A guidebook for DISCO tissue clearing
2021; Springer Nature; Volume: 17; Issue: 3 Linguagem: Inglês
10.15252/msb.20209807
ISSN1744-4292
AutoresMüge Molbay, Zeynep Ilgin Kolabas, Mihail Ivilinov Todorov, Tzu‐Lun Ohn, Ali Ertürk,
Tópico(s)Molecular Biology Techniques and Applications
ResumoReview26 March 2021Open Access A guidebook for DISCO tissue clearing Muge Molbay Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-University Munich, Munich, Germany Munich Medical Research School (MMRS), Munich, GermanyThese authors contributed equally to this work Search for more papers by this author Zeynep Ilgin Kolabas Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-University Munich, Munich, Germany Graduate School for Systemic Neurosciences (GSN), Munich, GermanyThese authors contributed equally to this work Search for more papers by this author Mihail Ivilinov Todorov Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-University Munich, Munich, Germany Graduate School for Systemic Neurosciences (GSN), Munich, Germany Search for more papers by this author Tzu-Lun Ohn Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-University Munich, Munich, Germany Search for more papers by this author Ali Ertürk Corresponding Author [email protected] orcid.org/0000-0001-5163-5100 Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-University Munich, Munich, Germany Munich Cluster for Systems Neurology (SyNergy), Munich, Germany Search for more papers by this author Muge Molbay Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-University Munich, Munich, Germany Munich Medical Research School (MMRS), Munich, GermanyThese authors contributed equally to this work Search for more papers by this author Zeynep Ilgin Kolabas Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-University Munich, Munich, Germany Graduate School for Systemic Neurosciences (GSN), Munich, GermanyThese authors contributed equally to this work Search for more papers by this author Mihail Ivilinov Todorov Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-University Munich, Munich, Germany Graduate School for Systemic Neurosciences (GSN), Munich, Germany Search for more papers by this author Tzu-Lun Ohn Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-University Munich, Munich, Germany Search for more papers by this author Ali Ertürk Corresponding Author [email protected] orcid.org/0000-0001-5163-5100 Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-University Munich, Munich, Germany Munich Cluster for Systems Neurology (SyNergy), Munich, Germany Search for more papers by this author Author Information Muge Molbay1,2,3, Zeynep Ilgin Kolabas1,2,4, Mihail Ivilinov Todorov1,2,4, Tzu-Lun Ohn1,2 and Ali Ertürk *,1,2,5 1Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany 2Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-University Munich, Munich, Germany 3Munich Medical Research School (MMRS), Munich, Germany 4Graduate School for Systemic Neurosciences (GSN), Munich, Germany 5Munich Cluster for Systems Neurology (SyNergy), Munich, Germany *Corresponding author. Tel: +49 89 3187 43234; E-mail: [email protected] Mol Syst Biol (2021)17:e9807https://doi.org/10.15252/msb.20209807 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Histological analysis of biological tissues by mechanical sectioning is significantly time-consuming and error-prone due to loss of important information during sample slicing. In the recent years, the development of tissue clearing methods overcame several of these limitations and allowed exploring intact biological specimens by rendering tissues transparent and subsequently imaging them by laser scanning fluorescence microscopy. In this review, we provide a guide for scientists who would like to perform a clearing protocol from scratch without any prior knowledge, with an emphasis on DISCO clearing protocols, which have been widely used not only due to their robustness, but also owing to their relatively straightforward application. We discuss diverse tissue-clearing options and propose solutions for several possible pitfalls. Moreover, after surveying more than 30 researchers that employ tissue clearing techniques in their laboratories, we compiled the most frequently encountered issues and propose solutions. Overall, this review offers an informative and detailed guide through the growing literature of tissue clearing and can help with finding the easiest way for hands-on implementation. Tissue transparency Biological tissues are remarkably complex. While several histological techniques are currently available for the analysis of biological tissues, a variety of challenges remains to be tackled. For example, imaging large samples requires destroying tissue integrity by sectioning them into thin slices for visualization. This process is labor-intensive and re-assembling information from serial tissue sections into a three-dimensional reconstruction is an extremely time-consuming process, likely causing loss of important information (Ertürk et al, 2012a). Rendering the tissues transparent is an option for circumventing the need for sectioning and allows imaging tissues while keeping them intact and preserving their details. Optical tissue clearing is the term that spans a variety of methods applying this concept. From the very first clearing protocol to the most recent ones, transparency is achieved by homogenizing the refractive index (RI) within the tissue. When light waves propagate through a tissue with nonhomogeneous refractive indices, the differences in the RIs will result in refraction at the interface, distorting the shape of the wave front. This process is also referred to as scattering. Having a wider distribution of the RI in a medium causes more scattering, degrading the quality of the acquired image. Therefore, minimizing the refraction by homogenizing the RI allows light to penetrate deeper into the medium (Tuchin, 2015), hence rendering a sample transparent. The first tissue clearing method was described in the 1900s by Werner Spalteholz, who used a mixture of Methyl salicylate/Benzyl benzoate (5:3 vol:vol) (Spalteholz, 1914) to apply the RI homogenization protocol. Derivatives of the Spalteholz’ method were used by embryologists by substituting methyl salicylate with benzyl alcohol. This new combination formed Murray’s clear (BABB) containing 1:2 combination of benzyl alcohol:benzyl benzoate (Dent et al, 1989; Klymkowsky & Hanken, 1991). Currently, tissue clearing, especially when paired with optical imaging and fluorescent labeling of cellular structures has become a powerful tool for collecting information deep within tissues at single-cell resolution. Due to the wide applicability of tissue clearing, several different approaches have emerged including hydrogel-based clearing, aqueous-based clearing, and those that followed Spalteholz's historical recipe on tissue dehydration—solvent-based clearing (Fig 1) (Silvestri et al, 2016). These approaches have expanded the application of tissue clearing by providing specialized protocols for a variety of purposes. Figure 1. The most common tissue clearing methods Tissue clearing methods can be separated into three main categories based on their chemistry: hydrogel-based, hydrophilic, and hydrophobic. The subtypes mainly differ according to reagents/approaches used. Several parameters will influence the choice of the procedure such as the labeling method, the size of the tissue, the species, and the nature of the molecule of interest. Download figure Download PowerPoint Cleared lipid-extracted acryl-hybridized rigid immunostaining/in situ hybridization-compatible tissue hydrogel (CLARITY) (Chung et al, 2013), passive CLARITY technique (PACT) and perfusion-assisted agent-release in situ (PARS) (Yang et al, 2014) are hydrogel-based protocols. They utilize an acrylamide monomer to form a tissue-hydrogel hybrid, which allows immobilizing most of the amino group containing molecules by cross-linking, thus keeping large tissues intact and decreasing structural damage. This process involves electrophoresis or diffusion depending on the nature of the biological specimen and the specific scientific question. After fixing the tissue in a hydrogel scaffold, the sample is subjected to delipidation using sodium dodecyl sulfate (SDS) detergent. The composition of the reagents can be modified depending on the intended purpose of imaging (e.g., RNA or protein imaging). The nature of the reagents also facilitates tissue expansion which potentially reveals hidden structures. Among the downsides of this approach are the longer incubation times required for clearing and the need for special equipment in order to employ some of the techniques (Gradinaru et al, 2018). All subtypes derived from the CLARITY approach such as PACT and PARS (Yang et al, 2014), stabilization under harsh conditions via intramolecular epoxide linkages to prevent degradation (SHIELD) (Park et al, 2019) and BoneCLARITY (Greenbaum et al, 2017) are protocols optimized toward a certain use case. As an example, PACT is a passive CLARITY technique involving a protocol suitable for small samples, while PARS is a perfusion-assisted agent-release in situ protocol for whole-body clearing with solution delivery through the vasculature. Other CLARITY-based protocols are modified for different applications or specific tissues, such as BoneCLARITY, developed for investigating the notoriously difficult bone tissue. An elaborate review on hydrogel-based tissue clearing methods is available by Gradinaru et al (2018). The second major type of tissue clearing methods is hydrophilic or aqueous-based approaches. These methodologies initially surfaced around the 1990s utilizing various water-soluble agents such as sugars, dextran, sucrose, urea, and amino alcohols. The main distinctive feature of aqueous-based methods is that the employed water-soluble agents are less destructive to the tissue and display high levels of biocompatibility and biosafety. The different method subtypes stem from the different reagents that are used in protocols for decolorization, delipidation, or RI-matching steps, i.e., urea in Sca/e (Hama et al, 2011), urea with sorbitol in Sca/eS (Hama et al, 2015), fructose for See Deep Brain (SeeDB) (Ke et al, 2013), and amino alcohols in clear, unobstructed brain or body imaging cocktails and computational analysis (CUBIC) (Tainaka et al, 2014). Hydrophilic methods involve several alternative mechanistic approaches for homogenizing the RI. For instance, the sample can be passively immersed in an RI-matching solution. Alternatively, lipids can be removed from the sample and tissues can be hyperhydrated to lower the RI. For more details on hydrophilic approaches, an extensive review is available by Tainaka et al (2018). Lastly, hydrophobic or solvent-based clearing methods rely on organic solvents to render the tissues transparent. While this approach dates back to Spalteholz’ protocol described in 1911, it did not draw much attention at the time with the exception of a few publications elaborating on the method (Dent et al, 1989; Klymkowsky & Hanken, 1991). It later re-emerged with 3D imaging of solvent-cleared organs (3DISCO) that was used to achieve transparency of a whole mouse brain in 1–2 days (Ertürk et al, 2012a). Every hydrophobic clearing method involves three fundamental steps: dehydration, delipidation and RI matching. The dehydration step is the distinctive step for hydrophobic methods and together with delipidation, the aim is to remove the two most abundant chemical components of biological tissue, namely water and lipids. The RI of water is 1.33 (Hale & Querry, 1973) and greatly differs from that of the remaining tissue (~ 1.55). Therefore, replacing water with a liquid with a similar RI to that of the remaining tissue can greatly reduce scattering (Richardson & Lichtman, 2015). Applications of tissue clearing Tissue clearing offers the opportunity to understand biology at a whole organism and systems level and presents several advantages for making new biological discoveries. One major feature of tissue clearing is its potential to reveal 3-dimensional structural information. The ability to keep tissues intact during imaging has revealed previously unknown microscopic details and anatomical connections. For instance, imaging of cleared bone marrow using 3DISCO showed that non-dividing stem cells localize in perisinusoidal areas in the bone marrow (Acar et al, 2015). Another study used FocusCLEAR to reveal the spatial organization of cockroach brain nuclei (Chiang et al, 2001). In the mouse gut, immunolabeling-enabled imaging of solvent-cleared organs (iDISCO) revealed the peripheral nervous system adapting to perturbations (Gabanyi et al, 2016). New cortical brain regions, which are downstream of whisker-evoked sensory processing, were discovered using iDISCO+ in mice (Renier et al, 2016). More recently, imaging of whole-body neuronal projections in adult mice using vDISCO (the ‘v’ refers to the variable domain of heavy-chain antibodies; that is, nanobodies) revealed vascular connections between the skull and the meninges (Cai et al, 2019), similar to the discovery of trans-cortical vessels in the long bones using the simpleCLEAR protocol (Grüneboom et al, 2019). Additionally, clearing agent fructose, urea, and glycerol for imaging (FUnGI) provided an organoid clearing protocol for multi-color lineage tracing of tumor heterogeneity (Rios et al, 2019). Besides revealing detailed information, keeping the tissues intact also maximizes the information that can be obtained from a large specimen. Elegant examples of this are whole-body analyses that allowed the unbiased assessment of treatment efficacy and enabled analyzing the biodistribution of nanoparticles, antibodies, and various other targeting agents. This powerful application was demonstrated with the use of CUBIC to detect cancer metastases throughout a whole mouse body sample (Kubota et al, 2017). More recently, vDISCO, in conjunction with deep learning-based algorithms allowed the quantification of cancer cells that had been targeted by therapeutic antibodies (Pan et al, 2019). Beyond cancer research, further medically relevant applications that are worth noting include the analysis of the spatial distribution of transplanted stem cells in adult mice body using ultimate DISCO (uDISCO) (Pan et al, 2016) and screening the transfection efficiency of adeno-associated virus (AAV) variant with CLARITY (Deverman et al, 2016). Furthermore, iDISCO+ enabled automated cell detection of cFos staining and Allen Brain Atlas registration revealing the brainstem circuit control for feeding behavior and B-amyloid plaques in an Alzheimer’s disease (AD) mouse model (Liebmann et al, 2016; Nectow et al, 2017) (Fig 2). Figure 2. Tissue clearing can be applied to samples ranging from organs, to mouse embryos to adult rodent bodies and reveals macroscopic and microscopic details (A). Nerve and nerve endings are revealed in an E12.5 mouse embryo cleared with iDISCO protocol and immunostained for motor (red) and sensory (magenta) nerves and nerve endings (cyan). (B). Kidney vasculature is visualized after fDISCO clearing using CD31-A647 labeling. (C). B-amyloid plaques in the brain with neurofilament H staining in a 10-month-old in AD mouse cortex cleared with iDISCO (Scale bar: 200 μm in upper and 30 μm in lower figures). (D). Neuronal projections in the whole adult mouse are shown in a Thy1-GFPM mouse boosted with Anti-eGFP nanobodies using the vDISCO clearing protocol. (E). A 3D reconstruction of whole adult human kidney performed using SHANEL. The autofluorescence signal at 780 nm (cyan), the glomeruli and vessels from TO-PRO-3 labeling (magenta), the vessels from the dextran labeling (green), and the merged image are shown, respectively. (F). Whole-brain vasculature is shown using PEGASOS protocol in Tie2-Cre;tTAflox;tetO-H2BGFP (TTH) mice in lateral view. Used with permission from: (A) Dr. Gist Croft, Rockefeller University, (B) Science Advances (Qi et al, 2019), (C) Cell (Liebmann et al, 2016), (D) Nature Neuroscience (Cai et al, 2019), (E) Cell (Zhao et al, 2020), (F) Cell Research (Jing et al, 2018). Download figure Download PowerPoint Another benefit of whole intact tissue visualization is that it provides less error-prone measurements and more informative quantification. For example, the growth of glioblastomas in mice was measured under different conditions using 3DISCO clearing (Garofalo et al, 2015) and the distance between neural stem cells and blood vessels was assessed with Sca/e (Hama et al, 2011). Moreover, clearing combined with unbiased quantification methods, i.e., deep learning algorithms, made it possible to obtain a system-level understanding at single-cell resolution. The complex mouse brain vasculature was inspected using a combination of 3DISCO and a convolutional neural network and revealed structural differences in the cerebral angioarchitecture among common inbred and outbred mice strains (Todorov et al, 2020; Fig 3). The same pipeline, but relying on manual analysis instead of machine learning, would be significantly more time-consuming and might potentially result in a subjective end result. A similar approach involving iDISCO+ revealed the local adaptations and functional correlates across brain regions and indicated vascular plasticity in diverse disease models (Kirst et al, 2020). Figure 3. Analysis tools for tissue clearing applications Analysis tools include: (A). TubeMap pipeline which demonstrates the fine-scale organization on the brain vasculature. In this study, Kirst et al (2020) demonstrated how stroke affects the brain, using antibody labeling. (B). The ClearMAP pipeline is used for examining parental behavior through Fos activity in the whole brain followed by a filter-based analysis. (C). SHANEL pipeline has one of the recent algorithms that include deep learning methods to analyze big tissues to quantify cleared human brain tissues identified in the six layers of primary visual cortex. The summary of the cell properties from different brain regions taken from cortex and hippocampus were analyzed using the authors’ CNN. (D). The VesSAP pipeline, which can extract features and registers the mouse brain vasculature to Allen Brain Atlas. Images represent the steps of feature extraction, radius illustration, and vascular segmentation. (E). The DeepMACT pipeline, which detects metastasis throughout organs in adult mice. Each dot represents a metastasis. Used with permission from (A) Cell (Kirst et al, 2020), (B) Cell (Renier et al, 2016), (C) Cell (Zhao et al, 2020), (D) Nature Methods (Todorov et al, 2020), (E) Cell (Pan et al, 2019). Download figure Download PowerPoint The ability of clearing to reveal fine details deep within tissues makes it a powerful tool in the context of disease pathology. Analyses of tumors using CUBIC indicated a heterogenous nature of macrophage infiltration in lung carcinoma (Cuccarese et al, 2017), while BABB clearing revealed growth patterns of prostate cancer (van Royen et al, 2016). Similarly, tissue clearing has been applied to neurological pathology, where 3DISCO showed microvessel reorganization following stroke (Lugo-Hernandez et al, 2017), and CLARITY elucidated the relationship between monoaminergic fibers and Lewy bodies in Parkinson patients (Liu et al, 2016), and AD plaques (Ando et al, 2014). CLARITY was also used to examine the dynamics of pancreatic innervations in pathological conditions such as in diabetes (Hsueh et al, 2017). Overall, the ability of clearing techniques to reveal information that has been inaccessible to other methodologies has been instrumental in the analysis of several pathologies. In summary, a variety of clearing techniques is available, with each of them presenting certain advantages depending on various parameters or being better suited for given applications. These methods are undergoing continuous improvements and are being adapted to specific applications. Further applications are shown in Table 1, and two recent reviews provide an in-depth discussion of the different applications (Ueda et al, 2020; Tian et al, 2021). In the following section, we narrow down our focus to solvent-based method by first sharing some of the known protocols, developed for various needs. Table 1. Tissue clearing applications based on tissue clearing method and labeling. Sample Tissue clearing method used Labeling Whole mouse organ Development of heart CUBIC (Li et al, 2016) AAV, Ab labeling Lung 3DISCO (Ertürk et al, 2012a; Mzinza et al, 2018) Endogenous GFP SWITCH (Murray et al, 2015) Endogenous GFP CLARITY (Kim et al, 2015) Endogenous GFP uDICSO (von Neubeck et al, 2018) Endogenous mCherry Spleen 3DISCO (Ertürk et al, 2012a) Endogenous GFP Lymph node 3DISCO (Ertürk et al, 2012a), Ab labeling, Endogenous xFP 3DISCO (Ertürk et al, 2012a) Ab labeling, Endogenous xFP Mammary gland CUBIC (Davis et al, 2016) Endogenous GFP FUnGI (Rios et al, 2019) Ab labeling Heart CLARITY (Kim et al, 2015) Endogenous GFP SWITCH (Murray et al, 2015) Ab labeling Kidney CLARITY (Kim et al, 2015) Endogenous GFP Liver CLARITY (Kim et al, 2015) Endogenous GFP SWITCH (Murray et al, 2015) Endogenous GFP 3DISCO (Mzinza et al, 2018) Endogenous GFP Uterus 3DISCO (Yuan et al, 2018) Endogenous GFP Intestine iDISCO (Gabanyi et al, 2016) Ab labeling Human organ parts Lung CUBIC (Tainaka et al, 2018; Nojima et al, 2017) Ab labeling Kidney CUBIC (Tainaka et al, 2018; Nojima et al, 2017) Ab labeling Pancreas CLARITY (Hsueh et al, 2017) Ab labeling Spleen CUBIC (Nojima et al, 2017) Ab labeling Intestine CUBIC (Nojima et al, 2017) Ab labeling Lymph node CUBIC (Nojima et al, 2017) Ab labeling Embryo Human 3DISCO/iDISCO (Yuan et al, 2018) Ab labeling Mouse 3DISCO (Ertürk et al, 2012b) Endogenous mGFP, Tomato iDISCO (Renier et al, 2014) Ab labeling Sca/e (Hama et al, 2011) Endogenous YFP, Ab labeling CUBIC (Cuccarese et al, 2017; Li et al, 2016) Endogenous GFP, Ab labeling Brain organoid iDISCO (Birey et al, 2017) Ab labeling Bone Long bones CUBIC (Chen et al, 2016, 5) Endogenous mCherry, Ab labeling simpleCLEAR (Grüneboom et al, 2019) Endogenous tdTomato, Ab labeling 3DISCO (Acar et al, 2015) Endogenous GFP, Ab labeling Whole-body vDISCO (Cai et al, 2019) Endogenous GFP, Nanobooster PARS (Yang et al, 2014) Ab labeling CUBIC (Tainaka et al, 2014; Susaki et al, 2015) Endogenous GFP, Ab labeling uDISCO (Pan et al, 2016) Endogenous GFP vDISCO (Pan et al, 2019) Endogenous mCherry, Ab labeling CUBIC (Kubota et al, 2017) Endogenous mCherry, Ab labeling Brain Whole mouse brain 3DISCO (Ertürk et al, 2012a; Ertürk et al, 2012b; Lin et al, 2018; Lugo-Hernandez et al, 2017) Endogenous GFP, FITC Dextran uDISCO (Pan et al, 2016; Li et al, 2018) Endogenous GFP CUBIC (Tatsuki et al, 2016; Susaki et al, 2014; Tainaka et al, 2018; Susaki et al, 2015) Ab labeling iDISCO (Renier et al, 2014) Ab labeling iDISCO (Liebmann et al, 2016) Ab labeling iDISCO+ (Renier et al, 2016; Nectow et al, 2017) Ab labeling CLARITY (Kim et al, 2015; Bedbrook et al, 2018; Deverman et al, 2016; Chung et al, 2013) Endogenous GFP, AAV AAV, Endogenous YFP Sca/eS (Hama et al, 2015) Endogenous YFP, Ab labeling SWITCH (Murray et al, 2015) Ab labeling SeeDB2 (Ke et al, 2016) Endogenous YFP Human brain slice SHANEL (Zhao et al, 2020) Ab labeling SWITCH (Murray et al, 2015) Ab labeling CUBIC (Tainaka et al, 2018; Nojima et al, 2017) Ab labeling CLARITY (Ando et al, 2014; Phillips et al, 2016; Morawski et al, 2018; Liu et al, 2016; Chung et al, 2013) Ab labeling Cockroach brain FocusClear (Chiang et al, 2001; Liu & Chiang, 2003) Ab labeling, WGA Rat brain FluoClearBabb (Stefaniuk et al, 2016) Ab labeling Fly brain SeeDB2G (Ke et al, 2016) Ab labeling Vasculature Mouse whole brain 3DISCO (Todorov et al, 2020) WGA, small dye iDISCO+ (Kirst et al, 2020) Ab labeling Tissue Spinal cord injury 3DISCO (Ertürk et al, 2012a; Zhu et al, 2015) Endogenous GFP CLARITY (Hsueh et al, 2017; Glaser et al, 2017) Ab labeling, WGA Tumor–Human CUBIC (Nojima et al, 2017) Ab labeling BABB (van Royen et al, 2016) Ab labeling DIPCO (Tanaka et al, 2017) Ab labeling FUnGI (Rios et al, 2019) Ab labeling Others Bio-artificial skeletal muscle 3DISCO, ClearT2, ScaleA2 (Decroix et al, 2015) Endogenous GFP, Ab labeling Cancer Lung carcinoma CUBIC (Cuccarese et al, 2017) Endogenous GFP, RFP, Ab labeling 3DISCO (Garofalo et al, 2015) Ab labeling Glioma vDISCO (Pan et al, 2019) Endogenous mCherry, Ab labeling Whole body CUBIC (Kubota et al, 2017) Endogenous mCherry, Ab labeling Organic solvent-based tissue clearing techniques Solvent-based or hydrophobic methods have improved drastically in the past decade. New protocols have emerged either addressing the shortcomings of the original protocol or optimizing it for a more specific application. Dehydration, lipid extraction, and RI matching around 1.55 are the essential steps common to this type of clearing approach. The first method to revive the century-old technique was 3DISCO, which is known to be robust and to work on a number of different tissue types. The major advantages were the speed, the transparency, and the ease of storing the samples, because of their solid nature following processing. However, at the time the main shortcomings were quenching the genetically expressed fluorescence and tissue shrinkage. Nevertheless, these shortcomings also triggered the further optimization of this approach. Spalteholz’s tissue clearing At the beginning of the 20th century, Werner Spalteholz mixed Methyl salicylate and Benzyl benzoate (5:3 vol:vol) to make various human organs transparent (Spalteholz, 1914). Examples of analyzed organs include the human heart which was made transparent in order to observe the blood vessels and the development of an infarctio
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