Three-Dimensional Virtual Histology of Human Cerebellum by X-Ray Phase

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Submission. Direct PNAS Board. a is article This interest. of conflict no samples declare the authors provided The C.S. and samples; interpretation. the data prepared neurological F.v.d.M. C.S., and paper; M.T., data; the analyzed wrote M.T. T.S. experiments; and synchrotron exper- performed laboratory T.S. performed and M.T. 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BIOPHYSICS AND COMPUTATIONAL BIOLOGY and choice of image recording parameters and reconstruction AB algorithms. To this end, we use two optimized tomography biopsy punch instruments designed and implemented by our group, a laboratory µ-CT setup equipped with a liquid jet anode source, as well as a high-resolution SR instrument with special X-ray waveguide optics for coherence and spatial filtering (20). While the µ-CT offers large volume, the SR setup provides a zoom-in paraffin into the laboratory dataset at higher resolution. In both cases, tissue we have optimized optical and geometrical parameters for samples of low contrast and have identified suitable reconstruction algorithms. Importantly, we have implemented and validated algorithms for automated 3D image segmentation and extraction of neuron locations. For this proof-of-concept demonstration, C we have chosen the example of the human cerebellum. Specifi- cally, we quantify the positions of all neurons and from these data the distribution functions describing difference vectors between neighboring neurons in the densely packed granular layer. We also cover the interface to the Purkinje cell layer where we show that image quality and contrast are high enough to segment the characteristic dendritic trees of Purkinje cells, as well as the molecular layer with its much sparser population of neurons. Several approaches have already been introduced to imple- ment automated segmentation. For example, Dyer et al. (8) first used manual training of an object classifier to obtain probability maps for neurons or for specific sample features. This was Fig. 1. Virtual histology of human cerebellum. (A) Sample preparation shown to work well for osmium-stained mouse cortex recorded for tomographic experiments. Biopsy punches were taken from paraffin- with synchrotron radiation. Hieber et al. (9) used a Frangi fil- embedded human cerebellum and placed into a Kapton tube for mounting tering step to extract tubular and spherical microstructures of in the experimental setups. (B) Transverse slice through the reconstructed Purkinje cells in unstained human cerebellum embedded in volume of the synchrotron dataset revealing the interface between the paraffin, also recorded with synchrotron data. To locate up to 106 low-cell molecular and the cell-rich granular layer, including a cell of the neurons in a 1-mm punch from a human cerebellum embedded monocellular Purkinje cell layer. (C, Left) Corresponding slice of the labora- in paraffin, we here make use of the spherical Hough transform tory dataset (same plane, same specimen as in B), showing the larger volume which we tune to detect the cell nuclei of expected size in the accessible by the laboratory setup while maintaining the resolution required for single-cell identification. (C, Right) Magnified view of the region marked entire 3D search space. The algorithm does not need any man- by the rectangle in C, Left corresponding to the field of view of the syn- ual training and can accurately locate even unstained and small chrotron dataset in B. [Scale bars: 50 µm (B and C, Right) and 200 µm (C, neurons. At the achieved level of data volume, image quality, Left).] and segmentation reliability, more statistical quantifications of anatomical structure become possible. We illustrate this by showing highly resolved histograms of structural parameters, such The cells of the granular and molecular layer are significantly as cell–cell distance and gray values representing electron den- smaller compared with the Purkinje cells which have a highly sity. Borrowing concepts from condensed-matter physics, we also branched dendritic tree. The segmentation of the single Purk- compute the associated pair correlation function and structure inje cells in Fig. 2A, Right (note that the cell in Fig. 2A, Lower factor from the retrieved cellular position vectors. The result- Right is the same as shown in Fig. 1C) also shows the typical flat ing curves indicate a highly structured 3D assembly for the shape of the Purkinje cell which is almost 2D. granular layer where the first and second correlation shells of nuclear positions can clearly be distinguished from the associated Cell Quantification in the Molecular and Granular Layer. The output maxima. of the automatic cellular segmentation based on the spherical Hough transform is depicted in Fig. 3, both for an exemplary slice Results and for the entire volume. In total, the algorithm determined High-Resolution Synchrotron CT. The synchrotron measurements ∼40,000 cells in the granular and 1,700 cells in the molecular were recorded in a highly coherent and divergent 8-keV radia- layer. To obtain a measure for cellular density, the volume of tion cone behind the X-ray waveguide optics of the Gottingen¨ the two layers is estimated via an envelope around all cells con- Instrument for Nano-Science with X-rays (GINIX) endstation tained in that layer (Fig. 3C). As a criterion for the separation (SI Appendix, Fig. S4A), installed at the P10/PETRAIII beam- between the low-cell molecular layer and the cell-rich granu- line (20). Fig. 1B shows a virtual slice through the tomographic lar layer the mean distance to the 35 nearest neighbors is used. reconstruction, clearly resolving the transition between the cell- The density is determined as ρ = 9.9 · 104 mm−3 in the molecu- rich granular layer in the bottom and the low-cell molecular layer lar layer and ρ = 2.7 · 106 mm−3 in the granular layer. Note that at the top. At the interface, the monocellular Purkinje cell layer this corresponds to the average density of the entire layer. In SI can be identified. One exemplary cell of this layer is depicted in Appendix, Fig. S12, a local density estimation for the granular the presented slice, including its large dendritic tree protruding layer is depicted, showing large variations throughout the layer. into the molecular layer. To better visualize the 3D structure of The cell density is also found to decrease toward the molecular the reconstructed volume, a cellular segmentation is shown in layer, resulting in a smooth transition between these two layers. Fig. 2A and in SI Appendix, Movie S1. The automated segmen- From the positions of the cell nuclei within the volume, several tation of cells in the molecular and granular layer based on the statistical measures can be determined (Fig. 4). The distribu- spherical Hough transform (21, 22) is detailed in Material and tion of nearest-neighbor distances in the molecular layer (ML) Methods. For the Purkinje cell layer, a semiautomatic approach and granular layer (GL) is shown in Fig. 4 A and B, reveal- was used, as detailed in Materials and Methods. Different char- ing mean distances of 9.7 ± 0.8 µm (ML) and 4.00 ± 0.02 µm acteristics of the cerebellar layers become immediately evident. (GL). Considering the mean radius of cellular nuclei in the two

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BIOPHYSICS AND COMPUTATIONAL BIOLOGY A C in the GL the algorithm found ∼1,760,000 cells, resulting in densities of 7.4·104 mm−3 and 3.4·106 mm−3, respectively. The deviation in the ML compared with the synchrotron results can be explained by the lower precision and recall for this layer. Contrarily, in the GL where precision and recall are high in both cases, the difference in the determined cell density must be B D attributed to the different probing volumes, as the synchrotron dataset probes a much smaller subvolume compared with the laboratory dataset. As can be recognized in SI Appendix, Fig. S13, the cellular density decreases when approaching the transition to the ML and the highest cell densities occur in the center of the GL. This indicates that the dataset recorded at the synchrotron comprises a less dense part of the GL, which explains EF the difference in overall cell density. To conﬁrm this assumption, the cell density in the corresponding subvolume of the laboratory dataset is determined as well, resulting in 2.9·106 mm−3. This value deviates by about 11% from the synchrotron dataset, which is in agreement with the precision and recall values for this layer. The laboratory dataset also conﬁrms that the grainy structure of the local density, resulting from a clustering of cells, persists throughout the entire GL. This is further visualized in SI G Appendix, Fig. S13. From the segmentation results the same statistical measures as for the synchrotron dataset are determined (Fig. 5). The mean cell radii for the ML and GL are 1.6 µm and 1.5 µm, respectively. The distribution of nearest-neighbor distances in the ML and the GL is shown in Fig. 5 A and B. An estimate for the relative electron density is given by the mean gray value within

Fig. 4. Statistical measures obtained from the automatically determined A 103 C 102 cell positions. (A) Histogram showing the nearest-neighbor distances in the Gaussian fit 8 2-Gaussian fit ML. The Gaussian fit reveals a mean nearest-neighbor distance of 9.7 ± 0.8 0.8 µm (95% confidence interval) with SD σ = 7.3 ± 0.8 µm. (B) Histogram 4 of the nearest-neighbor distances in the GL, with a Gaussian fit indicat- 0.4 ing a mean of 4.00 ± 0.02 µm with SD σ = 0.45 ± 0.02 µm. (C) Histogram showing the mean gray value within the automatically detected cell volume 10-4 0 0 in the ML. A Gaussian fit leads to (1.17 ± 0.02) · 10−3 with SD (σ = 2.9 ± 5 10 15 20 25 30 35 40 1.2 1.6 2 2.4 2.8 104 104 0.2) · 10−4.(D) Histogram of the mean gray value in the GL. The shape is best B 6 D 2-Gaussian fit 4 2-Gaussian fit fitted by a 2-Gaussian function with peak values of (1.24 ± 0.01) · 10−3 and −3 −4 −4 (1.6 ± 0.1) · 10 and SDs σ1 = (1.6 ± 0.1) · 10 and σ2 = (3.4 ± 0.1) · 10 4 in an approximate ratio of 30:70. (E) Angular averaged pair correlation func- 2 tion of the cells in the GL revealing two distinct peaks at 4.16 ± 0.04 µm 2 and 8.5 ± 0.3 µm. (F) Angular averaged structure factor of the cells in the -4 10 ◦ 0 0 GL. (G) Angular distribution of nearest neighbors in the GL, where θ ' 90 4 6 8 1 2 3 corresponds to the plane in which the dendritic tree of the Purkinje cells is EFnearest neighbor distance [µm] mean gray value ◦ spreading and φ = 90 is approximately parallel to the interface between 1.6 1.8 the ML and GL (SI Appendix, SI Methods). 1.2 1.4 0.8 dataset single cells are resolved in all layers of the cerebellum. 1 0.4 structure factor The magnified region in Fig. 1C, Right also reveals the large pair correlation function 0 0.6 Purkinje cell where the thick branches of the dendritic tree can 010205 15 25 123 4 5 6 already be resolved. In Fig. 2B a cellular segmentation of the radial distance r [µm] scattering vector q [µm-1 ] sample is shown, again via a semiautomatic approach for the Fig. 5. Statistical measures obtained from the laboratory dataset. (A) His- Purkinje cell layer and the automatic approach based on the togram of the nearest-neighbor distances in the ML. The Gaussian fit reveals spherical Hough transform for the GL and ML (see SI Appendix, a mean nearest-neighbor distance of 8.6 ± 0.4 µm (95% confidence inter- Fig. S5 and Movie S2, for more details and visualization). val) with SD σ = 7.8 ± 0.4 µm. (B) The same histogram for the cells detected To quantify the performance of the segmentation algorithm, in the GL. A 2-Gaussian function with peaks at 3.93 ± 0.02 µm and 2.64 ± the precision and recall are also determined for the laboratory 0.02 µm with SDs σ1 = 0.77 ± 0.03 µm and σ2 = 0.35 ± 0.04 µm (approxi- dataset. As the manual segmentation proved to be challenging mate aspect ratio 86:14) fitted the data best. (C) Histogram of the mean gray due to the lower resolution and signal-to-noise ratio, the auto- value within the volume of a single cell. The 2-Gaussian fit leads to peaks at −4 −4 −5 (1.72 ± 0.01) · 10 and (1.21 ± 0.02) · 10 with SDs σ1 = (2.4 ± 0.1) · 10 matic segmentation results from the synchrotron are considered −5 as ground truth (SI Appendix, Figs. S8 and S9). The analysis yields and σ2 = (1.1 ± 0.2) · 10 and an approximate weight ratio of 90:10. (D) Histogram of the mean gray values within the detected cells of the GL. The a precision and recall of (p, r) = (0.71, 0.72) for the ML and 2-Gaussian fit reveals peaks at 1.936 ± 0.001 · 10−4 and 1.483 ± 0.001 · 10−4 (p, r) = (0.85, 0.93) for the GL. This shows that especially for −5 −5 with SDs σ1 = (2.92 ± 0.01) · 10 and σ2 = (1.23 ± 0.02) · 10 (approxi- the GL the performance of the algorithm is remarkably high and mate aspect ratio 90:10). (E) Pair correlation function of the cells in the GL comparable to results obtained at synchrotron sources (8). In the with the two principle peaks at 4.74 ± 0.04 µm and 8.7 ± 0.1 µm and also ML around 26,000 cells were identified automatically whereas a minor modulation at 2.50 ± 0.05 µm. (F) Structure factor of the GL.

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BIOPHYSICS AND COMPUTATIONAL BIOLOGY Erythrocytes, which are contained in the blood vessels, are of similar size value, the values of the pixels contained in each single cell were summed to that of the granule cell nuclei (SI Appendix, Fig. S11) and are there- up and divided by the corresponding cell volume. A pixel was included fore segmented as well. To remove these false positives, they are manually if its radial distance to the center pixel was smaller than the radius. The excluded from the automatic segmentation. In the future, vessel segmenta- pair correlation function was calculated by counting the number of cells tion based for example on the Frangi filter (9) could be used to automatize in a spherical shell of a given radius and 0.5-pixel width around a single the identification of these regions. For the division into ML and GL, the cell in the GL and averaging this over all cells. For the structure factor the mean distance to the 35 nearest neighbors was chosen as a measure and angular average over the 3D Fourier transform of the array containing the the threshold was again set based on visual inspection. The volume of center positions of each sphere was computed. The procedure for determin- each of the layers, used for the calculation of the cell density, was deter- ing the angular distribution of neighboring cells in the GL is described in SI mined via the Matlab-implemented function boundary which determines a Appendix, SI Methods. 3D hull enveloping all cells contained within the ML or GL. This function also enables the choice of a shrinking factor, where 0 gives a convex hull and 1 ACKNOWLEDGMENTS. We thank Michael Sprung for support at the beam- gives a compact boundary. Here, the standard shrinking factor of 0.5 was line as well as Leon Merten Lohse for help in data visualization. This work chosen. was supported by the Cluster of Excellence 171 Nanoscale Microscopy and The nearest neighbor was determined by calculating all cell-to-cell dis- Molecular Physiology of the Brain and the Collaborative Research Center tances and finding the minimum for each individual cell. For the mean gray 755 Nanoscale Photonic Imaging of the German Science Foundation (DFG).

1. Paganin D, Nugent KA (1998) Noninterferometric phase imaging with partially 16. Topperwien¨ M, et al. (2017) Three-dimensional mouse brain cytoarchitecture coherent light. Phys Rev Lett 80:2586–2589. revealed by laboratory-based x-ray phase-contrast tomography. Sci Rep 7:42847. 2. Cloetens P, et al. (1999) Holotomography: Quantitative phase tomography with 17. Muller¨ M, et al. (2017) Myoanatomy of the velvet worm leg revealed by laboratory- micrometer resolution using hard synchrotron radiation x rays. Appl Phys Lett based nanofocus X-ray source tomography. Proc Natl Acad Sci USA 114:12378– 75:2912–2914. 12383. 3. Zhu P, et al. (2010) Low-dose, simple, and fast grating-based X-ray phase-contrast 18. Khimchenko A, et al. (2016) Extending two-dimensional histology into the third imaging. Proc Natl Acad Sci USA 107:13576–13581. dimension through conventional micro computed tomography. Neuroimage 139: 4. Zanette I, et al. (2012) Trimodal low-dose X-ray tomography. Proc Natl Acad Sci USA 26–36. 109:10199–10204. 19. Busse M, et al (2018) Three-dimensional virtual histology enabled through cytoplasm- 5. Munro PR, Ignatyev K, Speller RD, Olivo A (2012) Phase and absorption retrieval using speciﬁc X-ray stain for microscopic and nanoscopic computed tomography. Proc Natl incoherent X-ray sources. Proc Natl Acad Sci USA 109:13922–13927. Acad Sci USA 115:2293–2298. 6. Zhao Y, et al. (2012) High-resolution, low-dose phase contrast X-ray tomography 20. Salditt T, et al. (2015) Compound focusing mirror and X-ray waveguide optics for for 3D diagnosis of human breast cancers. Proc Natl Acad Sci USA 109:18290– coherent imaging and nano-diffraction. J Synchrotron Radiat 22:867–878. 18294. 21. Hough PV (1962) US Patent 3,069,654. 7. Zanette I, et al. (2015) X-ray microtomography using correlation of near-ﬁeld speckles 22. Peng T, Balijepalli A, Gupta SK, LeBrun T (2007) Algorithms for on-line monitoring for material characterization. Proc Natl Acad Sci USA 112:12569–12573. of micro spheres in an optical tweezers-based assembly cell. J Comput Inf Sci Eng 8. Dyer EL, et al. (2017) Quantifying mesoscale neuroanatomy using x-ray microtomog- 7:330–338. raphy. eNeuro 4:ENEURO.0195-17.2017. 23. Andersen BB, Gundersen HJG, Pakkenberg B (2003) Aging of the human cerebellum: 9. Hieber SE, et al. (2016) Tomographic brain imaging with nucleolar detail and A stereological study. J Comp Neurol 466:356–365. automatic cell counting. Sci Rep 6:32156. 24. Jiao Y, Berman H, Kiehl TR, Torquato S (2011) Spatial organization and correlations 10. Topperwien¨ M, Krenkel M, Muller¨ K, Salditt T (2016) Phase-contrast tomography of cell nuclei in brain tumors. PloS One 6:e27323. of neuronal tissues: From laboratory- to high resolution synchrotron CT. Proc SPIE 25. Chen HY, Hoffmann S, Salditt T (2015) X-ray beam compression by tapered 9967:99670T. waveguides. Appl Phys Lett 106:194105. 11. Schulz G, et al. (2010) High-resolution tomographic imaging of a human cerebellum: 26. Zabler S, Cloetens P, Guigay JP, Baruchel J, Schlenker M (2005) Optimization of phase Comparison of absorption and grating-based phase contrast. J R Soc Interface 7:1665– contrast imaging using hard x rays. Rev Sci Instrum 76:073705. 1676. 27. Ketcham RA (2006) New algorithms for ring artifact removal. Proc SPIE 6318:63180O. 12. Otendal M, Tuohimaa T, Vogt U, Hertz HM (2008) A 9 keV electron-impact liquid- 28. Witte YD, Boone M, Vlassenbroeck J, Dierick M, Hoorebeke LV (2009) Bronnikov- gallium-jet x-ray source. Rev Sci Instrum 79:016102. aided correction for x-ray computed tomography. J Opt Soc Am A 26:890–894. 13. Larsson DH, Vagberg˚ W, Yaroshenko A, Yildirim AO,¨ Hertz HM (2016) High-resolution 29. Palenstijn W, Batenburg K, Sijbers J (2011) Performance improvements for iterative short-exposure small-animal laboratory x-ray phase-contrast tomography. Sci Rep electron tomography reconstruction using graphics processing units (GPUs). J Struct 6:39074. Biol 176:250–253. 14. Bartels M, Hernandez VH, Krenkel M, Moser T, Salditt T (2013) Phase contrast 30. van Aarle W, et al. (2015) The ASTRA toolbox: A platform for advanced algorithm tomography of the mouse cochlea at microfocus x-ray sources. Appl Phys Lett development in electron tomography. Ultramicroscopy 157:35–47. 103:083703. 31. Xie L (2014) Spherical hough transform for 3D images. Available at https://de. 15. Topperwien¨ M, Krenkel M, Quade F, Salditt T (2016) Laboratory-based x-ray phase- mathworks.com/matlabcentral/fileexchange/48219-spherical-hough-transform- contrast tomography enables 3D virtual histology. Proc SPIE 9964:99640I. for-3d-images. Accessed May 5, 2017.

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