High Speed Imaging with the Andor Zyla

We’ve been having some more fun with our Andor Zyla. While it runs at 100 fps when you image the full field of view, if you work with smaller regions of interest (ROIs), it goes quite a bit faster. The fastest we’ve had it running is 2000 fps for a 1024 x 64 ROI, and it runs at 420 fps for a 528 x 512 ROI. Here’s a movie of a swimming Tetrahymena recorded at 420 fps. It’s been slowed down by 10-fold for display here.

Swimming tetrahymena, recorded at 420 fps with an Andor Zyla camera using a 100x / 1.4 NA lens and DIC optics.

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Fluorescent Protein Aggregation

I’m currently at MBL, teaching microscopy in the Physiology course, hence the reduced rate of posting. Not surprisingly there’s been a lot of discussion about microscopy, including a great talk from Hari Shroff (about which more later). Today we had a talk from John Allen, standing in for Mike Davidson. John gave a very nice overview of the state of the art in the fluorescent protein field.  Most of what was covered is published and reasonably well known, but I did learn about a recent paper for a quantitative way of assessing fluorescent protein aggregation by fusing proteins to an ER membrane protein. Protein aggregation restructures the ER into smooth-ER like whorls that can be detected by imaging; the amount of such structures correlates with the aggregation of fluorescent proteins [1]. How well this correlates with protein function in other contexts remains to be seen, but a systematic assay for fluorescent protein aggregation potential is very welcome.

References

  1. L.M. Costantini, M. Fossati, M. Francolini, and E.L. Snapp, "Assessing the tendency of fluorescent proteins to oligomerize under physiologic conditions.", Traffic (Copenhagen, Denmark), 2012. http://www.ncbi.nlm.nih.gov/pubmed/22289035

Paper Roundup: May 2013

  • There is a new study looking at DNA damage in cells as a result of fluorescence imaging. They compare the effect of illumination at different wavelengths in both unlabeled cells and those labeled with different dyes. [1]
  • A new single molecule localization algorithm for super-resolution imaging that specifically incorporates the noise properties of sCMOS cameras. It is also extremely fast. [2]
  • A protocol for doing simultaneous stimulation and recording using optogenetic tools and reporters in neural systems [3]
  • A brief introduction to Endrov, an open source image analysis and acquisition program. This looks like it has some nice features, particularly for working with large data sets. [4]
  • A Fourier ring correlation method for determining image resolution in super-resolution techniques. This borrows a technique from cryo-EM to estimate the resolution  of a data set. [5]
  • A group at Hamamatsu has used an FPGA similar to those onboard sCMOS cameras to do real time spot extraction from single molecule switching data (STORM data). While this is a long way from being in a commercial camera, it opens up the interesting possibility of data processing on the fly and only delivering interesting pixels to the end user, rather than the entire image, dramatically reducing data rate. [6]
  • A paper demonstrating that Vectashield can be used as a mounting medium for STORM microscopy. They report good switching of Cy5/Alexa 647, switching of Alexa-555, and switching of Cy3 in Vectashield plus N-propyl gallate and DABCO. [7]

References

  1. J. Ge, D.K. Wood, D.M. Weingeist, S. Prasongtanakij, P. Navasumrit, M. Ruchirawat, and B.P. Engelward, "Standard fluorescent imaging of live cells is highly genotoxic", Cytometry Part A, vol. 83A, pp. 552-560, 2013. http://dx.doi.org/10.1002/cyto.a.22291
  2. F. Huang, T.M.P. Hartwich, F.E. Rivera-Molina, Y. Lin, W.C. Duim, J.J. Long, P.D. Uchil, J.R. Myers, M.A. Baird, W. Mothes, M.W. Davidson, D. Toomre, and J. Bewersdorf, "Video-rate nanoscopy using sCMOS camera–specific single-molecule localization algorithms", Nature Methods, vol. 10, pp. 653-658, 2013. http://dx.doi.org/10.1038/Nmeth.2488
  3. N.R. Wilson, J. Schummers, C.A. Runyan, S.X. Yan, R.E. Chen, Y. Deng, and M. Sur, "Two-way communication with neural networks in vivo using focused light", Nature Protocols, vol. 8, pp. 1184-1203, 2013. http://dx.doi.org/10.1038/nprot.2013.063
  4. J. Henriksson, J. Hench, Y.G. Tong, A. Johansson, D. Johansson, and T.R. Bürglin, "Endrov: an integrated platform for image analysis", Nature Methods, vol. 10, pp. 454-456, 2013. http://dx.doi.org/10.1038/nmeth.2478
  5. R.P.J. Nieuwenhuizen, K.A. Lidke, M. Bates, D.L. Puig, D. Grünwald, S. Stallinga, and B. Rieger, "Measuring image resolution in optical nanoscopy", Nature Methods, vol. 10, pp. 557-562, 2013. http://dx.doi.org/10.1038/nmeth.2448
  6. H. Ma, H. Kawai, E. Toda, S. Zeng, and Z. Huang, "Localization-based super-resolution microscopy with an sCMOS camera part III: camera embedded data processing significantly reduces the challenges of massive data handling", Optics Letters, vol. 38, pp. 1769, 2013. http://dx.doi.org/10.1364/OL.38.001769
  7. N. Olivier, D. Keller, V.S. Rajan, P. Gönczy, and S. Manley, "Simple buffers for 3D STORM microscopy", Biomedical Optics Express, vol. 4, pp. 885, 2013. http://dx.doi.org/10.1364/BOE.4.000885