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CODA: quantitative 3D reconstruction of large tissues at cellular resolution

Nature Methods volume 19, pages 1490–1499 (2022)Cite this article

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Abstract

A central challenge in biology is obtaining high-content, high-resolution information while analyzing tissue samples at volumes relevant to disease progression. We address this here with CODA, a method to reconstruct exceptionally large (up to multicentimeter cubed) tissues at subcellular resolution using serially sectioned hematoxylin and eosin-stained tissue sections. Here we demonstrate CODA’s ability to reconstruct three-dimensional (3D) distinct microanatomical structures in pancreas, skin, lung and liver tissues. CODA allows creation of readily quantifiable tissue volumes amenable to biological research. As a testbed, we assess the microanatomy of the human pancreas during tumorigenesis within the branching pancreatic ductal system, labeling ten distinct structures to examine heterogeneity and structural transformation during neoplastic progression. We show that pancreatic precancerous lesions develop into distinct 3D morphological phenotypes and that pancreatic cancer tends to spread far from the bulk tumor along collagen fibers that are highly aligned to the 3D curves of ductal, lobular, vascular and neural structures. Thus, CODA establishes a means to transform broadly the structural study of human diseases through exploration of exhaustively labeled 3D microarchitecture.

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Fig. 1: CODA.
Fig. 2: Validation of CODA registration and ability to skip z sections.
Fig. 3: CODA processing of additional organs.
Fig. 4: Interpatient pancreas analysis from cm scale to single cell resolution.
Fig. 5: Microarchitectural patterns in pancreatic precancers.
Fig. 6: 3D Patterns in pancreatic cancer invasion.

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Data availability

Due to the extremely large size of the digital files described, data are available upon request from the corresponding author. Source data are provided with this paper.

Code availability

Code is available on the following GitHub page: https://github.com/ashleylk/CODA.

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Acknowledgements

We thank J. Phillip and D. Gilkes for their important feedback in this work. We would additionally like to thank sources of funding for additional projects in our groups: grant nos. NIH/NCI P50 CA62924; NIH/NIDDK K08 DK107781; Sol Goldman Pancreatic Cancer Research Center; Buffone Family Gastrointestinal Cancer Research Fund; Carol S. and Robert M. Long Pancreatic Cancer Research Fund; Allegheny Health Network, Johns Hopkins Cancer Research Fund; American Cancer Society, The Cornelia T. Bailey Foundation Research Scholar grant no. RSG-18-143-01; AACR-Bristol-Myers Squibb Midcareer Female Investigator Grant; Emerson Collective Cancer Research Fund; Robert L. Fine Pancreatic Cancer Research Foundation; Rolfe Pancreatic Cancer Foundation; Joseph C Monastra Foundation; The Gerald O Mann Charitable Foundation (H. and A. Wulfstat, Trustees); S. Wojcicki and D. Troper; The Carl and Carol Nale Fund for Pancreatic Cancer Research. The Johns Hopkins University Oncology Tissue Services core used for sectioning and staining is funded by the SKCCC Cancer Center Support grant (CCSG; grant no. P30 CA006973). Funding came from the National Institutes of Health/National Cancer Institute grant no. U54CA268083. (D.W., P.W. and A.L.K.); National Institutes of Health/National Cancer Institute grant no. U54CA210173. (D.W.); National Institutes of Health/National Institute on Aging grant no. U01AG060903 (D.W.); National Institutes of Health/National Cancer Institute grant no. UG3CA275681 (P.W.); The Sol Goldman Pancreatic Cancer Research Center (A.M.B., L.D.W., F.A., E.D.T., R.H.H., P.W. and D.W.); S. Wojcicki and D. Troper (A.L.K., A.M.B., L.D.W., F.A. and E.D.T.); The Rolfe Foundation for Pancreatic Cancer Research, Allegheny Health Network—Johns Hopkins Cancer Research Fund (A.M.B.); ARCS Foundation, Inc. (A.L.K.); Nanotechnology for Cancer Research T32 Training grant no. 5T32CA153952 (A.L.K.) and an NVIDIA GPU grant (P.W.).

Author information

Author notes

  1. Ashley L. Kiemen

    Present address: Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

Authors and Affiliations

  1. Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA

    Ashley L. Kiemen, Mia P. Grahn, Kyu Sang Han, Jocelyn Hsu, Denis Wirtz & Pei-Hsun Wu

  2. Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA

    Ann C. Jiang

  3. Department of Mechanical Engineering, The Johns Hopkins University, Baltimore, MD, USA

    Bridgette Kim

  4. Department of Plastic and Reconstructive Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Sashank Reddy

  5. Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

    Seung-Mo Hong

  6. Department of Pathology, University of Colorado School of Medicine, Aurora, CO, USA

    Toby C. Cornish

  7. Department of Biostatistics, The Johns Hopkins University, Baltimore, MD, USA

    Peng Huang

  8. Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Peng Huang, Laura D. Wood, Ralph H. Hruban & Denis Wirtz

  9. Department of Materials Science and Engineering, The Johns Hopkins University, Baltimore, MD, USA

    Denis Wirtz

  10. Johns Hopkins Physical Sciences—Oncology Center, The Johns Hopkins University, Baltimore, MD, USA

    Denis Wirtz & Pei-Hsun Wu

  11. Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Alicia M. Braxton, Jaanvi Mahesh Babu, Rebecca Reichel, Falone Amoa, Elizabeth D. Thompson, Laura D. Wood, Ralph H. Hruban & Denis Wirtz

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Contributions

Conceptualization was done by L.D.W., R.H.H., P.-H.W. and D.W. Image registration methodology was carried out by P.-H.W. and A.L.K. Deep learning methodology was done by A.L.K. 3D reconstruction methodology was done by A.L.K. 3D quantification methodology was devised by P.-H.W. and A.L.K. CODA validation was done by A.C.J., P.-H.W. and A.L.K. Collagen alignment methodology was developed by P.-H.W., K.S.H. and A.L.K. Tissue annotation was done by M.P.G., A.M.B., J.M.B., R.R., F.A., A.L.K., A.C.J., B.K. and J.H. Tissue collection, sectioning and scanning were carried out by S.R., T.C.C. and A.M.B. Pathology consultation was done by S.-M.H., E.D.T., L.D.W. and R.H.H. Biostatistics calculations were done by A.L.K., P.-H.W. and P.H. The original draft was written by A.L.K., P.-H.W. and D.W. Review and editing of the draft were done by D.W., P.-H.W., L.D.W., R.H.H., P.H., M.P.G., A.M.B., J.M.B., R.R., F.A., A.C.J., B.K., J.H., K.S.H., S.M.H., E.D.T., T.C.C., S.R. and A.L.K.

Corresponding authors

Correspondence to Denis Wirtz or Pei-Hsun Wu.

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Extended data

Extended Data Fig. 1 Histological image registration sample workflow.

Tissue cases registered with reference at center z-height of sample. Example fixed and moving images shown. Global registration performed with rotational reference at center of fixed image. Fixed and moving images smoothed by conversion to greyscale, removal of non-tissue objects in image, intensity complementing, and Gaussian filtering to reduce pixel-level noise in images. Radon transforms calculated filtered fixed and moving for discrete degrees 0–360. Maximum of 2D cross correlation of radon transforms yields registration angle. Maximum of 2D cross correlation of filtered images yields registration translation. Local registration performed at discrete intervals across fixed image. For each reference point, tiles are cropped from fixed and moving images and coarse registration is performed on tiles. Results from all tiles are interpolated on 2D grids to create nonlinear whole-image displacement fields. Sample overlays of pre and postregistration.

Extended Data Fig. 2 Overview of semantic segmentation workflow and training data design.

(a) For each case, a minimum of seven images were extracted from for manual annotation. For each extracted image, minimum 50 examples of each tissue type were annotated, and the annotations cropped from the larger image. (b) To construct training and validation sets, cropped annotations were overlayed on a large image until the image was >65% full and such that the number of annotations of each type was roughly equal. (c) These large tiles were cut into smaller tiles for training and validation. Additional tiles were created for the testing set where the annotation was not cropped from the image. Testing accuracy was assessed as the percentage of the annotated area of the tile classified correctly. Following model training, the serial images were cropped into tiles and semantically segmented.

Source data

Extended Data Fig. 3 Additional methodological supplement.

(a) Sample predicted vs. true outcomes for deep learning models for sample P1 (left) and P8 (right). (b) Workflow for creation of multi-patient semantic segmentation of nerves. Nerve annotations collected from thirteen pancreas samples. Original tissue annotations reformatted to: 1. smooth muscle, 2. collagen, 3. other tissue (islets, normal ducts, acini, precursor, lymph, PDAC), 4. white (whitespace, fat). Nerve annotations combined with original annotations to create a dataset for nerve recognition in H&E images. (c) Sample P7 average and per class testing accuracy as a function of percent of training annotations used. (d) Incidence of pancreatic phenotypes in eight samples. (e) Comparison of nuclear aspect ratio measurements performed by person 1 and person 2 (N = 150 nuclei per person per condition) show nonsignificant differences between measurements using the Wilcoxon rank sum test.

Extended Data Fig. 4 Validation of cell count and 2D to 3D cell count extrapolation.

(a) Sample histological section and corresponding color deconvolved hematoxylin channel of image. All cells in five validation images were manually annotated by two persons. Annotations were compared to CODA outputs and outputs from two existing cell counting methods27,28. (b) Cell diameters of each tissue subtype were measured using Aperio ImageScope. 2D cell counts were extrapolated to 3D using the formula listed. It was assumed that cells could be detected by the algorithm if any part of the nucleus touched the tissue section. Therefore, effective tissue section thickness equals true tissue section thickness plus the diameter of the cell. 3D cell counts were estimated by multiplying 2D cell counts by the true thickness of the tissue section, multiplied by three because two sections were skipped during scanning, divided by the effective thickness of the section.

Source data

Extended Data Fig. 5 Sample histology of venous invasions identified in samples.

Thirteen distinct venous invasions were identified in eight of the thirteen samples. For each, an H&E image was reviewed to confirm the venous invasion.

Extended Data Fig. 6 Sample histology of perineural / neural invasions identified in samples.

Ten distinct neural invasions were identified in seven of the thirteen samples, many containing regions of perineural invasion. For each, an H&E image was reviewed to confirm the structure.

Extended Data Fig. 7 Sample histology of invasion along regions of aligned collagen.

Nine distinct regions of invasion along aligned collagen were identified in five of the thirteen samples, including invasion along periductal collagen, invasion along perivascular collagen, and invasion along interlobular collagen. For each, an H&E image was reviewed to confirm the structure.

Supplementary information

Supplementary Information

Supplementary Tables 1–3.

Reporting Summary

Supplementary Video 1

Video 1. 3D reconstruction of P1: normal human pancreas.

Supplementary Video 2

Video 2. 3D reconstruction of P2: human pancreas containing PanIN.

Supplementary Video 3

Video 3. 3D reconstruction of P5: human pancreas containing IPMN.

Supplementary Video 4

Video 4. 3D reconstruction of P7: human pancreas containing PDAC.

Supplementary Video 5

Video 5. 3D reconstruction of P8: human pancreas containing PDAC.

Supplementary Video 6

Video 6. 3D reconstruction of P11: human pancreas containing PDAC.

Supplementary Video 7

Video 7. Identification of 38 distinct PanIN in a sample of human pancreas in P2.

Supplementary Video 8

Video 8. Identification of two phenotypes of pancreatic precancer in P2.

Source data

Source Data Fig. 1

Data for Fig. 2a–d registration methods comparison raw data, normalized registration performance graph, change in tissue composition with skipping sections and change in cell count with skipping sections.

Source Data Fig. 4

Data for Fig. 4b normal versus cancer cell density, and data for Fig. 4e comparison of participants 1 and 2 nuclear aspect ratio measurements.

Source Data Fig. 5

Data for Fig. 5b precursor 2D to 3D count overestimation factor.

Source Data Fig. 6

Data for Fig. 6c extracellular matrix anisotropy index and nuclear aspect ratio.

Source Data Extended Data Fig. 2

Data for Extended Data Fig. 4a precision and recall data.

Source Data Extended Data Fig. 4

Data for Extended Data Fig. 2c per-class testing accuracy as a function of percentage of annotations used.

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Kiemen, A.L., Braxton, A.M., Grahn, M.P. et al. CODA: quantitative 3D reconstruction of large tissues at cellular resolution. Nat Methods 19, 1490–1499 (2022). https://doi.org/10.1038/s41592-022-01650-9

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