JoVE Logo
Faculty Resource Center

Sign In

Summary

Abstract

Introduction

Protocol

Representative Results

Discussion

Acknowledgements

Materials

References

Neuroscience

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published: August 1st, 2022

DOI:

10.3791/64096

1UNC Neuroscience Center, University of North Carolina, Chapel Hill, 2Department of Genetics, University of North Carolina, Chapel Hill, 3Department of Psychiatry, University of North Carolina, Chapel Hill, 4Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, 5Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, 6Department of Neurology, The University of North Carolina at Chapel Hill, 7Department of Computer Science, The University of North Carolina at Greensboro
* These authors contributed equally

This protocol describes methods for conducting magnetic resonance imaging, clearing, and immunolabeling of intact mouse brains using iDISCO+, followed by a detailed description of imaging using light-sheet microscopy, and downstream analyses using NuMorph.

Tissue clearing followed by light-sheet microscopy (LSFM) enables cellular-resolution imaging of intact brain structure, allowing quantitative analysis of structural changes caused by genetic or environmental perturbations. Whole-brain imaging results in more accurate quantification of cells and the study of region-specific differences that may be missed with commonly used microscopy of physically sectioned tissue. Using light-sheet microscopy to image cleared brains greatly increases acquisition speed as compared to confocal microscopy. Although these images produce very large amounts of brain structural data, most computational tools that perform feature quantification in images of cleared tissue are limited to counting sparse cell populations, rather than all nuclei.

Here, we demonstrate NuMorph (Nuclear-Based Morphometry), a group of analysis tools, to quantify all nuclei and nuclear markers within annotated regions of a postnatal day 4 (P4) mouse brain after clearing and imaging on a light-sheet microscope. We describe magnetic resonance imaging (MRI) to measure brain volume prior to shrinkage caused by tissue clearing dehydration steps, tissue clearing using the iDISCO+ method, including immunolabeling, followed by light-sheet microscopy using a commercially available platform to image mouse brains at cellular resolution. We then demonstrate this image analysis pipeline using NuMorph, which is used to correct intensity differences, stitch image tiles, align multiple channels, count nuclei, and annotate brain regions through registration to publicly available atlases.

We designed this approach using publicly available protocols and software, allowing any researcher with the necessary microscope and computational resources to perform these techniques. These tissue clearing, imaging, and computational tools allow measurement and quantification of the three-dimensional (3D) organization of cell-types in the cortex and should be widely applicable to any wild-type/knockout mouse study design.

Whole-brain imaging at single-cell resolution is an important challenge in neuroscience. Cellular-resolution brain images allow for detailed analysis and system-level mapping of brain circuitry and how that circuitry is disrupted by genetic or environmental risk factors for neuropsychiatric disorders, cellular behavior in developing embryos, as well as neural circuits in the adult brain1,2,3. There are multiple histological methods that allow for high-resolution images of the reconstructed 3D brain; however, these techniques require expensive, specialized equipment, may not b....

Log in or to access full content. Learn more about your institution’s access to JoVE content here

All mice were used in accordance with and approved by the Institutional Animal Care and Use Committee (IACUC) at the University of North Carolina at Chapel Hill.

1. Mouse dissection and perfusion

NOTE: The following dissections were performed on P4 and P14 mice using a syringe. The volume of perfusion fluid will vary depending on the age of the animal.

  1. Perfusion
    CAUTION: Paraformaldehyde (PFA) is a hazardous chemical. Perform all .......

Log in or to access full content. Learn more about your institution’s access to JoVE content here

As the iDISCO+ protocol introduces significant tissue shrinkage, which is easily noticeable by eye (Figure 2B), we added an MRI step to this pipeline prior to tissue clearing to quantify the shrinkage induced by tissue clearing. The workflow starts with removal of the non-brain tissue from the MR image (Figure 2C). Next, a rigid transformation (3 translation and 3 rotation angles) is applied to align the MR image to the light-sheet image (Fi.......

Log in or to access full content. Learn more about your institution’s access to JoVE content here

Tissue clearing methods are useful techniques for measuring 3D cellular organization of the brain. There are a host of tissue clearing methods described in the literature, each with its advantages and limitations6,7,8,9. The options for computational tools to analyze the cell types in the tissue-cleared images are relatively limited. Other available tools have been implemented to sparse cell po.......

Log in or to access full content. Learn more about your institution’s access to JoVE content here

This work was supported by the NIH (R01MH121433, R01MH118349, and R01MH120125 to JLS and R01NS110791 to GW) and the Foundation of Hope. We thank Pablo Ariel of the Microscopy Services Laboratory for assisting in sample imaging. The Microscopy Services Laboratory in the Department of Pathology and Laboratory Medicine is supported in part by Cancer Center Core Support Grant P30 CA016086 to the University of North Carolina (UNC) Lineberger Comprehensive Cancer Center. The Neuroscience Microscopy Core is supported by grant P30 NS045892. Research reported in this publication was supported in part by the North Carolina Biotech Center Institutional Support Grant 2016-ID....

Log in or to access full content. Learn more about your institution’s access to JoVE content here

NameCompanyCatalog NumberComments
Bruker 9.4T/30 cm MRI ScannerBruker BiospecHorizontal Bore Animal MRI System
Dibenzyl etherSigma-Aldrich108014-1KG
Dichloromethane (DCM)Sigma-Aldrich270997-1L
Dimethyl sulfoxide (DMSO)Fisher-ScientificICN19605590
Donkey serumSigma-AldrichS30-100ML
EVO 860 4TB external SSD
Fomblin YSpeciality Fluids CompanyYL-VAC-25-6perfluoropolyether lubricant
gadolinium contrast agent (ProHance)Bracco DiagnosticsA9576
gadolinium contrast agent(ProHance)Bracco Diagnostics0270-1111-03
GeForce GTX 1080 Ti 11GB GPUEVGA08G-P4-6286-KR
GlycineSigma-AldrichG7126-500G
Heparin sodium saltSigma-AldrichH3393-10KUDissolved in H2O to 10 mg/mL
Hydrogen peroxide solution, 30%Sigma-AldrichH1009-100ML
ImSpector ProLaVision BioTecMicroscope image acquisition software
ITK Snapsegmentation software
MethanolFisher-ScientificA412SK-4
MVPLAPO 2x/0.5 NA ObjectiveOlympus
Paraformaldehyde, powder, 95% (PFA)Sigma-Aldrich30525-89-4Dissolved in 1x PBS to 4%
Phosphate Buffered Saline 10x (PBS)Corning46-013-CMDiluted to 1x in H2O
Sodium AzideSigma-AldrichS2002-100GDissolved in H2O to 10%
Sodium deoxycholateSigma-AldrichD6750-10G
Tergitol type NP-40Sigma-AldrichNP40S-100ML
TritonX-100Sigma-AldrichT8787-50ML
Tween-20Fisher-ScientificBP337 500
Ultramicroscope II Light Sheet MicroscopeLaVision BioTec
Xeon Processor E5-2690 v4IntelE5-2690
Zyla sCMOS CameraAndorComplementary metal oxide semiconductor camera
AntibodyWorking concentration
Alexa Fluor Goat 790 Anti-RabbitThermofisher ScientificA11369(1:50)
Alexa Fluor Goat 568 Anti-RatThermofisher ScientificA11077(1:200)
Rat anti-Ctip2Abcamab18465(1:400)
Rabbit anti-Brn2Cell Signaling Technology12137(1:100)
To-Pro 3 (TP3)Thermofisher ScientificT3605(1:400)

  1. Dodt, H. U., et al. Ultramicroscopy: Three-dimensional visualization of neuronal networks in the whole mouse brain. Nature Methods. 4 (4), 331-336 (2007).
  2. Hägerling, R., et al. A novel multistep mechanism for initial lymphangiogenesis in mouse embryos based on ultramicroscopy. The EMBO Journal. 32 (5), 629-644 (2013).
  3. Tomer, R., Khairy, K., Keller, P. J. Shedding light on the system: studying embryonic development with light sheet microscopy. Current Opinion in Genetics & Development. 21 (5), 558-565 (2011).
  4. Li, A., et al. Micro-optical sectioning tomography to obtain a high-resolution atlas of the mouse brain. Science. 330 (6009), 1404-1408 (2010).
  5. Stoner, R., et al. Patches of disorganization in the neocortex of children with autism. The New England Journal of Medicine. 370 (13), 1209-1219 (2014).
  6. Renier, N., et al. IDISCO: A simple, rapid method to immunolabel large tissue samples for volume imaging. Cell. 159 (4), 896-910 (2014).
  7. Susaki, E. A., et al. Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis. Cell. 157 (3), 726-739 (2014).
  8. Richardson, D. S., et al. Tissue clearing. Nature Reviews. Methods Primers. 1 (1), 84 (2021).
  9. Richardson, D. S., Lichtman, J. W. Clarifying tissue clearing. Cell. 162 (2), 246-257 (2015).
  10. Renier, N., et al. Mapping of brain activity by automated volume analysis of immediate early genes. Cell. 165 (7), 1789-1802 (2016).
  11. Santi, P. A. Light sheet fluorescence microscopy: a review. The Journal of Histochemistry and Cytochemistry: Official Journal of the Histochemistry Society. 59 (2), 129-138 (2011).
  12. Girkin, J. M., Carvalho, M. T. The light-sheet microscopy revolution. Journal of Optics. 20 (5), 053002 (2018).
  13. Budday, S., et al. Mechanical properties of gray and white matter brain tissue by indentation. Journal of the Mechanical Behavior of Biomedical Materials. 46, 318-330 (2015).
  14. Johnson, G. A., et al. High-throughput morphologic phenotyping of the mouse brain with magnetic resonance histology. NeuroImage. 37 (1), 82-89 (2007).
  15. Herculano-Houzel, S., Mota, B., Lent, R. Cellular scaling rules for rodent brains. Proceedings of the National Academy of Sciences of the United States of America. 103 (32), 12138-12143 (2006).
  16. Matsumoto, K., et al. Advanced CUBIC tissue clearing for whole-organ cell profiling. Nature Protocols. 14 (12), 3506-3537 (2019).
  17. Fürth, D., et al. An interactive framework for whole-brain maps at cellular resolution. Nature Neuroscience. 21 (1), 139-149 (2018).
  18. Chandrashekhar, V., et al. CloudReg: automatic terabyte-scale cross-modal brain volume registration. Nature Methods. 18 (8), 845-846 (2021).
  19. Krupa, O., et al. NuMorph: Tools for cortical cellular phenotyping in tissue-cleared whole-brain images. Cell Reports. 37 (2), 109802 (2021).
  20. Petiet, A., Delatour, B., Dhenain, M. Models of neurodegenerative disease - Alzheimer's anatomical and amyloid plaque imaging. Methods in Molecular Biology. 771, 293-308 (2011).
  21. Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., Smith, S. M. FSL. NeuroImage. 62, 782-790 (2012).
  22. Jenkinson, M., Bannister, P., Brady, M., Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage. 17 (2), 825-841 (2002).
  23. Avants, B. B., Epstein, C. L., Grossman, M., Gee, J. C. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis. 12 (1), 26-41 (2008).
  24. . iDISCO method Available from: https://idisco.info/ (2022)
  25. Ariel, P. UltraMicroscope II - A User Guide. University of North Carolina at Chapel Hill. , (2019).
  26. . Anaconda Distribution - Anaconda documentation Available from: https://docs.anaconda.com/anaconda/ (2022)
  27. Peng, T., et al. A BaSiC tool for background and shading correction of optical microscopy images. Nature Communications. 8, 14836 (2017).
  28. Klein, S., Staring, M., Murphy, K., Viergever, M. A., Pluim, J. P. W. elastix: a toolbox for intensity-based medical image registration. IEEE Transactions on Medical Imaging. 29, 196-205 (2010).
  29. Lowe, G. Sift-the scale invariant feature transform. International Journal. 2 (91-110), 2 (2004).
  30. Young, D. M., et al. Whole-brain image analysis and anatomical atlas 3D generation using MagellanMapper. Current Protocols in Neuroscience. 94 (1), 104 (2020).
  31. Schindelin, J., et al. Fiji: an open-source platform for biological-image analysis. Nature Methods. 9 (7), 676-682 (2012).
  32. Velíšek, L. Under the (Light) sheet after the iDISCO. Epilepsy Currents / American Epilepsy Society. 16 (6), 405-407 (2016).
  33. Young, D. M., et al. Constructing and optimizing 3D atlases from 2D data with application to the developing mouse brain. eLife. 10, (2021).
  34. Yun, D. H., et al. Ultrafast immunostaining of organ-scale tissues for scalable proteomic phenotyping. bioRxiv. , (2019).
  35. Silvestri, L., et al. Universal autofocus for quantitative volumetric microscopy of whole mouse brains. Nature Methods. 18 (8), 953-958 (2021).
  36. Frasconi, P., et al. Large-scale automated identification of mouse brain cells in confocal light sheet microscopy images. Bioinformatics. 30 (17), 587 (2014).
  37. Kruskal, J. B. On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical Society. American Mathematical Society. 7 (1), 48-50 (1956).
  38. Wang, Q., et al. The allen mouse brain common coordinate framework: A 3D reference atlas. Cell. 181, 936-953 (2020).
  39. Borland, D., et al. Segmentor: a tool for manual refinement of 3D microscopy annotations. BMC Bioinformatics. 22 (1), 260 (2021).
  40. Kumar, A., et al. Dual-view plane illumination microscopy for rapid and spatially isotropic imaging. Nature Protocols. 9 (11), 2555-2573 (2014).

Tags

Whole brain

This article has been published

Video Coming Soon

JoVE Logo

Privacy

Terms of Use

Policies

Research

Education

ABOUT JoVE

Copyright © 2024 MyJoVE Corporation. All rights reserved