Paper roundup – July 2015

  • A set of oxidation resistant fluorescent proteins that have also been carefully assessed for monomeric behavior [1]
  • Single molecule super-resolution imaging by transient binding of fluorescently labeled probes [2]
  • Speckle imaging with a single photon avalanche diode (SPAD) array. This is interesting not so much for the imaging, but for the SPAD array which looks like it may be a interesting new type of camera giving quantitative photon counts at very high speed. [3]
  • A monomeric infrared fluorescent protein [4]
  • Imaging of RNA-protein binding in vivo [5].
  • A nice review of tissue clearing methods [6]
  • BigNeuron: a community effort to develop algorithms to reconstruct neurons from image data [7]
  • Combining light sheet microscopy and optical trapping [8]
  • Using 488nm light and infrared light simultaneously to get photoconversion of Dendra2 and mEos2 [9]
  • A hyperspectral imaging microscope based on wavelength dispersion of focused spots on the sample [10]
  • A nice review on image transforms, compression, and denoising [11]
  • A long fluorescence lifetime GFP variant [12]
  • Construction of an iSIM (instant SIM) microscope [13]
  • Using neural networks for tomographic reconstruction [14] (News & Views in Nature [15]
  • A model-based approach to deconvolution [16]
  • Two photon adaptive optics by placing the defomable mirror outside of the pupil plane [17]
  • A comparison of objective lens performance for two-photon microscopy in scattering samples [18]

References

  1. L.M. Costantini, M. Baloban, M.L. Markwardt, M. Rizzo, F. Guo, V.V. Verkhusha, and E.L. Snapp, "A palette of fluorescent proteins optimized for diverse cellular environments", Nature Communications, vol. 6, pp. 7670, 2015. http://dx.doi.org/10.1038/ncomms8670
  2. T. Kiuchi, M. Higuchi, A. Takamura, M. Maruoka, and N. Watanabe, "Multitarget super-resolution microscopy with high-density labeling by exchangeable probes", Nature Methods, vol. 12, pp. 743-746, 2015. http://dx.doi.org/10.1038/nmeth.3466
  3. T. Dragojević, D. Bronzi, H.M. Varma, C.P. Valdes, C. Castellvi, F. Villa, A. Tosi, C. Justicia, F. Zappa, and T. Durduran, "High-speed multi-exposure laser speckle contrast imaging with a single-photon counting camera", Biomedical Optics Express, vol. 6, pp. 2865, 2015. http://dx.doi.org/10.1364/BOE.6.002865
  4. D. Yu, M.A. Baird, J.R. Allen, E.S. Howe, M.P. Klassen, A. Reade, K. Makhijani, Y. Song, S. Liu, Z. Murthy, S. Zhang, O.D. Weiner, T.B. Kornberg, Y. Jan, M.W. Davidson, and X. Shu, "A naturally monomeric infrared fluorescent protein for protein labeling in vivo", Nature Methods, vol. 12, pp. 763-765, 2015. http://dx.doi.org/10.1038/nmeth.3447
  5. B. Wu, A. Buxbaum, Z. Katz, Y. Yoon, and R. Singer, "Quantifying Protein-mRNA Interactions in Single Live Cells", Cell, vol. 162, pp. 211-220, 2015. http://dx.doi.org/10.1016/j.cell.2015.05.054
  6. D. Richardson, and J. Lichtman, "Clarifying Tissue Clearing", Cell, vol. 162, pp. 246-257, 2015. http://dx.doi.org/10.1016/j.cell.2015.06.067
  7. H. Peng, M. Hawrylycz, J. Roskams, S. Hill, N. Spruston, E. Meijering, and G. Ascoli, "BigNeuron: Large-Scale 3D Neuron Reconstruction from Optical Microscopy Images", Neuron, vol. 87, pp. 252-256, 2015. http://dx.doi.org/10.1016/j.neuron.2015.06.036
  8. Z. Yang, P. Piksarv, D.E. Ferrier, F.J. Gunn-Moore, and K. Dholakia, "Macro-optical trapping for sample confinement in light sheet microscopy", Biomedical Optics Express, vol. 6, pp. 2778, 2015. http://dx.doi.org/10.1364/BOE.6.002778
  9. W.P. Dempsey, L. Georgieva, P.M. Helbling, A.Y. Sonay, T.V. Truong, M. Haffner, and P. Pantazis, "In vivo single-cell labeling by confined primed conversion", Nature Methods, vol. 12, pp. 645-648, 2015. http://dx.doi.org/10.1038/nmeth.3405
  10. A. Orth, M.J. Tomaszewski, R.N. Ghosh, and E. Schonbrun, "Gigapixel multispectral microscopy", Optica, vol. 2, pp. 654, 2015. http://dx.doi.org/10.1364/OPTICA.2.000654
  11. L.P. Yaroslavsky, "Compression, restoration, resampling, ‘compressive sensing’: fast transforms in digital imaging", Journal of Optics, vol. 17, pp. 073001, 2015. http://dx.doi.org/10.1088/2040-8978/17/7/073001
  12. K. Sarkisyan, A. Goryashchenko, P. Lidsky, D. Gorbachev, N. Bozhanova, A. Gorokhovatsky, A. Pereverzeva, A. Ryumina, V. Zherdeva, A. Savitsky, K. Solntsev, A. Bommarius, G. Sharonov, J. Lindquist, M. Drobizhev, T. Hughes, A. Rebane, K. Lukyanov, and A. Mishin, "Green Fluorescent Protein with Anionic Tryptophan-Based Chromophore and Long Fluorescence Lifetime", Biophysical Journal, vol. 109, pp. 380-389, 2015. http://dx.doi.org/10.1016/j.bpj.2015.06.018
  13. A. Curd, A. Cleasby, K. Makowska, A. York, H. Shroff, and M. Peckham, "Construction of an instant structured illumination microscope", Methods, vol. 88, pp. 37-47, 2015. http://dx.doi.org/10.1016/j.ymeth.2015.07.012
  14. U.S. Kamilov, I.N. Papadopoulos, M.H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, "Learning approach to optical tomography", Optica, vol. 2, pp. 517, 2015. http://dx.doi.org/10.1364/OPTICA.2.000517
  15. L. Waller, and L. Tian, "Computational imaging: Machine learning for 3D microscopy", Nature, vol. 523, pp. 416-417, 2015. http://dx.doi.org/10.1038/523416a
  16. L. DAO, B. GLANCY, B. LUCOTTE, L. CHANG, R.S. BALABAN, and L. HSU, "A Model-based approach for microvasculature structure distortion correction in two-photon fluorescence microscopy images", Journal of Microscopy, vol. 260, pp. 180-193, 2015. http://dx.doi.org/10.1111/jmi.12281
  17. J. Park, W. Sun, and M. Cui, "High-resolution in vivo imaging of mouse brain through the intact skull", Proceedings of the National Academy of Sciences, vol. 112, pp. 9236-9241, 2015. http://dx.doi.org/10.1073/pnas.1505939112
  18. A. Singh, J.D. McMullen, E.A. Doris, and W.R. Zipfel, "Comparison of objective lenses for multiphoton microscopy in turbid samples", Biomedical Optics Express, vol. 6, pp. 3113, 2015. http://dx.doi.org/10.1364/BOE.6.003113

Denoising plugin for ImageJ

There has been a lot of excitement around the use of denoising algorithms to allow reconstruction of microscopy images to allow data collection at very low light levels, thus allowing fast long-term timelapse imaging of samples that would otherwise suffer too much photodamage. Much of this work has been done by the Sedat lab and colleagues here, so I hear a lot about it [1][2].  The algorithm they use comes from the work of Jerome Boulanger and Charles Kevrann, and apparently performs very well. However, it’s been hard for me to test because obtaining the software is relatively difficult.

Yesterday, a new ImageJ plugin for denoising was posted on the ImageJ mailing list. It’s called CANDLE-J, and a preprint describing it is here. I haven’t had a chance to try it yet, but the results reported in the preprint look promising, and it is freely available for download. Binaries for Mac and Linux are available as is the source code. I’m guessing building it on Windows won’t be too hard.

An earlier version that runs in Matlab is also available.

References

  1. M. Arigovindan, J.C. Fung, D. Elnatan, V. Mennella, Y.M. Chan, M. Pollard, E. Branlund, J.W. Sedat, and D.A. Agard, "High-resolution restoration of 3D structures from widefield images with extreme low signal-to-noise-ratio", Proceedings of the National Academy of Sciences, vol. 110, pp. 17344-17349, 2013. http://dx.doi.org/10.1073/pnas.1315675110
  2. P.M. Carlton, J. Boulanger, C. Kervrann, J. Sibarita, J. Salamero, S. Gordon-Messer, D. Bressan, J.E. Haber, S. Haase, L. Shao, L. Winoto, A. Matsuda, P. Kner, S. Uzawa, M. Gustafsson, Z. Kam, D.A. Agard, and J.W. Sedat, "Fast live simultaneous multiwavelength four-dimensional optical microscopy", Proceedings of the National Academy of Sciences, vol. 107, pp. 16016-16022, 2010. http://dx.doi.org/10.1073/pnas.1004037107