QSIM: quantitative structured illumination microscopy image processing in ImageJ
© Gao; licensee BioMed Central. 2015
Received: 16 October 2014
Accepted: 7 January 2015
Published: 14 January 2015
Structured illumination microscopy has been extensively used in biological imaging due to its low cost and easy implementation. However, the lack of quantitative imaging capability limits its application in absolute irradiance measurements.
We have developed a quantitative structured illumination microscopy image processing algorithm (QSIM) as a plugin for the widely used ImageJ software. QSIM can work with the raw images acquired by a traditional structured illumination microscope and can quantitatively measure photon numbers, with noise estimates for both wide-field images and sectioned images.
Results and conclusion
We demonstrated the quantitative image processing capability of QSIM by imaging a mouse kidney section in 3D. The results show that QSIM can transform structured illumination microscopy from qualitative to quantitative, which is essential for demanding fluorescence imaging applications.
KeywordsStructured illumination microscopy Quantitative imaging 3D imaging ImageJ
Structured illumination microscopy (SIM), a three-dimensional (3D) optical imaging technique, has been widely used in biomedical research because of its relatively low cost and easy implementation [1–4]. Currently, SIM is commercially available as an add-on module (e.g., Zeiss Apotome) for most wide-field optical microscopes, enabling acquisition of high resolution sectioned images, much as a scanning confocal microscope does [5, 6].
where is the mean of the intensity grey level distribution and σ is its standard deviation . Equation 2 is valid because in a shot-noise-limited imaging system the image noise σ obeys a Poisson distribution. For SIM, however, the calculation of N d cannot follow the same approach because the out-of-focus light is removed after being detected. In fact, the image noise σ in SIM is attributed to three sources – photon noise from the sectioned depth layer, photon noise from the out-of-focus depth layers, and noise caused by nonlinear demodulation (Eq. 1).
The lack of quantification limits SIM’s application in absolute irradiance measurements [8, 9]. To overcome this problem, we recently proposed a quantitative SIM image reconstruction algorithm . By calibrating the camera’s gain and estimating the modulation contrast in the detected images, the number of detected photons from a single sectioned depth layer can be derived. This gives SIM the same quantitative capability to measure photons as a confocal microscope. To make our algorithm easily available to the biological research community, here we present QSIM, a free open-source plugin for the widely used ImageJ software (http://rsb.info.nih.gov/ij/). QSIM integrates system calibration and image processing in one software package. By performing two simple calibration experiments prior to SIM imaging (Implementation), QSIM is able to calculate photons and noise maps for both reconstructed sectioned and wide-field images.
Calibration experiment 1: camera gain measurement
Step 1: Set up Koehler illumination on the microscope sample stage.
Step 2: Choose the camera gain settings and digitization level that will be used in SIM imaging experiments.
Step 3: Image a uniformly illuminated field with a microscope objective. Increase the illumination intensity step by step from zero to the level where the camera is almost saturated. The number of illumination steps should be larger than five. At each illumination level, capture two empty field images with the same integration time.
Calibration experiment 2: illumination modulation contrast measurement
Step 1: Prepare an evenly-distributed fluorescent microsphere sample. Choose fluorescence microspheres with mean diameters less than the targeted sectioning thickness. Uniformly suspend the fluorescence microspheres by vortex mixing and sonicating the suspension. Deposit the suspension onto a microscope slide and seal it by a coverslip.
Step 2: Image the microsphere slide with SIM, and save the raw images (unprocessed with grid patterns). A separate measurement is required for each objective and grid combination because the modulation contrast is specific to the microscope’s objective and illumination grid’s frequency.
Results and discussion
To demonstrate the advantages of QSIM in processing biological SIM images, we imaged a mouse kidney section (~10 μm thick, Life technologies) on a Zeiss Axio Imager Z1 microscope equipped with an Apotome module and an AxioCam MRM monochromatic camera (1388 × 1040 pixels). The tissue sample was stained with a fluorescent dye, Alexa Fluor 488 (495 nm excitation maximum, 519 nm emission maximum), on convoluted tubules and illuminated by a HBO100 light source. The fluorescence was collected by a Zeiss Plan-Apochromatic 20× objective with a numerical aperture of 0.8. The acquisition of raw SIM images was accomplished by Zeiss Apotome’s companion operating software, AxioVision.
In summary, using QSIM software in conjunction with a traditional SIM hardware provides a complete solution for the measuring photon count from a sectioned depth layer. From the same SIM raw images, QSIM can provide additional quantitative information about the sample, not available from current commercially-available SIM image processing software. QSIM is expected to facilitate the conversion of SIM from being a qualitative imaging technique into a confocal-like, quantitative imaging modality, and to make it accessible to a broad biomedical research community.
Availability and requirements
Project name: Quantitative structured illumination microscopy.
Project home page: http://code.google.com/p/quantitative-sim/.
Operating systems: Windows 7 or 8, Mac OS X, and Linux.
Programming language: Java.
Compatible ImageJ versions: 1.49m or newer.
Licence: QSIM is distributed under the terms of the GNU General Public License 2.0.
The QSIM java code is freely available at http://code.google.com/p/quantitative-sim/. Users need to compile this java code in ImageJ to generate a Java class file as instructed in the companion Additional file 1.
The author thanks Dr. Nathan Hagen and Prof. Tomasz Tkaczyk for constructive discussions, and appreciates Prof. James Ballard’s close reading of the manuscript. The author also would like to thank Dr. Matthew Kyrish for providing the SIM images for software testing.
- Neil MAA, Juskaitis R, Wilson T: Method of obtaining optical sectioning by using structured light in a conventional microscope. Opt Lett 1997, 22:1905. 10.1364/OL.22.001905View ArticleGoogle Scholar
- Karadaglic D, Wilson T: Image formation in structured illumination wide-field fluorescence microscopy. Micron 2008, 39:808–18. 10.1016/j.micron.2008.01.017View ArticleGoogle Scholar
- Le Couteur DG, Cogger VC, McNerney GP, Nyunt T, DeLeve LD, et al.: Three-dimensional structured illumination microscopy of liver sinusoidal endothelial cell fenestrations. J Struct Biol 2010, 171:382–8. 10.1016/j.jsb.2010.06.001View ArticleGoogle Scholar
- Cremer C, Best G, Amberger R, Baddeley D, Ach T, et al.: Structured illumination microscopy of autofluorescent aggregations in human tissue. Micron 2011, 42:330–5. 10.1016/j.micron.2010.06.016View ArticleGoogle Scholar
- Smalley MJ, Signoret N, Robertson D, Tilley A, Hann A, et al.: Dishevelled (Dvl-2) activates canonical Wnt signalling in the absence of cytoplasmic puncta. J Cell Sci 2005, 118:5279–89. 10.1242/jcs.02647View ArticleGoogle Scholar
- Ohlstein B, Spradling A: The adult Drosophila posterior midgut is maintained by pluripotent stem cells. Nature 2006, 439:470–4. 10.1038/nature04333View ArticleGoogle Scholar
- Sheppard CJR, Gan X, Gu M, Roy M: Signal-to-noise in confocal microscopes. In Handbook of biological confocal microscopy. Edited by: Pawley JB. New York, NY: Springer; 2006.Google Scholar
- Murray JM, Appleton PL, Swedlow JR, Waters JC: Evaluating performance in three-dimensional fluorescence microscopy. J Microsc-Oxford 2007, 228:390–405. 10.1111/j.1365-2818.2007.01861.xView ArticleMathSciNetGoogle Scholar
- Waters JC: Accuracy and precision in quantitative fluorescence microscopy. J Cell Biol 2009, 185:1135–48. 10.1083/jcb.200903097View ArticleGoogle Scholar
- Hagen N, Gao L, Tkaczyk TS: Quantitative sectioning and noise analysis for structured illumination microscopy. Opt Express 2012, 20:403–13. 10.1364/OE.20.000403View ArticleGoogle Scholar
- Pietraszewska-Bogiel A, Gadella T: FRET microscopy: from principle to routine technology in cell biology. J Microsc 2010, 241:111–8.View ArticleGoogle Scholar
- Haustein E, Schwille P: Fluorescence correlation spectroscopy: novel variations of an established technique. Annu Rev Biophys Biomol Struct 2007, 36:151–69. 10.1146/annurev.biophys.36.040306.132612View ArticleGoogle Scholar
- Diamond KR, Farrell TJ, Patterson MS: Measurement of fluorophore concentrations and fluorescence quantum yield in tissue-simulating phantoms using three diffusion models of steady-state spatially resolved fluorescence. Phys Med Biol 2003, 48:4135–49. 10.1088/0031-9155/48/24/011View ArticleGoogle Scholar
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