3D reconstruction of skin and spatial mapping of immune cell density, vascular distance and effects of sun exposure and aging

Mapping the human body at single cell resolution in three dimensions (3D) is important for understanding cellular interactions in context of tissue and organ organization. 2D spatial cell analysis in a single tissue section may be limited by cell numbers and histology. Here we show a work fl ow for 3D reconstruction of multiplexed sequential tissue sections: MATRICS-A (Multiplexed Image Three-D Reconstruction and Integrated Cell Spatial - Analysis). We demonstrate MATRICS-A in 26 serial sections of fi xed skin (stained with 18 biomarkers) from 12 donors aged between 32 – 72 years. Comparing the 3D reconstructed cellular data with the 2D data, we show signi fi cantly shorter distances between immune cells and vascular endothelial cells (56 µ m in 3D vs 108 µ m in 2D). We also show 10 – 70% more T cells (total) within 30 µ m of a neighboring T helper cell in 3D vs 2D. Distances of p53, DDB2 and Ki67 positive cells to the skin surface were consistent across all ages/sun exposure and largely localized to the lower stratum basale layer of the epidermis. MATRICS-A provides a framework for analysis of 3D spatial cell relationships in healthy and aging organs and could be further extended to diseased organs.

[1]  Cody N. Heiser,et al.  Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer , 2023, Cell.

[2]  A. Roebroeck,et al.  Efficient 3D light-sheet imaging of very large-scale optically cleared human brain and prostate tissue samples , 2022, bioRxiv.

[3]  L. Wood,et al.  CODA: quantitative 3D reconstruction of large tissues at cellular resolution , 2022, Nature Methods.

[4]  Wendy J. Fantl,et al.  CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images , 2022, BMC Bioinformatics.

[5]  M. Nykter,et al.  Spatial analysis of histology in 3D: quantification and visualization of organ and tumor level tissue environment , 2022, Heliyon.

[6]  S. Tavaré,et al.  Three-dimensional imaging mass cytometry for highly multiplexed molecular and cellular mapping of tissues and the tumor microenvironment , 2021, Nature Cancer.

[7]  Serestina Viriri,et al.  Dental Images' Segmentation Using Threshold Connected Component Analysis , 2021, Comput. Intell. Neurosci..

[8]  Ellen M. Quardokus,et al.  Anatomical structures, cell types and biomarkers of the Human Reference Atlas , 2021, Nature Cell Biology.

[9]  P. Hof,et al.  3D molecular phenotyping of cleared human brain tissues with light-sheet fluorescence microscopy , 2021, Communications Biology.

[10]  Tyler T. Risom,et al.  Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning , 2021, Nature Biotechnology.

[11]  J. I. Izpisúa Belmonte,et al.  A Single-Cell Transcriptomic Atlas of Human Skin Aging. , 2020, Developmental cell.

[12]  David J. Barry,et al.  GIANI – open-source software for automated analysis of 3D microscopy images , 2020, bioRxiv.

[13]  Daniel J Verdon,et al.  Cellular and Molecular Mechanisms of CD8+ T Cell Differentiation, Dysfunction and Exhaustion , 2020, International journal of molecular sciences.

[14]  M. Mann,et al.  Spatially and cell-type resolved quantitative proteomic atlas of healthy human skin , 2020, Nature Communications.

[15]  Adam J. Rubin,et al.  Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma , 2020, Cell.

[16]  K. Rippe,et al.  Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming , 2020, Communications Biology.

[17]  Matteo Mancini,et al.  A multimodal computational pipeline for 3D histology of the human brain , 2020, Scientific Reports.

[18]  E. S. Chambers,et al.  Skin barrier immunity and ageing , 2019, Immunology.

[19]  Katy Borner,et al.  Considerations for Using the Vasculature as a Coordinate System to Map All the Cells in the Human Body , 2019, Frontiers in Cardiovascular Medicine.

[20]  Eugene W. Myers,et al.  Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[21]  Hidekata Hontani,et al.  Robust 3D image reconstruction of pancreatic cancer tumors from histopathological images with different stains and its quantitative performance evaluation , 2019, International Journal of Computer Assisted Radiology and Surgery.

[22]  Sharla L. White,et al.  Three-dimensional imaging and quantitative analysis in CLARITY processed breast cancer tissues , 2019, Scientific Reports.

[23]  Shila Ghazanfar,et al.  The human body at cellular resolution: the NIH Human Biomolecular Atlas Program , 2019, Nature.

[24]  Ayush Goyal,et al.  Image-based clustering and connected component labeling for rapid automated left and right ventricular endocardial volume extraction and segmentation in full cardiac cycle multi-frame MRI images of cardiac patients , 2019, Medical & Biological Engineering & Computing.

[25]  Sébastien Ourselin,et al.  A Survey of Methods for 3D Histology Reconstruction , 2018, Medical Image Anal..

[26]  B. Chaput,et al.  3D imaging of cleared human skin biopsies using light‐sheet microscopy: A new way to visualize in‐depth skin structure , 2018, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

[27]  Carolina Wählby,et al.  Automated Training of Deep Convolutional Neural Networks for Cell Segmentation , 2017, Scientific Reports.

[28]  J. Monserrat,et al.  Using surface molecule expression on lymphocytes to classify septic shock patients , 2009, Critical care.

[29]  Francesco Saverio Pavone,et al.  Clearing of fixed tissue: a review from a microscopist’s perspective , 2016, Journal of biomedical optics.

[30]  Jurgen Fripp,et al.  MRI-alone radiation therapy planning for prostate cancer: Automatic fiducial marker detection. , 2016, Medical physics.

[31]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[32]  Shenghao Xu,et al.  Supplementary Information , 2014, States at War, Volume 3.

[33]  J. D'Orazio,et al.  Ultraviolet Radiation, Aging and the Skin: Prevention of Damage by Topical cAMP Manipulation , 2014, Molecules.

[34]  N. McGovern,et al.  A Three-Dimensional Atlas of Human Dermal Leukocytes, Lymphatics, and Blood Vessels , 2013, The Journal of investigative dermatology.

[35]  Qing Li,et al.  Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue , 2013, Proceedings of the National Academy of Sciences.

[36]  S. Navani,et al.  The human protein atlas , 2011 .

[37]  Hui Kong,et al.  Partitioning Histopathological Images: An Integrated Framework for Supervised Color-Texture Segmentation and Cell Splitting , 2011, IEEE Transactions on Medical Imaging.

[38]  R. Clark.  Skin-resident T cells: the ups and downs of on site immunity. , 2010, The Journal of investigative dermatology.

[39]  P. Jeffrey,et al.  Structural Basis of UV DNA-Damage Recognition by the DDB1–DDB2 Complex , 2008, Cell.

[40]  A. Cochran,et al.  Immunohistochemical characteristics of melanoma , 2008, Journal of cutaneous pathology.

[41]  R. Clark,et al.  Response to Comment on “The Vast Majority of CLA+ T Cells Are Resident in Normal Skin” , 2006, The Journal of Immunology.

[42]  Grégoire Malandain,et al.  Fusion of autoradiographs with an MR volume using 2-D and 3-D linear transformations , 2004, NeuroImage.

[43]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[44]  J Streicher,et al.  External marker‐based automatic congruencing: A new method of 3D reconstruction from serial sections , 1997, The Anatomical record.

[45]  Jianquan Xu,et al.  High-Resolution Microscopy for Imaging Cancer Pathobiology , 2019, Current Pathobiology Reports.

[46]  G. Fisher,et al.  Natural and sun-induced aging of human skin. , 2015, Cold Spring Harbor perspectives in medicine.

[47]  Joachim Hornegger,et al.  Landmark-constrained 3-D Histological Imaging: A Morphology-preserving Approach , 2011, VMV.

[48]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.

[49]  R. Clark,et al.  IL-15 and dermal fibroblasts induce proliferation of natural regulatory T cells isolated from human skin. , 2007, Blood.

[50]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[51]  J. Uitto,et al.  Expression of p53 in normal sun-exposed and protected skin (type IV-V) in different decades of age. , 2003, Acta dermato-venereologica.

[52]  Michael J Ackerman,et al.  Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit. , 2002, Studies in health technology and informatics.

[53]  Sébastien Ourselin,et al.  Reconstructing a 3D structure from serial histological sections , 2001, Image Vis. Comput..

[54]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[55]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[56]  T. Sørensen,et al.  A method of establishing group of equal amplitude in plant sociobiology based on similarity of species content and its application to analyses of the vegetation on Danish commons , 1948 .