Abstract
Simultaneous, high-resolution imaging across a large number of synaptic and dendritic sites is critical for understanding how neurons receive and integrate signals. Yet, functional imaging that targets a large number of submicrometer-sized synaptic and dendritic locations poses significant technical challenges. We demonstrate a new parallelized approach to address such questions, increasing the signal-to-noise ratio by an order of magnitude compared to previous approaches. This selective access multifocal multiphoton microscopy uses a spatial light modulator to generate multifocal excitation in three dimensions (3D) and a Gaussian–Laguerre phase plate to simultaneously detect fluorescence from these spots throughout the volume. We test the performance of this system by simultaneously recording Ca2+ dynamics from cultured neurons at 98–118 locations distributed throughout a 3D volume. This is the first demonstration of 3D imaging in a “single shot” and permits synchronized monitoring of signal propagation across multiple different dendrites.
1. INTRODUCTION
Synaptic transmission between neurons is the most basic unit of information flow in a neural circuit. How the activity of heterogeneous synaptic inputs is integrated within and across its individual dendrites is a key computation for each neuron in the brain. While synaptic integration at a neuronal level, in particular as it relates to excitatory inputs, has been extensively modeled [1–3], to date it has not been possible to simultaneously monitor synaptic activity across the entire dendritic arbor of a neuron in vivo due to the difficulty of monitoring sparsely distributed points across a large three-dimensional (3D) volume at sufficient speed. In the mouse neocortex, pyramidal neuron dendritic arbor can occupy a volume in the range of 1–5 × 106 μm3, depending on depth [4], with synaptic sites on the scale of a few micrometers distributed across the arbor [5].
Tackling this class of neurobiological questions requires high-speed methods for detecting activity at synaptic resolution across many distributed sites. Functional synaptic imaging in a living mouse brain further requires imaging at depths of at least 1–200 μm, which is best accomplished by multiphoton excitation. Today, the majority of in vivo neurobiological multiphoton imaging studies rely on point excitation with raster scanning [6–8]. To scan the entire volume, one must move the single excitation point very rapidly throughout the volume. The settling time of the scanner, as well as the sampling rates required to accurately detect fluctuations in the Ca2+ indicator fluorescence, place very strict limits on the available dwell time for each voxel. The image signal-to-noise ratio (SNR) typically suffers due to the short signal integration time at each voxel.
For monitoring events such as dendritic signal propagation, scanning speeds can be improved by using optical devices to steer the excitation beam [9–13]. Yet, the essential problem of SNR remains the same. All existing methods sequentially target the selected locations, which significantly reduces signal integration time and SNR, even when only 10 or so loci are measured. These constraints have restricted previous approaches to image Ca2+ activity at synaptic resolution to a relatively small field of view (FOV), allowing the tracking of signal propagation across only a few tens of micrometers of dendrite at any given time [9–11,14,15].
Parallelized imaging can improve scanning speed without incurring an SNR penalty by simultaneously exciting and detecting at multiple locations. Assuming sufficient excitation power can be provided, this parallelization improves imaging speed proportional to the degree of parallelization without compromising SNR. Some recent methods in multiphoton parallelized imaging include multifocal excitation [16–19], temporal focusing (TF) plane or line illumination [20–23], multiple plane simultaneous imaging [24,25], light field microscopy [26,27], and holographical imaging [24,28–31]. Many of these methods are typically used for monitoring neuronal activity across a large brain area at relatively coarse spatial resolution, on the order of several micrometers [22–28,31]. This level of resolution is not sufficient for studying neuronal integration of synaptic signals, which would require imaging with 3D resolution on the submicrometer scale. In addition, dendrites and spines are smaller than somas; thus their fluorescent signal is weaker, requiring new, higher SNR methods.
To address these challenges, we developed a high-resolution, high-SNR method for simultaneous functional imaging called selective access multifocal multiphoton microscopy (saMMM). This method has three advantages over previously developed techniques, such as random access imaging [9–12] and Bessel beam imaging [32,33] [Fig. 1(a)]. First, wavefront shaping [34] with a spatial light modulator (SLM) enables simultaneous generation of multiple Gaussian excitation spots in 3D at submicrometer resolution. Through the use of higher peak energy (microjoule–millijoule level) femtosecond lasers, over a hundred excitation spots can be generated simultaneously. Second, a Gaussian–Laguerre (GL) phase plate in the detection path extends the imaging depth of field (DoF), enabling 3D simultaneous detection on a 2D camera [26,31,35] without axial scanning. Third, saMMM allows imaging in a truly “scanless” manner from a 3D-distributed region of interest (ROI), so functional time traces are strictly simultaneous as compared to rapid sequential scanning among ROIs [9–13] or whole volume imaging [32,33].
Fig. 1.
Outline of experimental flow for saMMM and comparison with other methods. (a) Comparison of similar state-of-the-art methods. (a1) Outline of RAS; (a2) outline of Bessel beam or big Gaussian spot scanning; (a3) outline of saMMM, emphasizing the differences from (a1) and (a2); (b) experimental flow of saMMM; (b1) neuronal structure z stack is first captured using line-scan TF; (b2) manual or automated structural tracing is used to compute the SLM phase mask for targeting the ROI by calculating the 3D phase on an Ewald sphere using the Gerchberg–Saxton algorithm and projecting it onto 2D phase mask. (b3) 3D simultaneous excitation; (b4) 3D simultaneous detection within the DoF; GL phase plate elongates the DoF compared to Gaussian PSF. (b5) Camera captures one image per plane; without GL phase plate, only spots in focus are clearly detected, with out-of-focus locations appearing as blurs (DoF ≈1.8 μm); with GL phase plate, all spots in the slab (DoF ≈15 μm) are detected.
To demonstrate saMMM, we monitored Ca2+ signals from more than a hundred selected locations along the dendrites of a single cultured neuron. Our results show that the GL phase plate provides a higher out-of-focus SNR than Gaussian spot targeting, improving 3D resolution, and that the time gains from simultaneous excitation and scanless detection allow increased dwell times that further benefit the SNR. In addition, the scanless nature of saMMM provides a simultaneous time stamp at multiple loci, more faithful than can be provided by sequential scanning.
2. METHODS
A. saMMM with Auxiliary Line-Scan TF Microscopy Setup
The setup is illustrated in Fig. 2(a). The two microscopes share one high peak energy femtosecond laser at 1030 nm (Monaco, Coherent Inc., California). The excitation light is split with a polarizing beam splitter with individual intensities regulated with the half-wave plate. The P-polarized beam is phase-modulated by an SLM (PLUTO-NIR-HR phase-only reflective SLM, HOLOEYE Photonics AG, Germany) that is placed in a conjugate position to the back aperture of the objective (XLUMPlanFL, 20 × , 0.95 NA, Olympus), projecting holographic patterns that generate excitation foci defined by the ROI. The incident angle to the SLM is about 8°. A mirror [M3 in Fig. 2(a)] is inserted before the microscope tube lens [L4 in Fig. 2(a)] to direct the light from the SLM to the specimen while blocking the light from the grating. Two-photon excited fluorescence is deflected by a dichroic mirror into the detection path. A GL phase plate (Customer design, manufactured by HOLO/OR Ltd., Israel) is inserted in the Fourier plane of the image for 3D imaging.
Fig. 2.
saMMM setup (with additional line-scan TF multiphoton microscopy) and GL phase plate modulated PSF. (a) System diagram. L1, L2, to collimate beam; HWP, half-wave plate; PBS, polarizing beam splitter; SLM, spatial light modulator; SM, scan mirror; CL, cylindrical lens; DM, dichroic mirror; L4, L5, tube lenses; GL, Gaussian–Laguerre phase plate; L6, L7, relay lenses to generate Fourier plane for GL phase plate; (b) image of a fluorescent nanoparticle (FluoSphere carboxylate, 0.04 μm, yellow-green (505/515), Life Technologies, California) viewed in 3D volume. Scale bar, 5 μm. (c) Double helix PSF modulated by the GL phase plate [measured with a fluorescent nanoparticle, same as (b)], where each small image on the right side shows the cross section at the corresponding white dashed line position on the left 3D PSF. The distance between each dashed line is about 3 μm. Scale bar, 5 μm. The intensity is normalized so that the color scale of each image is [0,1], corresponding to the gray scale. (d) Axial Gaussian PSF of the system is about 1.1 μm. (e) Lateral Gaussian PSF of the system is about 0.43 μm. (f) Total intensity of GL PSF (red) and Gaussian PSF (blue) along z axis. (summing area: 16 × 16 μm in each z position); (g) total intensity ratio between GL PSF and Gaussian PSF along z axis; (h) encoding relation between axial difference and rotation angle (°) of GL PSF.
For structural imaging using line-scan TF excitation, the mirror [M3 in Fig. 2(a)] is removed before the microscope tube lens [L4 in Fig. 2(a)] to pass the light from the grating to the specimen while a beam block is placed to block the light from the SLM. In the line-scan TF system, the beam is mechanically scanned in the vertical direction by a galvano motor-driven scanning mirror (6350, Cambridge Technology Inc., Massachusetts). The excitation beam is then focused into a line on a diffraction grating (20RG1200-1000-2, 1200 grooves/mm, 50 × 50 × 6 mm, Newport, USA) by a cylindrical lens. The grating, conjugated to the image plane, diffracts the line in the horizontal direction. The saMMM and the line-scan TF systems also share the same tube lens and objective lens. By changing the incident angle to grating and voltage driving the scanning mirror, the FOV can be adjusted. The FOV of our setup is about 200 μm × 200 μm. For volumetric imaging by line-scan TF, a piezo objective-translator (MIPOS 5, Piezosystem jena GmbH, Germany) is used for axial scanning. Line-scan TF and saMMM share the same sCMOS camera (Prime95B, Photometrics, Arizona) for detection.
B. Rapid Selection of Discrete Targets for Scanning
Using MATLAB, an automatic tracing algorithm is used to select the local maximum of the image for the 2D targeting. For 3D targeting, the automatic tracing is done with neuTube [36], called from the command line within MATLAB. After the automatic tracing, the spots can be edited manually to adjust positions for better alignment, to distribute locations more regularly, and to add or delete spots for a specific sampling goal. For the volume size described here, the structural image tracing and subsequent calculation of the SLM phase mask takes approximately 1 min.
C. Cell Culture Preparation
Cortical neurons from E18 Sprague-Dawley rat embryos were cultured for 14 days on 18 mm glass coverslips in Neurobasal-A media with 2% B27 supplement and 1% Glutamax. On day in vitro (DIV) 7, cultures were infected with 2 × 108 genome copies of AAV1.Syn.NES-jRGECO1a.WPRE.SV40 (Janelia GENIE project supplied through Penn Vector Core). On DIV 14, the coverslips were transferred to a custom imaging chamber, and the Neurobasal media was replaced with Tyrode’s solution. A custom-made warming plate was used to maintain the culture at 37°C during imaging.
3. RESULTS
A. saMMM with Auxiliary Line-Scan TF
Figure 1(b) outlines the experimental flow for generating scanless excitation and detection using the saMMM setup with the auxiliary line-scan TF multiphoton system (see Methods). Since a selective addressing approach requires a coordinate “map” for targeting specific locations, we started with a structural imaging step to map out the dendritic arbor and determine potential ROIs. Accurately locating several hundreds of submicrometer excitation spots on the targeted positions from the 3D structure “map” requires highly precise registration between the TF system and the saMMM. Thus, we integrated the auxiliary line-scan TF multiphoton imaging system within the saMMM setup [Figs. 1(b), 2(a) and Fig. S1 of Supplement 1]. Running the line-scan TF system at 10 fps allows imaging of a 200 × 200 × 10 μm3 volume in about 1 s. The imaging speed of the line-scan TF is faster than point-scanning (PS) methods by 1 to 2 orders of magnitude [37], while spatial resolution of the line-scan TF is similar to standard PS two-photon microscopy (Fig. S1 of Supplement 1) due to spectral and spatial filling of the objective back aperture [20–22,37].
After structural imaging with the line-scan TF, we selected spots along multiple dendrites of the same cell. The x, y, z coordinates of these spots were used as targets for functional imaging by saMMM. The Gaussian point spread function (PSF) achieves submicrometer resolution for selected spot targeting [Figs. 2(b), 2(d), and 2(e)]. The lateral size of the GL PSF changes with depth. From top to bottom, the distance between two lobes are: 2.55, 2.02, 0.6, 2.66, and 2.86 μm [Fig. 2(c)]. The main advantage of the GL PSF is the elongation of the DoF to about 15 μm (full width at half-maximum) in our setup [Fig. 2(c)]. This enables out-of-focus fluorescence to be more efficiently captured by the camera, since the Gaussian PSF without GL phase encoding has a much shorter DoF [Figs. 2(b)–2(d)]. Under the same input power and a fixed lateral integration area of (16 μm × 16 μm) dictated by synaptic density, the total intensity of the GL PSF is the same as the Gaussian PSF at the focal point, but higher than the Gaussian PSF at out-of-focus locations [Figs. 2(f) and 2(g)]. Above ± 1 μm, GL spots have progressively better SNR as defocusing is increased. This statement holds when shot noise is the dominant noise source and readout noise from the camera is negligible. Another advantage of including the GL phase plate is that the GL PSF encodes different axial positions with different rotation angles [Figs. 2(c) and 2(h)]. In our setup, 1 μm axial difference corresponds to 11.5° ± 2.5° rotation [Fig. 2(h)]. Because the foci positions are known a priori, the depth-encoding properties of the GL phase plate is a nice bonus, but not essential. The ability to improve SNR for out-of-focus targets with the GL phase plate allows us to record signals from a 200 × 200 × 10 μm3 volume with a 2D camera.
B. High-Throughput Neuronal Ca2+ Imaging in a Single 2D Plane
As a first demonstration of the utility of the saMMM system, we tested its performance for high-throughput, high-resolution monitoring of Ca2+ dynamics in 2D (Fig. 3). Cultured neurons were transfected with the red Ca2+ sensor, jRGECO1a. These cells form arbors over a relatively large area on the culture dish but have a relatively short axial volume of only a few dozen micrometers, providing a mostly 2D specimen. A neuron expressing jRGECO1a was first structurally imaged using the line-scan TF system, rapidly auto-traced in MATLAB, and 113 ROIs were selected, targeting multiple dendritic branches and varying distances from the cell soma, as well as different somas in the FOV (see Methods). Ca2+ signals were recorded by saMMM at 100 Hz frame rate simultaneously from all spots for 120 s. Figure 3(a) shows the overlap of the structural image obtained with line-scan TF (magenta) with emission at targeted locations after selective excitation by saMMM (green). We selectively show Ca2+ signals recorded from every other spot along three dendrites (labeled in blue, red, and black), sorted based on the distance to the soma. This is the first demonstration of simultaneous monitoring of Ca2+ dynamics at more than a hundred locations distributed across a large FOV at submicrometer-level resolution and 100 Hz temporal resolution.
Fig. 3.
Monitoring spontaneous Ca2+ dynamics from 113 foci “at once” on cultured neurons expressing jRGECO1 using 2D saMMM. (a) Superposition of line-scan TF structural image (magenta) with saMMM functional image (green). Scale bar, 50 μm. (b) The ΔF/F traces of every other spot on the three branches shown in (a), sorted by their distances to the soma. The ΔF/F of each dendrite is normalized to the global maximum.
C. High-Throughput 3D Neuronal Ca2+ Imaging
To extend the utility of saMMM from 2D to 3D, we placed a GL phase plate in the Fourier plane of the detected image [Fig. 2(a)]. Because cultured cells are essentially planar, to demonstrate 3D detection we positioned the cell at different distances of defocus. As in the 2D case, we could achieve 3D targeting and acquisition in one shot. As outlined in Fig. 1, we acquired the 3D structural image of a cultured neuron (200 × 200 × 10 μm3) expressing jRGECO1a using the line-scan TF system, rapidly auto-traced in MATLAB, and selected 98 (ΔZ = 0), 118 (ΔZ = 3 μm), and 99 (ΔZ = 6 μm) ROIs sequentially in a 3D volume. We then simultaneously monitored spontaneous Ca2+ signals from these foci, recorded at 100 Hz (ΔZ = 0) and 50 Hz (ΔZ = 3 μm and ΔZ = 6 μm), with and without the GL phase plate (Fig. 4). For defocused recording, 50 Hz frame rate gives better SNR than recording at 100 Hz. In Fig. 4(b), we show representative Ca2+ traces for individual spots marked by arrows in Fig. 4(a). When the foci are in focus, Gaussian and GL spots both show a clear Ca2+ signal. When the foci are far out of focus, with the GL phase plate the Ca2+ signal can still be detected thanks to the elongated DoF, while Gaussian spots are barely detectable. These results demonstrate that with the GL phase plate, Ca2+ signals for individual foci are detectable within an axial range from −6 to 6 μm.
Fig. 4.
Monitoring spontaneous Ca2+ dynamics from around one hundred GL spots in 3D simultaneously. (a) Overlay of line-scan TF in focus image with the 3D targeted foci on a single plane. The foci are effectively in different planes of the line-scan TF in focus plane. (a1), (a2) 98 Gaussian and GL spots at ΔZ = 0; (a3), (a4) 118 Gaussian and GL foci at ΔZ = 3 μm; and (a5), (a6) 99 Gaussian and GL foci at ΔZ = 6 μm. Scale bar, 50 μm. (b) Ca2+ activities from representative saMMM targeted foci; figure number corresponding to the figure number in (a). The ΔF/F traces are from foci pointed by the yellow arrows in (a). Blue line is calcium signal from the Gaussian spot, and red line is from the GL spot; (c) SNR comparison of ΔF/F traces between Gaussian foci and GL foci in different axial planes corresponding to (a).
To quantitatively compare the SNR of Ca2+ signals recorded from Gaussian foci versus GL foci, we defined the “signal” as the peak ΔF/F value, where ΔF and F are the change in fluorescence and the steady-state fluorescence observed at a given dendritic location, respectively. We further defined the “noise” as the standard deviation of ΔF/F in the absence of Ca2+ events. For statistical analysis, we repeated this calculation for about 20 spots. For each spot, both signal and noise were averaged from multiple firings [Fig. 4(c)]. The absolute value of SNR is related to the jRGECO1a expression level in the neuron, so it varies from cell to cell. However, the relative SNR between Gaussian foci and GL foci show the advantage of GL. When the foci are in focus, ΔF/F recorded by Gaussian and GL foci have similar SNR (median of Gaussian, 2.39; median of GL, 2.34). As the foci become more defocused, the extended DoF of the GL foci gradually provides a higher SNR advantage as compared to Gaussian foci. At 3 μm defocus, the GL foci already have better SNR than Gaussian foci (median of Gaussian, 2.38; median of GL, 3.16), and the advantage of GL becomes more obvious at 6 μm defocus (median of Gaussian, 2.02; median of GL, 3.26). These results match the intensity comparison between the Gaussian and GL PSFs in Figs. 2(f)–2(g).
We also evaluated the thermal damage during our experiments. Thermal damage in living systems when using high-energy lasers is the greatest limitation and often constrains the amount of laser power than can be used for excitation [38,39]. For cultured cells, we illuminated a single cell with 100–160 mW (0.8 mW per spot, in total, 125–200 spots) for 30 min (Supplement 1, Fig. S2). The cell was partially photo-bleached but still firing after 30 min illumination. This shows that even with several hundred foci targeted per neuron, the thermal damage is negligible.
This experiment demonstrates that saMMM can perform 3D volumetric functional imaging “at once.” In the case of Gaussian spots, the DoF covers about 1.8 μm, while for GL spots, the DoF is extended to about 15 μm. The saMMM records Ca2+ signal from all locations simultaneously, so the temporal information obtained across all locations is not staggered, as would be the case for sequential scanning. Because of the parallelization, for the same voxel resident time saMMM affords about 35-fold improvement in SNR as compared to traditional PS.
D. Improvements in SNR Using saMMM
The SNR is equal to the square root of signal photons when shot noise dominates. For saMMM, the SNR is times higher than that of PS two-photon microscopy, in which M is the number of foci, R is ratio of the voxels in a volume and the number of voxels to be monitored corresponding to synaptic locations and branch points, and L is a parameter characterizing the relative two-photon excited fluorescence generation efficiency of the laser sources compared with standard titanium-sapphire oscillators used in PS systems. Among these parameters, M and R are related to imaging strategy, and L is mostly related to laser technology.
The SNR improvement of saMMM comes mainly from M and R. However, future improvements in lasers could enable much higher SNR compared to traditional systems. In a 200 × 200 × 10 μm3 volume containing a single neuron, the total volume of ROIs, including synapses and branch points, is about 500 μm3. Thus, compared to exhaustive sampling methods, R is the ratio between the 200 × 200 × 10 μm3 sampling amount and the 500 μm3 ROIs, typically about 800. L is related to the ratio of repetition rate and pulse width. The regenerative amplifier used in the saMMM system has a slower repetition rate and broader pulse width (1 MHz and 200 fs) than typical titanium-sapphire oscillators (80 MHz and 50 fs). Therefore, fluorescence generation for single-excitation foci is less efficient in the saMMM system by a factor of 320 (L), given the same optimal pulse energy for the fluorophore [38,39]. This parameter is limited by current laser technology. With a dispersion compensation unit, it would be possible to achieve a narrower pulse width below 50 fs, potentially improving L by a factor of 4 compared to the current version of saMMM. Thus saMMM could potentially (saMMMp) be further improved to double the SNR.
According to the SNR equation and the parameters above, comparing saMMM with standard PS, our approach currently has an SNR advantage of 35-fold in the 3D case. After compressing the pulse width of saMMM, the SNR of saMMMp has an advantage of 70-fold in the 3D case. As compared to saMMM, other competitive methods that employ single-spot random access scanning (RAS) [9–12], suffer from higher M values due to the lack of parallel excitation, on the order of 500-fold. With these methods, L is decreased on the order of 320 relative to saMMM, while R is the same. Thus, saMMM improves the SNR by 1.25 times, and saMMMp improves the SNR by 2.5 times over RAS.
For Bessel beam scanning (BBS), the DoF under NA = 0.93 is 11–20 μm 32,33], which is similar to the DoF of saMMM GL foci. The Bessel beam approach effectively generates a column of synchronous excitation at each axial location and scans them exhaustively across one plane, which does not need to have the “map” at the beginning. As compared to BBS, the saMMM system improves R by 800 and M by 50, while decreasing L by 320. Thus, saMMM improves the SNR by 12 times, and saMMMp improves the SNR by 24 times over BBS. A summary comparing the SNR of saMMM and saMMMp with other prior approaches is presented in Table 1.
Table 1.
Comparison of saMMM with Other Methods
| PS | BBS | RAS | saMMM | saMMMp | |
|---|---|---|---|---|---|
| R | 1 | 1 | 800 | 800 | 800 |
| M | 1 | 10 | 1 | 500 | 500 |
| L | 1 | 1 | 1 | 1/320 | 1/80 |
| SNR | 1 | 3 | 28 | 35 | 70 |
4. DISCUSSION
In this work, we demonstrate scanless excitation and detection by saMMM that can monitor spontaneous Ca2+ signals in cultured neurons at more than a hundred locations in a 3D volume. Our selective access approach applies an SLM to flexibly control the excitation positions, combined with a GL phase plate to elongate the DoF so that a single frame image collects signals across a 10 μm thick volume. This approach significantly improves the SNR and volumetric imaging speed across an FOV of hundreds of micrometers retaining submicrometer spatial resolution.
We expect that future developments in saMMM will allow higher speed volumetric imaging with even higher SNR. First, SNR always improves with further parallelization. The SLM could generate more spots in the current FOV, leading to a larger M value. Two factors currently limit the total number of spots that can be generated holographically. First, with limited laser output, the pulse energy available at each spot drops with increasing number. Second, the diffraction efficiency of the SLM decreases when increasing the number of spots [29]. In our experiment, hundreds of spots can be generated with good efficiency. For generating more diffraction spots, an SLM with more pixels would probably be helpful. In the 2D case, the SLM phase pattern is exactly the Fourier transform of the 2D targeting spots. In the 3D case, the SLM phase pattern is the parallel projection of an Ewald sphere [34,40], which is exactly the 3D Fourier transform of the targeted spots. In our experiment, the radius of the Ewald sphere equals kxy = 2NA/λ = 1.9λ−1. Correspondingly, the depth of the Ewald sphere is kz = NA2/(2λ) ≈ 0.45λ−1. Because kz is comparably small, the parallel projection approximation barely influences the diffraction efficiency. So, hundreds of spots distributed in 3D volume could be generated with reasonable efficiency.
We also show that saMMM can be used to monitor Ca2+ activity in cultured neurons that have limited axial depth. To explore how tissue scattering influences saMMM, we measured the GL PSF inside a 50 μm mouse brain slice (Fig. S3 of Supplement 1). We found that tissue aberration and scattering barely influences the extended DoF and the lateral size of the GL PSF. Also, tissue scattering was not significant within an imaging depth on the order of 1–2 mean free path (MFP) [7]. For deeper imaging (several MFPs), adaptive optics (AO) could be used for aberration correction [41]. Thus, with minor modifications, saMMM may potentially be suitable for larger-volume, lower-resolution studies inside tissues.
To summarize, the saMMM realizes simultaneous excitation and detection of fluorescence in a 3D volume. We demonstrate greatly improved SNR of functional imaging by parallelization of over a hundred foci with selective addressing. This selective targeting method improves functional imaging speed while reducing photodamage and can potentially be applied to 3D high-throughput imaging and monitoring of local activities across entire neurons within a living animal.
Supplementary Material
Acknowledgment.
E. N., P. T. C. S., and J. R. B. acknowledge the support from NIH/NINDS; Hamamatsu Corporation; Samsung Advanced Institute of Technology; SMART: Center, BioSystems and Micromechanics (BioSyM). Y. X. designed, implemented, and tested the optical system, and wrote the paper draft. K. P. B. and J. R. B. designed and performed the neurobiological part of the experiments. C. J. R. participated in the design of the optical system. Y. T. designed and simulated the GL phase plate. E. N. posed the neurobiological problem for application of the technology and supervised the neurobiological sample preparation and analysis. P. T. C. S. conceived the major approach, designed and supervised the optical system and its application. All authors contributed to paper revision.
Funding. National Institutes of Health (NIH) (1-R01-HL121386-01A1, 1R21NS105070-01, 1U01CA202177-01, 1-U01-NS090438-01, 5-P41-EB015871-27, 5R21NS091982-02, F32 MH115441); Singapore-MIT Alliance for Research and Technology Centre (SMART); Samsung.
Footnotes
See Supplement 1 for supporting content.
REFERENCES
- 1.Jadi MP, Behabadi BF, Poleg-Polsky A, Schiller J, and Mel BW, “An augmented two-layer model captures nonlinear analog spatial integration effects in pyramidal neuron dendrites,” Proc. IEEE 102, 782–798 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Stuart GJ and Spruston N, “Dendritic integration: 60 years of progress,” Nat. Neurosci 18, 1713–1721 (2015). [DOI] [PubMed] [Google Scholar]
- 3.Rall W, “Core conductor theory and cable properties of neurons,” in Comprehensive Physiology (Wiley, 2011). [Google Scholar]
- 4.Benavides-Piccione R, Hamzei-Sichani F, Ballesteros-Yáñez I, DeFelipe J, and Yuste R, “Dendritic size of pyramidal neurons differs among mouse cortical regions,” Cereb. Cortex 16, 990–1001 (2006). [DOI] [PubMed] [Google Scholar]
- 5.DeFelipe J and Fariñas I, “The pyramidal neuron of the cerebral cortex: morphological and chemical characteristics of the synaptic inputs,” Prog. Neurobiol 39, 563–607 (1992). [DOI] [PubMed] [Google Scholar]
- 6.Svoboda K and Yasuda R, “Principles of two-photon excitation microscopy and its applications to neuroscience,” Neuron 50, 823–839 (2006). [DOI] [PubMed] [Google Scholar]
- 7.Helmchen F and Denk W, “Deep tissue two-photon microscopy,” Nat. Methods 2, 932–940 (2005). [DOI] [PubMed] [Google Scholar]
- 8.Denk W, Delaney KR, Gelperin A, Kleinfeld D, Strowbridge BW, Tank DW, and Yuste R, “Anatomical and functional imaging of neurons using 2-photon laser scanning microscopy,” J. Neurosci. Methods 54, 151–162 (1994). [DOI] [PubMed] [Google Scholar]
- 9.Duemani Reddy G, Kelleher K, Fink R, and Saggau P, “Three-dimensional random access multiphoton microscopy for functional imaging of neuronal activity,” Nat. Neurosci 11, 713–720 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Katona G, Szalay G, Maák P, Kaszás A, Veress M, Hillier D, Chiovini B, Sylvester Vizi E, Roska B, and Rózsa B, “Fast two-photon in vivo imaging with three-dimensional random-access scanning in large tissue volumes,” Nat. Methods 9, 201–208 (2012). [DOI] [PubMed] [Google Scholar]
- 11.Chen X, Leischner U, Rochefort NL, Nelken I, and Konnerth A, “Functional mapping of single spines in cortical neurons in vivo,” Nature 475, 501–505 (2011). [DOI] [PubMed] [Google Scholar]
- 12.Nadella KMNS, Roš H, Baragli C, Griffiths VA, Konstantinou G, Koimtzis T, Evans GJ, Kirkby PA, and Silver RA, “Random-access scanning microscopy for 3D imaging in awake behaving animals,” Nat. Methods 13, 1001–1004 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Ducros M, Goulam Houssen Y, Bradley J, de Sars V, and Charpak S, “Encoded multisite two-photon microscopy,” Proc. Natl. Acad. Sci. USA 110, 13138–13143 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Chen T-W, Wardill TJ, Sun Y, Pulver SR, Renninger SL, Baohan A, Schreiter ER, Kerr RA, Orger MB, Jayaraman V, Looger LL, Svoboda K, and Kim DS, “Ultrasensitive fluorescent proteins for imaging neuronal activity,” Nature 499, 295–300 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Yuste R and Denk W, “Dendritic spines as basic functional units of neuronal integration,” Nature 375, 682–684 (1995). [DOI] [PubMed] [Google Scholar]
- 16.So PT, Dong CY, Masters BR, and Berland KM, “Two-photon excitation fluorescence microscopy,” Annu. Rev. Biomed. Eng 2, 399–429 (2000). [DOI] [PubMed] [Google Scholar]
- 17.Bewersdorf J, Pick R, and Hell SW, “Multifocal multiphoton microscopy,” Opt. Lett 23, 655–657 (1998). [DOI] [PubMed] [Google Scholar]
- 18.König K, “Multiphoton microscopy in life sciences,” J. Microsc 200, 83–104 (2000). [DOI] [PubMed] [Google Scholar]
- 19.Bahlmann K, So PT, Kirber M, Reich R, Kosicki B, McGonagle W, and Bellve K, “Multifocal multiphoton microscopy (MMM) at a frame rate beyond 600 hz,” Opt. Express 15, 10991–10998 (2007). [DOI] [PubMed] [Google Scholar]
- 20.Zhu G, van Howe J, Durst M, Zipfel W, and Xu C, “Simultaneous spatial and temporal focusing of femtosecond pulses,” Opt. Express 13, 2153–2159 (2005). [DOI] [PubMed] [Google Scholar]
- 21.Tal E, Oron D, and Silberberg Y, “Improved depth resolution in video-rate line-scanning multiphoton microscopy using temporal focusing,” Opt. Lett 30, 1686–1688 (2005). [DOI] [PubMed] [Google Scholar]
- 22.Dana H, Marom A, Paluch S, Dvorkin R, Brosh I, and Shoham S, “Hybrid multiphoton volumetric functional imaging of large-scale bioengineered neuronal networks,” Nat. Commun 5, 3997 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Prevedel R, Verhoef AJ, Pernía-Andrade AJ, Weisenburger S, Huang BS, Nöbauer T, Fernández A, Delcour JE, Golshani P, Baltuska A, and Vaziri A, “Fast volumetric calcium imaging across multiple cortical layers using sculpted light,” Nat. Methods 13, 1021–1028 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Yang W, Miller J-EK, Carrillo-Reid L, Pnevmatikakis E, Paninski L, Yuste R, and Peterka DS, “Simultaneous multi-plane imaging of neural circuits,” Neuron 89, 269–284 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Cheng A, Gonçalves JT, Golshani P, Arisaka K, and Portera-Cailliau C, “Simultaneous two-photon calcium imaging at different depths with spatiotemporal multiplexing,” Nat. Methods 8, 139–142 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Cong L, Wang Z, Chai Y, Hang W, Shang C, Yang W, Bai L, Du J, Wang K, and Wen Q, “Rapid whole brain imaging of neural activity in freely behaving larval zebrafish (danio rerio),” Elife 6, e28158 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Prevedel R, Yoon Y-G, Hoffmann M, Pak N, Wetzstein G, Kato S, Schrödel T, Raskar R, Zimmer M, Boyden ES, and Vaziri A, “Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy,” Nat. Methods 11, 727–730 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Papagiakoumou E, Anselmi F, Bègue A, de Sars V, Glückstad J, Isacoff EY, and Emiliani V, “Scanless two-photon excitation of channelrhodopsin-2,” Nat. Methods 7, 848–854 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Nikolenko V, Watson BO, Araya R, Woodruff A, Peterka DS, and Yuste R, “SLM microscopy: scanless two-photon imaging and photostimulation with spatial light modulators,” Front. Neural Circuits 2, 5 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Rosen J and Brooker G, “Non-scanning motionless fluorescence three-dimensional holographic microscopy,” Nat. Photonics 2, 190–195 (2008). [Google Scholar]
- 31.Quirin S, Jackson J, Peterka DS, and Yuste R, “Simultaneous imaging of neural activity in three dimensions,” Front. Neural Circuits 8, 29 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Botcherby EJ, Juškaitis R, and Wilson T, “Scanning two photon fluorescence microscopy with extended depth of field,” Opt. Commun 268, 253–260 (2006). [Google Scholar]
- 33.Lu R, Sun W, Liang Y, Kerlin A, Bierfeld J, Seelig JD, Wilson DE, Scholl B, Mohar B, Tanimoto M, Koyama M, Fitzpatrick D, Orger MB, and Ji N, “Video-rate volumetric functional imaging of the brain at synaptic resolution,” Nat. Neurosci 20, 620–628 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Ren H, Lin H, Li X, and Gu M, “Three-dimensional parallel recording with a Debye diffraction-limited and aberration-free volumetric multifocal array,” Opt. Lett 39, 1621–1624 (2014). [DOI] [PubMed] [Google Scholar]
- 35.Pavani SRP, Thompson MA, Biteen JS, Lord SJ, Liu N, Twieg RJ, Piestun R, and Moerner WE, “Three-dimensional, singlemolecule fluorescence imaging beyond the diffraction limit by using a double-helix point spread function,” Proc. Natl. Acad. Sci. USA 106, 2995–2999 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Feng L, Zhao T, and Kim J, “neutube 1.0: a new design for efficient neuron reconstruction software based on the SWC format,” eNeuro. 2 (1) (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Xue Y, Berry KP, Boivin JR, Wadduwage D, Nedivi E, and So PTC, “Scattering reduction by structured light illumination in line-scanning temporal focusing microscopy,” Biomed. Opt. Express 9, 5654–5666 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Nagy A, Wu J, and Berland KM, “Characterizing observation volumes and the role of excitation saturation in one-photon fluorescence fluctuation spectroscopy,” J. Biomed. Opt 10, 44015 (2005). [DOI] [PubMed] [Google Scholar]
- 39.Masters BR, So PTC, Buehler C, Barry N, Sutin JD, Mantulin WW, and Gratton E, “Mitigating thermal mechanical damage potential during two-photon dermal imaging,” J. Biomed. Opt 9, 1265–1270 (2004). [DOI] [PubMed] [Google Scholar]
- 40.Whyte G and Courtial J, “Experimental demonstration of holographic three-dimensional light shaping using a Gerchberg-Saxton algorithm,” New J. Phys 7, 117 (2005). [Google Scholar]
- 41.Débarre D, Botcherby EJ, Watanabe T, Srinivas S, Booth MJ, and Wilson T, “Image-based adaptive optics for two-photon microscopy,” Opt. Lett 34, 2495–2497 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
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