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A machine learning algorithm for identifying and tracking bacteria in three dimensions using Digital Holographic Microscopy

1 Department of Medical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
2 Department of Biomedical Engineering, McGill University, Montreal, QC H3A 2B4, Canada
3 Department of Engineering and Applied Sciences, California Institute of Technology, Pasadena, CA 91125, USA
4 Department of Physics, Portland State University, Portland, OR 97201, USA

Topical Section: Physical Models of Cell Motility

Digital Holographic Microscopy (DHM) is an emerging technique for three-dimensional imaging of microorganisms due to its high throughput and large depth of field relative to traditional microscopy techniques. While it has shown substantial success for use with eukaryotes, it has proven challenging for bacterial imaging because of low contrast and sources of noise intrinsic to the method (e.g. laser speckle). This paper describes a custom written MATLAB routine using machine-learning algorithms to obtain three-dimensional trajectories of live, lab-grown bacteria as they move within an essentially unrestrained environment with more than 90% precision. A fully annotated version of the software used in this work is available for public use.
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Keywords interferometric microscopy; digital holographic microscopy; machine learning; particle tracking

Citation: Manuel Bedrossian, Marwan El-Kholy, Daniel Neamati, Jay Nadeau. A machine learning algorithm for identifying and tracking bacteria in three dimensions using Digital Holographic Microscopy. AIMS Biophysics, 2018, 5(1): 36-49. doi: 10.3934/biophy.2018.1.36

References

  • 1. Taute KM, Gude S, Tans SJ, et al. (2015) High-throughput 3D tracking of bacteria on a standard phase contrast microscope. Nat Commun 6: 8776.    
  • 2. Molaei M, Barry M, Stocker R, et al. (2014) Failed escape: Solid surfaces prevent tumbling of Escherichia coli. Phys Rev Lett 113: 068103.    
  • 3. Stocker R (2011) Reverse and flick: Hybrid locomotion in bacteria. Proc Natl Acad Sci USA 108: 2635–2636.    
  • 4. Schnars U, Jüptner W (1994) Direct recording of holograms by a CCD target and numerical reconstruction. Appl Optics 33: 179–181.    
  • 5. Cuche E, Bevilacqua F, Depeursinge C (1999) Digital holography for quantitative phase-contrast imaging. Opt Lett 24: 291–293.    
  • 6. Kühn J, Niraula B, Liewer K, et al. (2014) A Mach-Zender digital holographic microscope with sub-micrometer resolution for imaging and tracking of marine micro-organisms. Rev Sci Instrum 85: 123113.    
  • 7. Lee KR, Kim K, Jung J, et al. (2013) Quantitative phase imaging techniques for the study of cell pathophysiology: From principles to applications. Sensors 13: 4170–4191.    
  • 8. Kim T, Zhou R, Mir M, et al. (2014) White-light diffraction tomography of unlabelled live cells. Nat Photonics 8: 256–263.    
  • 9. Chengala A, Hondzo M, Sheng J (2013) Microalga propels along vorticity direction in a shear flow. Phys Rev E 87: 052704.
  • 10. Sheng J, Malkiel E, Katz J, et al. (2007) Digital holographic microscopy reveals prey-induced changes in swimming behavior of predatory dinoflagellates. Proc Natl Acad Sci USA 104: 17512–17517.    
  • 11. Sheng J, Malkiel E, Katz J, et al. (2010) A dinoflagellate exploits toxins to immobilize prey prior to ingestion. Proc Natl Acad Sci USA 107: 2082–2087.    
  • 12. Vater SM, Finlay J, Callow ME, et al. (2015) Holographic microscopy provides new insights into the settlement of zoospores of the green alga Ulva linza on cationic oligopeptide surfaces. Biofouling 31: 229–239.    
  • 13. Liu PY, Chin LK, Ser W, et al. (2016) Cell refractive index for cell biology and disease diagnosis: Past, present and future. Lab Chip 16: 634–644.    
  • 14. Wallace JK, Rider S, Serabyn E, et al. (2015) Robust, compact implementation of an off-axis digital holographic microscope. Opt Express 23: 17367–17378.    
  • 15. Molaei M, Sheng J (2014) Imaging bacterial 3D motion using digital in-line holographic microscopy and correlation-based de-noising algorithm. Opt express 22: 32119–32137.    
  • 16. Bishara W, Sikora U, Mudanyali O, et al. (2011) Holographic pixel super-resolution in portable lensless on-chip microscopy using a fiber-optic array. Lab Chip 11: 1276–1279.    
  • 17. Chengala A, Hondzo M, Sheng J (2013) Microalga propels along vorticity direction in a shear flow. Phys Rev E 87: 052704.
  • 18. Phan AH, Park JH, Kim N (2011) Super-Resolution Digital Holographic Microscopy for three dimensional sample using multipoint light source illumination. Jpn J Appl Phys 50: 092503–092504.    
  • 19. Yuan C, Situ G, Pedrini G, et al. (2011) Resolution improvement in digital holography by angular and polarization multiplexing. Appl Optics 50: B6–B11.    
  • 20. Rappaz B, Marquet P, Cuche E, et al. (2005) Measurement of the integral refractive index and dynamic cell morphometry of living cells with digital holographic microscopy. Opt Express 13: 9361–9373.    
  • 21. Balaev AE, Dvoretski KN, Doubrovski VA (2002) Refractive index of escherichia coli cells. Proc SPIE 4707: 253–260.    
  • 22. Chenouard N, Smal I, Chaumont FD, et al. (2014) Objective comparison of particle tracking methods. Nat Methods 11: 281–289.    
  • 23. Hosmer Jr DW, Lemeshow S (2004) Applied logistic regression. John Wiley Sons.
  • 24. Junge K, Eicken H, Deming JW (2003) Motility of Colwellia psychrerythraea strain 34H at subzero temperatures. Appl Environ Microbiol 69: 4282–4284.    
  • 25. Tinevez JY, Cao Y (2016) Simple Tracker. MATLAB Cent File Exc.
  • 26. LynceeTec. Koala acquisition & analysis. Available from: http://www.lynceetec.com/koala-acquisition-analysis/.
  • 27. Schindelin J, Argandacarreras I, Frise E, et al. (2012) Fiji: An open-source platform for biological-image analysis. Nat Methods 9: 676–682.    
  • 28. Ito M, Terahara N, Fujinami S, et al. (2005) Properties of motility in Bacillus subtilis powered by the H+-coupled MotAB flagellar stator, Na+-coupled MotPS or hybrid stators MotAS or MotPB. J Mol Biol 352: 396–408.    
  • 29. Bedrossian M, Lindensmith C, Nadeau JL (2011) Digital holographic microscopy, a method for detection of microorganisms in plume samples from Enceladus and other icy worlds. Astrobiology 17: 913–925.

 

This article has been cited by

  • 1. Carl Snyder, David Cohoe, Maximilian Schadegg, Jay Nadeau, Large Data Considerations in Digital Holographic Microscopy, Microscopy and Microanalysis, 2019, 25, S2, 1390, 10.1017/S1431927619007682

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