A Novel Technique for Imaging and Analysis of Hair Cells in the Organ of Corti Using Modified Sca/eS and Machine Learning

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Jan 2019



Here, we describe a sorbitol-based optical clearing method, called modified Sca/eS that can be used to image all hair cells (HCs) in the mouse cochlea. This modification of Sca/eS is defined by three steps: decalcification, de-lipidation, and refractive index matching, which can all be completed within 72 h. Furthermore, we established automated analysis programs that perform machine learning-based pattern recognition. These programs generate 1) a linearized image of HCs, 2) the coordinates of HCs, 3) a holocochleogram, and 4) clusters of HC loss. In summary, a novel approach that integrates modified Sca/eS and programs based on machine learning facilitates quantitative and comprehensive analysis of the physiological and pathological properties of all HCs.

Keywords: Cochlea (耳蜗), Optical tissue clearing (光学组织清除), Auto hair cell analysis (自动毛细胞分析), Sca/eS (天平), Machine learning (机器学习)


Sound waves reach the inner ear via conduction mechanisms in the external and middle ear. The cochlea is an organ in the inner ear that transduces mechanical stimulation into electronic signals. As a result, generated auditory information is transferred to the brain. Hair cells (HCs) in the organ of Corti, an auditory sensory epithelium, are protected by the temporal bone and are precisely disposed to detect every frequency band from the base (high pitch) to the apex (low pitch) (von Békésy, 1990). Functional analysis of the cochlea is limited due to its anatomical specificity. Surface preparation and sectioning (paraffin or frozen sections) are the most popular methods in histological analysis; however, these techniques do not preserve the three-dimensional structure (Fujimoto et al., 2017; Mizushima et al., 2017). To better understand the processing of auditory information, the location and diversity of HCs must be accurately determined. Consequently, a novel technique to identify intact HCs has long been sought. Established optical clearing methods such as CLARITY, 3DISCO, Sca/eS, iDISCO, and CUBIC (Dodt et al., 2007; Chung et al., 2013; Renier et al., 2014; Susaki et al., 2014; Hama et al., 2015) enable imaging of intact brains and other organs, but are not optimized for imaging of all HCs (Urata et al., 2019). Based on recently developed optical clearing methods for hard tissues such as vDISCO, Bone CLARITY, PEGASOS, CUBIC-X, PACT, and PARS (Calve et al., 2015; Treweek et al., 2015; Berke et al., 2016; Greenbaum et al., 2017; Cai et al., 2018; Jing et al., 2018; Tainaka et al., 2018), we present a sorbitol-based optical clearing method (modified Sca/eS) that is optimized for intact imaging of all mouse HCs in the cochlea (Urata et al., 2019).

By acquiring successive images of HCs using modified Sca/eS, we established a novel algorithm for comprehensive analysis of HCs. These programs are available on GitHub (https://github.com/okabe-lab/cochlea-analyzer.git). Use of modified Sca/eS in combination with these programs facilitates quantitative and comprehensive analysis of the physiological and pathological properties of all HCs.

Materials and Reagents

  1. Blu Tack (Bostik, catalog number: 371351)
  2. Glass microscope slide (Matsunami, catalog number: S024410)
  3. Glass microscope coverslip (Matsunami, catalog number: C018181)
  4. Basukoku (Cemedine; silicone-based adhesive, catalog number: HJ-148)
  5. Alexa Fluor 488-conjugated goat anti-rabbit IgG (H+L) (Life Technologies, catalog number: 1937195)
  6. 4% paraformaldehyde diluted in phosphate-buffered saline (PBS) (Wako, catalog number: 163-20145)
  7. Triton X-100 (Nacalai-Tesque, catalog number: 12967-45)
  8. Urea (SIGMA, catalog number: U0631-1KG)
  9. Guanidine hydrochloride (Nacalai-Tesque, catalog number: 17318-82)
  10. D-sorbitol (SIGMA, catalog number: S1816-1KG)
  11. D-glucose (SIGMA, catalog number: G8270-100G)
  12. Rabbit polyclonal anti-myosin VIIa antibody (Proteus Biosciences, catalog number: 25-6790)
  13. 10% EDTA-2Na (Muto Pure Chemicals, catalog number: 85653)
  14. NaCl
  15. KCl
  16. Na2HPO4·12H2O
  17. KH2PO4
  18. Solution 1 (see Recipes)
  19. Solution 2 (see Recipes)
  20. PBS (see Recipes)


  1. Dumont tweezers, straight (World Precision Instruments, catalog number: 500233)
  2. Dumont tweezers, straight (World Precision Instruments, catalog number: 14098)
  3. Stereoscopic microscope (for microdissection), e.g., SMZ-2B (Nikon)
  4. Fluorescence microscope
    Confocal microscope (for imaging at a depth of up to 300-500 μm), e.g., A1MP confocal microscope (Nikon)
    Multiphoton microscope (recommended for deeper imaging), e.g., A1MP two-photon microscope (Nikon)
  5. Objective lenses (long working distance objectives), e.g., N25X-APO-MP (NA1.10, WD2.00) for multiphoton imaging and VC 20x (NA0.75, WD1.00) for confocal imaging
  6. Water bath at 37 °C
  7. Fridge at 4 °C


  1. Microsoft Excel (Microsoft) 
  2. MATLAB (MathWorks, version R2017b)
    Image Processing Toolbox
    Statistics and Machine Learning Toolbox
    Neural Network Toolbox


  1. Modified Sca/eS protocol (see Figure 1)

    Figure 1. Time course of modified Sca/eS. The inner ear embedded in the temporal bone was removed (panel A) and dissected along the fistula (B) (dotted lines in the left of panel A). The cochlea was separated (dotted line in panel C) from the semicircular canals along the fissure (red arrow in panel C). The cochlea was removed [panel D and (i)] and decalcified (ii). The sample was cleared immediately after being submerged in solution 1 (iii). The sample was then submerged in solution 2 and its transparency gradually increased (iv-viii). A: anterior, P: posterior, M: medial, L: lateral, C: cochlea, SC: semicircular canal, RI: refractive index. Scale bar: 1 mm.

    1. Extraction of the cochlea from the temporal bone
      1. Fix the cochlea in 4% paraformaldehyde prepared in PBS overnight at 4 °C with gentle shaking.
      2. Wash the cochlea three times (15 min each) with PBS.
      3. Incubate the samples in 500 mM EDTA prepared in PBS for 48-120 h at 37 °C.
        Note: The incubation duration depends on the sample size. For example, 48 h for pups, 72 h for young (2-week-old) mice, and 120 h for middle-age (6-month-old) mice.
      4. Wash the cochlea three times (15 min each) with PBS.
      5. Remove excess bone tissue surrounding the cochlea.
      6. Remove the vestibule and semicircular canal.
    2. Tissue de-lipidation (see Figure 1 and Solution 1 in Recipes)
      Incubate the samples in Solution 1 for 2 h at 37 °C.
    3. Antibody staining (see Figure 1)
      1. Wash the samples with PBS containing 0.1% Triton X-100 for 30 min with continuous rocking at 40 rpm.
      2. Incubate the samples in a solution containing the appropriate dilutions of primary antibodies for 2-48 h at 37 °C.
      3. Wash the samples three times (30 min each) with PBS containing 0.1% Triton X-100 with continuous rocking at 40 rpm.
      4. Detect primary antibodies by incubating the samples with a solution containing secondary antibodies for 12-48 h at 37 °C followed by three washes with PBS containing 0.1% Triton X-100.
    4. Sample preparation for imaging (see Figures 2 and 3)
      1. Roll up a piece of Blu Tack into a cylindrical shape that is slightly thicker than the cochlea [Figure 2 (i)].
      2. Align Basukoku in a horseshoe shape on the glass slide [Figure 2 (ii)].
      3. Place the Blu Tack on the Basukoku [Figure 2 (iii, iv)].
      4. Place a drop of Basukoku inside the Blu Tack horseshoe to make a pedestal for the cochlea [Figure 2 (v)]. 
      5. Carefully place the cochlea on the pedestal [Figure 2 (vi)].
        Note: Position the cochlea so that the axis of the center of the cochlea is aligned perpendicular to the glass slide (Figure 2).
      6. Place the cover glass on the surface of the Blu Tack and gently push it toward the surface of the cochlea [Figure 2 (vii)].
        Note: Keep pushing the cover glass until it reaches the surface of the cochlea. This process is critical to image the whole cochlea [Figure 2 (vii-a)].
      7. With the horseshoe opening facing upward, add 100 μl of solution 2 (see Recipes) to the gap within the horseshoe until the imaging chamber is filled [Figure 2 (viii)].
        Note: Images iv-viii in Figure 1 show how transparent the cochlea becomes after 20 min (images were acquired at increments of 5 min).
      8. Fill the gap between the Blu Tack with Basukoku to seal the horseshoe opening [Figure 2 (ix)].
        Note: Air bubbles should not remain in the chamber; this would indicate potential leaking of solution 2 and cause the sample to dry out.

      Figure 2. Sample preparation for the imaging step

      Figure 3. Cochlea position. The center of the first (green) and second (red) turns of the cochlea. Images on the left are the ideal position for imaging of HCs in the whole cochlea, where d represents the distance between the centers of the green and red areas. Upper panels (A) show all HCs imaged using a two-photon microscope in a successful case. Lower panels (B) are representative cases where the approach failed. Images on the left are from the top view, while images on the right are from the side view. The modiolus of the cochlea should be perpendicular to the slide glass [i.e., centers should overlap (purple)].

    5. Imaging of the sample
      1. Place one drop of distilled water on the cover glass and then place the objective lens against the cover glass.
        Note: Use an appropriate immersion media for high-resolution imaging. Water-immersion lenses perform much better than air-immersion objective lenses.
      2. To image the whole cochlea, images should be successively acquired with 10-40% overlap [Figure 2 (i)].
      3. See example images of sample processed with previous well known clearing methods (Figure 4).

      Figure 4. Comparison with other clearing methods. MYO7A-immunopositive HCs in samples were not detected by 3DISCO, iDISCO, CLARITY, or CUBIC (A). Microdissection of the membrane labyrinth of the iDISCO-processed samples in panel A confirmed the presence of MYO7A-immunopositive HCs, suggesting that the surrounding bone tissue prevented the detection of fluorescence (B). Scale bars: 500 µm in panel A and upper image of panel B, 100 µm in lower image of panel B.

  2. Automated HC analysis protocol based on machine learning
    HCs were automatically detected and analyzed using custom MATLAB scripts (R2017b, MathWorks), the details of which were provided in a previous study (Urata et al., 2019). The MATLAB source code is available on GitHub (https://github.com/okabe-lab/cochlea-analyzer.git). Data analysis procedures including statistical analysis were described in the original publication of the protocol (Urata et al., 2019). The actual running of the program was shown in this protocol paper.
    1. The main0.m program (1 min) checks the integrity of the software so that automated analysis can be flawlessly performed (see Video 1).
      Note: If an error occurs during the process, double check to see if any required files are absent under “Function Files” on GitHub (https://github.com/okabe-lab/cochlea-analyzer.git).

      Video 1. Checking the integrity of the software before performing automated analysis using main0.m

    2. The main1.m program merges all the files together to create a linearized image of HCs, which takes about 15 min in total (Step B2 in Figure 5 and see Video 2).
      Note: If the main0.m program completes without an error, it is guaranteed to function properly. A TIFF image is produced in this step.

      Video 2. Merging of all the acquired files together to create a linearized image of HCs using main1.m

      Figure 5. Image generation by the programs. Raw images are stitched (Step 1), and the lines between inner and outer HCs are illustrated (orange line in Step 2). Finally, HCs are linearized and representative images of the apical, middle, and basal portions are enlarged. Colored dots in Step 3 and Step 4 indicate the presence of HCs, and asterisks represent loss of HCs.

    3. The main2.m (inner HCs, duration 5 min, see Video 3) and main3.m (outer HCs, duration 3 min, see Video 4) programs detect 169 HCs.
      1. An EXCEL file containing the coordinates of recorded (or imaged) HCs is generated in this step.
      2. Advanced analysis is available using the main4-6.m programs. The main4.m program analyzes (< 1 min) the spatial distribution of outer HCs and the main5.m program generates (< 1 min) a holocochleogram (see Video 5). The main6.m program analyzes (10 min) clusters of HC loss (see Video 6).

      Video 3. Imaging of inner HCs using the main2.m program

      Video 4. Imaging of outer HCs using the main3.m program

      Video 5. Analysis of the spatial distribution of outer HCs using the main4.m program and generation of a holocochleogram using the main5.m program

      Video 6. Analysis of clustered HC loss using the main6.m program


  1. Solution 1 (20 ml)
    5.7 g of guanidinium chloride
    7 g of D-sorbitol
    3 g of D-glucose
    800 µl (w/v) of Triton X-100 in PBS (pH 6.0-8.0)
  2. Solution 2 (20 ml)
    5.7 g of guanidinium chloride (or 4.8 g of urea)
    12 g of D-sorbitol
    20 µl of Triton X-100 in PBS (pH 7.1)
    Note: The solution can be kept at room temperature for up to 2 months for good clearing.
  3. PBS
    80 g of NaCl
    2 g of KCl
    29 g of Na2HPO4·12H2O
    2 g of KH2PO4
    Add distilled H2O to prepare 1 L of 10x PBS
    Dilute 10-fold with distilled H2O to prepare 1x PBS


We thank Dr. Shigeo Okabe for helpful suggestions and supervision. This method was adapted from our original publication of the protocol (Urata et al., 2019). This work was supported by grants from the Japan Society for the Promotion of Science KAKENHI (15K10743, 2653081, and 16K15717), the Japan Science and Technology Agency (JPMJCR14W2), the Japan Agency for Medical Research and Development (17gm5010003), the Japan Society for the Promotion of Science (17H01387), the UTokyo Center for Integrative Science of Human Behavior, and the Ministry of Education, Culture, Sports, Science, and Technology (18H04727 and 26111506).

Competing interests

The authors have no conflicts of interest to declare.


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  8. Hama, H., Hioki, H., Namiki, K., Hoshida, T., Kurokawa, H., Ishidate, F., Kaneko, T., Akagi, T., Saito, T., Saido, T. and Miyawaki, A. (2015). ScaleS: an optical clearing palette for biological imaging. Nat Neurosci 18(10): 1518-1529.
  9. Jing, D., Zhang, S., Luo, W., Gao, X., Men, Y., Ma, C., Liu, X., Yi, Y., Bugde, A., Zhou, B. O., Zhao, Z., Yuan, Q., Feng, J. Q., Gao, L., Ge, W. P. and Zhao, H. (2018). Tissue clearing of both hard and soft tissue organs with the PEGASOS method. Cell Res 28(8): 803-818.
  10. Mizushima, Y., Fujimoto, C., Kashio, A., Kondo, K. and Yamasoba, T. (2017). Macrophage recruitment, but not interleukin 1 beta activation, enhances noise-induced hearing damage. Biochem Biophys Res Commun 493(2): 894-900.
  11. Renier, N., Wu, Z., Simon, D. J., Yang, J., Ariel, P. and Tessier-Lavigne, M. (2014). iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging. Cell 159(4): 896-910.
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在这里,我们描述了一种基于山梨醇的光学清除方法,称为改良的Sca / eS,可用于对小鼠耳蜗中的所有毛细胞(HC)进行成像。 Sca / eS的这种修饰由三个步骤定义:脱钙,脱脂和折射率匹配,它们都可以在72小时内完成。 此外,我们建立了自动分析程序,执行基于机器学习的模式识别。 这些程序产生1)HC的线性化图像,2)HC的坐标,3)全耳蜗图,以及4)HC损失的簇。 总之,结合修改的Sca / eS和基于机器学习的程序的新方法促进了对所有HC的生理和病理特性的定量和综合分析。
【背景】声波通过外耳和中耳的传导机制到达内耳。耳蜗是内耳中的器官,其将机械刺激转换成电子信号。结果,生成的听觉信息被传递到大脑。 Corti器官(听觉感觉上皮)中的毛细胞(HC)受到颞骨的保护,并且精确地用于检测从基部(高间距)到顶点(低间距)的每个频带(vonBékésy,1990) )。由于其解剖学特异性,耳蜗的功能分析受到限制。表面处理和切片(石蜡或冷冻切片)是组织学分析中最常用的方法;然而,这些技术不能保留三维结构(Fujimoto et al。,2017; Mizushima et al。,2017)。为了更好地理解听觉信息的处理,必须准确地确定HC的位置和多样性。因此,长期以来一直在寻找一种识别完整HC的新技术。建立了光学清除方法,如CLARITY,3DISCO,Sca / eS,iDISCO和CUBIC(Dodt et al。,2007; Chung et al。,2013; Renier et al。,2014; Susaki et al。,2014; Hama et al。,2015)能够对完整的大脑和其他器官进行成像,但是没有针对所有HC的成像进行优化(Urata et al。,2019)。基于最近开发的用于硬组织的光学清除方法,例如vDISCO,Bone CLARITY,PEGASOS,CUBIC-X,PACT和PARS(Calve et al。,2015; Treweek et al。,2015; Berke et al。,2016; Greenbaum et al。,2017; Cai et al。,2018; Jing et al。,2018; Tainaka et al。,2018),我们提出了一种基于山梨醇的光学清除方法(改良的Sca / eS),该方法针对所有人的完整成像进行了优化小鼠HCs在耳蜗中(Urata 等人,2019)。
通过使用改进的Sca / eS获取HC的连续图像,我们建立了一种用于HCs综合分析的新算法。这些程序可在GitHub上获得( https://github.com/okabe-lab/cochlea- analyzer.git )。将改良的Sca / eS与这些程序结合使用有助于定量和全面分析所有HC的生理和病理特性。

关键字:耳蜗, 光学组织清除, 自动毛细胞分析, 天平, 机器学习


  1. Blu Tack(Bostik,目录号:371351)
  2. 玻璃显微镜载玻片(Matsunami,目录号:S024410)
  3. 玻璃显微镜盖玻片(Matsunami,目录号:C018181)
  4. Basukoku(Cemedine;硅基粘合剂,目录号:HJ-148)
  5. Alexa Fluor 488-缀合的山羊抗兔IgG(H + L)(Life Technologies,目录号:1937195)
  6. 在磷酸盐缓冲盐水(PBS)中稀释的4%多聚甲醛(Wako,目录号:163-20145)
  7. Triton X-100(Nacalai-Tesque,目录号:12967-45)
  8. 尿素(SIGMA,目录号:U0631-1KG)
  9. 盐酸胍(Nacalai-Tesque,目录号:17318-82)
  10. D-山梨糖醇(SIGMA,目录号:S1816-1KG)
  11. D-葡萄糖(SIGMA,目录号:G8270-100G)
  12. 兔多克隆抗肌球蛋白VIIa抗体(Proteus Biosciences,目录编号:25-6790)
  13. 10%EDTA-2Na(武藤纯化学品,目录号:85653)
  14. 氯化钠
  15. 氯化钾
  16. 娜<子> 2 HPO <子> 4 ·12H <子> 2 0
  17. KH <子> 2 PO <子> 4
  18. 解决方案1(见食谱)
  19. 解决方案2(见食谱)
  20. PBS(见食谱)


  1. Dumont镊子,直(World Precision Instruments,目录号:500233)
  2. Dumont镊子,笔直(World Precision Instruments,目录号:14098)
  3. 立体显微镜(用于显微切割),例如,SMZ-2B(尼康)
  4. 荧光显微镜
  5. 物镜(长工作距离物镜),例如,N25X-APO-MP(NA1.10,WD2.00)用于多光子成像,VC 20x(NA0.75,WD1.00)用于共焦成像
  6. 在37°C水浴
  7. 冰箱在4°C


  1. Microsoft Excel(Microsoft)&nbsp;
  2. MATLAB(MathWorks,版本R2017b)


  1. 修改过的Sca / eS协议(见图1)

    图1.修改的Sca / eS的时间过程。移除嵌入颞骨的内耳(图A)并沿瘘管(B)解剖(图A左侧的虚线) 。耳蜗沿着裂缝与半圆形管道分离(图C中的虚线)(图C中的红色箭头)。移除耳蜗[图D和(i)]并脱钙(ii)。将样品浸没在溶液1(iii)中后立即清除。然后将样品浸没在溶液2中并且其透明度逐渐增加(iv-viii)。 A:前部,P:后部,M:内侧,L:侧面,C:耳蜗,SC:半规管,RI:折射率。比例尺:1毫米。

    1. 从颞骨中提取耳蜗
      1. 将耳蜗在PBS中制备的4%多聚甲醛中在4℃下轻轻摇动固定过夜。
      2. 用PBS清洗耳蜗三次(每次15分钟)。
      3. 将样品在PBS中制备的500 mM EDTA中孵育48-120小时。
      4. 用PBS清洗耳蜗三次(每次15分钟)。
      5. 去除耳蜗周围多余的骨组织。
      6. 去除前庭和半规管。
    2. 组织脱脂(参见图1和食谱中的溶液1)
    3. 抗体染色(见图1)
      1. 用含有0.1%Triton X-100的PBS洗涤样品30分钟,同时以40rpm连续摇动。
      2. 将样品在含有适当稀释的一抗的溶液中在37℃孵育2-48小时。
      3. 用含有0.1%Triton X-100的PBS洗涤样品三次(每次30分钟),并以40rpm连续摇动。
      4. 通过将样品与含有第二抗体的溶液在37℃温育12-48小时,然后用含有0.1%Triton X-100的PBS洗涤三次来检测一抗。
    4. 成像的样品制备(见图2和3)
      1. 将一块Blu Tack卷成圆柱形,比耳蜗略厚[图2(i)]。
      2. 在玻璃载玻片上以马蹄形对齐Basukoku [图2(ii)]。
      3. 将Blu Tack放在Basukoku上[图2(iii,iv)]。
      4. 在Blu Tack马蹄内放一滴Basukoku,为耳蜗做一个基座[图2(v)]。&nbsp;
      5. 小心地将耳蜗放在基座上[图2(vi)]。
      6. 将盖玻片放在Blu Tack的表面上,轻轻地将其推向耳蜗表面[图2(vii)]。
      7. 将马蹄形开口朝上,将100μl溶液2(见食谱)添加到马蹄内的间隙,直到成像室充满[图2(viii)]。
      8. 填补Blu Tack与Basukoku之间的空白,以密封马蹄形开口[图2(ix)]。



    5. 样品成像
      1. 将一滴蒸馏水放在盖玻片上,然后将物镜放在盖玻片上。
      2. 为了对整个耳蜗进行成像,应该连续采集图像,重叠率为10-40%[图2(i)]。
      3. 参见使用先前众所周知的清除方法处理的样品的示例图像(图4)。

      图4.与其他清除方法的比较。 3DISCO,iDISCO,CLARITY或CUBIC(A)未检测到样品中的MYO7A-免疫阳性HC。在图A中iDISCO处理的样品的膜迷路的显微切割证实了MYO7A-免疫阳性HC的存在,表明周围的骨组织阻止了荧光的检测(B)。比例尺:面板A中的500μm和面板B的上部图像,面板B的下部图像中的100μm。

  2. 基于机器学习的自动HC分析协议
    使用自定义MATLAB脚本(R2017b,MathWorks)自动检测和分析HC,其详细信息在之前的研究中提供(Urata et al。,2019)。 MATLAB源代码可在GitHub上获得( https://github.com/okabe-lab/耳蜗-analyzer.git )。包括统计分析在内的数据分析程序在该协议的原始出版物中描述(Urata 等人,,2019)。该协议文件显示了该计划的实际运行情况。
    1. main0.m程序(1分钟)检查软件的完整性,以便可以完美地执行自动分析(参见视频1)。
      注意:如果在此过程中发生错误,请仔细检查GitHub上“功能文件”下是否缺少任何所需文件( https://github.com/okabe-lab/cochlea-analyzer.git )。


    2. main1.m程序将所有文件合并在一起以创建HC的线性化图像,总共需要15分钟(图5中的步骤B2,参见视频2)。



    3. main2.m(内部HC,持续时间5分钟,见视频3)和main3.m(外部HC,持续时间3分钟,见视频4)程序检测169个HC。
      1. 在此步骤中生成包含已记录(或已成像)HC的坐标的EXCEL文件。
      2. 使用main4-6.m程序可以进行高级分析。 main4.m程序分析(<1分钟)外部HC的空间分布,并且main5.m程序生成(<1分钟)全耳蜗图(参见视频5)。 main6.m程序分析(10分钟)HC损失集群(见视频6)。






  1. 溶液1(20毫升)
    PBS中的800μl(w / v)Triton X-100(pH 6.0-8.0)
  2. 溶液2(20毫升)
    在PBS(pH 7.1)中加入20μlTritonX-100 注意:溶液可以在室温下保存长达2个月,以便清洁。
  3. PBS
    29克Na 2 HPO 4 ·12H 2 O
    2克KH 2 PO 4
    加入蒸馏的H 2 O以制备1L的10x PBS
    用蒸馏的H 2 O稀释10倍以制备1x PBS


我们感谢Shigeo Okabe博士的有益建议和监督。该方法改编自我们最初的协议出版物(Urata et al。,2019)。这项工作得到了日本科学促进会KAKENHI(15K10743,2653081和16K15717),日本科学技术厅(JPMJCR14W2),日本医学研究和发展机构(17gm5010003),日本学会的资助。促进科学(17H01387),UTokyo人类行为综合科学中心,教育,文化,体育,科学和技术部(18H04727和26111506)。




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引用: Readers should cite both the Bio-protocol article and the original research article where this protocol was used:
  1. Urata, S., Iida, T., Suzuki, Y., Lin, S., Mizushima, Y., Fujimoto, C., Matsumoto, Y. and Yamasoba, T. (2019). A Novel Technique for Imaging and Analysis of Hair Cells in the Organ of Corti Using Modified Sca/eS and Machine Learning. Bio-protocol 9(16): e3342. DOI: 10.21769/BioProtoc.3342.
  2. Urata, S., Iida, T., Yamamoto, M., Mizushima, Y., Fujimoto, C., Matsumoto, Y., Yamasoba, T. and Okabe, S. (2019). Cellular cartography of the organ of Corti based on optical tissue clearing and machine learning. Elife 8: e40946.