Name: artificial intelligence approaches to assessing primary cilia
Uploaded: 2021-05-01T14:00:00
Duration: 8 min 58 s
Description: Watch this Scientific Journal Video about Artificial Intelligence Approaches to Assessing Primary Cilia at JoVE.com

This work was funded by National Institute of Diabetes and Digestive and Kidney Diseases R01 DK114008 to NFB and the American Heart Association Fellowship Grant #18PRE34020122 to RB. We thank Rich Gruskin General Manager of Nikon Software, Melissa Bentley, Courtney Haycraft and Teresa Mastracci for insightful comments on the manuscript.

1. Acquire raw images
<ol>
	<li>Fix and immunolabel samples as needed20.
	<ol>
		<li>Image cilia using a confocal microscope at maximum bit depth using the same pixel size with Nyquist resolution.</li>
		<li>Export images as monochrome Tagged Image Format (.tif) files. 
		&#8203;NOTE: This protocol outlines how to use the Ai module specifically within NIS Elements software. If images were acquired as .nd2 files, exporting images as .tif files is not necessary and the user can proceed directly to step 2.3. If images were acquired on a different system, a NIS Elements license can be purchased separately and .tif files can be converted as outlined in the next steps.</li>
	</ol>
	</li>
</ol>
2. Train Ai to identify cilia
<ol>
	<li>Open the training dataset.
	<ol>
		<li>Select approximately 50 sample images with at least one cilium per frame to train the software and copy them in a single folder. This folder is used to direct the software when opening the images. Open these 50 frames within a single ND2 document with at least one cilium&#160;per frame. Select File &#62;&#160;Import/Export &#62;&#160;Create ND File from File Sequence.</li>
		<li>Select the folder containing the training dataset. This will open the list of files in the center of the dialogue window. Define manually the organization of the files using at least one option in the drop-down menu above. The options are multipoint (for multiple maximum projection files), Z series (for a z stack image), time (for a time-lapse image), and wavelength (for files from multiple channels).</li>
		<li>Enter the corresponding numerical values under each selected option. Select None wherever options are not selected. Click Convert to open the ND document.</li>
	</ol>
	</li>
	<li>Calibrate images.
	<ol>
		<li>Enter the pixel size in the lower left-hand corner of the image: Right click Uncalibrated &#62;&#160;Calibrate Document &#62;&#160;Pixel Size.</li>
	</ol>
	</li>
	<li>Identify cilia.
	<ol>
		<li>Hand-identify the cilia by precisely tracing individual ciliary structures on all the opened frames using either AutoDetect&#160;or Draw Object in the Binary Toolbar. This will draw binary masks on the objects of interest. These binaries will serve as sample objects for training the software to identify cilia on pixel-based characteristics in future experimental image analysis. Select View &#62;&#160;Analysis Controls &#62;&#160;Binary Toolbar &#62; Draw Object. 
		NOTE: Remove any frame that does not have any binaries as the software will not begin training unless it is able to detect binaries on all of the opened frames.</li>
	</ol>
	</li>
	<li>Train Ai.
	<ol>
		<li>Begin training the software. This will open the train Segment.ai box. Select NIS.ai &#62;&#160;Train Segment.ai.</li>
		<li>In the Train Segment.ai box, select the source channel to be used for training. If files from multiple channels are open, select only one channel as the source channel. Then select the appropriate ground truth binaries on which to train the Ai. Finally select the number of iterations needed to train the Ai depending on the size and distribution of the binaries. 
		NOTE: If the binaries are easily detectable from the surroundings and well distributed throughout the image, the software may need less than 1000 iterations to get trained to identify the images. If the images have a low signal-to-noise ratio, it is ideal to run at least 1000 iterations while training to enable the Ai to identify cilia in test samples with high confidence.</li>
		<li>Select the destination folder to save the trained Ai file (.sai) and click Train to train the software. The software will now proceed to train itself to identify cilia based on the traced binaries. This process takes several hours. 
		NOTE: When training, the software will display a graph showing training loss. The graph will initially show a spike before tapering to ideally around 1% loss where it plateaus for the rest of the training. Save the graph for future reference by checking the box for Save Graph Screenshot on the Train Segment.ai box (Supplemental Figure 1).</li>
		<li>If further refinement to the training is required, then continue training on the same dataset. Alternatively, train on a new dataset with the exact same parameters. It is not advisable to train an already trained Ai on a new dataset with different parameters or different objects of interest. Select Train segment.ai &#62;&#160;Continue Training On &#62; Select Trained Ai File.</li>
	</ol>
	</li>
</ol>
3. Identify cilia using trained Ai
<ol>
	<li>Open the experimental dataset.
	<ol>
		<li>Open the experimental confocal images of cilia in the software by converting the sample .tif files to .nd2 files, similar to step 2.1. Select File &#62; Import/Export &#62; Create ND File from File Sequence. 
		NOTE: The images should be of the same pixel size as those used for training the Ai. If images are already in ND2 format, skip to step 3.3.</li>
	</ol>
	</li>
	<li>Calibrate images.
	<ol>
		<li>Enter the pixel size in the lower left-hand corner of the image. Right click Uncalibrated &#62; Calibrate Document &#62; Pixel Size.</li>
	</ol>
	</li>
	<li>Run the trained Ai on the opened files.
	<ol>
		<li>Begin identifying the cilia using Ai. The software will now draw binaries on the cilia based on the training it received in the prior step. This process will take a few seconds. Select NIS.ai &#62; Segment.ai. 
		NOTE: The software will prompt to select the channel if multiple channels are open. Channels are listed by their respective names here. If not, the box marked &#39;Mono&#39; will be automatically selected.</li>
	</ol>
	</li>
	<li>Check images for misidentified binaries.
	<ol>
		<li>Once the Ai has identified cilia and drawn binaries, check the images for any mistakenly identified objects. If desired, manually delete any misidentified binaries. Select View &#62; Analysis Controls &#62; Binary Toolbar &#62; Delete Object.</li>
	</ol>
	</li>
</ol>
4. Measuring cilia length and intensity
<ol>
	<li>Create a new General Analysis 3 (GA3) recipe.
	<ol>
		<li>Now that the cilia have been identified and segmented, proceed to analyze different parameters of cilia such as lengths and intensities using GA3 tool. This will open a new window with a blank space in the center where the analysis will be defined. Select Image &#62; New GA3 Recipe. </li>
	</ol>
	</li>
	<li>Select the binaries to analyze.
	<ol>
		<li>Since the cilia are already segmented using Segment.ai, GA3 will automatically detect the binaries appropriately labelled according to the Ai and include the node. Select &#39;Binaries &#62; Auto Detect_AI&#39; or &#39;Binaries &#62; Draw Object_AI&#39;.</li>
	</ol>
	</li>
	<li>Select the channels required for analysis. GA3 will also automatically detect the channels in the images and display their tabs under Channels.</li>
	<li>Remove objects touching the border of the frame.
	<ol>
		<li>Since the Ai will segment all cilia like objects in the frame, it will also detect incomplete cilia along the edges of the frame. These objects can either be removed manually in step 3.4 or they can be removed automatically in GA3. Select Binary processing &#62;&#160;Remove objects &#62; Touching Borders.</li>
	</ol>
	</li>
	<li>Select parameters to measure cilia.
	<ol>
		<li>Drag and drop the parameters to measure such as cilia length (Length) and intensities (Sum Object Intensity). Connect the nodes to the appropriate binary node (connection A) and channel nodes (connection B). Hover over the node connection for a tooltip to show which connection the node belongs to. Select Measurement &#62;&#160;Object Size &#62;&#160;Length and Measurement &#62;&#160;Object Intensity &#62;&#160;Sum Obj Intensity. 
		NOTE: The node Length connects only to the binary node, whereas Sum Obj Intensity connects to both the binary and the channel nodes.</li>
	</ol>
	</li>
	<li>Append the measurements in a single table.
	<ol>
		<li>Combine all the measurements in a single output table by dragging and dropping the node Append Column&#160;to the analysis flow chart and connect it to the measurement nodes, Length and Sum Obj Intensity. Select Data Management &#62;&#160;Basic &#62;&#160;Append Column.</li>
	</ol>
	</li>
	<li>Measure cilia.
	<ol>
		<li>Measure cilia by clicking Run. This process takes a few moments to measure all the cilia in the experimental images. The lengths and intensities will appear in a new Analysis Results window. 
		&#8203;NOTE: The table can sometimes include data from masks that Ai recognized as cilia but were too small to be detected by the human eye and eliminated in step 3.4. These objects can be removed from the data set by using a filter before statistical analysis. Here, a filter of 1 &#181;m was used for in vitro cilia length measurements in Figure 2 and 2 &#181;m for in vivo cilia. This can be done prior to exporting data using the pathway below. Select Analysis Results window &#62;&#160;Define filter &#62;&#160;Enter Value &#62;&#160;Use Filter.</li>
	</ol>
	</li>
	<li>Export data for statistical analysis.</li>
</ol>
5. Colocalization studies
NOTE: Colocalization analysis can be included in the same GA3 recipe used for measurements of cilia length and intensity analysis. If using the same recipe, open files as described below and measure the lengths and intensities of both channels along with the colocalization coefficients in the same analysis pipeline.
<ol>
	<li>Open the experimental dataset.
	<ol>
		<li>Open the experimental confocal images of cilia in the software by converting the sample .tif files to .nd2 files. Select File &#62;&#160;Import/Export &#62;&#160;Create ND File from File Sequence.</li>
		<li>In the pop-up window, select the 16-bit depth monochrome files from all channels of interest from the window explorer located in the first column of the pop-up window. Select Multipoint or Z Series from the first drop down menu and enter a value corresponding to the total number of images or stacks, respectively.</li>
		<li>In the second drop down box, select Wavelength and change the value to the total number of channels in the folder. The software will automatically unlock a Wavelength selection window located at the bottom right end of the pop-up window. Use the Color drop down menu to select the color of each channel. Provide each channel with a different name under the Name column. Once all information is updated, click Convert. The software will automatically generate an All image file overlayed with all the individual images from all the selected channels.</li>
	</ol>
	</li>
	<li>Calibrate images.
	<ol>
		<li>Enter the pixel size in the lower left-hand corner of the image. Right click Uncalibrated &#62;&#160;Calibrate Document &#62;&#160;Pixel Size.</li>
	</ol>
	</li>
	<li>Run the trained Ai on the first channel.
	<ol>
		<li>Begin identifying the cilia on one of the opened channels (e.g., ACIII; Figure 5A) using Ai. The software will now draw binaries on ACIII labelled cilia based on the training it received for this channel. This process will take a few seconds. Select NIS.ai &#62; Segment.ai &#62;&#160;Source channels &#62;&#160;ACIII.</li>
	</ol>
	</li>
	<li>Run the trained Ai on the second channel.
	<ol>
		<li>Begin identifying the cilia on the other opened channel (e.g., MCHR1; Figure 5B) using Ai. The software will now draw binaries on MCHR1 labelled cilia based on the training it received for this channel. This process will take a few moments. Select NIS.ai &#62;&#160;Segment.ai &#62;&#160;Source channels &#62;&#160;MCHR1.</li>
	</ol>
	</li>
	<li>Check images for misidentified binaries.
	<ol>
		<li>Once the Ai has identified cilia and drawn binaries, check the images for any misidentified objects. Manually delete any misidentified binaries if necessary. Select View &#62;&#160;Analysis Controls &#62;&#160;Binary Toolbar &#62;&#160;Delete Object.</li>
	</ol>
	</li>
	<li>Create new GA3 recipe.
	<ol>
		<li>Now that the cilia have been identified and segmented, proceed to the colocalization analysis using the GA3 tool. This will open a new window with a blank space in the center where the analysis will be defined. A window with all identified binaries and channels will be generated. Verify that all desired channels and binaries required for analysis are present and selected. Select Image &#62;&#160;New GA3 Recipe.</li>
	</ol>
	</li>
	<li>Remove objects touching the border of the frame.
	<ol>
		<li>Since the Ai will segment all cilia-like objects in the frame, it will also detect incomplete cilia along the edges of the frame. These objects can either be removed manually in step 5.5 or they can be removed automatically in GA3. Select Binary processing &#62;&#160;Remove objects &#62;&#160;Touching Borders.</li>
	</ol>
	</li>
	<li>Set up the colocalization pathway in GA3.
	<ol>
		<li>To measure overlap of the two channels within cilia, use Mander&#39;s Coefficient Correlation. Drag and drop the Manders Coefficient node into the blank space of the GA3 recipe and connect it to the appropriate binary and channels. Here, &#39;connection A&#39; connects with the ACIII binary, &#39;connection B&#39; with the MCHR1 channel and &#39;connection C&#39; with the ACIII channel to determine the overlap of MCHR1 within ACIII binary. Select Measurement &#62; Object Ratiometry &#62; Manders Coefficient. 
		NOTE: The software allows for measuring colocalization using Pearson Coefficient Correlation using the same steps as described in this protocol40.</li>
	</ol>
	</li>
	<li>Append the measurements in a single table.
	<ol>
		<li>Combine all the measurements in a single output table. Select Data Management &#62;&#160;Basic &#62;&#160;Append Column.</li>
	</ol>
	</li>
	<li>Measure colocalization.
	<ol>
		<li>Measure cilia by clicking Run. This process takes a few moments to measure all the cilia in the experimental images. The data will appear in a new Analysis Results window.</li>
	</ol>
	</li>
	<li>Export data for statistical analysis.</li>
</ol>

Co-author Wesley Lewis is an employee of Nikon. There are no financial disclosures.

Length and intensity measurements are common ways primary cilia are analyzed, however, there is not a standardized conventional method used in the field. Identification and quantification of primary cilia using software such as ImageJ is time consuming and prone to user bias and error. This makes it difficult to accurately analyze large data sets. Here we show that using an Ai program can overcome many of these challenges making high throughput analysis of primary cilia achievable. Herein we describe the procedure for training an Ai-based application to recognize primary cilia and outline the steps required to analyze length and intensity.
While the initial training of the Ai to recognize cilia requires significant time from the user, once completed it can be used on any data set acquired with the same parameters. The binary mask generated by the Ai is modifiable such that any errors can be corrected. However, errors in cilia identification should signal the user that the Ai needs to be further trained with additional images. One major advantage of this method is that the Ai can be trained to recognize cilia in different sample types in both 2D and 3D. Previous analysis methods generated within labs have various limitations including requiring manual thresholding for identification and problems identifying cilia imaged from tissue sections where the cell density is high36,46,47. These methods are also specialized for cilia analysis whereas analysis using NIS Elements software can evaluate several aspects of the images simultaneously. Because the Ai described here is part of the NIS Elements software package, images acquired using a Nikon microscope can be easily continued through to analysis. However, imaging with Nikon is not required for use of this method. Regardless of the captured raw data file format, &#34;.tif&#34; files can be opened by NIS Elements to use in the Ai.
This Ai application within NIS Elements is widely available and possibly already part of image analysis software in use by labs studying primary cilia. With the prevalence of Ai technology expanding, other imaging software may expand their analysis options to include a similar Ai module. Applying Ai analysis to cilia identification can be used for several different aspects of cilia analysis. While we outlined methods for a few simple analyses such as length (Figure 2 and 3), intensity (Figure 4) and colocalization (Figure 5) more sophisticated analysis can be added to the GA3 analysis workflow as in Figure 6. For example, instead of measuring the intensity of a complete cilium, differences in intensity within a sub-region of a cilium may be of interest to assess sub-ciliary localization. Differences in intensity within a subregion of a cilium could indicate the protein is accumulating at the tip or the base of the cilium, such as how Gli proteins are enriched at the tip of cilia48. In addition, this Ai application can be used to readily identify differences between genotypes or treatment groups. While our lab primarily uses this method to analyze cilia imaged from brain sections or neuronal cultures, it can be applied to images acquired from various cell lines or other tissue types. The flexibility of sample type that this application can be used on makes this method of analysis valuable for many different groups studying primary cilia or any discrete organelle that is being assessed such as mitochondria, nucleus, or ER.

Primary cilia are sensory organelles protruding from most mammalian cell types1,2,3,4. They are generally solitary appendages critical for coordinating diverse cell signaling pathways by integrating extracellular signals5,6,7. Primary cilia play important roles during embryonic development and adult tissue homeostasis, and disruption of their function or morphology is associated with several genetic disorders, which are collectively called ciliopathies. Due to the near ubiquitous nature of cilia, ciliopathies are associated with a wide range of clinical features that can impact all organ systems8,9,10,11,12. In animal models of ciliopathies, loss of ciliary-structure or signaling capacity manifests in several clinically relevant phenotypes including hyperphagia-associated obesity3,13,14,15. In many model systems, cilia length changes have been shown to impact their signaling capacity and functions16,17,18,19. However, there are several time consuming and technical challenges associated with accurately and reproducibly assessing cilia length and composition.
The adult mammalian central nervous system (CNS) is one biological context that has posed a challenge for understanding cilia morphology and function. While it appears that neurons and cells throughout the CNS possess cilia, due to the limited tools and abilities to observe and analyze these cilia an understanding of their functions remains elusive20. For example, the prototypical cilia marker, acetylated &#945;-tubulin, does not label neuronal cilia20. The difficulty of studying these cilia was partly resolved with the discovery of several G protein-coupled receptors (GPCR), signaling machinery and membrane associated proteins that are enriched on the membrane of neuronal cilia21,22. All of these straightforward basic observations hint at the importance and diversity of CNS cilia, which thus far appears unparalleled by other tissues. For example, variation in cilia length and GPCR localization can be observed throughout the brain, with lengths in certain neuronal nuclei being different when compared with other nuclei19,23. Similarly, their GPCR content and signaling machinery compliment show diversity based on neuroanatomical location and neuronal type2,24,25,26,27,28,29. These simple observations demonstrate that mammalian CNS cilia length and composition are tightly regulated, just as in model organisms, like Chlamydomonas reinhardtii, but the impact of these length differences on cilia function, signaling and ultimately behavior remains unclear16,30,31,32.
Accurately measuring cilia length and composition proves to be a technical challenge prone to user error and irreproducibility. Currently cilia in vivo and in vitro are most often identified using immunofluorescent approaches that label ciliary proteins or cilia-enriched fluorescent reporter alleles33,34,35. The lengths of these fluorescently tagged cilia are then measured from a 2-dimensional (2D) image using line measurement tools in image analysis programs such as ImageJ36. This process is not only tedious and labor intensive but also prone to bias and error. These same obstacles arise when measuring cilia intensities, which help indicate changes in cilia structure37. To minimize the inconsistencies in these types of image analyses, artificial intelligence (Ai) programs are becoming more prevalent and affordable options38.
Ai is the advancement of computer systems that use the advantage of computer algorithms and programming to execute tasks that would usually require human intelligence39. Ai devices are taught to perceive recurring patterns, parameters, and characteristics and take actions to maximize the odds of creating successful outcomes. Ai is versatile and can be trained to recognize specific objects or structures of interest, such as cilia, and then be programmed to run a variety of analyses on the identified objects. Therefore, complex image data can be rapidly and reproducibly generated by Ai38. Automation and Ai analysis of captured images will increase efficacy and efficiency while limiting any potential human error and bias39. Establishing an Ai based methodology for cilia identification creates a consistent way for all research groups to analyze and interpret cilia data.
Here we utilize an Ai module to identify cilia both in vivo and in vitro on 2D images. Using a set of sample images the Ai is trained to identify cilia. Once training is complete, the designated Ai is used to apply a binary mask over Ai identified cilia in an image. The binaries applied by the Ai are modifiable, if necessary, to ensure all cilia in the images are properly identified and non-specific identification is eliminated. After utilizing the Ai to identify cilia, custom-built general analysis (GA) programs are used to perform different analyses such as measuring cilia length and fluorescence intensity. The data collected is exported into a table that can be easily read, interpreted, and used for statistical analyses (Figure 1). The use of automated technology and Ai to identify cilia and obtain specific measurements between experimental groups will aid in future studies aimed to understand the impact of CNS cilia function and morphology on cell-cell communication and behavior.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Intel Xeon, 3.6 GHz, 32GB RAM</td>
			<td>Intel Corporation</td>
			<td>W-2123</td>
			<td>Processor used for running NIS Elements.</td>
		</tr>
		<tr>
			<td>Nikon Elements Software</td>
			<td>Nikon Instruments Inc.</td>
			<td>-</td>
			<td>Ai and GA3 software</td>
		</tr>
		<tr>
			<td>Quadro RTX 4000 Graphics card</td>
			<td>NVIDIA Corporation</td>
			<td>Quadro RTX 4000</td>
			<td></td>
		</tr>
		<tr>
			<td>Windows 10 Professional 64-bit</td>
			<td>Microsoft Inc.</td>
			<td>-</td>
			<td>Operating system used for running NIS Elements</td>
		</tr>
		<tr>
			<td>Workstation</td>
			<td>HP Development Company, L.P.</td>
			<td>HP&#160;Z4G4</td>
			<td>Workstation used for running NIS Elements</td>
		</tr>
	</tbody>
</table>

artificial intelligence approaches to assessing primary cilia

Cilia are microtubule based cellular appendages that function as signaling centers for a diversity of signaling pathways in many mammalian cell types. Cilia length is highly conserved, tightly regulated, and varies between different cell types and tissues and has been implicated in directly impacting their signaling capacity. For example, cilia have been shown to alter their lengths in response to activation of ciliary G protein-coupled receptors. However, accurately and reproducibly measuring the lengths of numerous cilia is a time-consuming and labor-intensive procedure. Current approaches are also error and bias prone. Artificial intelligence (Ai) programs can be utilized to overcome many of these challenges due to capabilities that permit assimilation, manipulation, and optimization of extensive data sets. Here, we demonstrate that an Ai module can be trained to recognize cilia in images from both in vivo and in vitro samples. After using the trained Ai to identify cilia, we are able to design and rapidly utilize applications that analyze hundreds of cilia in a single sample for length, fluorescence intensity and co-localization. This unbiased approach increased our confidence and rigor when comparing samples from different primary neuronal preps in vitro as well as across different brain regions within an animal and between animals. Moreover, this technique can be used to reliably analyze cilia dynamics from any cell type and tissue in a high-throughput manner across multiple samples and treatment groups. Ultimately, Ai-based approaches will likely become standard as most fields move toward less biased and more reproducible approaches for image acquisition and analysis.

Watch this Scientific Journal Video about Artificial Intelligence Approaches to Assessing Primary Cilia at JoVE.com

Artificial Intelligence Approaches to Assessing Primary Cilia

The use of artificial intelligence (Ai) to analyze images is emerging as a powerful, less biased, and rapid approach compared with commonly used methods. Here we trained Ai to recognize a cellular organelle, primary cilia, and analyze properties such as length and staining intensity in a rigorous and reproducible manner.

Cilia are microtubule based cellular appendages that function as signaling centers for a diversity of signaling pathways in many mammalian cell types. ...

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