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. ...

artificial-intelligence-approaches-to-assessing-primary-cilia

Research

JoVE Journal

Biology

3.3K Views.  Indiana University-Purdue University Indianapolis. 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. 

Video: Artificial Intelligence Approaches to Assessing Primary Cilia

Harvesting Sperm and Artificial Insemination of Mice

Rodents of the genus Peromyscus (deer mice) are the most prevalent native North American mammals. Peromyscus species are used in a wide range of research including toxicology, epidemiology, ecology, behavioral, and genetic studies. Here they provide a useful model for demonstrations of artificial insemination. 

Methods similar to those displayed here have previously been used in several deer mouse studies, yet no detailed protocol has been published. Here we demonstrate the basic method of artificial insemination. This method entails extracting the testes from the rodent, then isolating the sperm from the epididymis and vas deferens. The mature sperm, now in a milk mixture, are placed in the female&#x2019;s reproductive tract at the time of ovulation. Fertilization is counted as day 0 for timing of embryo development. Embryos can then be retrieved at the desired time-point and manipulated.

Artificial insemination can be used in a variety of rodent species where exact embryo timing is crucial or hard to obtain. This technique is vital for species or strains (including most Peromyscus) which may not mate immediately and/or where mating is hard to assess. In addition, artificial insemination provides exact timing for embryo development either in mapping developmental progress and/or transgenic work. Reduced numbers of animals can be used since fertilization is guaranteed. This method has been vital to furthering the Peromyscus system, and will hopefully benefit others as well.

This protocol demonstrates methods for extracting sperm from the testes of males and then inseminating female mice. This procedure is useful when precise time is needed in developmental studies as well as transgenic work.

Rodents of the genus Peromyscus (deer mice) are the most prevalent native North American mammals.  Peromyscus species are used in a wide range of research ...

Experimental Approaches to Tissue Engineering

Preparation of Artificial Bilayers for Electrophysiology Experiments

Planar lipid bilayers, also called artificial lipid bilayers, allow you to study ion-conducting channels in a well-defined environment. These bilayers can be used for many different studies, such as the characterization of membrane-active peptides, the reconstitution of ion channels or investigations on how changes in lipid bilayer properties alter the function of bilayer-spanning channels.  Here, we show how to form a planar bilayer and how to isolate small patches from the bilayer, and in a second video  will also demonstrate a procedure for using gramicidin channels to determine changes in lipid bilayer elastic properties.   We also demonstrate the individual steps needed to prepare the bilayer chamber, the electrodes and how to test that the bilayer is suitable for single-channel measurements.

Planar lipid bilayers, also called artificial lipid bilayers, allow you to study ion-conducting channels in a well-defined environment. Here, we demonstrate the individual steps needed to prepare the bilayer chamber, the electrodes and how to test that the bilayer is suitable for single-channel measurements.

Planar lipid bilayers, also called artificial lipid bilayers, allow you to study ion-conducting channels in a well-defined environment. These bilayers can ...

Analysis of Gene Function and Visualization of Cilia-Generated Fluid Flow in Kupffer's Vesicle

Internal organs such as the heart, brain, and gut develop left-right (LR) asymmetries that are critical for their normal functions1. Motile cilia are involved in establishing LR asymmetry in vertebrate embryos, including mouse, frog, and zebrafish2-6. These 'LR cilia' generate asymmetric fluid flow that is necessary to trigger a conserved asymmetric Nodal (TGF-&beta; superfamily) signaling cascade in the left lateral plate mesoderm, which is thought to provide LR patterning information for developing organs7. Thus, to understand mechanisms underlying LR patterning, it is essential to identify genes that regulate the organization of LR ciliated cells, the motility and length of LR cilia and their ability to generate robust asymmetric flow.
In the zebrafish embryo, LR cilia are located in Kupffer's vesicle (KV)2,4,5. KV is comprised of a single layer of monociliated epithelial cells that enclose a fluid-filled lumen. Fate mapping has shown that KV is derived from a group of ~20-30 cells known as dorsal forerunner cells (DFCs) that migrate at the dorsal blastoderm margin during epiboly stages8,9. During early somite stages, DFCs cluster and differentiate into ciliated epithelial cells to form KV in the tailbud of the embryo10,11. The ability to identify and track DFCs&mdash;in combination with optical transparency and rapid development of the zebrafish embryo&mdash;make zebrafish KV an excellent model system to study LR ciliated cells.
Interestingly, progenitors of the DFC/KV cell lineage retain cytoplasmic bridges between the yolk cell up to 4 hr post-fertilization (hpf), whereas cytoplasmic bridges between the yolk cell and other embryonic cells close after 2 hpf8. Taking advantage of these cytoplasmic bridges, we developed a stage-specific injection strategy to deliver morpholino oligonucleotides (MO) exclusively to DFCs and knockdown the function of a targeted gene in these cells12. This technique creates chimeric embryos in which gene function is knocked down in the DFC/KV lineage developing in the context of a wild-type embryo. To analyze asymmetric fluid flow in KV, we inject fluorescent microbeads into the KV lumen and record bead movement using videomicroscopy2. Fluid flow is easily visualized and can be quantified by tracking bead displacement over time.
Here, using the stage-specific DFC-targeted gene knockdown technique and injection of fluorescent microbeads into KV to visualize flow, we present a protocol that provides an effective approach to characterize the role of a particular gene during KV development and function.

Cilia-generated fluid flow in Kupffer&#x2019;s Vesicle (KV) controls left-right patterning of the zebrafish embryo. Here, we describe a technique to modulate gene function specifically in KV cells. In addition, we show how to deliver fluorescent beads into KV to visualize fluid flow.

Internal organs such as the  heart, brain, and gut develop left-right (LR) asymmetries that are critical for  their normal functions1. Motile cilia are ...

Experimental Approaches to Study Mitochondrial Localization and Function of a Nuclear Cell Cycle Kinase, Cdk1

Although mitochondria possess their own transcriptional machinery, merely 1% of mitochondrial proteins are synthesized inside the organelle. The nuclear-encoded proteins are transported into mitochondria guided by their mitochondria targeting sequences (MTS); however, a majority of mitochondrial localized proteins lack an identifiable MTS. Nevertheless, the fact that MTS can instruct proteins to go into the mitochondria provides a valuable tool for studying mitochondrial functions of normally nuclear and/or cytoplasmic proteins. We have recently identified the cell cycle kinase CyclinB1/Cdk1 complex in the mitochondria. To specifically study the mitochondrial functions of this complex, mitochondrial overexpression and knock-down of this complex without interfering with its nuclear or cytoplasmic functions were essential. By tagging CyclinB1/Cdk1 with MTS, we were able to achieve mitochondrial overexpression of this complex to study its mitochondrial targets as well as functions. Via tagging dominant-negative Cdk1 with MTS, inhibition of Cdk1 activity was accomplished particularly in the mitochondria. Potential mitochondrial targets of CyclinB1/Cdk1 complex were identified using a gel-based proteomics approach. Unlike traditional 2D gel analysis, we employed 2-dimensional difference gel electrophoresis (2D-DIGE) technology followed by phosphoprotein staining to fluorescently label differentially phosphorylated proteins in mitochondrial Cdk1 expressing cells. Identification of phosphoprotein spots that were altered in wild type versus dominant negative Cdk1 bearing mitochondria revealed the identity of mitochondrial targets of Cdk1. Finally, to determine the effect of CyclinB1/Cdk1 mitochondrial localization in cell cycle progression, a cell proliferation assay using a synthetic thymidine analogue EdU (5-ethynyl-2&#8242;-deoxyuridine) was used to monitor the cells as they go through the cell cycle and replicate their DNA. Altogether, we demonstrated a variety of approaches available to study mitochondrial localization and activity of a cell cycle kinase. These are advanced, yet easy to follow methods that will be beneficial to many cell biology researchers.

Here, we outline how to study mitochondrial localization of a (cell cycle) kinase, and how to determine its sub-mitochondrial location as well as potential mitochondrial substrates/targets. Forced expression of proteins into the mitochondria provides a useful tool for studying the functional consequences of mitochondrial localization of a protein of interest.

Although mitochondria possess their own transcriptional machinery, merely 1% of mitochondrial proteins are synthesized inside the organelle. The ...

Using Microfluidic Devices to Measure Lifespan and Cellular Phenotypes in Single Budding Yeast Cells

Budding yeast Saccharomyces cerevisiae is an important model organism in aging research. Genetic studies have revealed many genes with conserved effects on the lifespan across species. However, the molecular causes of aging and death remain elusive. To gain a systematic understanding of the molecular mechanisms underlying yeast aging, we need high-throughput methods to measure lifespan and to quantify various cellular and molecular phenotypes in single cells. Previously, we developed microfluidic devices to track budding yeast mother cells throughout their lifespan while flushing away newborn daughter cells. This article presents a method for preparing microfluidic chips and for setting up microfluidic experiments. Multiple channels can be used to simultaneously track cells under different conditions or from different yeast strains. A typical setup can track hundreds of cells per channel and allow for high-resolution microscope imaging throughout the lifespan of the cells. Our method also allows detailed characterization of the lifespan, molecular markers, cell morphology, and the cell cycle dynamics of single cells. In addition, our microfluidic device is able to trap a significant amount of fresh mother cells that can be identified by downstream image analysis, making it possible to measure the lifespan with higher accuracy.

This article presents a protocol optimized for the production of microfluidic chips and the setup of microfluidic experiments to measure the lifespan and cellular phenotypes of single yeast cells.

Budding yeast Saccharomyces cerevisiae is an important model organism in aging research. Genetic studies have revealed many genes with conserved effects ...

In Vitro and In Vivo Approaches to Determine Intestinal Epithelial Cell Permeability

The intestinal barrier defends against pathogenic microorganism and microbial toxin. Its function is regulated by tight junction permeability and epithelial cell integrity, and disruption of the intestinal barrier function contributes to progression of gastrointestinal and systemic disease. Two simple methods are described here to measure the permeability of intestinal epithelium. In vitro, Caco-2BBe cells are plated in tissue culture wells as a monolayer and transepithelial electrical resistance (TER) can be measured by an epithelial (volt/ohm) meter. This method is convincing because of its user-friendly operation and repeatability. In vivo, mice are gavaged with 4 kDa fluorescein isothiocyanate (FITC)-dextran, and the FITC-dextran concentrations are measured in collected serum samples from mice to determine the epithelial permeability. Oral gavage provides an accurate dose, and therefore is the preferred method to measure the intestinal permeability in vivo. Taken together, these two methods can measure the permeability of the intestinal epithelium in vitro and in vivo, and hence be used to study the connection between diseases and barrier function.

In Vitro and In Vivo Approaches to Determine Intestinal Epithelial Cell Permeability

Two methods are presented here to determine intestinal barrier function. An epithelial meter (volt/ohm) is used for measurements of transepithelial electrical resistance of cultured epithelia directly in tissue culture wells. In mice, the FITC-dextran gavage method is used to determine the intestinal permeability in vivo.

The intestinal barrier defends against pathogenic microorganism and microbial toxin. Its function is regulated by tight junction permeability and ...

Magnetic-Activated Cell Sorting Strategies to Isolate and Purify Synovial Fluid-Derived Mesenchymal Stem Cells from a Rabbit Model

Mesenchymal stem cells (MSCs) are the main cell source for cell-based therapy. MSCs from articular cavity synovial fluid could potentially be used for cartilage tissue engineering. MSCs from synovial fluid (SF-MSCs) have been considered promising candidates for articular regeneration, and their potential therapeutic benefit has made them an important research topic of late. SF-MSCs from the knee cavity of the New Zealand white rabbit can be employed as an optimized translational model to assess human regenerative medicine. By means of CD90-based magnetic activated cell sorting (MACS) technologies, this protocol successfully obtains rabbit SF-MSCs (rbSF-MSCs) from this rabbit model and further fully demonstrates the MSC phenotype of these cells by inducing them to differentiate to osteoblasts, adipocytes, and chondrocytes. Therefore, this approach can be applied in cell biology research and tissue engineering using simple equipment and procedures.

This article presents a simple and economic protocol for the straightforward isolation and purification of mesenchymal stem cells from New Zealand white rabbit synovial fluid.

Mesenchymal stem cells (MSCs) are the main cell source for cell-based therapy. MSCs from articular cavity synovial fluid could potentially be used for ...

Simple Detection of Primary Cilia by Immunofluorescence

Primary cilia are dynamically regulated during cell cycle progression, specifically during the G0/G1 phases of the cell cycle, being resorbed prior to mitosis. Primary cilia can be visualized with highly sophisticated methods, including transmission electron microscopy, 3D imaging, or using software for the automatic detection of primary cilia. However, immunofluorescent staining of primary cilia is needed to perform these methods. This publication describes a protocol for the easy detection of primary cilia in vitro by staining acetylated alpha tubulin (axoneme) and gamma tubulin (basal body). This immunofluorescent staining protocol is relatively simple and results in high-quality images. The present protocol describes how four cell lines (C2C12, MEF, NHLF, and skin fibroblasts) expressing primary cilia were fixed, immunostained, and imaged with a fluorescent or confocal microscope.

Primary cilia are extracellular structures associated with the centriole. Primary cilia detection by immunofluorescent staining is a relatively simple procedure that results in extremely high-quality images. In this protocol, fibroblasts expressing primary cilia were fixed, immunostained, and imaged in a fluorescent or confocal microscope.

Primary cilia are dynamically regulated during cell cycle progression, specifically during the G0/G1 phases of the cell cycle, being resorbed prior to ...

Large-Scale Preparation of Synovial Fluid Mesenchymal Stem Cell-Derived Exosomes by 3D Bioreactor Culture

Exosomes secreted by mesenchymal stem cells (MSCs) have been suggested as promising candidates for cartilage injuries and osteoarthritis treatment. Exosomes for clinical application require large-scale production. To this aim, human synovial fluid MSCs (hSF-MSCs) were grown on microcarrier beads, and then cultured in a dynamic three-dimension (3D) culture system. Through utilizing 3D dynamic culture, this protocol successfully obtained large-scale exosomes from SF-MSC culture supernatants. Exosomes were harvested by ultracentrifugation and verified by a transmission electron microscope, nanoparticle transmission assay, and western blotting. Also, the microbiological safety of exosomes was detected. Results of exosome detection suggest that this approach can produce a large number of good manufacturing practices (GMP)-grade exosomes. These exosomes could be utilized in exosome biology research and clinical osteoarthritis treatment.

Here, we present a protocol to produce a large number of GMP-grade exosomes from synovial fluid mesenchymal stem cells using a 3D bioreactor.

Exosomes secreted by mesenchymal stem cells (MSCs) have been suggested as promising candidates for cartilage injuries and osteoarthritis treatment. ...