Testing the Prime95B – a back-illuminated sCMOS camera with 95% QE

A few weeks ago I mentioned that Photometrics had released a new camera, the Prime95B, featuring a back-illuminated sCMOS sensor with 95% peak QE. I got a chance to play with it last week, and I’m pleased to say that it performs as well as you would expect. We compared it to an iXon 888 EMCCD mounted on our CSU-W1 spinning disk confocal. We had purchased this EMCCD for imaging those samples that were too dim to get good images with the Zyla 4.2 sCMOS camera we also have mounted on the confocal. (You can see a sketch of how everything is configured in this previous post). For testing the Prime95B, we replaced the Zyla 4.2 with the Prime95B, allowing us to directly compare the Prime95B and the iXon 888.

Before I get to the data, however, what performance do we expect? To get a sense of what to expect I wrote a Matlab script that calculates the theoretical performance for a number of different cameras, using their quantum efficiency, read noise, and excess noise factor (for EMCCDs). You can get the script here.   You can read more about how to calculate the signal-to-nosie ratio for a camera in this Hamamatsu white paper. Here, I’m ignoring the different pixel sizes of the various cameras by assuming that they all receive the same photon flux per pixel, as if the magnification had been adjusted to produce the same effective pixel size at the sample.

Theoretical performance of different cameras.

Theoretical performance of different cameras. Ideal is a theoretical ideal camera with QE=1 and no read noise. EMCCD assumes a high EM gain, ~200x; 82% QE sCMOS is a Flash4.0v2 or Zyla 4.2 Plus; 72% QE sCMOS is a Flash 4.0 or Zyla 4.2; ICX285 is a Coolsnap HQ2 or similar interline CCD camera.

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USB3 Flash drives for high speed data transfer

In the past few years, we’ve switched the NIC almost exclusively to sCMOS cameras. We still have a few EMCCDs, but we have no interline CCDs in the NIC anymore. This change has greatly increased data acquisition rates – we’ve gone from 1.4 megapixel images to 4+ megapixel images. It’s now very common for someone to sit down at a microscope for a few hours and end up with 20 GB of data, and time lapse experiments often produce 1 TB.

This increase in the amount of data has made data transfer a bottleneck. USB2.0 flash drives, in our experience, top out around 20-30 MB/sec transfer rates. At that speed, a 20 GB data set takes 10-15 minutes to transfer. To reduce this time, we’ve begun to upgrade all of our PCs to add USB3 ports. We’ve been using Startech cards and have had good luck with them. With a good USB3 thumb drive – we use a SanDisk Extreme for testing – we can get transfer speeds of ~180 MB/sec, a 6-8x improvement in speed. In the process of doing this I’ve learned a few useful things. First, USB2 cables do not support USB3 transfer speeds – you need to have a USB3 cable. Second, not all USB3 hubs are equal – we have one that doesn’t manage USB3 transfer speeds. We’ve had good luck with this Anker USB3 hub. Finally, some devices (in particular the Nikon Ti) do not like to run over USB3 ports, so you still need some USB2.0 ports for controlling hardware.

We also have a network server for data transfer, but with gigabit ethernet, it maxes out at about 100 MB/sec transfer speeds, so the USB3 drives end up being somewhat faster. We’re hoping that the USB drives will be reliable enough to allow direct acquisition of data to them (rather than saving to the local hard drive and then copying), although we haven’t tried that yet. We have had problems with transfer glitches causing experiments to be interrupted when we save over the network or to USB2.0 drives, so we don’t recommend that.

There is now a USB3.1 Gen 2 specification that promises a 2x speed improvement over USB3.0, but very few drives support it, so we haven’t started looking at that.

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", Nat Biotechnol, 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 Chem. Biol., 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", J. Am. Chem. Soc., 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., 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. "Clearing and Labeling Techniques for Large-Scale Biological Tissues", Mol Cells, 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, 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, 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.

Paper Roundup – May 2016

  • Labeling of multiple genomic loci in different colors with CRISPRainbow [1]
  • 3D localization super-resolution microscopy over 4 μm using an astigmatic multifocus microscope [2]
  • Contrast and resolution enhancement by using subtraction of an image acquired from a donut beam from one acquired with a gaussian beam [3]
  • A single objective light sheet system using a micro-fabricated 45º mirror [4]
  • A comparison of OCT and OPT for murine embryo imaging [5]
  • Real time imaging of translation [6]
  • A protocol for coverslip cleaning and functionalization for TIRF microscopy [7]
  • A review of single molecule imaging in live cells [8]
  • Making a Bessel light sheet with a slit and an annulus [9]
  • Hyperspectral imaging of quantum dots for multiple particle tracking [10]

References

  1. H. Ma, L. Tu, A. Naseri, M. Huisman, S. Zhang, D. Grunwald, and T. Pederson, "Multiplexed labeling of genomic loci with dCas9 and engineered sgRNAs using CRISPRainbow", Nat Biotechnol, vol. 34, pp. 528-530, 2016. http://dx.doi.org/10.1038/nbt.3526
  2. L. Oudjedi, J. Fiche, S. Abrahamsson, L. Mazenq, A. Lecestre, P. Calmon, A. Cerf, and M. Nöllmann, "Astigmatic multifocus microscopy enables deep 3D super-resolved imaging", Biomedical Optics Express, vol. 7, pp. 2163, 2016. http://dx.doi.org/10.1364/BOE.7.002163
  3. K. Korobchevskaya, C. Peres, Z. Li, A. Antipov, C.J.R. Sheppard, A. Diaspro, and P. Bianchini, "Intensity Weighted Subtraction Microscopy Approach for Image Contrast and Resolution Enhancement", Sci. Rep., vol. 6, pp. 25816, 2016. http://dx.doi.org/10.1038/srep25816
  4. M.B.M. Meddens, S. Liu, P.S. Finnegan, T.L. Edwards, C.D. James, and K.A. Lidke, "Single objective light-sheet microscopy for high-speed whole-cell 3D super-resolution", Biomedical Optics Express, vol. 7, pp. 2219, 2016. http://dx.doi.org/10.1364/BOE.7.002219
  5. M. Singh, R. Raghunathan, V. Piazza, A.M. Davis-Loiacono, A. Cable, T.J. Vedakkan, T. Janecek, M.V. Frazier, A. Nair, C. Wu, I.V. Larina, M.E. Dickinson, and K.V. Larin, "Applicability, usability, and limitations of murine embryonic imaging with optical coherence tomography and optical projection tomography", Biomedical Optics Express, vol. 7, pp. 2295, 2016. http://dx.doi.org/10.1364/BOE.7.002295
  6. C. Wang, B. Han, R. Zhou, and X. Zhuang, "Real-Time Imaging of Translation on Single mRNA Transcripts in Live Cells", Cell, vol. 165, pp. 990-1001, 2016. http://dx.doi.org/10.1016/j.cell.2016.04.040
  7. E.M. Kudalkar, Y. Deng, T.N. Davis, and C.L. Asbury, "Coverslip Cleaning and Functionalization for Total Internal Reflection Fluorescence Microscopy", Cold Spring Harbor Protocols, vol. 2016, pp. pdb.prot085548, 2016. http://dx.doi.org/10.1101/pdb.prot085548
  8. J. Yu, "Single-Molecule Studies in Live Cells", Annual Review of Physical Chemistry, vol. 67, pp. 565-585, 2016. http://dx.doi.org/10.1146/annurev-physchem-040215-112451
  9. T. Zhao, S.C. Lau, Y. Wang, Y. Su, H. Wang, A. Cheng, K. Herrup, N.Y. Ip, S. Du, and M.M.T. Loy, "Multicolor 4D Fluorescence Microscopy using Ultrathin Bessel Light Sheets", Sci. Rep., vol. 6, pp. 26159, 2016. http://dx.doi.org/10.1038/srep26159
  10. S. Labrecque, J. Sylvestre, S. Marcet, F. Mangiarini, B. Bourgoin, M. Verhaegen, S. Blais-Ouellette, and P. De Koninck, "Hyperspectral multiplex single-particle tracking of different receptor subtypes labeled with quantum dots in live neurons", J. Biomed. Opt, vol. 21, pp. 046008, 2016. http://dx.doi.org/10.1117/1.JBO.21.4.046008

Paper Roundup: April 2016

  • Axial super-resolution using multi-angle TIRF and photobleaching [1]
  • A tool for simulating localization microscopy data [2]
  • Lattice Light Sheet plus PAINT for 3D localization microscopy of large volumes [3]
  • Monomeric streptavidin combined with enzymatic biotinylation as a labeling probe [4]
  • Massively parallel single-molecule FRET measurements with sCMOS cameras [5]
  • A microfluidic light sheet microscope [6]

References

  1. Y. Fu, P.W. Winter, R. Rojas, V. Wang, M. McAuliffe, and G.H. Patterson, "Axial superresolution via multiangle TIRF microscopy with sequential imaging and photobleaching", Proceedings of the National Academy of Sciences, vol. 113, pp. 4368-4373, 2016. http://dx.doi.org/10.1073/pnas.1516715113
  2. V. Venkataramani, F. Herrmannsdörfer, M. Heilemann, and T. Kuner, "SuReSim: simulating localization microscopy experiments from ground truth models", Nature Methods, vol. 13, pp. 319-321, 2016. http://dx.doi.org/10.1038/nmeth.3775
  3. W.R. Legant, L. Shao, J.B. Grimm, T.A. Brown, D.E. Milkie, B.B. Avants, L.D. Lavis, and E. Betzig, "High-density three-dimensional localization microscopy across large volumes", Nature Methods, vol. 13, pp. 359-365, 2016. http://dx.doi.org/10.1038/nmeth.3797
  4. I. Chamma, M. Letellier, C. Butler, B. Tessier, K. Lim, I. Gauthereau, D. Choquet, J. Sibarita, S. Park, M. Sainlos, and O. Thoumine, "Mapping the dynamics and nanoscale organization of synaptic adhesion proteins using monomeric streptavidin", Nature Communications, vol. 7, pp. 10773, 2016. http://dx.doi.org/10.1038/ncomms10773
  5. M.F. Juette, D.S. Terry, M.R. Wasserman, R.B. Altman, Z. Zhou, H. Zhao, and S.C. Blanchard, "Single-molecule imaging of non-equilibrium molecular ensembles on the millisecond timescale", Nature Methods, vol. 13, pp. 341-344, 2016. http://dx.doi.org/10.1038/nmeth.3769
  6. P. Paiè, F. Bragheri, A. Bassi, and R. Osellame, "Selective plane illumination microscopy on a chip", Lab Chip, vol. 16, pp. 1556-1560, 2016. http://dx.doi.org/10.1039/c6lc00084c

Paper Roundup: March 2016

  • Coupled robotics, imaging, and machine learning to automatically determine effects of compounds on protein localization [1]
  • Combining lattice light sheet microscopy and PAINT staining to achieve 3D super-resolution localization microscopy over large volumes [2]
  • A scanning Bessel beam light sheet scope for imaging of 3D cell behavior [3]
  • Building an interferometric scattering microscope [4]
  • A detailed analysis of the Beer-Lambert law and absorption spectroscopy [5]
  • An optofluidic gradient refractive index lens [6]
  • Combined spectral and lifetime imaging for imaging many cellular labels at once [7]
  • A nice review of clearing methods [8]
  • A 3D visualization tool for light sheet data [9]
  • Monomeric streptavidin as a probe for super-resolution imaging of biotinylated proteins [10]
  • Use of phase masks at the pupil plane to make more uniform light sheets [11]
  • A light sheet microscope compatible with multiwell plates and other coverslip bottom chambers [12]
  • Combining light sheet microscopy with RESOLFT to improve the Z-resolution of light sheet microscopy [13]
  • Using split GFP as a protein tagging system [14]
  • Tiling light sheet to optimize both field of view and spatial resolution [15]
  • Improved refractive index matching for sample clearing [16]
  • An open source structured illumination (SIM) reconstruction program [17]
  • A post hoc algorithm for estimating shading corrections [18]
  • Reprogramming CRISPR-Cas9 for fluorescent labeling of RNA [19]
  • Diagonal scanning light sheet microscopy for high resolution imaging of adherent cells [20]

References

  1. A.W. Naik, J.D. Kangas, D.P. Sullivan, and R.F. Murphy, "Active machine learning-driven experimentation to determine compound effects on protein patterns", eLife, vol. 5, 2016. http://dx.doi.org/10.7554/eLife.10047
  2. W.R. Legant, L. Shao, J.B. Grimm, T.A. Brown, D.E. Milkie, B.B. Avants, L.D. Lavis, and E. Betzig, "High-density three-dimensional localization microscopy across large volumes", Nature Methods, vol. 13, pp. 359-365, 2016. http://dx.doi.org/10.1038/nmeth.3797
  3. E. Welf, M. Driscoll, K. Dean, C. Schäfer, J. Chu, M. Davidson, M. Lin, G. Danuser, and R. Fiolka, "Quantitative Multiscale Cell Imaging in Controlled 3D Microenvironments", Developmental Cell, vol. 36, pp. 462-475, 2016. http://dx.doi.org/10.1016/j.devcel.2016.01.022
  4. J. Ortega Arroyo, D. Cole, and P. Kukura, "Interferometric scattering microscopy and its combination with single-molecule fluorescence imaging", Nat Protoc, vol. 11, pp. 617-633, 2016. http://dx.doi.org/10.1038/nprot.2016.022
  5. T.G. Mayerhöfer, H. Mutschke, and J. Popp, "Employing Theories Far beyond Their Limits-The Case of the (Boguer-) Beer-Lambert Law", ChemPhysChem, vol. 17, pp. 1948-1955, 2016. http://dx.doi.org/10.1002/cphc.201600114
  6. H.T. Zhao, Y. Yang, L.K. Chin, H.F. Chen, W.M. Zhu, J.B. Zhang, P.H. Yap, B. Liedberg, K. Wang, G. Wang, W. Ser, and A.Q. Liu, "Optofluidic lens with low spherical and low field curvature aberrations", Lab Chip, vol. 16, pp. 1617-1624, 2016. http://dx.doi.org/10.1039/C6LC00295A
  7. T. Niehörster, A. Löschberger, I. Gregor, B. Krämer, H. Rahn, M. Patting, F. Koberling, J. Enderlein, and M. Sauer, "Multi-target spectrally resolved fluorescence lifetime imaging microscopy", Nature Methods, vol. 13, pp. 257-262, 2016. http://dx.doi.org/10.1038/nmeth.3740
  8. V. Marx, "Optimizing probes to image cleared tissue", Nature Methods, vol. 13, pp. 205-209, 2016. http://dx.doi.org/10.1038/nmeth.3774
  9. A. Bria, G. Iannello, L. Onofri, and H. Peng, "TeraFly: real-time three-dimensional visualization and annotation of terabytes of multidimensional volumetric images", Nature Methods, vol. 13, pp. 192-194, 2016. http://dx.doi.org/10.1038/nmeth.3767
  10. I. Chamma, M. Letellier, C. Butler, B. Tessier, K. Lim, I. Gauthereau, D. Choquet, J. Sibarita, S. Park, M. Sainlos, and O. Thoumine, "Mapping the dynamics and nanoscale organization of synaptic adhesion proteins using monomeric streptavidin", Nature Communications, vol. 7, pp. 10773, 2016. http://dx.doi.org/10.1038/ncomms10773
  11. D. Wilding, P. Pozzi, O. Soloviev, G. Vdovin, C.J. Sheppard, and M. Verhaegen, "Pupil filters for extending the field-of-view in light-sheet microscopy", Optics Letters, vol. 41, pp. 1205, 2016. http://dx.doi.org/10.1364/OL.41.001205
  12. R. McGorty, and B. Huang, "Selective-plane illumination microscopy for high-content volumetric biological imaging", High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management, 2016. http://dx.doi.org/10.1117/12.2212889
  13. P. Hoyer, G. de Medeiros, B. Balázs, N. Norlin, C. Besir, J. Hanne, H. Kräusslich, J. Engelhardt, S.J. Sahl, S.W. Hell, and L. Hufnagel, "Breaking the diffraction limit of light-sheet fluorescence microscopy by RESOLFT", Proceedings of the National Academy of Sciences, vol. 113, pp. 3442-3446, 2016. http://dx.doi.org/10.1073/pnas.1522292113
  14. D. Kamiyama, S. Sekine, B. Barsi-Rhyne, J. Hu, B. Chen, L.A. Gilbert, H. Ishikawa, M.D. Leonetti, W.F. Marshall, J.S. Weissman, and B. Huang, "Versatile protein tagging in cells with split fluorescent protein", Nature Communications, vol. 7, pp. 11046, 2016. http://dx.doi.org/10.1038/ncomms11046
  15. Q. Fu, B.L. Martin, D.Q. Matus, and L. Gao, "Imaging multicellular specimens with real-time optimized tiling light-sheet selective plane illumination microscopy", Nature Communications, vol. 7, pp. 11088, 2016. http://dx.doi.org/10.1038/ncomms11088
  16. M. Ke, Y. Nakai, S. Fujimoto, R. Takayama, S. Yoshida, T. Kitajima, M. Sato, and T. Imai, "Super-Resolution Mapping of Neuronal Circuitry With an Index-Optimized Clearing Agent", Cell Reports, vol. 14, pp. 2718-2732, 2016. http://dx.doi.org/10.1016/j.celrep.2016.02.057
  17. M. Müller, V. Mönkemöller, S. Hennig, W. Hübner, and T. Huser, "Open-source image reconstruction of super-resolution structured illumination microscopy data in ImageJ", Nature Communications, vol. 7, pp. 10980, 2016. http://dx.doi.org/10.1038/ncomms10980
  18. P. KASK, K. PALO, C. HINNAH, and T. POMMERENCKE, "Flat field correction for high-throughput imaging of fluorescent samples", Journal of Microscopy, pp. n/a-n/a, 2016. http://dx.doi.org/10.1111/jmi.12404
  19. D. Nelles, M. Fang, M. O’Connell, J. Xu, S. Markmiller, J. Doudna, and G. Yeo, "Programmable RNA Tracking in Live Cells with CRISPR/Cas9", Cell, vol. 165, pp. 488-496, 2016. http://dx.doi.org/10.1016/j.cell.2016.02.054
  20. K. Dean, P. Roudot, C. Reis, E. Welf, M. Mettlen, and R. Fiolka, "Diagonally Scanned Light-Sheet Microscopy for Fast Volumetric Imaging of Adherent Cells", Biophysical Journal, vol. 110, pp. 1456-1465, 2016. http://dx.doi.org/10.1016/j.bpj.2016.01.029

Triggering a device from multiple cameras

I’m finishing up work on our high speed widefield / CSU-W1 spinning disk confocal system (previously discussed here). This microscope is about as complicated a system as I ever want to assemble – it has three cameras, two fluorescence light sources, a photobleaching system, motorized XYZ stages, and a brightfield LED (see the figure).

CSU-W1

Sketch of microscope layout. The Zyla 5.5 camera is used for widefield imaging; the other two cameras are for spinning disk confocal imaging.

We’d like to be able to trigger most of these devices for fast acquisition. Here, I’m using triggering to mean that every time the camera takes an image, the triggered devices automatically advance to the next state, allowing acquisition to proceed at the full frame rate of the camera. This works for devices with negligible switching times such as lasers, LEDs, and our piezoelectric Z-stage. You can read more about triggered acquisition on the Micro-manager website and on Austin’s blog. In particular, we’d like to be able to trigger the piezo Z stage of any of the three cameras, the spinning disk lasers should trigger off either spinning disk camera, and so on. The full list of triggers is shown in the table below. Continue reading