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, pp. n/a-n/a, 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

Converting an air objective into a dipping objective

If you’ve ever used an air objective to image into a liquid sample, you may have encountered the problem that as you image deeper, your image quality degrades. This is due to the refractive index mismatch causing aberration of the objective focus in the sample.  An easy way to think about this is by thinking about the optical path length between the objective and the focal plane.  As you image deeper into the sample, you’re replacing air (with a refractive index of 1) with liquid (with a higher refractive index).  This causes the optical path length to increase, and this gets worse the deeper in the sample you image (as you’re replacing more air with liquid).

SphericalAberration

Spherical aberration caused by the refractive index mismatch between the sample and the medium the objective was designed for.

This primarily introduces spherical aberration, although other aberrations are induced too. This is a particular problem with low magnification light sheet microscopes of the ‘Ultramicroscope’ type [1], where you use a low magnification air lens to image many millimeters into a cleared tissue sample. What’s particularly problematic is that the spherical aberration gets worse the deeper you image, requiring some adjustable correction to eliminate it. Continue reading

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

Paper Roundup: February 2016

  • Modeling the performance of light sheet microscopes in highly scattering tissues [1]
  • A review  of clearing techniques [2]
  • Combining multifocal microscopy and a cylindrical lens for 3D single-molecule localization over very large Z ranges [3]
  • Endogenous fluorescence tagging using CRISPR/Cas9 [4]
  • Improved versions of Clover and mRuby2 [5]
  • An improved multifocal microscope [6]
  • In situ hybridization in CLARITY-cleared tissues [7]
  • An inverted light-sheet microscope for imaging mouse embryo development [8]
  • Two papers describing imaging neuronal activity in free moving C. elegans [9] [10]
  • The 2016 single molecule localization microscopy challenge (see http://bigwww.epfl.ch/smlm/challenge2016/) [11]

References

  1. A.K. Glaser, Y. Wang, and J.T. Liu, "Assessing the imaging performance of light sheet microscopies in highly scattering tissues", Biomedical Optics Express, vol. 7, pp. 454, 2016. http://dx.doi.org/10.1364/BOE.7.000454
  2. E. Susaki, and H. Ueda, "Whole-body and Whole-Organ Clearing and Imaging Techniques with Single-Cell Resolution: Toward Organism-Level Systems Biology in Mammals", Cell Chemical Biology, vol. 23, pp. 137-157, 2016. http://dx.doi.org/10.1016/j.chembiol.2015.11.009
  3. B. Hajj, M. El Beheiry, and M. Dahan, "PSF engineering in multifocus microscopy for increased depth volumetric imaging", Biomedical Optics Express, vol. 7, pp. 726, 2016. http://dx.doi.org/10.1364/BOE.7.000726
  4. J. Stewart-Ornstein, and G. Lahav, "Dynamics of CDKN1A in Single Cells Defined by an Endogenous Fluorescent Tagging Toolkit", Cell Reports, vol. 14, pp. 1800-1811, 2016. http://dx.doi.org/10.1016/j.celrep.2016.01.045
  5. B.T. Bajar, E.S. Wang, A.J. Lam, B.B. Kim, C.L. Jacobs, E.S. Howe, M.W. Davidson, M.Z. Lin, and J. Chu, "Improving brightness and photostability of green and red fluorescent proteins for live cell imaging and FRET reporting", Sci. Rep., vol. 6, pp. 20889, 2016. http://dx.doi.org/10.1038/srep20889
  6. S. Abrahamsson, R. Ilic, J. Wisniewski, B. Mehl, L. Yu, L. Chen, M. Davanco, L. Oudjedi, J. Fiche, B. Hajj, X. Jin, J. Pulupa, C. Cho, M. Mir, M. El Beheiry, X. Darzacq, M. Nollmann, M. Dahan, C. Wu, T. Lionnet, J.A. Liddle, and C.I. Bargmann, "Multifocus microscopy with precise color multi-phase diffractive optics applied in functional neuronal imaging", Biomedical Optics Express, vol. 7, pp. 855, 2016. http://dx.doi.org/10.1364/BOE.7.000855
  7. E. Sylwestrak, P. Rajasethupathy, M. Wright, A. Jaffe, and K. Deisseroth, "Multiplexed Intact-Tissue Transcriptional Analysis at Cellular Resolution", Cell, vol. 164, pp. 792-804, 2016. http://dx.doi.org/10.1016/j.cell.2016.01.038
  8. P. Strnad, S. Gunther, J. Reichmann, U. Krzic, B. Balazs, G. de Medeiros, N. Norlin, T. Hiiragi, L. Hufnagel, and J. Ellenberg, "Inverted light-sheet microscope for imaging mouse pre-implantation development", Nature Methods, vol. 13, pp. 139-142, 2015. http://dx.doi.org/10.1038/nmeth.3690
  9. V. Venkatachalam, N. Ji, X. Wang, C. Clark, J.K. Mitchell, M. Klein, C.J. Tabone, J. Florman, H. Ji, J. Greenwood, A.D. Chisholm, J. Srinivasan, M. Alkema, M. Zhen, and A.D.T. Samuel, " Pan-neuronal imaging in roaming Caenorhabditis elegans ", Proceedings of the National Academy of Sciences, vol. 113, pp. E1082-E1088, 2015. http://dx.doi.org/10.1073/pnas.1507109113
  10. J.P. Nguyen, F.B. Shipley, A.N. Linder, G.S. Plummer, M. Liu, S.U. Setru, J.W. Shaevitz, and A.M. Leifer, " Whole-brain calcium imaging with cellular resolution in freely behaving Caenorhabditis elegans ", Proceedings of the National Academy of Sciences, vol. 113, pp. E1074-E1081, 2015. http://dx.doi.org/10.1073/pnas.1507110112
  11. S. Holden, and D. Sage, "Imaging: Super-resolution fight club", Nature Photonics, vol. 10, pp. 152-153, 2016. http://dx.doi.org/10.1038/nphoton.2016.22

Interlock distribution board

I assembled the interlock distribution box I mentioned previously. It was pretty straightforward to solder up three relays on a piece of perfboard. There is a single BNC input for the interlock loop, and BNC and phono jack outputs for our laser interlocks. Power is drawn from a 5V wall transformer. Pretty straightforward, and it works when installed on the microscope. The only surprising thing I learned is that the CSU-W1 interlock doesn’t close until the shutter on the CSU-W1 is open, so that shutter needs to be open for any lasers to operate.

DistributionBox