Visualizing diverse anatomical and functional traits that span many spatial scales with high spatio-temporal resolution provides insights into the fundamentals of living organisms. Light-field microscopy (LFM) has recently emerged as a scanning-free, scalable method that allows for high-speed, volumetric functional brain imaging. Given those promising applications at the tissue level, at its other extreme, this highly-scalable approach holds great potential for observing structures and dynamics in single-cell specimens. However, the challenge remains for current LFM to achieve a subcellular level, near-diffraction-limited 3D spatial resolution. Here, we report high-resolution LFM (HR-LFM) for live-cell imaging with a resolution of 300-700 nm in all three dimensions, an imaging depth of several micrometers, and a volume acquisition time of milliseconds. We demonstrate the technique by imaging various cellular dynamics and structures and tracking single particles. The method may advance LFM as a particularly useful tool for understanding biological systems at multiple spatio-temporal levels.
© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Light-field microscopy (LFM) simultaneously captures both the 2D spatial and 2D angular information of the incident light, allowing computational reconstruction of the full 3D volume of a specimen from a single camera frame [1–4]. Conventionally, fluorescent imaging techniques acquire 3D spatial information in a sequential or scanning fashion [5–12], inevitably compromising temporal resolution and increasing photodamage for live imaging. The 4D imaging scheme of LFM effectively liberates volume acquisition time (limited primarily by the camera’s frame rate) from the spatial parameters (e.g. the field of view (FOV) and spatial resolution), thus making LFM a promising tool for high-speed, volumetric imaging of living biological systems with low photodamage across many spatial levels.
Towards the tissue level, the latest LFM techniques have demonstrated promising results for functional brain imaging with cellular level spatial resolution of several micrometers across a depth of tens to hundreds of micrometers, and at a fast volume acquisition time on the order of 10 milliseconds [4,13–15]. At its other extreme, this highly-scalable approach holds great potential for visualizing delicate structures and dynamics within single-cell specimens. However, the challenge remains for current LFM to achieve subcellular level, near-diffraction-limited 3D spatial resolution.
For the existing LFM techniques, in practice, a microlens array (MLA) is placed at the native image plane (NIP) of a wide-field microscope, and the optical signal is recorded in an aliased manner across the microlenses at the back focal plane of the MLA [1,2]. The development of wave-optics models allows high-resolution reconstruction of densely aliased high-spatial frequencies through point-spread function (PSF) deconvolution [3,4]. However, near the NIP, the sampling pattern of the spatial information becomes redundant, resulting in prohibitive artifacts [3,4]. This limitation causes non-uniform resolution across depth, hindering applications that require visualization of fine 3D information spreading over a large axial range in a sample. Current LFM techniques circumvent the artifacts mainly by imaging on one side of the NIP [3,4,14] or using a coded wavefront . These approaches are feasible for functional brain imaging (e.g. using 20 × or 40 × objective lenses) despite compromised depth of focus (DOF) or spatial resolution. However, they may be prohibitive for high-resolution microscopy, because they would either sacrifice the already tightened DOF (typically <1 µm for a 100 × , 1.45NA oil objective lens) or worsen the 3D spatial resolution away from the diffraction limit. Alternatively, the MLA may instead form an imaging relationship between the NIP and the camera sensor, as used in the focused plenoptic camera . While the spatial information at the NIP can thereby be recovered using this scheme, the sampling geometry of the angular information becomes less aliased and redundant, inherently impairing the refocusing (or volumetric imaging) capability for practical applications. Hence, new optical design and computational framework are highly desired for high-resolution light-field imaging in single-cell specimens.
Here, we develop a HR-LFM method for live-cell imaging by simultaneous, dense sampling of both spatial and angular information. HR-LFM achieves a 3D spatial resolution of 300-700 nm, an imaging depth of several micrometers, and a volume acquisition time down to milliseconds. We demonstrated the technique by imaging various cellular systems and tracking single particles.
2.1 Experimental setup
We constructed HR-LFM on an epifluorescence microscope (Nikon Eclipse Ti-U) using a 100 × , NA = 1.45, oil immersion objective lens (Nikon CFI-PLAN 100 × , 1.45 NA) (Fig. 1(a) and Appendix 1). The sample stage was controlled by a nano-positioning system (Prior). The samples were illuminated with 488-nm, 561-nm and 647-nm lasers (MPB Communications). The corresponding emitted fluorescence was collected using dichroic mirrors (T495lpxr, T560lpxr and T660lpxr, Chroma, respectively) and emission filters (ET525/50, Chroma; FF02-617/73, Semrock; ET700/75, Chroma, respectively). A NA-matched MLA (S125-F30, RPC Photonics) was aligned in a five-axis kinematic mount (K5X1, Thorlabs). The light field was imaged using a 1:1 relay lens (Nikon AF-S VR Micro-Nikkor 105mm f/2.8G IF-ED) and recorded on a scientific complementary metal-oxide-semiconductor (sCMOS) camera (ORCA-Flash4.0, Hamamatsu).
In the setup, the MLA forms a defocused imaging relationship as 1/a + 1/b > 1/fml, where a and b denote the distances to the NIP and the camera sensor, respectively, and fml is the focal length of the MLA. This design contrasts with conventional LFM  (a = 0 and b = fml) and the focused plenoptic camera  (1/a + 1/b = 1/fml) (Appendix 2), and benefits HR-LFM in two main ways. First, the distance a can displace the artifact region away from the DOF by a/M2 in the object space, where M is the magnification of the objective lens. Second, the distance b facilitates optimum dense sampling of both the spatial and the angular information of optical signals on the camera sensor. In this work, the values of a and b (a = 25 mm and b = 4 mm) were carefully determined using numerical simulations to obtain the optimized spatio-angular distribution for reconstruction of point-emitters distributed in the 3D space (Appendix 3, Eqs. (1)-(5)). Using this scheme, the optical signals imaged by the objective lens can be densely aliased compared to conventional LFM, capturing different perspectives of the sample onto the camera sensor (Fig. 1(b)-1(d)). The aliasing of the recorded data can thus effectively suppress the reconstruction artifacts at the NIP, substantially improving the DOF and spatial resolution.
2.2 Reconstruction algorithm
To reconstruct the volumetric data, the Fresnel propagation of light by the distances of a and b, i.e. a defocused PSF, was established using the scalar diffraction theory . Specifically, the final intensity image at the camera plane is described by (Appendix 3, Eq. (5)), where represents the real coordinates on the camera plane, are the real coordinates of a point source in a volume in the object domain, whose combined intensities are distributed according to . represents the complex-valued PSF, which considers, sequentially, the light propagation through the high-NA objective lens, Fresnel propagation of light by the distance of a, modulation induced by the MLA, and another Fresnel propagation to the camera plane by the distance of b (Appendix 3, Eqs. (1)-(4)). In practice, considering the discrete model, is represented by the measurement matrix , which elements hkj describe the projection of light arriving at the pixel on the camera plane from the kth voxel in the object space. The volumetric information was then reconstructed employing the wave-optics model [3,4] based on an inverse-problem deconvolution framework  (Appendix 3 and Code 1 ). To verify the algorithm, we recorded light-field images of 100-nm fluorescent beads placed on the native object plane (NOP) of the objective lens, where the aggregated beads can be properly reconstructed on the NIP with near-diffraction-limited widths of ~400 nm without artifacts using HR-LFM, compared to conventional LFM (Fig. 1(e)).
2.3 System characterization
Next, to characterize HR-LFM, we imaged 100 nm fluorescent beads and measured the reconstructed PSFs of the system at varying depths (Fig. 2(a), 2(b), Appendix 4 and 5). The numbers of iterations taken for the reconstruction at varying depths were determined based on the distribution of the optical signals across the MLA and the corresponding signal-to-noise ratio (SNR). These numbers were consistently used in this work for other samples (Appendix 4). The full-width at half-maximum (FWHM) values of these reconstructed PSFs at each depth exhibited a near-diffraction-limited 300-700 nm resolution in all three dimensions over a >3 μm range, ~4 × larger than the corresponding DOF of wide-field microscopy. Detailed features of the system are shown in Appendix 4. First, when the beads were scanned near the axial position z = a/M2 = 2.5 µm (a = 25 mm and M = 100; i.e. forming images near the MLA), the optical signals were mainly restrained into a single microlens as in conventional LFM. Thus, HR-LFM performed as a NA-matched single-lens imaging system, exhibiting spatial resolution of ~300 nm and 600 nm in the lateral and axial dimensions, respectively, consistent with the PSF measured in wide-field microscopy using the same 100 × , 1.45NA objective lens. Second, when the beads were scanned toward the focal plane z = 0 (i.e. forming images between the MLA and the NIP), the optical signals became broadly distributed on the MLA, which led to higher angular sensitivity, thereby improving the axial resolution (Appendix 5). Notably, the lateral resolution was steadily maintained using the wave-optics model, and thus the system achieved a near-isotropic 3D resolution within this axial range. Lastly, beyond the NIP (z < 0), the optical signals became further broadened on the MLA due to the diffraction of light. While the high axial sensitivity was maintained, the lateral resolution became worse due to the degraded signal detection from an increased number of microlenses. In general, the implementation of wave-optics based reconstruction effectively overcame the tradeoff and maintained a high resolution in both the axial and lateral dimensions. Furthermore, the use of the MLA permits sensitive angular detection of the wavefront (i.e. spatial frequencies), improving axial-resolving capability within a substantial range of the DOF. These features make HR-LFM distinct from a conventional diffraction-limited imaging system, providing a new type of optical engineering scheme for high-resolution microscopy.
3.1 Imaging caliber structures and fixed biological samples
Further measurements, in good agreement with the reconstructed PSF values, have been obtained using known caliber structures and fixed biological samples. We first imaged surface-stained, 1-µm fluorescent microspheres (F14791, ThermoFisher), which sub-micrometer hollow structure was resolved using HR-LFM (Fig. 2(c)-2(e)). The corresponding lateral and axial cross-sectional profiles exhibited FWHMs of the stained surface of 300-500 nm in all three dimensions (Fig. 2(f)), consistent with the measured values in Fig. 2(b) and Appendix 4.
Next, we imaged immuno-labeled mitochondria in HeLa cells. For sample preparation, HeLa cells were obtained from the American Type Culture Collection (ATCC), maintained in 1 × Minimum Essential Medium (MEM) (Corning CellGro) with 10% fetal bovine serum (FBS) (Atlanta Biologicals) and 50 μg/ml gentamycin (Amresco), and incubated at 37°C with 5% CO2. The cells were plated on a 35 mm2 MatTek glass-bottom dishes (MatTek), incubated at 37°C for 16 hours, and fixed with 4% (vol/vol) formaldehyde (15735, Electron Microscopy Sciences) prepared in phosphate buffered saline (PBS) for 10 mins at 37°C. The cells were then blocked and permeabilized with blocking and antibody dilution buffer (1% (vol/vol) bovine serum albumin (BSA) (Santa Cruz Biotechnologies) and 0.25% (vol/vol) Triton X-100 prepared in PBS) for 1 hour at room temperature. The cells were then incubated with the mitochondrial marker primary antibody mouse anti-Tom20 (Santa Cruz Biotechnologies F10, SC-17764), at 1 µg/ml in blocking and antibody dilution buffer for 2 hours while gently shaking at room temperature. The cells were then washed 3 times with PBS for 5 mins each. Secondary antibody (AlexaFluor 647-conjugated AffiniPure Goat Anti-Mouse IgG, 1 mg/ml, Jackson ImmunoResearch) was diluted 1:1000 in 1% BSA in PBS and incubated with gentle shaking for 1 hour at room temperature. The cells were washed 3 times with PBS for 5 mins. The cells were placed in imaging buffer (20 mM HEPES pH 7.4, 135 mM NaCl, 5 mM KCl, 1 mM MgCl2, 1.8 mM CaCl2, 5.6 mM glucose) before imaging.
As seen, HR-LFM recorded the full 4D light-field information (Fig. 3(a)), allowing us to synthesize the focal stacks of the entire volume of the specimen (Fig. 3(b)). Remarkably, HR-LFM captured mitochondria that were out-of-focus and poorly detected by wide-field microscopy due to its limited DOF (Fig. 3(c) and Appendix 6). The mitochondrial structures were well-resolved by HR-LFM across a ~3-µm axial range in all three dimensions without the need for any sample or focal-plane scanning. Furthermore, as a comparison, conventional LFM can only image cells on one side of the NIP to avoid the prohibitive artifacts, resulting in substantially degraded volumetric imaging capability, especially in the axial dimension such as the DOF and axial resolution (Fig. 3(d)-3(f)).
Furthermore, we imaged the Golgi complex in HeLa cells, immuno-labeled for the Golgi marker GM130 with DyLight 549, where the nearby Golgi structures as close as ~400 nm in all three dimensions were resolved by HR-LFM (Appendix 7).
3.2 Imaging mitochondria in living Drp1−/− mouse embryo fibroblasts (MEFs)
To demonstrate live-cell imaging, we first recorded mitochondrial dynamics in living Drp1−/− MEFs , labeled with MitoTracker, a mitochondrial fluorescent tracking dye (Fig. 4). The dynamin-related GTPase (Drp1) mediates mitochondrial division and distribution, playing a critical role in mammalian development ; cells lacking Drp1 have low rates of mitochondrial fission and thus have long mitochondria.
Drp1−/− MEFs were generously provided by Hiromi Sesaki (Johns Hopkins University). For sample preparation, Drp1−/− MEFs were cultured in DMEM + 10% FBS and plated into tissue culture dishes containing sterile coverslips and cultured for 1-2 days until 50-80% confluent. The cells were incubated in medium containing a 1:5000 dilution of MitoTracker Deep Red (M22426, ThermoFisher) for 30 mins at 37°C, washed 3 times with PBS, and then placed back into growth medium lacking MitoTracker.
Using the full sCMOS camera chip (2048 × 2048 pixels), we captured the entire FOV (>100 × 100 µm laterally and >3-5 µm axially) enclosing several cells at a volume acquisition time of 0.1 s (Fig. 4(a)). The high spatial resolution allows us to visualize fine mitochondrial structures and distributions as close as 500 nm in all three dimensions (Fig. 4(b)-4(g), Visualization 1 and Visualization 2). Without the need for scanning, HR-LFM permits low light exposure (0.05-0.5 W cm−2) for time-lapse acquisition over more than thousands of time-points (up to minutes) without obvious photodamage or photobleaching . We observed extension and retraction of mitochondrial tubules, where mitochondrial movements of up to 0.53 μm s−1 were recorded, as well as occasional mitochondrial division (Visualization 1 and Visualization 3).
3.3 Imaging Golgi-derived membrane vesicles in living COS-7 cells
We next imaged Golgi-derived membrane vesicles in living COS-7 cells, labeled with a trans-Golgi marker, mEmerald-Golgi-7 (Fig. 5 and Appendix 8). These vesicles occasionally undergo rapid movement between the Golgi complex and other intracellular organelles, posing a challenge for capturing their volumetric dynamics using scanning-based imaging methods.
For sample preparation, COS-7 cells were obtained from ATCC and grown in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% FBS and 100 U/ml penicillin-streptomycin. Cells were seeded in a 35 mm2 MatTek glass-bottom dishes (MatTek), transfected with the trans-Golgi marker mEmerald-Golgi-7 (Addgene 54108), which carries the 1-82 aa residues of β-1,4-galactosyltransferase-I (β-1,4-GalT-I) at the N-terminus, using Lipofectamine 2000 (ThermoFisher), and imaged 24 hours post-transfection.
Using the full camera chip, we captured their rapid 3D motions at a volume acquisition time of 0.01 s over hundreds to thousands of camera frames (Fig. 5(a)). Notably, the high spatio-temporal resolution allowed us to observe the rapid interactions of individual vesicles separated as close as 300-500 nm and moving up to 2.06 μm s−1 in all three dimensions (Fig. 5(b)-5(g) and Visualization 4). In addition, the volume acquisition speed can be further accelerated by imaging only a region of interest on the camera chip. We imaged the same samples at a volume acquisition time faster than 5 ms without noticeable degradation in image quality or resolution (Appendix 9).
3.4 Tracking hydrophobically-modified glycol chitosan (HGC) nanoparticles
Finally, we tracked HGC nanoparticles suspended in water, labeled with a fluorescent dye Cyanine3 (cy3; Fig. 6). HGC nanoparticles were prepared through the covalent attachment of 5β-cholanic acid to glycol chitosan using a previously published protocol . Specifically, 150 mg of 5β-cholanic acid dissolved in 60 ml of methanol was activated with 1.5 mol equivalents of N-hydroxysulfosuccinimide (NHS, ThermoFisher) and 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDC, ThermoFisher). The activated 5β-cholanic solution was slowly added to the glycol chitosan solution (500 mg/60 ml, in HPLC water) and stirred for 24 hours at room temperature to ensure complete reaction. The resulting mixture was dialyzed using 10 kDa molecular weight cut-off dialysis cassettes for 24 hours against a water-methanol mixture (1:4 vol/vol), for another 24 hours against water, and lyophilized. For labeling, cy3 dye (Lumiprobe, 1 mg) was dissolved in dimethyl sulfoxide (DMSO, 200 µl) and added dropwise to HGC (100 mg/40 ml, in DMSO) under gentle stirring at room temperature for 6 hours in darkness. The mixture was dialyzed using 3.5 kDa molecular weight cut-off dialysis cassettes for 2 days against HPLC water, and lyophilized. The cy3-HGC nanoparticles were suspended in HPLC water at a concentration of 1 mg/ml and treated with a probe-type sonicator (S-450D Sonifier, Branson Ultrasonics) at 90 W for 6 mins. One drop (5 µl) of cy3-HGC suspension was added to a microscope slide and coverslipped prior to imaging.
At a volume acquisition time of 1 ms, single nanoparticles moving up to 92.81 μm s−1 have been recorded using HR-LFM (Fig. 6(a)-6(c) and Visualization 5). The 3D positions and trajectories of the nanoparticles were determined by localizing the reconstructed particles using Gaussian fitting with nanometer-level precision in all three dimensions  (Fig. 6(d)). The measurements of the nanoparticles are consistent with the reconstructed PSF values using the fluorescent beads (Fig. 2(b) and Appendix 4), showing no compromise in spatial precision as the acquisition is accelerated. The system thus has demonstrated the capability of recording dynamic particle behavior in a volumetric context, which has been a spatio-temporal-limiting step for live-cell imaging.
In summary, we have developed HR-LFM for volumetric live-cell imaging with a spatial resolution of 300-700 nm in all three dimensions, an imaging depth of several micrometers, and a volume acquisition time on the order of milliseconds. Defocusing the MLA effectively mitigates the prohibitive reconstruction artifacts in the previous LFM design, providing four- to five-fold larger DOF than conventional high-resolution wide-field microscopy. In addition, due to its greater axial sensitivity and discrimination, we demonstrated the remarkable resolution improvement especially in the axial dimension (i.e. near-isotropic) within a substantial axial range. These findings may lead to new imaging physics and applications for MLA-facilitied microscopy. Advancing current LFM to the subcellular level, the system enables high-speed, volumetric visualization of dynamics and structures in single-cell specimens with low photodamage. Combining the molecular specificity of fluorescent labeling, great scalability, engineered MLAs and deconvolution algorithms, LFM is becoming a particularly promising tool for imaging diverse anatomical and functional traits, spanning molecular, cellular and tissue levels.
Appendix 1: Details on system alignment
The sCMOS camera was first installed on the camera port of the microscope, and the position of the objective lens was adjusted to form an in-focus image of the sample. Next, the 1:1 relay lens was mounted onto the camera. The camera was placed on a dovetail optical rail (RLA150/M, Thorlabs), aligned to the optical axis, and translated until the same in-focus wide-field image was captured. At this point, the camera was conjugated to the NIP. The MLA, mounted in a translational stage, was first inserted near the NIP where the modulation of the MLA (edges of the microlenses) can be observed on the camera. The MLA was then slightly adjusted until the modulation of the MLA disappeared on the camera. At this point, the MLA was aligned to the NIP, and the camera recorded the wide-field images reported in this work. For light-field imaging, the camera was translated away from the sample by the focal length of the MLA (fml = 3.75 mm), where the system formed conventional LFM (Appendix 2 and Fig. 7; a = 0 and b = fml = 3.75 mm). To establish the design in this work, the MLA was translated away from the sample by a = 25 mm, and the camera, accordingly, by a + b – fml = 25.25 mm. At this point, the HR-LFM system was established as reported (a = 25 mm and b = 4 mm) (Fig. 1(a) and Appendix 2).
Appendix 2: Design schematics of conventional LFM, focused plenoptic camera, and HR-LFM
Appendix 3: Model of light-field propagation and image formation
Projecting the 3D volume in the object domain to the 2D imaging space, the wavefunction at the NIP using the high-NA objective lens, is predicted by the Debye theory as :
When the MLA is located by the distance from the NIP, the wavefront on the MLA can be represented by Fresnel propagation from the NIP, given as:
Next, the light propagation over the distance of from the MLA to the camera can be modelled using Fresnel propagation. The final complex-valued PSF is described as:3]. The volume reconstruction utilized a deconvolution algorithm based on the inverse problem in tomographic image formation , where the volumetric information was obtained from multiple different perspectives of a 3D volume using deconvolution. The algorithm was further modified combining the wave-optics model for reconstruction of light-field data Code 1 [3,4,24]
Appendix 4: Full-width at half-maximum (FWHM) values of the reconstructed point-spread functions (PSFs) at varying depths
Appendix 5: Improved axial-resolving capability of HR-LFM
Appendix 6: Imaging mitochondria in Drp1−/− mouse embryo fibroblasts (MEFs) using HR-LFM
Appendix 7: Imaging Golgi complex in HeLa cells using HR-LFM
Appendix 8: Imaging Golgi-derived membrane vesicles in living COS-7 cells using HR-LFM
Appendix 9: Imaging Golgi-derived membrane vesicles in living COS-7 cells at a volume acquisition time of 5 ms using HR-LFM
Appendix 10: Acquisition parameters for all images in Figs. 1-6, 8-13
|Image Property||Fig. 1(e)||Fig. 2(a)||Fig. 2(c), 2(e)||Fig. 3|
|sample||100-nm bead||100-nm bead||1-µm microsphere||HeLa (fixed)|
|supplier||T7279, ThermoFisher||T7279, ThermoFisher||F14791, ThermoFisher||ATCC|
|fluorescent label||Blue, Dark Red, Green, Orange||Blue, Dark Red, Green, Orange||Dark Red, Green, Orange||AlexaFluor 647 /|
|excitation wavelength||647 nm||647 nm||647 nm||647 nm|
|emission wavelength||680 nm||680 nm||680 nm||667 nm|
|dichroic mirror||T660lpxr, Chroma||T660lpxr, Chroma||T660lpxr, Chroma||T660lpxr, Chroma|
|emission filter||ET700/75, Chroma||ET700/75, Chroma||ET700/75, Chroma||ET700/75, Chroma|
|FOV (µm2)||133 x 133||133 x 133||133 x 133||133 x 133|
|sCMOS frame rate /|
volume acquisition time
|100 ms||100 ms||100 ms||100 ms|
|Image Property||Fig. 4||Fig. 5||Fig. 6|
|sample||Drp1−/− Mouse embryo fibroblasts (live)||COS-7 (live)||Hydrophobically-modified glycol chitosan (HGC) nanoparticles|
|supplier||Hiromi Sesaki (JHU)||ATCC||Yizhi Meng (SBU)|
|fluorescent label||MitoTracker Deep Red||mEmerald-Golgi-7||Cyanine3|
|excitation wavelength||647 nm||488 nm||561 nm|
|emission wavelength||680 nm||518 nm||570 nm|
|dichroic mirror||T660lpxr, Chroma||T495lpxr, Chroma||T560lpxr, Chroma|
|emission filter||ET700/75, Chroma||ET525/50, Chroma||FF02-617/73, Semrock|
|FOV (µm2)||133 x 133||133 x 133||8.3 x 133|
|sCMOS frame rate /|
volume acquisition time
|10 Hz / 100 ms||100 Hz / 10 ms||1000 Hz / 1 ms|
|Image Property||Fig. 8||Fig. 9||Fig. 10||Fig. 11|
|sample||100-nm bead||100-nm bead||Drp1−/− Mouse embryo fibroblasts (live)||HeLa (fixed)|
|supplier||T7279, ThermoFisher||T7279, ThermoFisher||Hiromi Sesaki (JHU)||ATCC|
|fluorescent label||Blue, Dark Red, Green, Orange||Blue, Dark Red, Green, Orange||MitoTracker Deep Red||DyLight 549 /|
|excitation wavelength||647 nm||647 nm||647 nm||561 nm|
|emission wavelength||680 nm||680 nm||680 nm||576 nm|
|dichroic mirror||T660lpxr, Chroma||T660lpxr, Chroma||T660lpxr, Chroma||T560lpxr, Chroma|
|emission filter||ET700/75, Chroma||ET700/75, Chroma||ET700/75, Chroma||FF02-617/73, Semrock|
|FOV (µm2)||133 x 133||133 x 133||133 x 133||133 x 133|
|sCMOS frame rate /|
volume acquisition time
|10 Hz / 100 ms||10 Hz / 100 ms||10 Hz / 100 ms||10 Hz / 100 ms|
|Image Property||Fig. 12||Fig. 13|
|sample||COS-7 (live)||COS-7 (live)|
|excitation wavelength||488 nm||488 nm|
|emission wavelength||518 nm||518 nm|
|dichroic mirror||T495lpxr, Chroma||T495lpxr, Chroma|
|emission filter||ET525/50, Chroma||ET525/50, Chroma|
|FOV (µm2)||133 x 133||67 x 133|
|sCMOS frame rate /|
volume acquisition time
|100 Hz / 10 ms||200 Hz / 5 ms|
Appendix 11: Additional parameters for all Visualizations 1-5
|Visualization 1||Time-lapse video of reconstructed mitochondria in living Drp1−/− MEFs (FOV = 133 x 133 µm, volume acquisition rate = 10 Hz). Scale bars, 10 µm (main), 1 µm (insets). Color scale bar, z = −1.0 µm (blue) to 1.5 µm (red).|
|Visualization 2||Zoomed-in, 3D view of the corresponding region (marked with ‘triangle’) in Supplementary Video 1 (volume acquisition rate = 10 Hz).|
|Visualization 3||Zoomed-in, 3D view of the corresponding region (marked with ‘cross’) in Supplementary Video 1 (volume acquisition rate = 10 Hz).|
|Visualization 4||Time-lapse video of reconstructed Golgi-derived membrane vesicles in living COS-7 cells (FOV = 133 x 133 µm, volume acquisition rate = 100 Hz). Scale bars, 10 µm (main), 1 µm (insets). Color scale bar, z = −1.5 µm (blue) to 1.0 µm (red).|
|Visualization 5||Time-lapse video of a reconstructed diffusing nanoparticle suspended in water (FOV = 8.3 x 133 µm, volume acquisition rate = 1000 Hz).|
National Institutes of Health grants 1R35GM124846 (to S.J.) and R01GM084251 (to M.F.), National Science Foundation grants CBET1604565 and EFMA1830941 (to S.J.).
We acknowledge the support of the NSF-CBET Biophotonics program, the NSF-EFMA program, and the NIH-NIGMS MIRA program.
The authors declare that there are no conflicts of interest related to this article.
1. M. Levoy, R. Ng, A. Adams, M. Footer, and M. Horowitz, “Light field microscopy,” ACM Trans. Graph. 25(3), 924 (2006). [CrossRef]
3. M. Broxton, L. Grosenick, S. Yang, N. Cohen, A. Andalman, K. Deisseroth, and M. Levoy, “Wave optics theory and 3-D deconvolution for the light field microscope,” Opt. Express 21(21), 25418–25439 (2013). [CrossRef] [PubMed]
4. R. Prevedel, Y.-G. Yoon, M. Hoffmann, N. Pak, G. Wetzstein, S. Kato, T. Schrödel, R. Raskar, M. Zimmer, E. S. Boyden, and A. Vaziri, “Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy,” Nat. Methods 11(7), 727–730 (2014). [CrossRef] [PubMed]
6. P. J. Keller, A. D. Schmidt, J. Wittbrodt, and E. H. K. Stelzer, “Reconstruction of Zebrafish Early Embryonic Development by Scanned Light Sheet Microscopy,” Science 322(5904), 1065–1069 (2008). [CrossRef] [PubMed]
7. L. Schermelleh, P. M. Carlton, S. Haase, L. Shao, L. Winoto, P. Kner, B. Burke, M. C. Cardoso, D. A. Agard, M. G. L. Gustafsson, H. Leonhardt, and J. W. Sedat, “Subdiffraction multicolor imaging of the nuclear periphery with 3D structured illumination microscopy,” Science 320(5881), 1332–1336 (2008). [CrossRef] [PubMed]
8. M. B. Bouchard, V. Voleti, C. S. Mendes, C. Lacefield, W. B. Grueber, R. S. Mann, R. M. Bruno, and E. M. C. Hillman, “Swept confocally-aligned planar excitation (SCAPE) microscopy for high speed volumetric imaging of behaving organisms,” Nat. Photonics 9(2), 113–119 (2015). [CrossRef] [PubMed]
9. T. Schrödel, R. Prevedel, K. Aumayr, M. Zimmer, and A. Vaziri, “Brain-wide 3D imaging of neuronal activity in Caenorhabditis elegans with sculpted light,” Nat. Methods 10(10), 1013–1020 (2013). [CrossRef] [PubMed]
10. K. M. Dean, P. Roudot, E. S. Welf, T. Pohlkamp, G. Garrelts, J. Herz, and R. Fiolka, “Imaging subcellular dynamics with fast and light-efficient volumetrically parallelized microscopy,” Optica 4(2), 263–271 (2017). [CrossRef] [PubMed]
11. T. A. Planchon, L. Gao, D. E. Milkie, M. W. Davidson, J. A. Galbraith, C. G. Galbraith, and E. Betzig, “Rapid three-dimensional isotropic imaging of living cells using Bessel beam plane illumination,” Nat. Methods 8(5), 417–423 (2011). [CrossRef] [PubMed]
12. R. Tomer, M. Lovett-Barron, I. Kauvar, A. Andalman, V. M. Burns, S. Sankaran, L. Grosenick, M. Broxton, S. Yang, and K. Deisseroth, “SPED Light Sheet Microscopy: Fast Mapping of Biological System Structure and Function,” Cell 163(7), 1796–1806 (2015). [CrossRef] [PubMed]
13. N. C. Pégard, H.-Y. Liu, N. Antipa, M. Gerlock, H. Adesnik, and L. Waller, “Compressive light-field microscopy for 3D neural activity recording,” Optica 3(5), 517 (2016). [CrossRef]
14. T. Nöbauer, O. Skocek, A. J. Pernía-Andrade, L. Weilguny, F. M. Traub, M. I. Molodtsov, and A. Vaziri, “Video rate volumetric Ca2+ imaging across cortex using seeded iterative demixing (SID) microscopy,” Nat. Methods 14(8), 811–818 (2017). [CrossRef] [PubMed]
15. M. A. Taylor, T. Nöbauer, A. Pernia-Andrade, F. Schlumm, and A. Vaziri, “Brain-wide 3D light-field imaging of neuronal activity with speckle-enhanced resolution,” Optica 5(4), 345 (2018). [CrossRef]
16. N. Cohen, S. Yang, A. Andalman, M. Broxton, L. Grosenick, K. Deisseroth, M. Horowitz, and M. Levoy, “Enhancing the performance of the light field microscope using wavefront coding,” Opt. Express 22(20), 24817–24839 (2014). [CrossRef] [PubMed]
17. A. Lumsdaine and T. Georgiev, “The focused plenoptic camera,” in 2009 IEEE International Conference on Computational Photography, ICCP 09 (2009). [CrossRef]
18. M. Gu, Advanced Optical Imaging Theory (Springer, 2000).
19. A. C. Kak and M. Stanley, “Algorithms for Reconstruction with Nondiffracting Sources,” Princ. Comput. Tomogr. Imaging 49–112 (1999).
20. J. Wakabayashi, Z. Zhang, N. Wakabayashi, Y. Tamura, M. Fukaya, T. W. Kensler, M. Iijima, and H. Sesaki, “The dynamin-related GTPase Drp1 is required for embryonic and brain development in mice,” J. Cell Biol. 186(6), 805–816 (2009). [CrossRef] [PubMed]
22. G. Suarato, S. I. Lee, W. Li, S. Rao, T. Khan, Y. Meng, and M. Shelly, “Micellar nanocomplexes for biomagnetic delivery of intracellular proteins to dictate axon formation during neuronal development,” Biomaterials 112, 176–191 (2017). [CrossRef] [PubMed]
24. Supplementary code used for HR-LFM reconstruction, https://doi.org/10.6084/m9.figshare.6850409.