dataesr/scientific-paragraphs-categorization huggingface dataset:
More generally, this experiment showed how SXRT can provide context -cellular identity-to an EM-powered analysis of tissue ultrastructure. We next aimed to corroborate how SXRT imaging can benefit the wider neuroscience community. Thus, we imaged a number of other brain regions with SXRT, including the mouse neocortex, striatum, and cerebellum (Fig. 5 ). Again, cell bodies, neurites and axon bundles were all readily visible in SXRT datasets of all brain regions, emphasising the versatility of the approach. This suggests that SXRT can generally provide subcellular context across multiple mm 3 of soft tissue non-destructively. This in turn can be used to inform follow-up higher resolution imaging of smaller targeted regions of interest employing compatible techniques such as vEM. Bridging in vivo function with ex vivo structure.
One of the key challenges in mammalian circuit neuroscience is to bring together large-scale functional studies in vivo with their structural anatomical underpinning  8, 37, 73. Full neural circuits are embedded in volumes often exceeding several mm  3 3. However, acquiring synaptic resolution datasets of volumes nearing a 1 mm 3 using vEM has only been achieved in individual exceptional circumstances  37, 74. Targeting more readily obtainable highresolution ~(10-100 µm)  3  volumes acquired with vEM to specific locations within mm 3 volumes previously imaged in vivo is typically highly challenging and error-prone. The different length scales and staining protocols associated with the different imaging modalities of this multi-step process often result in structural distortions that need to be untangled to robustly correlate features across datasets.
Thus, we hypothesised that the large volumes and high level of structural detail provided by SXRT should substantially simplify this CMI pipeline. To assess this we aimed to directly link in vivo 2-photon Ca 2+ functional data to underlying anatomical features in the mouse OB circuitry. We presented a panel of 48 distinct odours to anaesthetised mice  75  and recorded the activity of projection neurons (mitral and tufted cells) across a glomerular column  76. We narrowed our analysis to the glomerular layer, where the dendrites of projection neurons delineate glomerular contours. We collected the averaged odour-evoked response profiles of 20 glomeruli on the dorsal surface of the OB (Fig. 6a-d ). Following in vivo functional imaging, we labelled the blood vessels by injecting the fluorophore sulforhodamine (SR101) intraperitoneally and acquired in the same in vivo 2-photon setup a fluorescence dataset containing the entire tissue volume with higher spatial resolution (~0.
36 μm horizontal and 5 μm vertical spatial voxel size). Mice were then sacrificed, the OB extracted and a 600 µm dorsal section including all imaged glomeruli dissected out. Imaging the tissue section in the 2-photon microscope after fixation ('2p ex vivo') provided a dataset where glutaraldehydeinduced autofluorescence delineates glomeruli alongside numerous histological landmarks. Subsequently, tissue was stained as described above and imaged with LXRT, SXRT, and finallyafter trimming to a tissue block of suitable dimensions (C525a: 930 × 1080 × 630 µm, C525b: 970 × 960 × 590 µm, wide × long × tall)-in SBEM (Supplementary Fig. 8 ). Histological landmarks from glutaraldehyde autofluorescence were used to warp the 2p ex vivo and the LXRT datasets, while the 2p in vivo anatomical dataset was warped to the SXRT and SBEM datasets directly using the unique patterns of the blood vessel network as landmarks (Supplementary Fig.
Soma colours refer to their position in the pyramidal layer. Apical dendrites (traced in the SXRT dataset) are shown in blue. The dashed line shows the average depth of all seeded somata. The dendrites of seven cells were traced exhaustively in SBEM (shown in the same colour as their soma). c Dendrites and spines traced in SBEM. Spines containing a spine apparatus are highlighted in red. d Examples of spines with (d2) and without (d1) spine apparatus. e CA1 pyramidal neurons. The apical dendrite traced in the SXRT dataset is shown in blue, dendrites traced in SBEM are shown in green (dark: trunk; light: apical oblique). Spines are shown as small protrusions, a red mark highlighting those with a spine apparatus. f Density of spine apparati. n = 7 cells from one dataset, linear regression, p = 0.
092, 0. 039 for apical oblique and trunk dendrites, respectively. The shaded area represents the 95% confidence interval of the linear regression line. Source data for (f) are provided as a Source Data file. CTX, cerebral cortex; HPF, hippocampal formation; CA1, Ammon's horn field CA1; so, stratum oriens; sp, stratum pyramidale; sr, stratum radiatum; mo, molecular layers; DG, dentate gyrus. this use of X-ray microscopy, "bridging use"  10. Moreover, we have demonstrated that SXRT provides sufficient resolution to delineate individual cell bodies and at least some neurites in a dense fashion. This information on the subcellular context over several millimetres can be quantified alongside analyses relying on targeted high-resolution vEM measurements. Thus, SXRT not only provides bridging information but can also be employed for "context use"  10.
7 ), and that this CMI pipeline can be used to delineate the anatomical regions-glomeruli-where functional imaging data has been recorded. This was more accurate than by 2p imaging alone and can help to disambiguate signal sources. While glomeruli are ~100 µm in diameter, the resolution of SXRT will allow correlating smaller regions reliably, possibly down to individual cells. The tools developed here will then allow to transform annotations (structural ones on different length scales such as subcellular or regional segmentation as well as e. g. functional signals) from the modality with the highest resolution to the corresponding location in the other modalities and vice versa. For example, neurites traced in EM can be visualised in and linked up with other circuit elements in the SXRT space, or neurons outlined in SXRT can be attributed functional signals from an in vivo 2p dataset.
Odour stimulation. Odour stimuli were delivered using a set of custom-made 6channel airflow dilution olfactometers. In brief, volumes of 3 ml of a set of 48 monomolecular odorants (Sigma-Aldrich, St. Louis MO, USA) were pipetted freshly for each experimental day into 15 ml glass vials (27160-U, Sigma-Aldrich, St. Louis MO, USA). Odours were diluted 1:20 in air before being presented to the animal at 0. 3 litres/min using custom Python Software (PulseBoy; github. com/ RoboDoig). Odours were prepared for 3 sec in tubing before a final odour valve was triggered to open at the beginning of an inhalation cycle. During anaesthetised recordings all stimuli were presented with a 30 s inter-stimulus interval. To minimise contamination between odour presentations, a high flow clean air stream was passed through the olfactometer manifolds during this time.
Data analysis. Motion correction, segmentation and trace extraction were performed using the Suite2p package (https://github. com/MouseLand/suite2p). Regions of Interest (ROIs) corresponding to glomeruli were manually delineated based on the mean fluorescence image in Fiji  91. The fluorescence signal from all pixels within each ROI was averaged and extracted as a time series with ΔF/F = (F-F 0 )/F 0 , where F = raw fluorescence and F 0 = median of the fluorescence signal distribution. Further analysis was performed with custom Matlab scripts.
for a comparison of 20x and 40x objectives at PSI TOMCAT). A set of 2-24 local tomography scans (with an overlap of 0. 22 mm in the horizontal and 0. 1 mm in the vertical direction) was acquired for each specimen to cover a total volume of 1. 2-9. 9 mm 3 (Supplementary Fig. 1b ). For each tomography, 3001 projections were recorded at equi-distant viewing angles over 180°of sample rotation with an exposure time of 0. 4 s per frame, which resulted in a total scan time of approximately 20 min per tomography dataset. All data were saved in hdf5 container files. We used the reconstruction pipeline savu  94  for reconstructing the SRXT datasets. The full processing pipeline steps were: a. Loading of HDF5 dataset; b. Correction for dark and flat-field images; c. Paganin filter; d. Ring removal; e.
Automatic centre-finding; f. Reconstruction; g. Saving images as tiff files. Dark images, flat-field images and projections were extracted from the HDF5 dataset (a. ) and all projections p were corrected for detector dark current d and the flat-field f intensity variation (b. ). The normalised projections pnorm were calculated using: p norm ¼ pÀd f Àd. The normalized projections were then filtered with a Paganin filter (also see below)  95  using the following settings: detector distance 52 mm, X-ray energy 22 keV and detector pixel size 325 nm. Afterwards, processing followed the algorithms outlined in  96. In brief, ring artefacts were detected using algorithm #4 (from  96  ) and rings were removed using algorithms #6, #5, and #3 (from  96. For algorithms #6 and #5, a signal-to-noise ratio (SNR) of 3.
Fourier shell correlation and power spectral density calculations. To measure resolution we performed Fourier Shell Correlation (FSC) analysis (Supplementary Fig. 7 ) as described before  104, 105. In brief, for each measurement (Supplementary Fig. 7 ), two paired volume ROIs acquired independently and containing data of the same volume were generated. The ROIs were large enough to contain relevant features (500 voxels wide cubes in all cases except for LXRT, where ROIs were 100 voxels wide) and their contents were checked to ensure they contained biological tissue data throughout. In SXRT and LXRT datasets, the two paired ROIs were generated reconstructing half-tomograms composed by the odd and even projection angles, respectively. A FSC analysis was then performed on the paired ROIs. Resolution readouts were obtained for each FSC curve at the spatial frequency the FSC curve fell below the resolution criterion.
EM imaging. Three serial block-face electron microscopy (SBEM) datasets were obtained for this study, to explore in detail the olfactory bulb (C525a,b) and the hippocampus (C556) of samples previously imaged with other imaging modalities. In all cases, the parent stained sample was scanned with LXRT (Zeiss Versa 510) and a trimming plan was designed to render the regions of interest optimally accessible for the SBEM. Additional datasets obtained from other modalities (2p ex vivo datasets of regions containing genetically tagged fluorescent glomeruli) were also warped to the same scene in some cases (C525a,b) to determine the ROI (Supplementary Fig. 8a, b ). Several LXRT datasets taken iteratively at different steps of trimming were warped onto the same scene iteratively using BigWarp 59 (Supplementary Fig.
8  c1-c2, d1-d2), guiding the trimming process and ensuring the ROI was preserved at all times. The trimming process involved the use of tools of increasing precision as required: hand-held single-edge blades, ultramicrotome-guided glass knives and a 4 mm diamond knife (Leica UC7 ultramicrotome, Diatome histo knife). The final sample was a <1 mm 3 tissue cuboid with a diamond knife-polished surface displaying some tissue. The sample sides were then covered with a silver colloidal suspension (SPI supplies), and the sample was sputter-coated with 10 nm platinum (Quorum Q150R S). All SBEM datasets were obtained with single images covering the desired field of view. Datasets C525a and C556 were acquired using a 3View2-variable pressure Zeiss Sigma SBEM using focal charge compensation  30. In both cases, datasets were acquired using a 2.
5 kV electron beam and a 30 µm aperture, and multiple ROIs were acquired at high and low resolution covering narrower and wider fields of view, respectively (Supplementary Table  1 ). In particular, C525a contained 6004 slices across 11 concatenated datasets in z. Seven high-resolution regions (acquired in z-order 1, 2, 3, 5, 7, 9, 11) were composed altogether by 2078 images acquired at 2. 0 µs/pixel, each covering (200 µm) 2 with (10 nm) 2 pixels, separated in z by a slice thickness of 40 nm. A reconstructed continuous low-resolution dataset contained 4970 images acquired at 0. 5 µs/pixel covering (737 µm) 2 with (80 nm) 2 pixels, separated in z by a slice thickness of 80 nm. Acquiring the C525a datasets took 25 days. C556 was composed by one low-resolution dataset and three highresolution datasets from narrower regions in the same volume, all containing 787 images separated in z by a slice thickness of 50 nm.
The images of the lowresolution dataset were acquired at 0. 5 µs/pixel and covered (655 µm) 2 with (80 nm) 2 pixels. The images of the high-resolution dataset were acquired at 2. 0 µs/ pixel and covered (82 µm) 2 with (10 nm) 2 pixels. Acquiring the C556 datasets took 5 days. C525b was acquired using a 3View2 -Zeiss Merlin SBEM under high vacuum, a 2. 5 kV electron beam with a landing current of 0. 5 nA and a Gatan OnPoint backscattered electron detector. The dataset was composed by 9899 images acquired at 0. 5 µs/pixel covering (700 µm) 2 with (50 nm) 2 pixels, resulting in a dose of 0,62e -/nm 2 , separated in z by a slice thickness of 50 nm. Acquiring the C525b dataset took 18 days. Additionally, the micrographs shown in Supplementary Fig. 6a-b  were taken from C046, a SBEM dataset previously reported in reference  106.
Image registration. Each SBEM dataset was registered using Voxelytics Align (scalable minds, Potsdam, Germany), which uses pairwise feature matching between adjacent slices, RANSAC inlier detection and global relaxation to create mesh-tessellated affine transformations. When a number of slices had to be discarded, the closest non-discarded slices were duplicated in the final dataset thereby keeping the total number of slices unaltered. C525a datasets (Supplementary Fig. 4a2 ) were registered by removing 3 slices (Supplementary Fig. 4a4 ). C556 datasets were registered without any slice loss (Supplementary Fig. 4a5 ). For C525b (Supplementary Fig. 4a1 ), 595 slices (6%) had to be discarded to register the dataset (Supplementary Fig. 4a3 ), generating one single continuous gap of 10 slices (0.
5 µm) and an average gap size of 2. 7 slices (135 nm). These slices were detected by looking at maximum variation and at minimum matched features between any two adjacent slices. These artefact detections and the registration were run on a masked version of the dataset only containing tissue voxels. This mask was essential to avoid picking up statistics from imaging artefacts present in areas filled with resin (such as charging artefacts). The final step involved correcting for lateral drift across z, which is most likely a registration artefact arising from the presence of features in the dataset that have a preferred orientation. Fourier shell correlation. Resolution was estimated using FSC as described above except that in the registered SBEM dataset (50 nm voxels), the two ROIs were generated by extracting the odd and even slices and further downscaling by 1/2 in x, y (and therefore the voxel size of the ROIs became 100 nm isotropic).
Data processing of SXRT and EM data. Datasets were stored as a chunked file format with a resolution pyramid (wkw) and explored in webKnossos  107. Warping. Linked datasets obtained with different imaging modalities were warped into a common framework using BigWarp 59 (Supplementary Fig. 4b ). Conserved landmarks were manually identified, and a first affine transformation of the moving dataset was generated allowing to find further local paired landmarks between both datasets. This iterative process of landmark deposition and dataset transformation was performed until no observable improvement in overlap was found, using 11-171 paired landmarks. The number of landmarks depended on the modalities being warped: 27-53 landmarks were used for warping 2-photon datasets of fixed tissue into a LXRT (Supplementary Fig.
4b1 -b3, b8); 11-25 to warp LXRT and SXRT (b4-b5, b8), 50-51 to warp SXRT and SBEM, and 171 to warp in vivo 2-photon to SBEM (b6-b7, b9). The nature of landmarks also depended on the modalities being warped, since this relies on conserved features across both modalities: glomerular contours and tissue boundaries were features present in all datasets, blood vessels were well defined in the in vivo 2-photon dataset, and these and nuclei were easily found in SXRT and SBEM. This set of landmarks was then used to generate a warp field to move between both datasets. The Bigwarp engine was integrated into Matlab using custom code and integrated with a webKnossos skeleton analysis toolbox  6. We constructed a graph with nodes representing datasets and edges representing transform functions, either involving spline transformations as introduced above or scale-offset transformations.
Data analysis of SXRT and EM data Apical dendrite tracing and SXRT traceable length analyses. Mitral cell apical dendrites were traced in SBEM and SXRT datasets. All datasets were processed and managed using a webKnossos data storage, display and annotation infrastructure  107. Somata of mitral cells were identified based on their histological and morphological features  108  , mainly a large cell body located in a monolayer parallel to the glomerular layer, 200-400 µm below it, with a large, pale nucleus containing a prominent nucleolus and surrounded by abundant cytoplasm. All mitral cell somata (372) in the SBEM dataset (C525b) were manually tagged by creating a node in their nucleus and their names and coordinates were stored in a webKnossos skeleton file (*. nml). 50 somata were randomly selected without replacement for this analysis and saved as a new skeleton.
This file was warped onto the SXRT dataset space using the integrated warping toolbox. This enabled manually tracing the same cell in the native version of each imaging modality. Three trained tracers were assigned the task of manually tracing all apical dendrites in both SBEM and SXRT datasets. Briefly, it involved identifying the apical dendrite that arises from the cell body of the seeded nucleus, and then tracing that apical dendrite centrifugally until reaching the glomerulus without creating any branches. Once inside the glomerulus, the most prominent dendrite was to be traced as well. All SBEM skeletons (with 3 traces per neuron) were pooled and manually curated (all could be reconstructed: 34/50 were correctly traced by all, 16/ 50 were correctly traced by two tracers). The longest tree of the correctly traced ones was proofread and chosen as consensus apical dendrite for that one neuron, generating ground truth apical dendrite skeletons for all 50 mitral cells.
These ground truth skeletons were warped into the SXRT space using the integrated warping toolbox described above. For each tracer, the apical dendrites traced in the SXRT dataset were paired with the warped ground truth (SBEM) apical dendrite of the same cell. Both trees would start at the same position (nucleus). A polynomial cubic spline function (Matlab File Exchange) was fitted to both trees, and evenly spaced nodes were created every 320 nm. For every node in the SXRT-traced tree, the euclidean distance to the closest node in the warped EM tree was calculated and plotted as a function of the position in the SXRT-traced tree. These distances displayed a bimodal distribution with the two peaks arising from correctly and incorrectly traced dendrites respectively. A tree was considered "lost" when the closest distance to the consensus tree was >12 µm, and the distance from the soma to the node at which it was lost was defined as the correctly traced distance.
Most tree losses took place within the first 40 µm of dendrite, which can be attributed to the beginning of the apical dendrite. We named this challenge as the "linkage" between the soma and the apical dendrite. We called "linked" all trees that were correctly traced for at least 35 µm. This enabled splitting all trees between kept or lost, linked or not linked, or shorter than both thresholds 'linked' and 'kept' (Supplementary Fig. 3a ). A similar approach was used in the hippocampus (Supplementary Fig. 3c-h ). In this experiment, the EM dataset was configured so the xy plane matched the direction of CA1 apical dendrites and the z-axis evolved almost perpendicular to the pyramidal layer (Supplementary Fig. 3c, d ). A bounding box of 180 × 200 µm 2 in x, y and spanning the entire z of the dataset (39 µm) was defined so it would contain the CA1a region.
Pyramidal neurons were easily identifiable based on their large soma with a nucleolus and condensed chromatin inclusions located in a packed layer with a clearly distinguishable apical dendrite arising from the cell body. All 175 CA1 pyramidal somata in the bounding box were tagged by creating a node in their nucleus. One trained tracer was assigned to trace all their dendrites in both EM and SXRT datasets (Supplementary Fig. 3d ). In EM, the dendrites of 132 somata could be traced, generating ground truth apical dendrite skeletons for these 132 CA1 pyramidal neurons. 90 of these could be traced in the SXRT data. In this case, a warped version of the SXRT dataset on the EM space was also used to complement the tracings when helpful. The distances between SXRT and EM trees also displayed a bimodal distribution, which was used to set the "lost" threshold to 6 µm.
The "linkage" threshold was set to 35 µm (Supplementary Fig. 3e ). The same tracing and analysis methods were used to compare the tracing accuracy for apical dendrites of OB mitral cells in SXRT datasets acquired in two different microtomography beamlines (Supplementary Fig. 3i-l ) (as in previous OB analyses: interpolation step = 320 nm, lost threshold = 12 µm). Here, I13-2 tracings were used as references and TOMCAT tracings were evaluated against them. Since the distance analysis only applies to dendrite tracing pairs, only cells traced in both datasets were analysed (33 out of 50 randomly seeded mitral cell somata). From these, 91% of the dendrites were accurately traced in both SXRT datasets (30 out of 33) (Supplementary Fig. 3l ). Correlative SXRT-SBEM analyses of hippocampal CA1 neurons Soma depth of CA1 pyramidal neurons.
A linear regression line was fitted to the x,y coordinates of all seeded CA1 pyramidal neuron somata. Soma depth was measured as the euclidean distance between a soma and the regression line. Here, the soma z coordinate was ignored as apical dendrites were approximately parallel to the xy image plane. Deeper cells have more positive scores. A subset of the 90 CA1 apical dendrites traced in the SXRT dataset passed through the high-resolution SBEM dataset. Among them, seven dendrites with varying soma depths were chosen for further spine and spine apparatus analyses. Dendritic spine tracing and spine apparatus tagging. By warping SXRT apical dendrite tracings into the high-resolution SBEM dataset space, dendrites corresponding to the seven target cells were identified. Like in apical dendrite tracings, one trained tracer manually annotated all dendrite branches, dendritic spines and spine apparati on webKnossos.
2-photon data was acquired with commercially available Scientifica software (SciScan 1. 3). Epifluorescence data was acquired using the commercially available ZEISS ZEN software. LXRT tomograms were acquired in a ZEISS Versa 510 micro-CT using ZEISS Scout and Scan and reconstructed using ZEISS Reconstructor. SXRT tomograms were acquired using GDA (at Diamond, http://www. opengda. org/) and reconstructed with savu (at Diamond I13-2, https://github. com/ DiamondLightSource/Savu) and GridRec (at PSI TOMCAT, https://github. com/ esther279/Gridrec-MS). At ID19 data was collected using Bliss (https://bliss. gitlab-pages. esrf. fr/bliss/master/) and reconstructed with Nabu (https://tomotools. gitlab-pages. esrf. fr/ nabu/). At ID16A data was acquired using Spec and Tango (https://www. tango-controls.