Imaging intact human organs with local resolution of cellular structures using hierarchical phase-contrast tomography

Abstract

Imaging intact human organs from the organ to the cellular scale in three dimensions is a goal of biomedical imaging. To meet this challenge, we developed hierarchical phase-contrast tomography (HiP-CT), an X-ray phase propagation technique using the European Synchrotron Radiation Facility (ESRF)'s Extremely Brilliant Source (EBS). The spatial coherence of the ESRF-EBS combined with our beamline equipment, sample preparation and scanning developments enabled us to perform non-destructive, three-dimensional (3D) scans with hierarchically increasing resolution at any location in whole human organs. We applied HiP-CT to image five intact human organ types: brain, lung, heart, kidney and spleen. HiP-CT provided a structural overview of each whole organ followed by multiple higher-resolution volumes of interest, capturing organotypic functional units and certain individual specialized cells within intact human organs. We demonstrate the potential applications of HiP-CT through quantification and morphometry of glomeruli in an intact human kidney and identification of regional changes in the tissue architecture in a lung from a deceased donor with coronavirus disease 2019 (COVID-19).

Fig. 1. A HiP-CT pipeline for multiscale 3D imaging from whole-organ to cellular resolution within large intact soft tissue samples.

a, Flow chart of HiP-CT sample preparation and imaging procedure; the ability to select specific higher-resolution scan regions based on lower-resolution scans provides hierarchical tissue structure images in a data-efficient manner. b, Left, 2D image slice (25 µm per voxel) showing the location of a series of regions of 2.5 µm per voxel that transect the organ’s radius (red circles). Right, HiP-CT scans at 2.5 µm per voxel every 7 mm from the external kidney surface (left) to the center of the sample (right). Scans are overlapped and stitched to provide a complete organ. The magnified view shows a constant level of data quality and precision over the complete transect through the use of the reference scan procedure. c, Photograph of an intact human brain mounted in a polyethylene terephthalate jar with ethanol–agar stabilization and with the reference jar on top. d, Left, maximum intensity projection of a whole human lung with two randomly selected VOI imaged at a resolution of 2.45 µm per voxel shown in green (VOI1) and blue (VOI2). Three-dimensional reconstructions of the two high-resolution VOI are shown with 2D slices in the insets. In the 3D high-resolution VOI, the fine mesh of pulmonary blood vessels and the complex network of pulmonary alveoli and their septa can be seen. Yellow arrows denote occluded capillaries in 2D slices. Top right, image stack histograms for the green (VOI1) and blue (VOI2) high-resolution VOI, respectively (fixed bin width, 0.0001). Intensity distributions are comparable with positive skew (1.82 and 2.68) and kurtosis (6.44 and 11.88) for VOI1 and VOI2, respectively; the histogram intersection is 71 ± 3% for fixed bin width in the range 1 × 10⁻² – 3 × 10⁻⁴. Bottom right, box-and-whisker plot showing the structural similarity index between n = 200 pairs of 2D slices independently sampled either from within the same VOI (1-1 and 2-2) or from different VOI (1-2 and 2-1) for each group, respectively; one-way ANOVA (two sided); P = 0.8765, three degrees of freedom, F = 0.23). Box plots show the median (center line), interquartile range (75th–25th percentiles) of data (box bounds) and data range excluding outliers (whiskers)); values more than 1.5 times the interquartile range above or below box bounds are denoted as outliers (red crosses). e, Single representative slices of high-resolution scans from a HiP-CT image of an intact whole human lung lobe affected by COVID-19 (donor 3) and a biopsy taken from the same patient’s contralateral lung. Both VOI are captured from the upper peripheral region of each upper lung lobe. In HiP-CT images, fine structure of the tissue including blood capillaries (red arrows) and alveoli (blue arrows) as well as thin alveolar septa (yellow arrows in insets) is depicted. Source data

Fig. 2. HiP-CT enables 3D imaging of organotypic functional units across intact human organs.

HiP-CT of brain (a), lung (b), heart (c) kidney (d) and spleen (e); for each organ, 3D rendering (i) of the whole organ is shown using scans at 25 µm per voxel. Subsequent 2D slices (ii–iv) show positions of the higher-resolution VOI relative to the previous scan. (v), Digital magnification of the highest-resolution image with annotations depicting characteristic structural features in the brain (ml, molecular layer; gl, granule cell layer; pc, Purkinje cell), in the lung (c, blood capillary; ec/m, epithelial cell or macrophage), in the heart (mc, myocardium; ca, coronary artery; ad, adipose tissue), in the kidney (e/a, efferent or afferent arteriole; g, glomerulus) and in the spleen (rp, red pulp; wp, white pulp; a, arteriole; ss, splenic sinus). All images are shown using 2× binning.

Fig. 3. HiP-CT analysis of the kidney to measure glomerular morphology and nephron number.

a, Top left, HiP-CT datasets at three resolutions (25, 6 and 1.3 µm per voxel) obtained from a human kidney, aligned and overlaid. a, Top right, measurement of the parenchymal volume, semi-automatically segmented (green). a, Middle, the dataset at 6 µm per voxel with the virtual biopsy cylinder is shown in white; the parenchymal volume within the cylinder was measured. A representative 2D slice with an inset shows four labeled glomeruli. In total, 853 glomeruli that were within the cylinder were counted; a blue + sign is used to denote the approximate center of each glomerulus. a, Bottom, the dataset at 1.3 µm with virtual biopsy (red cylinder). The 13 glomeruli within this cylinder were segmented in 3D as shown in the 2D representative slice with inset. b, Comparison of HiP-CT with an aligned histopathological section (n = 1) (stained with hematoxylin and eosin (H&E)) taken after all HiP-CT scanning was finished. The left-hand column shows light micrographs of H&E-stained histopathological sections, and the right-hand column shows 2D tomograms of HiP-CT; yellow boxes denote images that have been pseudocolored (from HiP-CT) or converted in gray levels and inverted in contrast (from histological observations). Source data

Fig. 4. HiP-CT with 3D image analysis and morphometry in the lung of a patient with COVID-19.

a(i), A 3D reconstruction from HiP-CT scanning at 25 µm per voxel of the intact upper left lung lobe from the autopsy of a patient who died from COVID-19-related ARDS. High-resolution VOI are shown in red (6.5 µm per voxel) and blue (2.5 µm per voxel). a(ii), At 25 µm per voxel, high-intensity regions are observed in the lung periphery. The yellow dotted line delineates a secondary pulmonary lobule. a(iii), At 6.5 µm per voxel, heterogeneity in the lung parenchyma included (1) dilated alveolar ducts and diffuse loss of alveolar structural organization and (2) comparatively well-preserved alveolar structure with some edematous changes. a(iv), At 2.5 µm per voxel, we observed (3) alveolar obstruction, likely representing clotted blood based on its high intensity and (4) interstitial thickening of alveolar septa. b, A 3D reconstruction of the COVID-19 lung with segmentation of two adjacent secondary pulmonary lobules with differing degrees of parenchymal deterioration. c, A 3D reconstruction of segmented acinus structure within the SARS-CoV-2-uninfected (control) lung (top) and the SARS-CoV-2-infected lung (bottom). d(i–iii), A 3D reconstruction of representative VOI at high resolution (2.5 µm per voxel) for the control, COVID_S and COVID_C groups, respectively. Duplicated volumes show visual representations of the airspace–tissue interface in the COVID-19 lung, where a smaller distance between a voxel of airspace and a voxel of tissue is colored blue, whereas larger distances are colored yellow. d(iv–viii), Box plots showing quantitative comparisons between n = 6 independent COVID_S, COVID_C and control VOI. Box plots show data median (center line), the interquartile range (25th–75th percentiles) of data and data range without outliers (box bounds); outliers were considered as values 1.5 times above or below the box bounds (whiskers). Quantification of the mean surface area-to-volume (SA:V) ratio, airspace connectivity and mean septal thickness are shown, respectively (**P < 0.001, *P < 0.05 and ~P = 0.08) (calculated by two-tailed one-way ANOVA with Tukey’s comparison; P values and F statistics can be found in the Supplementary Information). d(ix), The distribution of airspace diameters for all six VOI in each group (modal values for COVID_C, COVID_S and control, 8.9, 152 and 351 µm, respectively). Source data

Extended Data Fig. 1. Minimum scan times.

Minimum scan times for quarter and half acquisition modes at voxel sizes presented in this work.

Extended Data Fig. 2. Signal to noise variation with lateral distance from center of rotation.

Graph showing the signal to noise variation with lateral off-set from centre of rotation. Pearson correlation coefficient (r) = -0.25 and p-value =0.21 (two-tailed test) shown on graph. It should be noted that the exposure time of the camera and final selection of the X-ray filters were set-up on the most off-centre scan, at the angle where the beam was crossing the smallest amount of material, in order to ensure that detector saturation would not occur during scanning. B) showing an example of the regions used to calculate the SNR. Signal was taken from tubule walls while the open tubule lumen was used as the background region.

Extended Data Fig. 3. Resolution estimation via Fourier shell correlation.

Resolution estimation from fourier shell correlation (FSC) measure. FSC curves (black) with standard deviation (grey shaded) and ½ bit criterion (red hatched) for each voxel size imaged Donor 1 lung. Intercept point is marked and labelled with the equivalent spatial resolution. FSC was calculated in each case using 9 subvolumes cropped at randomly varying spatial locations from the total image volume. Subvolume sizes were varied (200,500 and 1000 voxels).

Extended Data Fig. 4. Purkinje cell signal to noise ratio.

Purkinje cell signal to noise ratio calculation PC= Purkinje cell, BG= Background.

Extended Data Fig. 5. High X-ray dose does not cause detectable damage to kidney as assessed standard histology H&E.

Large scale view of Haemotoxylin and Eosin (H&E) stained slice of the kidney with outlines of the 6 µm/voxel (blue) and 1.3 µm/voxel (green) scanning regions shown. There is no visible difference to suggest X-ray damage to parenchyma between these regions (N = 1).

Extended Data Fig. 6. Imaging protocol developments.

The attenuation scanning protocol was originally developed to image dense fossils and adapted for HiP-CT of complete human organs. In this protocol, the centre of rotation is in the middle of the cylinder used to mount the sample. There are four key principles: 1) single distance phase retrieval using Paganin et al. algorithm coupled with an unsharp mask on the radiographs after phase retrieval; 2) optimization of references absorption profile for local tomography; 3) optimization of dynamical range by adjusting detector saturation level through the sample; 4) a combination of 1, 2, 3 and the attenuation protocol adapted to organs. A) classical scan in edge detection mode. The saturation level of the detector is set on the beam reference without the sample B) Same scan reconstructed using single distance phase retrieval. C) Same scan but references are calculated from an equivalent jar filled with mounting media instead of using pictures of the beam without sample. D) Scan performed off-axis to have a lateral gradient of power in the beam to fit with absorption profile of the sample. Saturation level of the detector tuned through the sample, normal references without sample in the beam. E) Scan in attenuation protocol using references in equivalent jar filled with mounting media, dynamic range optimization and single distance phase retrieval. Scalebar 50mm.

Extended Data Fig. 7. Histological staining following imaging of lung tissue of BM05.

H&E and IHC staining of COVID-19 lung biopsy and control (Donor 4) lung biopsy after HiP-CT imaging. H&E staining of overview, parenchyma and alveoli in columns 1-3; CD31 (column 4), Thyroid transcription factor 1 (TTF-1 (column 5)) and Fibrin staining (column 6) (N=1). For histological comparison, representative lung samples were cut and embedded in paraffin. 2 µm thick sections were cut followed by histological staining using Hematoxylin and Eosin (HE) at the Institute of Pathology at Hannover Medical School. Immunohistochemistry for CD31 1:75 (M0823, Agilent Dako, California, USA), TTF-1 RTU (790-4756, Hoffmann-La Roche, Basel, Swiss) and Fibrin 1:500 (MABS2155, Merck Millipore, Massachusetts, USA) were stained on a VENTANA BenchMark ULTRA (Hoffmann-La Roche, Basel, Swiss) with the aforementioned dilutions and pretreatment and incubation times according to the manufacturers advice. Representative images were acquired with a Olympus CS50 camera (Olympus, Tokyo, Japan) using Olympus cellSens Software (Olympus, Tokyo, Japan) on a routine diagnostic light microscope (BX43, Olympus, Tokyo, Japan).