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]


  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 Nanotechnology, vol. 11, pp. 798-807, 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", Nature Biotechnology, vol. 34, pp. 774-780, 2016. http://dx.doi.org/10.1038/nbt.3563
  5. F. Huang, G. Sirinakis, E.S. Allgeyer, L.K. Schroeder, W.C. Duim, E.B. Kromann, T. Phan, F.E. Rivera-Molina, J.R. Myers, I. Irnov, M. Lessard, Y. Zhang, M.A. Handel, C. Jacobs-Wagner, C.P. Lusk, J.E. Rothman, D. Toomre, M.J. 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, vol. 27, pp. 3385-3394, 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", Nature Biotechnology, vol. 34, pp. 987-992, 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", Nature Biotechnology, 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