High Speed PCIe SSDs

Those of you who’ve been reading this blog since it’s inception will remember that I used to post a lot about solid state drives, because we spent a lot of time trying to handle the 1 GB/sec bandwidth of sCMOS cameras back in 2013. We standardized on RAID 0 arrays of four Samsung Pro SSDs, and I stopped thinking about it, because that was good enough for our purposes.

Since then, however, quite a bit has changed. You can now get an 512 GB SSD card that can write at 1.5 GB/sec and read at 3 GB/sec (the Samsung Pro 950) for $350. Newer Samsung products promise slightly faster read / write speeds at disk sizes up to 1 TB. These use the M.2 interface, designed for SSDs, but PCIe to M.2 adapters are readily available if your motherboard doesn’t have an M.2 slot. To get full speeds you’ll need a motherboard with a PCIe 3.0  x4 slot available.

Another thing that’s changed is that sCMOS cameras have made it really easy to capture large data sets. In the last year people really seen to have taken advantage of this and we’re seeing a lot of people acquiring 100GB+ data sets, and often up to 1 TB. A big consequence of this is that data processing is increasingly becoming I/O bound. A mechanical hard drive tops out at around 120 MB/sec sequential read/write speed. At those speeds, just reading a 20 GB file takes around 3 min. We’ve seen this become a major issue where exporting a 500 GB data set from Nikon ND2 to TIFF takes hours.

If you’re doing any processing of large data sets, it’s very advantageous to have a fast drive like this to speed up I/O. There’s still a challenge in getting data on and off of these drives, since gigabit ethernet tops out at around 100 MB/sec, and most USB3 drives max out around 250 MB/sec (as an aside, the Samsung T3 looks pretty promising, with 450 MB/sec transfer rates). Finally, most people (at least here) still want their data on a mechanical drive for long term storage. But at least once you have data on the fast drive the I/O bottleneck gets better by about 10 – 20-fold.

All of this would be well worth thinking about if you’re building the compute environment for a microscopy core from scratch. I imagine you’d want fast local storage for data processing, 10 Gbit networking to move data around, and some kind of slow archiving to long term storage. You’d also want to educate people about data handling so that they think about these issues when designing their experiments.

Confocal imaging of a tick

I recently came back from the east coast with an unwanted guest attached to me: a tick, probably a lone star tick, Amblyomma americanum. After removing it, I decided to have some fun with it – I dehydrated it in methanol, cleared it in methyl salicylate, and then imaged it on our spinning disk confocal. The movie below is stitched from four images, and is about 1.8 mm on a side and 1.2 mm thick. Total image size is ~2800 x 2800 x 306. The fluorescence is endogenous autofluorescence excited at 488 and 561 nm. This is probably a nymphal tick, and it looks like the mouthparts are missing.

Paper Roundup – July 2016

  • A review of Raman imaging [1]
  • DNA-PAINT imaging of DNA origami with nanometer resolution [2]
  • A self-assembling icosahedron that can be used as a fluorescence standard in microscopy [3]
  • A split horseradish peroxidase for labelling of protein interactions [4]
  • A 4Pi single molecule switching microscope, with isotropic 20 nm resolution imaging [5]
  • A comparison of fluorescent protein performance in C. elegans [6]
  • Expansion microscopy with RNA FISH [7] and with conventional fluorescent proteins and antibodies [8]
  • A custom system for calcium imaging of freely walking flies [9]
  • Cell tracking software tools [10]
  • An in-incubator Fourier ptychography system for rapid imaging of 6-well plate [11]

References

  1. M. Cicerone, "Molecular imaging with CARS micro-spectroscopy", Current Opinion in Chemical Biology, vol. 33, pp. 179-185, 2016. http://dx.doi.org/10.1016/j.cbpa.2016.05.010
  2. M. Dai, R. Jungmann, and P. Yin, "Optical imaging of individual biomolecules in densely packed clusters", Nature Nanotech, 2016. http://dx.doi.org/10.1038/nnano.2016.95
  3. Y. Hsia, J.B. Bale, S. Gonen, D. Shi, W. Sheffler, K.K. Fong, U. Nattermann, C. Xu, P. Huang, R. Ravichandran, S. Yi, T.N. Davis, T. Gonen, N.P. King, and D. Baker, "Design of a hyperstable 60-subunit protein icosahedron", Nature, vol. 535, pp. 136-139, 2016. http://dx.doi.org/10.1038/nature18010
  4. J.D. Martell, M. Yamagata, T.J. Deerinck, S. Phan, C.G. Kwa, M.H. Ellisman, J.R. Sanes, and A.Y. Ting, "A split horseradish peroxidase for the detection of intercellular protein–protein interactions and sensitive visualization of synapses", Nat Biotechnol, vol. 34, pp. 774-780, 2016. http://dx.doi.org/10.1038/nbt.3563
  5. F. Huang, G. Sirinakis, E. Allgeyer, L. Schroeder, W. Duim, E. Kromann, T. Phan, F. Rivera-Molina, J. Myers, I. Irnov, M. Lessard, Y. Zhang, M. Handel, C. Jacobs-Wagner, C. Lusk, J. Rothman, D. Toomre, M. Booth, and J. Bewersdorf, "Ultra-High Resolution 3D Imaging of Whole Cells", Cell, vol. 166, pp. 1028-1040, 2016. http://dx.doi.org/10.1016/j.cell.2016.06.016
  6. J.K. Heppert, D.J. Dickinson, A.M. Pani, C.D. Higgins, A. Steward, J. Ahringer, J.R. Kuhn, and B. Goldstein, "Comparative assessment of fluorescent proteins for in vivo imaging in an animal model system", Molecular Biology of the Cell, 2016. http://dx.doi.org/10.1091/mbc.E16-01-0063
  7. F. Chen, A.T. Wassie, A.J. Cote, A. Sinha, S. Alon, S. Asano, E.R. Daugharthy, J. Chang, A. Marblestone, G.M. Church, A. Raj, and E.S. Boyden, "Nanoscale imaging of RNA with expansion microscopy", Nature Methods, vol. 13, pp. 679-684, 2016. http://dx.doi.org/10.1038/nMeth.3899
  8. P.W. Tillberg, F. Chen, K.D. Piatkevich, Y. Zhao, C.(. Yu, B.P. English, L. Gao, A. Martorell, H. Suk, F. Yoshida, E.M. DeGennaro, D.H. Roossien, G. Gong, U. Seneviratne, S.R. Tannenbaum, R. Desimone, D. Cai, and E.S. Boyden, "Protein-retention expansion microscopy of cells and tissues labeled using standard fluorescent proteins and antibodies", Nat Biotechnol, 2016. http://dx.doi.org/10.1038/nbt.3625
  9. D. Grover, T. Katsuki, and R.J. Greenspan, "Flyception: imaging brain activity in freely walking fruit flies", Nature Methods, vol. 13, pp. 569-572, 2016. http://dx.doi.org/10.1038/nmeth.3866
  10. O. Hilsenbeck, M. Schwarzfischer, S. Skylaki, B. Schauberger, P.S. Hoppe, D. Loeffler, K.D. Kokkaliaris, S. Hastreiter, E. Skylaki, A. Filipczyk, M. Strasser, F. Buggenthin, J.S. Feigelman, J. Krumsiek, A.J.J. van den Berg, M. Endele, M. Etzrodt, C. Marr, F.J. Theis, and T. Schroeder, "Software tools for single-cell tracking and quantification of cellular and molecular properties", Nat Biotechnol, vol. 34, pp. 703-706, 2016. http://dx.doi.org/10.1038/nbt.3626
  11. J. Kim, B.M. Henley, C.H. Kim, H.A. Lester, and C. Yang, "Incubator embedded cell culture imaging system (EmSight) based on Fourier ptychographic microscopy", Biomedical Optics Express, vol. 7, pp. 3097, 2016. http://dx.doi.org/10.1364/BOE.7.003097

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.

Continue reading

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, vol. 34, pp. 760-767, 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., vol. 11, pp. 2101-2104, 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., vol. 45, pp. 4651-4667, 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. J. Seo, M. Choe, and S. Kim, "Clearing and Labeling Techniques for Large-Scale Biological Tissues", Molecules and Cells, vol. 39, pp. 439-446, 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, vol. 89, pp. 633-643, 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