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]


  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.
  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.
  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.
  4. J. Ortega Arroyo, D. Cole, and P. Kukura, "Interferometric scattering microscopy and its combination with single-molecule fluorescence imaging", Nature Protocols, vol. 11, pp. 617-633, 2016.
  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.
  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 on a Chip, vol. 16, pp. 1617-1624, 2016.
  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.
  8. V. Marx, "Optimizing probes to image cleared tissue", Nature Methods, vol. 13, pp. 205-209, 2016.
  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.
  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, 2016.
  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.
  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.
  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.
  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, 2016.
  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, 2016.
  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.
  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, 2016.
  18. P. KASK, K. PALO, C. HINNAH, and T. POMMERENCKE, "Flat field correction for high‐throughput imaging of fluorescent samples", Journal of Microscopy, vol. 263, pp. 328-340, 2016.
  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.
  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.

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).


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).


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


  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.