Paper Roundup – March 2014

  • An automated computational approach for recognizing cell phenotypes from images [1]. Nicely, they make their source code open source.
  • A spinning TIRF system with polarization control, so that constant illumination polarization is maintained at the sample while the beam rotates around the back aperture [2]
  • A pair of papers on identifying and removing the background scattered light in TIRF imaging which gives rise to non-TIRF background fluorescence. [3][4], and a commentary on it [5]
  • Lavis and Raines have a new review out on the chemistry of fluorophores, focusing on the synthesis and application of common fluorophore scaffolds. [6]
  • Calculations of expected FRET signals from non-interacting proteins in the membrane. Important for separating ‘bystander’ FRET from FRET due to true protein interaction. [7][8]
  • A head-to-head competition of particle-tracking algorithms: article [9] and commentary [10].
  • A detailed protocol for generating and testing monomeric fluorescent proteins. [11]
  • A low-cost, homebuilt motorized microscope stage [12]
  • mCardinal – an intrinisically fluorescent protein that is the brightest protein to date for imaging in the far-red (~Cy5) region. [13]
  • ChemPhysChem has an issue focused on super-resolution imaging, including articles on localization imaging using stochastic binding of dyes to fluorogen activating proteins [14], a review on combining single molecule super resolution imaging and light sheet microscopy [15], and a review of structured illumination microscopy [16]
  • An image-based assay using five fluorescent channels and six stains to extract seven cellular components. The paper includes detailed protocols and the CellProfiler pipelines used to analyze the data. [17]
  • Using fluorescent protein – TALE fusions to mark satellite sequences in Drosophila [18]
  • A low cost microscope built from Thorlabs parts for neuroscience imaging [19]
  • High-throughput PALM imaging of bacteria [20]


  1. S. Basu, S. Kolouri, and G.K. Rohde, "Detecting and visualizing cell phenotype differences from microscopy images using transport-based morphometry", Proceedings of the National Academy of Sciences, vol. 111, pp. 3448-3453, 2014.
  2. D. Johnson, R. Toledo-Crow, A. Mattheyses, and S. Simon, "Polarization-Controlled TIRFM with Focal Drift and Spatial Field Intensity Correction", Biophysical Journal, vol. 106, pp. 1008-1019, 2014.
  3. M. Brunstein, M. Teremetz, K. Hérault, C. Tourain, and M. Oheim, "Eliminating Unwanted Far-Field Excitation in Objective-Type TIRF. Part I. Identifying Sources of Nonevanescent Excitation Light", Biophysical Journal, vol. 106, pp. 1020-1032, 2014.
  4. M. Brunstein, K. Hérault, and M. Oheim, "Eliminating Unwanted Far-Field Excitation in Objective-Type TIRF. Part II. Combined Evanescent-Wave Excitation and Supercritical-Angle Fluorescence Detection Improves Optical Sectioning", Biophysical Journal, vol. 106, pp. 1044-1056, 2014.
  5. C. Yip, "Star Light, Star Bright, First Molecule I See Tonight", Biophysical Journal, vol. 106, pp. 987-988, 2014.
  6. L.D. Lavis, and R.T. Raines, "Bright Building Blocks for Chemical Biology", ACS Chemical Biology, vol. 9, pp. 855-866, 2014.
  7. C. King, S. Sarabipour, P. Byrne, D. Leahy, and K. Hristova, "The FRET Signatures of Noninteracting Proteins in Membranes: Simulations and Experiments", Biophysical Journal, vol. 106, pp. 1309-1317, 2014.
  8. A. Clayton, and A. Chattopadhyay, "Taking Care of Bystander FRET in a Crowded Cell Membrane Environment", Biophysical Journal, vol. 106, pp. 1227-1228, 2014.
  9. N. Chenouard, I. Smal, F. de Chaumont, M. Maška, I.F. Sbalzarini, Y. Gong, J. Cardinale, C. Carthel, S. Coraluppi, M. Winter, A.R. Cohen, W.J. Godinez, K. Rohr, Y. Kalaidzidis, L. Liang, J. Duncan, H. Shen, Y. Xu, K.E.G. Magnusson, J. Jaldén, H.M. Blau, P. Paul-Gilloteaux, P. Roudot, C. Kervrann, F. Waharte, J. Tinevez, S.L. Shorte, J. Willemse, K. Celler, G.P. van Wezel, H. Dan, Y. Tsai, C.O. de Solórzano, J. Olivo-Marin, and E. Meijering, "Objective comparison of particle tracking methods", Nature Methods, vol. 11, pp. 281-289, 2014.
  10. M.J. Saxton, "A particle tracking meet", Nature Methods, vol. 11, pp. 247-248, 2014.
  11. H. Ai, M.A. Baird, Y. Shen, M.W. Davidson, and R.E. Campbell, "Engineering and characterizing monomeric fluorescent proteins for live-cell imaging applications", Nature Protocols, vol. 9, pp. 910-928, 2014.
  12. R.A.A. Campbell, R.W. Eifert, and G.C. Turner, "Openstage: A Low-Cost Motorized Microscope Stage with Sub-Micron Positioning Accuracy", PLoS ONE, vol. 9, pp. e88977, 2014.
  13. J. Chu, R.D. Haynes, S.Y. Corbel, P. Li, E. González-González, J.S. Burg, N.J. Ataie, A.J. Lam, P.J. Cranfill, M.A. Baird, M.W. Davidson, H. Ng, K.C. Garcia, C.H. Contag, K. Shen, H.M. Blau, and M.Z. Lin, "Non-invasive intravital imaging of cellular differentiation with a bright red-excitable fluorescent protein", Nature Methods, vol. 11, pp. 572-578, 2014.
  14. Q. Yan, S.L. Schwartz, S. Maji, F. Huang, C. Szent-Gyorgyi, D.S. Lidke, K.A. Lidke, and M.P. Bruchez, "Localization Microscopy using Noncovalent Fluorogen Activation by Genetically Encoded Fluorogen-Activating Proteins.", ChemPhysChem, vol. 15, pp. 687-695, 2013.
  15. Y.S. Hu, M. Zimmerley, Y. Li, R. Watters, and H. Cang, "Single-Molecule Super-Resolution Light-Sheet Microscopy", ChemPhysChem, vol. 15, pp. 577-586, 2014.
  16. J.R. Allen, S.T. Ross, and M.W. Davidson, "Structured Illumination Microscopy for Superresolution", ChemPhysChem, vol. 15, pp. 566-576, 2014.
  17. S.M. Gustafsdottir, V. Ljosa, K.L. Sokolnicki, J. Anthony Wilson, D. Walpita, M.M. Kemp, K. Petri Seiler, H.A. Carrel, T.R. Golub, S.L. Schreiber, P.A. Clemons, A.E. Carpenter, and A.F. Shamji, "Multiplex Cytological Profiling Assay to Measure Diverse Cellular States", PLoS ONE, vol. 8, pp. e80999, 2013.
  18. K. Yuan, A.W. Shermoen, and P.H. O’Farrell, "Illuminating DNA replication during Drosophila development using TALE-lights", Current Biology, vol. 24, pp. R144-R145, 2014.
  19. L. Beltran-Parrazal, C. Morgado-Valle, R.E. Serrano, J. Manzo, and J.L. Vergara, "Design and construction of a modular low-cost epifluorescence upright microscope for neuron visualized recording and fluorescence detection", Journal of Neuroscience Methods, vol. 225, pp. 57-64, 2014.
  20. S.J. Holden, T. Pengo, K.L. Meibom, C. Fernandez Fernandez, J. Collier, and S. Manley, "High throughput 3D super-resolution microscopy reveals Caulobacter crescentus in vivo Z-ring organization", Proceedings of the National Academy of Sciences, vol. 111, pp. 4566-4571, 2014.

Point Grey Cameras

I just came across this interesting PDF summarizing the performance of Point Grey cameras. Point Grey is a machine vision camera manufacturer, and I don’t normally think of using machine vision cameras for microscopy. However, some of their cameras have respectable performance – I wouldn’t want to use them for low light fluorescence work, but for brightfield imaging, and possible for routine fluorescence imaging, they would be fine. Most interestingly, they are very cheap – all are under $2k, and many are under $1k.

For example, the GS3-U3-23S6M-C is a 1920 x 1200 pixel CMOS camera, with 6.8 e read noise, and 76% QE at 525 nm. It has 5.86 μm pixels, and runs at up to 162 frames per second. Best, it’s only $1295.

Or, the FL3-GE-20S4M-C – it’s a 1624 x 1224 pixel CCD camera, wih 8.35 e- read noise, and 59% QE at 525 nm. It has 4.4 μm pixels, and runs at 15 fps.  It’s only $995.

These cameras are uncooled, so noise will be an issue at longer exposure times. Driver support for these in microscopy software is also an issue, but some of the firewire and GigE cameras may work in Micro-manager.  It would be nice to see support for these low cost cameras appearing in microscope software and would open up some interesting possibilities for cheap imaging.


Updates to Fluorescent Protein Visualization

I made some updates to the fluorescent protein visualization website that I’ve been meaning to do for some time: proteins that require an extrinsic chromophore (like the iRFPs) are now shown as squares, and the oligomerization state (dimer, etc.) is also shown on the graph.

Up next I intend to add a few new fluorescent proteins, including mCardinal, and then I want to allow the excitation and emission axes to be plotted in units of energy or wavenumber. As a reminder, the source code is available, and I encourage contributions.

Here’s what it currently looks like:


Matlab to Python conversion

As readers of this blog will have guessed, I’m a big fan of open solutions to problems, including open source software. Despite that, I’ve used Matlab for many years to develop data analysis code, but I’ve always felt a little bad about developing code that requires someone else to buy Matlab if they want to use it.

Today I was looking at Maria Kilfoil’s particle tracing micro-rheology code, and saw that they’ve reimplemented all their Matlab code in Python. The Python code looks auto-generated from the Matlab code, so I went looking, and indeed there are Matlab-to-Python converters. It doesn’t look like they will generate Python code that will automatically run and replace your Matlab code, but if like me, you’ve been looking to migrate from Matlab to Python, they may well come in handy.

CUDA Deconvolution

We’ve recently been testing the graphics card accelerated deconvolution software from the Butte lab [1]. It’s very impressive – we can deconvolve a 1024 x 1024 x 50 slice image stack in about 8 seconds.  The test data we were using has some spherical aberration, so the resulting deconvolved images aren’t that nice and I won’t post them, but I think that’s the fault of our data and not of the software.

The data set size you can deconvolve is limited by the amount of memory on the graphics card, so the 1024 x 1024 x 50 data set fit fully into the graphics card RAM, a 1536 x 1024 x 50 data set required using some CPU RAM in order to deconvolve, and I was unable to process a 2048 x 2048 x 50 data set.

We’ve tried two different graphics cards; here is the time required to deconvolve the 1024 x 1024 x 50 data set if you are interested:

Quadro K2000: 12.5 sec
GTX 750Ti: 8.1 sec

I hope to do some more comprehensive testing and comparison of different deconvolution tools, but this one is the fastest of all the ones I’ve seen.


  1. M.A. Bruce, and M.J. Butte, "Real-time GPU-based 3D Deconvolution", Optics Express, vol. 21, pp. 4766, 2013.

Fluorescence calibration slides

Since my previous post on flat-field correction, I’ve become aware of two commercial sources for slides with uniform fluorescent films deposited on them: Valley Scientific and Argolight. These are more expensive than the DIY solution but more convenient.  The Argolight slide also includes a number of very small features for measurement of resolution and distortion (this also makes it fairly expensive). I don’t have personal experience with either one, but they may come in handy.

I hope to have a report soon on the testing of all the flat-fielding dyes.  We need to do more testing, but we have promising initial results on using Acid Blue 9 to calibrate the Cy5 channel.

Paper Roundup – February 2014

  • A fluorescence method for measuring absolute membrane potential [1]
  • Protocols for single particle tracking using QDots, organic dyes, and gold nanoparticles [2]
  • Color calibration for microscopy, using the DataColor ChromaCal system [3]
  • A microscope that uses quantum entanglement to improve the signal to noise ratio for DIC microscopy. Not practical for biology yet, but an interesting proof-of-concept [4]
  • A nice paper describing an automated pipeline for quantifying cell number and type during growth of Arabidopsis hypocotyls [5]
  • A short article on motion compensation / drift correction in imaging applications [6]
  • A review on fixation protocols for a planarian for both histology and EM; while focused on a specific organism it is a nice discussion of how to optimize a fixation protocol [7]
  • A new genetically encoded calcium sensor based on aequorin and GFP [8]
  • An ImageJ based protocol for segmentation, colocalization, and shape analysis of subcellular objects [9]
  • A quantitative phase contrast microscopy method that allows measuring the phase shift introduced by a sample [10]. News and Views [11]
  • A review of the openSPIM and openSpinMicroscopy platforms [12]
  • A review of localization-based super-resolution with genetically encodable probes [13]
  • A review of single molecule localization precision and accuracy [14] and a review of localization algorithms [15]
  • Using transient binding of labeled DNA strands to generate super-resolution images via PAINT [16]


  1. J. Hou, V. Venkatachalam, and A. Cohen, "Temporal Dynamics of Microbial Rhodopsin Fluorescence Reports Absolute Membrane Voltage", Biophysical Journal, vol. 106, pp. 639-648, 2014.
  2. L. Cognet, B. Lounis, and D. Choquet, "Tracking Receptors Using Individual Fluorescent and Nonfluorescent Nanolabels", Cold Spring Harbor Protocols, vol. 2014, pp. pdb.prot080416, 2014.
  3. B. Foster, and J. Sedgewick, "Color Integrity: Is What You See What You Saw?", Microscopy Today, vol. 22, pp. 12-17, 2014.
  4. T. Ono, R. Okamoto, and S. Takeuchi, "An entanglement-enhanced microscope", Nature Communications, vol. 4, 2013.
  5. M. Sankar, K. Nieminen, L. Ragni, I. Xenarios, and C.S. Hardtke, "Automated quantitative histology reveals vascular morphodynamics during Arabidopsis hypocotyl secondary growth", eLife, vol. 3, 2014.
  6. B. LUCOTTE, and R. BALABAN, " Motion compensation for in vivo subcellular optical microscopy ", Journal of Microscopy, vol. 254, pp. 9-12, 2014.
  7. J.L. Brubacher, A.P. Vieira, and P.A. Newmark, "Preparation of the planarian Schmidtea mediterranea for high-resolution histology and transmission electron microscopy", Nature Protocols, vol. 9, pp. 661-673, 2014.
  8. A. Rodriguez-Garcia, J. Rojo-Ruiz, P. Navas-Navarro, F.J. Aulestia, S. Gallego-Sandin, J. Garcia-Sancho, and M.T. Alonso, "GAP, an aequorin-based fluorescent indicator for imaging Ca2+ in organelles", Proceedings of the National Academy of Sciences, vol. 111, pp. 2584-2589, 2014.
  9. A. Rizk, G. Paul, P. Incardona, M. Bugarski, M. Mansouri, A. Niemann, U. Ziegler, P. Berger, and I.F. Sbalzarini, "Segmentation and quantification of subcellular structures in fluorescence microscopy images using Squassh", Nature Protocols, vol. 9, pp. 586-596, 2014.
  10. T. Kim, R. Zhou, M. Mir, S.D. Babacan, P.S. Carney, L.L. Goddard, and G. Popescu, "White-light diffraction tomography of unlabelled live cells", Nature Photonics, vol. 8, pp. 256-263, 2014.
  11. A. Bouwens, and T. Lasser, "Imaging: White-light diffraction tomography", Nature Photonics, vol. 8, pp. 173-174, 2014.
  12. E. Gualda, N. Moreno, P. Tomancak, and G.G. Martins, "Going "open" with Mesoscopy: a new dimension on multi-view imaging", Protoplasma, vol. 251, pp. 363-372, 2014.
  13. P.N. Hedde, and G.U. Nienhaus, "Super-resolution localization microscopy with photoactivatable fluorescent marker proteins", Protoplasma, vol. 251, pp. 349-362, 2013.
  14. H. Deschout, F.C. Zanacchi, M. Mlodzianoski, A. Diaspro, J. Bewersdorf, S.T. Hess, and K. Braeckmans, "Precisely and accurately localizing single emitters in fluorescence microscopy", Nature Methods, vol. 11, pp. 253-266, 2014.
  15. A. Small, and S. Stahlheber, "Fluorophore localization algorithms for super-resolution microscopy", Nature Methods, vol. 11, pp. 267-279, 2014.
  16. R. Jungmann, M.S. Avendaño, J.B. Woehrstein, M. Dai, W.M. Shih, and P. Yin, "Multiplexed 3D cellular super-resolution imaging with DNA-PAINT and Exchange-PAINT", Nature Methods, vol. 11, pp. 313-318, 2014.