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Real-time volumetric microscopy of in vivo dynamics and large-scale samples with SCAPE 2.0

Nature Methods volume 16, pages 1054–1062 (2019)Cite this article

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Abstract

The limited per-pixel bandwidth of most microscopy methods requires compromises between field of view, sampling density and imaging speed. This limitation constrains studies involving complex motion or fast cellular signaling, and presents a major bottleneck for high-throughput structural imaging. Here, we combine high-speed intensified camera technology with a versatile, reconfigurable and dramatically improved Swept, Confocally Aligned Planar Excitation (SCAPE) microscope design that can achieve high-resolution volumetric imaging at over 300 volumes per second and over 1.2 GHz pixel rates. We demonstrate near-isotropic sampling in freely moving Caenorhabditis elegans, and analyze real-time blood flow and calcium dynamics in the beating zebrafish heart. The same system also permits high-throughput structural imaging of mounted, intact, cleared and expanded samples. SCAPE 2.0’s significantly lower photodamage compared to point-scanning techniques is also confirmed. Our results demonstrate that SCAPE 2.0 is a powerful, yet accessible imaging platform for myriad emerging high-speed dynamic and high-throughput volumetric microscopy applications.

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Fig. 1: SCAPE 2.0 system configuration and sample geometries.
Fig. 2: High-resolution and high-speed SCAPE 2.0 imaging of C. elegans worms.
Fig. 3: Real-time high-speed SCAPE 2.0 imaging of the beating zebrafish heart.
Fig. 4: High-throughput, large FOV structural imaging with SCAPE 2.0.

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Data availability

The datasets shown in the current study are available from the corresponding author on reasonable request.

Code availability

Analysis code for the datasets is available from the corresponding author on reasonable request.

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Acknowledgements

We would like to thank J. Herz from Lambert Instruments and P. Clemenceau from Axiom Optics for demonstrating and helping to support the HiCAM Fluo camera, and B. Witover from Morell Instruments for assistance with lens testing. C. elegans strains were provided by the Caenorhabditis Genetics Center (funded by NIH Office of Research Infrastructure Program no. P40OD010440). We sincerely thank D. Cosio, O. Celiker and E. Boyden (MIT), O. Hobert and E. Yemini (Columbia), F. Randi and A. Leifer (Princeton) and R. Dunn and S. Kato (UCSF) for their help and advice with respect to C. elegans imaging. We thank C. de Sena Tomás (Columbia) for assistance in providing zebrafish embryos for heart studies. We thank T. Zheng, S. Huang and O. Gerard (Columbia) for their advice and assistance with TrackPy and C. elegans cell tracking and L. Hammond (Columbia) for help with spinning disk confocal photobleaching experiments. We thank B. Young and N. Tian (University of Utah) for providing the retinal flat-mount sample and A. Narasimhan and P. Osten (Cold Spring Harbor) for providing the mCUBIC-cleared mouse brain sample. We thank F. Shen and D. Cai (University of Michigan) for preparing the expanded spinal cord slice and G. Valdez (Virginia Tech) for providing the spinal cord tissue. We thank M. Bouchard for training and foundational work on SCAPE, other members of the Hillman laboratory and our collaborators including W. Grueber, D. Schoppik and R. Bruno for support and assistance on many aspects of this work. Funding for this work was provided by National Institutes of Health BRAIN initiative grants (nos. 5U01NS09429, UF1NS108213 and U19NS104649 (PI - Costa) to E.M.C.H. and R01HL13143801A1 to K.L.T.), the National Science Foundation (grant no. NSF-GRFP DGE - 1644869 to K.B.P.), IGERT 0801530 (to V.V.), CAREER (grant no. CBET-0954796 to E.M.C.H.), the Simons Foundation Collaboration on the Global Brain (grant no. 542951 to E.M.C.H.), the Department of Defense (grant no. MURI W911NF-12-1-0594 to R.W.Y. and E.M.C.H.), the Kavli Institute for Brain Science and the Columbia-Coulter Translational Research Partnership (37) and Coulter Foundation Early Career program (to E.M.C.H.) and KAKENHI JP16H06545 by MEXT, Japan (to K.K.).

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Author notes

  1. These authors contributed equally: Kripa B. Patel, Wenze Li.

Authors and Affiliations

  1. Laboratory for Functional Optical Imaging, Department of Biomedical Engineering, Columbia University, New York, NY, USA

    Venkatakaushik Voleti, Kripa B. Patel, Wenze Li, Citlali Perez Campos, Srinidhi Bharadwaj, Hang Yu, Malte J. Casper, Richard Wenwei Yan, Wenxuan Liang & Elizabeth M. C. Hillman

  2. Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA

    Venkatakaushik Voleti, Kripa B. Patel, Wenze Li, Citlali Perez Campos, Srinidhi Bharadwaj, Hang Yu, Malte J. Casper, Richard Wenwei Yan, Wenxuan Liang & Elizabeth M. C. Hillman

  3. Department of Pediatrics, College of Physicians & Surgeons, Columbia University, New York, NY, USA

    Caitlin Ford & Kimara L. Targoff

  4. Graduate School of Natural Sciences, Nagoya City University, Nagoya, Japan

    Chentao Wen & Koutarou D. Kimura

  5. Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan

    Koutarou D. Kimura

  6. Department of Radiology, Columbia University Medical Center and New York-Presbyterian Hospital New York, New York, NY, USA

    Elizabeth M. C. Hillman

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  1. Venkatakaushik Voleti
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Contributions

V.V. and E.M.C.H. conceived and designed the system. V.V., W.Li, K.B.P., C.P.C. and E.M.C.H constructed the system. H.Y. and W.Liang assisted and provided information for extended designs. V.V., W.Li, K.B.P., C.P.C., S.B., R.W.Y. and E.M.C.H. performed the experiments. V.V., K.B.P., S.B., R.W.Y., M.J.C and E.M.C.H. analyzed data. C.F. and K.L.T. created the zebrafish lines and performed husbandry. C.W. and K.D.K. developed the deep-learning tracking algorithm used for cardiomyocyte nuclear tracking and tracked the cells. V.V. and E.M.C.H. wrote and prepared the manuscript. All authors reviewed, edited and consulted on the manuscript text.

Corresponding author

Correspondence to Elizabeth M. C. Hillman.

Ethics declarations

Competing interests

SCAPE intellectual property is licensed to Leica Microsystems for commercial development. V.V., W.Li., K.B.P., C.P.C. and E.M.C.H. have a potential financial conflict of interest relating to SCAPE microscopy.

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Peer review information Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Skew correction for SCAPE microscopy.

a. Galvo scanning an oblique light sheet results in oblique sections acquired within the sample b. Each frame captured by the camera corresponds to an oblique plane c. If successive planes are simply stacked into a rectangular matrix, single x-y planes will be undistorted, but viewing x-y planes from successive depths will reveal a shift along the x-direction, while image planes in the x-z direction will appear skewed diagonally. Raw data can be easily examined in this space. Previewing data (for example, to check for calcium responses or sample motion), image denoising and image stitching are all operations that can be performed in this raw “camera coordinate” system d. Correction of data into 3D object-space can be achieved by de-skewing. This transform is as simple as shifting x-y image planes at each depth along x to line up true vertical pixels with each other. We typically use an affine transform based on the known sheet angle and x-step size. Empty triangular regions at the top-left and lower-right edges (as shown in c.) are zero-padded. This operation results in a rhomboid field of view when viewed along x-z and increases the system’s apparent x-direction field of view as noted in Supplementary Table 2.

Supplementary Figure 2 Immobilized C. elegans worm cellular RFP signal fluctuations over 10 minutes of imaging.

RFP signals extracted from the tracked cells shown in Fig. 2a, plotted on the same percentage change scale as the GCaMP time courses shown in Fig. 2c. While GCaMP signals showed very large (>500%) changes in fluorescence, the only features in the RFP signals were mild photobleaching. To explore the factors affecting this RFP photobleaching, additional experiments were performed in comparison to spinning disk confocal as detailed in Supplementary Note SN2.

Supplementary Figure 3 A SCAPE 2.0, HiCAM volume acquired in a freely moving young adult C. elegans at 10 VPS.

The high pixel bandwidth of the HiCAM camera enabled dense sampling over a large 525x465x60 µm of view at 10 VPS. Image shows a single volume from a time-series on a freely moving C. elegans worm expressing panneuronal GFP (IM324: edls20[F25B3.3::GFP+rol-6(su1006)]). The colors represent depth into the sample and the data set has been square-rooted for visualization purposes to enhance contrast between the bright cell bodies and the dimmer dendritic process. See Supplementary Video 5 for full run, see Online Methods for experimental details, configuration AH. Scale bar 100 µm.

Supplementary Figure 4 Neuronal GCaMP signaling in freely moving C. elegans.

SCAPE 2.0 data was collected in a freely moving a lite-1(ce314) background (AML70: wtfls5[rab-3p::NLS::GCaMP6s +rab-3p::NLS:tagRFP]) worm constrained in a circular agarose arena at 20 VPS for 3 minutes (see Supplementary Video 6). The 3-views of the data shown in the Video (a) reveal clear dynamics of the green GCaMP signal. Without tracking individual cells, it is possible to validate the presence of these dynamics by using maximum intensity kymographs. Taking a 50 second window of this data (just prior to initiation of vigorous movement), a z and x-direction projection of the green channel ROI over the head as indicated in (a) confirms the presence of a single cell that is repeatedly pulsating with a characteristic time-course every 1–2 s (b, top) (see red arrowheads). This pulsating feature is not seen in the same projection of red (RFP) data (b – middle), and is enhanced in the ratio of green / red ratio kymographs (b-bottom). This cell is highlighted in 3D renderings in c(i), further confirming that the signal is not motion-related (although a slight motion of the head at a similar frequency suggests a role for this cell in relation to this motion). The kymographs reveal further patches of brightening corresponding to other cells (green, blue and gray arrowheads), which were then identified in 3D renderings in c(ii). Simple extraction of signals from the kymograph (by tracing the shaded regions lines shown in b(bottom) produced the plots shown in d(i-iii) demonstrating good signal to noise (raw signal with 5-point temporal smoothing). The image sequence in e) shows the x-y position of the worm during this entire time-period, and includes clear signals along the body and tail corresponding to movements.

Supplementary Figure 5 Supervised automated cell tracking over 2 minutes of dual-color SCAPE 2.0 data acquired on an unrestrained C. elegans at 26.4 VPS.

After pre-processing the RFP channel 4D images in ImageJ to enhance contrast, cells were automatically segmented using Imaris (Bitplane) (a). Cells in successive planes were then automatically tracked using an autoregression model. Resultant tracks were refined by hand, with 30 cells (22 in the head, 8 in the tail) chosen for tracking throughout the 2 minute run (b.) (see Supplementary Video 7 for complete tracking Video). Signals were then extracted from tracked positions in raw red (RFP) and green (GCaMP) datasets. Exclusion criteria were applied to extracted signals to further exclude cell-hopping and non-centered cells. (c.) shows resultant red and green signals, with gaps corresponding to rejected points. The green / red ratio was interpolated over missing points and smoothed over a 20 time-point window. Corrections were applied to correct RFP signals for photobleaching, and for differing spatially-dependent sensitivity of the red and green channels (see Online Methods). (d). and (e). show the corresponding positions of each cell at each time-point. This worm was relatively stationary during this run, with several sharp movements followed by more significant movement at the end. A relatively small number of cells exhibited characteristic transients in GCaMP. Although this demonstration shows that SCAPE’s spatial and temporal resolution are sufficient for commercial software to be used for tracking, tracking of cells in 4D in such large datasets remains a significant challenge requiring eventual development of sophisticated, customized algorithms to fully exploit SCAPE 2.0 datasets featuring vigorous movement.

Supplementary Figure 6 Further analysis of 321 VPS SCAPE 2.0 data acquired on the beating zebrafish heart.

a. 3D trajectories of all tracked cells. Color encodes cardiac phase determined using the auto-correlation function of the mean of the entire green channel over an ~11 sec acquisition. The circular color-map represents the periodicity of the heartbeat with markers along the colormap (b.i-vi) corresponding to the montage in (b). Cardiac phase encoding permits comparison of the behavior of red blood cells extracted over multiple heartbeats b. The montage (i-vi) contains top-down (XY) projections of the red channel containing RBC’s in Fig. 3b. An outline of the atrial wall is shown in blue while the ventricular wall is shown in red. Images are a time-encoded color projection showing 20 timepoints that span 0.06 sec prior to the timestamp indicated at the bottom-left of each image. Projections highlighting the direction and speed of cellular motion. Scale bar 50 µm.

Supplementary Figure 7 Individual red blood cell trajectories tracked from 321 VPS SCAPE 2.0 data acquired on the beating zebrafish heart.

(Detail of analysis shown in Fig. 3b). Maps show top-down (XY) projections of the trajectories of 16 red blood cells encoded as a function of normalized time tracked (left), cardiac phase as explained by Supplementary Fig. 6, and speed.

Supplementary Figure 8 GCaMP calcium dynamics of cells tracked in beating zebrafish heart at 100 VPS using SCAPE 2.0.

(Detail of cells shown in Fig. 3e). a. GCaMP, nDsRed and ratio-ed signals of the atrial cardiomyocyte shown in Fig. 3f.i-iv shown before and after the addition of a 30 mM BDM solution b. GCaMP, nDsRed and ratio-ed signals of the ventricular cardiomyocyte shown in Fig. 3f.v-iix shown before and after the addition of a 30 mM BDM solution.

Supplementary Figure 9 Comparison of high and low resolution retinal flat-mount imaging with SCAPE 2.0.

Left shows high-magnification, high-NA (26.6x, 0.35NA, configuration Az) data, while right shows lower-magnification, lower-NA (4.66x, 0.23NA, configuration Dz). The top-down (XY) view is shown as a depth-projection of the top ~35-88 µm of the sample, in which the colors encode the depth of a given feature within the sample. Retinal ganglion cells, as well as their axonal projections can be clearly visualized in both views. Scale bar – 100 µm in both.

Supplementary Figure 10 High magnification (66.6x) SCAPE 2.0 imaging of dendritic spines in cleared brain tissue.

a. XY MIP of entire 536 x 193 x 47 µm volume of mCUBIC cleared Thy1-GFP brain tissue. (see Supplementary Video 15 for a depth fly-through of this dataset) b. YZ MIP of a +/- 5 µm substack centered along the green line shown in (a) c. XZ MIP of a +/- 5 µm substack centered along the yellow line shown in (a). Scale bar is 50 µm and is shared for (a, b, c) d. Zoomed in XY MIP of the red square shown in (a) over the indicated depth ranges. (see Supplementary Video 16 for a depth fly-through of this zoomed in region) Scale bar 10 µm, configuration Gz. All data shown was square-rooted to improve contrast between the larger dendritic stalks and lower intensity finer processes.

Supplementary Figure 11 SCAPE 2.0 depth-stitched image of a ~2mm thick expanded mouse spinal cord section.

a. 3D volume rendering (Imaris) of a dual color depth-stitched 2 x 1 x 1.8 mm (X, Y, Z) volume of a 4x expanded spinal cord slice from a ChAT-Tomato/Thy1-YFP double transgenic mouse expressing fluorescence in motor neurons. For better visualization of 3D details, magenta and green channels were gamma corrected with values of 1.1 and 1.3, respectively. The volume is composed of 11 successive volumes stitched together over a 1.8 mm depth range, with a total image acquisition time of 44 seconds. See Online Methods for experimental and analysis details, configuration Dz. Volumes were imaged from below through a glass-bottom dish, and acquired by moving the sample gradually closer to the primary objective lens (here a 20x 1.0 NA water immersion objective lens with a 2mm working distance). Top-down (X-Y) MIPs of 100 slices centered around depths b(i) 480µm and b(ii) 1690µm. c. A side-facing (Y-Z) MIP of 1000 slices in the same volume. See Supplementary Video 17 for visualization of 3D rendering. d. A 3D volume rendering (Imaris) of a 0.5 x 1 x 1.73 mm (X,Y,Z) region in the sample at slightly higher (~11.4x) magnification (configuration Ez) showing Thy1-YFP labeling with a total image acquisition time of 45 seconds. A gamma correction of 1.1 was applied for better 3D visualization. Top-down (X-Y) MIPs of 100 slices centered at depths e(i) 440 µm and e(ii) 1445 µm with insets showing zoomed in regions where spines, small processes and intracellular structures are clearly visible. White arrows indicate spines. Scale bars in b-c are 200 μm and in e are 100 µm. Note that the distance acquired along X is easily increased, and limited only by the range of the motorized stage. Additional 1mm wide volume strips along the Y direction can also be acquired and stitched together.

Supplementary information

Supplementary Information

Supplementary Figs. 1–11, and thumbnails and captions for Supplementary Videos 1–17.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–3

Supplementary Notes

Supplementary Notes 1–3 and Supplementary References.

Supplementary Video 1

SCAPE 2.0 imaging of GCaMP activity in the head of an immobilized C. elegans worm.

Supplementary Video 2

C. elegans photobleaching comparison between SCAPE 2.0 and spinning disk confocal microscopy.

Supplementary Video 3

SCAPE 2.0 imaging of GCaMP activity in the head of a moving, but immobilized C. elegans worm.

Supplementary Video 4

SCAPE 2.0 imaging of a freely moving C. elegans GCaMP/RFP worm at 25.7 VPS.

Supplementary Video 5

Large field of view SCAPE 2.0 imaging of a freely moving C. elegans GFP worm.

Supplementary Video 6

SCAPE 2.0 imaging of a freely moving C. elegans GCaMP/RFP worm at 20 VPS for 3 min.

Supplementary Video 7

Automated cell tracking during a 2-min C. Elegans SCAPE 2.0 run at 26.4 VPS.

Supplementary Video 8

Real-time SCAPE 2.0 imaging of a beating embryonic zebrafish heart at 100 VPS.

Supplementary Video 9

Real-time SCAPE 2.0 imaging of a beating embryonic zebrafish heart at 321 VPS.

Supplementary Video 10

Real-time SCAPE 2.0 imaging of calcium dynamics in the beating embryonic zebrafish heart at 100 VPS.

Supplementary Video 11

Real-time SCAPE 2.0 imaging of calcium dynamics in the paralyzed embryonic zebrafish heart at 100 VPS.

Supplementary Video 12

High magnification SCAPE 2.0 imaging of uncleared flat-mount mouse retina.

Supplementary Video 13

High-throughput SCAPE 2.0 imaging of an entire uncleared flat-mount mouse retina.

Supplementary Video 14

High-throughput SCAPE 2.0 imaging of an mCUBIC cleared mouse brain.

Supplementary Video 15

High-resolution SCAPE 2.0 imaging of an mCUBIC cleared mouse brain.

Supplementary Video 16

Detail of a high-resolution SCAPE 2.0 image of an mCUBIC cleared mouse brain.

Supplementary Video 17

Extended-depth SCAPE 2.0 imaging of a 4× expanded mouse spinal cord section.

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Voleti, V., Patel, K.B., Li, W. et al. Real-time volumetric microscopy of in vivo dynamics and large-scale samples with SCAPE 2.0. Nat Methods 16, 1054–1062 (2019). https://doi.org/10.1038/s41592-019-0579-4

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