Paper Roundup – June 2016

  • Quantitative comparison of fluorescent proteins. A great resource with measurements of photobleaching, brightness, and monomericness for a large number of fluorescent proteins [1]
  • Metalenses for focusing visible light with an NA of 0.8 [2]
  • A cyan-excitable orange-emitting fluorescent protein [3]
  • A mutant of UnaG that is nonfluorescent and destabilizing in the absence of ligand, and fluorescent and stable in the presence of ligand [4]
  • Spectral imaging for single particle tracking of motor proteins [5]
  • A fluorescent reporter for beta-galactosidase that can be used for cell and animal imaging [6]
  • All-optical electrophysiology [7]
  • A review of small molecule dyes for super-resolution imaging [8]
  • An improved protocol for expansion microscopy [9]
  • A review of clearing techniques [10]
  • A generative model for testing spatial distributions of puncta within the cell [11]
  • Mammalian protein tagging with CRISPR/Cas9 and split GFP for simple, scalable tagging of endogenous mammalian proteins [12]
  • Miniature light sheet generator modules [13]
  • An electrically-tunable lens to move the waist of a light sheet synchronously with the virtual detection slit on a sCMOS camera to make narrow light sheets over large areas [14]
  • A review of cyanine photobleaching mechanisms and their applications [15]

References

  1. P.J. Cranfill, B.R. Sell, M.A. Baird, J.R. Allen, Z. Lavagnino, H.M. de Gruiter, G. Kremers, M.W. Davidson, A. Ustione, and D.W. Piston, "Quantitative assessment of fluorescent proteins", Nature Methods, vol. 13, pp. 557-562, 2016. http://dx.doi.org/10.1038/nmeth.3891
  2. M. Khorasaninejad, W.T. Chen, R.C. Devlin, J. Oh, A.Y. Zhu, and F. Capasso, "Metalenses at visible wavelengths: Diffraction-limited focusing and subwavelength resolution imaging", Science, vol. 352, pp. 1190-1194, 2016. http://dx.doi.org/10.1126/science.aaf6644
  3. J. Chu, Y. Oh, A. Sens, N. Ataie, H. Dana, J.J. Macklin, T. Laviv, E.S. Welf, K.M. Dean, F. Zhang, B.B. Kim, C.T. Tang, M. Hu, M.A. Baird, M.W. Davidson, M.A. Kay, R. Fiolka, R. Yasuda, D.S. Kim, H. Ng, and M.Z. Lin, "A bright cyan-excitable orange fluorescent protein facilitates dual-emission microscopy and enhances bioluminescence imaging in vivo", Nature Biotechnology, vol. 34, pp. 760-767, 2016. http://dx.doi.org/10.1038/nbt.3550
  4. R. Navarro, L. Chen, R. Rakhit, and T.J. Wandless, "A Novel Destabilizing Domain Based on a Small-Molecule Dependent Fluorophore", ACS Chemical Biology, vol. 11, pp. 2101-2104, 2016. http://dx.doi.org/10.1021/acschembio.6b00234
  5. T. Kakizuka, K. Ikezaki, J. Kaneshiro, H. Fujita, T.M. Watanabe, and T. Ichimura, "Simultaneous nano-tracking of multiple motor proteins via spectral discrimination of quantum dots", Biomedical Optics Express, vol. 7, pp. 2475, 2016. http://dx.doi.org/10.1364/BOE.7.002475
  6. K. Gu, Y. Xu, H. Li, Z. Guo, S. Zhu, S. Zhu, P. Shi, T.D. James, H. Tian, and W. Zhu, "Real-Time Tracking and In Vivo Visualization of β-Galactosidase Activity in Colorectal Tumor with a Ratiometric Near-Infrared Fluorescent Probe", Journal of the American Chemical Society, vol. 138, pp. 5334-5340, 2016. http://dx.doi.org/10.1021/jacs.6b01705
  7. H. Zhang, E. Reichert, and A.E. Cohen, "Optical electrophysiology for probing function and pharmacology of voltage-gated ion channels", eLife, vol. 5, 2016. http://dx.doi.org/10.7554/eLife.15202
  8. Z. Yang, A. Sharma, J. Qi, X. Peng, D.Y. Lee, R. Hu, D. Lin, J. Qu, and J.S. Kim, "Super-resolution fluorescent materials: an insight into design and bioimaging applications", Chem. Soc. Rev., vol. 45, pp. 4651-4667, 2016. http://dx.doi.org/10.1039/C5CS00875A
  9. T.J. Chozinski, A.R. Halpern, H. Okawa, H. Kim, G.J. Tremel, R.O.L. Wong, and J.C. Vaughan, "Expansion microscopy with conventional antibodies and fluorescent proteins", Nature Methods, vol. 13, pp. 485-488, 2016. http://dx.doi.org/10.1038/nmeth.3833
  10. J. Seo, M. Choe, and S. Kim, "Clearing and Labeling Techniques for Large-Scale Biological Tissues", Molecules and Cells, vol. 39, pp. 439-446, 2016. http://dx.doi.org/10.14348/molcells.2016.0088
  11. Y. Li, T.D. Majarian, A.W. Naik, G.R. Johnson, and R.F. Murphy, "Point process models for localization and interdependence of punctate cellular structures", Cytometry Part A, vol. 89, pp. 633-643, 2016. http://dx.doi.org/10.1002/cyto.a.22873
  12. M.D. Leonetti, S. Sekine, D. Kamiyama, J.S. Weissman, and B. Huang, "A scalable strategy for high-throughput GFP tagging of endogenous human proteins", Proceedings of the National Academy of Sciences, vol. 113, pp. E3501-E3508, 2016. http://dx.doi.org/10.1073/pnas.1606731113
  13. T. BRUNS, M. BAUER, S. BRUNS, H. MEYER, D. KUBIN, and H. SCHNECKENBURGER, "Miniaturized modules for light sheet microscopy with low chromatic aberration", Journal of Microscopy, vol. 264, pp. 261-267, 2016. http://dx.doi.org/10.1111/jmi.12439
  14. P.N. Hedde, and E. Gratton, "Selective plane illumination microscopy with a light sheet of uniform thickness formed by an electrically tunable lens", Microscopy Research and Technique, 2016. http://dx.doi.org/10.1002/jemt.22707
  15. A.P. Gorka, and M.J. Schnermann, "Harnessing cyanine photooxidation: from slowing photobleaching to near-IR uncaging", Current Opinion in Chemical Biology, vol. 33, pp. 117-125, 2016. http://dx.doi.org/10.1016/j.cbpa.2016.05.022

Destriping of Light Sheet data

We’ve been working on a simple, home-built light sheet system in the NIC. It’s designed for imaging cleared organs, and so uses a cylindrical lens to produce a light sheet, about the simplest illumination system you can use for such a microscope (it’s similar to the system described in [1]). Because the illumination traverses the sample, if there is an opaque or scattering part of the sample, it blocks part of the illumination beam, casting shadows through the sample that show up as stripes in the resulting images.

I recently discovered a software tool for removing stripes from these images [2]. It’s not perfect – in particular, it assumes that the noise is additive, when it is really multiplicative – but it does a good job. You can download a Fiji plugin that implements it here, and you can see the results below.

Raw image

Raw image

After destriping

After destriping

References

  1. H. Dodt, U. Leischner, A. Schierloh, N. Jährling, C.P. Mauch, K. Deininger, J.M. Deussing, M. Eder, W. Zieglgänsberger, and K. Becker, "Ultramicroscopy: three-dimensional visualization of neuronal networks in the whole mouse brain", Nature Methods, vol. 4, pp. 331-336, 2007. http://dx.doi.org/10.1038/nmeth1036
  2. J. Fehrenbach, P. Weiss, and C. Lorenzo, "Variational Algorithms to Remove Stationary Noise: Applications to Microscopy Imaging", IEEE Transactions on Image Processing, vol. 21, pp. 4420-4430, 2012. http://dx.doi.org/10.1109/TIP.2012.2206037

95% QE, Back-illuminated sCMOS camera

Photometrics has just announced a new sCMOS camera, the Prime 95B, featuring a back-side illuminated sCMOS sensor with 95% peak QE and over 90% QE from about 500 – 650 nm. It’s using a version of this 4 MP sensor from Gpixel. It’s a 1200 x 1200 pixel sensor, with 11 μm pixels and 1.3 e read noise, so it should be substantially more sensitive than a conventional sCMOS camera, and close in performance to an EMCCD camera.

If it performs as well as the specs indicate, this should be a real game changer for cameras, and could displace EMCCDs from all but the lowest light applications. Tucsen had previously released a back-side illuminated sCMOS camera based on the Gpixel sensor, but earlier versions used a sensor with peak QE at ~420 nm (it now uses the version with peak QE in the visible), and distribution in the US has been a bit of a mystery (I was not able to get one to demo, although I didn’t try that hard).

I hope to get a chance to test out the Prime 95B soon and will definitely report results from it here once I have a chance to try it out.