Nikon and UCSF parting ways; thoughts on managing core facility funding

Nikon has supported the Nikon Imaging Center at UCSF since its inception in 2006. During that time they have provided both direct financial support as well as providing six microscopes to the center, and then replacing and upgrading those microscopes in 2009/2010. Our relationship with Nikon is now coming to an end; we will retain the microscopes we have and Nikon will provide service on them for some time, but we will not get new hardware, nor we will get financial support after 2017.

(For those of you worried about the future of the NIC; we’ll be fine. Recharge rates will go up, but that will be the only change in the near term.)

Not surprisingly, given this change in affairs, I’ve been thinking a lot about our financial model and I wanted to share some of that thinking here in case it’s useful to someone else who is new at core management. Continue reading

Database of fluorescent protein sequences

A paper recently came out on a web server for predicting the oligomeric state of fluorescent proteins [1]. It’s only about 80% accurate, which isn’t so nice, but they have a github page with sequences for hundreds of fluorescent proteins in a csv file. Tracking down all these sequences can be tedious, so this is a nice resource.


  1. S. Simeon, W. Shoombuatong, N. Anuwongcharoen, L. Preeyanon, V. Prachayasittikul, J.E.S. Wikberg, and C. Nantasenamat, "osFP: a web server for predicting the oligomeric states of fluorescent proteins", Journal of Cheminformatics, vol. 8, 2016.

More thoughts on fluorescent lifetime

Since my previous post on fluorescent lifetimes of fluorescent proteins, I’ve been doing some more reading on what governs fluorescence lifetime of fluorophores. It turns out that there is a theoretical model for the instrinsic radiative lifetime, the Strickler-Berg equation [1]. This is a generalization of an equation originally derived by Einstein for predicting atomic spectra, and is roughly valid for for molecules where the structure does not change between ground and excited states.

The Strickler-Berg Equation. kr is the radiative rate (1/lifetime), n is refractive index, I(ν) is the emission spectrum, and ε(ν) is the absorption spectrum, where ν is the wavenumber.

This gives results accurate to within about a factor of 2 for CFP and YFP [2], so it’s not completely accurate. Interestingly, however, it only depends on the absorption and emission spectra of the molecule, so this provides an explanation for why all fluorescent proteins seem to have similar intrinsic radiative lifetimes – they have similar spectra (the whole visible range of light only spans a factor of two in wavenumber) and absorption coefficients (mostly in the range of 50,000 – 100,000). So from first principles we might expect that intrinsic lifetimes of all visible dyes should be within a small range, unless they have very small or very large extinction coefficients.

It predicts that molecules with larger absorption coefficients should have shorter lifetimes, which makes sense if you think of the absorption coefficient as measuring the coupling strength between light and the molecule. It also predicts that molecules with emission at longer wavenumbers ( = shorter wavelengths) should have shorter lifetimes.


  1. S.J. Strickler, and R.A. Berg, "Relationship between Absorption Intensity and Fluorescence Lifetime of Molecules", The Journal of Chemical Physics, vol. 37, pp. 814-822, 1962.
  2. J.W. Borst, M.A. Hink, A. van Hoek, and A.J.W.G. Visser, "Effects of Refractive Index and Viscosity on Fluorescence and Anisotropy Decays of Enhanced Cyan and Yellow Fluorescent Proteins", Journal of Fluorescence, vol. 15, pp. 153-160, 2005.

Michael Davidson and Roger Tsien Commemorative Travel Awards from Addgene

Addgene is offering travel awards for students or postdocs using fluorescent proteins in their research. The award is named in honor of Michael Davidson and Roger Tsien – two giants in the fluorescent protein field. While Roger Tsien is well known, Michael Davidson is one of the unsung heroes of fluorescent protein research – his lab built thousands of fluorescent protein fusions to test the performance of fluorescent proteins in a wide variety of contexts. He then distributed these plasmids to many labs, and eventually deposited nearly all of them in Addgene.

Fluorescence lifetime and quantum yield

Two months ago I saw a tweet noting the linear relationship between quantum yield and fluorescence lifetime in fluorescent proteins. I hadn’t seen this before, so I wanted to see if it held on a wider range of fluorescent proteins, so I added the ability to plot lifetimes on my fluorescent protein visualization and added lifetimes for all the proteins I could find (37 in total).

Quantum yield vs. fluorescent lifetime (ns) for 37 fluorescent proteins, colored by emission wavelength and brightness. Click for full size image.

Continue reading

Paper Roundup – December 2016

  • A software tool for cell segmentation, tracking, and lineage tracing from phase contrast images [1]
  • A turn-on fluorescence probe for specifc RNAs [2]
  • Using a bacterial pore-forming toxin to get cell-impermeant molecules and fluorophores into mammalian cells [3]
  • Optical techniques for membrane voltage measurement in freely moving mice [4]
  • A nice review of super-resolution microscopy as applied to biology [5]
  • A discussion of the future of computational image analysis [6]
  • Interferometeric imaging for nm precision imaging of single molecules in vivo [7]
  • A 3D random access scanning microscope using an acousto-optic lens system [8]
  • Photoactivatible versions of the Janelia Fluor dyes [9]
  • An aberration-corrected doublet metalens [10]
  • Imaging of RNAs throughout a cleared Drosophila brain [11]
  • Nanometer precision localization of single molecules with minimal photon fluxes [12]
  • A new far-red emitting (598 nm ex / 671 nm em) fluorescent protein [13]


  1. M. Winter, W. Mankowski, E. Wait, S. Temple, and A.R. Cohen, "LEVER: software tools for segmentation, tracking and lineaging of proliferating cells", Bioinformatics, pp. btw406, 2016.
  2. W.Q. Ong, Y.R. Citron, S. Sekine, and B. Huang, "Live Cell Imaging of Endogenous mRNA Using RNA-Based Fluorescence “Turn-On” Probe", ACS Chemical Biology, vol. 12, pp. 200-205, 2017.
  3. K.W. Teng, Y. Ishitsuka, P. Ren, Y. Youn, X. Deng, P. Ge, S.H. Lee, A.S. Belmont, and P.R. Selvin, "Labeling proteins inside living cells using external fluorophores for microscopy", eLife, vol. 5, 2016.
  4. J.D. Marshall, J.Z. Li, Y. Zhang, Y. Gong, F. St-Pierre, M.Z. Lin, and M.J. Schnitzer, "Cell-Type-Specific Optical Recording of Membrane Voltage Dynamics in Freely Moving Mice", Cell, vol. 167, pp. 1650-1662.e15, 2016.
  5. T.J. Lambert, and J.C. Waters, "Navigating challenges in the application of superresolution microscopy", The Journal of Cell Biology, vol. 216, pp. 53-63, 2016.
  6. E. Meijering, A.E. Carpenter, H. Peng, F.A. Hamprecht, and J. Olivo-Marin, "Imagining the future of bioimage analysis", Nature Biotechnology, vol. 34, pp. 1250-1255, 2016.
  7. G. Wang, J. Hauver, Z. Thomas, S.A. Darst, and A. Pertsinidis, "Single-Molecule Real-Time 3D Imaging of the Transcription Cycle by Modulation Interferometry", Cell, vol. 167, pp. 1839-1852.e21, 2016.
  8. K.M.N.S. Nadella, H. Roš, C. Baragli, V.A. Griffiths, G. Konstantinou, T. Koimtzis, G.J. Evans, P.A. Kirkby, and R.A. Silver, "Random-access scanning microscopy for 3D imaging in awake behaving animals", Nature Methods, vol. 13, pp. 1001-1004, 2016.
  9. J.B. Grimm, B.P. English, H. Choi, A.K. Muthusamy, B.P. Mehl, P. Dong, T.A. Brown, J. Lippincott-Schwartz, Z. Liu, T. Lionnet, and L.D. Lavis, "Bright photoactivatable fluorophores for single-molecule imaging", Nature Methods, vol. 13, pp. 985-988, 2016.
  10. A. Arbabi, E. Arbabi, S.M. Kamali, Y. Horie, S. Han, and A. Faraon, "Miniature optical planar camera based on a wide-angle metasurface doublet corrected for monochromatic aberrations", Nature Communications, vol. 7, pp. 13682, 2016.
  11. X. Long, J. Colonell, A.M. Wong, R.H. Singer, and T. Lionnet, "Quantitative mRNA Imaging Throughout the Entire Drosophila Brain", 2016.
  12. F. Balzarotti, Y. Eilers, K.C. Gwosch, A.H. Gynna, V. Westphal, F.D. Stefani, J. Elf, and S.W. Hell, "Nanometer resolution imaging and tracking of fluorescent molecules with minimal photon fluxes", Science, 2016.
  13. G. Matela, P. Gao, G. Guigas, A.F. Eckert, K. Nienhaus, and G. Ulrich Nienhaus, "A far-red emitting fluorescent marker protein, mGarnet2, for microscopy and STED nanoscopy", Chem. Commun., vol. 53, pp. 979-982, 2017.

New sensor company with back-illuminated CMOS sensors

Image Sensors World linked to an interesting presentation from Caeleste, a Belgian company doing custom sensor design. In it, they mention a back-illuminated CMOS sensor with 3840 x 2160 6.5 µm pixels and 2 e read noise. I have no idea if this will ever see wide distribution, but it does suggest that the future for high sensitivity, low noise cameras is promising.

New Sutter LED combiner

Sutter recently announced a clever new LED combiner that allows you to combine multiple LED light sources into a single output beam. It relies on the fact that interference filters are very good reflectors of the wavelengths they don’t transmit, so you can use a filter to simultaneously pass the output of one LED while reflecting wavelengths from other LEDs. The concept is shown in the diagram below:

The Sutter 421 LED combiner. The number at each position indicates how many reflections that LED undergoes before reaching the output. Image courtesy of Sutter.

One of the many nice things about this concept is that changing LED wavelengths is really easy: you just replace the LED and the filter in front of it. You can also mount a second pentagon on the first to combine up to seven wavelengths (in principle you could even cascade a third pentagon to get 10 wavelengths, but at some point the filter designs get pretty tricky and the losses add up). You can also combine light sources other than LEDs, provided you can find appropriate interference filters.

We demoed a six color version of this a few weeks ago, using two pentagons and LEDs for DAPI, FITC, Cy3, Cy5, CFP, and YFP. We tested it with a Semrock Sedat Quad filter set and Chroma GFP/RFP and CFP/YFP filter sets. At all wavelengths tested it was as bright or brighter (in some cases as much as 10-fold brighter) than the Lambda XL we were using as a reference.  We’re now working with Sutter to get a seven color version of this (including 340 nm excitation for Fura-2) to install on our microscope. This will allow us to synchronize the LEDs to the camera, so that the LEDs are only on when the camera is exposing, minimizing photobleaching and phototoxicity. This should be a very nice LED illumination option for microscopy, particularly for users who want a modular system that’s easy to modify as needed.

Preprint: Review of Genetically Encoded Fluorescent Tags

I was recently asked to write a brief Technical Perspective on fluorescent tags for Molecular Biology of the Cell. These are meant to be introductions to a topic for novices in the field; I previously wrote one on light microscopy.

I’ve posted a preprint of the fluorescent tag review here; please send me any comments and I will incorporate them into the final version. I would have posted the preprint on BioRxiv, but it seems that they don’t host reviews.

Paper Roundup – November 2016

  • A detailed investigation of ER structure by multiple super-resolution methods [1]
  • Using deep convolutional neural networks to segment cells automatically with high accuracy [2]
  • A light sheet microscope that automatically adjusts the illumination plane to correct for sample-induced distortion [3]
  • Tools for scanning angle interference microscopy (SAIM) acquisition and analysis [4]
  • Super-resolution mapping of fluorophore orientation [5]
  • Isotropic point spread functions for fast cellular resolution 2-photon imaging [6]
  • CyRFP1, a long Stokes shift fluorescent protein co-excited with GFP but with separable emission [7]
  • mMaroon1, a new far-red fluorescent protein, and a four-color Fucci cell cycle sensor [8]
  • Multi-color electron microscopy [9]
  • A detailed review of fluorescent proteins [10]
  • A nice discussion of challenges in live cell time lapse imaging [11]
  • Ni2+ as a triplet state quencher for improved light output from Cy3 and Cy5 [12]
  • A python tool for image analysis [13]
  • Optimal reconstruction of 2D-SIM data [14]
  • A new bright monomeric red fluorescent protein, mScarlet [15]
  • A review of fluorescent tagging methods [16]
  • Adaptive SIM microscopy to reduce bleaching [17]
  • Tools for cluster analysis of single molecule localization microscopy methods [18]
  • A super-resolution microscope based on incoherent holography [19]


  1. J. Nixon-Abell, C.J. Obara, A.V. Weigel, D. Li, W.R. Legant, C.S. Xu, H.A. Pasolli, K. Harvey, H.F. Hess, E. Betzig, C. Blackstone, and J. Lippincott-Schwartz, "Increased spatiotemporal resolution reveals highly dynamic dense tubular matrices in the peripheral ER", Science, vol. 354, pp. aaf3928-aaf3928, 2016.
  2. D.A. Van Valen, T. Kudo, K.M. Lane, D.N. Macklin, N.T. Quach, M.M. DeFelice, I. Maayan, Y. Tanouchi, E.A. Ashley, and M.W. Covert, "Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments", PLOS Computational Biology, vol. 12, pp. e1005177, 2016.
  3. L.A. Royer, W.C. Lemon, R.K. Chhetri, Y. Wan, M. Coleman, E.W. Myers, and P.J. Keller, "Adaptive light-sheet microscopy for long-term, high-resolution imaging in living organisms", Nature Biotechnology, vol. 34, pp. 1267-1278, 2016.
  4. C.B. Carbone, R.D. Vale, and N. Stuurman, "An acquisition and analysis pipeline for scanning angle interference microscopy", Nature Methods, vol. 13, pp. 897-898, 2016.
  5. K. Zhanghao, L. Chen, X. Yang, M. Wang, Z. Jing, H. Han, M.Q. Zhang, D. Jin, J. Gao, and P. Xi, "Super-resolution dipole orientation mapping via polarization demodulation", Light: Science & Applications, vol. 5, pp. e16166, 2016.
  6. R. Prevedel, A.J. Verhoef, A.J. Pernía-Andrade, S. Weisenburger, B.S. Huang, T. Nöbauer, A. Fernández, J.E. Delcour, P. Golshani, A. Baltuska, and A. Vaziri, "Fast volumetric calcium imaging across multiple cortical layers using sculpted light", Nature Methods, vol. 13, pp. 1021-1028, 2016.
  7. T. Laviv, B.B. Kim, J. Chu, A.J. Lam, M.Z. Lin, and R. Yasuda, "Simultaneous dual-color fluorescence lifetime imaging with novel red-shifted fluorescent proteins", Nature Methods, vol. 13, pp. 989-992, 2016.
  8. B.T. Bajar, A.J. Lam, R.K. Badiee, Y. Oh, J. Chu, X.X. Zhou, N. Kim, B.B. Kim, M. Chung, A.L. Yablonovitch, B.F. Cruz, K. Kulalert, J.J. Tao, T. Meyer, X. Su, and M.Z. Lin, "Fluorescent indicators for simultaneous reporting of all four cell cycle phases", Nature Methods, vol. 13, pp. 993-996, 2016.
  9. S. Adams, M. Mackey, R. Ramachandra, S. Palida Lemieux, P. Steinbach, E. Bushong, M. Butko, B. Giepmans, M. Ellisman, and R. Tsien, "Multicolor Electron Microscopy for Simultaneous Visualization of Multiple Molecular Species", Cell Chemical Biology, vol. 23, pp. 1417-1427, 2016.
  10. E.A. Rodriguez, R.E. Campbell, J.Y. Lin, M.Z. Lin, A. Miyawaki, A.E. Palmer, X. Shu, J. Zhang, and R.Y. Tsien, "The Growing and Glowing Toolbox of Fluorescent and Photoactive Proteins", Trends in Biochemical Sciences, vol. 42, pp. 111-129, 2017.
  11. S. Skylaki, O. Hilsenbeck, and T. Schroeder, "Challenges in long-term imaging and quantification of single-cell dynamics", Nature Biotechnology, vol. 34, pp. 1137-1144, 2016.
  12. V. Glembockyte, J. Lin, and G. Cosa, "Improving the Photostability of Red- and Green-Emissive Single-Molecule Fluorophores via Ni2+ Mediated Excited Triplet-State Quenching", The Journal of Physical Chemistry B, vol. 120, pp. 11923-11929, 2016.
  13. T.S.G. Olsson, and M. Hartley, "jicbioimage: a tool for automated and reproducible bioimage analysis", PeerJ, vol. 4, pp. e2674, 2016.
  14. V. Perez, B. Chang, and E.H.K. Stelzer, "Optimal 2D-SIM reconstruction by two filtering steps with Richardson-Lucy deconvolution", Scientific Reports, vol. 6, pp. 37149, 2016.
  15. D.S. Bindels, L. Haarbosch, L. van Weeren, M. Postma, K.E. Wiese, M. Mastop, S. Aumonier, G. Gotthard, A. Royant, M.A. Hink, and T.W.J. Gadella, "mScarlet: a bright monomeric red fluorescent protein for cellular imaging", Nature Methods, vol. 14, pp. 53-56, 2016.
  16. E.A. Specht, E. Braselmann, and A.E. Palmer, "A Critical and Comparative Review of Fluorescent Tools for Live-Cell Imaging", Annual Review of Physiology, vol. 79, pp. 93-117, 2017.
  17. N. Chakrova, A.S. Canton, C. Danelon, S. Stallinga, and B. Rieger, "Adaptive illumination reduces photobleaching in structured illumination microscopy", Biomedical Optics Express, vol. 7, pp. 4263, 2016.
  18. J. Griffié, M. Shannon, C.L. Bromley, L. Boelen, G.L. Burn, D.J. Williamson, N.A. Heard, A.P. Cope, D.M. Owen, and P. Rubin-Delanchy, "A Bayesian cluster analysis method for single-molecule localization microscopy data", Nature Protocols, vol. 11, pp. 2499-2514, 2016.
  19. N. Siegel, V. Lupashin, B. Storrie, and G. Brooker, "High-magnification super-resolution FINCH microscopy using birefringent crystal lens interferometers", Nature Photonics, vol. 10, pp. 802-808, 2016.