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An integrated pipeline for the multidimensional analysis of branching morphogenesis

Abstract

Developmental branching morphogenesis establishes organ architecture, and it is driven by iterative interactions between epithelial and mesenchymal progenitor cell populations. We describe an approach for analyzing this interaction and how it contributes to organ development. After initial in vivo cell labeling with the nucleoside analog 5-ethynyl-2′-deoxyuridine (EdU) and tissue-specific antibodies, optical projection tomography (OPT) and confocal microscopy are used to image the developing organ. These imaging data then inform a second analysis phase that quantifies (using Imaris and Tree Surveyor software), models and integrates these events at a cell and tissue level in 3D space and across developmental time. The protocol establishes a benchmark for assessing the impact of genetic change or fetal environment on organogenesis that does not rely on ex vivo organ culture or section-based reconstruction. By using this approach, examination of two developmental stages for an organ such as the kidney can be undertaken by a postdoctoral-level researcher in 6 weeks, with a full developmental analysis in mouse achievable in 5 months.

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Figure 1: Flowchart of the protocol.
Figure 2: Correlative microscopy.
Figure 3: Operation of Tree Surveyor.
Figure 4: Activating processes and data management in Tree Surveyor.
Figure 5: Sample processing options in Tree Surveyor.
Figure 6: Tree Surveyor sample display.
Figure 7: Skeleton editing functions in Tree Surveyor.
Figure 8: Analysis of Tree Surveyor data using Microsoft Excel.
Figure 9: Niche counting.
Figure 10: Surface rendering, spot counts and QC.
Figure 11: Assessing the number of nephrons per niche.

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Acknowledgements

This work was supported by the National Health and Medical Research Council of Australia (APP1002748, APP1022239 and APP1063989), the Australian Research Council (DP10310086) and the Human Frontiers in Science Program (RGP0039/2011). Microscopy was performed at the Australian Cancer Research Foundation (ARCF)/Institute for Molecular Bioscience Cancer Biology Imaging Facility, which was established with the support of the ARCF. I.M.S. holds a Future Fellowship from the Australian Research Council. M.H.L. is a Senior Principal Research Fellow of the Australian National Health and Medical Research Council.

Author information

Authors and Affiliations

Authors

Contributions

A.N.C. and J.L. developed the methodologies used in analyzing confocal data, cell cycle length and population modeling. K.M.S. developed the Tree Surveyor protocol and tools to examine tree morphometrics. I.M.S. undertook the correlative microscopy experiments. N.A.H. oversaw and contributed to the development of approaches to mathematical analyses. I.M.S. and M.H.L. were responsible for the initial conception of the project, directed the collaboration and were involved in the design of experiments, ongoing planning, and analysis and interpretation of the data. K.M.S., A.N.C., J.L. and I.M.S. wrote the manuscript with substantive input from M.H.L. and N.A.H.

Corresponding authors

Correspondence to Melissa H Little or Ian M Smyth.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Whole Organ Confocal Z-stack.

Confocal imaging data from a whole 14.5 dpc kidney demonstrate that the quality of imaging can be maintained throughout the sample. Panels show every 10th optical slice throughout the Z-stack (depth from the fist slice is indicated in each panel). The whole file is composed of 101 Z-slices that encompass a total thickness of 352 microns. Scale bar = 100 μm.

Supplementary information

Supplementary Figure 1 and Supplementary Manual

(PDF 213 kb)

Introduction to the Tree Surveyor application.

The movie explains the interface, project setup, loading data, and an explanation of the algorithms used and the sliders and buttons which control them. (MOV 6995 kb)

A guide to running a Tree Surveyor process.

Discussed are the processing algorithms, the assessment of the first stage of skeletonization, editing of the tree and placement of root. (MOV 6082 kb)

Completion of processing and export of data from Tree Surveyor.

Visualisation modes, explanation of segment/branch naming conventions and introduction to data export and analysis. (MOV 7006 kb)

Supplementary Data 1: Cell Modeling.

This zip file contains an excel spreadsheet (Supp Data 1 worksheet.xl) and a Word document with instructions for its use (Supp Data 1 instructions.doc). The excel spreadsheet contains the formulas required to determine whole organ estimates and to model population growth and exit from cell niches. (ZIP 33 kb)

Supplementary Data 2: An example OPT z-stack.

This data is from a normal E14.5 C57BL/6 embryonic kidney with ureteric tree detected with Trop-2 antibody. The stack can be used as a guide to test Tree Surveyor usage and analysis. On analysis, this sample should have approximately 327 tips, total length of 44 μm, a convex hull volume of 0.56 mm3, and tips with an approximate mean length of 56 μm. (ZIP 8367 kb)

Supplementary Data 3: Example confocal dataset.

An example of a high resolution 40× confocal z-stack of a normal E15.5 kidney (Zeiss LSM format, compatible with Imaris). The stack contains data from four channels marking nuclei (blue), cap mesenchyme (red), Cytokeratin (white), GFP (green). This can be used as a guide to test the included protocols in Imaris. (ZIP 51871 kb)

Supplementary Software: R Studio Script to model EdU incorporation.

This file provides code and demonstration data for EdU incorporation curve modelling. An R Studio script (curvefitting.R) calculates cell cycle length estimates and standard errors, and plots modelled curves. The script has example data embedded which can be replaced. Instructions for use are embedded in the script. (ZIP 5 kb)

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Combes, A., Short, K., Lefevre, J. et al. An integrated pipeline for the multidimensional analysis of branching morphogenesis. Nat Protoc 9, 2859–2879 (2014). https://doi.org/10.1038/nprot.2014.193

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