Name: lipidunet-machine learning-based method of characterization and quantification of lipid deposits using ipsc-derived retinal pigment epithelium
Uploaded: 2023-07-28T12:50:00.000+00:00
Duration: 6 min 16 s
Description: Watch this Scientific Journal Video about Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies at JoVE.com

We thank National Eye Institute (NEI) histology core for the use of the Zeiss confocal system. This work was supported by NEI IRP funds (grant number ZIA EY000533-04).

All protocol steps adhere to the guidelines set forth by the NIH&#39;s human research ethics committee. Stem cell&#160;work and patient sample collection were approved by the Combined NeuroScience Institutional Review Board (CNS IRB) under the Office of Human Research Protection (OHRP), NIH, as per 45 CFR 46 guidelines of the U.S. Government. Patient samples were collected using CNS IRB-approved consent form in accordance with the criteria set by the Declaration of Helsinki under the protocol number NCT01432847 (https://clinicaltrials.gov/ct2/show/NCT01432847?cond=NCT01432847&#38;draw=2&#38;rank=1).
1. iRPE generation
<ol>
	<li>Generate iRPE from patient blood-derived iPSC&#160;following the published protocol by Sharma et al., 202211 (Figure 2 and Figure 3).</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65503/65503fig02v2.jpg" /> 
Figure 2: Schematic of iRPE differentiation and maturation. To generate iRPE, an established differentiation protocol was followed, and the cells were allowed to mature for 5 weeks. The resulting cell culture acts as an in vitro model that can be manipulated with various treatments to mimic RPE dysfunction in diseases like AMD and Stargardt disease. <a href="https://www.jove.com/files/ftp_upload/65503/65503fig02largev2.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65503/65503fig03.jpg" /> 
Figure 3: Representative images of successful and unsuccessful RPE differentiation and maturation. Two brightfield images at 10x magnification of TJP1 RPE are shown at Day 42 of the iRPE protocol. (A) Successful differentiation and maturation will show confluent RPE with pigmentation and polygonal morphology. (B) Unsuccessful differentiation and maturation will show clusters of dying cells, as shown here. <a href="https://www.jove.com/files/ftp_upload/65503/65503fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. RPE maintenance media (RPE-MM) preparation
<ol>
	<li>Thaw the N2 supplement at 4 &#176;C overnight. Thaw all other reagents at room temperature (RT).</li>
	<li>Under sterile conditions, add the reagents listed in Table 1 at the listed dilution factors, as per the protocol established by Sharma et al., 202211.</li>
	<li>Mix the media well and filter it using a 0.22 &#181;m filtration unit. 
	&#8203;NOTE: Media is suitable for use within 2 weeks if stored at 4 &#176;C.</li>
</ol>
3. 96-well plate seeding
<ol>
	<li>Thaw an aliquot of vitronectin at RT for 3-5 min or until the ice is completely melted.</li>
	<li>Dilute the vitronectin with 1x Dulbecco&#39;s phosphate-buffered saline (DPBS) to obtain the desired working solution using a 1:200 dilution (vitronectin: DPBS). For a 96-well plate, coat each well with 200 &#181;L of the working solution.</li>
	<li>Combine thawed ROCK inhibitor (Y-27632 dihydrochloride) with the RPE-MM at a 1:1000 dilution to achieve a final concentration of 10 &#181;M. This is the plating media for RPE cells.</li>
	<li>Thaw the iRPE vial using an automated cell thawing system and transfer the iRPE cell suspension to a 50 mL tube.</li>
	<li>Dilute the cell suspension with the plating media at a 1:10 dilution. Centrifuge the tube at 400 x g for 5 min.</li>
	<li>Carefully aspirate the supernatant and resuspend the cells in 10 mL of the plating media.</li>
	<li>Mix 400 &#181;L of the plating media with 100 &#181;L of the resuspended cell solution for cell counting. Use this aliquot to determine the viable cell concentration of the cell suspension using a cell viability counter.</li>
	<li>Dilute the cell suspension with the plating media to a final concentration of 60,000 cells/mL.</li>
	<li>Completely aspirate the vitronectin coating solution from the 96-well plate and dispense 200 &#181;L of the cell suspension into each well. There will be roughly 12,000 cells/well or ~200 cells/mm2.</li>
	<li>Incubate the seeded cell plates for 48 h at 37 &#176;C and 5% CO2. After 48 h, change the media to RPE-MM without ROCK inhibitor supplement. Change the media every 2-3 days during the 5-week maturation period.</li>
</ol>
4. In vitro disease models
<ol>
	<li>Complement competent human serum (CC-HS) treatment
	<ol>
		<li>Thaw human complement competent serum at 4&#176;C overnight.</li>
		<li>Prepare CC-HS and complement incompetent human serum (CI-HS) media.
		<ol>
			<li>To prepare 5% CC-HS media, mix the thawed complement competent human serum with RPE-MM at a 1:20 dilution. Filter the solution through a 0.22 &#181;m media filter before use.</li>
			<li>To prepare 5% complement incompetent human serum (CI-HS) media, first heat inactivate the CC-HS in a 57 &#176;C water bath for 30 min and then mix with the culture media at a 1:20 dilution. Filter the solution through a 0.22 &#181;m media filter before use.</li>
		</ol>
		</li>
		<li>Serum treat the cells with 200 &#181;L of either 5% CC-HS or 5% CI-HS media for a total incubation time of 48 h, refreshing the media after 24 h.</li>
		<li>Wash the cells with 1x DPBS and fix them with 4% paraformaldehyde for 20 min at RT. Wash once more with 1x DPBS and store the samples at 4 &#176;C, immersed in 200 &#181;L of DPBS.</li>
		<li>OPTIONAL: If desired, lyse the cells from the plate to show only sub-RPE lipid deposition.
		<ol>
			<li>To lyse the cells and leave only lipid deposits, remove the media and add 200 &#181;L of deionized water to each well.</li>
			<li>Incubate for 10-15 min, pipette up and down until cells are removed. Wash once more with 200 &#181;L of deionized water and immediately fix the cells with 4% paraformaldehyde.</li>
			<li>Confirm the cell removal efficacy with nuclear staining using Hoechst. Add Hoechst at 1:2000 dilution to a 1x DPBS solution containing 1% bovine serum albumin (BSA), 0.5% Tween 20, and 0.5% Triton-X 100. Incubate at RT for 1 h in the dark. Subsequently, wash with 1x DPBS.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Photoreceptor outer segment (POS) treatment on iRPE
	<ol>
		<li>POS preparation
		<ol>
			<li>Remove the POS pellet tube from -80 &#176;C storage and thaw overnight at 4 &#176;C in a covered ice bucket.</li>
			<li>Prepare POS wash buffer by mixing 10 g of sucrose in 40 mL of double-deionized H2O (ddH2O).</li>
			<li>Heat mixture at 40-50 &#176;C while gently stirring for 15 min. Add 840 mg of sodium bicarbonate to the mixture and stir while heating for 10 min.</li>
			<li>Adjust the total volume of the POS wash buffer to 100 mL with ddH2O and adjust the pH of the solution to 8.3 with 1 N HCl or 1 N NaOH, as needed. Filter the wash solution using a 0.22 &#181;m filter. 
			NOTE: The protocol can be paused here; the POS wash buffer can be stored at 4 &#176;C overnight.</li>
			<li>Once thawed, suspend the pellet in 15 mL of POS wash buffer. Be gentle during pellet suspensions to ensure POS integrity. Centrifuge the POS suspension at 600 x g at 4 &#176;C for 20 min and then aspirate the supernatant.</li>
			<li>Resuspend the POS pellet in 10 mL of the POS wash buffer.</li>
			<li>Remove a 100 &#181;L aliquot of the POS + POS wash buffer (POS solution) and dilute in 400 &#181;L of 1x DPBS. Spread 50 &#181;L of the diluted POS solution on a blood agar plate and an agarose plate to check for bacterial and fungal contaminants. Prepare positive controls for each and incubate all plates for 48 h at 37 &#176;C.</li>
			<li>Perform a qPCR assay by adding 1 &#181;L of POS solution to the detection well to test for mycoplasma. To amplify DNA fragments, perform 40 cycles of denaturation (95&#176;C, 15 s), and annealing&#160;and elongation (60&#176;C, 1 min). The forward and reverse primers for detecting mycoplasma in the POS sample are as follows: 
			Forward primer: GGA TTA GAT ACC CTG GTA GTC CAC G 
			&#8203;Reverse primer: CGT CAA TTC CTT TAA GTT TCA CTC TTG GC</li>
			<li>Measure POS concentration using a cell analyzer and aliquot according to need. For one well of 96 well plates with RPE cells, 3 x 106 POS is sufficient. The desired ratio is 10 POS/RPE cell. Store the aliquots at 80 &#176;C for future use.</li>
		</ol>
		</li>
		<li>Addition of POS to cells
		<ol>
			<li>Thaw vials of POS in an ice bath.</li>
			<li>Mix the calculated amount of prepared POS with RPE-MM and treat the cells with POS once daily for 7 days. 
			&#8203;NOTE: Prepare POS solution fresh daily.</li>
			<li>Wash the cells with 1x DPBS and then fix them with 4% paraformaldehyde for 20 min at RT. Wash with DPBS once more and store samples at 4 &#176;C, immersed in 200 &#181;L of DPBS.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
5. Staining for sub-RPE deposits
<ol>
	<li>Nile red staining protocol
	<ol>
		<li>After PFA fixation, wash the samples 3 times&#160;with 1x DPBS. 
		&#8203;NOTE: If not used immediately, the protocol can be paused here, but samples must be stored in a 1x DPBS + 0.02% sodium azide solution at 4 &#176;C.</li>
		<li>To prepare the Nile Red stock solution, dissolve Nile Red powder in acetone at 3 mg/mL concentration. Incubate for 15 min at RT with periodic mixing. Filter the solutions with a 0.22 &#181;m filter once or twice depending on the level of precipitate remaining in the solution. 
		NOTE: Protect the stock solution from light.</li>
		<li>To prepare the working solution, dilute the stock solution at a ratio of 1:500 in 1x DPBS. Add 200 &#181;L of the working solution to the sample for 30 min at RT on a shaker and protect it from light.</li>
		<li>Wash 3 times&#160;with 1x PBS and store the samples at 4 &#176;C, immersed in 200 &#181;L of DPBS. 
		NOTE: If conducting an experiment on transwells instead of a 96-well plate, samples can be mounted on a slide with mounting media, covered with a glass coverslip, and sealed with transparent nail polish. Care should be taken to mount the sample with the cells facing up.</li>
	</ol>
	</li>
	<li>BODIPY staining protocol
	<ol>
		<li>For stock solution, dissolve BODIPY in anhydrous dimethyl sulfoxide (DMSO) to attain a stock concentration of 3.8 mM.</li>
		<li>For PFA fixed samples, dilute BODIPY stock at 1:300 in 1x DPBS. Add 200 &#181;L to cells and incubate overnight on a rocker at RT.</li>
		<li>Wash 3 times&#160;with 1x DPBS and store the samples at 4 &#176;C, immersed in 200 &#181;L of DPBS. 
		NOTE: If conducting an experiment on transwells instead of a 96-well plate, samples can be mounted on a slide with mounting media, covered with a glass coverslip, and sealed with transparent nail polish. Care should be taken to mount the sample with the cells facing up.</li>
	</ol>
	</li>
	<li>APOE immunostaining protocol
	<ol>
		<li>Combine 1x DPBS with 1% bovine serum albumin (BSA), 0.5% Tween 20, and 0.5% Triton-X 100 to create a buffer solution.</li>
		<li>For PFA fixed samples, block and permeabilize the sample in 200 &#181;L of the buffer solution for 1 h at RT.</li>
		<li>Add APOE primary antibody diluted at 1:100 in the buffer solution and incubate overnight at RT.</li>
		<li>The following day, wash the samples 3 times&#160;with 1x DPBS.</li>
		<li>Add a secondary antibody at 1:1000 dilution in the buffer solution and add 200 &#181;L of the solution to the cells for 1 h at RT.</li>
		<li>Wash 3 times&#160;with 1x DPBS and store samples at 4 &#176;C, immersed in 200 &#181;L of DPBS. 
		&#8203;NOTE: If conducting an experiment on transwells instead of a 96-well plate, samples can be mounted on a slide with mounting media (Fluoromount), covered with a glass coverslip, and sealed with transparent nail polish. Care should be taken to mount the sample with the cells facing up.</li>
	</ol>
	</li>
</ol>
6. Image automation and processing
<ol>
	<li>Automated image scan 
	NOTE: Zeiss LSM 800 inverted confocal scanning microscope and ZEN 3.2 (blue edition) software were used in this study. Ensure the 96-well plate is warmed to RT for a minimum of 60 min before imaging to avoid focal plane drift during the scan due to a change in the refractive index of the medium with temperature change.
	<ol>
		<li>Using a confocal microscope and a 40x objective, create a scan profile with the appropriate fluorescent channels for the lipid marker used and any additional antibodies.</li>
		<li>Use the Tiles checkbox to set up image automation. To calibrate the 96-well plate, ensure the correct sample carrier measurements are entered and selected. Then, click the Calibrate button to calibrate the plate according to the instructions, which requires using the 10x objective.</li>
		<li>Choose the Advanced Setup view to select appropriate wells and add 3 different imaging points near the center of the well using the Positions function. This can be done manually under the Position subtab or randomly using the Position Setup tab and selecting Setup by Carrier. Repeat for all the wells of the same staining.</li>
		<li>For optimal focusing and Z-stack positioning during automation, go to the Focus Strategy tab to select Use Focus Surface/Z values Defined by Tiles Setup. Alternative methods can use other focus strategies, but it is recommended to use this setting for the most consistent results.</li>
		<li>Under the Tiles tab, click Verify Positions and manually set the central Z plane for each position. The settings in the Options subtab will order the acquisition of images, so check this before starting to image. To acquire images in the order the positions were selected, deselect the Tile Regions/Positions and Carrier Wells/Container checkboxes. Select Split Scenes into Separate Files for ease of image processing.</li>
		<li>Ensure the Z-Stack tab is set to Center, a range is inputted to the user&#39;s preferences, and the Optimal button is selected to set the slice interval.</li>
		<li>After optimizing the Acquisition Mode, Channels, Focus Strategy, Z-Stack, and Tiles tabs, start the experiment.</li>
	</ol>
	</li>
	<li>Image processing
	<ol>
		<li>Using a batch image processing method, create maximum projections of each Z-stack with the Extended Depth of Focus method.</li>
		<li>Using a batch image processing method, export the maximum projection files as 16-bit TIFF images. Set the compression to None and ensure Original Data is checked. The resulting image should be a maximum projection greyscale TIFF of only the fluorescence channel on which the lipid marker is expressed.</li>
	</ol>
	</li>
</ol>
7. Segmentation and quantification
NOTE: The LipidUNet program was trained on 40x images from a 96-well plate. It is highly recommended to use images that were obtained using a 40x objective.
<ol>
	<li>Install the LipidUNet software. LipidUNet can be downloaded from the following GitHub repository: &#160;https://github.com/RPEGoogleMap/LipidUNet</li>
	<li>Identify the TIFF images representing either Nile Red, Bodipy, or APOE and move them into a folder named imgs within a directory named either Nile_Red, Bodipy, or APOE, depending on the method being used. 
	NOTE: Exact naming conventions must be used for the LipidUNet program to recognize the directories.</li>
	<li>Open the LipidUNet software (Figure 4).</li>
	<li>In the Predict tab of the software, select the relevant directory (Nile_Red, Bodipy, or APOE) by clicking on the ellipsis and navigating to the named directory. Confirm that the LipidUNet program has correctly identified the images by checking the Class entry.</li>
	<li>Select a probability threshold for the algorithm between 0.01 and 0.99. A higher value will eliminate more false positives but might cause more false negatives, and lower values might introduce more false positives while eliminating more false negatives. A value of 0.65 is the default and is recommended.</li>
	<li>Click Predict. 
	&#8203;NOTE: The software will iterate through all the images automatically and create a new folder called predicted_masks in the selected directory.</li>
	<li>Use a Mask Analysis tool to iterate through the generated masks and provide a quantitative count of the thresholded lipid deposits from the mask images.</li>
	<li>Analyze the generated counts data to compare treatment conditions.</li>
</ol>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65503/65503fig04.jpg" /> 
Figure 4: LipidUNet user interface. The LipidUNet software has different sections to select for the training data directory, where images of lipid deposits have been identified correctly; the model weights directory, which is produced from the training data; and the prediction data directory in which the user will input their images for segmentation. <a href="https://www.jove.com/files/ftp_upload/65503/65503fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Modeling and Analyzing Retinal Degeneration

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	<li>Kanow, M. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biochemical+adaptations+of+the+retina+and+retinal+pigment+epithelium+support+a+metabolic+ecosystem+in+the+vertebrate+eye.">Biochemical adaptations of the retina and retinal pigment epithelium support a metabolic ecosystem in the vertebrate eye.</a> eLife. 6, e28899 (2017).</li><li>Adijanto, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+retinal+pigment+epithelium+utilizes+fatty+acids+for+ketogenesis.">The retinal pigment epithelium utilizes fatty acids for ketogenesis.</a> The Journal of Biological Chemistry. 289 (30), 20570-20582 (2014).</li><li>Farnoodian, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell-autonomous+lipid-handling+defects+in+Stargardt+iPSC-derived+retinal+pigment+epithelium+cells.">Cell-autonomous lipid-handling defects in Stargardt iPSC-derived retinal pigment epithelium cells.</a> Stem Cell Reports. 17 (11), 2438-2450 (2022).</li><li>Curcio, C. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Soft+drusen+in+age-related+macular+degeneration:+Biology+and+targeting+via+the+oil+spill+strategies.">Soft drusen in age-related macular degeneration: Biology and targeting via the oil spill strategies.</a> Investigative Ophthalmology &amp; Visual Science. 59 (4), AMD160-AMD181 (2018).</li><li>Cankova, Z., Huang, J. D., Kruth, H. S., Johnson, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Passage+of+low-density+lipoproteins+through+Bruch's+membrane+and+choroid.">Passage of low-density lipoproteins through Bruch's membrane and choroid.</a> Experimental Eye Research. 93 (6), 947-955 (2011).</li><li>Curcio, C. 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J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=AMPK+modulation+ameliorates+dominant+disease+phenotypes+of+CTRP5+variant+in+retinal+degeneration.">AMPK modulation ameliorates dominant disease phenotypes of CTRP5 variant in retinal degeneration.</a> Communications Biology. 4 (1), 1360 (2021).</li><li>Sharma, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Epithelial+phenotype+restoring+drugs+suppress+macular+degeneration+phenotypes+in+an+iPSC+model.">Epithelial phenotype restoring drugs suppress macular degeneration phenotypes in an iPSC model.</a> Nature Communications. 12 (1), 7293 (2021).</li><li>Farnoodian, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell-autonomous+lipid-handling+defects+in+Stargardt+iPSC-derived+retinal+pigment+epithelium+cells.">Cell-autonomous lipid-handling defects in Stargardt iPSC-derived retinal pigment epithelium cells.</a> Stem Cell Reports. 17 (11), 2438-2450 (2022).</li><li>Hallam, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+induced+pluripotent+stem+cell+patient+specific+model+of+complement+factor+H+(Y402H)+polymorphism+displays+characteristic+features+of+age-related+macular+degeneration+and+indicates+a+beneficial+role+for+UV+light+exposure.">An induced pluripotent stem cell patient specific model of complement factor H (Y402H) polymorphism displays characteristic features of age-related macular degeneration and indicates a beneficial role for UV light exposure.</a> Stem Cells (Dayton, Ohio). 35 (11), 2305-2320 (2017).</li><li>Sharma, R., Bose, D., Montford, J., Ortolan, D., Bharti, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Triphasic+developmentally+guided+protocol+to+generate+retinal+pigment+epithelium+from+induced+pluripotent+stem+cells.">Triphasic developmentally guided protocol to generate retinal pigment epithelium from induced pluripotent stem cells.</a> STAR Protocols. 3 (3), 101582 (2022).</li><li>Issa, P. 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No disclosures.

This protocol provides a method to efficiently label, image, and quantify lipid deposits in monogenic and polygenic in vitro disease models for degenerative eye diseases. The AI-based software, LipidUNet, can be applied to three common lipid markers, APOE, Nile Red, and BODIPY, and provides a fast, automatic method for analysis that allows quantification to be standard and unbiased.
The main limitation of LipidUNet is the fact that the training dataset for the AI was limited to 40x magnification images of cells cultured in a 96-well plate. As a result of the training image set, LipidUNet, in its current form, is limited to analyzing 40x magnification images. The software can be used to analyze 40x images of cells cultured on other culture surfaces besides a 96 well plate, but care should be taken to examine the generated output masks to verify accurate thresholding by the software. More image sets (at different magnifications) will be needed to expand the scope of what samples/images it can analyze.
The protocol has several critical steps. In the lipid marker step, the user should confirm that their chosen labeling compound (BODIPY, APOE, Nile Red) has labeled their sample effectively. Mature RPE cells are often heavily pigmented, which can impair the fluorescent signal of antibody immunostaining. When the fluorescence signal is weak or when there is too much background staining, LipidUNet cannot discern lipid droplets accurately. For a similar reason, properly selected acquisition settings for the automatic imaging step of the protocol must be used. If the images acquired are of poor quality, LipidUNet will struggle to properly mask images and therefore, quantification will be inaccurate (Figure 6A-L). Finally, post-processing of the images is an important step, as LipidUNet has specific requirements for the software to work.
When compared to workflows for lipid analysis that use manual thresholding, or techniques that involve automatic thresholding in software like Fiji, LipidUNet offers a nonbiased and reliable segmentation across images with variable lipid deposition, as reflected by a small error rate in the identification of lipid particles (Figure 7). The software allows for user entry of additional training images, allowing for analysis of image sets beyond those that utilize a 40x magnification objective or even those that utilize a different lipid marker, as outlined in the protocol. In the future, the software will be trained to analyze 3D images so that the quantification of lipid deposit volume is possible. Degenerative eye diseases that implicate lipid deposition as a major contributor to pathology are prevalent, and cases are predicted to increase as the elderly population is expanding13. Accurate disease models and efficient analysis tools, as we have outlined in this protocol, will allow for the development of novel therapeutic interventions.

The retinal pigment epithelium (RPE) is a monolayer of cells located in the back of the eye adjacent to retinal photoreceptors. RPE plays a vital role in maintaining proper vision by providing metabolic and structural support to the photoreceptors. Healthy RPE cells are characterized by a distinct hexagonal morphology. They are connected by tight junctions, which allow the RPE to act as a barrier between the choriocapillaris located on its basal side and photoreceptors located apically. To maintain the retinal ecosystem, RPE shuttles key metabolites, e.g., glucose, to photoreceptors in a way that minimizes glucose consumption in the RPE1. Due to this limitation, RPE depends on other metabolites to maintain their metabolic needs, including fatty acids, which RPE converts to ketones through &#946;-oxidation2. Given the propensity of RPE to utilize fatty acids, which are likely recycled from photoreceptor outer segment (POS) digestion, as an energy source, detrimental changes to the lipid processing pathways in RPE often lead to, or are implicated in, both monogenic and polygenic degenerative retinal diseases3.
Age-related macular degeneration (AMD), a polygenic degenerative eye disease that causes RPE degeneration, has also been linked to aberrant autophagy and lipid metabolism in the RPE monolayer. The failure of a dysfunctional RPE monolayer to process POS and perform other critical functions leads to extracellular (sub-RPE) deposits called basal linear deposits (BLinD) located between the RPE and Bruch&#39;s membrane - a hallmark of AMD pathologies. Major components of BLinD include lipoproteins, the most abundant of which is apolipoprotein E (APOE)4. Accumulation of thin layers of BLinD can lead to soft drusen, which is recognized as a clinical symptom of AMD5,6.
Several groups have shown that stem cell-derived in vitro disease models that cause RPE dysfunction feature sub-RPE lipid accumulation7,8,9. Hallam et al. (2017) generated induced pluripotent stem cell-derived RPE (iRPE) from patients with a high risk for AMD due to a polymorphism of the CFH gene. The iRPE showed drusen accumulation, as marked by APOE, and the high-risk RPE accumulated larger deposits than iRPE generated from low-risk patients10.
To create an in vitro model that recapitulates cellular hallmarks of AMD, such as lipid droplets and drusen deposition, iRPE lines generated from patient blood samples were established using a previously published developmentally guided protocol11. The iRPE were subjected to complement-competent human serum (CC-HS), a solution containing anaphylatoxins that mimic one possible cause of AMD: increased alternate complement signaling8. The resulting cellular and sub-cellular deposition of lipid deposits was measured using commonly used lipid and lipoprotein markers, APOE, Nile Red, and&#160;BODIPY. Through these markers, it was shown that activated complement signaling via CC-HS exacerbated lipid accumulation in iRPE cells8.
To develop a disease model for a monogenic retinal degenerative disease, iRPE lines were developed from patients with Stargardt disease, a disease caused by mutations to the ABCA4 gene in RPE. It has previously been shown that when ABCA4 is knocked out, A2E lipofuscin, an intracellular deposit known to contain high levels of phospholipids and light-dependent lipid peroxidation products, accumulates inside the RPE12. ABCA4 knockout lines were developed alongside the patient lines, and both were subjected to POS feeding. The Stargardt iRPE demonstrated POS phagocytosis-induced pathology, exhibiting increased lipid accumulation quantified by BODIPY staining. RPE derived from ABCA4 KO iPSCs were subjected to CC-HS treatment; quantification of the BODIPY signal showed a defect in lipid handling in the Stargardt disease model as well9.
Given the prevalence of these diseases and the need for effective therapeutics, along with the relevant disease models described above, there is a need to establish robust methods for quantifying the efficacy of potential treatments. To quantify lipid deposits in a way that is objective, automated, and standardized, a machine-learning-based software, LipidUNet, was created so that, when paired with mask analysis tools, lipid deposition can quickly and effectively be identified using the common markers Nile Red, BODIPY, and APOE. The summary statistics obtained using this analysis pipeline can then be analyzed and displayed graphically, allowing for easy comparison of treatment conditions. The schematic of the protocol is shown in Figure 1.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65503/65503fig01.jpg" /> 
Figure 1: Schematic of the protocol: RPE cells are grown on a 96-well plate and challenged with active human serum or purified bovine outer segments to model different types of retinal degenerations in vitro. RPE cells are fixed and stained for lipoprotein deposits with Nile Red, BODIPY, and APOE. A confocal microscope is used to acquire Z-stacks of fluorescently-labeled lipid particles, which are subsequently processed into 2D maximum intensity projections. A machine-learning algorithm was trained to recognize and correctly segment lipoprotein particles. Summary tables containing particle count and various shape metrics are generated and can be used for subsequent plotting and statistical analysis. <a href="https://www.jove.com/files/ftp_upload/65503/65503fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table><tbody><tr><td>0.22 &amp;micro;m Steriflip filter system</td><td>EMD Millipore</td><td>SCGP00525</td><td></td></tr><tr><td>1x Dulbecco&#039;s Phosphate Buffered Saline</td><td>Gibco</td><td>14190-144</td><td></td></tr><tr><td>3,3&#039;,5-Triiodo-L-thyronine</td><td>Sigma</td><td>T5516</td><td></td></tr><tr><td>Albumin Bovine, Fraction V</td><td>MP Biomedical</td><td>160069</td><td></td></tr><tr><td>Alexa Fluor 555 rabbit anti-goat IgG (H+L)</td><td>Invitrogen</td><td>A21431</td><td>APOE secondary antibody</td></tr><tr><td>APOE primary antibody</td><td>Millipore Sigma</td><td>AB947</td><td></td></tr><tr><td>BODIPY 493/503</td><td>Invitrogen</td><td>D3922</td><td>Protect from light</td></tr><tr><td>Complement competent human serum</td><td>Millipore Sigma</td><td>S1-LITER</td><td></td></tr><tr><td>CTS N2 Supplement</td><td>Life Technologies</td><td>A13707-01</td><td></td></tr><tr><td>Fetal Bovine Serum</td><td>Hyclone</td><td>SH30071.03</td><td></td></tr><tr><td>Fluoromount-G</td><td>SouthernBiotech</td><td>0100-01</td><td>Slide mounting media</td></tr><tr><td>Glass Cover Slips #1 1/2 22 mm x 22 mm</td><td>Electron Microscopy Sciences</td><td>72204-01</td><td></td></tr><tr><td>Glass Microscope Slide 25 mm x 75 mm- 1.2 mm Thick</td><td>Electron Microscopy Sciences</td><td>71870-01</td><td></td></tr><tr><td>Hydrocortisone</td><td>Sigma</td><td>H0396</td><td></td></tr><tr><td>MEM Alpha</td><td>Life Technologies</td><td>12571-063</td><td></td></tr><tr><td>MEM non-essential Amino Acids</td><td>Life Technologies</td><td>11140</td><td></td></tr><tr><td>Nile Red</td><td>Sigma</td><td>72485-100MG</td><td>Protect from light</td></tr><tr><td>Paraformaldehyde 16% Solution, EM Grade</td><td>Electron Microscopy Sciences</td><td>15710</td><td></td></tr><tr><td>Penicillin-Strep</td><td>Life Technologies</td><td>15140-148</td><td></td></tr><tr><td>Phosphate Buffered Saline 10x</td><td>Gibco</td><td>70011-044</td><td></td></tr><tr><td>Rod Outer Segments (OS)</td><td>InVision Bioresources</td><td>98740</td><td></td></tr><tr><td>Sodium bicarbonate</td><td>Sigma Aldrich</td><td>S5761</td><td></td></tr><tr><td>Sodium Pyruvate</td><td>Life Technologies</td><td>11360-070</td><td></td></tr><tr><td>Sucrose</td><td>Sigma Aldrich</td><td>S1888</td><td></td></tr><tr><td>SYBR Green Master Mix</td><td>Bio-Rad</td><td>1725274</td><td></td></tr><tr><td>Taurine</td><td>Sigma</td><td>T0625</td><td></td></tr><tr><td>Triton X-100</td><td>Sigma</td><td>9002-93-1</td><td></td></tr><tr><td>Tween 20 Ultrapure</td><td>Affymetrix</td><td>9005-64-5</td><td></td></tr><tr><td>Vitronectin</td><td>Life Technologies</td><td>A14701SA</td><td></td></tr><tr><td>Y-27632 dihydrochloride</td><td>R&amp;amp;D Systems</td><td>1254</td><td></td></tr></tbody></table>

lipidunet-machine learning-based method of characterization and quantification of lipid deposits using ipsc-derived retinal pigment epithelium

The retinal pigment epithelium (RPE) is a monolayer of hexagonal cells located at the back of the eye. It provides nourishment and support to photoreceptors and choroidal capillaries, performs phagocytosis of photoreceptor outer segments (POS), and secretes cytokines in a polarized manner for maintaining the homeostasis of the outer retina. Dysfunctional RPE, caused by mutations, aging, and environmental factors, results in the degeneration of other retinal layers and causes vision loss. A hallmark phenotypic feature of degenerating RPE is intra and sub-cellular lipid-rich deposits. These deposits are a common phenotype across different retinal degenerative diseases. To reproduce the lipid deposit phenotype of monogenic retinal degenerations in vitro, induced pluripotent stem cell-derived RPE (iRPE) was generated from patients&#39; fibroblasts. Cell lines generated from patients with Stargardt and Late-onset retinal degeneration (L-ORD) disease were fed with POS for 7 days to replicate RPE physiological function, which caused POS phagocytosis-induced pathology in these diseases. To generate a model for age-related macular degeneration (AMD), a polygenic disease associated with alternate complement activation, iRPE was challenged with alternate complement anaphylatoxins. The intra and sub-cellular lipid deposits were characterized using Nile Red, boron-dipyrromethene (BODIPY), and apolipoprotein E (APOE). To quantify the density of lipid deposits, a machine learning-based software, LipidUNet, was developed. The software was trained on maximum-intensity projection images of iRPE on culture surfaces. In the future, it will be trained to analyze three-dimensional (3D) images and quantify the volume of lipid droplets. The LipidUNet software will be a valuable resource for discovering drugs that decrease lipid accumulation in disease models.

This protocol provides a workflow to identify lipid deposits stained by Nile Red, BODIPY, and APOE. The developed software can automatically identify and quantify lipid deposits and performs best when the protocol outlined is optimized. Included are examples of successfully differentiated RPE (Figure 3A) and poorly differentiated RPE (Figure 3B), as the quality of the cell model greatly impacts the quality of proper image segmentation.
Two of the three markers described in the protocol, Nile Red and BODIPY, are identified as small circular points that are distinctly bright in fluorescent images (Figure 5 and Figure 6). A &#34;positive&#34; image from the protocol would be an appropriate identification of these distinct deposits (Figure 5A-D and Figure 5E-H). A &#34;negative&#34; result would show incorrect segmentation of the image by mistaking background fluorescence as a deposit, either due to weak staining (Figure 6A-C and Figure 6D-F) or due to high background intensity (Figure 6G-I).
APOE deposits have a variety of sizes and shapes, appearing more oval or irregular rather than the circular deposits of Nile Red and BODIPY. These deposits are also less punctate, and signal intensity can differ between deposits due to variations in the permeabilization of the sample. Correct identification will identify each deposit, including those that are less saturated (Figure 5I-L), while incorrect segmentation will not pick up these deposits (Figure 6J-L). Therefore, it is important to optimize staining and imaging methods to avoid drastic variation. One way to do this is by paying careful attention to the sample permeabilization steps while immunostaining. To optimize fluorescent signal, cells can be lysed prior to fixation and immunostaining for APOE, which results in even saturation and better segmentation of the APOE deposits.
Provided are also segmented images of cells matured on a culture platform other than a 96 well plate. The LipidUNet software was run on images of cells cultured on a transwell, and while the lipid deposits are thresholded, so too are the pores in the transwell membrane (Figure 6M-O). Because of the similarity in shape and size, the LipidUNet software in its current form will mask both the lipid deposits and transwell pores indiscriminately.
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/65503/65503fig05.jpg" /> 
Figure 5: Representative Results. (A,E,I) 96-well plated RPE are stained with Hoechst nuclear staining (blue) and either Nile Red (magenta), BODIPY (green), or APOE (orange) and are the maximum intensity projections of a Z-stack. (B,F,J) The greyscale input images for the LipidUNet software after image processing. (C,G,K) Masks generated by LipidUNet, where all deposits are identified correctly. (D,H,L) Outlines of each masked particle are numbered. These labels allow to connect each particle in the image to an entry in the spreadheet with the raw data. (A-D) shows Nile Red staining, and the software is able to recognize the deposits against the background accurately despite a weaker signal. (E-H) shows a strong contrast between the BODIPY signal and background, which is ideal. LipidUNet correctly identifies every deposit in the image. (I-L) shows a strong APOE signal and represents the variability of signal saturation that is often seen with this stain. Nonetheless, image segmentation is able to identify the borders of each APOE deposit. <a href="https://www.jove.com/files/ftp_upload/65503/65503fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/65503/65503fig06.jpg" /> 
Figure 6: Suboptimal Results. (A,D,G,J,M) 96-well-plated RPE are stained with Hoechst nuclear staining (blue) and either Nile Red (magenta), BODIPY (green), or APOE (orange) and are the maximum intensity projections of a Z-stack. (B,E,H,K,N) The greyscale input images for the LipidUNet software after image processing. (C,F,I,L,O) The incorrect masks generated by LipidUNet. Red circles indicate where the software has incorrectly identified a lipid deposit. (A-C) Nile Red processing is incorrect because the software has identified the background staining as a deposit. This can happen more often when there is high background but few lipid deposits in the image.&#160;Two examples of BODIPY staining are shown: a poor-quality image due to (D-F) weak BODIPY staining and (G - I) a strong BODIPY signal with high background. In both cases, the software is unable to distinguish small, circular lipid deposits from the background circular ring surrounding the nucleus. While staining and imaging should be optimized to avoid these errors, the most recent version of LipidUNet is largely improved for these images. (J-L) Incorrect APOE segmentation. Since the deposits are more variable in size and saturation of signal, the software has difficulty recognizing some deposits. (M-O) RPE seeded onto a transwell and stained with Nile Red. A slice of the Z-stack is shown here with both Nile Red lipid deposits and transwell pores. The software is unable to distinguish between the two, as shown by the red circle containing transwell pores and the green arrow pointing to Nile Red deposits. <a href="https://www.jove.com/files/ftp_upload/65503/65503fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/65503/65503fig07.jpg" /> 
Figure 7: Mask Tool Comparison. (A,B,C)&#160;96-well plated RPE with variable amounts of lipid deposition are identified with Nile Red (red). The images are masked using three different common masking methods, Find Maxima, Max Entropy, and Renyi Entropy, and compared to the LipidUNet-generated mask. The original image is accompanied by a manual count of the lipid deposits, while the masks display the predicted counts by each segmentation method. The average error rate was calculated for each method of segmentation using the following formula: mean[(|Predicted Count - Manual Count|/Manual Count) x 100]. The LipidUNet-generated mask more accurately identifies lipid deposits across images with variable deposition when compared to other masking methods (Average error rates: 23% LipidUnet, 1164% Find Maxima, 851% Max Entropy, 203% Renyi Entropy). <a href="https://www.jove.com/files/ftp_upload/65503/65503fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Component</td>
			<td>Cat number</td>
			<td>Stock Conc.</td>
			<td>Final Conc.</td>
			<td>mL</td>
		</tr>
		<tr>
			<td>MEM alpha</td>
			<td>12571-063</td>
			<td>NA</td>
			<td></td>
			<td>500</td>
		</tr>
		<tr>
			<td>N2 supplement</td>
			<td>17502-048</td>
			<td>NA</td>
			<td>1%</td>
			<td>5</td>
		</tr>
		<tr>
			<td>Heat Inactivated FBS</td>
			<td>SH30071.03</td>
			<td>NA</td>
			<td>5%</td>
			<td>25</td>
		</tr>
		<tr>
			<td>NMEM NEAA</td>
			<td>11140-050</td>
			<td>10mM</td>
			<td>0.01mM</td>
			<td>5</td>
		</tr>
		<tr>
			<td>Sodium Pyruvate</td>
			<td>11360-070</td>
			<td>100mM</td>
			<td>1mM</td>
			<td>5</td>
		</tr>
		<tr>
			<td>Penicillin-streptomycin</td>
			<td>15140-122</td>
			<td>10000u/mL</td>
			<td>100U/mL</td>
			<td>5</td>
		</tr>
		<tr>
			<td>Taurine</td>
			<td>T4571</td>
			<td>50mg/mL</td>
			<td>250ug/mL</td>
			<td>2.5</td>
		</tr>
		<tr>
			<td>Hydrocortisone</td>
			<td>H6909</td>
			<td>18.1mg/L</td>
			<td>20ug/L</td>
			<td>0.553</td>
		</tr>
		<tr>
			<td>T3</td>
			<td>T5516</td>
			<td>20ug/L</td>
			<td>0.013ug/L</td>
			<td>0.33</td>
		</tr>
		<tr>
			<td colspan="4">Total volume, mL</td>
			<td>548.383</td>
		</tr>
	</tbody>
</table>
Table 1:&#160;RPE-MM reagent composition. A list of reagents and optimal concentrations for RPE-MM.

Watch this Scientific Journal Video about Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies at JoVE.com

Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies 

Degenerative eye diseases that affect the retinal pigment epithelium layer of the eye have monogenic and polygenic origins. Several disease models and a software application, LipidUNet, have been developed to study mechanisms of disease, as well as potential therapeutic interventions.

LipidUNet-Machine Learning-Based Method of Characterization and Quantification of Lipid Deposits Using iPSC-Derived Retinal Pigment Epithelium

The retinal pigment epithelium (RPE) is a monolayer of hexagonal cells located at the back of the eye. It provides nourishment and support to ...

lipidunet-machine-learning-based-method-characterization

Research

JoVE Journal

Biology

2.4K Views.  National Institutes of Health (NIH). Degenerative eye diseases that affect the retinal pigment epithelium layer of the eye have monogenic and polygenic origins. Several disease models and a software application, LipidUNet, have been developed to study mechanisms of disease, as well as potential therapeutic interventions.

Video: Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies 

A Protocol to Evaluate and Quantify Retinal Pigmented Epithelium Pathologies in Mouse Models of Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is a debilitating retinal disorder in aging populations. It is widely believed that dysfunction of the retinal pigmented epithelium (RPE) is a key pathobiological event in AMD. To understand the mechanisms that lead to RPE dysfunction, mouse models can be utilized by researchers. It has been established by previous studies that mice can develop RPE pathologies, some of which are observed in the eyes of individuals diagnosed with AMD. Here, we describe a phenotyping protocol to assess RPE pathologies in mice. This protocol includes the preparation and evaluation of retinal cross-sections using light microscopy and transmission electron microscopy, as well as that of RPE flat mounts by confocal microscopy. We detail the common types of murine RPE pathologies observed by these techniques and ways to quantify them through unbiased methods for statistical testing. As proof of concept, we use this RPE phenotyping protocol to quantify the RPE pathologies observed in mice overexpressing transmembrane protein 135 (Tmem135) and aged wild-type C57BL/6J mice. The main goal of this protocol is to present standard RPE phenotyping methods with unbiased quantitative assessments for scientists using mouse models of AMD.

Mouse models can be useful tools for investigating the biology of the retinal pigmented epithelium (RPE). It has been established that mice can develop an array of RPE pathologies. Here, we describe a phenotyping protocol to elucidate and quantify RPE pathologies in mice using light, transmission electron, and confocal microscopy.

Age-related macular degeneration (AMD) is a debilitating retinal disorder in aging populations. It is widely believed that dysfunction of the retinal ...

Genetics

Analysis of Lipid Signaling in Drosophila Photoreceptors using Mass Spectrometry

The activation of phospholipase C&#946; (PLC&#946;) is an essential step during sensory transduction in Drosophila photoreceptors. PLC&#946; activity results in the hydrolysis of the membrane lipid phosphatidylinositol 4,5 bisphosphate [PI(4,5)P2] leading ultimately to the activation of transient receptor potential (TRP) and TRP like (TRPL) channels. The activity of PLC&#946; also leads subsequently to the generation of many lipid species several of which have been proposed to play a role in TRP and TRPL activation. In addition, several classes of lipids have been proposed to play key roles in organizing the cell biology of photoreceptors to optimize signaling reactions for optimal sensory transduction. Historically, these discoveries have been driven by the ability to isolate Drosophila mutants for enzymes that control the levels of specific lipids and perform analysis of photoreceptor physiology in these mutants. More recently, powerful mass spectrometry methods for isolation and quantitative analysis of lipids with high sensitivity and specificity have been developed. These are particularly suited for use in Drosophila where lipid analysis is now possible from photoreceptors without the need for radionuclide labeling. In this article, the conceptual and practical considerations in the use of lipid mass spectrometry for the robust, sensitive, and accurate quantitative assessment of various signaling lipids in Drosophila photoreceptors are covered. Along with existing methods in molecular genetics and physiological analysis such lipid is likely to enhance the power of photoreceptors as a model system for discoveries in biology.

Analysis of Lipid Signaling in Drosophila Photoreceptors using Mass Spectrometry

The manuscript presents versatile, robust, and sensitive mass spectrometry protocols to identify and quantify several classes of lipids from Drosophila photoreceptors.

The activation of phospholipase C&#946; (PLC&#946;) is an essential step during sensory transduction in Drosophila photoreceptors. PLC&#946; activity ...

Biochemistry

Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases

The retinal pigment epithelium (RPE) is juxtaposed to the overlying sensory retina, and supports the function of the visual system. Among the tasks performed by the RPE are phagocytosis and processing of outer photoreceptor segments through lysosome-derived organelles. These degradation products, stored and referred to as lipofuscin granules, are composed partially of bisretinoids, which have broad fluorescence absorption and emission spectra that can be detected clinically as fundus autofluorescence with confocal scanning laser ophthalmoscopy (cSLO). Lipofuscin accumulation is associated with increasing age, but is also found in various patterns in both acquired and inherited degenerative diseases of the retina. Thus, studying its pattern of accumulation and correlating such patterns with changes in the overlying sensory retina are essential to understanding the pathophysiology and progression of retinal disease. Here, we describe a technique employed by our lab and others that uses cSLO in order to quantify the level of RPE lipofuscin in both healthy and diseased eyes.

The retinal pigment epithelium (RPE) supports the sensory retina through recycling visual cycle byproducts, which accumulate as lipofuscin. These products are autofluorescent and can be qualitatively imaged in vivo. Here, we describe a method to quantitatively image RPE lipofuscin using confocal scanning laser ophthalmoscopy.

The retinal pigment epithelium (RPE) is juxtaposed to the overlying sensory retina, and supports the function of the visual system. Among the tasks ...

Medicine

MicroRNA Expression Profiles of Human iPS Cells, Retinal Pigment Epithelium Derived From iPS, and Fetal Retinal Pigment Epithelium

The objective of this report is to describe the protocols for comparing the microRNA (miRNA) profiles of human induced-pluripotent stem (iPS) cells, retinal pigment epithelium (RPE) derived from human iPS cells (iPS-RPE), and fetal RPE. The protocols include collection of RNA for analysis by microarray, and the analysis of microarray data to identify miRNAs that are differentially expressed among three cell types. The methods for culture of iPS cells and fetal RPE are explained. The protocol used for differentiation of RPE from human iPS is also described. The RNA extraction technique we describe was selected to allow maximal recovery of very small RNA for use in a miRNA microarray. Finally, cellular pathway and network analysis of microarray data is explained. These techniques will facilitate the comparison of the miRNA profiles of three different cell types.

The microRNA (miRNA) profiles of human induced-pluripotent stem (iPS) cells, retinal pigment epithelium (RPE) derived from human induced-pluripotent stem (iPS) cells (iPS-RPE), and fetal RPE, were compared.

The objective of this report is to describe the protocols for comparing the microRNA (miRNA) profiles of human induced-pluripotent stem (iPS) cells, ...

Primary Cell Cultures from the Mouse Retinal Pigment Epithelium

The retinal pigment epithelium (RPE) is a highly polarized multi-functional epithelium that is located between the neural retina and the choroid of the eye. It is a single sheet of pigmented cells that are hexagonally packed and connected by tight junctions. The main functions of the RPE include absorption of light, phagocytosis of the shed photoreceptor outer segments, spatial buffering of ions, transport of nutrients, ions and water as well as active involvement in the visual cycle. With such important and diverse functions, it is critically important to study the biology of RPE cells. A number of RPE cell lines have been established; however, passaged and immortalized cells are known to quickly lose some of the morphological and physiological characteristics of natural RPE cells. Thus, primary cells are more suitable for studying different aspects of RPE cell biology and function. Mouse primary RPE cell culture is very useful to researchers since mouse models are widely used in biological studies, however collecting RPE cells from mouse is also very challenging due to their small size. Here, we present a protocol for establishing primary mouse RPE cell cultures which includes enucleation and dissection of the eyes and isolation of the RPE sheets to yield the cells for culturing. This method enables efficient cell recovery. The RPE cells obtained from two mice&#160;can reach confluency on one 12 mm polyester membrane insert pre-loaded in culture plate after one week of culture and display some of the original properties of bona fide RPE cells such as hexagonal shape and pigmentation after two weeks of culture.

The retinal pigment epithelium (RPE) is a multi-functional epithelium of the eye. Here we present a protocol to establish primary cell cultures derived from the murine RPE.

The retinal pigment epithelium (RPE) is a highly polarized multi-functional epithelium that is located between the neural retina and the choroid of the ...

Quantification of Lipid Abundance and Evaluation of Lipid Distribution in Caenorhabditis elegans by Nile Red and Oil Red O Staining

Caenorhabditis elegans is an exceptional model organism in which to study lipid metabolism and energy homeostasis. Many of its lipid genes are conserved in humans and are associated with metabolic syndrome or other diseases. Examination of lipid accumulation in this organism can be carried out by fixative dyes or label-free methods. Fixative stains like Nile red and oil red O are inexpensive, reliable ways to quantitatively measure lipid levels and to qualitatively observe lipid distribution across tissues, respectively. Moreover, these stains allow for high-throughput screening of various lipid metabolism genes and pathways. Additionally, their hydrophobic nature facilitates lipid solubility, reduces interaction with surrounding tissues, and prevents dissociation into the solvent. Though these methods are effective at examining general lipid content, they do not provide detailed information about the chemical composition and diversity of lipid deposits. For these purposes, label-free methods such as GC-MS and CARS microscopy are better suited, their costs notwithstanding.

Quantification of Lipid Abundance and Evaluation of Lipid Distribution in Caenorhabditis elegans by Nile Red and Oil Red O Staining

Nile red staining of fixed Caenorhabditis elegans is a method for quantitative measurement of neutral lipid deposits, while oil red O staining facilitates qualitative assessment of lipid distribution among tissues.

Caenorhabditis elegans is an exceptional model organism in which to study lipid metabolism and energy homeostasis. Many of its lipid genes are conserved ...

Measurement of Energy Metabolism in Explanted Retinal Tissue Using Extracellular Flux Analysis

High acuity vision is a heavily energy-consuming process, and the retina has developed several unique adaptations to precisely meet such demands while maintaining transparency of the visual axis. Perturbations to this delicate balance cause blinding illnesses, such as diabetic retinopathy. Therefore, the understanding of energy metabolism changes in the retina during disease is imperative to the development of rational therapies for various causes of vison loss. The recent advent of commercially-available extracellular flux analyzers has made the study of retinal energy metabolism more accessible. This protocol describes the use of such an analyzer to measure contributions to retinal energy supply through its two principle arms - oxidative phosphorylation and glycolysis - by quantifying changes in oxygen consumption rates (OCR) and extracellular acidification rates (ECAR) as proxies for these pathways. This technique is readily performed in explanted retinal tissue, facilitating assessment of responses to multiple pharmacologic agents in a single experiment. Metabolic signatures in retinas from animals lacking rod photoreceptor signaling are compared to wild-type controls using this method. A major limitation in this technique is the lack of ability to discriminate between light-adapted and dark-adapted energy utilization, an important physiologic consideration in retinal tissue.

This technique describes real time recording of oxygen consumption and extracellular acidification rates in explanted mouse retinal tissues using an extracellular flux analyzer.

High acuity vision is a heavily energy-consuming process, and the retina has developed several unique adaptations to precisely meet such demands while ...

Analyzing the &alpha;-Actinin Network in Human iPSC-Derived Cardiomyocytes Using Single Molecule Localization Microscopy

The maturation of iPSC-derived cardiomyocytes is a critical issue for their application in regenerative therapy, drug testing and disease modeling. Despite the development of multiple differentiation protocols, the generation of iPSC cardiomyocytes resembling an adult-like phenotype remains challenging. One major aspect of cardiomyocytes maturation involves the formation of a well-organized sarcomere network to ensure high contraction capacity. Here, we present a super resolution-based approach for semi-quantitative analysis of the &#945;-actinin network in cardiomyocytes. Using photoactivated localization microscopy a comparison of sarcomere length and z-disc thickness of iPSC-derived cardiomyocytes and cardiac cells isolated from neonatal tissue was performed. At the same time, we demonstrate the importance of proper imaging conditions to obtain reliable data. Our results show that this method is suitable to quantitatively monitor the structural maturity of cardiac cells with high spatial resolution, enabling the detection of even subtle changes of sarcomere organization.

Analyzing the &#945;-Actinin Network in Human iPSC-Derived Cardiomyocytes Using Single Molecule Localization Microscopy

The formation of a proper sarcomere network is important for the maturation of iPSC-derived cardiomyocytes. We present a super resolution-based approach that allows for the quantitative evaluation of the structural maturation of stem cell derived cardiomyocytes, to improve culture conditions promoting cardiac development.

The maturation of iPSC-derived cardiomyocytes is a critical issue for their application in regenerative therapy, drug testing and disease modeling. ...

Efficient and Consistent Generation of Retinal Pigment Epithelium/Choroid Flatmounts from Human Eyes for Histological Analysis

The retinal pigment epithelium (RPE) and retina are functionally and structurally connected tissues that work together to regulate light perception and vision. Proteins on the RPE apical surface are tightly associated with proteins on the photoreceptor outer segment surface, making it difficult to consistently separate the RPE from the photoreceptors/retina. We developed a method to efficiently separate the retina from the RPE of human eyes to generate complete RPE/choroid and retina flatmounts for separate cellular analysis of the photoreceptors and RPE cells. An intravitreal injection of a high-osmolarity solution of D-mannitol, a sugar not transported by the RPE, induced the separation of the RPE and retina across the entire posterior chamber without causing damage to the RPE cell junctions. No RPE patches were observed attached to the retina. Phalloidin labeling of actin showed RPE shape preservation and allowed morphometric analysis of the entire epithelium. An artificial intelligence (AI)-based software was developed to accurately recognize and segment the RPE cell borders and quantify 30 different shape metrics. This dissection method is highly reproducible and can be easily extended to other animal models.

We describe a method to efficiently separate retinal pigment epithelium (RPE) from the retina in human eyes and generate whole RPE/choroid flatmounts for histological and morphometric analyses of the RPE.

The retinal pigment epithelium (RPE) and retina are functionally and structurally connected tissues that work together to regulate light perception and ...

Quantifying Retinal Pigment Epithelial Phagocytosis and Lipofuscin Accumulation

Improved Lipofuscin Models and Quantification of Outer Segment Phagocytosis Capacity in Highly Polarized Human Retinal Pigment Epithelial Cultures

The daily phagocytosis of photoreceptor outer segments by the retinal pigment epithelium (RPE) contributes to the accumulation of an intracellular aging pigment termed lipofuscin. The toxicity of lipofuscin is well established in Stargardt's disease, the most common inherited retinal degeneration, but is more controversial in age-related macular degeneration (AMD), the leading cause of irreversible blindness in the developed world. Determining lipofuscin toxicity in humans has been difficult, and animal models of Stargardt's have limited toxicity. Thus, in vitro models that mimic human RPE in vivo are needed to better understand lipofuscin generation, clearance, and toxicity. The majority of cell culture lipofuscin models to date have been in cell lines or have involved feeding RPE a single component of the complex lipofuscin mixture rather than fragments/tips of the entire photoreceptor outer segment, which generates a more complete and physiologic lipofuscin model. Described here is a method to induce the accumulation of lipofuscin-like material (termed undigestible autofluorescence material, or UAM) in highly differentiated primary human pre-natal RPE (hfRPE) and induced pluripotent stem cell (iPSC) derived RPE. UAM accumulated in cultures by repeated feedings of ultraviolet light-treated OS fragments taken up by the RPE via phagocytosis. The key ways that UAM approximates and differs from lipofuscin in vivo are also discussed. Accompanying this model of lipofuscin-like accumulation, imaging methods to distinguish the broad autofluorescence spectrum of UAM granules from concurrent antibody staining are introduced. Finally, to assess the impact of UAM on RPE phagocytosis capacity, a new method for quantifying outer segment fragment/tips uptake and breakdown has been introduced. Termed "Total Consumptive Capacity", this method overcomes potential misinterpretations of RPE phagocytosis capacity inherent in classic outer segment "pulse-chase" assays. The models and techniques introduced here can be used to study lipofuscin generation and clearance pathways and putative toxicity.

Author Spotlight: Improved Lipofuscin Models and Quantification of Outer Segment Phagocytosis Capacity in Highly Polarized Human Retinal Pigment Epithelial Cultures  

This protocol describes a lipofuscin accumulation model in highly differentiated and polarized human retinal pigment epithelial (RPE) cultures and an improved outer segment (OS) phagocytosis assay to detect the total OS consumption/degradation capacity of the RPE. These methods overcome the limitations of previous lipofuscin models and classical pulse-chase outer segment phagocytosis assays.

The daily phagocytosis of photoreceptor outer segments by the retinal pigment epithelium (RPE) contributes to the accumulation of an intracellular aging ...

LipidUNet-Machine Learning-Based Method of Characterization and Quantification of Lipid Deposits Using iPSC-Derived Retinal Pigment Epithelium

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Efficient and Consistent Generation of Retinal Pigment Epithelium/Choroid Flatmounts from Human Eyes for Histological Analysis

Improved Lipofuscin Models and Quantification of Outer Segment Phagocytosis Capacity in Highly Polarized Human Retinal Pigment Epithelial Cultures