Research ArticleLIVE CELL IMAGING

Optical computed tomography for spatially isotropic four-dimensional imaging of live single cells

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Science Advances  06 Dec 2017:
Vol. 3, no. 12, e1602580
DOI: 10.1126/sciadv.1602580
  • Fig. 1 Principle and experimental approach of optical CT.

    (A) Typically, 300 to 400 2D projection images at varying angular orientations of the cell are collected during a full 360° rotation perpendicular to the optical axis of the imaging system. The 3D volumetric image of the cell is computationally reconstructed using either classical [filtered back projection (FBP)] or algebraic (iterative) algorithms. (B) Setup of instrumentation for 2D projection acquisition. CCD, charge-coupled device. A high–numerical aperture (NA) objective lens is scanned along the optical (z) axis with an open camera shutter during a full scan in one direction resulting in a single image per scan (C). This mode of acquisition in the optical spectral range produces images equivalent to 2D projections in x-ray CT. (D) Microfabricated device for rotating live cells using high-frequency electric fields rotating in 3D space. The device comprises a fluidic channel for cell delivery and an octupole of microelectrodes for creating 3D spatially distributed electric field.

  • Fig. 2 Characterization of rotation stability and spatial resolution.

    (A) Projection images obtained at four orientations of the cell during a full rotation in the electrocage device. Scale bars, 5 μm. (B) Lateral shift of the cell along the x and y axes during one full rotation. Small relative planar average relative deviations were observed in both directions (0.057 and 0.084 of the total cell size in the x and y directions, respectively). (C) Rotation rate stability. The plot shows average coefficient of variation (CV) of rotation speed calculated using the data of five cells over five to seven rotations each. The box charts in (B) and (C) show the following statistical values: open square, mean; solid line, median; upper and lower box lines, 75th and 25th percentiles, respectively; upper and lower whiskers, 95th and 5th percentiles, respectively; x, maximal and minimal values. rel. u., relative units. (D) Spatial resolution characterization using 200-nm fluorescent beads. Beads were internalized by the cells before the experiment. Images of one such bead are shown in the xy and xz directions. (E and F) Analysis of axial (E) and lateral (F) spatial resolution. FWHM, full width at half maximum. (G) A comparison with confocal imaging reveals the advantage of the LCCT approach of distortion-free, orientation-independent 3D imaging due to isotropic spatial resolution. MIP renderings of the nucleus of the same reconstructed 3D image of a K562 cell using either a conventional confocal Z-stack or PP (LCCT) data are shown. The arrow indicates the orientation of the optical axis for confocal imaging. Scale bar, 5 μm.

  • Fig. 3 Volumetric 3D reconstruction of the nucleus and mitochondria of a live human myelogenous leukemia cell (K562 cell line).

    (A) MIP images at three orthogonal orientations (grayscale) and volume rendering (color, lower right) of the nuclear DNA stained with Hoechst 33342. Lighter regions in the MIP images correspond to higher density of the DNA. A fold-like feature can be seen in the volume rendering (arrow). (B) The mitochondria of the same cell as in (A) stained with the mitochondrial marker MitoTracker Red. A volume rendering (lower right) indicates some of the mitochondria colocalized with the fold in the nucleus in (A). (C) Overlay of the volume renderings of the nuclear and mitochondrial data shows some of the mitochondria aligned with the fold in the nucleus in (A). (D) A cutaway presentation of the overlay reveals mitochondria located deep in the nuclear fold (movie S5). (E and F) Partial segmentation of the bright (70 to 100% range of the intensity histogram) nuclear and mitochondrial structures, respectively, using an intensity threshold above 70% of the camera detection dynamic range highlights the brightest features of the two organelles. The nucleus and mitochondrial data sets were acquired simultaneously. Both sets of data were deconvolved using a Gaussian PSF. Scale bars, 5 μm. (G) Reconstructed volumetric LCCT image of the mitochondria of the same cell presented as a series of slices through the cell at different depths (left). The contrast was enhanced in the intensity images to make less intense structures visible. The panel on the right shows segmentation of the mitochondria using the Niblack local threshold approach. The segmentation results were smoothed to enhance visibility. Scale bar, 5 μm. (H) 3D surface rendering of the mitochondrial network of the same cell with fluorescence intensity and segmented mitochondria shown in gray and orange, respectively (movies S11 and S12). (I and J) Mitochondrial fluorescence intensity (I; surface rendering) and the corresponding segmentation result (J) of a selected small region of interest (ROI) of the same cell. (K) 3D MIP image of the nucleoli of a live K562 cell. The nucleoli appear as bright clusters and were segmented (red structures) using an intensity threshold (movie S15). The diffuse fluorescence is a result of the nucleolar stain binding to RNA. The MIP image was deconvoluted and filtered using a Gaussian filter for increased clarity.

  • Fig. 4 Biological validation study using the mitochondrial fission inhibitor 8-bromo-cAMP in mouse macrophage J774A.1 cells.

    As expected, larger mitochondrial structures (bright spots, arrows) were observed as a result of mitochondrial fission inhibition. (A) Comparison of an untreated J774A.1 cell (left) and one treated with 8-bromo-cAMP (right). MIP (top row) and isosurface rendering (bottom row) show a marked shift toward larger structures. (B) Quantitative analysis of the mitochondrial structure volumes corroborates the qualitative findings of (A) showing the appearance of structures with larger volume in the treated cell. (C) A comparison of the average values of the volume distribution demonstrates statistically significant (P = 0.001, Kolmogorov-Smirnov test) increase in the volume of the structures after treatment. (D) Combined mitochondrial structure volume distribution of 30 control and 30 treated cells. A trend toward increasing volume of the structures can be seen. (E) The average values of the volume distribution show less pronounced but statistically significant (P = 0.008, Kolmogorov-Smirnov test) increase in the mitochondrial volume in treated versus control cells.

  • Fig. 5 Mitochondrial and nuclear DNA dynamic studies.

    Time-lapse data over 4 to 5 min of one J774A.1 cell were acquired, reconstructed, and segmented, followed by feature volume calculation. (A) Alterations in the nuclear DNA structure spatial distribution. The graph shows changes in the structure volume distribution over time, revealing most dynamics in the structures with larger volumes (2 to 12 fl, dashed rectangle), whereas the smaller features show little change. (B) Mitochondrial structure volume dynamics showed qualitatively similar behavior as the nucleus. Most of the changes can be seen in the structures with larger volumes (3 to 20 fl; dashed rectangle), whereas the smaller structures show relatively little alteration. The stacked graphs in each panel represent spatial distribution at a particular time point, as indicated to the right of each graph.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/3/12/e1602580/DC1

    fig. S1. Microfabrication steps of the electrocage chip for live-cell rotation.

    fig. S2. Determination of the optimal voltage range of the electric field used to rotate live cells.

    fig. S3. Cell rotation speed as a function of electric field frequency applied to the microelectrodes of the electrocage.

    fig. S4. A live K562 cell incubated with 200-nm fluorescent polystyrene beads.

    fig. S5. Photobleaching kinetics of the Hoechst 33342 dye in a live K562 cell.

    fig. S6. Confocal micrographs showing enlarged mitochondrial structures in J774A.1 cells after treatment with 8-bromo-cAMP, a mitochondrial fission inhibitor.

    fig. S7. A device used to simulate electric field conditions in the electrocage during cell rotation on bulk cell samples for assessing potential stress levels introduced by the electric field.

    fig. S8. Testing potential cell stress caused by the exposure to high-frequency electric fields used in the electrocage for cell rotation using the device shown in fig. S7.

    fig. S9. Assessing potential changes in cellular morphology as a result of exposure to high-frequency electric fields via imaging.

    fig. S10. Imaging system setup and implementation.

    fig. S11. A diagram of the overall LCCT system design and control.

    fig. S12. Piezo scanning, control, and synchronization time diagrams (green, objective scanning voltage; red, triggering pulse; and yellow, acquisition exposure control waveform).

    fig. S13. Comparison of the reconstruction of the various registration methods.

    fig. S14. Computational workflow used in the modified SIRT and blind deconvolution SIRT methods of volumetric image reconstruction.

    fig. S15. Performance demonstration of four different volumetric reconstruction approaches used in the study.

    fig. S16. The principle of sinogram generation.

    fig. S17. Examples of detector and slice sinograms generated from simulated projection images of two beads moving in circular, distortion-free trajectories.

    fig. S18. Description of the GeoFit algorithm computational pipeline, which estimates and corrects in-plane projection perturbations—Lateral shift and in-plane orientation changes of the rotation axis.

    fig. S19. Correction of in-plane perturbations of rotation using the GeoFit algorithm.

    fig. S20. Slice of a volumetric image reconstructed using raw/uncorrected data (left) and after correction using the GeoFit algorithm (right).

    fig. S21. Pipeline description of the FixPP algorithm to correct out-of-plane perturbations.

    fig. S22. Reconstruction of simulated Shepp-Logan data with 10° axis elevation using the FixPP algorithm.

    movie S1. Raw PP images of a live human myelogenous leukemia (K562 cell line) cell rotating in the electrocage.

    movie S2. Raw PP images of a live K562 cell with internalized 200-nm fluorescent beads rotating in the electrocage.

    movie S3. Reconstructed 3D volumetric image (surface rendering) of the K562 cell shown in movie S2.

    movie S4. Comparison between the confocal and LCCT imaging modalities.

    movie S5. Surface rendering of a reconstructed 3D volumetric image of a live K562 cell with stained nucleus (blue-green) and mitochondria (red-yellow).

    movie S6. Nuclear feature segmentation of a reconstructed 3D volumetric image of a live K562 cell.

    movie S7. Mitochondrial feature segmentation of a reconstructed 3D volumetric image of the same cell as shown in movie S4.

    movie S8. Overlay of nuclear (green) and mitochondrial (red) feature segmentation results and their corresponding MIP renderings shown in movies S4 and S5.

    movie S9. MIP and separate slices of the reconstructed 3D volumetric images of mitochondria in the same cell shown in movies S5 to S8.

    movie S10. Mitochondrial fluorescence intensity and segmentation results using the Niblack local threshold approach.

    movie S11. 3D view of mitochondrial segmentation overlaid with fluorescence intensity (both surface renderings).

    movie S12. A representative example of mitochondrial segmentation in 3D illustrated as a small ROI from movie S11.

    movie S13. 3D rendering and Z-stack of the mitochondrial network in a fixed K562 cell imaged using confocal microscopy.

    movie S14. Mitochondrial fluorescence intensity and segmentation results of the cell shown in movie S13.

    movie S15. Fluorescence intensity and segmentation results of the nucleoli in a live K562 cell.

    movie S16. Maximum intensity renderings of reconstructed 3D images of an untreated (left) and treated (right) J774A.1 cell line (mouse macrophage cell) with the mitochondrial fission inhibitor 8-bromo-cAMP.

    movie S17. Surface rendering of a reconstructed 3D image of a live J774A.1 cell.

  • Supplementary Materials

    This PDF file includes:

    • fig. S1. Microfabrication steps of the electrocage chip for live-cell rotation.
    • fig. S2. Determination of the optimal voltage range of the electric field used to rotate live cells.
    • fig. S3. Cell rotation speed as a function of electric field frequency applied to the microelectrodes of the electrocage.
    • fig. S4. A live K562 cell incubated with 200-nm fluorescent polystyrene beads.
    • fig. S5. Photobleaching kinetics of the Hoechst 33342 dye in a live K562 cell.
    • fig. S6. Confocal micrographs showing enlarged mitochondrial structures in J774A.1 cells after treatment with 8-bromo-cAMP, a mitochondrial fission inhibitor.
    • fig. S7. A device used to simulate electric field conditions in the electrocage during cell rotation on bulk cell samples for assessing potential stress levels introduced by the electric field.
    • fig. S8. Testing potential cell stress caused by the exposure to high-frequency electric fields used in the electrocage for cell rotation using the device shown in
    • fig. S9. Assessing potential changes in cellular morphology as a result of exposure to high-frequency electric fields via imaging.
    • fig. S10. Imaging system setup and implementation.
    • fig. S11. A diagram of the overall LCCT system design and control.
    • fig. S12. Piezo scanning, control, and synchronization time diagrams (green, objective scanning voltage; red, triggering pulse; and yellow, acquisition exposure control waveform).
    • fig. S13. Comparison of the reconstruction of the various registration methods.
    • fig. S14. Computational workflow used in the modified SIRT and blind deconvolution SIRT methods of volumetric image reconstruction.
    • fig. S15. Performance demonstration of four different volumetric reconstruction approaches used in the study.
    • fig. S16. The principle of sinogram generation.
    • fig. S17. Examples of detector and slice sinograms generated from simulated
      projection images of two beads moving in circular, distortion-free trajectories.
    • fig. S18. Description of the GeoFit algorithm computational pipeline, which estimates and corrects in-plane projection perturbations—Lateral shift and in-plane orientation changes of the rotation axis.
    • fig. S19. Correction of in-plane perturbations of rotation using the GeoFit algorithm.
    • fig. S20. Slice of a volumetric image reconstructed using raw/uncorrected data (left) and after correction using the GeoFit algorithm (right).
    • fig. S21. Pipeline description of the FixPP algorithm to correct out-of-plane perturbations.
    • fig. S22. Reconstruction of simulated Shepp-Logan data with 10° axis elevation using the FixPP algorithm.
    • Legends for movies S1 to S17

    Download PDF

    Other Supplementary Material for this manuscript includes the following:

    • movie S1 (.avi format). Raw PP images of a live human myelogenous leukemia (K562 cell line) cell rotating in the electrocage.
    • movie S2 (.avi format). Raw PP images of a live K562 cell with internalized 200-nm fluorescent beads rotating in the electrocage.
    • movie S3 (.mpg format). Reconstructed 3D volumetric image (surface rendering) of the K562 cell shown in movie S2.
    • movie S4 (.mpg format). Comparison between the confocal and LCCT imaging modalities.
    • movie S5 (.mpg format). Surface rendering of a reconstructed 3D volumetric image of a live K562 cell with stained nucleus (blue-green) and mitochondria (red-yellow).
    • movie S6 (.mpg format). Nuclear feature segmentation of a reconstructed 3D volumetric image of a live K562 cell.
    • movie S7 (.mpg format). Mitochondrial feature segmentation of a reconstructed 3D volumetric image of the same cell as shown in movie S4.
    • movie S8 (.mpg format). Overlay of nuclear (green) and mitochondrial (red) feature segmentation results and their corresponding MIP renderings shown in movies S4 and S5.
    • movie S9 (.mpg format). MIP and separate slices of the reconstructed 3D volumetric images of mitochondria in the same cell shown in movies S5 to S8.
    • movie S10 (.mpg format). Mitochondrial fluorescence intensity and segmentation results using the Niblack local threshold approach.
    • movie S11 (.mpg format). 3D view of mitochondrial segmentation overlaid with fluorescence intensity (both surface renderings).
    • movie S12 (.mpg format). A representative example of mitochondrial segmentation in 3D illustrated as a small ROI from movie S11.
    • movie S13 (.mpg format). 3D rendering and Z-stack of the mitochondrial network in a fixed K562 cell imaged using confocal microscopy.
    • movie S14 (.mpg format). Mitochondrial fluorescence intensity and segmentation results of the cell shown in movie S13.
    • movie S15 (.mpg format). Fluorescence intensity and segmentation results of the nucleoli in a live K562 cell.
    • movie S16 (.mpg format). Maximum intensity renderings of reconstructed 3D images of an untreated (left) and treated (right) J774A.1 cell line (mouse macrophage cell) with the mitochondrial fission inhibitor 8-bromo-cAMP.
    • movie S17 (.mpg format). Surface rendering of a reconstructed 3D image of a live J774A.1 cell.

    Files in this Data Supplement:

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