Paper Roundup – January 2014

  • An interesting method for image-based cell sorting where photopolymerization is used to trap unwanted cells, allowing the selected cells to be washed off and collected [1]
  • Best Practices for Scientific Computing. Only microscopy related insofar as you write software or macros to analyze images, but very good to bear in mind if you do [2]
  • Trimethoprim tagging for STORM imaging with Alexa 647 and Atto 655 [3]
  • MS2 fusions to SNAP-tags and DHFR for targeting small molecule dyes to RNAs in vivo[4]
  • Luminescence imaging using a short focal length tube lens [5]
  • An overview of the Vaa3D image analysis package [6]
  • X-ray microtomography of Xenopus development. [7]
  • Super-resolution imaging and quantitation of RNA polymerase using a mirror to perform light sheet imaging of the cell nucleus [8]
  • A fast-folding Dronpa variant, and a variant that is green-to-red photoconvertible (though tetrameric) [9]
  • New fluorophores to change the spectral properties of the Spinach / Spinach2 fluorescent RNA apatamers [10]
  • A blue light inducible degron [11]
  • A blue light inducible homodimerization system [12]
  • Using the CRISPR/Cas system for imaging of genomic loci in live cells [13]
  • A new ratiometric calcium senso [14]
  • A direct measure of photoactivation efficency for photoactivatible fluorescent proteins [15]
  • A real-time FISH assay for measurement of RNA transcription rates [16]
  • Metal-induced energy transfer for measuring distances between dyes and a metal film, used to map the height of the basal membrane of cells with one nanometer accuracy. [17]
  • A review of genetically encoded fluorescent biosensors for phosphorylation [18]
  • A clever trick using SNAP-tag labeling of a solvatochromic dye for no-wash imaging of plasma membrane proteins [19]

References

  1. T. Sun, J. Kovac, and J. Voldman, "Image-Based Single-Cell Sorting via Dual-Photopolymerized Microwell Arrays", Analytical Chemistry, vol. 86, pp. 977-981, 2014. http://dx.doi.org/10.1021/ac403777g
  2. G. Wilson, D.A. Aruliah, C.T. Brown, N.P. Chue Hong, M. Davis, R.T. Guy, S.H.D. Haddock, K.D. Huff, I.M. Mitchell, M.D. Plumbley, B. Waugh, E.P. White, and P. Wilson, "Best Practices for Scientific Computing", PLoS Biology, vol. 12, pp. e1001745, 2014. http://dx.doi.org/10.1371/journal.pbio.1001745
  3. T. Wang, L. Friedman, J. Gelles, W. Min, A. Hoskins, and V. Cornish, "The Covalent Trimethoprim Chemical Tag Facilitates Single Molecule Imaging with Organic Fluorophores", Biophysical Journal, vol. 106, pp. 272-278, 2014. http://dx.doi.org/10.1016/j.bpj.2013.11.4488
  4. T.J. Carrocci, and A.A. Hoskins, "Imaging of RNAs in live cells with spectrally diverse small molecule fluorophores", The Analyst, vol. 139, pp. 44-47, 2014. http://dx.doi.org/10.1039/c3an01550e
  5. K. OGOH, R. AKIYOSHI, . MAY-MAW-THET, T. SUGIYAMA, S. DOSAKA, Y. HATTA-OHASHI, and H. SUZUKI, "Bioluminescence microscopy using a short focal-length imaging lens", Journal of Microscopy, vol. 253, pp. 191-197, 2014. http://dx.doi.org/10.1111/jmi.12109
  6. H. Peng, A. Bria, Z. Zhou, G. Iannello, and F. Long, "Extensible visualization and analysis for multidimensional images using Vaa3D", Nature Protocols, vol. 9, pp. 193-208, 2014. http://dx.doi.org/10.1038/nprot.2014.011
  7. J. Moosmann, A. Ershov, V. Weinhardt, T. Baumbach, M.S. Prasad, C. LaBonne, X. Xiao, J. Kashef, and R. Hofmann, "Time-lapse X-ray phase-contrast microtomography for in vivo imaging and analysis of morphogenesis", Nature Protocols, vol. 9, pp. 294-304, 2014. http://dx.doi.org/10.1038/nprot.2014.033
  8. Z.W. Zhao, R. Roy, J.C.M. Gebhardt, D.M. Suter, A.R. Chapman, and X.S. Xie, "Spatial organization of RNA polymerase II inside a mammalian cell nucleus revealed by reflected light-sheet superresolution microscopy", Proceedings of the National Academy of Sciences, vol. 111, pp. 681-686, 2013. http://dx.doi.org/10.1073/pnas.1318496111
  9. B. Moeyaert, N. Nguyen Bich, E. De Zitter, S. Rocha, K. Clays, H. Mizuno, L. van Meervelt, J. Hofkens, and P. Dedecker, "Green-to-Red Photoconvertible Dronpa Mutant for Multimodal Super-resolution Fluorescence Microscopy", ACS Nano, vol. 8, pp. 1664-1673, 2014. http://dx.doi.org/10.1021/nn4060144
  10. W. Song, R.L. Strack, N. Svensen, and S.R. Jaffrey, "Plug-and-Play Fluorophores Extend the Spectral Properties of Spinach", Journal of the American Chemical Society, vol. 136, pp. 1198-1201, 2014. http://dx.doi.org/10.1021/ja410819x
  11. K.M. Bonger, R. Rakhit, A.Y. Payumo, J.K. Chen, and T.J. Wandless, "General Method for Regulating Protein Stability with Light", ACS Chemical Biology, vol. 9, pp. 111-115, 2014. http://dx.doi.org/10.1021/cb400755b
  12. Y. Nihongaki, H. Suzuki, F. Kawano, and M. Sato, "Genetically Engineered Photoinducible Homodimerization System with Improved Dimer-Forming Efficiency", ACS Chemical Biology, vol. 9, pp. 617-621, 2014. http://dx.doi.org/10.1021/cb400836k
  13. B. Chen, L. Gilbert, B. Cimini, J. Schnitzbauer, W. Zhang, G. Li, J. Park, E. Blackburn, J. Weissman, L. Qi, and B. Huang, "Dynamic Imaging of Genomic Loci in Living Human Cells by an Optimized CRISPR/Cas System", Cell, vol. 155, pp. 1479-1491, 2013. http://dx.doi.org/10.1016/j.cell.2013.12.001
  14. T. Thestrup, J. Litzlbauer, I. Bartholomäus, M. Mues, L. Russo, H. Dana, Y. Kovalchuk, Y. Liang, G. Kalamakis, Y. Laukat, S. Becker, G. Witte, A. Geiger, T. Allen, L.C. Rome, T. Chen, D.S. Kim, O. Garaschuk, C. Griesinger, and O. Griesbeck, "Optimized ratiometric calcium sensors for functional in vivo imaging of neurons and T lymphocytes", Nature Methods, vol. 11, pp. 175-182, 2014. http://dx.doi.org/10.1038/nmeth.2773
  15. N. Durisic, L. Laparra-Cuervo, Ã. Sandoval-Álvarez, J.S. Borbely, and M. Lakadamyali, "Single-molecule evaluation of fluorescent protein photoactivation efficiency using an in vivo nanotemplate", Nature Methods, vol. 11, pp. 156-162, 2014. http://dx.doi.org/10.1038/nmeth.2784
  16. Z. Zhang, A. Revyakin, J.B. Grimm, L.D. Lavis, and R. Tjian, "Single-molecule tracking of the transcription cycle by sub-second RNA detection", eLife, vol. 3, 2014. http://dx.doi.org/10.7554/eLife.01775
  17. A.I. Chizhik, J. Rother, I. Gregor, A. Janshoff, and J. Enderlein, "Metal-induced energy transfer for live cell nanoscopy", Nature Photonics, vol. 8, pp. 124-127, 2014. http://dx.doi.org/10.1038/nphoton.2013.345
  18. L. Oldach, and J. Zhang, "Genetically Encoded Fluorescent Biosensors for Live-Cell Visualization of Protein Phosphorylation", Chemistry & Biology, vol. 21, pp. 186-197, 2014. http://dx.doi.org/10.1016/j.chembiol.2013.12.012
  19. E. Prifti, L. Reymond, M. Umebayashi, R. Hovius, H. Riezman, and K. Johnsson, "A Fluorogenic Probe for SNAP-Tagged Plasma Membrane Proteins Based on the Solvatochromic Molecule Nile Red", ACS Chemical Biology, vol. 9, pp. 606-612, 2014. http://dx.doi.org/10.1021/cb400819c

Fluorescent dyes for shading correction

Since my recent post on shading correction of microscopy images, I’ve become aware of two papers by Michael Model describing the use of concentrated dye solutions for shading correction and intensity calibration of microscopes. The first paper [1] describes the testing of the solutions, while the second paper [2] provides recipes for green, red, and far-red calibration solutions. He finds that concentrated solutions (10% w/v fluorescein, for instance) perform best, and also identifies dyes that are highly water soluble and can be prepared at these high concentrations for measurement of shading images.

In particular, he recommends fluorescein for the correction of green images, rose bengal or acid fuchsin for red images (all available from Sigma-Aldrich), and acid blue 9 for far-red (Cy5 images).

References

  1. M.A. Model, and J.K. Burkhardt, "A standard for calibration and shading correction of a fluorescence microscope.", Cytometry, 2001. http://www.ncbi.nlm.nih.gov/pubmed/11500847
  2. M.A. Model, "Intensity calibration and shading correction for fluorescence microscopes.", Current protocols in cytometry, 2006. http://www.ncbi.nlm.nih.gov/pubmed/18770832

Shading correction of fluorescence images

I posted previously about automated acquisition and stitching of tiled fluorescence images in Micro-Manager. Today I want to talk about how to properly flat-field correct them. In the previous post I mentioned that I have been developing tools for flat-fielding images with independent correction images in each channel. However, if you looked at the linked stitched image from the previous post you will notice that there is still some uncorrected shading in the images, which manifests itself as the checkerboard pattern in the final stitched image.

I suspected that this was because the correction image was not a good match to the true shading image. Normally, we measure flat-field correction images using 1 mm thick fluorescent plastic slides. Chroma gives these out at conferences, and they’re easy to use, but you might expect that a 1 mm thick fluorescent slide is not a good way to measure the correction image for a 20 μm thick tissue section. To test this, I measured correction images from one of these fluorescent plastic slide and from concentrated and dilute solutions of dye (fluorescein or rhodamine). To image the dye samples a drop of dye was placed between a coverslip and slide to produce a thin layer of dye. The dilute dye solutions produced poor correction images due to high variability in intensity from position to position. The concentrated dye solutions (a spatula-full of dye dissolved in 5 mL of PBS) produced good correction images. These were tested by tiling image acquisition to look for uniformity in the stitched image. The results are shown below.

A 6x4 stitched image of a mouse kidney section with no shading correction applied.

A 6×4 stitched image of a mouse kidney section with no shading correction applied.

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Fluorescence stitching

Regular readers of this blog will have noticed that I post about large image acquisition and stitching a lot. Partly this is due to demand from users of our facility who want to acquire images of tissue sections. In particular, there’s a lot of demand for imaging fluorescently-labeled mouse brain sections. There doesn’t seem to be a readily available slide scanner at UCSF, so I’ve been trying to come up with some easy methods for acquiring these images. I’ve been working on developing an easy workflow for doing tiled image acquisition and image stitching in Micro-Manager.

Recently, I’ve found that the Grid/Collection stitching plugin in FIJI works well for stitching images acquired in Micro-Manager. Because Micro-Manager saves its images in the OME-TIFF format, they include the coordinates that each image was acquired at.  This makes stitching the images pretty easy, because the stitcher knows exactly where each image came from. Because of this, acquiring tiled images in Micro-Manager and stitching them is seamless – simply use the create grid function in Micro-Manager to acquire the images and then open and stitch them using Grid/Collection stitching. I’ve written up a full description of how to do this on the NIC Wiki, along with some details about other stitching programs we’ve looked at.

Micro-Manager also includes a plugin for flat-field correcting images, but it doesn’t allow different flat-field images for different channels. On our microscope, there is some variation in shading from image to image, so I’ve put together a plugin that allows different flat-field correction images for different channels. This plugin has been submitted to Micro-Manager and should be available in future releases.

Putting this all together, here’s a fluorescence image of a kidney section, composed of 180 individual 3-color images. It took 3.5 min to acquire using the high speed scanning system I’ve posted about before and 11 min to stitch on a dual quad-core Xeon with 32 GB of RAM. The flat-fielding isn’t perfect, but I hope to improve that soon.