This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project-ID 259130777 - SFB 1177 (project E03). Figure 1 was created using BioRender.

1. Culturing and seeding primary cells
<ol>
	<li>Fetch the vial with frozen cells from the liquid nitrogen tank, and keep it on ice until reaching the cell culture room. In a previously sterilized hood, hold the vial in the hand so the cell suspension thaws. Resuspend the cells, and transfer them to a 10 cm cell culture plate with 9 mL of prewarmed DMEM with 10% fetal calf serum (FCS) and 1% penicillin-streptomycin (P/S). Shake gently so that the cells distribute evenly on the plate, and incubate overnight at 37 &#176;C and 5% CO2 </li>
	<li>The following morning, look at the cells to confirm that they are attached. Change the medium to fresh DMEM + 10% FCS + 1%P/S, and let the cells grow until they reach 80% confluency.</li>
	<li>Remove the culture medium, wash 2x with DPBS, add 1 mL of trypsin to the plate, and incubate it at 37 &#176;C and 5% CO2 for 10 min. Add 9 mL of DMEM + 10% FCS to the plate, and resuspend the cells.</li>
	<li>Transfer the cell suspension to a 15 mL centrifuge tube. Determine the cell count manually using a Neubauer chamber, and dilute the cell suspension in DMEM + 10% FCS until a cell concentration of 40,000 cells/mL is achieved.</li>
	<li>In a glass-bottom 96-well plate, seed 100 &#181;L/well of the cell suspension. Incubate at 37 &#176;C and 5% CO2 for 16 h.</li>
</ol>
2. Treatments
<ol>
	<li>Prepare the following treatments according to the Table of Materials: control medium, control medium plus deferasiox (DFX) for iron chelation, or control medium plus ferric ammonium citrate (FAC) for iron loading. Prepare all the treatments in both the presence and absence of a lysosomal inhibitor (bafilomycin A1) for the assessment of the ferritinophagy flux.</li>
	<li>Remove the culture medium from the plate, and replace it with the corresponding treatment (100 &#181;L/well).</li>
	<li>Incubate at 37 &#176;C and 5% CO2 for 6 h.</li>
</ol>
3. Fixation and staining
<ol>
	<li>Aspirate the treatment medium, and wash 2x with DPBS (Table of Materials).</li>
	<li>Add 100 &#181;L of 3.7% paraformaldehyde (PFA, Table of Materials) per well. Fix the cells for 20 min at room temperature in darkness.</li>
	<li>Remove the PFA, and wash the cells 2x with PBS/T (Table of Materials).</li>
	<li>Block in PBS/T containing 1% bovine serum albumin (BSA) for 1 h at 4 &#176;C.</li>
	<li>Prepare the primary antibody (Table of Materials) mix in PBS/T (ferritin antibody 1:50, LAMP2 antibody 1:50).</li>
	<li>Wash the cells 2x with PBS/T, and apply 50 &#181;L of the antibody mix per well. Wrap the plates with parafilm, and incubate overnight at 4 &#176;C.</li>
	<li>The following morning, prepare the secondary antibody (Table of Materials) mix (1:200) in PBS/T.</li>
	<li>Wash the cells 2x with PBS/T, and apply 50 &#181;L of the secondary antibody mix per well. Incubate at 4 &#176;C for 1 h in the dark.</li>
	<li>Prepare 5 &#181;g/&#181;L 4&#39;,6-diamidino-2-phenylindole (DAPI, Table of Materials) solution in room-temperature PBS.</li>
	<li>Wash the cells 2x with PBS/T, and add 100 &#181;L/well of DAPI stain. Incubate for 20 min at room temperature in the dark.</li>
	<li>Wash the cells 2x with PBS, and add 50 &#181;L of PBS to each well. Wrap the plates with parafilm, and store at 4 &#176;C in the dark.</li>
</ol>
4. Automated imaging using confocal laser-scanning microscopy
<ol>
	<li>Exchange the PBS in the plate for 50 &#181;L of fresh PBS at room temperature.</li>
	<li>Cover the plate with aluminum foil and take it to the microscopy room.</li>
	<li>Turn the confocal laser-scanning microscope on, set a sample holder for 96-well plates on the microscope table, and select an appropriate objective (here: 40x). Refer to Table 1 for details.</li>
	<li>Adjust the imaging settings (Table 1).
	<ol>
		<li>In Smart Setup, select the lasers and filters, and assign them to tracks (Tracks 1-3) for optimal image quality and speed (Table 1).</li>
		<li>When selecting the type of experiment, click on Tiles to enable the option of selecting and saving the determined X,Y positions on the plate.</li>
		<li>In the Imaging Setup module, visualize the acquisition channels, the tracks assigned to them, and their excitation and emission wavelengths (Table 1).</li>
		<li>In the Acquisition Mode module, select the following: Scan Speed: 6; Direction: bidirectional; Averaging: 2x; Bits per pixel: 8.</li>
		<li>In the Channels module, adjust the laser power, pinhole, and master gain for each channel individually (Table 2). Fine-tune these parameters to achieve properly exposed images without oversaturating them.</li>
		<li>Click on the Focus strategy module.
		<ol>
			<li>Select Combine Software Autofocus and Definite Focus.</li>
			<li>Under Reference Channel and Offsets, select the most stable/brightest channel as a reference (normally DAPI or 488 nm).</li>
			<li>Under Stabilization Event Repetitions and Frequency, select Standard.</li>
		</ol>
		</li>
		<li>In the Software Autofocus module, select the following: Mode | Intensity; Search | Smart; Sampling | Fine; Relative Range.</li>
		<li>Click on the Tiles module.
		<ol>
			<li>Select Positions | single positions.</li>
			<li>Under Advanced Setup, navigate through the plate, identify a position for imaging, and click on the + button under the Position Setup tab.</li>
			<li>Label each position with additional information that will be recorded on the image metadata. To do so, open the Properties tab, click on the wheel icon next to the Category dropdown menu, and click on New to open a new dialog box where the new category name is to be entered. To assign a category to a certain position, select the position, click on the Properties tab, go to the Categories dropdown menu, and select the corresponding label. 
			&#8203;NOTE: It is recommended to create a category for each treatment to label each position with the treatment that the cells were subjected to.</li>
			<li>Under Sample Carrier, select Multiwell 96-Cellvis glass bottom #0. Click on Calibrate to calibrate the microscope to the plate surface. Choose between 1 point or 7 point calibration depending on the system&#39;s specifications.</li>
			<li>Under Options, select a tile overlap of 10%. Under Travel in Tile Regions, select Comb. Tick the following: use stage speed from stage control, use stage acceleration from stage control, and image pyramid during acquisition.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Run image acquisition, and save the image results (czi files) and the image metadata (csv files) on a portable hard drive.</li>
	<li>Export the images as TIFF files.</li>
</ol>
5. High-throughput image analysis
<ol>
	<li>Create a separate metadata spreadsheet file with the following columns: Position, Well, Condition, Series, and Set. The appropriate data to fill these columns are retrieved from the original metadata file that comes from confocal laser-scanning microscopy.</li>
	<li>Download the open-source software CellProfiler 4.2.4 (https://cellprofiler.org/previous-releases).</li>
	<li>Launch CellProfiler 4.2.4 by double-clicking, and import the CellProfiler pipeline (Supplemental File 1) by drag and drop. 
	NOTE: Find below the steps to compile the CellProfiler pipeline (Supplemental File 1) for the automated recognition of fibroblasts using DAPI staining (Module 1, nuclei recognition, see Table 2) and background fluorescence in single cells derived from AF488 (Module 2, cell recognition, see Table 2). Puncta (termed objects) recognition is determined by the puncta size and the puncta fluorescence intensity over background (Module 3, puncta recognition, see Table 2). Finally, according to the cell boundaries, the puncta recognized by the software are assigned to the corresponding cells (Assign relationships). A detailed description of how to implement a CellProfiler pipeline for autophagy studies has been published previously9. In addition, Table 2 contains the CellProfiler settings used in this study. Moreover, Supplemental File 2 displays the CellProfiler pipeline (Supplemental File 1) with screenshots taken in consecutive order and referring to the modules detailed in Table 2.</li>
	<li>Before adapting the CellProfiler pipeline (Supplemental File 1), drag and drop individual .czi image files or a folder containing multiple .czi image files&#160;to the Image module.</li>
	<li>Customize the image recognition pipeline. In the Names and Types module, replace the c1-c3 code with a code that allows the software to identify and sort the single-channel images to be analyzed.</li>
	<li>Tune the pipeline settings for the IdentifyPrimaryObjects and IdentifySecondaryObjects modules by optimizing the numeric entries in the modules. The values used in this study are presented in Table 2.</li>
	<li>To track the adjustments, select the Test Mode option by clicking on the Start Test Mode button. Then click the&#160;Step button between the modules. Wait for pop-up windows (the original input image, the recognition outlines drawn over the original image, and the analysis result) showing how an image would be analyzed with the settings that were just entered.</li>
	<li>Once the pipeline is optimized, end the test mode. Execute the analysis by activating the Exit Test Mode, followed by clicking on the Analyze Images button.</li>
	<li>After the analysis is complete, verify the accuracy of the cell and puncta detection by comparing the input images with the overlay outputs (cell boundaries, puncta) generated by CellProfiler. If not satisfactory, adjust the numerical values in the different modules (Table 2) until the analysis result is sufficiently accurate.</li>
</ol>
6. Data analysis
<ol>
	<li>After running the analysis, wait for the software to create a set of tabular text files with the results. If using the provided CellProfiler pipeline (Supplemental File 1), look for the following output files:
	<ol>
		<li>The CellProfilerOutputCells file, which lists the image number assigned by the software, the cell number assigned to single cells, the original file name, and the path indicating where the files are stored, the number of puncta (ferritin, LAMP2) per cell, and the mean puncta area (ferritin, LAMP2) per cell.</li>
		<li>The CellProfilerOutputExperiment file, which provides information on the technical details related to the experiment (e.g., the software version, the image channels, the metadata tags).</li>
	</ol>
	</li>
	<li>Open these files as spreadsheets.</li>
	<li>Proceed to conduct the statistical analysis of the puncta counts and puncta co-localization numbers using the preferred statistics software (Table of Materials).</li>
</ol>

<ol>
	<li>Proikas-Cezanne, T., Takacs, Z., D&#246;nnes, P., Kohlbacher, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=WIPI+proteins:+Essential+PtdIns3P+effectors+at+the+nascent+autophagosome.">WIPI proteins: Essential PtdIns3P effectors at the nascent autophagosome.</a> Journal of Cell Science. 128 (2), 207-217 (2015).</li><li>Proikas-Cezanne, T., 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=WIPI-1&#945;+(WIPI49),+a+member+of+the+novel+7-bladed+WIPI+protein+family,+is+aberrantly+expressed+in+human+cancer+and+is+linked+to+starvation-induced+autophagy.">WIPI-1&#945; (WIPI49), a member of the novel 7-bladed WIPI protein family, is aberrantly expressed in human cancer and is linked to starvation-induced autophagy.</a> Oncogene. 23 (58), 9314-9325 (2004).</li><li>Bakula, 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=WIPI3+and+WIPI4+&#946;-propellers+are+scaffolds+for+LKB1-AMPK-TSC+signalling+circuits+in+the+control+of+autophagy.">WIPI3 and WIPI4 &#946;-propellers are scaffolds for LKB1-AMPK-TSC signalling circuits in the control of autophagy.</a> Nature Communications. 8 (1), 15637 (2017).</li><li>Hayflick, S. 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=&#946;-Propeller+protein-associated+neurodegeneration:+A+new+X-linked+dominant+disorder+with+brain+iron+accumulation.">&#946;-Propeller protein-associated neurodegeneration: A new X-linked dominant disorder with brain iron accumulation.</a> Brain. 36, 1708-1717 (2013).</li><li>Wilson, J. A. -. O., 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=Consensus+clinical+management+guideline+for+beta-propeller+protein-associated+neurodegeneration.">Consensus clinical management guideline for beta-propeller protein-associated neurodegeneration.</a> Developmental Medicine and Child Neurology. 63 (12), 1402-1409 (2021).</li><li>Gao, G., Li, J., Zhang, Y., Chang, Y. Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cellular+iron+metabolism+and+regulation.">Cellular iron metabolism and regulation.</a> Advances in Experimental Medicine and Biology. 1173, 21-32 (2019).</li><li>Mancias, J. D., Wang, X., Gygi, S. P., Harper, J. W., Kimmelman, A. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+proteomics+identifies+NCOA4+as+the+cargo+receptor+mediating+ferritinophagy.">Quantitative proteomics identifies NCOA4 as the cargo receptor mediating ferritinophagy.</a> Nature. 509 (7498), 105-109 (2014).</li><li>Voss, P., Horakova, L., Jakstadt, M., Kiekebusch, D., Grune, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ferritin+oxidation+and+proteasomal+degradation:+Protection+by+antioxidants.">Ferritin oxidation and proteasomal degradation: Protection by antioxidants.</a> Free Radical Research. 40 (7), 673-683 (2006).</li><li>Sch&#252;ssele, D. S., 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=Autophagy+profiling+in+single+cells+with+open+source+CellProfiler-based+image+analysis.">Autophagy profiling in single cells with open source CellProfiler-based image analysis.</a> Autophagy. 19 (1), 338-351 (2023).</li><li>Goodwin, J. 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=Autophagy-independent+lysosomal+targeting+regulated+by+ULK1/2-FIP200+and+ATG9.">Autophagy-independent lysosomal targeting regulated by ULK1/2-FIP200 and ATG9.</a> Cell Reports. 20 (10), 2341-2356 (2017).</li><li>Quiles del Rey, M., Mancias, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NCOA4-mediated+ferritinophagy:+A+potential+link+to+neurodegeneration.">NCOA4-mediated ferritinophagy: A potential link to neurodegeneration.</a> Frontiers in Neuroscience. 13, 238 (2019).</li><li>Zachari, 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=Selective+autophagy+of+mitochondria+on+a+ubiquitin-endoplasmic-reticulum+platform.">Selective autophagy of mitochondria on a ubiquitin-endoplasmic-reticulum platform.</a> Developmental Cell. 50 (5), 627-643 (2019).</li></ol>

The authors declare no conflicts of interest.

This method, which uses automated fluorescence microscopy combined with open-access CellProfiler-based image analysis9 to assess the intracellular localization of key ferritinophagy factors, was designed to provide valuable information about the efficacy of lysosomal ferritin clearance via selective autophagy, referred to as ferritinophagy7. This protocol enables the handling of big sample sets with multiple cell lines and treatments simultaneously and the assessment of desired readouts, such as ferritin puncta numbers and ferritin puncta numbers colocalizing with LAMP2, which is a typical lysosomal marker for assessing the ferritinophagy flux. However, it should be emphasized here that accurate, automated image analysis depends on the image quality, which, in turn, depends on the antibody specificity and optimal imaging. Therefore, software-based image analysis should only be performed with high-quality images.
The modulation of iron levels in cells can provide valuable information about the mechanisms that are activated to regulate iron homeostasis in the context of disease. This paper describes a method to study BPAN diseases in patient-derived cells treated with iron-loading and iron-chelating agents. Deferasiox (DFX) is a known iron chelator used extensively in the field10. Upon application, it traps the free iron in the cytoplasm and triggers a cellular response to iron starvation. Since the aim of this study was to assess ferritinophagy, depriving the cells of iron was a straightforward way to promote ferritin degradation via autophagy as a compensation mechanism to replenish the cellular iron pool. Ferric ammonium citrate (FAC), on the contrary, is used to load cells with iron. Cells activate ferritin synthesis as a response to excessive and, thus, toxic cytosolic iron concentrations11. Ferric ammonium citrate, hence, promotes ferritin synthesis and the entrapment of iron within the ferritin heteropolymers, which can, in turn, activate ferritinophagy.
To study the ferritinophagic flux, in this protocol, we selectively stain the key players in the process-ferritin and the lysosomes (here marked by LAMP2)-and assess their colocalization. A high percentage of colocalizing puncta is indicative of effective lysosomal degradation of ferritin. To maximize the information retrieved from such experiments, additional dyes or marker antibodies can be used. 
Of note, there are technical challenges inherent in this method. Primary fibroblasts derived from different human donors may proliferate and grow differently in vitro. Hence, for comparable results, different cells should be seeded at the same passage. Therefore, it is advisable to perform a test experiment before proceeding with this protocol to examine the doubling times of the cells or cell lines that are to be used. 
This method is not restricted to the study of ferritinophagy; by simply changing the treatments and antibodies used, it can be repurposed to assess various other selective autophagy pathways; for example, mitophagy can be examined by using CCCP as a mitochondrial stress inducer and optineurin, LC3, and LAMP2 antibodies for the staining12. 
Overall, the combination of automated image acquisition and CellProfiler analysis constitutes a powerful tool for the high-throughput functional assessment of single human fibroblasts. This is especially relevant for studying human diseases, for which the analysis and comparison of large cohorts of patient-derived cells and healthy donor-derived cells are of great interest.

The WIPI (WD-repeat interacting with phosphoinositides) proteins are evolutionarily conserved PI3P effectors with distinct roles in autophagy1. The four human WIPI proteins (WIPI1 through WIPI4) fold into seven-bladed &#946;-propellers with two evolutionarily conserved regions in which homologous and invariant amino acids cluster on opposite sites of the propeller. One site is aligned with the phosphoinositide-binding region in propeller blade 5 and blade 6. The other site is aligned with the protein-protein interaction region where WIPIs associate with distinct members of the autophagy machinery or autophagy regulatory factors2.
WIPI4 functions in controlling energy-driven autophagy regulation and at the level of controlling the size of the growing nascent autophagosome3. A mutation in the WIPI4 gene, referred to as WDR45, is causative of beta-propeller-associated neurodegeneration (BPAN), a neurodegeneration with brain iron accumulation (NBIA) subtype in human patients that is characterized by a hypointense iron halo in the substantia nigra and globus pallidus4. The presence of abnormal iron deposits in BPAN patients provokes neuronal damage that translates into encephalopathy, global developmental delay, seizures, and ultimately, parkinsonism, dementia, and spasticity5.
Iron homeostasis is tightly regulated by multiple cellular mechanisms, with the concentration of cytosolic iron being controlled by the expression levels and functionality of iron carriers, iron transporters, and iron storage proteins6 . The concentration of cytosolic iron, in turn, determines whether degradation pathways such as ferritinophagy7 or the proteasomal clearance of oxidized ferritin8 are involved.
During ferritinophagy, ferritin is selectively recruited to autophagosomes by the cargo receptor NCOA4 and targeted for lysosomal degradation in response to low intracellular iron levels7. Given the role of WIPI4 in regulating the size of autophagosomal membranes3, it is reasonable to predict that the loss of this specific function may affect the selective sequestration of ferritin in autophagosomes and, thus, ferritinophagy. Studying this key step in iron metabolism may open doors to therapeutic options for BPAN patients; however, commonly used protocols to study ferritinophagy in human cells are only now being developed.
This paper describes a high-throughput, fluorescence-based method to assess ferritinophagy in human cells. The application of this assay to patient-derived BPAN cells can provide valuable insight into how ferritinophagy differs compared to healthy human cells and may serve as a benchmark for understanding the molecular mechanisms underlying this rare human disease.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Cell lines</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Primary skin-derived human fibroblasts&#160;</td>
			<td>Skin biopsy, healthy human donor, Biobank University Clinic T&#252;bingen, Ethical approvement 389/2019BO2</td>
			<td>F-CO-60, passage 9</td>
			<td></td>
		</tr>
		<tr>
			<td>Cell culture treatments</td>
			<td></td>
			<td></td>
			<td>Final concentration (compound)</td>
		</tr>
		<tr>
			<td>Control medium</td>
			<td>DMEM 10%FCS</td>
			<td></td>
			<td>(DMSO only)</td>
		</tr>
		<tr>
			<td>Control medium + bafilomycinA1</td>
			<td>DMEM 10%FCS</td>
			<td></td>
			<td>100 nM bafilomycin A1</td>
		</tr>
		<tr>
			<td>Control medium + deferasiox (DFX)</td>
			<td>DMEM 10%FCS</td>
			<td></td>
			<td>30 &#181;M DFX</td>
		</tr>
		<tr>
			<td>Control medium + DFX + bafilomycinA1</td>
			<td>DMEM 10%FCS</td>
			<td></td>
			<td>30 &#181;M DFX, 100 nM bafilomycin A1</td>
		</tr>
		<tr>
			<td>Control medium + ferric ammonium citrate (FAC)</td>
			<td>DMEM 10%FCS</td>
			<td></td>
			<td>0.05 mg/mL FAC</td>
		</tr>
		<tr>
			<td>Control medium + FAC + bafilomycinA1</td>
			<td>DMEM 10%FCS</td>
			<td></td>
			<td>0.05 mg/mL FAC, 100 nM bafilomycin A1</td>
		</tr>
		<tr>
			<td>Material</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Albumin [BSA] Fraction V</td>
			<td>AppliChem</td>
			<td>A1391</td>
			<td></td>
		</tr>
		<tr>
			<td>Alexa 488 goat a-rabbit</td>
			<td>Life Technologies</td>
			<td>A-11008</td>
			<td></td>
		</tr>
		<tr>
			<td>Alexa 546 goat a-mouse</td>
			<td>Life Technologies</td>
			<td>A-11003</td>
			<td></td>
		</tr>
		<tr>
			<td>Bafilomycin A1</td>
			<td>Sigma Aldrich</td>
			<td>196000</td>
			<td></td>
		</tr>
		<tr>
			<td>BioLite Cell Culture treated 10cm dishes</td>
			<td>Thermo Scientific</td>
			<td>130182</td>
			<td></td>
		</tr>
		<tr>
			<td>DAPI</td>
			<td>AppliChem</td>
			<td>A4099</td>
			<td></td>
		</tr>
		<tr>
			<td>Deferasiox (ICL-670)</td>
			<td>Selleckchem</td>
			<td>S1712</td>
			<td></td>
		</tr>
		<tr>
			<td>DMEM Glutamax</td>
			<td>Gibco</td>
			<td>31966</td>
			<td></td>
		</tr>
		<tr>
			<td>DMSO&#160;</td>
			<td>AppliChem</td>
			<td>A3672</td>
			<td></td>
		</tr>
		<tr>
			<td>DPBS no calcium nor magnesium</td>
			<td>Gibco</td>
			<td>14190094</td>
			<td></td>
		</tr>
		<tr>
			<td>Fetal bovine serum (FCS)</td>
			<td>Gibco</td>
			<td>10437-028</td>
			<td></td>
		</tr>
		<tr>
			<td>Ferric ammonium citrate (FAC)</td>
			<td>Merck</td>
			<td>F5879</td>
			<td></td>
		</tr>
		<tr>
			<td>Anti-FERRITIN (Human Spleen)</td>
			<td>Rockland</td>
			<td>200-401-090-0100</td>
			<td></td>
		</tr>
		<tr>
			<td>LAMP2 (H4B4)</td>
			<td>Santa Cruz</td>
			<td>Sc-18822</td>
			<td></td>
		</tr>
		<tr>
			<td>Paraformaldehyde (PFA)</td>
			<td>Sigma-Aldrich</td>
			<td>441244</td>
			<td></td>
		</tr>
		<tr>
			<td>PBS 10x Dulbecco&#8217;s</td>
			<td>AppliChem</td>
			<td>A0965</td>
			<td></td>
		</tr>
		<tr>
			<td>PenStrep (1,00U/mL penicillin, 100 mg/mL streptomycin)</td>
			<td>Life Technologies</td>
			<td>15140-122</td>
			<td></td>
		</tr>
		<tr>
			<td>Trypsin-EDTA (0.05%) with phenol red</td>
			<td>Gibco</td>
			<td>25300</td>
			<td></td>
		</tr>
		<tr>
			<td>Tween20</td>
			<td>AppliChem</td>
			<td>A4974</td>
			<td></td>
		</tr>
		<tr>
			<td>96 well glass-bottom #0 plates</td>
			<td>Cellvis</td>
			<td>P96-0-N</td>
			<td></td>
		</tr>
		<tr>
			<td>Solution</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>3.7% paraformaldehyde (PFA)</td>
			<td></td>
			<td></td>
			<td>PFA dissolved in PBS, pH 7.5&#160;</td>
		</tr>
		<tr>
			<td>Bafilomycin A1 100 mM stock</td>
			<td></td>
			<td></td>
			<td>Bafilomycin A1 diluted in DMSO</td>
		</tr>
		<tr>
			<td>Ferric ammonium citrate (FAC) 100 mg/mL stock</td>
			<td></td>
			<td></td>
			<td>FAC diluted in autoclaved double distilled water</td>
		</tr>
		<tr>
			<td>PBS/T</td>
			<td></td>
			<td></td>
			<td>PBS, supplemented with 10% Tween20&#160;</td>
		</tr>
		<tr>
			<td>PBS/T + BSA</td>
			<td></td>
			<td></td>
			<td>PBS, supplemented with 10% Tween20, 1% BSA&#160;</td>
		</tr>
		<tr>
			<td>Software</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>CellProfiler 4.2.4</td>
			<td>https://cellprofiler.org/</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>GraphPad Prism</td>
			<td>Dotmatics</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Microsoft Excel Version 16.50</td>
			<td>Microsoft Office Plus 365</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

ferritinophagy: assessing the selective degradation of iron by autophagy in human fibroblasts

Mutations in the autophagy gene WDR45/WIPI4 are the cause of beta-propeller-associated neurodegeneration (BPAN), a subtype of human diseases known as neurodegeneration with brain iron accumulation (NBIA) due to the presence of iron deposits in the brains of patients. Intracellular iron levels are tightly regulated by a number of cellular mechanisms, including the critical mechanism of ferritinophagy. This paper describes how ferritinophagy can be assessed in primary, skin-derived human fibroblasts. In this protocol, we use iron-modulating conditions for inducing or inhibiting ferritinophagy at the cellular level, such as the administration of bafilomycin A1 to inhibit lysosome function and ferric ammonium citrate (FAC) or deferasiox (DFX) treatments to overload or deplete iron, respectively. Such treated fibroblasts are then subjected to high-throughput imaging and CellProfiler-based quantitative localization analysis of endogenous ferritin and autophagosomal/lysosomal markers, here LAMP2. Based on the level of autophagosomal/lysosomal ferritin, conclusions can be drawn regarding the level of ferritinophagy. This protocol can be used to assess ferritinophagy in BPAN patient-derived primary fibroblasts or other types of mammalian cells.

Human skin-derived primary fibroblasts (F-CO-60) from healthy donors (Figure 1A) were prepared for ferritinophagy assessments using baflomycin A1 treatment followed by immunostaining of endogenous ferritin and LAMP2, automated image analysis, and CellProfiler-based analysis (Figure 1B).
The colocalization of endogenous ferritin and LAMP2 increased after the lysosomal degradation of ferritin was blocked by the addition of bafilomycin A1, consistent with its ability to inhibit lysosomal V-ATPase enzymes and, thus, block ferritinophagy7 (Figure 2). In addition, the software-based cell recognition, as well as the ferritin and LAMP2 recognition, are shown as an example (Figure 2).
In this regard, it should be noted that the CellProfiler pipeline outlined here is extremely versatile and can be adapted to additional and/or alternative readouts, such as to assess the effects of certain BPAN mutations on cell growth, mitochondria, or other organelles in the context of functional single-cell marker systems.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65110/65110fig01.jpg" /> 
Figure 1: Experimental overview. Graphical representation of (A) the process of ferritinophagy and (B) the experimental workflow to assess ferritinophagy in primary skin-derived human fibroblasts, as described in this protocol. <a href="https://www.jove.com/files/ftp_upload/65110/65110fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65110/65110fig02.jpg" /> 
Figure 2: Representative images. Primary skin-derived human fibroblasts in control medium (fed conditions) in the (A) absence or (B) presence of bafilomycin A1 were subjected to an indirect immunofluorescence analysis using anti-ferritin and anti-LAMP2 antibodies, while the cell nuclei were stained with DAPI. Exemplary fluorescence images acquired with confocal laser-scanning microscopy automation are presented (scale bars: 10 &#956;m). Changes in ferritin and LAMP2 abundance and colocalization could be observed upon lysosomal inhibition upon bafilomycin A1 administration. The CellProfiler-based analysis is also displayed, showing the software-derived image overlays of cell recognition (purple), nuclei (blue), ferritin (green), LAMP2 (red), and co-localizing ferritin/LAMP2 (yellow) puncta. (C) Magnified image sections (scale bar = 10 &#181;m). <a href="https://www.jove.com/files/ftp_upload/65110/65110fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Table 1: The confocal laser-scanning microscopy settings used in this study. <a href="https://www.jove.com/files/ftp_upload/65110/JoVE65110_Table 1.xlsx" target="_blank">Please click here to download this Table.</a>
Table 2: The numerical CellProfiler settings used in this study. <a href="https://www.jove.com/files/ftp_upload/65110/JoVE65110_Table 2.xlsx" target="_blank">Please click here to download this Table.</a>
Supplemental File 1: The CellProfiler pipeline used in this study. <a href="https://www.jove.com/files/ftp_upload/65110/JoVE65110_Supplemental File 1.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 2: Display of the CellProfiler pipeline (see Supplemental File 1) with screenshots taken in consecutive order (see Table 2). <a href="https://www.jove.com/files/ftp_upload/65110/JoVE65110_.Supplemental FIle 2.zip" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about Ferritinophagy: Assessing the Selective Degradation of Iron by Autophagy in Human Fibroblasts at JoVE.com

Ferritinophagy: Assessing the Selective Degradation of Iron by Autophagy in Human Fibroblasts

This protocol provides detailed instructions for performing high-throughput, immunofluorescence-based ferritinophagy assessments in primary, skin-derived human fibroblasts.

Mutations in the autophagy gene WDR45/WIPI4 are the cause of beta-propeller-associated neurodegeneration (BPAN), a subtype of human diseases known as ...

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740 Views.  Eberhard Karls University Tübingen. This protocol provides detailed instructions for performing high-throughput, immunofluorescence-based ferritinophagy assessments in primary, skin-derived human fibroblasts.

Video: Ferritinophagy: Assessing the Selective Degradation of Iron by Autophagy in Human Fibroblasts

Generation of Induced Pluripotent Stem Cells by Reprogramming Human Fibroblasts with the Stemgent Human TF Lentivirus Set

In 2006, Yamanaka and colleagues first demonstrated that retrovirus-mediated delivery and expression of Oct4, Sox2, c-Myc and Klf4 is capable of inducing the pluripotent state in mouse fibroblasts.1 The same group also reported the successful reprogramming of human somatic cells into induced pluripotent stem (iPS) cells using human versions of the same transcription factors delivered by retroviral vectors.2 Additionally, James Thomson et al. reported that the lentivirus-mediated co-expression of another set of factors (Oct4, Sox2, Nanog and Lin28) was capable of reprogramming human somatic cells into iPS cells.3
iPS cells are similar to ES cells in morphology, proliferation and the ability to differentiate into all tissue types of the body. Human iPS cells have a distinct advantage over ES cells as they exhibit key properties of ES cells without the ethical dilemma of embryo destruction. The generation of patient-specific iPS cells circumvents an important roadblock to personalized regenerative medicine therapies by eliminating the potential for immune rejection of non-autologous transplanted cells.
Here we demonstrate the protocol for reprogramming human fibroblast cells using the Stemgent Human TF Lentivirus Set. We also show that cells reprogrammed with this set begin to show iPS morphology four days post-transduction. Using the Stemolecule Y27632, we selected for iPS cells and observed correct morphology after three sequential rounds of colony picking and passaging. We also demonstrate that after reprogramming cells displayed the pluripotency marker AP, surface markers TRA-1-81, TRA-1-60, SSEA-4, and SSEA-3, and nuclear markers Oct4, Sox2 and Nanog.

We demonstrate the protocol for the generation of induced pluripotent stem cells from human somatic cells using lentivirus-mediated delivery of the human factors Oct4, Sox2, Nanog, and Lin28. Pluripotency was confirmed by morphology and the presence of embryonic stem (ES) cell-specific markers.

In 2006, Yamanaka and colleagues first demonstrated that retrovirus-mediated delivery and expression of Oct4, Sox2, c-Myc and Klf4 is capable of inducing ...

Selecting and Isolating Colonies of Human Induced Pluripotent Stem Cells Reprogrammed from Adult Fibroblasts

Herein we present a protocol of reprogramming human adult fibroblasts into human induced pluripotent stem cells (hiPSC) using retroviral vectors encoding Oct3/4, Sox2, Klf4 and c-myc (OSKM) in the presence of sodium butyrate 1-3. We used this method to reprogram late passage (&gt;p10) human adult fibroblasts derived from Friedreich's ataxia patient (GM03665, Coriell Repository). The reprogramming approach includes highly efficient transduction protocol using repetitive centrifugation of fibroblasts in the presence of virus-containing media. The reprogrammed hiPSC colonies were identified using live immunostaining for Tra-1-81, a surface marker of pluripotent cells, separated from non-reprogrammed fibroblasts and manually passaged 4,5. These hiPSC were then transferred to Matrigel plates and grown in feeder-free conditions, directly from the reprogramming plate. Starting from the first passage, hiPSC colonies demonstrate characteristic hES-like morphology. Using this protocol more than 70% of selected colonies can be successfully expanded and established into cell lines. The established hiPSC lines displayed characteristic pluripotency markers including surface markers TRA-1-60 and SSEA-4, as well as nuclear markers Oct3/4, Sox2 and Nanog. 
The protocol presented here has been established and tested using adult fibroblasts obtained from Friedreich's ataxia patients and control individuals 6, human newborn fibroblasts, as well as human keratinocytes.

We present a protocol for efficient reprogramming of human somatic cells into human induced pluripotent stem cells (hiPSC) using retroviral vectors encoding Oct3/4, Sox2, Klf4 and c-myc (OSKM) and identification of correctly reprogrammed hiPSC by live staining with Tra-1-81 antibody.

Herein we present a protocol of reprogramming human adult fibroblasts into human induced pluripotent stem cells (hiPSC) using retroviral vectors encoding ...

Isolation of Rat Portal Fibroblasts by In situ Liver Perfusion

Liver fibrosis is defined by the excessive deposition of extracellular matrix by activated myofibroblasts. There are multiple precursors of hepatic myofibroblasts, including hepatic stellate cells, portal fibroblasts and bone marrow derived fibroblasts 1. Hepatic stellate cells have been the best studied, but portal fibroblasts are increasingly recognized as important contributors to the myofibroblast pool, particularly in biliary fibrosis 2. Portal fibroblasts undergo proliferation in response to biliary epithelial injury, potentially playing a key role in the early stages of biliary scarring 3-5. A method of isolating portal fibroblasts would allow in vitro study of this cell population and lead to greater understanding of the role portal fibroblasts play in biliary fibrosis.
Portal fibroblasts have been isolated using various techniques including outgrowth 6, 7 and liver perfusion with enzymatic digestion followed by size selection 8. The advantage of the digestion and size selection technique compared to the outgrowth technique is that cells can be studied without the necessity of passage in culture. Here, we describe a modified version of the original technique described by Kruglov and Dranoff 8 for isolation of portal fibroblasts from rat liver that results in a relatively pure population of primary cells.

Isolation of Rat Portal Fibroblasts by In situ Liver Perfusion

A technique for isolating portal fibroblasts from rat liver is described. Livers are perfused and digested in situ with collagenase, followed by ex vivo digestion of the liver slurry and size selection of cells. This method provides a pure population of portal fibroblasts without the need for passage in culture.

Liver fibrosis is defined  by the excessive deposition of extracellular matrix by activated  myofibroblasts. There are multiple precursors of hepatic ...

Selective Capture of 5-hydroxymethylcytosine from Genomic DNA

5-methylcytosine (5-mC) constitutes ~2-8% of the total cytosines in human genomic DNA and impacts a broad range of biological functions, including gene expression, maintenance of genome integrity, parental imprinting, X-chromosome inactivation, regulation of development, aging, and cancer1. Recently, the presence of an oxidized 5-mC, 5-hydroxymethylcytosine (5-hmC), was discovered in mammalian cells, in particular in embryonic stem (ES) cells and neuronal cells2-4. 5-hmC is generated by oxidation of 5-mC catalyzed by TET family iron (II)/&alpha;-ketoglutarate-dependent dioxygenases2, 3. 5-hmC is proposed to be involved in the maintenance of embryonic stem (mES) cell, normal hematopoiesis and malignancies, and zygote development2, 5-10. To better understand the function of 5-hmC, a reliable and straightforward sequencing system is essential. Traditional bisulfite sequencing cannot distinguish 5-hmC from 5-mC11. To unravel the biology of 5-hmC, we have developed a highly efficient and selective chemical approach to label and capture 5-hmC, taking advantage of a bacteriophage enzyme that adds a glucose moiety to 5-hmC specifically12.
Here we describe a straightforward two-step procedure for selective chemical labeling of 5-hmC. In the first labeling step, 5-hmC in genomic DNA is labeled with a 6-azide-glucose catalyzed by &beta;-GT, a glucosyltransferase from T4 bacteriophage, in a way that transfers the 6-azide-glucose to 5-hmC from the modified cofactor, UDP-6-N3-Glc (6-N3UDPG). In the second step, biotinylation, a disulfide biotin linker is attached to the azide group by click chemistry. Both steps are highly specific and efficient, leading to complete labeling regardless of the abundance of 5-hmC in genomic regions and giving extremely low background. Following biotinylation of 5-hmC, the 5-hmC-containing DNA fragments are then selectively captured using streptavidin beads in a density-independent manner. The resulting 5-hmC-enriched DNA fragments could be used for downstream analyses, including next-generation sequencing.
Our selective labeling and capture protocol confers high sensitivity, applicable to any source of genomic DNA with variable/diverse 5-hmC abundances. Although the main purpose of this protocol is its downstream application (i.e., next-generation sequencing to map out the 5-hmC distribution in genome), it is compatible with single-molecule, real-time SMRT (DNA) sequencing, which is capable of delivering single-base resolution sequencing of 5-hmC.

Described is a two-step labeling process using &beta;-glucosyltransferase (&beta;-GT) to transfer an azide-glucose to 5-hmC, followed by click chemistry to transfer a biotin linker for easy and density-independent enrichment. This efficient and specific labeling method enables enrichment of 5-hmC with extremely low background and high-throughput epigenomic mapping via next-generation sequencing.

5-methylcytosine (5-mC) constitutes ~2-8% of the total cytosines in human genomic DNA and impacts a broad range of biological functions, including gene ...

Reconstitution Of &#946;-catenin Degradation In Xenopus Egg Extract

Xenopus laevis egg extract is a well-characterized, robust system for studying the biochemistry of diverse cellular processes. Xenopus egg extract has been used to study protein turnover in many cellular contexts, including the cell cycle and signal transduction pathways1-3. Herein, a method is described for isolating Xenopus egg extract that has been optimized to promote the degradation of the critical Wnt pathway component, &beta;-catenin. Two different methods are described to assess &beta;-catenin protein degradation in Xenopus egg extract. One method is visually informative ([35S]-radiolabeled proteins), while the other is more readily scaled for high-throughput assays (firefly luciferase-tagged fusion proteins). The techniques described can be used to, but are not limited to, assess &beta;-catenin protein turnover and identify molecular components contributing to its turnover. Additionally, the ability to purify large volumes of homogenous Xenopus egg extract combined with the quantitative and facile readout of luciferase-tagged proteins allows this system to be easily adapted for high-throughput screening for modulators of &beta;-catenin degradation.

Reconstitution Of &amp;#946;-catenin Degradation In Xenopus Egg Extract

A method is described for analyzing protein degradation using radiolabeled and luciferase-fusion proteins in Xenopus egg extract and its adaptation for high-throughput screening for small molecule modulators of protein degradation.

Xenopus laevis egg extract is a well-characterized, robust system for studying the biochemistry of diverse cellular processes. Xenopus egg extract has ...

Reporter-based Growth Assay for Systematic Analysis of Protein Degradation

Protein degradation by the ubiquitin-proteasome system (UPS) is a major regulatory mechanism for protein homeostasis in all eukaryotes. The standard approach to determining intracellular protein degradation relies on biochemical assays for following the kinetics of protein decline. Such methods are often laborious and time consuming and therefore not amenable to experiments aimed at assessing multiple substrates and degradation conditions. As an alternative, cell growth-based assays have been developed, that are, in their conventional format, end-point assays that cannot quantitatively determine relative changes in protein levels.
Here we describe a method that faithfully determines changes in protein degradation rates by coupling them to yeast cell-growth kinetics. The method is based on an established selection system where uracil auxotrophy of URA3-deleted yeast cells is rescued by an exogenously expressed reporter protein, comprised of a fusion between the essential URA3 gene and a degradation determinant (degron). The reporter protein is designed so that its synthesis rate is constant whilst its degradation rate is determined by the degron. As cell growth in uracil-deficient medium is proportional to the relative levels of Ura3, growth kinetics are entirely dependent on the reporter protein degradation.
This method accurately measures changes in intracellular protein degradation kinetics. It was applied to: (a) Assessing the relative contribution of known ubiquitin-conjugating factors to proteolysis (b) E2 conjugating enzyme structure-function analyses (c) Identification and characterization of novel degrons. Application of the degron-URA3-based system transcends the protein degradation field, as it can also be adapted to monitoring changes of protein levels associated with functions of other cellular pathways.

Here we describe a robust biological assay for quantifying the relative rate of proteolysis by the ubiquitin-proteasome system. The assay readout is yeast growth rate in liquid culture, which is dependent on the cellular levels of a reporter protein comprising a degradation signal fused to an essential metabolic marker.

Protein degradation by the ubiquitin-proteasome system (UPS) is a major regulatory mechanism for protein homeostasis in all eukaryotes. The standard ...

Assessment of Selective mRNA Translation in Mammalian Cells by Polysome Profiling

Regulation of protein synthesis represents a key control point in cellular response to stress. In particular, discreet RNA regulatory elements were shown to allow to selective translation of specific mRNAs, which typically encode for proteins required for a particular stress response. Identification of these mRNAs, as well as the characterization of regulatory mechanisms responsible for selective translation has been at the forefront of molecular biology for some time. Polysome profiling is a cornerstone method in these studies. The goal of polysome profiling is to capture mRNA translation by immobilizing actively translating ribosomes on different transcripts and separate the resulting polyribosomes by ultracentrifugation on a sucrose gradient, thus allowing for a distinction between highly translated transcripts and poorly translated ones. These can then be further characterized by traditional biochemical and molecular biology methods. Importantly, combining polysome profiling with high throughput genomic approaches allows for a large scale analysis of translational regulation.

The ability of cells to adapt to stress is crucial for their survival. Regulation of mRNA translation is one such adaptation strategy, providing for rapid regulation of the proteome. Here, we provide a standardized polysome profiling protocol to identify specific mRNAs that are selectively translated under stress conditions.

Regulation of protein synthesis represents a key control point in cellular response to stress. In particular, discreet RNA regulatory elements were shown ...

Analysis of Autophagy in Penicillium chrysogenum by Using Starvation Pads in Combination With Fluorescence Microscopy

The study of cellular quality control systems has emerged as a highly dynamic and relevant field of contemporary research. It has become clear that cells possess several lines of defense against damage to biologically relevant molecules like nucleic acids, lipids and proteins. In addition to organelle dynamics (fusion/fission/motility/inheritance) and tightly controlled protease activity, the degradation of surplus, damaged or compromised organelles by autophagy (cellular &#8216;self-eating&#8217;) has received much attention from the scientific community. The regulation of autophagy is quite complex and depends on genetic and environmental factors, many of which have so far not been elucidated. Here a novel method is presented that allows the convenient study of autophagy in the filamentous fungus Penicillium chrysogenum. It is based on growth of the fungus on so-called &#8216;starvation pads&#8217; for stimulation of autophagy in a reproducible manner. Samples are directly assayed by microscopy and evaluated for autophagy induction / progress. The protocol presented here is not limited for use with P. chrysogenum and can be easily adapted for use in other filamentous fungi.

Analysis of Autophagy in Penicillium chrysogenum by Using Starvation Pads in Combination With Fluorescence Microscopy

A convenient and powerful method for studying autophagy in Penicillium chrysogenum by using starvation pads (mixtures of agarose and tap water in a microscope slide containing a central cavity) is presented here.

The study of cellular quality control systems has emerged as a highly dynamic and relevant field of contemporary research. It has become clear that cells ...

Assays for the Degradation of Misfolded Proteins in Cells

Protein misfolding and aggregation are associated with various neurodegenerative diseases. Cellular mechanisms that recognize and degrade misfolded proteins may serve as potential therapeutic targets. To distinguish degradation of misfolding-prone proteins from other mechanisms that regulate their levels, one important method is to measure protein half-life in cells. However, this can be challenging because misfolding-prone proteins may exist in different forms, including the native form and misfolded forms of distinct characteristics. Here we describe assays to examine the half-life of misfolded proteins in mammalian cells using a highly aggregation-prone protein, Ataxin-1 with an extended polyglutamine (polyQ) stretch, and a conformationally unstable luciferase mutant as models. Cycloheximide chase is combined with cell fractionation to examine the turnover rate of misfolding-prone proteins in various cellular fractions. We further depict a fluorescence-based assay using an enhanced green fluorescence protein (EGFP)-fusion of the luciferase mutant, which can be adapted for high throughput screening on a microplate-reader.

This report describes protocols for measuring degradation rates of misfolded proteins by either western blot or fluorescence-based assays. The methods can be applied to analysis of other misfolded proteins and for high throughput screening.

Protein misfolding and aggregation are associated with various neurodegenerative diseases. Cellular mechanisms that recognize and degrade misfolded ...

Activating Autophagy by Aerobic Exercise in Mice

Autophagy is a lysosomal degradation pathway essential for cell homeostasis, function and differentiation. Under stress conditions, autophagy is induced and targets various cargos, such as bulk cytosol, damaged organelles and misfolded proteins, for degradation in lysosomes. Resulting nutrient molecules are recycled back to the cytosol for new protein synthesis and ATP production. Upregulation of autophagy has beneficial effects against the pathogenesis of many diseases, and pharmacological and physiological strategies to activate autophagy have been reported. Aerobic exercise is recently identified as an efficient autophagy inducer in multiple organs in mice, including muscle, liver, heart and brain. Here we show procedures to induce autophagy in vivo by either forced treadmill exercise or voluntary wheel running. We also demonstrate microscopic and biochemical methods to quantitatively analyze autophagy levels in mouse tissues, using the marker proteins LC3 and p62 that are transported to and degraded in lysosomes along with autophagosomes.

Autophagy activation is beneficial in the prevention of a number of diseases. One of the physiological approaches to induce autophagy in vivo is physical exercise. Here we show how to activate autophagy by aerobic exercise and measure autophagy levels in mice.

Autophagy is a lysosomal degradation pathway essential for cell homeostasis, function and differentiation. Under stress conditions, autophagy is induced ...