Expansion-assisted selective plane illumination microscopy for nanoscale imaging of centimeter-scale tissues

Biological tissue is organized over scales of nanometers to centimeters. Understanding individual cells and multi-cellular organization requires probing the architecture of tissue over these spatial scales simultaneously. However, standard microscope objectives with high resolutions have limited working distances (<1 mm) and fields of view (<1 mm). Imaging large tissue volumes at high resolutions therefore requires physical sectioning and extensive tiling. Physical sectioning distorts the imaged tissue, and focal planes have optical distortions at the edges of the field of view (Zhang and Gross, 2019b). These factors complicate stitching across sections and tile boundaries at sub-micrometer resolutions which in turn increases the complexity and cost of downstream image analysis.

Because light aberration and scattering limit high-resolution microscopy in tissue, including two-photon microscopy, to depths of hundreds of micrometers, sectioning and tiling have traditionally been viewed as necessary to image large specimens (Portera-Cailliau et al., 2005; Tsai et al., 2009; Oh et al., 2014; Economo et al., 2016). However, advances in histological methods, including clearing (Richardson and Lichtman, 2015; Spalteholz, 1914; Dodt et al., 2007; Tsai et al., 2009; Hama et al., 2011; Becker et al., 2012; Ertürk et al., 2012; Chung and Deisseroth, 2013; Ke et al., 2013; Susaki et al., 2014; Tainaka et al., 2014; Yang et al., 2014; Renier et al., 2014; Hou et al., 2015; Costantini et al., 2015; Chi et al., 2018) and expansion for microscopy (ExM; Chen et al., 2015; Chen et al., 2016; Chozinski et al., 2016; Ku et al., 2016), now promise diffraction-limited imaging deep in tissue. Avoiding sectioning and reducing tiling requires overcoming the ‘volumetric’ imaging barrier of microscopy (i.e. the maximum volume that may be imaged; Figure 1). Expansion up to 20×has been demonstrated (Chang et al., 2017), which provides access to molecular spatial scales (10’s of nm), approaching those of cryo electron microscopy (a few nm; Cheng et al., 2015; Fernandez-Leiro and Scheres, 2016; Nogales and Scheres, 2015). These ExM methods produce large, fragile three-dimensional samples, further emphasizing the need for overcoming the volumetric imaging barrier.

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We combine a new microscope and methods for tissue clearing and expansion, which we jointly refer to as Expansion-Assisted Selective Plane Illumination Microscopy (ExA-SPIM). Unlike expansion lattice light-sheet microscopy (Gao et al., 2023), which is limited to small tissue volumes << 1 mm³, ExA-SPIM works with expansion of large centimeter-scale tissue volumes, such as the mouse brain. Leveraging optics and detectors developed for the electronics metrology industry, ExA-SPIM has a field of view ~100 × larger (13.3 mm diameter) and a working distance ~10 × larger (35 mm) compared to objectives typically used for biological microscopy, while retaining a relatively high numerical aperture (NA)=0.305. When combined with 3×expansion, the system achieves an effective resolution of ~0.5 µm laterally, and ~1 µm axially, at imaging speeds of up to 946 megavoxels/s. Imaging with diffraction-limited resolution throughout centimeter-scale specimens requires tissue samples with small index of refraction variations (Weiss et al., 2021). Tailoring the expansion factor allows fine-tuning effective resolution and reduced light collection efficiency for specific tissue types and scientific questions (Figure 1—figure supplement 1).

We apply ExA-SPIM to imaging and reconstructing mammalian neurons. Axonal arbors of individual neurons are complex, branched structures that transmit electrical impulses over distances of centimeters, yet axons can be thinner than 100 nm (Economo et al., 2016; Winnubst et al., 2019). Axons contain numerous varicosities that make synapses with other neurons. Tracing the axonal arbors of single neurons is critical to define how signals are routed within the brain and is also necessary for classifying diverse neuron types into distinct types (Economo et al., 2016; Wang et al., 2021; Winnubst et al., 2019; Xu et al., 2021; Zhang et al., 2021). Large-scale projects based on single-cell transcriptomics in the mouse and human brain have revealed a great diversity of neuron types (Hodge et al., 2019; Tasic et al., 2018; Yao et al., 2023). However, the throughput of neuronal reconstructions has remained too low for single neuron reconstructions on a comparable scale, even in mice. Throughput is limited in part by speed and image quality of existing microscopy methods. We demonstrate that ExA-SPIM provides high-resolution fluorescence microscopy over teravoxel image volumes with minimal distortions, and thereby enables brain-wide imaging with high contrast, resolution, and speed.

We first describe the microscope optics and how they overcome specific requirements for multi-scale tissue imaging. We then outline a new histological method for clearing and expanding centimeter-scale specimens, although details are relegated to extensive protocols (Ouellette et al., 2023, dx.doi.org/10.17504/protocols.io.n92ldpwjxl5b/v1). Finally, we illustrate ExA-SPIM performance for imaging neurons in whole mouse brains and large samples of non-human primate and human cortex.

ExA-SPIM microscope

The ideal fluorescence microscope for large-scale tissue imaging would provide: (1) nanoscale resolution, (2) over centimeter-scale volumes, (3) with minimal tiling and sectioning, (4) high isotropy in resolution and contrast, and (5) fast imaging speed. Multiple imaging systems have been developed within this space, each with unique advantages but inevitable trade-offs (Huisken et al., 2004; Dodt et al., 2007; Wu et al., 2013; Kumar et al., 2014; Tomer et al., 2014; Economo et al., 2016; Narasimhan et al., 2017; Power and Huisken, 2017; Migliori et al., 2018; Chakraborty et al., 2019; Voigt et al., 2019; Voleti et al., 2019; Chen et al., 2020b; Chen et al., 2020a; Glaser et al., 2022; Wang et al., 2021; Xu et al., 2021; Zhang et al., 2021; Qi et al., 2023; Vladimirov et al., 2024).

The choice of microscope objective is a critical design choice. Microscope objectives impose trade-offs between the smallest objects that can be resolved (i.e. the resolution), how much of the specimen can be observed at once (i.e. the field of view), and how thick of a specimen may be imaged (i.e. the working distance). These trade-offs are not based on physical law but reflect limitations of optical design, engineering, and lens manufacturing.

The trade-off between resolution and field of view is related to the etendue (G), which is proportional to the number of resolution elements of an optical system. The etendue is a quadratic function of the lens field of view (FOV) and numerical aperture (NA).

(1) ${\displaystyle {\displaystyle G=\frac{\pi }{4}(FOV\times NA{)}^2}}$

Similarly, for a given NA, the required lens diameter increases proportionally with the required working distance (WD). The ideal lens for large-scale volumetric microscopy would provide large G and WD values.

90% of commercially available objectives for biological microscopy have G<1 mm² (Zhang and Gross, 2019a), and the vast majority of commercially available lenses have lens diameters < 2 cm. Several attempts have been made to develop custom lenses for specific applications. The ‘Mesolens’ provides an etendue of G=6.25 mm², with FOV = 6 mm, NA = 0.47, and WD = 3 mm (McConnell et al., 2016), but difficulties with manufacturing have limited adoption (McConnell, 2020). An objective with an etendue of G=7.07 mm², FOV = 5 mm, NA = 0.6, and WD = 2.7 mm has been developed for two-photon microscopy in vivo (Sofroniew et al., 2016). However, this lens is customized for infrared illumination and exhibits significant field curvature. Additional custom lenses have subsequently been developed for two-photon microscopy, although none of these lenses are suitable for large-scale fluorescence microscopy (Ota et al., 2020; Rumyantsev et al., 2020; Yu et al., 2021). A custom lens with a large etendue of G=18.86 mm², FOV = 14 mm, NA = 0.35, and WD = 19 mm has been developed for the real-time ultra-large-scale high-resolution (RUSH) microscope (Fan et al., 2019). Despite the large etendue, the lens is not commercially available and not designed for immersion, which is critical for imaging cleared or expanded tissues. Custom lenses such as the Schmidt (Voigt et al., 2024), Cousa (Yu et al., 2024), and Kyocera (https://www.ksoc.co.jp/en/seihin/immersion-objective/immersion-objective.html) lenses offer long working distances but small etendues. See Appendix 1—table 1 for a summary of the existing custom microscopy objectives.

Rather than designing a highly customized lens from scratch, we leveraged engineering investments that have been made in a different domain. High-resolution, high-speed imaging is widely used within the machine vision and metrology industry, where optical microscopes are used to map defects in semiconductors and other electronic devices. As the physical size of electronic components (e.g. pixels on flat panel displays) has become smaller, lenses for this domain have been designed with increasing NA, accessing biologically relevant resolutions (<1 µm). For a given NA, these lenses have remarkably large fields of view (and thus high etendues), low-field curvature, minimal distortion, and chromatic correction throughout the visible wavelengths. A comparison of the etendue as a function of NA and working distance for these lenses, and commercial and custom life sciences lenses is summarized in Figure 1. A more detailed summary of many electronics metrology technologies is provided as LABEL:Supplementary_File_1. Despite their superb specifications, both the lenses and camera sensors are readily available, are manufactured in large quantities, and are cost effective. We investigated the performance of metrology optics and cameras for imaging biological tissues and adapted a particular set of machine vision optics for biological microscopy.

The detection path of the system uses a lens with 5.0×magnification, with diffraction-limited imaging at NA = 0.305, and a 16.8 mm field of view (G=19.65 mm²) (VEO_JM DIAMOND 5.0×/F1.3, Schneider-Kreuznach, Germany) (Figure 2a). A 35-mm-thick glass beam splitter, normally used for co-axial illumination, is replaced with an optically equivalent thickness of liquid media. This enables spherical aberration-free imaging through 35–40 mm of liquid media (tunable for refractive indices between 1.33 and 1.56), including expanded hydrogels and cleared tissues. The lens is paired with a large-format CMOS sensor (VP-151MX, Vieworks Korea), based on the Sony IMX411 sensor, with a single-sided rolling shutter, 151 megapixels, 14192 (H)×10,640 (V), and a pitch of 3.76 µm. The camera captures a field of view of 10.6×8.0 mm (13.3 mm diagonal), capable of covering an entire 3×expanded mouse brain in only 15 tiles (Figure 2b). A conventional SPIM system would require 400+tiles to image an entire cleared brain at equivalent resolutions (Figure 2—figure supplement 1). A comparison of the selected lens and camera sensor with a state-of-the-art cleared tissue objective lens (Nikon 20×GLYC) and sCMOS camera (Hamamatsu Orca BT-Fusion) is shown in (Figure 2—figure supplement 2).

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To improve axial resolution, our system synchronizes an axially-swept light sheet with the rolling shutter of the Sony IMX411 sensor (Dean et al., 2015). The CMOS sensor parallelizes readout across 14192 pixels per row, thereby achieving equivalent or greater pixel rates than typical scientific CMOS (sCMOS) sensors, even with an increased time per line and a relatively low frame rate (<6.4 Hz). As a result, the ExA-SPIM microscope operates at a higher imaging speed with comparable signal-to-noise ratio (SNR) to sCMOS-based systems (Figure 2e). See (Figure 2—figure supplement 3) for noise comparisons between the Sony IMX411 and sCMOS sensors. The low frame rate of the sensor facilitates accurate axially swept imaging using generic scanning hardware at an imaging speed of 946 megavoxels/sec. The excitation lens (1–290419, Navitar) is infinity-corrected (to enable axially swept excitation) and provides diffraction-limited resolution at NA = 0.133 over a 16 mm field of view. When fitted with a custom dipping cap, the excitation lens provides a working distance of 53 mm in water. The beam shaping in the excitation path is configured to deliver a light sheet with NA = 0.10 and width of 12.5 mm (full-width half-maximum). A full CAD layout of the ExA-SPIM system is shown in (Figure 2—figure supplement 4). Due to the large light-sheet, the ExA-SPIM system uses high excitation laser powers (1000+mW; Figure 2—figure supplement 5), resulting in light intensities typically used in SPIM microscopy. Photobleaching is not detrimental at these imaging conditions (Figure 2—figure supplement 6).

Based on these lenses, and the travel limits of a mechanical motorized stage, the ExA-SPIM is capable of imaging a 200×52 × 35 mm³ volume, with ~1.5 µm lateral and ~3 µm axial native optical resolution, and minimal field curvature quantified using the methodology described in Vladimirov et al., 2024 and distortion (Figure 2c–d). We leveraged this large volume to image intact, expanded tissues.

With 3×tissue expansion, the system can image a native tissue volume of 67×17 × 12 mm³ with an effective optical resolution of ~0.5 µm laterally and ~1 µm axially. This amounts to >100 teravoxels, which can be captured without the need for physical sectioning and with minimal tiling. The large field of view of the ExA-SPIM system can also permit imaging cleared tissues, such as mouse brains, in a single tile (Figure 2—figure supplement 7). A summary plot comparing the focal volume, isotropy, and imaging speed of our new ExA-SPIM system to other large-scale volumetric imaging systems is shown in (Figure 2—figure supplement 8). Individual data points are listed in Appendix 1—table 2.

The large-scale and high-speed imaging made possible by the ExA-SPIM system required new software for controlling the microscope, as well as downstream computational pipelines for compressing and handling the resulting datasets (Figure 2—figure supplement 9). We developed custom acquisition software capable of interfacing with the VP-151MX camera driver (https://github.com/acquire-project), which streams imaging data directly to next-generation file formats (Beati et al., 2020; Clack et al., 2023; Moore et al., 2021). Using a combination of high-speed networking, fast on-premises storage, and real-time compression, our current imaging pipeline enables a throughput to cloud storage of >100 TB per day, using commodity hardware. This pipeline is actively being developed, with ongoing efforts focused on standardizing every processing step around the OME-Zarr file format (Moore et al., 2023).

Whole-brain expansion

Tissue expansion for microscopy (ExM) enables imaging optically transparent specimens at effective resolutions well below the diffraction limit of light microscopes (Chang et al., 2017; Chen et al., 2015; Tillberg and Chen, 2019; Figure 2—figure supplement 10). Moreover, ExM can produce optically clear specimens with low fluorescence background. Multiple ExM variations have emerged, driven by specific biological questions. These include engineered hydrogel chemistry for post-expansion molecular interrogation of proteins or RNA (Asano et al., 2018; Cho and Chang, 2022; Tillberg et al., 2016; Cui et al., 2022), formulations that provide gel stiffness (Chen et al., 2021) or tunable expansion up to >10 × (Damstra et al., 2022; Klimas et al., 2023; Sarkar et al., 2022; Truckenbrodt et al., 2018). Most of these protocols have been developed for specimens that are at most 100 micrometers thick. We developed ExM methods for centimeter-scale tissue samples, including entire mouse brains. A key requirement for ExA-SPIM is optical clearing so that the entire volume can be imaged with diffraction-limited resolution without sectioning. Index of refraction inhomogeneities in heavily myelinated fiber tracts pose particular challenges. Clearing was achieved by stringent dehydration and delipidation prior to gelation and expansion. We systematically evaluated dehydration agents, including methanol, ethanol, and tetrahydrofuran (THF), followed by delipidation with commonly used protocols on 1-mm-thick brain slices. Slices were expanded and examined for clarity under a macroscope. Dehydration using THF was followed by two sequential delipidation steps. First DISCO type clearing Chi et al., 2018; Renier et al., 2014 followed by aqueous delipidation (Chen and Svoboda, 2020c, dx.doi.org/10.17504/protocols.io.zndf5a6) rendered the samples extremely transparent (Figure 3a; Ouellette et al., 2023, dx.doi.org/10.17504/protocols.io.n92ldpwjxl5b/v1). In addition to clearing specimens, delipidation facilitates immunolabeling brain samples prior to gelation and expansion (Figure 3). Signal amplification facilitates high contrast imaging of small structures, especially since the increase in volume upon expansion dilutes the concentration of the fluorophore. For gelation, we used VA-044 as the initiator, instead of the more commonly used APS/TEMED (Tillberg and Chen, 2019). VA-044 initiates free radicals at a temperature-dependent rate. At low temperatures (4 °C), the gelling reagents are allowed to diffuse to the center of thick samples, followed by higher temperatures (37 °C) to trigger uniform polymerization. Our protocol provides nearly isotropic expansion of the whole sample (including internal brain structures). Although developed for the mouse brain, we have successfully used this protocol for other large specimens, such as a 1 cm×1 cm×1.5 cm piece of macaque motor cortex, as well as a 1 cm×1 cm×0.01 cm section of human visual cortex (Ouellette et al., 2023, dx.doi.org/10.17504/protocols.io.n92ldpwjxl5b/v1).

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Imaging single neurons across entire mouse brains

We cleared and expanded entire mouse brains and imaged individual neurons, resolving their dense axonal projections (Figure 3). Tracking the axons of individual neurons is a challenging problem—axon collaterals can be very thin (<100 nm) and traverse large distances (centimeters), spanning vast areas of the brain. This requires high-resolution, high-contrast imaging of the entire brain without loss of data. Current best-in-class approaches (Economo et al., 2016; Gong et al., 2013; Gong et al., 2016; Winnubst et al., 2019) require physical sectioning and extensive tiling, which complicates downstream data processing. In addition, imaging lasts multiple days, which increases the chance for experimental failure and data loss. Finally, the resolution of these imaging methods is highly anisotropic (typically <1:6), which compromises the ability to perform unambiguous axon tracing.

We expressed GFP in a sparse subset of neurons in an enhancer virus mouse to label subcortical projection neurons. Brains were expanded (3×) and imaged in 15 tiles in as little as 24 hr. Because of the lack of physical tissue slicing and minimal tiling, datasets are aligned with an average residual error of <2 pixels between interest point correspondences in neighboring tiles (Hörl et al., 2019). Tile alignment of a representative dataset based on initial stage coordinates (Video 1 and Video 2), after stitching optimization (Video 3), and after fusion (Video 4) are available as videos. Dense axonal projections and varicosities are clearly visible (Figure 3e–h), and long-range axons can be tracked across the brain (Figure 3c–d). Videos of the data displayed in Figure 3e and f are shown in Video 5 Video 6.

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Representative images of axons as a function of imaging depth are shown in (Figure 3—figure supplement 1). The achromatic performance of the ExA-SPIM optics also enables multiple fluorescence channels to be acquired from mouse brains (e.g. GFP and tdTomato; Figure 4).

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Imaging cortico-spinal tract neurons in macaque motor cortex

We next established that our methods are applicable to larger brains. With minor adaptations, we applied our clearing and expansion protocol to a 1 cm×1 cm×1.5 cm block of pigtail macaque brain from the hand-wrist and trunk regions of primary motor cortex. Due to its strong myelination, this region of the primate brain is particularly difficult to clear and image (Van Essen et al., 2019). The specimen contained cortico-spinal neurons expressing a fluorescent protein (tdTomato) driven by retrograde AAV injected into the intermediate and ventral laminae of the cervical spinal cord. ExA-SPIM image volumes revealed brightly labeled cortico-spinal neurons (Figure 5a–b and Video 7, Video 8, Video 9). Individual neurons, their dendritic arbors, and extensive dendritic spines as well as the descending axon and collaterals are clearly discernible (Figure 5c–f and Video 10). Additionally, even in down-sampled data (~1 µm effective voxel size), we can follow axonal pathways. This raises the possibility that with a small number of slices (~6 × 1 cm thick slabs) and reduced imaging resolution, a modified inverted (Wu et al., 2013; Kumar et al., 2014) or open-top (McGorty et al., 2015; Glaser et al., 2017; Glaser et al., 2019; Barner et al., 2020; Glaser et al., 2022) ExA-SPIM design (Figure 5—figure supplement 1) could provide a mesoscale map of white matter axons across the entire macaque brain in several days. Existing efforts to map pathways in the primate brain at this resolution require slicing the brain in 300 µm sections and laboriously assembling the resulting images into a coherent 3D volume (Xu et al., 2021).

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Visualizing axons in human neocortex and white matter

Heavy chain neurofilaments comprise the internal scaffolds of long-range projection axons. Visualizing these neurofilaments with immunofluorescence could provide detailed information about axonal trajectories without the need for gene transfer methods, and which cannot be achieved with other methods, such as diffusion magnetic resonance imaging (Huang et al., 2021; Van Essen et al., 2019) or high-resolution optical coherence tomography (Li et al., 2019). We next evaluated ExA-SPIM for imaging immunolabeled heavy chain neurofilaments in the human neocortex (Figure 6).

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We cleared and expanded (4×) a~1 cm×1 cm×0.01 cm piece of human neocortex (Figure 6a) and labeled the tissue with fluorescent SMI-32, which preferentially stains heavy chain neurofilaments. Individual axons and their trajectories are clearly visible through the 350-µm-thick sample (Figure 6b–d). The larger axons (>1 µm) are well separated from each other in both the gray and white matter. In the white matter, axons are arranged as multiple intercalated populations coursing in different directions, rather than as homogeneous fascicles. These results demonstrate the feasibility of using ExA-SPIM for visualizing white matter tract axons and lay the foundation for scaling this data acquisition to multiple, thick tissue sections, and ultimately the entire human brain.

Recent breakthroughs in histological methods (Richardson and Lichtman, 2015; Spalteholz, 1914; Dodt et al., 2007; Tsai et al., 2009; Hama et al., 2011; Becker et al., 2012; Ertürk et al., 2012; Chung and Deisseroth, 2013; Ke et al., 2013; Susaki et al., 2014; Tainaka et al., 2014; Yang et al., 2014; Renier et al., 2014; Hou et al., 2015; Costantini et al., 2015; Chi et al., 2018), fluorescent labeling strategies (Kim et al., 2015; Susaki et al., 2020; Mao et al., 2020), fast and sensitive cameras, and new microscopes (Huisken et al., 2004; Dodt et al., 2007; Wu et al., 2013; Kumar et al., 2014; Tomer et al., 2014; Economo et al., 2016; Narasimhan et al., 2017; Power and Huisken, 2017; Migliori et al., 2018; Chakraborty et al., 2019; Glaser et al., 2019; Voigt et al., 2019; Voleti et al., 2019; Chen et al., 2020b; Chen et al., 2020a; Wang et al., 2021; Xu et al., 2021; Zhang et al., 2021; Glaser et al., 2022; Qi et al., 2023; Vladimirov et al., 2024) are revolutionizing our ability to study the molecular organization of cells and tissues with fluorescence microscopy.

For many applications, it is necessary to reconstruct tissues with high resolution and contrast over large spatial scales, including cells, organs, or even entire organisms. Such multi-scale imaging is necessary to reconstruct individual neurons in the mouse brain, which can span many millimeters, but with axons that are often less than 100 nm thick. ExA-SPIM addresses this need for imaging of large tissue volumes with high resolution and contrast. The resulting image volumes are sufficient for manual neuron tracing, and preliminary data suggests that automated segmentation of thin axons is improved compared to previous approaches (Winnubst et al., 2019).

ExA-SPIM relies on tissue expansion and lower NA optics compared to microscopes that are typically used for high-resolution imaging. This approach has several advantages over imaging of cleared (non-expanded) tissues. First, optical engineering requirements are more forgiving for lenses with lower NA. As shown in (Figure 1b), lower NA lenses can have much higher etendues, fields of view, and much longer working distances. In other words, a large volumetric coverage (accessible resolvable voxels) is more feasible with lower NA lenses. In addition, these lenses are more easily corrected for common distortions, including field curvature, pincushion distortion, vignetting, and uniform resolution across the entire field of view (Zhang and Gross, 2019a). These lenses can be paired with large format CMOS sensors that offer voxel rates surpassing more commonly used scientific CMOS sensors. These technologies will continue to progress rapidly. For example, the recently announced 247-megapixel Sony IMX811 sensor will be capable of delivering 12-bit images at up to 3 gigavoxels/s.

Second, lower NA imaging is less sensitive to tissue-induced aberrations. Although clearing and expansion techniques render tissues transparent, remnant refractive index inhomogeneities still deteriorate image quality at larger imaging depths (Weiss et al., 2021). This is more noticeable at higher NA, because the rays entering the objective at higher angles have longer path lengths through the tissue and are more susceptible to aberrations (Figure 2—figure supplement 4). For lower NA systems, the differences in path lengths between the extreme and axial rays are smaller. For example, the effect of spherical aberration (e.g. loss of Strehl ratio, which may be a dominant aberration mode for imaging cleared and expanded tissues) increases with NA to the third power (Hecht, 2015).

Third, the refractive index gradients in tissue are expected to decrease with the third power of the expansion factor (proportional to density) (Jacques, 2013). As a consequence, tissue-induced aberrations reduce with the expansion factor to the third power. In contrast, the imaging path length through the tissue only increases with the expansion factor to the first power. These factors together may explain the advantage of expansion-assisted imaging (Figure 2—figure supplement 10). Further investigation into the optical properties of hydrogels and their constituents is necessary to fully support these hypotheses. ExA-SPIM still exhibits a decrease in image quality at larger imaging depths (Figure 2—figure supplement 1). This can be reduced with dual-sided excitation (Dodt et al., 2007) as well as multi-view detection (Tomer et al., 2012).

One limitation of the current implementation of the ExA-SPIM is related to inefficient collection of signal photons. Given that signal collection scales approximately with NA², signal collection is 10×lower compared to a NA = 1.0 system ((1/0.305)²). However, we find that the ExA-SPIM system can detect and resolve dim, nanoscale biological features with high signal-to-noise ratio (e.g. thin axons). Because of the large field of view, high SNR imaging requires 1000+mW lasers. However, light intensities are similar to traditional SPIM systems because the power is distributed over a ~1 cm wide light sheet. Under these imaging conditions, photobleaching is not detrimental (i.e. ~50% reduction in intensity after ~800 repeated exposures), suggesting that even higher laser intensities could be used for higher SNR imaging (Figure 2—figure supplement 6). To improve signal collection and enable even higher sensitivity ExA-SPIM imaging, a custom lens with NA = 0.5, FOV = 6.8, and a 35 mm working distance in liquid has been designed and is being fabricated. This new lens will enable ~50–150 nm effective lateral resolution in 3–12×expanded tissues, with 2.7×improved light collection efficiency.

Fundamentally, contrast, effective resolution, and imaging speed in expansion microscopy are limited by the density of fluorescent molecules labeling the structure of interest and the photon budget. ExA-SPIM allows distributing the technical burden between optics and tissue expansion (Figure 1—figure supplement 1). In practice, a target resolution should first be specified, corresponding to a viable combination of NA and tissue expansion, taking into consideration light collection efficiency (NA dependent) and required working distance (expansion factor dependent).

Although we have focused on the combination of new microscopy with tissue expansion, the ExA-SPIM microscope design will be useful for many additional imaging applications. For example, the field of view of the ExA-SPIM is sufficient to image a cleared mouse brain without the need for tiling. Cleared mouse brain datasets with ~1.5 µm lateral resolution could be acquired at a speed of ~36 min/channel (Figure 2—figure supplement 7). In addition, the entire system could be converted to an inverted (Wu et al., 2013; Kumar et al., 2014) or open-top (McGorty et al., 2015; Glaser et al., 2017; Glaser et al., 2019; Barner et al., 2020; Glaser et al., 2022) architecture, which would enable large-scale imaging of tissue slabs with large aspect ratios that are up to 1 cm thick (Figure 2—figure supplement 1). Similarly, an ExA-SPIM system with a lower resolution metrology lens could easily be built (see Appendix 1—table 3). These more mesoscale systems would provide extremely high imaging throughput (cm³ per day) and could pave the way for new large-scale neuroanatomy investigations of human and non-human primate tissues.

Finally, new types of applications may also require multi-scale microscopy. For example, expansion microscopy could provide a super-resolution view of interactions between immune cells and solid tumors. This application may require 10×expansion, for better than 100 nm effective resolution, to distinguish immunofluorescence in immune cells and tumor cells, throughout millimeter-scale tissue samples (10 mm after expansion). Given that 10×expanded samples are mechanically fragile and difficult to handle and section, the large field of view and reduced need for physical sectioning of ExA-SPIM would be highly advantageous for these demanding imaging experiments.

In summary, our new ExA-SPIM approach represents a new substrate for innovation within the realm of large-scale fluorescence imaging. By leveraging technologies from the electronics metrology industry, in combination with whole-mount tissue expansion, the system provides: (1) nanoscale lateral resolution, (2) over large centimeter-scale volumes, (3) with minimal tiling and no sectioning, (4) high isotropy, and (5) fast imaging speed.

Imaging datasets for this paper are available at:s3://aind-open-data/exaSPIM_708373_2024-04-02_19-49-38 s3://aind-open-data/exaSPIM_671477_2024-01-08_17-03-04 s3://aind-open-data/exaSPIM_Z13288-QN22-26-036_2023-03-26_09-27-21 s3://aind-open-data/exaSPIM_H17.24.006-CX55-31B_2023-05-11_14-59-09 Instructions for how the data is organized and how it can be accessed is available at: https://allenneuraldynamics.github.io/data.html.

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Author details

1. Adam Glaser

   Allen Institute for Neural Dynamics, Seattle, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Jayaram Chandrashekar

   For correspondence

   adam.glaser@alleninstitute.org

   Competing interests

   A.G., J.C., and K.S. have filed for patent WO2024129575A1 on aspects of the ExA-SPIM system

   "This ORCID iD identifies the author of this article:" 0000-0003-3558-8994

2. Jayaram Chandrashekar

   Allen Institute for Neural Dynamics, Seattle, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Adam Glaser

   For correspondence

   jayaramc@alleninstitute.org

   Competing interests

   A.G., J.C., and K.S. have filed for patent WO2024129575A1 on aspects of the ExA-SPIM system

3. Sonya Vasquez

   Allen Institute for Neural Dynamics, Seattle, United States

   Contribution

   Resources, Software, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

4. Cameron Arshadi

   Allen Institute for Neural Dynamics, Seattle, United States

   Contribution

   Resources, Software, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

5. Rajvi Javeri

   Allen Institute for Neural Dynamics, Seattle, United States

   Contribution

   Resources, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

6. Naveen Ouellette

   Allen Institute for Neural Dynamics, Seattle, United States

   Contribution

   Resources, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0005-4472-0880

7. Xiaoyun Jiang

   Allen Institute for Neural Dynamics, Seattle, United States

   Contribution

   Resources, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

8. Judith Baka

   Allen Institute for Neural Dynamics, Seattle, United States

   Contribution

   Resources, Software, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

9. Gabor Kovacs

   Allen Institute for Neural Dynamics, Seattle, United States

   Contribution

   Resources, Software, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

10. Micah Woodard

    Allen Institute for Neural Dynamics, Seattle, United States

    Contribution

    Resources, Software, Formal analysis, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

11. Shamishtaa Seshamani

    Allen Institute for Neural Dynamics, Seattle, United States

    Contribution

    Resources, Software, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

12. Kevin Cao

    Allen Institute for Neural Dynamics, Seattle, United States

    Contribution

    Software, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0226-7687

13. Nathan Clack

    Chan Zuckerberg Initiative, Redwood City, United States

    Contribution

    Resources, Software, Formal analysis, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

14. Andrew Recknagel

    Allen Institute for Neural Dynamics, Seattle, United States

    Contribution

    Resources, Software, Formal analysis, Investigation, Methodology

    Competing interests

    No competing interests declared

15. Anna Grim

    Allen Institute for Neural Dynamics, Seattle, United States

    Contribution

    Resources, Software, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

16. Pooja Balaram

    Allen Institute for Neural Dynamics, Seattle, United States

    Contribution

    Resources, Methodology, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

17. Emily Turschak

    Allen Institute for Neural Dynamics, Seattle, United States

    Contribution

    Resources, Investigation, Methodology, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

18. Marcus Hooper

    Allen Institute for Neural Dynamics, Seattle, United States

    Contribution

    Resources, Software, Investigation, Methodology

    Competing interests

    No competing interests declared

19. Alan Liddell

    Chan Zuckerberg Initiative, Redwood City, United States

    Contribution

    Resources, Software, Formal analysis

    Competing interests

    No competing interests declared

20. John Rohde

    Allen Institute for Neural Dynamics, Seattle, United States

    Contribution

    Resources, Formal analysis, Methodology

    Competing interests

    No competing interests declared

21. Ayana Hellevik

    Allen Institute for Neural Dynamics, Seattle, United States

    Contribution

    Resources, Methodology

    Competing interests

    No competing interests declared

22. Kevin Takasaki

    Allen Institute for Neural Dynamics, Seattle, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

23. Lindsey Erion Barner

    Allen Institute for Neural Dynamics, Seattle, United States

    Contribution

    Resources, Methodology, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

24. Molly Logsdon

    Allen Institute for Neural Dynamics, Seattle, United States

    Contribution

    Investigation, Methodology, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

25. Chris Chronopoulos

    Allen Institute for Neural Dynamics, Seattle, United States

    Contribution

    Software, Formal analysis, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

26. Saskia EJ de Vries

    Allen Institute for Neural Dynamics, Seattle, United States

    Contribution

    Resources, Software, Formal analysis, Funding acquisition, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-3704-3499

27. Jonathan T Ting

    Allen Institute for Brain Science, Seattle, United States

    Contribution

    Resources, Funding acquisition, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

28. Steven Perlmutter

    University of Washington, Seattle, United States

    Contribution

    Resources, Funding acquisition, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

29. Brian E Kalmbach

    Chan Zuckerberg Initiative, Redwood City, United States

    Contribution

    Resources, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3136-8097

30. Nikolai Dembrow

    Chan Zuckerberg Initiative, Redwood City, United States

    Contribution

    Resources, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

31. Bosiljka Tasic

    Chan Zuckerberg Initiative, Redwood City, United States

    Contribution

    Resources, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-6861-4506

32. R Clay Reid

    Chan Zuckerberg Initiative, Redwood City, United States

    Contribution

    Resources, Software, Formal analysis, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8697-6797

33. David Feng

    Allen Institute for Neural Dynamics, Seattle, United States

    Contribution

    Conceptualization, Resources, Software, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

34. Karel Svoboda

    Allen Institute for Neural Dynamics, Seattle, United States

    Contribution

    Conceptualization, Resources, Software, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

    Competing interests

    A.G., J.C., and K.S. have filed for patent WO2024129575A1 on aspects of the ExA-SPIM system

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