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Comparison of the Zyla 4.2 and 5.5 cameras

It’s been a long time since I posted – I’ve been kept busy with many things here, one of which has been demoing a new spinning disk confocal for the shared instrumentation grant we were recently awarded. Last week we demoed the Borealis CSU-W1, which performs very well. The Borealis upgrade really does result in impressive light delivery to the sample – with 150 mW lasers, we were getting about 25 mW of light at the sample. Even with the large field of view of the CSU-W1, this is way more light than we need to image samples. This is good because it means we can save on hardware by buying cheaper low power lasers.

While we were doing the demo, we took advantage of the dual camera ports on the CSU-W1 to compare different cameras. In particular, we compared the Zyla 5.5 to the Zyla 4.2. I’ve known that the Zyla 4.2 is the more sensitive camera on paper (going from 5 to 4 transistors per pixel improves the quantum efficiency from ~60% to ~72%) but I didn’t realize just how much a difference this makes in practice. Below, you can see images from both cameras. These were acquired one after the other with the same laser power and exposure time, and the images are autoscaled to saturate the brightest and darkest 0.01% of the pixels).


Image from the Zyla 4.2 camera


Image from the Zyla 5.5 camera.

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Paper Roundup – April 2015

  • An implantable CMOS sensor for brain imaging [1]
  • Using the same dye multiple times for STORM imaging by bleaching and restaining [2]
  • Multiplexed single molecule FISH to follow many RNA species simultaneously [3]
  • A review of protein labeling methods for imaging [4]
  • An imageJ plugin for tracking cell migration and membrane protrusion [5]
  • An enzyme-catalyzed method to covalently label genetically tagged RNAs [6]
  • Fusion of imaging mass spectrometry and microscopy data [7]
  • A review and protocol for FRAP data acquisition and analysis [8]
  • Labeling proteins using an expanded genetic code [9]
  • A light sheet microscope with computer control of sheet thickness by using a telescope made up of electrically tunable lenses [10]
  • A protocol for labeling proteins using unnatural amino acid incorporation and click chemistry [11]
  • A line-scanning confocal using the pixel rows on an sCMOS camera as a virtual slit [12]
  • A system for doing high-throughput fluorescence correlation spectroscopy [13]
  • Multi-color luciferases bright enough for microscopy [14]


  1. H. Takehara, Y. Ohta, M. Motoyama, M. Haruta, M. Nagasaki, H. Takehara, T. Noda, K. Sasagawa, T. Tokuda, and J. Ohta, "Intravital fluorescence imaging of mouse brain using implantable semiconductor devices and epi-illumination of biological tissue", Biomedical Optics Express, vol. 6, pp. 1553, 2015.
  2. C.C. Valley, S. Liu, D.S. Lidke, and K.A. Lidke, "Sequential Superresolution Imaging of Multiple Targets Using a Single Fluorophore", PLOS ONE, vol. 10, pp. e0123941, 2015.
  3. K.H. Chen, A.N. Boettiger, J.R. Moffitt, S. Wang, and X. Zhuang, "Spatially resolved, highly multiplexed RNA profiling in single cells", Science, vol. 348, pp. aaa6090-aaa6090, 2015.
  4. G. Zhang, S. Zheng, H. Liu, and P.R. Chen, "Illuminating biological processes through site-specific protein labeling", Chemical Society Reviews, vol. 44, pp. 3405-3417, 2015.
  5. D.J. Barry, C.H. Durkin, J.V. Abella, and M. Way, "Open source software for quantification of cell migration, protrusions, and fluorescence intensities", Journal of Cell Biology, vol. 209, pp. 163-180, 2015.
  6. F. Li, J. Dong, X. Hu, W. Gong, J. Li, J. Shen, H. Tian, and J. Wang, "A Covalent Approach for Site-Specific RNA Labeling in Mammalian Cells", Angewandte Chemie International Edition, vol. 54, pp. 4597-4602, 2015.
  7. R. Van de Plas, J. Yang, J. Spraggins, and R.M. Caprioli, "Image fusion of mass spectrometry and microscopy: a multimodality paradigm for molecular tissue mapping", Nature Methods, vol. 12, pp. 366-372, 2015.
  8. M. Fritzsche, and G. Charras, "Dissecting protein reaction dynamics in living cells by fluorescence recovery after photobleaching", Nature Protocols, vol. 10, pp. 660-680, 2015.
  9. C. Uttamapinant, J.D. Howe, K. Lang, V. Beránek, L. Davis, M. Mahesh, N.P. Barry, and J.W. Chin, "Genetic Code Expansion Enables Live-Cell and Super-Resolution Imaging of Site-Specifically Labeled Cellular Proteins", Journal of the American Chemical Society, vol. 137, pp. 4602-4605, 2015.
  10. A.K. Chmielewski, A. Kyrsting, P. Mahou, M.T. Wayland, L. Muresan, J.F. Evers, and C.F. Kaminski, "Fast imaging of live organisms with sculpted light sheets", Scientific Reports, vol. 5, 2015.
  11. I. Nikić, J.H. Kang, G.E. Girona, I.V. Aramburu, and E.A. Lemke, "Labeling proteins on live mammalian cells using click chemistry", Nature Protocols, vol. 10, pp. 780-791, 2015.
  12. T. Yang, T. Zheng, Z. Shang, X. Wang, X. Lv, J. Yuan, and S. Zeng, "Rapid imaging of large tissues using high-resolution stage-scanning microscopy", Biomedical Optics Express, vol. 6, pp. 1867, 2015.
  13. M. Wachsmuth, C. Conrad, J. Bulkescher, B. Koch, R. Mahen, M. Isokane, R. Pepperkok, and J. Ellenberg, "High-throughput fluorescence correlation spectroscopy enables analysis of proteome dynamics in living cells", Nature Biotechnology, vol. 33, pp. 384-389, 2015.
  14. A. Takai, M. Nakano, K. Saito, R. Haruno, T.M. Watanabe, T. Ohyanagi, T. Jin, Y. Okada, and T. Nagai, "Expanded palette of Nano-lanterns for real-time multicolor luminescence imaging", Proceedings of the National Academy of Sciences, vol. 112, pp. 4352-4356, 2015.