Protocol for Supervised Automation of Cell Counting in Confocal Microscopic Cochlear Imaging Datasets Using Macro

doi:10.21203/rs.3.pex-2212/v1

Method Article

Protocol for Supervised Automation of Cell Counting in Confocal Microscopic Cochlear Imaging Datasets Using Macro

https://doi.org/10.21203/rs.3.pex-2212/v1

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Regular image analysis derived from confocal microscopic data of biological tissue samples with the Imaris software package involves a significant amount of repetition on the part of the researchers for each image. We developed a supervised automation protocol in our lab to execute our everyday cell and spot counting tasks (e.g., neuron, nucleus, macrophages) on images obtained from mouse cochlear samples. The proposed semi-automated protocol can be reproduced and customized by scientists working on basic and experimental neuro-immunology and neuroanatomy research and on other disciplines as well. Though it has some limitations and challenges, the protocol can be further enhanced with the integration of machine learning. We believe our developed protocol may enlighten the scientific community on the controlled automation of image analysis tasks, may translate to a substantial reduction of the manual labor of the researchers, and can increase laboratory efficiency and productivity.

confocal microscopy

cochlea

macro

automation

surface

spot

mask

imaris

Confocal microscopy (CM) has emerged as a crucial tool for studying cochlear microstructure due to its capacity to resolve extremely minute structures, such as synapses of the auditory nerve onto the hair cells of the organ of Corti or neurons of the spiral ganglion, as well as the variety of immunocytochemical staining methods available to resolve numerous biomolecules in detail. [1] Compared to other biomedical imaging modalities available for experimental neuroscience research, such as optical coherence tomography (OCT) and ultrasound, CM has a higher resolution with a smaller penetration depth. With advancements in image acquisition algorithms to render three-dimensional (3D) high- resolution images of biological samples se and, combination of modern-day fluorescent markers and image analysis software, CM has become a critical tool in the scientists’ armamentarium for basic and experimental neuroimmunology and neuroanatomy research. [2-4]

Applications of commercially available software (e.g., Imaris, Zeiss ZEN, Nikon NIS, HCImage) and open-source software (e.g., ImageJ, Cellprofiler/Cell Analyst, Neuronstudio) for CM image analysis vary from scientist’s choice, preference, expertise, and institutional availability. However, image analysis with CM datasets using Imaris, a software developed by Oxford Instruments and one of the most commonly used software packages nowadays for CM image analysis, requires time-consuming and laborious repetitive processes for a researcher to execute in every day basic image analysis tasks (e.g., creating surfaces, masking, and spot counting). We report here a protocol for using macros in CM image analysis with Imaris software for mouse cochlear samples.

The supervised automation protocol can be generalized to other biological imaging datasets in the Imaris software as well. Though it has some limitations, our developed macro reduces repetitive tasks and thereby saves a significant amount of time compared to the manual process. The macro codes do not require prior programming knowledge and therefore can be reproduced by most scientists. Finally, we anticipate that our reproducible macros may enhance the efficiency and scientific productivity of the laboratories dealing with three-dimensional confocal imaging datasets of biological samples.

For a better understanding of the protocol described, we recommend a basic understanding of the anatomy of the mammalian cochlea. [Figure 1] Figure 2 shows one of our confocal microscopy raw image data sets (unsegmented) of the mouse cochlea. For the manual processing, the requirements are a suitable computer with a Windows or Mac operating system (64-bit) and Imaris 9.9.0 software. We need "Macro Scheduler 15" for the semi-automated protocol. [Figure 3]

Protocol for the Manual Processing

A. Running Imaris File Converter

1. Use Imaris File Converter Application to convert images before use.

2. Drag the image files to the “Input” area.

3. Choose to “Output” the file to the “Same Folder as Input File” or “Specific Folder” found by clicking “Browse”.

4. Click “Start All”.

B. Opening Image Files

1. Open the Imaris Application.

2. In the toolbar, choose “Arena” > “Observe Folder” and choose the folder containing the Imaris file converted images and click “Select Folder”.

3. In the “Arena” window, click the image to be viewed.

4. In the “Surpass” window, increase the “Rendering Quality” to the highest setting (towards the right).

5. In the toolbar, choose “Image Processing” > “Free Rotate” and change the “Angle” setting to 75º and click "OK". (He said 180?)

In the toolbar, choose “Edit” > “Show Display Adjustment” to display the channel selections. The intensity of each channel can be changed by moving the histogram ranges.

C. Making Surfaces

1. In the “Scene” window, choose the Surfaces tab to make a surface for every area of interest.

2. Rename each surface to the corresponding identity of the region of interest by using the mouse to right click the surface text. The resolution of surfaces are needed to be set at maximum.

3. Click the “Skip automatic creation, edit manually” button.

4. Choose the Draw tab click the “Contour” button then click the “Draw” button.

5. Using the DIC channel, draw the outline of the region of interest by clicking the border. Remove all other channels by clicking the respective channels in the “Display Adjustment” window.

6. Under Selection, click the “Copy” button. Change the “Slice Position” to the last slice and click the “Create Surface” button.

7. Choose the Edit tab. Under “Edit”, click the “Mask selection..” button. In the Mask Channel Window, change the channel under “Channel Selection”, check “Duplicate channel before applying mask”, then click “OK”. Repeat for all channels, always masking the original channels.

8. Choose the Statistics tab, click the “Selection” button, under the drop down “Specific Values” list choose “Volume”and record the value.

Here we used following surfaces for our cochlear image analysis task: ST-Scala Tympani (Basal turn), GB-Spiral Ganglion (Basal turn), LWB-Lateral Wall (Basal turn), GM -Spiral Ganglion (Middle Turn), LWM-Lateral Wall (Middle Turn), GA -Spiral Ganglion (Apex/Apical Turn), LWA -Lateral Wall (Apex/Apical Turn) and MOD-Modiolus.

D. Creating and Counting Spots

1. In the Display Adjustment” window, unclick all channels except the masked channel to be analyzed.

2. Choose the Spots tab, then click the blue forward arrow button to change the page.

3. On page 2/4, under the drop down “Source Channel” list choose the masked channel to be analyzed.

4. On page 2/4, change the “Estimated XY Diameter” to 7.58for macrophages and spiral ganglion neurons, or 6.00 for the nuclei. Click the blue forward arrow button.

Protocol for the Semi-automated Processing

Step 1: Creating the Surfaces

After selecting the first macro, “Run now” is clicked. The macro will automatically create eight desired surfaces. It will then rename the surfaces and set the resolution to the maximum. The output image is depicted in Figure 4. Once it is finished, we need to draw the surface manually (as described earlier).

Figure 5 shows manually drawn outline of four surfaces: scala tympani of base, spiral ganglion of base, lateral wall of base and modiolus of a cochlear section.

Step 2: Masking the Surface

1. Uncheck “Show/Hide all Channels” option.

2. Run the macro “Making mask”. It will create the masks.

3. Run the macro “Naming the Masks”, it will rename the created masks as per instructed. (Figure 6A). If needed, volumes or any other necessary statistics can be recorded at this step.

Step 3: Creating and counting the spots

1. Uncheck “Volume” option.

2. Run the macros for spotcreation and renaming. We divided it into two parts for smooth running it on the computer. The output spots for nucleus, neuron and macrophages are shown in Figure 6B.

3. Count the spots manually with desired standard deviation from estimated threshold. (Figure 6C). The count then should be recorded. (Figure 6D)

Figure 7 demonstrates a counting of neurons in spiral ganglion of base with thresholding.

Discussion

Our developed semi-automated macro tool can significantly reduce the number of repetitive clicks required in basic image analysis. For instance, creating and renaming a single surface with maximum resolution requires a minimum of six clicks for one image. For the creation of eight surfaces, we will need a minimum of forty-eight clicks on the computer before a researcher can draw the surface manually. This tedious process can be minimized to only one click through the developed macro. For a basic cell counting task of all the needed surfaces of each image, only three tasks should be done manually by a researcher: drawing the surface, selecting the desired range for spots, and recording them. Thus, it can significantly reduce the workload of the researchers.

However, there are some limitations. Our developed macro lacks ideal programming and intelligence. As a result, a minimal change to the window or the arena in the Imaris software disables the macro's functionality. So, before assigning each task, an operator should set the full window in a pre-defined setup. Also, missing any step of the developed protocol and changing its predefined setup in any window can result in errors while running the macros. Consequently, it needs prior customizations for each computer and window and at least one cycle of ideal manual completion of the analysis process to record the macros before running them. Future integration of programming and machine learning may overcome this caveat.

Another pitfall is that our developed macro does not significantly reduce the processing time required for each image analysis. Though the time can be reduced through editing the processing time in the recorded macro code, on occasion it may not run properly due to the computer operating system’s limitation to process such a large amount of data in such a period of time. This may be accounted for when determining the operating system the program is utilized on. Nevertheless, it enhances the efficiency and productivity of the lab in the sense that a researcher can do other sophisticated tasks requiring human interaction while running the macro in the computer. After finishing the task, the researcher can return to the computer to execute the next manual step of the image analysis task and run the next macro. The complete automation of the image analysis task may require integration of machine learning for each sample type. Again, an advantage of our protocol is its flexibility, editing and reproducibility of the method so that researchers with no prior programming knowledge can reproduce, record and run their own macro codes for their day-to-day image analysis tasks significantly decreasing time requirements for repetitive tasks. Lastly, the proposed protocol needs further development in sophistication and maneuverability.

Conclusions

Implementing macros and following our proposed semi-automated protocol for confocal microscopy image analysis can significantly decrease the amount of manual labor performed by a researcher in regular image analysis tasks for basic and applied experimental biological research. Though the method lacks sophistication of intelligence and maneuverability, it still enhances a researcher’s efficiency. As the method does not require previous programming knowledge, it can be reproduced by researchers working on cochlear samples or any other biological tissues from different disciplines. Future research should aim to integrate machine learning and increase the flexibility of the method in order to further automate the process. Finally, the supervised semi-automated protocol can augment the efficiency of labs dealing with confocal microscopy image analysis of biological samples and can be further synergized with the integration of machine learning.

[1] MacDonald GH, Rubel EW. Three-dimensional imaging of the intact mouse cochlea by fluorescent laser scanning confocal microscopy. Hear Res. 2008 Sep;243(1-2):1-10. doi: 10.1016/j.heares.2008.05.009. Epub 2008 Jun 6. PMID: 18573326; PMCID: PMC2566306.

[2] Neerken, S., Lucassen, G.W., Nuijs, T.A.M., Lenderink, E., Hendriks, R.F.M. (2004). Comparison of Confocal Laser Scanning Microscopy and Optical Coherence Tomography. In: Tuchin, V.V. (eds) Handbook of Coherent Domain Optical Methods. Springer, New York, NY. https://doi.org/10.1007/0-387-29989-0_22

[3] Ibne Mokbul M. Optical Coherence Tomography: Basic Concepts and Applications in Neuroscience Research. J Med Eng. 2017;2017:3409327. doi: 10.1155/2017/3409327. Epub 2017 Oct 29. PMID: 29214158; PMCID: PMC5682075.

[4] Perego C, Fumagalli S, De Simoni MG. Three-dimensional confocal analysis of microglia/macrophage markers of polarization in experimental brain injury. J Vis Exp. 2013 Sep 4;(79):50605. doi: 10.3791/50605. PMID: 24056862; PMCID: PMC3857388.

[5] Nyberg S, Abbott NJ, Shi X, Steyger PS, Dabdoub A. Delivery of therapeutics to the inner ear: The challenge of the blood-labyrinth barrier. Sci Transl Med. 2019 Mar 6;11(482):eaao0935. doi: 10.1126/scitranslmed.aao0935. PMID: 30842313; PMCID: PMC6488020.

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Protocol for Supervised Automation of Cell Counting in Confocal Microscopic Cochlear Imaging Datasets Using Macro

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Protocol for Supervised Automation of Cell Counting in Confocal Microscopic Cochlear Imaging Datasets Using Macro

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