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><tbody><tr><td>&lt;strong&gt;Cell lines&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Primary skin-derived human fibroblasts&amp;nbsp;</td><td>Skin biopsy, healthy human donor, Biobank University Clinic T&amp;uuml;bingen, Ethical approvement 389/2019BO2</td><td>F-CO-60, passage 9</td><td></td></tr><tr><td>&lt;strong&gt;Cell culture treatments&lt;/strong&gt;</td><td></td><td></td><td>&lt;strong&gt;Final concentration (compound)&lt;/strong&gt;</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 &amp;micro;M DFX</td></tr><tr><td>Control medium + DFX + bafilomycinA1</td><td>DMEM 10%FCS</td><td></td><td>30 &amp;micro;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>&lt;strong&gt;Material&lt;/strong&gt;</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&amp;nbsp;</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&amp;rsquo;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>&lt;strong&gt;Solution&lt;/strong&gt;</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&amp;nbsp;</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&amp;nbsp;</td></tr><tr><td>PBS/T + BSA</td><td></td><td></td><td>PBS, supplemented with 10% Tween20, 1% BSA&amp;nbsp;</td></tr><tr><td>&lt;strong&gt;Software&lt;/strong&gt;</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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Biology

819 Views.  Eberhard Karls University Tübingen. Hoge ophoping van ijzer in de hersenen bij BPAN wordt veroorzaakt door disfunctie van het autofagie-eiwit. Maar de onderliggende moleculaire mechanismen zijn onbekend. Dit protocol biedt een beoordeling voor lysosomale ferritineafbraak, zoals ferritinofagie.Deze techniek biedt de kwantitatieve, op beelden gebaseerde analysemethode om de afbraak van lysosomale ferritine in afzonderlijke cellen te meten. Handig is dat de multiplex-benadering kan worden u...

Video: Ferritinofagie: beoordeling van de selectieve afbraak van ijzer door autofagie in menselijke fibroblasten

Synthesis of Cationized Magnetoferritin for Ultra-fast Magnetization of Cells

Many important biomedical applications, such as cell imaging and remote manipulation, can be achieved by labeling cells with superparamagnetic iron oxide nanoparticles (SPIONs). Achieving sufficient cellular uptake of SPIONs is a challenge that has traditionally been met by exposing cells to elevated concentrations of SPIONs or by prolonging exposure times (up to 72 hr). However, these strategies are likely to mediate toxicity. Here, we present the synthesis of the protein-based SPION magnetoferritin as well as a facile surface functionalization protocol that enables rapid cell magnetization using low exposure concentrations. The SPION core of magnetoferritin consists of cobalt-doped iron oxide with an average particle diameter of 8.2 nm mineralized inside the cavity of horse spleen apo-ferritin. Chemical cationization of magnetoferritin produced a novel, highly membrane-active SPION that magnetized human mesenchymal stem cells (hMSCs) using incubation times as short as one minute and iron concentrations as lows as 0.2 mM.

A protocol for the synthesis and cationization of cobalt-doped magnetoferritin is presented, as well as a method to rapidly magnetize stem cells with cationized magnetoferritin.

Many important biomedical applications, such as cell imaging and remote manipulation, can be achieved by labeling cells with superparamagnetic iron oxide ...

Bioengineering

FACS Analysis of Lipid Hydroperoxides in Ferroptotic Medulloblastoma Cells

Revealing the Ferroptotic Phenotype of Medulloblastoma

The interaction of iron and oxygen is an integral part of the development of life on Earth. Nonetheless, this unique chemistry continues to fascinate and puzzle, leading to new biological ventures. In 2012, a Columbia University group recognized this interaction as a central event leading to a new type of regulated cell death named &#34;ferroptosis.&#34; The major feature of ferroptosis is the accumulation of lipid hydroperoxides due to (1) dysfunctional antioxidant defense and/or (2) overwhelming oxidative stress, which most frequently coincides with increased content of free labile iron in the cell. This is normally prevented by the canonical anti-ferroptotic axis comprising the cystine transporter xCT, glutathione (GSH), and GSH peroxidase 4 (GPx4). Since ferroptosis is not a programmed type of cell death, it does not involve signaling pathways characteristic of apoptosis. The most common way to prove this type of cell death is by using lipophilic antioxidants (vitamin E, ferrostatin-1, etc.) to prevent it. These molecules can approach and detoxify oxidative damage in the plasma membrane. Another important aspect in revealing the ferroptotic phenotype is detecting the preceding accumulation of lipid hydroperoxides, for which the specific dye BODIPY C11 is used. The present manuscript will show how ferroptosis can be induced in wild-type medulloblastoma cells by using different inducers: erastin, RSL3, and iron-donor. Similarly, the xCT-KO cells that grow in the presence of NAC, and which undergo ferroptosis once NAC is removed, will be used. The characteristic &#34;bubbling&#34; phenotype is visible under the light microscope within 12-16 h from the moment of ferroptosis triggering. Furthermore, BODIPY C11 staining followed by FACS analysis to show the accumulation of lipid hydroperoxides and consequent cell death using the PI staining method will be used. To prove the ferroptotic nature of cell death, ferrostatin-1 will be used as a specific ferroptosis-preventing agent.

Author Spotlight: Tracing the Ferroptotic Signatures and Cell Death Dynamics in Medulloblastoma for Advanced Therapeutics 

Lipid hydroperoxide content represents the most commonly used indicator of ferroptotic cell death. This article demonstrates the step-by-step flow cytometry analysis of lipid hydroperoxide content in cells upon ferroptosis induction.

The interaction of iron and oxygen is an integral part of the development of life on Earth. Nonetheless, this unique chemistry continues to fascinate and ...

Cancer Research

Exploring the Regulation of Lipid Droplet Catabolism through Lipophagy

Macroautophagy, commonly referred to as autophagy, is a highly conserved cellular process responsible for the degradation of cellular components. This process is particularly prominent under conditions such as fasting, cellular stress, organelle damage, cellular damage, or aging of cellular components. During autophagy, a segment of the cytoplasm is enclosed within double-membrane vesicles known as autophagosomes, which then fuse with lysosomes. Following this fusion, the contents of autophagosomes undergo non-selective bulk degradation facilitated by lysosomes. However, autophagy also exhibits selective functionality, targeting specific organelles, including mitochondria, peroxisomes, lysosomes, nuclei, and lipid droplets (LDs). Lipid droplets are enclosed by a phospholipid monolayer that isolates neutral lipids from the cytoplasm, protecting cells from the harmful effects of excess sterols and free fatty acids (FFAs). Autophagy is implicated in various conditions, including neurodegenerative diseases, metabolic disorders, and cancer. Specifically, lipophagy -- the autophagy-dependent degradation of lipid droplets -- plays a crucial role in regulating intracellular FFA levels across different metabolic states. This regulation supports essential processes such as membrane synthesis, signaling molecule formation, and energy balance. Consequently, impaired lipophagy increases cellular vulnerability to death stimuli and contributes to the development of diseases such as cancer. Despite its significance, the precise mechanisms governing lipid droplet metabolism regulated by lipophagy in cancer cells remain poorly understood. This article aims to describe confocal imaging acquisition and quantitative imaging analysis protocols that enable the investigation of lipophagy associated with metabolic changes in cancer cells. The results obtained through these protocols may shed light on the intricate interplay between autophagy, lipid metabolism, and cancer progression. By elucidating these mechanisms, novel therapeutic targets may emerge for combating cancer and other metabolic-related diseases.

Lipophagy is a selective form of autophagy that involves the degradation of lipid droplets. Dysfunctions in this process are associated with cancer development. However, the precise mechanisms are not yet fully understood. This protocol describes quantitative imaging approaches to better understand the interplay between autophagy, lipid metabolism, and cancer progression.

Macroautophagy, commonly referred to as autophagy, is a highly conserved cellular process responsible for the degradation of cellular components. This ...

In Vitro and In Vivo Detection of Mitophagy in Human Cells, C. Elegans, and Mice

Mitochondria are the powerhouses of cells and produce cellular energy in the form of ATP. Mitochondrial dysfunction contributes to biological aging and a wide variety of disorders including metabolic diseases, premature aging syndromes, and neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD). Maintenance of mitochondrial health depends on mitochondrial biogenesis and the efficient clearance of dysfunctional mitochondria through mitophagy. Experimental methods to accurately detect autophagy/mitophagy, especially in animal models, have been challenging to develop. Recent progress towards the understanding of the molecular mechanisms of mitophagy has enabled the development of novel mitophagy detection techniques. Here, we introduce several versatile techniques to monitor mitophagy in human cells, Caenorhabditis elegans (e.g., Rosella and DCT-1/ LGG-1 strains), and mice (mt-Keima). A combination of these mitophagy detection techniques, including cross-species evaluation, will improve the accuracy of mitophagy measurements and lead to a better understanding of the role of mitophagy in health and disease.

In Vitro and In Vivo Detection of Mitophagy in Human Cells, C. Elegans, and Mice

Mitophagy, the process of clearing damaged mitochondria, is necessary for mitochondrial homeostasis and health maintenance. This article presents some of the latest mitophagy detection methods in human cells, Caenorhabditis elegans, and mice.

Mitochondria are the powerhouses of cells and produce cellular energy in the form of ATP. Mitochondrial dysfunction contributes to biological aging and a ...

Medicine

Assessing Autophagic Flux by Measuring LC3, p62, and LAMP1 Co-localization Using Multispectral Imaging Flow Cytometry

Autophagy is a catabolic pathway in which normal or dysfunctional cellular components that accumulate during growth and differentiation are degraded via the lysosome and are recycled. During autophagy, cytoplasmic LC3 protein is lipidated and recruited to the autophagosomal membranes. The autophagosome then fuses with the lysosome to form the autolysosome, where the breakdown of the autophagosome vesicle and its contents occurs. The ubiquitin-associated protein p62, which binds to LC3, is also used to monitor autophagic flux. Cells undergoing autophagy should demonstrate the co-localization of p62, LC3, and lysosomal markers. Immunofluorescence microscopy has been used to visually identify LC3 puncta, p62, and/or lysosomes on a per-cell basis. However, an objective and statistically rigorous assessment can be difficult to obtain. To overcome these problems, multispectral imaging flow cytometry was used along with an analytical feature that compares the bright detail images from three autophagy markers (LC3, p62 and lysosomal LAMP1) and quantifies their co-localization, in combination with LC3 spot counting to measure autophagy in an objective, quantitative, and statistically robust manner.

Here, multispectral imaging flow cytometry with an analytical feature that compares bright detail images of 3 autophagy markers and quantifies their co-localization, along with LC3 spot counting, was used to measure autophagy in an objective, quantitative, and statistically robust manner.

Autophagy is a catabolic pathway in which normal or dysfunctional cellular components that accumulate during growth and differentiation are degraded via ...

In Situ Immunofluorescent Staining of Autophagy in Muscle Stem Cells

Increasing evidence points to autophagy as a crucial regulatory process to preserve tissue homeostasis. It is known that autophagy is involved in skeletal muscle development and regeneration, and the autophagic process has been described in several muscular pathologies and age-related muscle disorders. A recently described block of the autophagic process that correlates with the functional exhaustion of satellite cells during muscle repair supports the notion that active autophagy is coupled with productive muscle regeneration. These data uncover the crucial role of autophagy in satellite cell activation during muscle regeneration in both normal and pathological conditions, such as muscular dystrophies. Here, we provide a protocol to monitor the autophagic process in the adult Muscle Stem Cell (MuSC) compartment during muscle regenerative conditions. This protocol describes the setup methodology to perform in situ immunofluorescence imaging of LC3, an autophagy marker, and MyoD, a myogenic lineage marker, in muscle tissue sections from control and injured mice. The methodology reported allows for monitoring the autophagic process in one specific cell compartment, the MuSC compartment, which plays a central role in orchestrating muscle regeneration.

In Situ Immunofluorescent Staining of Autophagy in Muscle Stem Cells

Active autophagy is associated with productive muscle regeneration, which is essential for Muscle Stem Cell (MuSC) activation. Here, we provide a protocol for the in situ detection of LC3, an autophagy marker in MyoD-positive MuSCs of muscle tissue sections from control and injured mice.

Increasing evidence points to autophagy as a crucial regulatory process to preserve tissue homeostasis. It is known that autophagy is involved in skeletal ...

The Lactate Dehydrogenase Sequestration Assay &#8212; A Simple and Reliable Method to Determine Bulk Autophagic Sequestration Activity in Mammalian Cells

Bulk autophagy is characterized by the sequestration of large portions of cytoplasm into double/multi-membrane structures termed autophagosomes. Here a simple protocol to monitor this process is described. Moreover, typical results and experimental validation of the method under autophagy-inducing conditions in various types of cultured mammalian cells are provided. During bulk autophagy, autophagosomes sequester cytosol, and thereby also soluble cytosolic proteins, alongside other autophagic cargo. LDH is a stable and highly abundant, soluble cytosolic enzyme that is non-selectively sequestered into autophagosomes. The amount of LDH sequestration therefore reflects the amount of bulk autophagic sequestration. To efficiently and accurately determine LDH sequestration in cells, we employ an electrodisruption-based fractionation protocol that effectively separates sedimentable from cytosolic LDH, followed by measurement of enzymatic activity in sedimentable fractions versus whole-cell samples. Autophagic sequestration is determined by subtracting the proportion of sedimentable LDH in untreated cells from that in treated cells. The advantage of the LDH sequestration assay is that it gives a quantitative measure of the autophagic sequestration of endogenous cargo, as opposed to other methods that either involve ectopic expression of sequestration probes or semi-quantitative protease protection analyses of autophagy markers or receptors.

Here a simple and well-validated protocol for measuring bulk autophagic sequestration activity in mammalian cells is described. The method is based on quantifying the proportion of lactate dehydrogenase (LDH) in sedimentable cell fractions compared to total cellular LDH levels.

Bulk autophagy is characterized by the sequestration of large portions of cytoplasm into double/multi-membrane structures termed autophagosomes. Here a ...

Analyzing Starvation-Induced Autophagy in the Drosophila melanogaster Larval Fat Body

Autophagy is a cellular self-digestion process. It delivers cargo to the lysosomes for degradation in response to various stresses, including starvation. The malfunction of autophagy is associated with aging and multiple human diseases. The autophagy machinery is highly conserved-from yeast to humans.&#160;The larval fat body of Drosophila melanogaster, an analog for vertebrate liver and adipose tissue, provides a unique model for monitoring autophagy in vivo. Autophagy can be easily induced by nutrient starvation in the larval fat body. Most autophagy-related genes are conserved in Drosophila. Many transgenic fly strains expressing tagged autophagy markers have been developed, which facilitates the monitoring of different steps in the autophagy process. The clonal analysis enables a close comparison of autophagy markers in cells with different genotypes in the same piece of tissue. The current protocol details procedures for (1) generating somatic clones in the larval fat body, (2) inducing autophagy via amino acid starvation, and (3) dissecting the larval fat body, aiming to create a model for analyzing differences in autophagy using an autophagosome marker (GFP-Atg8a) and clonal analysis.

Analyzing Starvation-Induced Autophagy in the Drosophila melanogaster Larval Fat Body

The present protocol describes the induction of autophagy in the Drosophila melanogaster larval fat body via nutrient depletion and analyzes changes in autophagy using transgenic fly strains.

Autophagy is a cellular self-digestion process. It delivers cargo to the lysosomes for degradation in response to various stresses, including starvation. ...

Quantifying Mitophagy in C. elegans and a Liver Cancer Cell Line

Detection of Mitophagy in Caenorhabditis elegans and Mammalian Cells Using Organelle-Specific Dyes

Mitochondria are essential for various biological functions, including energy production, lipid metabolism, calcium homeostasis, heme biosynthesis, regulated cell death, and the generation of reactive oxygen species (ROS). ROS are vital for key biological processes. However, when uncontrolled, they can lead to oxidative injury, including mitochondrial damage. Damaged mitochondria release more ROS, thereby intensifying cellular injury and the disease state. A homeostatic process named mitochondrial autophagy (mitophagy) selectively removes damaged mitochondria, which are then replaced by new ones. There are multiple mitophagy pathways, with the common endpoint being the breakdown of the damaged mitochondria in lysosomes. 
Several methodologies, including genetic sensors, antibody immunofluorescence, and electron microscopy, use this endpoint to quantify mitophagy. Each method for examining mitophagy has its advantages, such as specific tissue/cell targeting (with genetic sensors) and great detail (with electron microscopy). However, these methods often require expensive resources, trained personnel, and a lengthy preparation time before the actual experiment, such as for creating transgenic animals. Here, we present a cost-effective alternative for measuring mitophagy using commercially available fluorescent dyes targeting mitochondria and lysosomes. This method effectively measures mitophagy in the nematode Caenorhabditis elegans and human liver cells, which indicates its potential efficiency in other model systems.

Author Spotlight: Detection of Mitophagy in Caenorhabditis elegans and Mammalian Cells Using Organelle-Specific Dyes  

Exploring mitophagy through electron microscopy, genetic sensors, and immunofluorescence requires costly equipment, skilled personnel, and a significant time investment. Here, we demonstrate the efficacy of a commercial fluorescence dye kit in quantifying the mitophagy process in both Caenorhabditis elegans and a liver cancer cell line.

Mitochondria are essential for various biological functions, including energy production, lipid metabolism, calcium homeostasis, heme biosynthesis, ...

Identification of Ferroptosis- and Hypoxia-related Genes in iPSC-derived Oligodendrocyte Precursor Cells from Multiple Sclerosis Patients

Multiple sclerosis (MS) is a chronic inflammatory disorder characterized by demyelination, with failed remyelination leading to progressive axon loss in chronic stages. Oligodendrocyte precursor cells (OPCs) are critical for remyelination. Recent studies suggest that both hypoxia and ferroptosis play crucial roles in the dysfunctional differentiation of OPCs. This research seeks to identify key genes linked to hypoxia and ferroptosis and immune infiltration characteristics in OPCs derived from induced pluripotent stem cells (iPSCs) of MS patients and to construct a diagnostic model centered on these pivotal genes.
We analyzed gene expression data from the GSE196575 and GSE147315 datasets and compared MS patients with healthy individuals. Using weighted gene coexpression network analysis (WGCNA), we pinpointed primary module genes and essential genes associated with hypoxia, ferroptosis, and MS. The ferroptosis Z score and the hypoxia Z score calculated via gene set variation analysis (GSVA) were greater in the iPSC-derived OPCs of MS patients than those of the control group. The implicated genes are predominantly linked to the PI3K/Akt/mTOR pathway, as identified through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses.
A protein-protein interaction (PPI) network of crucial genes revealed 10 central hub genes (COL4A1, COL4A2, ITGB5, ITGB1, ITGB8, ITGAV, VIM, FLNA, VCL, and SPARC). The robust expression of ITGB1, ITGB8, and VIM was validated in the GSE151306 dataset, supporting their role as key hub genes. Additionally, an interaction network between transcription factors (TFs) and hub genes was established via Transcriptional Regulatory Relationships Unraveled by Sentence-based Text (TRRUST), which identified five key TFs. The results of this study could help elucidatenovel biomarkers or therapeutic targets for MS.

This study provides novel insights into the interactions among hypoxia, ferroptosis, and immune infiltration in the pathogenesis of multiple sclerosis (MS) via bioinformatics analysis. By employing weighted gene coexpression network analysis (WGCNA) and protein-protein interaction (PPI) analysis, we identified three pivotal hub genes (ITGB1, ITGB8, and VIM).

Multiple sclerosis (MS) is a chronic inflammatory disorder characterized by demyelination, with failed remyelination leading to progressive axon loss in ...

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

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