To facilitate fine-scale phenotyping of whole specimens, we describe here a set of tissue fixation-embedding, detergent-clearing and staining protocols that can be used to transform excised organs and whole organisms into optically transparent samples within 1–2 weeks without compromising their cellular architecture or endogenous fluorescence. PACT (passive CLARITY technique) and PARS (perfusion-assisted agent release in situ) use tissue-hydrogel hybrids to stabilize tissue biomolecules during selective lipid extraction, resulting in enhanced clearing efficiency and sample integrity. Furthermore, the macromolecule permeability of PACT- and PARS-processed tissue hybrids supports the diffusion of immunolabels throughout intact tissue, whereas RIMS (refractive index matching solution) grants high-resolution imaging at depth by further reducing light scattering in cleared and uncleared samples alike. These methods are adaptable to difficult-to-image tissues, such as bone (PACT-deCAL), and to magnified single-cell visualization (ePACT). Together, these protocols and solutions enable phenotyping of subcellular components and tracing cellular connectivity in intact biological networks.
Subscribe to Journal
Get full journal access for 1 year
only $21.58 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Chen, T.W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).
Akerboom, J. et al. Optimization of a GCaMP calcium indicator for neural activity imaging. J. Neurosci. 32, 13819–13840 (2012).
Peters, A.J., Chen, S.X. & Komiyama, T. Emergence of reproducible spatiotemporal activity during motor learning. Nature 510, 263–267 (2014).
White, R.M. et al. Transparent adult zebrafish as a tool for in vivo transplantation analysis. Cell Stem Cell 2, 183–189 (2008).
Kaletta, T. & Hengartner, M.O. Finding function in novel targets: C. Elegans as a model organism. Nat. Rev. Drug Discov. 5, 387–399 (2006).
Lieschke, G.J. & Currie, P.D. Animal models of human disease: zebrafish swim into view. Nat. Rev. Genet. 8, 353–367 (2007).
Ke, M.-T., Fujimoto, S. & Imai, T. SeeDB: a simple and morphology-preserving optical clearing agent for neuronal circuit reconstruction. Nat. Neurosci. 16, 1154–1161 (2013).
Chung, K. et al. Structural and molecular interrogation of intact biological systems. Nature 497, 332–337 (2013).
Hama, H. et al. Scale: a chemical approach for fluorescence imaging and reconstruction of transparent mouse brain. Nat. Neurosci. 14, 1481–1488 (2011).
Kuwajima, T. et al. ClearT: a detergent-and solvent-free clearing method for neuronal and non-neuronal tissue. Development 140, 1364–1368 (2013).
Susaki, E.A. et al. Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis. Cell 157, 726–739 (2014).
Becker, K., Jährling, N., Saghafi, S., Weiler, R. & Dodt, H.-U. Chemical clearing and dehydration of GFP-expressing mouse brains. PLoS ONE 7, e33916 (2012).
Dodt, H.-U. et al. Ultramicroscopy: three-dimensional visualization of neuronal networks in the whole mouse brain. Nat. Methods 4, 331–336 (2007).
Ertürk, A. et al. Three-dimensional imaging of solvent-cleared organs using 3DISCO. Nat. Protoc. 7, 1983–1995 (2012).
Tomer, R., Ye, L., Hsueh, B. & Deisseroth, K. Advanced clarity for rapid and high-resolution imaging of intact tissues. Nat. Protoc. 9, 1682–1697 (2014).
Zhang, M.D. et al. Neuronal calcium-binding proteins 1/2 localize to dorsal root ganglia and excitatory spinal neurons and are regulated by nerve injury. Proc. Natl. Acad. Sci. USA 111, E1149–1158 (2014).
Hou, B. et al. Scalable and DiI-compatible optical clearance of the mammalian brain. Front. Neuroanat. 9, 19 (2015).
Yang, B. et al. Single-cell phenotyping within transparent intact tissue through whole-body clearing. Cell 158, 945–958 (2014).
Sakhalkar, H.S., Dewhirst, M., Oliver, T., Cao, Y. & Oldham, M. Functional imaging in bulk tissue specimens using optical emission tomography: fluorescence preservation during optical clearing. Phys. Med. Biol. 52, 2035–2054 (2007).
Aoyagi, Y., Kawakami, R., Osanai, H., Hibi, T. & Nemoto, T. A rapid optical clearing protocol using 2,2′-thiodiethanol for microscopic observation of fixed mouse brain. PLoS ONE 10, e0116280 (2015).
Tainaka, K. et al. Whole-body imaging with single-cell resolution by tissue decolorization. Cell 159, 911–924 (2014).
Ke, M.T. & Imai, T. Optical clearing of fixed brain samples using SeeDB. Curr. Protoc. Neurosci. 66, 2.22.21–22.22.19 (2014).
Renier, N. et al. Idisco: a simple, rapid method to immunolabel large tissue samples for volume imaging. Cell 159, 896–910 (2014).
Chung, K. & Deisseroth, K. CLARITY for mapping the nervous system. Nat. Methods 10, 508–513 (2013).
Kim, S.Y., Chung, K. & Deisseroth, K. Light microscopy mapping of connections in the intact brain. Trends Cogn. Sci. 17, 596–599 (2013).
Ertürk, A. & Bradke, F. High-resolution imaging of entire organs by 3-dimensional imaging of solvent cleared organs (3DISCO). Exp. Neurol. 242, 57–64 (2013).
Deisseroth, K.A. & Gradinaru, V. Functional targeted brain endoskeletonization. US patent US2014030192 (2012).
Richardson, D.S. & Lichtman, J.W. Clarifying tissue clearing. Cell 162, 246–257 (2015).
Ragan, T. et al. Serial two-photon tomography for automated ex vivo mouse brain imaging. Nat. Methods 9, 255–258 (2012).
Mccormick, B.H. Brain tissue scanner enables brain microstructure surveys. Neurocomputing 44, 1113–1118 (2002).
Kuan, L. et al. Neuroinformatics of the Allen mouse brain connectivity atlas. Methods 73, 4–17 (2015).
Rah, J.-C. et al. Thalamocortical input onto layer 5 pyramidal neurons measured using quantitative large-scale array tomography. Front. Neural Circuits 7, 177 (2013).
Zingg, B. et al. Neural networks of the mouse neocortex. Cell 156, 1096–1111 (2014).
Bohland, J.W. et al. A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale. PLoS Comput. Biol. 5, e1000334 (2009).
Craddock, R.C. et al. Imaging human connectomes at the macroscale. Nat. Methods 10, 524–539 (2013).
Miller, J.A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).
Oh, S.W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014).
Hintiryan, H. et al. Comprehensive connectivity of the mouse main olfactory bulb: analysis and online digital atlas. Front. Neuroanat. 6, 30 (2012).
George, M.S. et al. Vagus nerve stimulation: a new tool for brain research and therapy. Biol. Psychiatry 47, 287–295 (2000).
Berthoud, H.-R. & Neuhuber, W.L. Functional and chemical anatomy of the afferent vagal system. Auton. Neurosci. 85, 1–17 (2000).
Birmingham, K. et al. Bioelectronic medicines: a research roadmap. Nat. Rev. Drug Discov. 13, 399–400 (2014).
Ertürk, A. et al. Three-dimensional imaging of the unsectioned adult spinal cord to assess axon regeneration and glial responses after injury. Nat. Med. 18, 166–171 (2012).
Bucher, D., Scholz, M., Stetter, M., Obermayer, K. & Pflüger, H.J. Correction methods for three-dimensional reconstructions from confocal images: I. Tissue shrinking and axial scaling. J. Neurosci. Methods 100, 135–143 (2000).
Staudt, T., Lang, M.C., Medda, R., Engelhardt, J. & Hell, S.W. 2,2′-thiodiethanol: a new water-soluble mounting medium for high-resolution optical microscopy. Microsc. Res. Tech. 70, 1–9 (2007).
Ott, H.C. et al. Perfusion-decellularized matrix: using nature's platform to engineer a bioartificial heart. Nat. Med. 14, 213–221 (2008).
Rahman, A. & Brown, C.W. Effect of pH on the critical micelle concentration of sodium dodecyl sulphate. J. Appl. Polym. Sci. 28, 1331–1334 (1983).
Otzen, D.E. Protein unfolding in detergents: effect of micelle structure, ionic strength, pH, and temperature. Biophys. J. 83, 2219–2230 (2002).
Piatkevich, K.D. et al. Extended Stokes shift in fluorescent proteins: chromophore–protein interactions in a near-infrared TagRFP675 Variant. Sci. Rep. 3, 1847 (2013).
Kragh-Hansen, U., Le Maire, M. & Møller, J.V. The mechanism of detergent solubilization of liposomes and protein-containing membranes. Biophys. J. 75, 2932–2946 (1998).
Hammouda, B. Temperature effect on the nanostructure of SDS micelles in water. J. Res. Natl. Inst. Stand. Technol. 118, 151–167 (2013).
Desmyter, A., Spinelli, S., Roussel, A. & Cambillau, C. Camelid Nanobodies: killing two birds with one stone. Curr. Opin. Struct. Biol. 32, 1–8 (2015).
Pardon, E. et al. A general protocol for the generation of Nanobodies for structural biology. Nat. Protoc. 9, 674–693 (2014).
Lichtman, J.W. & Sanes, J.R. Ome sweet Ome: what can the genome tell us about the connectome? Curr. Opin. Neurobiol. 18, 346–353 (2008).
Lander, E.S. et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).
Lehrer, J. Neuroscience: making connections. Nature 457, 524–527 (2009).
Peng, H. et al. Bigneuron: large-scale 3D neuron reconstruction from optical microscopy images. Neuron 87, 252–256 (2015).
Kasthuri, N. et al. Saturated reconstruction of a volume of neocortex. Cell 162, 648–661 (2015).
Burns, R. et al. The open connectome project data cluster: scalable analysis and vision for high-throughput neuroscience. Sci. Stat. Database Manag. doi:10.1145/2484838.2484870 (2013).
Costa, M., Ostrovsky, A.D., Manton, J.D., Prohaska, S. & Jefferis, G.S.X.E. Nblast: rapid, sensitive comparison of neuronal structure and construction of neuron family databases. Biorxiv doi:10.1101/006346 (2014).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Schneider, C.A., Rasband, W.S. & Eliceiri, K.W. NIH image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).
Peng, H., Bria, A., Zhou, Z., Iannello, G. & Long, F. Extensible visualization and analysis for multidimensional images using Vaa3D. Nat. Protoc. 9, 193–208 (2014).
Peng, H., Ruan, Z., Long, F., Simpson, J.H. & Myers, E.W. V3d Enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nat. Biotechnol. 28, 348–353 (2010).
Longair, M.H., Baker, D.A. & Armstrong, J.D. Simple neurite tracer: open source software for reconstruction, visualization and analysis of neuronal processes. Bioinformatics 27, 2453–2454 (2011).
Dercksen, V.J., Hege, H.C. & Oberlaender, M. The Filament Editor: an interactive software environment for visualization, proof-editing and analysis of 3D neuron morphology. Neuroinformatics 12, 325–339 (2014).
Peng, H. et al. Virtual finger boosts three-dimensional imaging and microsurgery as well as terabyte volume image visualization and analysis. Nat. Commun. 5, 4342 (2014).
Feng, L., Zhao, T. & Kim, J. Neutube 1.0: a new design for efficient neuron reconstruction software based on the Swc Format. Eneuro doi:10.1523/Eneuro.0049-1514.2014 (2015).
Glaser, J.R. & Glaser, E.M. Neuron imaging with Neurolucida—a PC-based system for image combining microscopy. Comput. Med. Imaging Graph 14, 307–317 (1990).
Bria, A. & Iannello, G. TeraStitcher—a tool for fast automatic 3D-stitching of teravoxel-sized microscopy images. BMC Bioinformatics 13, 316 (2012).
Yu, Y. & Peng, H. Automated high speed stitching of large 3D microscopic images. Proc. of IEEE 2011 International Symposium on Biomedical Imaging: From Nano to Macro 238–241 (2011).
Preibisch, S., Saalfeld, S. & Tomancak, P. Globally optimal stitching of tiled 3D microscopic image acquisitions. Bioinformatics 25, 1463–1465 (2009).
Emmenlauer, M. et al. XuvTools: free, fast and reliable stitching of large 3D datasets. J. Microsc. 233, 42–60 (2009).
Model, M.A. & Blank, J.L. Concentrated dyes as a source of two-dimensional fluorescent field for characterization of a confocal microscope. J. Microsc. 229, 12–16 (2008).
Smith, K. et al. CIDRE: an illumination-correction method for optical microscopy. Nat. Methods 12, 404–406 (2015).
Edelstein, A.D. et al. Advanced methods of microscope control using μManager software. J. Biol. Methods 1, e10 (2014).
Schmid, B., Schindelin, J., Cardona, A., Longair, M. & Heisenberg, M. A high-level 3D visualization API for Java and ImageJ. BMC Bioinformatics 11, 274 (2010).
De Chaumont, F. et al. Icy: an open bioimage informatics platform for extended reproducible research. Nat. Methods 9, 690–696 (2012).
Kankaanpaa, P. et al. Bioimagexd: an open, general-purpose and high-throughput image-processing platform. Nat. Methods 9, 683–689 (2012).
Kvilekval, K., Fedorov, D., Obara, B., Singh, A. & Manjunath, B.S. Bisque: a platform for bioimage analysis and management. Bioinformatics 26, 544–552 (2010).
Pietzsch, T., Saalfeld, S., Preibisch, S. & Tomancak, P. Bigdataviewer: visualization and processing for large image data sets. Nat. Methods 12, 481–483 (2015).
Benmansour, F. & Cohen, L.D. Tubular structure segmentation based on minimal path method and anisotropic enhancement. Int. J. Comput. Vis. 92, 192–210 (2011).
Chothani, P., Mehta, V. & Stepanyants, A. Automated tracing of neurites from light microscopy stacks of images. Neuroinformatics 9, 263–278 (2011).
Ming, X. et al. Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling. PLoS ONE 8, e84557 (2013).
Luisi, J., Narayanaswamy, A., Galbreath, Z. & Roysam, B. The FARSIGHT trace editor: an open source tool for 3-D inspection and efficient pattern analysis aided editing of automated neuronal reconstructions. Neuroinformatics 9, 305–315 (2011).
Myatt, D.R., Hadlington, T., Ascoli, G.A. & Nasuto, S.J. Neuromantic—from semi-manual to semi-automatic reconstruction of neuron morphology. Front. Neuroinform. 6, 4 (2012).
Gleeson, P. et al. NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS Comput. Biol. 6, e1000815 (2010).
Parekh, R. & Ascoli, G.A. Neuronal morphology goes digital: a research hub for cellular and system neuroscience. Neuron 77, 1017–1038 (2013).
Meijering, E. Neuron tracing in perspective. Cytometry A 77, 693–704 (2010).
Scorcioni, R., Polavaram, S. & Ascoli, G.A. L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies. Nat. Protoc. 3, 866–876 (2008).
Gleeson, P., Steuber, V. & Silver, R.A. Neuroconstruct: a tool for modeling networks of neurons in 3D space. Neuron 54, 219–235 (2007).
Ascoli, G.A., Donohue, D.E. & Halavi, M. Neuromorpho.Org: a central resource for neuronal morphologies. J. Neurosci. 27, 9247–9251 (2007).
Guyette, J.P. et al. Perfusion decellularization of whole organs. Nat. Protoc. 9, 1451–1468 (2014).
Uygun, B.E. et al. Decellularization and recellularization of whole livers. J. Vis. Exp. doi:10.3791/2394 (2011).
Chen, F., Tillberg, P.W. & Boyden, E.S. Optical imaging. Expansion microscopy. Science 347, 543–548 (2015).
Bach, S.P., Renehan, A.G. & Potten, C.S. Stem cells: the intestinal stem cell as a paradigm. Carcinogenesis 21, 469–476 (2000).
Barry, E.R. et al. Restriction of intestinal stem cell expansion and the regenerative response by YAP. Nature 493, 106–110 (2013).
Wilson, A. & Trumpp, A. Bone-marrow haematopoietic-stem-cell niches. Nat. Rev. Immunol. 6, 93–106 (2006).
Vakoc, B.J. et al. Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging. Nat. Med. 15, 1219–1223 (2009).
Reardon, S. Electroceuticals spark interest. Nature 511, 18 (2014).
Grace, A.A. & Llinas, R. Morphological artifacts induced in intracellularly stained neurons by dehydration: circumvention using rapid dimethyl sulfoxide clearing. Neuroscience 16, 461–475 (1985).
Kasthuri, N. & Lichtman, J.W. Neurocartography. Neuropsychopharmacology 35, 342–343 (2010).
Henry, A.M. & Hohmann, J.G. High-resolution gene expression atlases for adult and developing mouse brain and spinal cord. Mamm. Genome 23, 539–549 (2012).
Dodt, H.U. Microscopy. The superresolved brain. Science 347, 474–475 (2015).
Betzig, E., Trautman, J.K., Harris, T.D., Weiner, J.S. & Kostelak, R.L. Breaking the diffraction barrier: optical microscopy on a nanometric scale. Science 251, 1468–1470 (1991).
Hell, S.W. & Wichmann, J. Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy. Opt. Lett. 19, 780–782 (1994).
Gustafsson, M.G. Nonlinear structured-illumination microscopy: wide-field fluorescence imaging with theoretically unlimited resolution. Proc. Natl. Acad. Sci. USA 102, 13081–13086 (2005).
Betzig, E. et al. Imaging intracellular fluorescent proteins at nanometer resolution. Science 313, 1642–1645 (2006).
Rust, M.J., Bates, M. & Zhuang, X. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat. Methods 3, 793–796 (2006).
Sumbul, U., Zlateski, A., Vishwanathan, A., Masland, R.H. & Seung, H.S. Automated computation of arbor densities: a step toward identifying neuronal cell types. Front. Neuroanat. 8, 139 (2014).
Videen, T.O. et al. Validation of a fiducial-based atlas localization method for deep brain stimulation contacts in the area of the subthalamic nucleus. J. Neurosci. Methods 168, 275–281 (2008).
Gutman, B. et al. Registering cortical surfaces based on whole-brain structural connectivity and continuous connectivity analysis. Med. Image Comput. Comput. Assist. Interv. 17, 161–168 (2014).
Pantazis, D. et al. Comparison of landmark-based and automatic methods for cortical surface registration. Neuroimage 49, 2479–2493 (2010).
Kuwajima, M., Mendenhall, J.M. & Harris, K.M. Large-volume reconstruction of brain tissue from high-resolution serial section images acquired by SEM-based scanning transmission electron microscopy. Methods Mol. Biol. 950, 253–273 (2013).
Masich, S., Östberg, T., Norlén, L., Shupliakov, O. & Daneholt, B. A procedure to deposit fiducial markers on vitreous cryo-sections for cellular tomography. J. Struct. Biol. 156, 461–468 (2006).
Kuwajima, M., Mendenhall, J.M., Lindsey, L.F. & Harris, K.M. Automated transmission-mode scanning electron microscopy (tSEM) for large volume analysis at nanoscale resolution. PLoS ONE 8, e59573 (2013).
Choi, H.M.T., Beck, V.A. & Pierce, N.A. Next-generation in situ hybridization chain reaction: higher gain, lower cost, greater durability. ACS Nano 8, 4284–4294 (2014).
Choi, H.M. et al. Programmable in situ amplification for multiplexed imaging of mRNA expression. Nat. Biotechnol. 28, 1208–1212 (2010).
Lubeck, E., Coskun, A.F., Zhiyentayev, T., Ahmad, M. & Cai, L. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11, 360–361 (2014).
Lubeck, E. & Cai, L. Single-cell systems biology by super-resolution imaging and combinatorial labeling. Nat. Methods 9, 743–748 (2012).
Huisken, J. & Stainier, D.Y.R. Selective plane illumination microscopy techniques in developmental biology. Development 136, 1963–1975 (2009).
Baumgart, E. & Kubitscheck, U. Scanned light-sheet microscopy with confocal slit detection. Opt. Express 20, 21805–21814 (2012).
Gage, G.J., Kipke, D.R. & Shain, W. Whole-animal perfusion fixation for rodents. J. Vis. Exp. doi:10.3791/3564 (2012).
Tremblay, M.-È., Riad, M. & Majewska, A.K. Preparation of mouse brain tissue for immunoelectron microscopy. J. Vis. Exp. doi:10.3791/2021 (2010).
Dominguez, E. et al. Non-invasive in vivo measurement of cardiac output in C57BL/6 mice using high-frequency transthoracic ultrasound: evaluation of gender and body weight effects. Int. J. Cardiovasc. Imaging 30, 1237–1244 (2014).
Tournoux, F. et al. Validation of non invasive measurements of cardiac output in mice using echocardiography. J. Am. Soc. Echocardiogr. 24, 465–470 (2011).
Janssen, B., Debets, J., Leenders, P. & Smits, J. Chronic measurement of cardiac output in conscious mice. Am. J. Physiol. Regul. Integr. Comp. Physiol. 282, R928–935 (2002).
Delp, M.D., Evans, M.V. & Duan, C.P. Effects of aging on cardiac output, regional blood flow, and body composition in Fischer-344 rats. J. Appl. Physiol. 85, 1813–1822 (1998).
Reineke, T. et al. Ultrasonic decalcification offers new perspectives for rapid FISH, DNA, and RT-PCR analysis in bone marrow trephines. Am. J. Surg. Pathol. 30, 892–896 (2006).
Fridy, P.C. et al. A robust pipeline for rapid production of versatile nanobody repertoires. Nat. Methods 11, 1253–1260 (2014).
Goldberg, I.G. et al. The Open Microscopy Environment (Ome) data model and XML file: open tools for informatics and quantitative analysis in biological imaging. Genome Biol. 6, R47 (2005).
Linkert, M. et al. Metadata matters: access to image data in the real world. J. Cell Biol. 189, 777–782 (2010).
Carnevale, N.T. & Hines,, M.L. The Neuron Book (Cambridge University Press, 2006).
Tsuriel, S., Gudes, S., Draft, R.W., Binshtok, A.M. & Lichtman, J.W. Multispectral labeling technique to map many neighboring axonal projections in the same tissue. Nat. Methods 12, 547–552 (2015).
Keppler, A. et al. A general method for the covalent labeling of fusion proteins with small molecules in vivo. Nat. Biotechnol. 21, 86–89 (2003).
Los, G.V. et al. Hatotag: a novel protein labeling technology for cell imaging and protein analysis. ACS Chem. Biol. 3, 373–382 (2008).
Gautier, A. et al. An engineered protein tag for multiprotein labeling in living cells. Chem. Biol. 15, 128–136 (2008).
Miller, L.W., Cai, Y.F., Sheetz, M.P. & Cornish, V.W. In vivo protein labeling with trimethoprim conjugates: a flexible chemical tag. Nat. Methods 2, 255–257 (2005).
Bedbrook, C.N. et al. Genetically encoded spy peptide fusion system to detect plasma membrane-localized proteins in vivo. Chem. Biol. 22, 1108–1121 (2015).
Kohl, J. et al. Ultrafast tissue staining with chemical tags. Proc. Natl. Acad. Sci. USA 111, E3805–E3814 (2014).
Keller, P.J. & Ahrens, M.B. Visualizing whole-brain activity and development at the single-cell level using light-sheet microscopy. Neuron 85, 462–483 (2015).
Stelzer, E.H.K. Light-sheet fluorescence microscopy for quantitative biology. Nat. Methods 12, 23–26 (2015).
Paxinos, G. & Franklin, K.B.J. The Mouse Brain In Stereotaxic Coordinates Compact 2nd edn. (Elsevier Academic Press, 2004).
Connell, B.J. & Lortat-Jacob, H. Human immunodeficiency virus and heparan sulfate: from attachment to entry inhibition. Front. Immunol. 4, 385 (2013).
Jones, C.T. et al. Real-time imaging of hepatitis C virus infection using a fluorescent cell-based reporter system. Nat. Biotechnol. 28, 167–171 (2010).
Sattentau, Q. Avoiding the void: cell-to-cell spread of human viruses. Nat. Rev. Microbiol. 6, 815–826 (2008).
Wu, Z., Asokan, A. & Samulski, R.J. Adeno-associated virus serotypes: vector toolkit for human gene therapy. Mol. Ther. 14, 316–327 (2006).
Fiege, J.K. & Langlois, R.A. Investigating influenza A virus infection: tools to track infection and limit tropism. J. Virol. 89, 6167–6170 (2015).
Foust, K.D. et al. Intravascular AAV9 preferentially targets neonatal neurons and adult astrocytes. Nat. Biotechnol. 27, 59–65 (2009).
Kollarik, M. et al. Transgene expression and effective gene silencing in vagal afferent neurons in vivo using recombinant adeno-associated virus vectors. J. Physiol. 588, 4303–4315 (2010).
Zhang, H. et al. Several rAAV vectors efficiently cross the blood–brain barrier and transduce neurons and astrocytes in the neonatal mouse central nervous system. Mol. Ther. 19, 1440–1448 (2011).
Tapia, J.C. et al. High-contrast en bloc staining of neuronal tissue for field emission scanning electron microscopy. Nat. Protoc. 7, 193–206 (2012).
Micheva, K.D. & Smith, S.J. Array tomography: a new tool for imaging the molecular architecture and ultrastructure of neural circuits. Neuron 55, 25–36 (2007).
Magnon, C. et al. Autonomic nerve development contributes to prostate cancer progression. Science 341, 1236361 (2013).
Colomba, A. & Ridley, A.J. Analyzing the roles of Rho GTPases in cancer cell migration with a live cell imaging 3D-morphology-based assay. Methods Mol. Biol. 1120, 327–337 (2014).
Fukamachi, K. et al. Total-circumference intraoperative frozen section analysis reduces margin-positive rate in breast-conservation surgery. Jpn. J. Clin. Oncol. 40, 513–520 (2010).
Louveau, A. et al. Structural and functional features of central nervous system lymphatic vessels. Nature 523, 337–341 (2015).
Goodell, M.A., Nguyen, H. & Shroyer, N. Somatic stem cell heterogeneity: diversity in the blood, skin and intestinal stem cell compartments. Nat. Rev. Mol. Cell. Biol. 16, 299–309 (2015).
Sharp, F.R., Liu, J.L. & Bernabeu, R. Neurogenesis following brain ischemia. Brain Res. Dev. Brain Res. 134, 23–30 (2002).
Nakatomi, H. et al. Regeneration of hippocampal pyramidal neurons after ischemic brain injury by recruitment of endogenous neural progenitors. Cell 110, 429–441 (2002).
Ross, J.D., Cullen, D.K., Harris, J.P., Laplaca, M.C. & Deweerth, S.P. A three-dimensional image processing program for accurate, rapid, and semi-automated segmentation of neuronal somata with dense neurite outgrowth. Front. Neuroanat. 9, 87 (2015).
Livet, J. et al. Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system. Nature 450, 56–62 (2007).
Alitalo, K. The lymphatic vasculature in disease. Nat. Med. 17, 1371–1380 (2011).
Bajénoff, M. et al. Stromal cell networks regulate lymphocyte entry, migration, and territoriality in lymph nodes. Immunity 25, 989–1001 (2006).
Hanoun, M., Maryanovich, M., Arnal-Estape, A. & Frenette, P.S. Neural regulation of hematopoiesis, inflammation, and cancer. Neuron 86, 360–373 (2015).
Gibson, E.M. et al. Neuronal activity promotes oligodendrogenesis and adaptive myelination in the mammalian brain. Science 344, 1252304 (2014).
Bartzokis, G. et al. Multimodal magnetic resonance imaging assessment of white matter aging trajectories over the lifespan of healthy individuals. Biol. Psychiatry 72, 1026–1034 (2012).
Callaway, E. et al. The discovery of Homo floresiensis: tales of the hobbit. Nature 514, 422–426 (2014).
Taupin, P. & Gage, F.H. Adult neurogenesis and neural stem cells of the central nervous system in mammals. J. Neurosci. Res. 69, 745–749 (2002).
Hsiao, E.Y. & Patterson, P.H. Placental regulation of maternal-fetal interactions and brain development. Dev. Neurobiol. 72, 1317–1326 (2012).
Zoukos, Y., Leonard, J.P., Thomaides, T., Thompson, A.J. & Cuzner, M.L. Beta-adrenergic receptor density and function of peripheral blood mononuclear cells are increased in multiple sclerosis: a regulatory role for cortisol and interleukin-1. Ann. Neurol. 31, 657–662 (1992).
Hsiao, E.Y., Mcbride, S.W., Chow, J., Mazmanian, S.K. & Patterson, P.H. Modeling an autism risk factor in mice leads to permanent immune dysregulation. Proc. Natl. Acad. Sci. USA 109, 12776–12781 (2012).
Hsiao, E.Y. & Patterson, P.H. Activation of the maternal immune system induces endocrine changes in the placenta via Il-6. Brain Behav. Immun. 25, 604–615 (2011).
Kaya, F. et al. Live imaging of targeted cell ablation in Xenopus: a new model to study demyelination and repair. J. Neurosci. 32, 12885–12895 (2012).
Hsiao, E.Y. et al. Microbiota modulate behavioral and physiological abnormalities associated with neurodevelopmental disorders. Cell 155, 1451–1463 (2013).
Kim, B.J. et al. Bacterial induction of Snail1 contributes to blood-brain barrier disruption. J. Clin. Invest. 125, 2473–2483 (2015).
Lucas, S.M., Rothwell, N.J. & Gibson, R.M. The role of inflammation in CNS injury and disease. Br. J. Pharmacol. 147, S232–S240 (2006).
Bianco, P., Riminucci, M., Gronthos, S. & Robey, P.G. Bone marrow stromal stem cells: nature, biology, and potential applications. Stem Cells 19, 180–192 (2001).
Morrison, S.J. & Scadden, D.T. The bone marrow niche for haematopoietic stem cells. Nature 505, 327–334 (2014).
Sugiyama, T., Kohara, H., Noda, M. & Nagasawa, T. Maintenance of the hematopoietic stem cell pool by CXCL12-CXCR4 chemokine signaling in bone marrow stromal cell niches. Immunity 25, 977–988 (2006).
Omatsu, Y., Seike, M., Sugiyama, T., Kume, T. & Nagasawa, T. Foxc1 is a critical regulator of haematopoietic stem/progenitor cell niche formation. Nature 508, 536–540 (2014).
Mendez-Ferrer, S. et al. Mesenchymal and haematopoietic stem cells form a unique bone marrow niche. Nature 466, 829–834 (2010).
Greenbaum, A. et al. CXCL12 in early mesenchymal progenitors is required for haematopoietic stem-cell maintenance. Nature 495, 227–230 (2013).
Singh, P.K. et al. Quorum-sensing signals indicate that cystic fibrosis lungs are infected with bacterial biofilms. Nature 407, 762–764 (2000).
Ernst, R.K. et al. Specific lipopolysaccharide found in cystic fibrosis airway Pseudomonas aeruginosa. Science 286, 1561–1565 (1999).
Ramsey, D.M. & Wozniak, D.J. Understanding the control of pseudomonas aeruginosa alginate synthesis and the prospects for management of chronic infections in cystic fibrosis. Mol. Microbiol. 56, 309–322 (2005).
Tan, S.Y., Chew, S.C., Tan, S.Y., Givskov, M. & Yang, L. Emerging frontiers in detection and control of bacterial biofilms. Curr. Opin. Biotechnol. 26, 1–6 (2014).
Basser, P.J., Mattiello, J. & Lebihan, D. MR diffusion tensor spectroscopy and imaging. Biophys. J. 66, 259–267 (1994).
Schain, A.J., Hill, R.A. & Grutzendler, J. Label-free in vivo imaging of myelinated axons in health and disease with spectral confocal reflectance microscopy. Nat. Med. 20, 443–449 (2014).
Huppi, P.S. et al. Microstructural development of human newborn cerebral white matter assessed in vivo by diffusion tensor magnetic resonance imaging. Pediatr. Res. 44, 584–590 (1998).
Alexander, A.L., Lee, J.E., Lazar, M. & Field, A.S. Diffusion tensor imaging of the brain. Neurotherapeutics 4, 316–329 (2007).
Seehaus, A. et al. Histological validation of high-resolution DTI in human postmortem tissue. Front. Neuroanat. 9, 98 (2015).
Ertürk, A., Lafkas, D. & Chalouni, C. Imaging cleared intact biological systems at a cellular level by 3DISCO. J. Vis. Exp. doi:10.3791/51382 (2014).
Epp, J.R. et al. Optimization of CLARITY for clearing whole-brain and other intact organs. Eneuro 2 doi:10.1523/Eneuro.0022-1515.2015 (2015).
Li, T. et al. Cell-penetrating Anti-GFAP VHH and corresponding fluorescent fusion protein VHH-GFP spontaneously cross the blood-brain barrier and specifically recognize astrocytes: application to brain imaging. FASEB J. 26, 3969–3979 (2012).
Perruchini, C. et al. Llama VHH antibody fragments against GFAP: better diffusion in fixed tissues than classical monoclonal antibodies. Acta Neuropathol. 118, 685–695 (2009).
Pifferi, A. et al. Optical biopsy of bone tissue: a step toward the diagnosis of bone pathologies. J. Biomed. Opt. 9, 474–480 (2004).
Genina, E.A., Bashkatov, A.N. & Tuchin, V.V. Optical clearing of cranial bone. Adv. Opt. Technol. 2008 doi:10.1155/2008/267867 (2008).
Duong, H. & Han, M. A multispectral led array for the reduction of background autofluorescence in brain tissue. J. Neurosci. Methods 220, 46–54 (2013).
Kupferschmidt, D.A., Cody, P.A., Lovinger, D.M. & Davis, M.I. Brain BLAQ: post hoc thick-section histochemistry for localizing optogenetic constructs in neurons and their distal terminals. Front. Neuroanat. 9 doi:10.3389/fnana.2015.0000 (2015).
We thank H. McBride, D.K. Newman and J. Allman for useful discussions on uses of tissue clearing across disciplines. We thank M. Brissova and A.C. Powers from Vanderbilt University for providing fixed human pancreas tissue used in Figure 1 and guidance with pancreatic markers and anatomy. This work was funded by grants to V.G.: the US National Institutes of Health (NIH) Director's New Innovator IDP20D017782-01; the NIH/National Institute on Aging (NIA) 1R01AG047664-01; the Beckman Institute for Optogenetics and CLARITY; the Pew Charitable Trust; and the Kimmel Foundation. Work in the Gradinaru Laboratory at Caltech is also funded by awards from the following (to V.G.): the NIH Brain Research through Advancing Innovative Neurotechnologies (BRAIN) 1U01NS090577; the NIH/National Institutes of Mental Health (NIMH) 1R21MH103824-01; the Human Frontiers in Science Program; the Mallinckrodt Foundation; the Gordon and Betty Moore Foundation through grant GBMF2809 to the Caltech Programmable Molecular Technology Initiative; the Michael J. Fox Foundation; Caltech-GIST; and the Caltech-City of Hope Biomedical Initiative. This work was also supported by grants to P.J.B. from the NIH (2 P50 GM082545-06; W.I. Sundquist, principal investigator) and gifts from the Gordon and Betty Moore Foundation and the Agouron Institute to support electron microscopy at Caltech; and by National Science Foundation (NSF) IIS-1253538 and DBI-1262547 grants to C.C.F. K.Y.C. and N.C.F. were supported by the NIH Predoctoral Training in Biology and Chemistry (2T32GM007616-36).
The authors declare no competing financial interests.
Integrated supplementary information
Supplementary Figure 1 Effects of bis-acrylamide crosslinker on clearing time and swelling of PACT-cleared sections.
(a) Representative images of the time course for PACT clearing of four 2 mm thick rat coronal brain slices, (displayed anterior to posterior, from left to right). Slices were embedded in either A4P0B0.05 or A4P0 and then cleared with 8% SDS-PBS (pH 7.5). The A4P0 slices were completely clear by 144 hours. Although some heavily myelinated brain sections seemed to resist clearing in A4P0B0.05-embedded sections initially, this effect did not persist, resulting in similar overall clearing time as slices embedded without bis-acrylamide. Likewise, tissue transparency was indistinguishable between conditions after their 48-hour incubation in RIMS. Unlike in 1 mm sections (Fig. 3), bis-acrylamide did limit tissue expansion in 2mm thick slices (A4P0: 38% total average linear expansion, A4P0B0.05: 28% average linear expansion). (b) RIMS formulation guide to optimize the RI to that of the cleared sample. RIMS formulated with 82% HistodenzTM (RI = 1.4655) should be broadly applicable to cleared brain tissue, while RIMS with a higher RI of 1.48-1.49 (RIMS-1.48, RIMS-1.49) is suggested for denser cleared tissue such as bone (Fig. 6).
The amount of protein lost while clearing was measured by performing a BCA on the clearing buffer, which was collected and replaced periodically while 1 mm tissue samples were undergoing PACT. A standard curve of BSA protein concentration in each of the four different clearing buffers was generated. Standard curves were fit with a third order polynomial and used to extrapolate all protein loss measurements. (a) A representative case, in which the absorbance in arbitrary units (a.u.) of standard solutions at 562 nm is plotted against known BSA concentrations in 8% SDS-PBS (pH 7.5). (b) Graphs show single-trial, representative protein loss measurements for each hydrogel condition in each clearing buffer. Protein content was measured at 12 hours into clearing, at 24 hours, and then every 24 hours until the samples were clear, and normalized to the initial weight of the slice. Experiments were performed in triplicates, representative single trials for each combination are shown. (c) Time to clear for 1 mm sections PACT-processed with all hydrogel embedding and clearing buffer combinations (n = 3 for A4P1, A4P2 and Unhybridized. n = 4 for all others).
(a-c) Representative images of thick section clearing with addition of CuSO4 or 0.2% SB compared to regular PACT. 0.5 mm and 1 mm coronal Thy1-YFP mouse brain sections are shown after A4P1 hydrogel polymerization (a) and during clearing with 8% SDS-BB (pH 8.5) and subsequent 24 hour incubation in RIMS (b and c for 0.5 mm and 1 mm, respectively). (d) The control, CuSO4, and 0.2% SB treated 0.5 mm slices from (a-b) were immunostained for parvalbumin (see Table 4) and then transferred to RIMS, degassed, and mounted. Shown are 500 μm thick maximum intensity projections of endogenous YFP (cyan) and parvalbumin (red) staining (top) as well as lipofuscin (white) autofluorescence (top and bottom). Red blood cell-derived (e.g. lipofuscin-like) autofluorescence was excited at 561 nm and collected between 562-606 nm. (e) Visualizing endogenous fluorescence and immunostaining deep within thick tissue. A 1 mm thick Thy1-YFP mouse brain coronal slice was treated with 0.2% SB, A4P1-embedded, cleared with 8% SDS-BB (pH 8.5), immunostained for parvalbumin (see Table 4), and then transferred to RIMS and mounted. Endogenous YFP (cyan) and immunolabeled PV (red) were imaged throughout the slice (left) in a region of the cortex. A 100 μm thick maximum intensity projection (right) was taken at a depth of 500 μm to show representative imaging in the middle of the section. Signal range of the red channel was adjusted for better visualization of PV staining at depth. All sections were imaged on a Zeiss LSM 780 confocal with the Plan-Apochromat 10× 0.45 N.A. M27 air objective (w.d. 2.0 mm).
(a) Fluorescence image of Thy1-YFP expression prior to expansion-clearing. A 70 μm thick maximum intensity projection of five cells expressing YFP represents the standard for imaging pre-expansion. A bright-field image of the pre-expansion 100 μm brain slice is shown in the top right, with the location of the cells being imaged indicated by the red arrowhead. Noteworthy features that may differ between pre- and post- expansion-cleared tissue, such as cell bodies, branching processes, and large projections, are numbered 1, 2, and 3, respectively. (b) Fluorescence image of Thy-YFP expression after 4× expansion-clearing. A 340 μm thick maximum intensity projection of the same five cells in (a) is shown, with the same features labeled again 1, 2, and 3. Of note, a cell body (1) and the neuronal processes of an adjacent cell (2) are both partially obstructed by tissue lipids in (a), but can be easily identified in (b) after clearing and expansion. However, the 4× expansion that contributes to this increased visibility through tissue also causes some tissue destruction, as apparent in the multiple severed processes, such as (3). A bright-field image of the expanded slice embedded in agarose is shown at the top right. (c) Native YFP fluorescence from the same cell in pre-expanded (blue box) and post-expanded (yellow box) tissue is shown. (d) Equipment for sample processing: (1) 2% bis-acrylamide, (2) 40% acrylamide, (3) sodium acrylate, (4) ammonium persulfate (APS), (5) N,N,N’,N’-Tetramethylethylenediamine (TEMED), (6) 4-hydroxy TEMPO, (7) 20% SDS, (8) collagenase, (9) low gelling temperature agarose, (10) Entellan, and assorted, unlabeled, glass slides, spacers, and plastic dishes. All images were taken on a Zeiss LSM 780 confocal with the Plan-Apochromat 10× 0.45 N.A. M27 air objective (w.d. 2.0 mm).
(a) Mice were perfusion-fixed, A4P0-embedded, PARS-cleared for 5 days with 8% SDS-BB (pH 8.5), and washed with 1× PBS at pH 7.5. Numbers correspond to the extracted organs in panel (b). (b) Extracted organs from the cleared mouse in panel (a), pictured before (top) and after (bottom) RIMS incubation for 3 days. Black arrowheads correspond to the adrenal gland on the kidney and to the ovaries on the fallopian tubes. Each square represents 0.5 cm2. Rodent husbandry and euthanasia conformed to all relevant governmental and institutional regulations; animal protocols were approved by the Institutional Animal Care and Use Committee (IACUC) and by the Office of Laboratory Animal Resources at the California Institute of Technology.
(a) For labeling thick tissue sections, camelid nanobodies are a promising alternative to traditional antibodies, either full immunoglobulins or their engineered formats (single-chain variable fragment (scFv), Fab, and F(ab’)2). A possible workflow for nanobody production consists of: inoculating 500 ml terrific broth with 5 ml overnight cultures and grow at 37 °C until IPTG induction at OD = 0.5, then lowering the temperature to 20 °C for 10 hours for nanobody expression. Cell pellets are then lysed, carried through alternating cycles of freeze-thaw with benzonase addition, followed by a final addition of 0.1% polyethyleneimine to the pellet lysate before pelleting debris and filtering the nanobody-containing fraction. The His-tagged GFAP fusion protein is purified by immobilized metal affinity chromatography on a Ni-NTA column. The His-tag must be removed prior to staining to avoid non-specific binding. (b) To stain for glial fibrillary acidic protein (GFAP) using a GFAP camelid nanobody, 1 mm thick PACT-cleared mouse brain sections were immunostained with 1:500 Atto 488 conjugated GFAP nanobody (see Table 4) at RT overnight with shaking. The stained sections were then washed 3 times in PBST over 1 hour, followed by a 1-hour incubation in RIMS. The transparent sections were RIMS-mounted and imaged on a Zeiss LSM 780 confocal with the Plan-Apochromat 10× 0.45 N.A. M27 air objective (w.d. 2.0 mm). (b, left) 850 μm thick 3D rendering of mouse internal capsule stained with GFAP nanobody. (b, right) Side view showing uniform labeling of GFAP nanobody throughout the entire 850 μm slice.
(a) neuTube. (b) Imaris. Computer screenshots depict the image processing workspace for each software during the 3D visualization (i) of labeled cells in Figure 10.
Supplementary Figures 1–7, Supplementary Methods and Supplementary Table 1 (PDF 8493 kb)
Zip file containing STL design file required for custom printing of Immersion chamber for LSFM. (ZIP 62 kb)
Zip file containing STL design file required for custom printing of Sample holder for LSFM. (ZIP 17 kb)
Zip file containing source file used to test the tracing functionalities of neuTube and Imaris. Picturing two labeled neurons in the mouse striatum across two fields of view, the test image, provided in TIFF file format, was acquired on an LSM 780 at 25× magnification and stitched in Zen (Zeiss) to produce a single channel, 8-bit, 300 MB image stack of size 3.15 × 108 voxels (1024 × 2048 × 150) covering approximately 0.08 mm3 (480 × 960 × 175 um3) of tissue. 3D image analysis of this test image generated the neuTube and Imaris traces depicted in Figure 10 and Figure S7. (ZIP 141502 kb)
About this article
Cite this article
Treweek, J., Chan, K., Flytzanis, N. et al. Whole-body tissue stabilization and selective extractions via tissue-hydrogel hybrids for high-resolution intact circuit mapping and phenotyping. Nat Protoc 10, 1860–1896 (2015). https://doi.org/10.1038/nprot.2015.122
ACS Chemical Neuroscience (2021)
Three-dimensional imaging of intact porcine cochlea using tissue clearing and custom-built light-sheet microscopy
Biomedical Optics Express (2020)
Self-reinoculation with fecal flora changes microbiota density and composition leading to an altered bile-acid profile in the mouse small intestine