==== Front Commun Biol Commun Biol Communications Biology 2399-3642 Nature Publishing Group UK London {{PHONE+SOCIAL_SECURITY_NUMBER}}/s42003-021-02548-6 Article Cellular 3D-reconstruction and analysis in the human cerebral cortex using automatic serial sections http://orcid.org/0000-0003-4663-2284 Larsen Nick Y. luisestrada@example.com 1234 Li Xixia 56 Tan Xueke 56 Ji Gang 56 Lin Jing 7 http://orcid.org/0000-0002-4348-4688 Rajkowska Grazyna 8 Møller Jesper 29 http://orcid.org/0000-0002-7507-8473 Vihrs Ninna 9 http://orcid.org/0000-0003-1261-6702 Sporring Jon 27 http://orcid.org/0000-0002-0351-5144 Sun Fei 3456 Nyengaard Jens R. 12310 1 grid.7048.b 0000 0001 1956 2722 Core Centre for Molecular Morphology, Section for Stereology and Microscopy, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark 2 grid.5117.2 0000 0001 0742 471X Centre for Stochastic Geometry and Advanced Bioimaging, Aalborg University, Aarhus University and University of Copenhagen, Aalborg, Aarhus and Copenhagen, Denmark 3 Sino-Danish Center for Education and Research, Aarhus, Denmark 4 grid.410726.6 0000 0004 1797 8419 University of the Chinese Academy of Sciences, Beijing, China 5 grid.418856.6 0000 0004 1792 5640 National Key Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China 6 grid.418856.6 0000 0004 1792 5640 Center for Biological Imaging, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China 7 grid.5254.6 0000 0001 0674 042X Department of Computer Science, University of Copenhagen, Copenhagen, Denmark 8 grid.410721.1 0000 0004 1937 0407 Department of Psychiatry and Human Behavior, University of Mississippi Medical Center, Jackson, MS USA 9 grid.5117.2 0000 0001 0742 471X Department of Mathematical Sciences, Aalborg University, Aalborg, Denmark 10 grid.7048.b 0000 0001 1956 2722 Department of Pathology, Aarhus University, Aarhus, Denmark 2 9 2021 2 9 2021 2021 4 103736-27-8749 9 8 2021 © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Techniques involving three-dimensional (3D) tissue structure reconstruction and analysis provide a better understanding of changes in molecules and function. We have developed AutoCUTS-LM, an automated system that allows the latest advances in 3D tissue reconstruction and cellular analysis developments using light microscopy on various tissues, including archived tissue. The workflow in this paper involved advanced tissue sampling methods of the human cerebral cortex, an automated serial section collection system, digital tissue library, cell detection using convolution neural network, 3D cell reconstruction, and advanced analysis. Our results demonstrated the detailed structure of pyramidal cells (number, volume, diameter, sphericity and orientation) and their 3D spatial organization are arranged in a columnar structure. The pipeline of these combined techniques provides a detailed analysis of tissues and cells in biology and pathology. Nick Larsen et al. developed a pipeline to collect and image serial sections from fixed human cortex, then apply deep learning to detect pyramidal cells from 3D reconstructions of these sections. Their results reiterate that cortical pyramidal cells are organized in a columnar structure and highlight the potential of this method, which is universally applicable to characterize cells for various tissues. Subject terms Cell biology Cells Scientific community Cellular neuroscience Centre for stochastic geometry and advanced bioimaging, Sino-Danish center, Villum Foundation.https://doi.org/10.13039/100000002 U.S. Department of Health & Human Services | National Institutes of Health (NIH) The National Institutes of Health - GM103328The Danish Council for Independent Research | Natural Sciences, grant DFF - 7014-00074 'Statistics for point processes in space and beyond'.The National Natural Science Foundation of China (31925026), Academy of Sciences (QYZDB-SSW-SMC004).issue-copyright-statement© The Author(s) 2021 ==== Body pmcIntroduction Life science aims at a better understanding of multiple biological functions, such as healthy organ development with cellular proliferation, migration and organization, tumor formation, and general pathology. Several techniques have been developed to study biological structure in 3D like serial block-face scanning electron microscopy (EM), focused ion beam scanning EM, serial-section transmission EM, automatic tape-collecting ultramicrotome-scanning EM, and many types of light microscopy with or without tissue clearing1–4. In life science, EM is widely used to explore the subcellular components, which are several orders of magnitude smaller than the spatial neuron networks. Clearing techniques can be very helpful in attempting to explain neuron networks in greater brain volumes, such as the adult mouse brain, in an entire state without disassembly5,6. However, some of the disadvantages for tissue clearance are that immunostaining of archival tissues is usually complicated as a result of the antigen masking due to formaldehyde protein cross-linking7. Furthermore, practicing immunostaining with tissue clearing remains difficult in human tissues due to factors such as inadequate antibody penetration depth, physicochemical properties, and tissue composition7,8. A typical human neuron has thousands of complex connections with neighboring neurons, which is essential for normal function, yet the organization of these neurons is still under debate9. The cellular organization in the human neocortex has been described as a local network of vertical columns containing neurons. Neurons with similar functions are grouped together and according to different theories, these cortical columns may contain smaller columns known as minicolumns, which are the smallest unit capable of processing information in the cerebral cortex10,11. Cortical columns are radially oriented cell bodies that span through the laminar pattern perpendicular to the pial surface and can be seen using regular Nissl preparations or another cell body-revealing histological procedures. The introduction of minicolumns was in response to studies of the patterning of apical dendrites of pyramidal cells with somata situated in layers II, III, and V12,13. Studies have attempted to characterize and analyze the morphology of minicolumns with a 2D computerized method designed to detect subtle differences among patient groups such as schizophrenia, autism, and Alzheimer14–17. As a result, much of our understanding of cellular organization is focused on 2D histological images, which could potentially misrepresent biological structures and malpositioning of cells in 3D-space. This paper aims to create a method that is accessible to the broader science community and analyze 3D tissue organization through the use of archival tissue to make detailed inferences about pathologies. In the present study, we developed Automatic Collector of Ultrathin Sections for Light microscopy (AutoCUTS-LM) to measure the neuronal cell morphology and their spatial organization in 3D-space of archived tissue. This is accomplished by modifying and adjusting the original AutoCUTS, which was designed for scanning EM array tomography18–20, to image archival human brain tissues (~20 years) in layer III of Brodmann Area 46 (BA46). BA46 was chosen since it involves working memory, attention and has been the subject of studies related to mental disorders like schizophrenia and depression21–28. Myelinated axon bundles are potentially cortical efferents that originated in layer II/III pyramidal cells as these bundles descend to the white matter29. Pyramidal neurons in layer III project to other cortical areas and play a key role in cortical and thalamic cortical circuits, and have been found to be the most affected layer by these disorders in BA4630–32. Here, we report the applied technical workflow that is able to uncover morphological properties of pyramidal neurons in human brain autopsy tissue: First, we identified BA46 and applied advanced sampling procedures of biopsies. After embedding biopsies in resin, the AutoCUTS-LM cut them automatically into semi-thin sections (300 and 400 nm), and collected them on tape. Sections were stained and mounted on glass slides, and a library for each sample was organized. The digitalization of sections was stored, aligned, and stacked as a volumetric structure. Then, we detected neurons using the UNetDense architecture. Finally, the 3D spatial arrangement and structural parameters of pyramidal neurons in layer III of BA46 were analyzed in three human brains applying recently developed methods. Our findings present valuable insight into neuronal morphology and architecture by characterizing pyramidal neurons in 3D from old archived human brain tissue. We discovered that pyramidal cells are not randomly distributed but are clustered in small columnar structures, which may be relevant for understanding the formation and function of the cortical network. Results Sampling strategy and preparation of tissue A block of tissue from the dorsolateral prefrontal cortex that contained all of BA46 was removed from each brain33, see Fig. 1. We used the MATLAB script we developed to delineate BA46 and sample two biopsies on a highly complex surface like the brain, with the second biopsy kept as a reserve. The cortical columns of neurons in the cortex could be successively extracted by a biopsy perpendicularly to the cortical surface; therefore, only neurons from gyri and not sulci were analyzed, illustrated with the yellow color in Fig. 1D. The sampling area was divided into four quarters to avoid any overlap or adjacent biopsies, see Fig. 1E, F.Fig. 1 Sampling of luisestrada@example.com. A Formalin-fixed tissue from three human brains was selected from the brain collection at Aarhus University Hospital. The red box marks the excised area of the tissue. B Fixed coronal block of tissue containing BA46. C Manual delineation of BA46 performed by MATLAB. D Infused image between global threshold image and sample area. The filtered surface of a coronal block of tissue containing BA46 was marked with a red and yellow map that shows the available sample area. E Summation of all white pixels for each row of the binary picture of the sample area. The 1st, 2nd, and 3rd quarter of pixels were marked with a green dashed line. F The two biopsies can only be sampled in either the 1st and 3rd quarter(white area) or the 2nd and 4th quarter (red area). In this case, random points in the 2nd and 4th quarters, marked with blue dots, were chosen by the algorithm. G The two chosen biopsies are marked with blue dots on the original block of tissue from (B). The brain tissue samples were obtained by using a biopsy punch positioned on the two sampling points with a diameter of 1.5 mm and a depth around 3–5 mm. This meant that a sample included all six layers of the cerebral cortex. The tissue samples were fixed in resin and not stained with osmium since osmium fixation reduces the contrast of sections during light microscopy, see Fig. S1. Our strategy to successfully find neurons in the supragranular layers (I–III) and layer IV, region of interest (ROI), requires that the sample be positioned in such a manner that all six layers in the neocortex appear in every section during the AutoCUTS-LM procedure. Consequently, the biopsy was placed at the bottom of the embedding form such that the pial surface was perpendicular to the cutting direction, see Fig. S2. This orientation decreased the number of sections approximately two to three times relative to alternate orientations, thus shortened the time spent collecting and capturing images with the microscope. We used a light microscope to locate and measure the ROI by first examining and staining the outermost section of the block to delimit the area for collecting semi-thin sections (100–500 nm). We precisely trimmed the excess embedding resin around the sample to avoid wrinkles while cutting and removed layers V–VI, which resulted in an ROI of around 1.5 mm2. Collection and preparation of serial brain sections on tape We have modified and adapted the automatic serial section technique, which was originally developed for scanning EM18–20, so it operates for light microscopy. The main changes were the replacement of the double-sided conductive tape with a plasma cleaned transparent tape and mounting tape strips containing the sections on glass slides instead of on a silicon wafer. The transparent polyester tape was 7-mm wide and was put through plasma treatment, which influenced the hydrophilicity of the tape. The hydrophilicity was found to not only reduce wrinkles of the brain sections produced during the collection process20, but also made the sections more adhesive to the tape. Thus, the sections would later stick to the tape during collection and staining. Automated collection of the resin-embedded material was accomplished using a ultramicrotome attached to a custom-tape collection device (AutoCUTS-LM). We collected around 2400 serial sections with a total tissue depth of about 0.7 mm for each subject. The three human subjects' cutting thickness was chosen to be 400 nm for subject 1 and 300 nm for subjects 2 and 3 (sectional area was about 1.5 mm2, and the total volume was ~1 mm3). Sections were cut continuously with a diamond knife (Diatome, Switzerland) with an indoor humidity around 85%, see Fig. 2A. The pulling motion from the collecting tape moved the sections from the water onto the tape's surface, and its adhesiveness affected how flat these sections lay on the tape. Around 800 sections were collected per hour with our settings. Hence, we used less than 3 h to cut a tissue sample that is around 0.7 mm thick. It is important to notice that the ultramicrotome calibrates itself after sectioning the maximum useful range of 200 μm and had to be manually reset. In our case, a tissue sample to a depth of 0.7 mm could be sectioned with only three interruptions, and it was possible to move the sample to a different knife-edge, as dulling of the knife affects the section quality. Collecting thousands of sections was possible without any loss of tissue. However, we observed different technical and environmental factors that could generate wrinkles and disfigure the sections during cutting. The indoor humidity level was one of the main factors (see the "Methods" section).Fig. 2 Image of the AutoCUTS-LM and sample preparation on tape. A The supply-reel of the AutoCUTS-LM contains a transparent plastic tape that collects sections from the knife boat to the final take-up reel. (Red frame) Close-up view of the tape conveyor belt positioned in front of the knife boat with the mounted sample. B Collected sections on the transparent tape before staining. C Sections have been stained with toluidine blue. D Setup to glue the sections on the microscopic glass slide. E Stained sections glued on a microscopic slide and ready for image acquisition. Following section-collection, we chose to stain our sections with toluidine blue, since it interacts with most cells in the brain (both neurons and glial cells) and is thus excellent for revealing the neuronal patterns. However, to assess other biological results, methods such as immunolabeling or EM may also be used with the same AutoCUTS strategy, see Fig. S3. The spools holding the tape with attached sections were dried overnight in an oven at 50 ∘C. Sections were then stained with toluidine blue, and the tape with attached sections was cut into three consecutive strips with ~60 serial sections and glued onto a standard microscope slide (Fig. 2B–E). Each sample resulted in a section library of ~40 glass slides, which were digitalized. Data acquisition Digital images were acquired using the Apiro Versa 200 platform from Leica. The scanning speed was ~15 min per glass slide and required about 10 h to complete a section library of glass slides for each subject. We only sampled every second and third section because a distance of 800 nm (subject 1) and 900 nm (subject 2 and 3) between sections was considered sufficient for 3D-reconstruction of pyramidal cells, which have an average somal diameter of ~13 μm34, see Fig. 3A. The microscope included the commercial software Aperio ImageScope (Leica Biosystems Imaging, Inc., USA), which visualized the whole microscope slide image with high resolution. However, the user had to extract a region manually before an image could be exported as a TIF file. The uncompressed images were then automatically processed, ordered, and exported as individual images of each section using a MATLAB algorithm we developed, as shown in Fig. 3B. These individual images were then aligned and stacked by sequential image registration using our custom MATLAB scripts35. Next, the stack of images was prepared for further analysis by cropping tissue regions, see Fig. 3C.Fig. 3 Image acquisition and 3D-stacks of aligned sections. A Overview of a microscopic glass slide with the commercial Leica software. Systematic sampling of every third section was manually marked with three local points for the autofocus calibration. B In MATLAB, a TIF image was loaded where each second was of interest, which is different from (A) but easier to visualize as an example (i). Next, we used an entropy filter to detect sections of interest (ii). Binary masks have been computed for each section of interest and the images are ready for export (iii). The output of each exported section (iv). C Individual sections were aligned and stacked on top of each other. The stacked block of sections was then cropped down to a specific ROI (1.05 × 1.05 mm) which only contains tissue. Pixelwise performance of deep learning model for segmenting pyramidal cells We worked with UNetDense, a deep learning framework, which has provided promising results for image segmentation of pyramidal cells36. The performance of the UNetDense model was measured by reporting sensitivity, precision, and F1-score. Neither recall nor accuracy was calculated as the cell's pixel count is dominated by background pixels making these measurements less informative. These measures were calculated based on the confusion matrices shown in Table 1. In general, the output of using individual models per subject showed a better predictive result compared to the combined model, see Table 2. In general, individual models for each subject performed well with sensitivity, accuracy, and F1-score above 0.8.Table 1 Pixelwise validation. Model 1 (A) 0 (A) Subject 1 Subject 2 Subject 3 Subject 1 Subject 2 Subject 3 Individual 1 (P) 128937 TP 83370 TP 91266 TP 11710 FP 14400 FP 10717 FP Models 0 (P) 19923 FN 17810 FN 11512 FN 4033734 TN 4078724 TN 4080809 TN Combined 1 (P) 119351 TP 77409 TP 92189 TP 9506 FP 12994 FP 12337 FP Model 0 (P) 29509 FN 20717 FN 8931 FN 4035938 TN 4083184 TN 4080847 TN Pixel to pixel comparison between an MS and UDP image (2048 × 2048) for each subject. The confusion matrix is used to validate the UNetDense model on images that the model has not encountered before. The result shows the performance of individual trained UNetDense models for each subject and a combined UNetDense model used for all subjects. Pixels belonging to the background have label 0, those belonging to the pyramidal cells have label 1, (A) stands for actual (value in MS image), and (P) stands for predicted (value in UDP image). Table 2 Performance of pixelwise validation. Model Metrics Subject 1 Subject 2 Subject 3 Individual Models Sensitivity 0.87 0.82 0.89 Precision 0.92 0.85 0.89 F1 0.89 0.84 0.89 Combined Model Sensitivity 0.80 0.79 0.91 Precision 0.93 0.86 0.88 F1 0.86 0.82 0.90 Precision, sensitivity, and F1-score of 2048 × 2048 pixel image for each subject with individual trained UNetDense models and one combined UNetDense model. Pixel evaluation is based on the confusion matrix of Table 1. Objectwise performance of 3D-reconstruction of segmented pyramidal cells The performance of detecting pyramidal cells as 3D objects was evaluated by measuring the sensitivity, precision, and F1-score based on 3D-reconstructions of cells from a stack of 30 images. Manually Segmented (MS) and UNetDense Predicted (UDP) 3D-reconstructions were compared by checking whether estimated centroids from the MS cells fell within a cell profile of the UDP cells and vice versa. The sensitivity, precision, and F1-score were 0.98, 0.93, and 0.95, respectively, after removing datapoints from first and last three images, see Table 3. Thus, the 3D-reconstruction of pyramidal cells demonstrated a high performance across all three measurements.Table 3 Performance of objectwise validation. 3D-stack TP FN FP Sensitivity Precision F1 Original 472 19 33 0.96 0.93 0.95 Filtered 368 6 29 0.98 0.93 0.95 Validation of segmentation of pyramidal cells based on 3D-reconstruction. Precision, sensitivity, and F1-scores were calculated based on 491 reconstructed pyramidal cells from 30 stacked 2048 × 3840 manually marked images. Original refers to the case where centroid points from all 30 images are included. Filtered refers to the case where centroid points from the first and last three images of the stack were excluded. 3D-reconstruction and morphological analysis of pyramidal cells Layer III was located using a density plot of a 2D-projection of the centroids of every 3D-reconstructed neuron, with yellow representing areas of high density and blue representing areas of low density, see Fig. 4. Layer I got a very low density of neurons, while II and IV are denser than layer III in BA46. As Layer III has a smaller density than Layer II and Layer IV, the ROI is specified between the two dense yellow areas for our analysis.Fig. 4 Visualizing the steps to identify layer III. A) Illustration of four layers of BA46 and the observation window for the analysis is marked in green. B Raw image of a section. Scale bar = 300 μm. C Binary image of the output from the Deep learning model, UNetDense. D Position of centroids from 200 images projected to the x- and y-plane. E Density map of the positions of neurons to visualize the different layers in the cerebral cortex and the yellow color represents areas of high density, whereas the blue color represents areas of low density. The density was high at the yellow areas, which indicate the position of layer II and layer IV (from left to right) and low at the blue areas, which indicate fewer neurons and show a part of layer I and layer III. The volumetric stack of images was cropped within the squared marked with the red dashed lines that was selected by a user. F The part of the image in C, which is chosen for analysis. Further classifications into pyramidal or non-pyramidal neurons were needed as the UDP detects all neuronal shapes from 2D images. This is necessary because pyramidal cells' top and bottom image profiles can be mistaken for smaller neurons/glial cells, as seen in Fig. S4. The Gaussian mixture model (GMM) was used to classify the 3D-reconstructed pyramidal and non-pyramidal cells based on estimated sphericity and volume, see Fig. 5B. Big objects/cells were classified as outliers if the maximum Feret diameter in 2D or 3D measurements was three standard deviations from the mean. As a result, a total of 1, 19, and 28 datapoints for each subject were deemed outliers and hence excluded from the pyramidal cell data. The percentage of objects/neurons excluded from analysis using GMM and outlier detection accounted for 23, 25, 37, and 23% of the total number of detected objects for each dataset, as shown in Tab. S1. The mean density of pyramidal cells in layer III of BA46 after filtering was 28,155 mm−3, and the GMM categorization and data containing outliers are shown in Figs. S5–S6. The measurements and calculations for each subject's entire stack were examined just for the classified pyramidal cells, with the number of cells and the size of the ROI window for each subject shown in Table S1. Table 4 provides information on the size, shape, and orientation of pyramidal cells of layer III in BA46 for all three subjects.Fig. 5 Illustration of 3D-reconstruction of neurons and segmentation of pyramidal cells. A Visualization of the 3D-reconstruction of one layer neurons with a window height of 31 μm. B Zoomed image of the segmented pyramidal and non-pyramidal cells. Scale bar = 20 μm. C Overlay image of the binary segmented image with the gray-scale image of a section. Scale bar = 200 μm. D, E Close-up view of the overlay picture and 3D-reconstruction of pyramidal cells, with yellow lines representing orientations. Scale bar = 15 μm. F Measurement of diameter from the longest cell profile DiaL was utilized to construct 3D spherical objects with their corresponding 3D-reconstructed cell. The graphic indicates that the difference in 3D volume space between the spherical approximation and the 3D-reconstructed item is similar. Scale bar = 20 μm. Table 4 Quantitative values for pyramidal neurons from the entire stack of each subject. Brain Volume (μm3) Orientation (∘) Sphericity DiaL (μm) DiaAll (μm) Subject 1 867 ± 960 34 ± 23 0.38 ± 0.08 11.13 ± 3.46 7.23 ± 3.26 Subject 2 709 ± 806 28 ± 22 0.33 ± 0.07 11.13 ± 3.74 6.49 ± 3.1 Subject 3 808 ± 893 24 ± 18 0.35 ± 0.06 11.27 ± 3.61 7.37 ± 3.1 Mean 795 ± 65 29 ± 4.1 0.35 ± 0.02 11.17 ± 0.07 7.03 ± 0.39 CV 0.08 0.14 0.06 0.01 0.06 The table shows the neuronal volume, diameter, orientation, and sphericity of pyramidal cells for each subject based on the data which is summarized in Fig. S7. The entries for each subject state the average and ±one standard deviation. Mean is the average measurement for each column, coefficient of variation CV = SD/mean. The average neuronal volume across all three subjects is 795 μm3, and the shapes of pyramidal cells were assessed by approximating sphericity, giving an average value of 0.3537. The orientations of pyramidal cells relative to the direction of the vector pointing toward the pial surface had an average of 29∘, and some examples of orientation vectors are shown in Fig. 5E. The diameter was calculated using the nucleator probe by measuring the segment length from the largest cell profile (DiaL) and the average segment length from all cell profiles (DiaAll). The average neuronal diameter for DiaL and DiaAll were 11.17 and 7.03 μm, respectively. Estimated spheres of length DiaL were constructed and displayed with their matching 3D-reconstructed cell in Fig. 5F. Histograms of the different measurements can be found in Fig. S7 and as log-normal transformed in Fig. S8. 2D vs 3D comparison of pyramidal cells sizes The exact same neurons were used to compare the 2D and 3D analyses for each subject. The volumes of pyramidal cells from 2D images were approximated by constructing a spherical object based on the estimated radii measured by the nucleator probe. The volumes of cells from the three subjects were calculated from 2D images using the estimated segment length from the largest cell profile (VolL) and all cell profiles (VolAll). The average neuronal volume in 3D (Vol3D), VolL, and VolAll are 795, 730, and 183 μm3, respectively. An approximation to the mean diameter of a cell Dia3D was estimated from the mean volume Vol3D under the assumption that it is the volume of a perfect sphere. Dia3D was then assessed and compared to the diameter measured from the largest cell profile (DiaL) and all cell profiles (DiaAll). Because the nucleator probe is derived from the mathematical fact that the length of isotropic test lines between a unique point and the cell border, it provides the most accurate one-to-one comparison. The average diameter for Dia3D, DiaL, and DiaAll are 11.48, 11.17, and 7.03 μm3, respectively. The values for the 2D vs 3D comparison of volume and diameter can be seen in Tab. S2–S3. Point pattern analysis of pyramidal cells The coordinates of the centroids for the 3D analyzed pyramidal cells in layer III of BA46 form a spatial point pattern. We considered four such point patterns, which we refer to as 1_1, 1_2, 2, and 3 and they correspond to the three subjects (Subject 1 was divided into two parts since it was collected over two different days). In order to detect possible columnar structures in the datasets, we estimated the cylindrical K-function for each dataset, and compared it to the 95% global envelope obtained by simulations under the null hypothesis of complete spatial randomness (CSR). The results can be seen in Fig. 6. We considered the empirical cylindrical K-function in the directions of the three main axes. When the empirical cylindrical K-function is above the envelope, it indicates cylindrical clusters of points (centroids of cell locations) in the corresponding direction. There were signs of columnar clusters in all three directions for all subjects. However, it was most pronounced in the direction of the x-axis, which is the expected direction of the possible columnar structure of pyramidal cells, especially when looking at radii between 5 and 20 μm and heights between 20 and 80 μm. There were areas where the empirical curves were below the envelopes suggesting some repulsive behavior in the data. This was not unexpected since the point patterns only represent the centroids of cells, since cells cannot overlap, it was thus natural to see some repulsion between the points. The global envelope tests corresponding to the situations in Fig. 6A all yielded p-values below 0.05, and the tests corresponding to the situations in Fig. 6B all yielded p-values below 0.001, indicating that all datasets exhibited large deviations from CSR.Fig. 6 Results of analyses with the cylindrical K-function for the spatial point patterns of centroids of pyramidal cells. Each row represents a dataset which from top to bottom are Subject 1_1, 1_2, 2, and 3. A 95% global envelopes (shaded area) for the cylindrical K-function, with t = 80 fixed, and using 2000 CSR simulations. The theoretical value of the cylindrical K-function under CSR was subtracted from the curves for better visualization. The three curves correspond to the empirical cylindrical K-function for each dataset, in the direction of the x-axis (solid lines), y-axis (dotted lines), or z-axis (dashed lines). B Summary of 95% global envelopes for the four datasets based on the cylindrical K-function when varying both the height t and radius r. The envelopes are each based on 4000 CSR simulations. The direction of the cylinders is stated at the bottom of each plot. The plots indicate whether the empirical cylindrical K-function for the observed point pattern is above the envelope (black), within the envelope (gray), or below the envelope (white). Tissue deformation Every single-cell location research technique is vulnerable to tissue deformation. Epon embedding is used for the analysis in this paper to minimize shrinkage. We compared the tissue area of three Epon-embedded biopsies before and after processing. The human gray matter brain biopsies showed an areal shrinkage equal to 0.1%, 4.2%, and 7.9%, respectively. Based on these data and our previous publications38,39, it was decided not to correct our results for shrinkage. Discussion The presented method can reveal fundamental characteristics of specific brain areas, which can be used to improve our understanding of neuronal morphology and their spatial relationship. A variety of efforts have been made toward developing technologies for revealing the intricate patterns of neural circuits. By using different slicing or optical methods to recreate neuronal tissues, multiple studies have provided 3D data on the microscopic morphology or gene expression of neurons6,40–46. Due to a number of difficulties, such as size limitations of the slide scanner and microtome, low homogeneity of serial whole organ sectioning and staining, the time-consuming design of the operation, computer constraints, and limited digital processing capability, the entire human brain is difficult to recreate entirely on a mesoscopic scale. High-resolution, 3D models of animal and human brains are difficult to acquire. Chemical clearance methods make the tissue transparent and enable the entire mouse brain to be reconstructed6,44, but 3D-reconstruction and digitization of fine neuronal morphology continue to be challenging. One explanation is that in the nervous system, cells are closely arranged, making it difficult to distinguish one from another. Applying Golgi-staining, a 3D structural dataset of the entire mouse brain was also collected by micro-optical sectioning tomography, which can conduct imaging and sectioning simultaneously on centimeter-sized tissues42. However, such a section-based method demands costly specialized instruments, relatively long periods of sample treatment (weeks or months), and for a single brain imaging time can approach even one month47. Based on the reconstruction of histological sections of a human brain preserved in paraffin, Amunts et al. developed a 3D model of the whole human brain called BigBrain with a spatial resolution of 20 μm45. The tissue was embedded in paraffin, which can shrink tissue volume up to 50–60%39, and it is challenging to study neuronal morphology with a spatial resolution of 20 μm. To date, no optimal approach to the analysis and processing of the whole human brain with micro-resolution has been achieved. The majority of the approaches discussed above share the use of immunolabeling in order to detect and localize antigens or proteins within a cell at a specific location. The fundamental constraint of immunolabeling is that milder fixation conditions and a shorter detergent permeabilization incubation time are required to allow antibodies to penetrate the tissue. This is particularly important for soluble proteins in the cytoplasm, which are frequently damaged or destroyed during incubation. Hence, it is challenging to create neuronal antibodies that work in postmortem brains that have been in the fixative for months or years48. Another well-known concern is that many histological procedures can cause tissue samples to shrink and deform, potentially altering cell size, shape, and organization. Keeping the dimensions of the tissue is especially essential if studies want to assess changes in the size and distances of any cells or organelles within the tissue. Our methodology provides an effective technique for imaging smaller pieces of most archival tissue from semi-thin serial sections into a functional dataset with hardly any tissue deformation. Researchers have with our method a unique opportunity to study archived tissue samples, enabling researchers to investigate tissue and disease development in great detail. An automated section collection EM system has been altered into a unique 3D-reconstruction method for light microscopy, AutoCUTS-LM. We used histological staining to visualize cytoarchitecture and an autonomous slide scanner to produce high-resolution images of the brain tissue. Via deep learning, pyramidal neurons were characterized and their spatial distribution analyzed using advanced techniques. Our application provides valuable information on the neuronal 3D architecture from archived human brain tissue. We systematically sampled the tissue for our setup to reduce the scanning time and the processing of data. A thickness of 800 and 900 nm provided sufficient axial spatial resolution to detect and reconstruct pyramidal cells. The UNetDense architecture was applied to detect the pyramidal cells in our images, as threshold-based segmentation techniques typically yield poor output in medical image analysis for low-contrast images, unexplained noise, blurred boundaries, and different light condition49,50. The variation between brains made it challenging to create a model that suited all three brains since flaws occurred mainly along the edges of the images, and where the contrast was low due to less staining absorption. Therefore, we trained an individual UNetDense model for each subject in this study since the sensitivity, precision, and F1-scores for both pixelwise and objectwise segmentation were above 0.82 and 0.93, respectively. Overall, the results of the combined model showed smilar precision and F1-scores but lower sensitivity compared to the individual models. That being said, sensitivity was emphasized for this study as it demonstrates how effectively the model detects pyramidal cells present in the data, which is essential for conducting the point pattern analysis. The risk of having three models is overfitting. Overall, the validation-findings from the sensitivity, precision, and F1-scores showed that the outcomes of the three models are reliable. One reason for the difference between subjects was that the brains absorb staining differently, and this may be due to different ages, postmortem intervals, and long fixation time. With this limitation in mind, one model might be adequate if the captured images did not alter too much or if we annotated more images to train the model. We measured the volume, sphericity, orientation, and diameter of pyramidal cells and used the cylindrical K-function to identify columnar structures. The average volume was 795 μm3 with an equivalent diameter equal to 11.48 μm. The neuronal diameters of pyramidal neurons in layer III of BA46 have received little attention in the literature. Nonetheless, Rajkowska et al. examined 150–200 neurons in this region using stereological techniques and determined an average diameter of 13.45 μm. The process for measuring neuron radii was comparable to the nucleator probe as they measured the border outlines of cell profiles to compute the associated diameter. Similar to the procedure in this study, they only assessed neurons in the crown of a gyrus. The differences in radii could be related to the fact that we sampled pyramidal cells from a small concentrated area. Also, they quantified neurons in broader regions of BA46 using a succession of counting boxes. In comparison, our method offers information on thousands of counted neurons from each subject, whereas Rajkowska counted 150–200 neurons per subject34. In BA46, counting fewer neurons from smaller sample sizes over a broader area may overestimate neuronal radii. This is due to the fact that smaller pyramidal cells dominate layer III over bigger ones, resulting in a stronger proclivity toward smaller radii. The difference in radii measurements across individuals might also be related to biological variance for subjects, brain storage period, or different embedding media, as the difference is roughly 17%. Different factors such as age, disease, and toxicity have been reported to affect neuron volume in the cerebral cortex across other studies, however, they do not give any detail on how the shape of the neurons are affected51–54. A literature search revealed that no quantitative values to quantify the sphericity and orientation of pyramidal cells had previously been published. As a result, such measurements were not comparable to those of other studies. Sphericity is valuable as a general shape descriptor since it also applies to objects having holes, such as a torus. Yet, the main reasons sphericity is used for measuring shape are as follows: sphericity is a unitless number, hence is scale invariant. Furthermore, it is simple to compute since MATLAB easily calculates the surface area and volume of cells, which are fundamental descriptors of cells in their own right. Estimation of sphericity does not presume any shape before examining a 3D object, making it an appealing tool for differentiating between distinct forms. As a result, we implement sphericity estimates to comprehend a natural cell shape. The average sphericity of 0.35 found in this paper indicates that pyramidal cells appear elongated and far from spherical. The average angle between the vector that represents the orientation of the pyramid cell and the x-axis, which points to the pial surface, is 29∘, suggesting that pyramidal cells are focused perpendicular to the pial surface. Quantities such as cell sphericity and orientation in follow-up studies with different disease groups could be utilized to understand the morphological alterations induced by these disorders. The volumes of pyramidal cells calculated from 3D-reconstruction images were compared to the results of the nucleator probe measurements for the same pyramidal cells, which estimate the mean cell volume from 2D images. It is crucial to keep in mind that the volume estimates are based on two separate methodologies, and the volume may be overestimated or underestimated with either approach. The 3D approach measures every cell profile, and if a cell profile on the edge of a pyramidal cell is not recognized, this approach may underestimate the volume (top or bottom cell profile). On the other hand, the nucleator relies on measurements from a unique position (e.g., nucleolus), assuming that the section or cell is isotropic. If this criterion is not fulfilled, the estimate may be biased. The average estimated volumes of pyramidal cells were 795, 730, and 183 μm3 for Vol3D, VolL, and VolAll, respectively. The difference between Vol3D and VolL is 8.7%, whereas the difference between Vol3D and VolAll is 77%. It appears that using the nucleator probe on the largest cell profile to estimate volume yields a comparable overall result, but employing the nucleator probe on all cell profiles causes a huge difference. The estimated average volumes vary greatly depending on the approach used. The estimated diameter of pyramidal cells changed less than the volume estimates, with results of 11.48. 11.17, and 7.03 μm for Dia3D, DiaL, and DiaAll, respectively. The difference between Dia3D and DiaL is 2.9%, whereas the difference between Dia3D and DiaAll is 39%. Hence, if we look at estimated radii when employing the largest cell profile compared to the 3D estimation, there is no substantial disagreement between those two approaches for calculating diameter because the difference is nearly equivalent to the 272 nm pixel size. In contrast, estimating the average radius from all cell profiles makes a notable difference. As the neurons are not spherical in reality, comparing neuronal size based on volume rather than diameter is a more appropriate parameter to quantify changes in neuronal size in future references. Nevertheless, if only a 2D technique is available, adopting a spherical approximation by assuming general isotropy of these human pyramidal cells to estimate neuronal volume is not entirely inaccurate. It is vital to notice that a slight change in the radius can substantially change the volume value considering the radius is raised to the power of three while computing the volume. Even though the nucleator is a 2D procedure, it is nevertheless based on a 3D sampling methodology since the biggest cell profile detected during the sample is required before the analysis can be completed. The nucleator is therefore a helpful tool to use when it is challenging to distinguish smaller cell profiles at the edges and only larger cell profiles are required. Another important consideration is that the nucleator will be less sensitive to tissue shrinkage in the direction of the z-axis as it estimates the volume from a single 2D plane. The nucleator is advantageous for estimating volumes compared to other embedding materials that are sensitive to shrinkage, such as frozen sections or vibratome sections, which rarely deform in the x- and y-axes but shrink in the z-axis. The observed pyramidal neuron density in layer III of BA46 after the classification between pyramidal and non-pyramidal cells is 28,160 mm−3. A stereology study by Francine M. Benes found a neuronal density of 36,800 mm−3 in layer III of postmortem brains from nine healthy controls in the prefrontal cortex55. Cullen et al. estimated a neuronal density of 37,000 mm−3 in the prefrontal cortex of 10 adults with no history of mental illnesses, similar to Benes et al. However, Cullen et al. also measured the density of pyramidal cells in layer III to be 25,650 mm−3, a difference of 30% compared to the total neuronal density. Rajkowska et al. characterized and mapped the cytoarchitecture of BA46 in 17 healthy people. They were able to estimate a neuronal density of 51,510 mm−3 by blending cortical layers I–III34. Because the density in layer II varies from 48,000 to 78,000 mm−3 and covers a smaller area than layer III, it is reasonable to assume that the density in layer III would be about 36,000–44,000 mm−3 55,56. If the difference between neuronal and pyramidal density is roughly 30%, then the projected density should be ~25,200–30,800 mm−3, which is consistent with our findings. One thing all of the three studies listed above have in common is that they solely measured prefrontal cortex neuronal density34,55,56. Number densities indicate changes in the number of cells as well as tissue volume under consideration, because they are ratios. It is commonly assumed that as the number of detected objects rises, so does the density. However, the density increases as well if the number of identified objects remains constant, but the examined volume shrinks. As noted in other studies, making any definite claim regarding changes in 3D structures based purely on density estimates may lead to questionable findings57–59. Because this assumption is so widespread, the phrase "reference trap" refers to circumstances in which wrong conclusions are drawn only on the basis of density. As a result, comparing densities should be handled very carefully. Some studies have shown that pyramidal cells are organized in columns that are perpendicular to the pial surface of the cerebral cortex60–63, other studies suggest otherwise64,65. It is an attractive idea to explain the neuronal organization because interconnected neuron groups typically share similar physiological properties, and the conditions that excite a neuron are also likely to excite a considerable fraction of its afferent input. The loss or changes in the spatial organization of neurons may interfere with information processing between distributed networks, thereby promoting cognitive decline. The spatial distribution of pyramidal neurons was analyzed with the use of the cylindrical K-function, which does not assume any columnarity a priori, and where we applied the cylindrical K-function on much larger point pattern datasets than so far analyzed in the literature66–68. Our results suggest that there is evidence of a columnar structure in the directions of the x-, y-, and z-axes for all three subjects, but it was most pronounced in the direction of the x-axis, which is pointing toward the pial surface. Thus, the results support the theory of a columnar pattern perpendicular to the pial surface. The method can be used to detect potential cytoarchitectural distortions that may impact the neuronal columnar organization in the brain cortex. However, more human brains need to be studied in order to draw any final conclusions. Technical and environmental factors could affect the sampling quality of our sections. The plasma treatment required the transparent collection tape to be hydrophilic to ensure that the sections would adhere to the tape20,69. If the tape was too hydrophilic, the sections would not have time to unfold before they landed on the tape and folds were unavoidable. Variations in density between tissue samples and resin meant that the remaining blank resin had to be trimmed off4,20,70. We observed the advantage of higher indoor humidity, which provided a favorable environment for collecting sections. The impact of induced folds can be seen with a humidity level about 10%, 60%, and 90% in Figs. S10–S11. The test showed that a stable indoor humidity level around 80% and 90% could help avoid folds. If larger structures were to be explored in the future, the use of an optimized resin embedding protocol that could be used for thicker sections (>500 nm) would ease the workload. Applying a resin ratio test to match the tissue density and select the most suitable one before collecting samples with AutoCUTS-LM would have a beneficial effect on reducing the folds in the section. Reducing the hardness of the resin to suit a particular tissue type can be achieved by controlling the ratio of two different anhydride curing agents (Dodecenyl Succinic Anhydride and NADIC Methyl Anhydride). This could avoid the excessive cutting of thousands of needless sections, which would result in the sharpness of the knife being extended, using less tape, and a reduced workload for data collection and image processing. Other labeling methods can also be carried out with the AutoCUTS-LM, as our sections can be combined with immunofluorescence labeling to identify the distribution and co-localization of proteins as shown in Fig. S3A, and it may also be combined with in situ hybridization in order to localize a specific DNA or RNA sequence71. Due to the use of a hard resin, sections can also be viewed in an electron microscope, Fig. S3B, in which case cellular ultrastructure can be visualized. In essence, this methodology can be designed for both light microscopy and electron microscopy to bridge the localization of important molecules with cellular ultrastructure. In conclusion, the AutoCUTS-LM method could benefit research in cell and developmental biology, model organism analysis to address mechanisms as cell migration, 3D tissue modeling, and morphological changes between animal/patient groups. This method is applicable for any disease and can potentially enhance the research of normal and disease processes, particularly those involving morphological alterations or in which the spatial interaction of disease features is essential. Methods Subjects Three healthy human brains (two women, one male) aged 30, 53, and 58 (Subjects 1, 2, and 3) with no history of psychiatric or neurological diseases were selected from the brain collection at Core Centre for Molecular Morphology, Section for Stereology and Microscopy, Aarhus University Hospital, Aarhus, Denmark. These archived brains have been stored for 19, 21, and 21 years, respectively, in 4% formaldehyde in phosphate buffer at neutral pH and were collected in compliance with Danish law and with approval from the Central Denmark Area Health Research Ethics Committees (license number: M-2017-91-17). Sample extraction We have developed an algorithm in MATLAB that can assist any user in delineating a ROI in a block of tissue and systematically sample two (or more) points for biopsies on the cortex, see Fig. 1. The user had to capture a picture of the tissue block and define the biopsy diameter. Then the image was transformed into gray scale, and the ROI was delineated to produce the sample area. This picture was then transformed into a binary image indicating the sample area by means of a global threshold. Next, to prevent the biopsy from being placed on the crown of the gyrus, the edges of the binary image were removed. This was done by using morphological erosion with a filter size equivalent to the biopsy diameter and is shown with the red color in Fig. 1D. The sampling area was divided into four quarters. The two biopsy punches should either be performed in the first and third quarters or in the second and fourth quarters to avoid overlapping biopsies. The four quarters were determined by
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