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Published in final edited form as:
Nat Methods. ; 9(3): 255–258. doi:10.1038/nmeth.1854.

Serial two-photon tomography: an automated method for ex-vivo
mouse brain imaging
Timothy Ragan1,*, Lolahon R. Kadiri2,*, Kannan Umadevi Venkataraju2, Karsten Bahlmann1,
Jason Sutin1, Julian Taranda2, Ignacio Arganda-Carreras3, Yongsoo Kim2, H. SebastianSeung3, and Pavel Osten2
1TissueVision, Inc., One Kendall Square, Cambridge, MA 02139
2Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY 11724
3Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Howard

Hughes Medical Institute, Cambridge, MA 02139

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Abstract
Here we describe an automated method, which we call serial two-photon (STP) tomography, that
achieves high-throughput fluorescence imaging of mouse brains by integrating two-photon
microscopy and tissue sectioning. STP tomography generates high-resolution datasets that are freeof distortions and can be readily warped in 3D, for example, for comparing multiple anatomical
tracings. This method opens the door to routine systematic studies of neuroanatomy in mousemodels of human brain disorders.

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Since the pioneering work of Ramón y Cajal, advances in light microscopy have been
central to many discoveries in neuroanatomy. The recent focus on systematic generation ofwhole-brain datasets, for example the Allen Mouse Brain Atlas for gene expression1 and theongoing Mouse Brain Architecture Project for mesoscopic connectivity2, has created apressing need for the development of new instrumentation for high-throughput whole-brain
imaging. Here we describe a microscopy method, which we call serial two-photon (STP;
pronounced “step”) tomography, that achieves automated high-throughput imaging offluorescently labeled mouse brains. This method uses whole-mount two-photon
microscopy3, 4 and thus generates datasets of well aligned, high-resolution serial opticalsections. We tested the versatility of STP tomography by imaging four mouse brains withcell type-specific fluorescent protein expression and systematically mapping input andoutput connections of mouse somatosensory cortex. These experiments show that STP
tomography is a robust imaging method that can transform the emerging field of systematicwhole-brain anatomy, until now limited to dedicated atlasing initiatives1, 2, into a routine
methodology applicable, for example, to the study of mouse models of human brain
disorders in standard laboratory settings.

Correspondence to: Pavel Osten.
*equal contribution
Conflict of Interest
Karsten Bahlmann and Timothy Ragan are shareholders and employees of TissueVision, Inc., Jason Sutin is an employee ofTissueVision Inc., Pavel Osten is a consultant of TissueVision Inc. and a shareholder and consultant of Certerra Inc.
Author Contribution
J.S., T.R., K.B. and P.O. planned the redesign of the instrument, T.R., J.S., and K.B. engineered and built the instrument, T.R and J.S.
wrote the operating software, L.R.K did all imaging experiments, J.T. contributed to early imaging experiments, Y.K. validated
warping accuracy, K.U.V. and I.A-C. set up all image processing methods, H.S.S. supervised image processing, T.R and K.B.
contributed to writing instrument portions of the paper, P.O. supervised imaging, coordinated all work, and wrote the paper.

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STP tomography works as follows (Fig. 1; Supplementary Video 1). First, a fixed agarembedded mouse brain is placed in a water bath on integrated XYZ stages under theobjective of a two-photon microscope and imaging parameters are entered in the operating
software (Online Methods). Once these are set, the instrument works fully automatically: 1)
the XYZ stages move the brain under the objective so that an optical section (or an optical
Z-stack) is imaged as a mosaic of fields of view (FOVs); 2) a builtin vibrating blade
microtome mechanically cuts off a tissue section from the top; and 3) the steps ofoverlapping optical and mechanical sectioning are repeated until the dataset is collected. The
instrument is based on a previous prototype4 that was redesigned for imaging offluorescently labeled mouse brains, including the integration of a custom-build vibrating
blade microtome instead of a milling machine and the use of high-speed galvanometricscanners instead of a rotating polygonal scanner (Online Methods). Sectioning by vibrating
blade microtome allows the use of brains prepared by simple procedures of formaldehyde
fixation and agar embedding, which have minimal detrimental effects on fluorescence andbrain morphology. High-speed galvanometric scanning enables fast imaging at different
sampling resolutions.

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In the first set of experiments, we used Thy1-GFPM mice, which express green fluorescentprotein (GFP) mainly in hippocampal and cortical pyramidal neurons, to determine theoptimal conditions for imaging mouse brains at different sampling resolutions. We imaged
the GFPM brain as a dataset of 260 coronal sections, evenly spaced by 50 μm, with 10x and
20x objectives at XY imaging resolution 2.0, 1.0 and 0.5 μm (Fig. 1; Supplementary Figs.
1–3; Supplementary Video 2). The 10x objective (0.6 NA) allows fast imaging at aresolution sufficient to visualize the distribution and morphology of GFP-labeled neurons,
including their dendrites and axons (Supplementary Figs. 2–3). The data collection times fora 10×-objective dataset of 260 coronal sections are ~6 1/2 and 8 1/2 hrs at x-y sampling of 2
and 1 μm, respectively (Supplementary Table 1). The 20x objective (1.0 NA) enables
visualization of dendritic spines and fine axonal arborizations (Fig. 1 and SupplementaryFigs. 2–3; note that in this application axons are detected within single XY optical sections,but not traced in Z, because of the spacing of 50 μm between each section). The datacollection times for a 260-section dataset using the 20x objective are ~15 1/2 and 24 hrs at
x-y sampling of 1 and 0.5 μm, respectively (Supplementary Table 1). Taken together, theseexperiments show that STP tomography can be used as an automated method for collectionof high resolution datasets of fluorescently labeled mouse brains.

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Transgenic mice with cell type-specific fluorescent protein expression allow easy
identification of different types of neurons and glia5. Next, we show examples of wholebrain mapping of different cell types in two BAC transgenic mice and one gene-targeted(knockin) mouse. The Mobp-GFP5 mouse reveals a pattern of whole-brain myelination as aresult of GFP expression in oligodendrocytes from the promoter of myelin-associatedoligodendrocyte basic protein (Mobp) (Supplementary Video 3). The ChAT-GFP5 mouseallows visualization of whole-brain cholinergic innervation as a result of GFP expression incholinergic neurons from the choline acetyltransferase (ChAT) promoter (Supplementary
Video 4). The SST-ires-Cre::Ai96 mouse reveals brain-wide distribution of somatostatinexpressing interneurons as a result of Cre recombinase expression from the somatostatin(SST) gene6 (Supplementary Video 5). These experiments show the ease of generating brain
atlas-like datasets of cell-type distribution and innervation by STP tomography in transgenic
mice. In addition, a more complete visualization of a specific cell-type distribution can beachieved by imaging Z-stack volumes, instead of single optical sections, between the stepsof mechanical tissue sectioning. As an example of this application, we include a dataset of
800 optical sections (2.5 μm Z-spacing) revealing the distribution of all somatostatinexpressing interneurons in the olfactory bulbs of the SST-ires-Cre::Ai96 mouse(Supplementary Video 6 and 7). Imaging with high Z resolution, of course, increases the
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acquisition time and currently it would take about 7 days to image a whole mouse brain at
the same resolution. However, increasing the imaging speed (at present 0.4 μs pixel
residence time) by, for example, integration of resonant scanners, should make high Zresolution imaging of whole mouse brains by STP tomography more practical in the future.

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In the final set of experiments, we demonstrate the use of STP tomography for mapping
brain connectivity by imaging mouse brains injected with anatomical tracers in thesomatosensory barrel cortex, a brain region with projections well documented by both retroand anterograde tracing7, 8. We imaged brains injected with CTB-Alexa-488 for retrograde
tracing and adeno-associated virus expressing GFP (AAV-GFP) for anterograde tracing at 1μm XY resolution (20x objective). As expected, Alexa-488-labeled neurons were found inbrain areas known to project to the mouse barrel cortex7, 8 and GFP-labeled axons weredetected in brain areas known to receive barrel cortex projections7 (Figs. 2 and 3,
Supplementary Figs. 4 and 5). The experiments also revealed two brain regions with sparselabeling that were not previously reported in the literature: retrogradely labeled contralateralorbital cortex (Supplementary Fig. 4b panel 2) and anterogradely labeled contralateral motor
cortex (Supplementary Fig. 5b panel 2). Taken together, the replication of the previously
described pattern of connectivity and the detection of putative new connections in thecontralateral cortical areas demonstrate that STP tomography is both a high-throughput andhighly sensitive method for whole mouse brain anatomy. The 3D alignment of the datasets,in addition, facilitates direct comparison between different samples. We demonstrate this bywarping the AAV-GFP brain onto the CTB Alexa-488 brain for direct comparison ofanterograde and retrograde tracings (Fig. 3; Online Methods; Supplementary Video 7). We
estimated the precision of co-registration of anatomical landmarks between two brains to beapproximately 100 μm (Supplementary Fig. 7). Warping of multiple brains to one spacethus provides a simple alternative to multiple tracer injections and can be extended toinclude many brains in a virtual brainbow9-like tracing.
STP tomography is part of a growing field of automated methods for ex-vivo imaging ofanimal (mainly mouse) brain tissue1, 3, 10–14. The combination of serial imaging andsectioning was pioneered by block-face imaging in light microscopy15 and more recently
adapted for two-photon microscopy3, 4, confocal microscopy16, and electron microscopy10.
The use of two-photon microscopy allows deep tissue imaging, which is advantageous forfocusing below the surface to obtain undisturbed optical sections and to collect highresolution Z-stacks between sectioning steps, as pioneered in all-optical histology3.

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What are the advantages of STP tomography compared to other methods for whole mousebrain imaging? One comparison is with robotized wide-field fluorescence microscopy ofmounted serial brain sections1. This approach, however, is more laborious and prone toerror, because it requires brain sectioning and mounting of the sections on microscopic
slides. In addition, enhancements in scanning speed of STP tomography should allow theuse of our method for complete whole-brain anatomical tracings and cell counts, whereas
imaging of cut tissue sections will always result in some loss of data.
A second comparison is with light-sheet fluorescence microscopy (LSFM; also known as
selective-plane illumination microscopy (SPIM)17 and ultramicroscopy11), which can image
mouse brains without mechanical sectioning, but at a lower resolution11. Until now, the useof LSFM for whole mouse brain imaging has also been limited by the need for chemicalclearing that quenches fluorescent proteins, but this may be overcome by the development of
improved clearing reagents18. Finally, a third comparison is with micro-optical sectioning
tomography (MOST14; also known as knife-edge scanning microscopy13), which combinesbright-field line-scan imaging and ultra-microtome cutting and was used to image Golgistained mouse brains in two studies13, 14. This method, however, has yet to be implemented

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for fluorescence microscopy and since the tissue preparation requires dehydration andplastic embedding, which decrease GFP intensity, it is not clear how MOST will perform foranatomical tracing and other applications described in our study.
In summary, we conclude that STP tomography can be used to generate high-resolution
anatomical datasets that can be readily warped for comparison of multiple brains. STP
tomography is particularly well-suited for systematic studies of brain anatomy in genetic
mouse models of cognitive disorders, such as autism and schizophrenia. To provide
quantitative measurements for such studies, we are currently focusing on anatomicalregistration19 and the development of computational methods for detection of fluorescencesignals in whole-brain datasets generated by STP tomography.

Online Methods
Tissue Preparation

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The following mouse strains were used: GENSAT ChAT-GFP Tg(Chat-EGFP)GH293Gsat/
Mmcd (#000296-UCD) and Mobp-GFP Tg(Mobp-EGFP)103Gsat/Mmcd (#030483-UCD)5(MMRRC; www.mmrrc.org); GFPM20; SST-ires-Cre::Ai96; and wild type C57BL/6J (Jax
mice; # 000664). As anatomical tracers, we used CTB Alexa Fluor-488 (Invitrogen;#C22841; 0.5 % wt/vol in phosphate buffer) and AAV-GFP with synapsin promoter21, 22.
AAV was produced at the Salk vector core (http://vectorcore.salk.edu) as a chimeric 1/2
serotype23, purified by iodoxinal gradient and concentrated to 5.3 × 1011 genomic copy per
ml. Stereotaxic injections of the tracers were done as described24. Briefly, the mice wereanaesthetized by 1% isoflurane inhalation. A small craniotomy (approximately 300 × 300μm) was opened over the left primary somatosensory cortex and ~50 nl of virus or 50 nl of
0.05 % CTB Alexa FluorR 488 was injected into layer 2/3 barrel cortex at stereotaxic
coordinates: caudal 1.6, lateral 3.2, ventral 0.3 mm relative to bregma. The skin incision wasthen closed with silk sutures, and the mice were allowed to recover with free access to foodand water (meloxicam was given at 1 mg/kg, s.c. for analgesia). The brains were prepared
for imaging 10–14 days later (see below).

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The mouse brains were prepared for STP tomography as follows. The mice were deeply
anesthetized by intraperitoneal (i.p.) injection of the mixture of ketamine (60 mg/kg) andmedetomidine (0.5 mg/kg) and transcardially perfused with ~15 ml cold saline (0.9 % NaCl)followed by ~30 ml cold neutral buffered formaldehyde (NBF, 4% w/v in phosphate buffer,
pH 7.4). The brains were dissected out and post-fixed in 4% NBF overnight at 4 °C. In orderto decrease formaldehyde-induced autofluorescence, the brains were incubated in 0.1 M
glycine (adjusted to pH 7. 4 with 1M Tris base) at 4 °C for 2–5 days. The brains were thenwashed in phosphate buffer (PB) and embedded in 3–5% oxidized agarose as
described25, 26. Briefly, agarose (Sigma, cat.# A6013) was oxidized by stirring in 10 mM
sodium periodate (NaIO4, Sigma cat.# S1878) solution for 2 hrs at RT, washed 3x and resuspended in PB to bring the final concentration to 3–5 %. The mouse brain was pat-dried
and embedded in melted oxidized agarose using a cube-shaped mold. Covalent crosslinking
between brain surface and agarose was activated by equilibrating in excess of 0.5–1 %sodium borohydrate (NaBH4, Sigma cat.# 452882) in 0.05 M sodium borate buffer (pH =9.0–9.5), gently shaking for 2–4 hrs at RT (or overnight at 4 °C) (note that after rinsing,activated agarose can be stored in PB at 4 °C for up to one week; sodium borohydrate buffer
should be prepared fresh). Covalent crosslinking of the agar-brain interface is important tokeep the brain firmly embedded during sectioning and to limit shadowing artifacts byinsufficiently cut meninges (see Troubleshooting, below).

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Page 5The Instrument and Software

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The experiments were performed on a high speed multiphoton microscope27 with integrated
vibratome sectioning (TissueCyte 1000, TissueVision, Inc.; Supplementary Video 1 shows
the instrument schema). Laser light from a titanium sapphire laser (Chameleon Ultra,
Coherent) is directed through a tube and scan lens assembly towards a pair of galvanometer
mirrors (6210H, Cambridge Technology) and reflected by a short pass dichroic towards a
microscope objective (either a 20x Olympus XLUMPLFLN20XW lens, NA 1.0, or a 10x
Olympus XLUMPLFL10XW-SP lens, NA 0.6). The fluorescent signal from the sample iscollected by the same objective, passes through the dichroic and is directed by a series ofmirrors and lens onto a photomultiplier tube detection system (PMT, Hamamatsu, R3896).
In two- and three-channel multicolor configuration the emission light is split by dichroic
mirror(s) onto, respectively, two and three PMTs to allow for simultaneous multichannel
data acquisition. 3D scanning of Z-volume stacks is achieved via a microscope objective
piezo (PI E-665 LVPZT amplifier, P-725 PIFOC long-travel objective scanner), whichtranslates the microscope objective with respect to the sample. Laser light intensity can bevaried by liquid crystal controller (Thorlabs, LCC25) for shuttering purposes and as afunction of imaging depth into the sample.

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Robust mechanical sectioning is achieved by a vibrating blade microtome that is integrated
into the imaging system. It is based on a novel dual flexure design. Flexures are compliant
mechanisms consisting of a series of rigid bodes connected by compliant elements that aredesigned to produce geometrically well defined motion upon application of force. Flexures
can achieve smooth displacements down to the sub-micron level with little parasitic motion.
The microtome consists of a primary flexure to which the blade is mounted and a secondary
flexure which connects the primary flexure to the actuator. The actuator consists of a DC
motor with an off-center cam attached to the shaft. The secondary flexure is designed to be
rigid in the direction of the cut and compliant in all other directions. In this way, only a force
along the direction of the cut is transmitted to the primary flexure which holds the
microtome blade and reduces any potential parasitic motion along unwanted axis of motion.
For this design, we experimentally verified that the parasitic Z-vertical deflection was lessthan 2 μm RMS by measuring the motion directly with capacitive sensors. Additional detailsof the microtome design will be presented in a forthcoming manuscript by Drs. Shih-Chi
Chen and Martin Culpepper (MIT, Cambridge, MA). The vibration frequency can be set
between 0–60 Hz and the blade angle between 5–30 degrees. By the use of different cams,
the amplitude can be adjusted from 0.8 mm to 2 mm. We empirically determined thesectioning parameters for brain tissue to be 0.8 mm amplitude at 60 Hz and at a blade angleof 11 degrees (the blade was Vibroslice from WPI). The reliability of sectioning wasverified by measurements of the brain surface and overlapping Z-planes before and after
sectioning during a whole brain dataset (Supplementary Fig. 6). As emphasized in the
Troubleshooting section below, to achieve reliable sectioning it is essential to use brains
covalently crosslinked in oxidized agarose (see Tissue Preparation, above).
The instrument is controlled by custom software, written in C++ and C#. It handles thescanning, stage motion, microtome control, and data acquisition. The software is comprisedof several discrete services, each of which controls a particular hardware component orfunction of the instrument. Sequences of events are coordinated by a master orchestrator
service. For instance, in order to scan a section, a command is sent from the orchestrator
service to the galvanometer scanner service commanding it to unshutter the laser and scan
an image. The orchestrator service waits until the scanner service reports that the image
acquisition has been completed, and then sends a command to the XY stages to move thesample to the next position. Once the XY stages completes the requested motion, acommand is sent back to the orchestrator service, which in turn issues a command to thescanner service to acquire a second image. During the imaging, background services handle
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the data acquisition and saving of the 16 bit TIFF images to a local or network attachedstorage device. The process continues until an entire section has been acquired. Similarly, toacquire a whole-brain dataset, at the end of each mosaic section acquisition the orchestrator
service commands the Z-stage service to move the sample upwards by the desired slice
thickness. Simultaneously, the sample is directed towards the microtome by the XY stage
service. Once in position, the microtome is turned on and the sample is translated throughthe microtome and a tissue section is cut. The sample is then translated back underneath theobjective, and the next section is imaged. This process repeats until all sections are imaged.
The software is highly modular and additional services can be introduced or specifichardware can be exchanged with minimal changes to higher level routines. For instance,services to automate additional features, such as the capture of the slices after sectioning,can be added in the future.

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In comparison to an earlier prototype4 there are a substantial number of improvements in thedesign of the current instrument. The previous version used a milling machine to machine
the surface of a paraffin embedded tissue. Because paraffin quenches fluorescence, anintegrated vibrating blade microtome (Supplementary Video 1) is now used. This allows
imaging of formaldehyde fixed brains embedded in agar, a histological procedure with low
quenching. As an additional advantage, the sections can be used for further histochemical
analysis as they are no longer destroyed by the milling process (currently, the sections sinkto the bottom of the water bath and can be collected and sorted at the end of the experiment).
The incorporation of low-magnification (10–20X) high-numerical aperture (NA 0.6–1.0)
lenses has increased fluorescence collection compared to a standard 60× objective, withoutcompromising the resolution at large imaging depths28. The combination of a lowmagnification lens with large aperture optics have increased the image field of view that canbe scanned with even illumination from ~200 to 1400 μm. High speed galvanometricscanning has replaced a polygonal scanning approach. Galvanometric scanners are far more
flexible than polygonal scanners and allow a wide range of pixel sizes and residence times tobe set depending on the requirements of the sample. Finally, high speed XYZ stages wereconstructed to allow positioning of the sample over centimeters of travel with sub-micron
accuracy. The custom Z-stage was designed to hold two commercial X and Y stages(Polytech, PI) and be rotationally rigid with a pitch and yaw of less than 1 micron over theentire travel range of the X and Y stage assembly. The X and Y axes have a 0.1 μm
positional accuracy, a settling time of 0.1 ms and a speed up to 50 mm/s. The high speed andsmall settling time allows for rapid positioning of the sample and minimizes acquisition timeof a section, while the positional accuracy decreases post-processing registration time. The
Z-axis has a precision of 0.15 μm, maximum velocity of 1 mm/s and maximum Z travel
distance 5 cm. Since this stage is only used to raise the sample to the microtome blade andobjective, its speed has negligible impact on the imaging time.
The Instrument Operation
Once the brain is positioned under the objective and the imaging and sectioning parameters
are chosen (see below), the instrument operates in a fully automated mode. The brain is
mounted in saline (we used 50 mM PB, pH 7.4) in a water bath positioned on the computer
controlled XYZ stages. After identifying Z-position of the brain surface under the objective,
the following parameters are set in the software: FOV size, FOV mosaic size, pixel size,
pixel residence time, laser power, sectioning speed, sectioning frequency, Z-step for eachsectioning cycle and a number of Z sections. The imaging plane is set below the brain
surface to ensure an undisturbed optical section throughout. We typically use 50 μm below
surface, but a comparable image resolution can be obtained down to about 100 μm below
surface with small adjustments in laser power. The laser power is set constant for imaging of
single optical sections between each sectioning steps. For collection of Z-volumes between

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sectioning steps, such as the dataset of SST-ires-Cre::Ai9 olfactory bulb imaged at Zresolution 2.5 μm (Supplementary Video 6), the laser power is adjusted based on the Z
depth to compensate for increased light scattering with increased depth.
The number of FOV tiles per mosaic is set to cover the extent of the sample and allow for asmall overlap between the FOV tiles for post-processing stitching (see below). The
experiments with the 10X objective employ 6 × 8 overlapping mosaic of 1.66 × 1.66 mm
FOV, the XY stage movement is 1.5 mm, pixel size 1 or 2 μm and pixel residence time
between 0.4 to 1.0 μs. The experiments with the 20x objective employ 11 × 17 mosaic of
0.83 × 0.83 mm FOV, the XY stage movement is 0.7 mm, pixel size 0.5 or 1μm and pixel
residence time between 0.4 to 1 μs. Once a mosaic is completed, the same XYZ stages used
for the mosaic imaging move the sample from the microscope objective towards a vibrating
blade microtome to section the uppermost portion of the tissue. The times for imaging of
260 section mouse brain datasets are given Table 1.
Image processing

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The images are constructed from the PMT signal, with the tile and pixel size set by a
combination of the scan angle and pixel sampling rate. The tiles are saved as tif files (named
as Tile_Z{zzz}_Y{yyy}_X{xxx}.tif) and processed in the following way. First, each tile iscropped to remove illumination artifacts near the edges (the number of pixels cropped isdetermined empirically based on the objective used and FOV; e.g. we crop 15 and 10 pixels
at each side of X and Y direction, respectively, for 832 × 832 pixel FOV). Second, all tiles
from one brain dataset (for example 52,360 tiles for 11×17 mosaic of 280 sections) are
loaded in Fiji (ImageJ-based image-processing software; http://fiji.sc/wiki/index.php/Fiji)and used to generate an average-intensity image for illumination correction by a Z-projectfunction. Third, all tiles are divided by the average-intensity image to correct for uneven
illumination (Plugins > TissueVision > Divide sequence by image). Fourth, illuminationcorrected tiles are used to stitch the sequence of mosaic images (Plugins > Stitching > Stitch
Sequence of Grids of Images; fusion method = linear blending, fusion alpha = 1.5,
regression threshold = 0.3, max/avg displacement = 2.5, absolute displacement = 3.5; select“compute overlap”). The transformation between the tiles is modeled as a translationtransform. For each section, the X and Y translations are determined by cross correlation29
between the tiles. At the overlapping regions, the pixels are blended linearly30, 31. The
overlapping regions may show some photobleaching when large power (>150 mW) is used
for samples with low fluorescence. In such case, since bleaching occurs mainly for thesecond overlapping tile, it is better to display the image from the first tile and use the second
tile only for XY registration. This can be achieved by rendering the tiles into the mosaic in
the reverse order they where scanned by the microscope: the pixels of the first scanned tile
overwrite the same pixels scanned later in the second. The whole brain dataset of 11×17
mosaic of 280 sections of raw tiles scanned at a 16-bit depth occupies ~40GB. The final
stitched slices occupy ~25GB with LZW compression on the final stitched TIFF slices. All
image processing was run on Mac/Linux desktop machines with at least 8 GB on RAM.
Image Warping
The warping was done by an affine registration followed by an elastic B-spline-basedtransformation32 using autofluorescence signal from STP tomography datasets
downsampled by factor of 20 (resolution 20 × 20 × 50 μm). The registration was done in a
multi-resolution approach for a more efficient and robust alignment33. The affine transformwas calculated using 4 resolution levels while the elastic step uses 6 resolution steps.
Advanced Mattes mutual information34 was used as the metric to measure the similarity ofregistration. In this parametric registration method, Mattes Mutual information is used as thesimilarity measure between the moving and fixed images. The registration problem is posed

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as an optimization problem, where the image discrepancy/similarity function is minimized
for a set of transformation parameters. The transformation parameters are then estimated in
multi-resolution approach, which ensures a more robust approach compared to a single
resolution approach. The image similarity function is estimated and minimized for a set of
randomly chosen samples with the images at each resolution in a iterative way. On a 8 core
CPU with 16 GB RAM, the registration takes 12 hrs on 650×450×300 sized image with
20×20×50 micron pixel spacing. The entire image warping experiment is setup usingelastix32, an image registration tool based on Kitware’s ITK with Parameters setup
according to our dataset. To determine the effectiveness of the warping procedure, we
compared the displacement of 42 anatomical manually identified landmark points of interestin two mouse brain scans before and after warping one dataset onto the other(Supplementary Fig. 7). The mean (± SEM) distance between the corresponding points inthe two brains was 749.5 ± 52.1 and 102.5 ± 45 μm before and after warping, respectively.
Troubleshooting

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We chose to establish STP tomography for imaging formaldehyde-fixed brains embedded inagar, because this provides maximal preservation of GFP fluorescence. However, vibrating
microtome-based sectioning of a relatively soft agar-brain block is mechanically morechallenging than sectioning of hard resin-embedded tissue and can lead to imaging artifacts.
First, the brain may become “loose” in the agar block after many hours of imaging and thiscan cause the remaining part of the brain to fall out of the agar or the brain edges to shift outof the imaging plane. These problems can be completely prevented by covalent crosslinkingof brain-agar after embedding (see above). Second, brain meninges sometime fail to becompletely cut with every sectioning step and the remaining small stripes of meninges cancause shadowing artifacts during imaging. This problem is again largely solved by brainagar crosslinking, but small uncut meninges sometimes remain in the regions between
hemispheres that are not in contact with agar (this is seen in a few sections in our datasets;
an example from the Thy1-GFPM dataset is shown in Supplementary Fig. 8a). These
artifacts are infrequent and only near the brain surface and thus are unlikely to have asubstantial effect on data interpretation in most experimental applications. Carefully
removing the meninges under a dissecting microscope will help to limit this problem. Third,in the current configuration of the instrument, cut brain sections freely sink to the bottom of
the water bath. However, a floating section may sometimes remain between the objective
and the brain block, causing substantial shadowing artifacts (an example from the Thy1GFPM dataset is shown in Supplementary Fig. 8b). This happens infrequently (typicallyonce per several samples) and can be largely avoided by setting a longer waiting time after
each sectioning step in the instrument software (we used only a 1 sec waiting time for fast
data collection). Recent software update also added a lateral movement of the stage at theend of sectioning, which helps to direct the sinking section away from the objective. In
addition, we are working on integrating a section retrieval module, which will allow an
automated capture of each brain section and eliminate the problem with floating sections.
Finally, meninges between the frontal cortex and the accessory olfactory bulb sometimes
show very bright autofluorescence (an example from the Chat-GFP is shown in
Supplementary Fig. 8c). This only occurs in the rostral brain areas and since it is on thebrain surface it does not affect data interpretation.

Supplementary Material
Refer to Web version on PubMed Central for supplementary material.

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Page 9Acknowledgments
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We thank Y. Bao and R. Palaniswamy for expert technical assistance, S.C. Chen and M. Culpepper (MIT, Boston)for design of the flexure-based microtome, Z. J. Huang (CSHL, CSH) for the SST-ires-Cre::Ai9 mouse, W. Denk
(Max Planck Institute for Medical Research, Heidelberg), K. Rockland (MIT, Boston), and F. Scalia (State
University of New York, Brooklyn) for critical reading of the manuscript, and J. Kuhl for art graphics andanimation. This work was supported by Simons Foundation grant (137480) to P.O., McKnight TechnologicalInnovations in Neuroscience Award to P.O., HHMI Collaborative Innovation Award #43667 to H.S.S., and NIH
grant 1 R43 HL093897-01 to T.R.

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Figure 1.

STP tomography. (a) Schema of the method. Computer-controlled XYZ stages move thebrain under the objective of a two-photon microscope, so that the top view is imaged as amosaic. The stages also deliver the brain to a built-in vibrating blade microtome forsectioning (Supplementary Video 1). (b) Coronal (top), horizontal (mid) and sagittal(bottom) views of a GFPM dataset of 260 coronal sections (Supplementary Fig. 1) after 3D
reconstruction. (c) 3D view of a coronal section of the GFPM brain imaged with a 20x
objective at 0.5 μm XY sampling. Lower left: position of the coronal plane in the imaged
mouse brain (approximately −2.5 mm from Bregma). (d-e) Enlarged views from regions (i)and (ii) in panel (c), respectively, demonstrating visualization of dendritic spines (d) and fine

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Page 12axon fibers (e); scale bar = 25 μm (d-e) and 5 μm (insert in d). See Supplementary Figs. 2
and 3 for a comparison of different imaging conditions.

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Figure 2.

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Retrograde tracing by CTB-Alexa-488. (a) 3D view of a coronal section comprising theinjection site (i) and several retrogradely labeled regions (ii-iv). Lower left: position of thesection in the whole brain (approximately −1.15 mm from Bregma). (b) Coronal (top),
sagittal (bottom), and horizontal views of the injection site. (c) Cortical regions marked upin (a), comprising: (i) the injection site in the barrel field of the primary somatosensory
cortex (S1BF), (ii) ipsilateral secondary somatosensory cortex (S2), (iii) granular insularcortex (GI), and (iv) contralateral S1BF. The panels (ii-iv) are shown with enlarged regions
from supragranular and infragranular cortical layers comprising CTB labeled cells. The
scale bar is 250 μm in panel (i) and 50 μm in the enlarged view of panel (ii).

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Figure 3.

Anterograde tracing by AAV-GFP and brain warping. (a) 3D view of a coronal section
comprising the injection site (i) and several anterogradely labeled regions (ii-v). Lower left:
position of the section in the whole brain (approximately −1.9 mm from Bregma). (b)Coronal (top) and sagittal (bottom) views of the injection site. (c) The injection site (S1BF).
(d) Brain regions (ii-v) marked up in (a), comprising: (ii) ipsilateral caudoputamen (CP);(iii) axon fibers in the internal capsule (ic) (iv) ventral posteromedial thalamus (VPM) andposterior thalamus (PO), and (v) contralateral barrel cortex (S1BF). The enlarged views
show inverted grayscale images for better visualization of axon fibers and varicosities. The
scale bar in (c) and (d-ii) and the enlarged view of (d-ii) is 250 μm. (e) One section from a
combined “virtual” two-tracer dataset generated by warping AAV-GFP brain onto CTBAlexa-488 brain (see Supplementary Video 8). (f) Brain region marked up in (e) comprising
motor cortex (M1) with overlapping anterograde (AAV-GFP, red color) and retrograde(CTB-Alexa-488, green color) labeling.

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