Cellular cartography of the organ of Corti based on optical tissue clearing and machine learning
- The University of Tokyo, Japan
- Kyoto University, Japan
Figures
Figure 1 with 2 supplements
Optical tissue clearing and whole-mount immunolabeling of the organ of Corti.
(A) Time course and individual steps of tissue clearing with or without immunostaining. (B) Three-dimensional imaging of the organ of Corti within the temporal bone. Top view (left), lateral view …
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Figure 1—source data 1
Source data for Figure 1B, E and Figure 1—figure supplement 2.
- https://doi.org/10.7554/eLife.40946.005
Figure 1—figure supplement 1
Protocol of modified Sca/eS.
(A) Comparison of optical transparency of mouse brain samples using 3DISCO, iDISCO, CLARITY and CUBIC. Scale, 3 mm. (B) Relationship between imaging depths and RIs using the decalcified cochlea as a …
Figure 1—figure supplement 2
Application of modified ScaleS to other tissues.
(A) Tubular bone samples were treated with modified ScaleS and CB-perfusion. CB-perfusion was designed for whole-body imaging (see Appendix 1 for the detail). Transmitted light Images before and …
Figure 2 with 2 supplements
Computational analysis of hair cell distribution in the organ of Corti.
(A) Detection of single hair cells stained with anti-MYO7A. The border between hair cells can be clearly detected. Scale bar, 10 μm. (B) Sequential steps in reconstruction of the linearized voxel …
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Figure 2—source data 1
Source data for Figure 2D, E, G and Figure 2—figure supplement 1.
- https://doi.org/10.7554/eLife.40946.009
Figure 2—figure supplement 1
Manual counting of lost hair cells and auditory brainstem-evoked response (ABR) in mice with age-related and noise-induced hearing loss.
(A) Numbers of total IHCs and OHCs of C57BL/6J mice at PND 5, PND 60, and PND 360. (n = 4 (PND 5), 4 (PND 60), and 3 (PND 360). One-way ANOVA with Bonferroni's post hoc test, ***p < 0.001.) (B) ABR …
Figure 2—figure supplement 2
Three-dimensional presentation of hair cell distribution projected to X-Y and Y-Z planes.
Positions of Inner hair cells were sampled with regular intervals and the position data from different samples were overlaid. Hair cells from different samples were plotted as dots with different …
Figure 3 with 1 supplement
Spatial pattern of hair cell loss.
(A) Pseudo-color presentation of hair cell loss along the longitudinal axis of the organ of Corti (PND 30, 60, and 120 and noise exposure at PND 60). Each row represents a single cochlear sample. …
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Figure 3—source data 1
Source data for Figure 3D and Figure 3—figure supplement 1.
- https://doi.org/10.7554/eLife.40946.015
Figure 3—figure supplement 1
Longitudinal and radial distribution of hair cell loss in the organ of Corti.
(A) Pseudo-color presentation of the OHC loss frequency along the radial axis. Each row represents a single cochlear sample. The sample IDs were written on the left of the map. Note that the …
Figure 4 with 1 supplement
Model-based analysis of clustered cell loss.
(A) Evaluation of the extent of clustered cell loss by comparison with the extent of clustering based on a model of random cell loss. The extent of cell clustering in the experimental data was much …
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Figure 4—source data 1
Source data for Figure 4A, C, D and E.
- https://doi.org/10.7554/eLife.40946.018
Figure 4—figure supplement 1
Simulation analysis of clustered cell loss.
(A) A pair of ‘probability matrix’ (upper panel) and ‘cell matrix’ (lower panel). The intensities of elements in the ‘probability matrix’ indicate the probability of cell loss for the next round of …
Figure 5
Efficient mapping of subcellular pathology and multiple cellular components.
(A) Automated detection of areas with variable degrees of hair cell loss, combined with evaluation of subcellular pathology. All sites of hair cell loss (white squares) were selected, and changes in …
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Figure 5—source data 1
Source data for Figure 5A.
- https://doi.org/10.7554/eLife.40946.020
Appendix 2—figure 1
First the line passing through the centers of two images were generated, and the line passing through the center of the image overlap and perpendicular to the first line was created (the center line).
Distance of each pixel to the center line was defined as . The pixel that has the largest distance was selected and its value was normalized to be 1.
Appendix 2—Figure 2
Distribution of intensity peaks within the plane of the first and second principal components.
The first principal component matched the longitudinal axis, while the second matched the radial axis.
Appendix 2—Figure 3
The method of stitching two arcs.
Among the dots on the two arcs, the open dots were removed and the closed dots were fitted with a spline curve.
Appendix 2—figure 4
Extraction of cell candidates by template matching.
Left; Template matching with the small template image (upper left) was performed for individual x-y images within the image stack. Right; Detection and labeling of correlation peaks distributed …
Appendix 2—figure 5
Functions of the third and fourth machine learning models.
Cell candidates detected by the first and the second machine learning models were further categorized into three rows by the third model (cells marked by dots with different colors correspond to the …
Appendix 2—figure 6
Comparison of detection efficiencies between a standard image processing method and our method (Paired t-test, both p < 0.0001, n = 10 linearized whole cochlear images).
https://doi.org/10.7554/eLife.40946.030
Appendix 2—figure 7
Comparison of 3D watershed and our machine learning based method.
There are many duplicate count and false detection with 3D watershed method.
Appendix 2—figure 8
An example of fitting the increasing distance between IHCs and the modiolus to a smooth spiral.
The line with the minimum sum of squared errors was chosen to be the longitudinal axis of the cylindrical coordinate system.
Appendix 2—figure 9
An example of inner hair cell locations (black dots) viewed from the axial direction.
The angle () was measured from the line (red line) connecting the center of the spiral and the inner hair cell located at the end of the apex (green dot).
Appendix 2—figure 10
Evaluation of the extent of correlation between two curves in the plane of -.
Within this plane, the positions of spiral A and B were aligned. First, the shift in was adjusted (middle). Subsequently, the shift in was adjusted (right).
Appendix 2—figure 11
Representative examples of hair cell images where manual estimation of cell loss was difficult.
The number of lost cells is difficult to estimate when the size of cell-negative area increases in the basal turn (left). Disorganized rows of OHCs were frequently observed in the apical turn …
Appendix 2—figure 12
Procedures of obtaining parameters necessary for radial alignment (along y-axis) of cell centers.
Calculation of an averaged y position of the cell group (a red circle) and a vertical spread of the cell group (a red vertical line). These two parameters were calculated in the area (colored in …
Appendix 2—figure 13
Procedures of obtaining parameters necessary for longitudinal alignment (along x-axis) of cell centers.
Calculation of the horizontal distance between adjacent cells (red horizontal line). The nearest cell in the rectangular area (colored in gray) was selected for the calculation. The variables x0 and …
Tables
Table 1
Details of machine learning models (related to Figure 2).
https://doi.org/10.7554/eLife.40946.010| Models | Type‡ | Algorithm | Configuration | Use | Predictor |
|---|---|---|---|---|---|
| IHC* 1 | Binary | Gentle Boost | 300 classification trees | Reduction of noise | Area, barycentric coordinates, maximum correlation coefficients, maximum intensity, same data set of the nearest neighbor group and relative position of the nearest neighbor group |
| IHC* 2 | Binary | Random Forest | 300 classification trees | Detection of cells | Adding to the above, prediction score by ‘IHC* 1’ of itself and that of adjacent groups in six directions¶, relative position of the adjacent groups, and cropped image†† |
| OHC† 1 | Binary | Gentle Boost | 300 classification trees | Reduction of noise | Same as ‘IHC* 1’ |
| OHC† 2 | Binary | Random Forest | 300 classification trees | Detection of cells | Same as ‘IHC* 2’ |
| OHC† 3 | Multiclass | Convolutional Neural Network | From the input, convolutional layer (filter size 5, number 60), ReLU§ layer, fully connected layer, Softmax Layer (three classes), and output. | Estimation of belonging row | Cropped image (39 × 69 pixels in width and height) |
| OHC† 4 | Binary | Convolutional Neural Network | From the input, convolutional layer (filter size 5, number 60), ReLU§ layer, convolutional layer (filter size 5, number 20), ReLU§ layer, fully connected layer, Softmax Layer (two classes), and output. | Detection of cells in spaces | Cropped image (39 × 69 pixels in width and height) |
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*. IHC, inner hair cell.
†. OHC, outer hair cell.
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‡. Classification type.
§. Rectified Linear Unit.
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¶. Adjacent groups in direction of 0–60°, 60–120°, 120–180°, 180–240°, 240–300°, 300–360° with the y-axis as an initial line in the x-y plane.
††. Initial image size is 21 × 69 pixels in width and height. The image is resized in 7 × 23 then reshaped in 1 × 161.
Table 2
Detection efficiency of hair cells (related to Figure 2).*
https://doi.org/10.7554/eLife.40946.011Inner hair cell | |||||
|---|---|---|---|---|---|
| Detect. (n)† | Undetect. (n)‡ | Err. detect. (n)§ | Recover Rate¶ | Accuracy rate†† | |
| Our Method | 576 ± 33 | 13 ± 12 | 2 ± 2 | 0.979 ± 0.021 | 0.997 ± 0.003 |
| 3D Watershed | 424 ± 98 | 152 ± 82 | 110 ± 78 | 0.733 ± 0.149 | 0.818 ± 0.100 |
| Outer hair cell | |||||
| Detect. (n)† | Undetect. (n)‡ | Err. Detect. (n)§ | Recover Rate¶ | Accuracy rate†† | |
| Our Method‡‡ | 1989 ± 133 | 24 ± 13 | 6 ± 4 | 0.988 ± 0.006 | 0.997 ± 0.002 |
| Principle 1 Only§§ | 1925 ± 131 | 69 ± 41 | 16 ± 13 | 0.966 ± 0.021 | 0.992 ± 0.006 |
| 3D Watershed | 1493 ± 197 | 496 ± 111 | 760 ± 381 | 0.748 ± 0.064 | 0.682 ± 0.103 |
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*. Data from 10 samples (PND30: two sample, PND60: three sample, ACL: two sample, NCL: three sample). Data are expressed as means ± SD.
†. Detection number.
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‡. Undetected number.
§. Erroneous detection number.
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¶. Recover rate of manually identified hair cells by the automated detection algorithm (almost synonymous with recall).
††. The number of hair cells identified by both manual and automated detection divided by the number of hair cells identified by automated detection (almost synonymous with precision).
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‡‡.The proposed method in this study (principle 1 + principle 2).
§§. The method using the first half of the proposed method. For details please see ‘Principles of auto-detection by machine learning’ in Appendix 2.
Table 3
Inter-operator percent match in void space detection (related to Experimental procedures).
https://doi.org/10.7554/eLife.40946.012| Inter-operator percent match | Number of detected void space | ||||||
|---|---|---|---|---|---|---|---|
| Sample number | A¶-B¶ | B¶-C¶ | A¶-C¶ | Auto††-HC‡‡ | Both | Auto††-only | HC‡‡-only |
| 1* | 0.960 | 0.880 | 0.917 | 0.920 | 24 | 1 | 1 |
| 2† | 0.898 | 0.917 | 0.906 | 0.952 | 84 | 2 | 2 |
| 3‡ | 0.923 | 0.885 | 0.958 | 0.889 | 24 | 3 | 0 |
| 4§ | 0.923 | 0.882 | 0.846 | 0.926 | 50 | 3 | 1 |
| Overall | 0.916 | 0.898 | 0.897 | 0.931 | 182 | 9 | 4 |
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*. Sample 1, two months old, total loss rate of OHCs: 1.7%.
†. Sample 2, two months old with noise exposure, total loss rate of OHCs: 8.1%.
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‡. Sample 3, one month old, total loss rate of OHCs: 2.2%.
§. Sample 4, four months old, total loss rate of OHCs: 4.2%.
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¶. Skilled human operators (A, B, and C).
††. Auto, automated OHC loss counting program.
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‡‡. HC, human consensus.
Key resources table
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
|---|---|---|---|---|
| Genetic reagent (M. musculus) | C57BL/6J | Sankyo Lab (JAPAN) | PRID:MGI:5658686 | |
| Genetic reagent (M. musculus) | CBA/Ca | Sankyo Lab (JAPAN) | PRID:MGI:2159826 | |
| Genetic reagent (M. musculus) | Thy1-GFP line-M | Jackson Lab | PRID:MGI:3766828 | |
| Genetic reagent (M. musculus) | GO-Ateam | PMID: 19720993 | Dr. M Yamamoto (Kyoto University, Japan) | |
| Antibody | Rabbit polyclonal anti- Myosin VIIa | Proteus Biosciences | cat# 25–6790 PRID:AB_10013626 | IHC (1:100) |
| Antibody | Mouse monoclonal anti-Neurofilament 200 | SIGMA | cat# N5389 PRID:AB_260781 | IHC (1:100) |
| Antibody | Mouse monoclonal anti-SOX-2 | EMD Millipore | cat# MAB4343 PRID:AB_827493 | IHC (1:200) |
| Antibody | Mouse monoclonal anti-CTBP2 | BD Bioscience | cat# 612044 PRID:AB_399431 | IHC (1:100) |
| Antibody | Guinea pig polyclonal anti-VGLUT3 | PMID: 20034056 | IHC (1:500), Dr. H Hioki (Juntendo University, Japan) | |
| Antibody | Alexa Fluor 488-conjugated mouse monoclonal anti-VE cadherin | eBioscience | cat# 16-1441-81 PRID:AB_15604224 | IHC (1:500) |
| Chemical compound, drug | Rhodamine phalloidin | Invitrogen | cat# R415 | IHC (1:500) |
| Chemical compound, drug | Triton X-100 | Nakalai-tesque | cat# 12967–45 | |
| Chemical compound, drug | Urea | SIGMA | cat# U0631-1KG | |
| Chemical compound, drug | N,N,N',N'-Tetrakis (2-eydroxypropyl) ethylendiamine | TCI | cat# T0781 | |
| Chemical compound, drug | D-sucrose | Wako | cat# 196–00015 | |
| Chemical compound, drug | 2,2',2''-nitrilotriethanol | Wako | cat# 145–05605 | |
| Chemical compound, drug | Dichloromethane | SIGMA | cat# 270997–100 ML | |
| Chemical compound, drug | Tetrahydrofuran | SIGMA | cat# 186562–100 ML | |
| Chemical compound, drug | Dibenzyl Ether | Wako | cat# 022–01466 | |
| Chemical compound, drug | Methanol | Wako | cat# 132–06471 | |
| Chemical compound, drug | D-glucose | SIGMA | cat# G8270-100G | |
| Chemical compound, drug | D-sorbitol | SIGMA | cat# S1816-1KG | |
| Chemical compound, drug | Thiodiethanol | Wako | cat# 205–00936 | |
| Chemical compound, drug | Acrylamide | Wako | cat# 011–08015 | |
| Chemical compound, drug | Bis-acrylamide | SIGMA | cat# 146072–100G | |
| Chemical compound, drug | VA-044 initiator | Wako | cat# 225–02111 | |
| Chemical compound, drug | Sodium dodecyl sulfate | TCI | cat# I0352 | |
| Chemical compound, drug | FocusClear | CelExplorer Labs | cat# F101-KIT | |
| Chemical compound, drug | Glycerol | Wako | cat# 075–00616 | |
| Chemical compound, drug | Dimethyl sulfoxide | Wako | cat# 043–07216 | |
| Chemical compound, drug | N-acetyl-L-hydroxyproline | TCI | cat# A2265 | |
| Chemical compound, drug | Methyl-β-cyclodextrin | TCI | cat# M1356 | |
| Chemical compound, drug | γ-cyclodextrin | TCI | cat# C0869 | |
| Chemical compound, drug | Tween-20 | Wako | cat# 167–11515 | |
| Software, algorithm | ImageJ | NIH | PRID: SCR_003070 | |
| Software, algorithm | GraphPad Prism 6 | GraphPad Software | PRID: SCR_002798 | |
| Software, algorithm | MATLAB | MathWorks | PRID: SCR_001622 | |
| Software, algorithm | Microsoft Excel | Microsoft | PRID: SCR_016137 | |
| Software, algorithm | Adobe Illustrator | Adobe | PRID: SCR_010279 | |
| Software, algorithm | Signal processor | Nihon Kouden | Neuropack MEB2208 | |
| Other | MATLAB codes | This paper | https://github.com/okabe-lab/cochlea-analyzer | |
| Other | 25x water- immersion objective lens | Nikon | N25X-APO-MP | |
| Other | 25x water- immersion objective lens | Olympus | XPLN25XWMP | |
| Other | Sound speaker | TOA | HDF-261–8 | |
| Other | Power amplifier | TOA | IP-600D | |
| Other | Condenser microphone | RION | UC-31 and UN14 | |
| Other | Sound calibrator | RION | NC-74 | |
| Other | Noise generator | RION | AA-61B | |
| Other | Dual channel programmable filter | NF corporation | 3624 |
Table 4
Number of training and test dataset, and performance evaluation of machine learning models (related to Figure 2).
https://doi.org/10.7554/eLife.40946.021| Model | Training | Test | Recall | Precision | F score | ||
|---|---|---|---|---|---|---|---|
| Total (n) | Positive labels (n) | Total (n) | Positive labels (n) | ||||
| IHC* 1 | 607,954 | 5906 | 578,851 | 5741 | 0.961 | 0.941 | 0.951 |
| IHC* 2 | 37,576 | 11,977 | 18,104 | 5753 | 0.977 | 0.986 | 0.981 |
| OHC† 1 | 1,112,659 | 20,576 | 1,099,519 | 19,959 | 0.978 | 0.914 | 0.945 |
| OHC† 2 | 28,702 | 20,576 | 27,185 | 19,959 | 0.959 | 0.979 | 0.969 |
| OHC† 3 | 20,416 | Row1: 6706 Row2: 6745 Row3: 6965 | 19,594 | Row1: 6421 Row2: 6450 Row3: 6723 | 0.993‡ | 0.993‡ | 0.993‡ |
| OHC† 4 | 4114 | 1365 | 2990 | 905 | 0.920 | 0.946 | 0.933 |
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*. IHC, inner hair cell.
†. OHC, outer hair cell.
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‡. Calculated by micro-average of recall and precision (Sokolova M and Lapalme G, 2009)
Additional files
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Transparent reporting form
- https://doi.org/10.7554/eLife.40946.022
