the quantum efficiency of photosynthesis. Limnol. Oceanogr. Methods 10:645-652 [doi:10. 4319/ lom. 2012. 10. 645].
66. Singh, J., and S. Gu. 2010. Commercialization potential of microalgae for biofuels production. Renew. Sust. Energ. Rev. 14:2596-2610 [doi:10. 1016/ j. rser. 2010. 06. 014].
67. Sosik, H. M., and R. J. Olson. 2007. Automated taxonomic classification of phytoplankton sampled with imaging-inflow cytometry. Limnol. Oceanogr. Methods 5:204-216 [doi:10. 4319/ lom. 2007. 5. 204].
68. Spizzichino, V., L. Fiorani, A. Lai, A. Palucci, K. Semyanov, E. Viaggiu, and P. Albertano. 2011. First studies of pico-and nanoplankton populations by a laser scanning flow cytometer. J. Quant. Spectrosc. Radiat. Transfer 112:876-882 [doi:10. 1016/ j. jqsrt. 2010. 10. 008].
69. Tang, X., W. K. Stewart, H. Huang, S. M. Gallager, C. S. Davis, L. Vincent, and M. Marra. 1998. Automatic plankton image recognition. Artif. Intell. Rev. 12:177-199 [doi:10. 1023/ A:1006517211724].
70. Thiel, S. U. and R. J. Wiltshire. 1995. The automated detection of cyanobacteria using digital image processing techniques. Environ. Int. 21:233-236 [doi:10.1016/0160-4120(95)00013-5].
71. Van Der Heijden, F., R. P. W. Duin, D. De Ridder, and D. Tax. 2004. Classification, parameter estimation and state estimation. Wiley Online Library [doi:10. 1002/ 0470090154].
72. Walker, R. F., and M. Kumagai. 2000. Image analysis as a tool for quantitative phycology: a computational approach to cyanobacterial taxa identification. Limnology (Tokyo) 1:107-115 [doi:10. 1007/ s102010070016].
73. Yamaguchi, I., and T. Zhang. 1997. Phase-shifting digital holography. Opt. Lett. 22:1268-1270 [doi:10. 1364/ OL. 22. 001268].
74. Yentsch, C. M., and others. 1983. Flow cytometry and cell sorting: A technique for analysis and sorting of aquatic particles. Limnol. Oceanogr. 28:1275-1280 [doi:10. 4319/ lo. 1983. 28. 6. 1275].
75. Yourassowsky, C., and F. Dubois. 2014. High throughput holographic imaging-in-flow for the analysis of a wide plankton size range. Opt. Express 22:6661-6673 [doi:10. 1364/ OE. 22. 006661].
76. Zarauz, L., X. Irigoien, and J. A. Fernandes. 2009. Changes in plankton size structure and composition, during the generation of a phytoplankton bloom, in the central Cantabrian Sea. J. Plankton Res. 31:193-207 [doi:10. 1093/ plankt/ fbn107].
77. Zhang, T., and I. Yamaguchi. 1998. Three-dimensional microscopy with phase-shifting digital holography. Opt. Lett. 23:1221-1223 [doi:10. 1364/ OL. 23. 001221].