I would like to thank my coworkers at MilliporeSigma, Philip J. Morrissey and Sherree L. Friend, for their support and guidance over the years. I would also like to thank Vidya Venkatachalam and Bryan R. Davidson for their help with the BDC3 feature in the IDEAS software, Ryan P. Jessup for help editing this manuscript, Raymond Kong for help on the day of shooting, and a special thank you to makeup artist Cynthia Xamonthiene.

<ol>
	<li>Levine, B., Klionsky, D. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+by+self-digestion:+molecular+mechanisms+and+biological+functions+of+autophagy.">Development by self-digestion: molecular mechanisms and biological functions of autophagy.</a> Developmental Cell. 6, 463-477 (2004).</li><li>Zhang, X. J., Chen, S., Huang, K. X., Le, W. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Why+should+autophagic+flux+be+assessed?.">Why should autophagic flux be assessed?.</a> Acta Pharmacologica Sinica. 34, 595-599 (2013).</li><li>Tanida, I., Takashi, U., Kominami, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=LC3+and+Autophagy.">LC3 and Autophagy.</a> Methods Mol Biol. 445, 77-88 (2008).</li><li>Larsen, K. B., Lamark, T., &#216;vervatn, A., Harneshaug, I., Johansen, T., Bj&#248;rk&#248;y, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+reporter+cell+system+to+monitor+autophagy+based+on+p62/SQSTM1.">A reporter cell system to monitor autophagy based on p62/SQSTM1.</a> Autophagy. 6 (6), 784-793 (2010).</li><li>Demishtein, A., Porat, Z., Elazar, Z., Shvets, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Applications+of+flow+cytometry+for+measurement+of+autophagy.">Applications of flow cytometry for measurement of autophagy.</a> Methods. 75, 87-95 (2015).</li><li>Mizushima, N., Yoshimori, T., Levine, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Methods+in+mammalian+autophagy+research.">Methods in mammalian autophagy research.</a> Cell. 140 (3), 313-326 (2010).</li><li>Klionsky, D. 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=Guidelines+for+the+use+and+interpretation+of+assays+for+monitoring+autophagy.">Guidelines for the use and interpretation of assays for monitoring autophagy.</a> Autophagy. 8 (4), 445-544 (2012).</li><li>Arsov, I., 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=BAC-mediated+transgenic+expression+of+fluorescent+autophagic+protein+Beclin+1+reveals+a+role+for+Beclin+1+in+lymphocyte+development.">BAC-mediated transgenic expression of fluorescent autophagic protein Beclin 1 reveals a role for Beclin 1 in lymphocyte development.</a> Cell Death Differ. 15 (9), 1385-1395 (2008).</li><li>Maloyan, A., Sayegh, J., Osinska, H., Chua, B. H., Robbins, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Manipulation+of+death+pathways+in+desmin-related+cardiomyopathy.">Manipulation of death pathways in desmin-related cardiomyopathy.</a> Circ Res. 106 (9), 1524-1532 (2010).</li><li>Altman, B. 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=Autophagy+is+essential+to+suppress+cell+stress+and+to+allow+BCR-Abl-mediated+leukemogenesis.">Autophagy is essential to suppress cell stress and to allow BCR-Abl-mediated leukemogenesis.</a> Oncogene. 30 (16), 1855-1867 (2011).</li><li>Suarez, A. L., Kong, R., George, T., He, L., Yue, Z., van Dyk, L. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gammaherpesvirus+68+infection+of+endothelial+cells+requires+both+host+autophagy+genes+and+viral+oncogenes+for+optimal+survival+and+persistence.">Gammaherpesvirus 68 infection of endothelial cells requires both host autophagy genes and viral oncogenes for optimal survival and persistence.</a> J Virol. 85 (13), 6293-6308 (2011).</li><li>Tra, 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=Autophagy+in+human+embryonic+stem+cells.">Autophagy in human embryonic stem cells.</a> PLoS One. 6 (11), (2011).</li><li>Yuan, N., 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=Bafilomycin+A1+targets+both+autophagy+and+apoptosis+pathways+in+pediatric+B-cell+acute+lymphoblastic+leukemia.">Bafilomycin A1 targets both autophagy and apoptosis pathways in pediatric B-cell acute lymphoblastic leukemia.</a> Haematologica. 100 (3), 345-356 (2015).</li><li>Leveque-El Mouttie, L., 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+is+required+for+stem+cell+mobilization+by+G-CSF.">Autophagy is required for stem cell mobilization by G-CSF.</a> Blood. 125 (19), 2933-2936 (2015).</li><li>Watson, A. 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+limits+proliferation+and+glycolytic+metabolism+in+acute+myeloid+leukemia.">Autophagy limits proliferation and glycolytic metabolism in acute myeloid leukemia.</a> Cell Death Discov. 1, 15008 (2015).</li><li>Stranks, A. 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=Autophagy+Controls+Acquisition+of+Aging+Features+in+Macrophages.">Autophagy Controls Acquisition of Aging Features in Macrophages.</a> J Innate Immun. 7 (4), 375-391 (2015).</li><li>Clarke, A. 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=Autophagy+is+activated+in+systemic+lupus+erythematosus+and+required+for+plasmablast+development.">Autophagy is activated in systemic lupus erythematosus and required for plasmablast development.</a> Ann Rheum Dis. 74 (5), 912-920 (2015).</li><li>Warnes, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flow+cytometric+assays+for+the+study+of+autophagy.">Flow cytometric assays for the study of autophagy.</a> Methods. 82, 21-28 (2015).</li><li>Bj&#248;rk&#248;y, G., 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=p62/SQSTM1+forms+protein+aggregates+degraded+by+autophagy+and+has+a+protective+effect+on+huntingtin-induced+cell+death.">p62/SQSTM1 forms protein aggregates degraded by autophagy and has a protective effect on huntingtin-induced cell death.</a> J Cell Biol. 171 (4), 603-614 (2005).</li><li>Phadwal, K., 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=A+novel+method+for+autophagy+detection+in+primary+cells:+Impaired+levels+of+macroautophagy+in+immunosenescent+T+cells.">A novel method for autophagy detection in primary cells: Impaired levels of macroautophagy in immunosenescent T cells.</a> Autophagy. 8 (4), 677-689 (2012).</li><li>Rajan, R., Karbowniczek, M., Pugsley, H. R., Sabnani, M. K., Astrinidis, A., La-Beck, N. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+autophagosomes+and+autolysosomes+in+cells+using+imaging+flow+cytometry.">Quantifying autophagosomes and autolysosomes in cells using imaging flow cytometry.</a> Cytometry A. 87 (5), 451-458 (2015).</li><li>Kwok, A. 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=HspB8+mutation+causing+hereditary+distal+motor+neuropathy+impairs+lysosomal+delivery+of+autophagosomes.">HspB8 mutation causing hereditary distal motor neuropathy impairs lysosomal delivery of autophagosomes.</a> J Neurochem. 119 (6), 1155-1161 (2011).</li><li>Lopez-Herrera, G., 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=Deleterious+Mutations+in+LRBA+Are+Associated+with+a+Syndrome+of+Immune+Deficiency+and+Autoimmunity.">Deleterious Mutations in LRBA Are Associated with a Syndrome of Immune Deficiency and Autoimmunity.</a> Am J Hum Genet. 90 (6), 986-1001 (2012).</li><li>Pike, L. R., Phadwal, K., Simon, A. K., Harris, A. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ATF4+orchestrates+a+program+of+BH3-only+protein+expression+in+severe+hypoxia.">ATF4 orchestrates a program of BH3-only protein expression in severe hypoxia.</a> Mol Biol Rep. 39 (12), 10811-10822 (2012).</li><li>Pike, L. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transcriptional+up-regulation+of+ULK1+by+ATF4+contributes+to+cancer+cell+survival.">Transcriptional up-regulation of ULK1 by ATF4 contributes to cancer cell survival.</a> Biochem J. 449 (2), 389-400 (2013).</li><li>Pugsley, H. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+autophagy:+Measuring+LC3+puncta+and+autolysosome+formation+in+cells+using+multispectral+imaging+flow+cytometry.">Quantifying autophagy: Measuring LC3 puncta and autolysosome formation in cells using multispectral imaging flow cytometry.</a> Methods. 112, 147-156 (2017).</li><li>IDEAS. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Image Data Exploration and Analysis Software User's Manual Version 6.2. , (2015).</li></ol>

I am employed by MilliporeSigma, the maker of the Amnis brand ImageStream, which was used in this study.

When performing this analysis, there are a few things to consider. It is important to have appropriate control samples. For this experiment, two different controls were used: Control and Control + Chloroquine. The Control sample was used to set the threshold for the regions. It represents the basal level of autophagy without stopping lysosomal degradation and is the control for the Starved sample. However, the Control sample is not an appropriate control to compare with the results from the Starved + Chloroquine sample because chloroquine itself influences the control sample. Therefore, it was necessary to have a Control + Chloroquine sample.
Prior to performing any analysis for autophagy, it is important to only analyze/gate on single cells that are in focus (i.e., gradient RMS greater than 60), as the results could be affected if there is more than one cell or the images are out of focus. It is crucial that the autophagosomes and lysosomes are in focus for proper spot counting and BDC3 analysis. Apoptotic cells must be removed prior to autophagy analysis, as they may skew results and should not be included. There should be appropriate staining for all three markers (i.e., LC3, p62, and LAMP1). For example, all three markers are intracellular and should have a punctate signal; if the signal is not punctate, or if it is on the surface of the cell, the staining would not be appropriate and the experiment/staining should be redone. In addition, for BDC3 to be accurate, it is essential to gate on cells that are bright for all three fluorescent markers of interest. Gating on positive events is needed to prevent the measurement of BDC3 on non-specific binding, imaging artifacts, and/or noise. This is crucial, as BDC3 can potentially amplify artifacts that are not true signals. Since BDC3 can only be performed on bright positive events, many cells may not be included in the final analysis; this is especially true for control cells that do not have any accumulation of LC3, p62, and/or LAMP1. Thus, a limitation of this assay is that BDC3 only examines cells that are positive for all three markers, which might not be appropriate for all autophagy experiments.
Like BDS, which has been previously reported15,16,17,20,21,22,23,24,25,26, BDC3 alone does not take into account the number of autophagy organelles that co-localize, resulting in a large degree of variability in the number of autophagosomes. Therefore, the bivariate approach presented by Rajan et al. was employed. Looking at the bivariate plots, one might ask if the cells must be positive for LC3, p62, and LAMP1; why are there so many cells that have 0 spots? This is because the LC3 signal might be bright enough for co-localization, but it might not meet the threshold set by the peak mask (Peak (M11, Ch11-LC3-AF647, Bright, 4)) necessary for it to be considered a spot.
The protocol presented here used MIFC to count LC3 puncta and the co-localization of three autophagy markers to measure autophagic flux. Under basal conditions (Control sample), the number of autophagosomes was low, and few cells were found with &#34;High Spots.&#34; With the addition of chloroquine, which inhibits autophagosome/lysosome fusion, there was an increase in the number of LC3 spots. Since the lysosome is unable to break down the autophagosome and the p62, which resides in the autophagosome, this leads to an increase in the co-localization of the LC3, p62, and LAMP1 autolysosome accumulation. This effect was amplified under nutrient starvation, which induces autophagy. However, without the addition of chloroquine, there was not a significant increase in the number of autophagosomes, most likely due to an increase in the rate of autophagic turnover. When cells were starved in the presence of chloroquine, there was an increase in the co-localization and number of autophagosomes, which supports the conclusion that starvation increases autophagic flux. EBSS is known to be a powerful inducer of autophagy. Therefore, it is expected that the increase would be large. If another method, such as drug induction, is used to induce autophagy, the difference between the control and treated samples may be subtler.
This particular protocol was designed to measure autophagy in human cell lines, but the assay could be adapted to other species by switching to antibodies for that particular species. In addition, the analysis method could be used for any intracellular application that requires the co-localization of three probes/markers.

Macroautophagy, hereafter referred to as autophagy, is a catabolic pathway in which damaged or dysfunctional components, long-lived proteins, and organelles are degraded via the lysosome and are recycled1. Autophagy is a dynamic, multistep process that involves the formation of an autophagosome; the fusion of the autophagosome with the lysosome, forming the autolysosome; and the degradation of the contents of the autolysosome2. The crucial biological marker used to identify autophagy in mammalian systems is the microtubule-associated protein 1A/1B-light chain 3 (LC3), which makes up the autophagosomal membrane. During autophagy, cytosolic LC3-1 is conjugated to phosphatidylethanolamine to form LC3-II; LC3-II is then incorporated into the autophagosomal membrane3. Another widely used marker for autophagic flux is the autophagy receptor sequestosome 1 (SQSTM1, p62) which physically links autophagic cargo to the autophagic membrane4,5.
Methods that have traditionally been used to measure autophagy are Western blots and fluorescence microscopy7. However, neither of these methods are considered to be the &#34;gold standard,&#34;2,6 as there are challenges associated with both of these techniques. Western blots only give an average because they use a homogenate sample, so observers cannot see what is happening in individual cells. On the other hand, fluorescence microscopy gives an observer information at the single-cell level, but it lacks throughput capabilities, making it difficult to obtain objective and statistically rigorous assessments. In recent years, the measurement of LC3 and p62 by multispectral imaging flow cytometry (MIFC, see the Table of Materials) has been gaining popularity due to its quantitative power, high throughput capabilities, multiplexing potential, and ability to image every cell. One of the most widely used methods to measure autophagy using MIFC, along with its companion analysis software (see the Table of Materials), is through the spot counting of LC3 puncta or autophagosomes8,9,10,11,12,13,14,15,16,17. However, an increase in autophagosomes does not necessarily mean that there is an increase in autophagy, as it could also represent a blockade in the process2. LC3-II turnover is a useful parameter to measure autophagic flux by analyzing cells in the presence and absence of a lysosomal degradation inhibitor, such as chloroquine. Chloroquine inhibits the fusion of the autophagosome to the lysosome, thereby permitting the quantitation of the autophagosome formation as a measure of the degree of autophagy by arresting autophagic flux before the lysosomal degradation can occur18. In addition, p62 is degraded primarily by autophagy, and if the lysosomal degradation of the autophagosome and its contents is blocked, an accumulation of p62 is expected17,19.
Autophagy is a multistep process, and the measurement of LC3 or p62 alone does not provide a complete picture of what is happening in the cells. Recent publications have emphasized the need to examine the concurrent formation of the autolysosome2,17,20,21. MIFC is uniquely able to measure the formation of the autolysosome by imaging the co-localization of the autophagosome to the lysosome15-17,20,21,22,23,24,25,26. In addition, the co-localization of p62 and LC3 using MIFC has also been explored to measure autophagic flux5. In the referenced analysis software, the &#34;feature&#34; that measures co-localization is called &#34;Bright Detail Similarity R3&#34; (BDS) and was specificallydesigned to compare the small, bright image details of two images. BDS is the log-transformed Pearson&#39;s correlation coefficient of the localized bright spots with a radius of 3 pixels or less; in other words, BDS calculates the degree of overlapping pixels from two different fluorescent channels. The bright spots are either correlated (i.e., same spatial location) or uncorrelated (i.e., different spatial locations). Therefore, the correlation coefficient varies between 0 (uncorrelated) and 1 (perfect correlation). The coefficient is log-transformed to increase the range to between zero and infinity26,27. BDS alone may not be sufficient; Rajan et al. found that using only BDS could lead to false-positive or false-negative results21. BDS evaluates the co-localization of two markers of autophagy but does not consider the number of autophagosomes. To account for the number of autophagosomes, Rajan et al. included spot-counting of the LC3 puncta21. Rajan et al. proposed using a bivariate scatter plot of LC3 spot count versus BDS. Using this bivariate plot, two populations were first identified: one with cells that have a high level of LC3 spots and one with cells that have a low level of LC3 spots. The high-LC3 spot population was further classified into two populations: cells with low co-localization (i.e., accumulation of autophagosomes) and cells with high co-localization (i.e., accumulation of autolysosomes). This bivariate plot allows one to distinguish between cells with very few autophagosomes and cells with an accumulation of autophagosomes and/or autolysosomes21.
Until now, the simultaneous co-localization of LC3, p62, and LAMP1 (lysosomal marker) was not possible using a single &#34;Feature Type&#34; in the MIFC companion analysis software (see the Table of Materials). However, a new feature, recently introduced in version 6.1, is called Bright Detail Colocalization 3 (BDC3). BDC3 compares the bright detail images from each of the three images (in this case, LC3, p62, and LAMP1) and quantifies the co-localization of the three probes (i.e., LC3, p62, and LAMP1). The BDC3 feature computes the Pearson&#39;s correlation coefficient modified to extend to three images. Since the bright spots in the three images are either correlated or uncorrelated, the correlation coefficient varies between 0 (uncorrelated) and 1 (perfect correlation). The coefficient is log-transformed to increase the dynamic range between 0 and infinity. By switching out the BDS feature with the BDC3 feature, the analysis method presented by Rajan et al. can now incorporate the three most-used markers to measure autophagy in one system at the same time. The ability to co-localize these three markers of autophagy in a single assay could lead to novel insights into the induction and regulation of autophagy. The following protocol outlines the steps to induce autophagy in Jurkat cells; label the cells with LC3, p62, and LAMP1 antibodies; acquire data on a multispectral imaging flow cytometer; and analyze the data to assess autophagic flux.

<table><tbody><tr><td>15 mL centrifuge tube</td><td>Falcon</td><td>352096</td><td></td></tr><tr><td>50 mL centrifuge tube</td><td>Falcon</td><td>352070</td><td></td></tr><tr><td>AF647 labeled Donkey anti-Mouse - IgG&amp;nbsp;</td><td>Invitrogen</td><td>A-31571</td><td>Donkey anti-Mouse IgG (H+L) Secondary Antibody, Alexa Fluor 647. Concntration 2 mg/mL. https://www.thermofisher.com/antibody/product/Donkey-anti-Mouse-IgG-H-L-Secondary-Antibody-Polyclonal/A-31571</td></tr><tr><td>anti-human CD107a-PE (LAMP1)</td><td>BioLegend</td><td>328607</td><td>Clone H4A3. Concentration 400 &amp;micro;g/mL. http://www.biolegend.com/pe-anti-human-cd107a-lamp-1-antibody-4967.html</td></tr><tr><td>anti-human CD45- AF488</td><td>BioLegend</td><td>304017</td><td>Clone HI30. http://www.biolegend.com/alexa-fluor-488-anti-human-cd45-antibody-2738.html</td></tr><tr><td>anti-human CD45- PE&amp;nbsp;</td><td>BioLegend</td><td>304008</td><td>Clone HI30.&amp;nbsp; http://www.biolegend.com/pe-anti-human-cd45-antibody-708.html</td></tr><tr><td>anti-human CD45-AF647&amp;nbsp;</td><td>BioLegend</td><td>304018</td><td>Clone HI30.&amp;nbsp; http://www.biolegend.com/alexa-fluor-647-anti-human-cd45-antibody-2739.html</td></tr><tr><td>Anti-LC3 (Human) mAb clone 4E12</td><td>MBL International Corporation</td><td>M152-3</td><td>Clone 4E+12.&amp;nbsp; Concentration 2 mg/mL.&amp;nbsp; https://www.mblintl.com/products/m152-3</td></tr><tr><td>Anti-SQSTM1 / p62 antibody [EPR4844] - Autophagosome Marker (Alexa Fluor 488)</td><td>abcam</td><td>ab185015</td><td>Clone EPR4844.&amp;nbsp; Concentration 0.5 mg/mL.&amp;nbsp; http://www.abcam.com/sqstm1-p62-antibody-epr4844-autophagosome-marker-alexa-fluor-488-ab185015.html</td></tr><tr><td>Chloroquine diphosphate Salt</td><td>Sigma-Aldrich</td><td>C6628-25G</td><td></td></tr><tr><td>Cleanser - Coulter Clenz&amp;nbsp;</td><td>Beckman Coulter</td><td>8546931</td><td>Fill container with 200 mL of Cleanser.&amp;nbsp; https://www.beckmancoulter.com/wsrportal/page/itemDetails?itemNumber=8546931#2/10//0/25/1/0/asc/2/8546931///0/1//0/</td></tr><tr><td>DAPI Stain 5 mg/mL</td><td>MilliporeSigma</td><td>508741</td><td>http://www.emdmillipore.com/US/en/product/DAPI-Stain---CAS-28718-90-3---Calbiochem,EMD_BIO-508741</td></tr><tr><td>Debubbler - 70% Isopropanol</td><td>MilliporeSigma</td><td>1.3704</td><td>Fill container with 200 mL of Debubbler.&amp;nbsp; http://www.emdmillipore.com/US/en/product/2-Propanol-70%25-%28V%2FV%29-0.1-%C2%B5m-filtred,MDA_CHEM-137040?ReferrerURL=https%3A%2F%2Fwww.google.com%2F</td></tr><tr><td>Dulbecco&#039;s Phosphate Buffered Saline 10x</td><td>MilliporeSigma</td><td>BSS-2010-B</td><td>&amp;nbsp;Ca++Mg++ Free&amp;nbsp;</td></tr><tr><td>Dulbecco&#039;s Phosphate Buffered Saline 1x</td><td>MilliporeSigma</td><td>BSS-1006-B</td><td>PBS Ca++Mg++ Free&amp;nbsp;</td></tr><tr><td>Earle&#039;s Balanced Salt Solution 1x</td><td>Gibco</td><td>14155</td><td></td></tr><tr><td>Fetal Bovine Serum</td><td>HyClone</td><td>SH30071.03</td><td></td></tr><tr><td>Formaldehyde, 10%, methanol free, Ultra Pure</td><td>Polysciences, Inc.</td><td>04018</td><td>This is what is used for the 4% and 1% Formalin. CAUTION: Formalin/Formaldehyde toxic by inhalation and if swallowed.&amp;nbsp; Irritating to the eyes, respiratory systems and skin.&amp;nbsp; May cause sensitization by inhalation or skin contact. Risk of serious damage to eyes.&amp;nbsp; Potential cancer hazard.&amp;nbsp; http://www.polysciences.com/default/catalog-products/life-sciences/histology-microscopy/fixatives/formaldehydes/formaldehyde-10-methanol-free-pure/</td></tr><tr><td>Hanks&#039; Balanced Salt Solution 1x</td><td>Gibco</td><td>14175</td><td></td></tr><tr><td>Jurkat, Clone E6-1 (ATCC TIB-152)</td><td>ATCC</td><td>TIB-152</td><td>https://www.atcc.org/products/all/TIB-152.aspx</td></tr><tr><td>MEM Non-Essential Amino Acids 100x</td><td>HyClone</td><td>SH30238.01</td><td></td></tr><tr><td>MIFC - ImageStreamX Mark II</td><td>MilliporeSigma</td><td>100220</td><td>A 2 camera ImageStreamX Mark II eqiped with the 405nm, 488nm, and 642nm lasers was used. http://www.emdmillipore.com/US/en/life-science-research/cell-analysis/amnis-imaging-flow-cytometers/imagestreamx-Mark-ii-imaging-flow-cytometer/VaSb.qB.QokAAAFLzRop.zHe,nav?cid=BI-XX-BDS-P-GOOG-FLOW-B325-0006</td></tr><tr><td>MIFC analysis software - IDEAS 6.2</td><td>MilliporeSigma</td><td>100220</td><td>The companion software to the MIFC (ImageStreamX MKII).&amp;nbsp; IDEAS version 6.2</td></tr><tr><td>MIFC software - INSPIRE</td><td>MilliporeSigma</td><td>100220</td><td>This is the software that runs the MIFC (ImageStreamX MKII). INSPIRE version 200.1.388.0</td></tr><tr><td>PE anti-human CD107a (LAMP-1) Antibody clone H4A3</td><td>BioLegend</td><td>328608</td><td>http://www.biolegend.com/pe-anti-human-cd107a-lamp-1-antibody-4967.html</td></tr><tr><td>Penicllin/Streptomycin/Glutamine solution 100x</td><td>HyClone</td><td>SV30082.1</td><td></td></tr><tr><td>Rinse - Ultrapure water or deionized water</td><td>NA</td><td>NA</td><td>You can use any ultrapure water or deionized water.&amp;nbsp; Fill container with 900 mL of Rinse.</td></tr><tr><td>RPMI-1640 Medium 1x</td><td>HyClone</td><td>SH30027.01</td><td></td></tr><tr><td>Sheath - PBS</td><td>MilliporeSigma</td><td>BSS-1006-B</td><td>This is the same as Dulbecco&#039;s Phosphate Buffered Saline 1X&amp;nbsp; Ca++MG++ free.&amp;nbsp; Fill container with 900 mL of Sheath.</td></tr><tr><td>Siliconized polypropylene microcentrifuge tubes</td><td>Fisherbrand</td><td>02-681-320</td><td>Fisherbran Siliconized Low-Retention Microcentrifuge Tubes 1.5 mL. https://www.fishersci.com/shop/products/fisherbrand-siliconized-low-retention-microcentrifuge-tubes-8/p-193936</td></tr><tr><td>Sodium Pyruvate solution (100 mM)</td><td>HyClone</td><td>SH30239.01</td><td></td></tr><tr><td>Sterilizer - 0.4 - 0.7% Hypochlorite</td><td>VWR</td><td>JT9416-1</td><td>This is assentually 10% Clorox bleach that can be made by deluting Clorox bleach with water.&amp;nbsp; Fill container with 200 mL of Sterilzer.</td></tr><tr><td>System Calibration Reagent - SpeedBead</td><td>MilliporeSigma</td><td>400041</td><td>Each tube holds ~10 mL.&amp;nbsp; https://www.emdmillipore.com/US/en/life-science-research/cell-analysis/amnis-imaging-flow-cytometers/support-training/XDqb.qB.wQMAAAFLBDUp.zHu,nav&amp;nbsp;</td></tr><tr><td>T75 flask</td><td>Falcon</td><td>353136</td><td></td></tr><tr><td>TRITON X-100, PROTEIN GRADE Detergent, 10% Solution, Sterile-Filtered</td><td>MilliporeSigma</td><td>648463-50ML</td><td>http://www.emdmillipore.com/US/en/product/TRITON-X-100,-PROTEIN-GRADE-Detergent,-10%25-Solution,-Sterile-Filtered---CAS-9002-93-1---Calbiochem,EMD_BIO-648463</td></tr><tr><td>Water, Cell Culture Grade&amp;nbsp;</td><td>HyClone</td><td>SH30529.03</td><td></td></tr></tbody></table>

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.

This analysis method uses multiple features to asses autophagic flux. In order to fully understand the final bivariate plot, the individual analysis features must first be investigated. The counting of autophagosomes is a logical way to measure autophagy; however, the size/shape/brightness of LC3 puncta can vary drastically between cells. Variation can make it difficult to count autophagosomes manually or using a spot-count feature in the analysis software. Therefore, no spot-count feature will be perfect due to this large variability in autophagosomes. However, a good spot-count feature will work on most cells26. Figure 3 shows examples of spot masking of LC3 puncta in Jurkat cells using different spot masks. The spot-count feature selected for this data set is Spot Count_Peak(M11, Ch11-LC3-AF647, Bright, 4)_4, meaning that the spot-count feature counted the spots that the peak mask identified within the default channel 11 mask (M11) on channel 11 (Ch11-LC3-AF647) with a spot-to-cell background ratio of 4 (Bright, 4). Figure 4 shows spot-count histograms and representative images for the mean spot count for the Control, Control + Chloroquine, Starved, and Starved + Chloroquine Jurkat cells labeled with anti-LC3-AF647. The Control and Starved mean spot counts are not significantly different; with the addition of chloroquine, there is a large difference in the mean of the Control + Chloroquine compared to the Starved + Chloroquine.
The next feature to investigate is BDC3. BDC3 is a measurement of the co-localization of three markers/probes, in this case, LC3, p62, and LAMP1. Figure 5A-5D shows BDC3 histograms for the Control, Control + Chloroquine, Starved, and Starved + Chloroquine Jurkat cells labeled with anti-p62-AF488, anti-LAMP1-PE, and anti-LC3-AF647. There is a shift between the Control mean to the Starved mean, as well as the Control + Chloroquine mean to the Starved + Chloroquine mean. However, looking at the images of cells from the mean BDC3 scores in Figure 5E-5H, there is a greater difference between the samples than the histograms may lead one to believe. This is because BDC3 does not consider the number of autophagy organelles that co-localize, resulting in a large degree of variability in the number of autophagosomes for the same BDC3 score. In most cases, there is overlap between all three probes because, even at basal levels, p62, LAMP1, and LC3 should, to a certain extent, co-localize or reside in similar regions in the cells. As a contrast, an example of three probes that should not co-localize are anti-p62-AF488, anti-LC3-AF647, and DAPI nuclear dye for the Starved + Chloroquine sample, shown in Figure 6.
When the spot-count feature and the BDC3 feature are combined, the presence of different subpopulations that improve the ability to distinguish between the various samples/conditions are evident. Figure 7 shows the bivariate plot of spot count of LC3 versus BDC3 p62/LAMP1/LC3 for the four samples: Control, Control + Chloroquine, Starved, and Starved + Chloroquine. The Control sample was used to set the gating strategy for three populations: Low Spots, High Spots/Low BDC3, and High Spots/High BDC3. The Control samples demonstrated that greater than 98% of the cells had 1 or fewer spots. The boundary between the High Spots/Low BDC3 and High Spots/High BDC3 was set to a BDC3 score of 1 because more than 91% of the Control sample had a BDC3 score of less than 1. A summary of the results for the bivariate plots is shown in Table 1.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/55637/55637fig1.jpg" /> 
Figure 1: MIFC Instrument Setting. A screenshot of the MIFC instrument settings used for this experiment, outlined in step 8 of the protocol. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55637/55637fig1large.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/55637/55637fig2.jpg" /> 
Figure 2: Analysis Software Gating Strategy. A screenshot of the analysis software gating scheme, outlined in step 9 of the protocol. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55637/55637fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/55637/55637fig3.jpg" /> 
Figure 3: LC3 Spot Masking. Jurkat cell images and masks used to create the spot-count feature. Shown are BrightField (BF), LC3-AF647, Peak(M11, Ch11-LC3-AF647, Bright,2), Peak(M11, Ch11-LC3-AF647, Bright,4), Peak(M11, Ch11-LC3-AF647, Bright,5), Spot(M11, Ch11-LC3-AF647, Bright,5,3,1), and Spot(M11, Ch11-LC3-AF647, Bright,6,2,1). The mask that worked best for all cells shown was Peak(M11, Ch11-LC3-AF647, Bright,4). <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55637/55637fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/55637/55637fig4.jpg" /> 
Figure 4: LC3 Spot-count Histograms. Using the spot-count feature Spot Count_Peak(M11, Ch11-LC3-AF647, Bright, 4)_4 and the LC3-AF647 spot-count histograms for Control (A, light blue), Control + Chloroquine (B, blue), Starved (C, pink), and Starved + Chloroquine (D, red). The mean spot counts for Control, Control + Chloroquine, Starved, and Starved + Chloroquine are 0.07, 1.13, 0.10, and 2.77, respectively. BF, LC3-AF647 (red), DAPI nuclear dye (blue), and a composite of the LC3-AF647 and DAPI images of representative cells for the mean spot count are shown for Control (E, 0 spots), Control + Chloroquine (F, 1 spot), Starved (G, 0 spots), and Starved + Chloroquine (H, 3 spots). <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55637/55637fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/55637/55637fig5.jpg" /> 
Figure 5: BDC3 Histograms of p62/LAMP1/LC3. BDC3 p62/LAMP1/LC3 histograms for Control (A, light blue), Control + Chloroquine (B, blue), Starved (C, pink), and Starved + Chloroquine (D, red). The mean BDC3 score for Control, Control + Chloroquine, Starved, and Starved + Chloroquine are 0.57, 0.82, 0.74, and 0.98, respectively. BF; p62-AF488 (green); LAMP1-PE (yellow); LC3-AF647 (red); and a composite of the p62-AF488, LAMP1-PE, and LC3-AF647 images of representative cells for the mean BDC3 are shown for Control (E), Control + Chloroquine (F), Starved (G), and Starved + Chloroquine (H). <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55637/55637fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/55637/55637fig6.jpg" /> 
Figure 6: BDC3 Histograms of p62/LC3/DAPI for the Starved + Chloroquine Sample. (A) BDC3 p62/LC3/DAPI histogram for the Starved + Chloroquine sample is shown. The mean BDC3 score is 0.07. (B) BF; p62-AF488 (green); LC3-AF647 (red); DAPI (blue); and a composite of the p62-AF488, LC3-AF647, and DAPI images of representative cells for the mean BDC3 is shown for the Starved + Chloroquine sample. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55637/55637fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/55637/55637fig7.jpg" /> 
Figure 7: Bivariate Plots of LC3 Spot Count versus BDC3 p62/LAMP1/LC3. (A) Bivariate plots of LC3 spot count versus BDC3 p62/LAMP1/LC3 for Control, Control + Chloroquine, Starved, and Starved + Chloroquine Jurkat cells. (B) BF; p62-AF488 (green); LAMP1-PE (yellow); LC3-AF647 (red); and a composite of the p62-AF488, LAMP1-PE, and LC3-AF647 images from three regions (i.e., Low Spots, High Spots/Low BDC3, and High Spots/High BDC3) are shown for the Starved + Chloroquine sample. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55637/55637fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>Cell Count</td>
			<td>Bright Detail Colocalization 3 Mean&#160;</td>
			<td>Spot Count LC3 Mean</td>
			<td>% Low Spot&#160;&#160;</td>
			<td>% High Spot/Low BDC3&#160;</td>
			<td>% High Spot/High BDC3&#160;</td>
		</tr>
		<tr>
			<td>Control</td>
			<td>439</td>
			<td>0.57</td>
			<td>0.07</td>
			<td>98.2</td>
			<td>1.8</td>
			<td>0.0</td>
		</tr>
		<tr>
			<td>Control + Chloroquine</td>
			<td>1432</td>
			<td>0.82</td>
			<td>1.13</td>
			<td>68.9</td>
			<td>19.5</td>
			<td>11.7</td>
		</tr>
		<tr>
			<td>Starved&#160;</td>
			<td>1204</td>
			<td>0.74</td>
			<td>0.10</td>
			<td>98.4</td>
			<td>0.6</td>
			<td>1.0</td>
		</tr>
		<tr>
			<td>Starved + Chloroquine</td>
			<td>1811</td>
			<td>0.98</td>
			<td>2.77</td>
			<td>32.1</td>
			<td>36.9</td>
			<td>31.0</td>
		</tr>
	</tbody>
</table>
Table 1: Summary of Jurkat LC3 Spot Count versus BDC3 p62/LAMP1/LC3 Bivariate Plot

Watch this Scientific Journal Video about Assessing Autophagic Flux by Measuring LC3, p62, and LAMP1 Co-localization Using Multispectral Imaging Flow Cytometry at JoVE.com

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

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 ...

assessing-autophagic-flux-measuring-lc3-p62-lamp1-co-localization

Nanyang Technological University

Research

JoVE Journal

Biology

31.1K Views.  MilliporeSigma. 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. 

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

A Fluorescence Microscopy Assay for Monitoring Mitophagy in the Yeast Saccharomyces cerevisiae

Autophagy is important for turnover of cellular components under a range of different conditions. It serves an essential homeostatic function as well as a quality control mechanism that can target and selectively degrade cellular material including organelles1-4. For example, damaged or redundant mitochondria (Fig. 1), not disposed of by autophagy, can represent a threat to cellular homeostasis and cell survival. In the yeast, Saccharomyces cerevisiae, nutrient deprivation (e.g., nitrogen starvation) or damage can promote selective turnover of mitochondria by autophagy in a process termed mitophagy 5-9.
We describe a simple fluorescence microscopy approach to assess autophagy. For clarity we restrict our description here to show how the approach can be used to monitor mitophagy in yeast cells. The assay makes use of a fluorescent reporter, Rosella, which is a dual-emission biosensor comprising a relatively pH-stable red fluorescent protein linked to a pH-sensitive green fluorescent protein. The operation of this reporter relies on differences in pH between the vacuole (pH ~ 5.0-5.5) and mitochondria (pH ~ 8.2) in living cells. Under growing conditions, wild type cells exhibit both red and green fluorescence distributed in a manner characteristic of the mitochondria. Fluorescence emission is not associated with the vacuole. When subjected to nitrogen starvation, a condition which induces mitophagy, in addition to red and green fluorescence labeling the mitochondria, cells exhibit the accumulation of red, but not green fluorescence, in the acidic vacuolar lumen representing the delivery of mitochondria to the vacuole. Scoring cells with red, but not green fluorescent vacuoles can be used as a measure of mitophagic activity in cells5,10-12.

A Fluorescence Microscopy Assay for Monitoring Mitophagy in the Yeast Saccharomyces cerevisiae

A robust approach to monitor the delivery of organelles to the acidic lumen of the yeast vacuole for degradation and recycling is described. The method relies on the specific labeling of target organelles with a genetically encoded dual-emission fluorescence pH-biosensor, and visualization of individual cells using fluorescence microscopy.

Autophagy is important for turnover of cellular components under a range of different conditions. It serves an essential homeostatic function as well as a ...

Assessing LC3-II-Positive EV Release in Autophagy with Impaired Lysosome Fusion

Evaluation of LC3-II Release via Extracellular Vesicles in Relation to the Accumulation of Intracellular LC3-positive Vesicles

(Macro)autophagy represents a fundamental cellular degradation pathway. In this process, double-membraned vesicles known as autophagosomes engulf cytoplasmic contents, subsequently fusing with lysosomes for degradation. Beyond the canonical role, autophagy-related genes also modulate a secretory pathway involving the release of inflammatory molecules, tissue repair factors, and extracellular vesicles (EVs). Notably, the process of disseminating pathological proteins between cells, particularly in neurodegenerative diseases affecting the brain and spinal cord, underscores the significance of understanding this phenomenon. Recent research suggests that the transactive response DNA-binding protein 43 kDa (TDP-43), a key player in amyotrophic lateral sclerosis and frontotemporal lobar degeneration, is released in an autophagy-dependent manner via EVs enriched with the autophagosome marker microtubule-associated proteins 1A/1B light chain 3B-II (LC3-II), especially when autophagosome-lysosome fusion is inhibited.
To elucidate the mechanism underlying the formation and release of LC3-II-positive EVs, it is imperative to establish an accessible and reproducible method for evaluating both intracellular and extracellular LC3-II-positive vesicles. This study presents a detailed protocol for assessing LC3-II levels via immunoblotting in cellular and EV fractions obtained through differential centrifugation. Bafilomycin A1 (Baf), an inhibitor of autophagosome-lysosome fusion, serves as a positive control to enhance the levels of intracellular and extracellular LC3-II-positive vesicles. Tumor susceptibility gene 101 (TSG101) is used as a marker for multivesicular bodies. Applying this protocol, it is demonstrated that siRNA-mediated knockdown of syntaxin-6 (STX6), a genetic risk factor for sporadic Creutzfeldt-Jakob disease, augments LC3-II levels in the EV fraction of cells treated with Baf while showing no significant effect on TSG101 levels. These findings suggest that STX6 may negatively regulate the extracellular release of LC3-II via EVs, particularly under conditions where autophagosome-lysosome fusion is impaired. Combined with established methods for evaluating autophagy, this protocol provides valuable insights into the role of specific molecules in the formation and release of LC3-II-positive EVs.

Here, we present the methodology for concisely assessing autophagosome marker LC3-II levels in extracellular vesicles (EVs) by immunoblotting. Analysis for LC3-II levels in EVs, autolysosome formation, and omegasome formation suggests the new role of STX6 in the release of LC3-II-positive EVs when autophagosome-lysosome fusion is inhibited.

(Macro)autophagy represents a fundamental cellular degradation pathway. In this process, double-membraned vesicles known as autophagosomes engulf ...

Biochemistry

Quantitative Analysis of Autophagy using Advanced 3D Fluorescence Microscopy

Prostate cancer is the leading form of malignancies among men in the U.S. While surgery carries a significant risk of impotence and incontinence, traditional chemotherapeutic approaches have been largely unsuccessful. Hormone therapy is effective at early stage, but often fails with the eventual development of hormone-refractory tumors. We have been interested in developing therapeutics targeting specific metabolic deficiency of tumor cells. We recently showed that prostate tumor cells specifically lack an enzyme (argininosuccinate synthase, or ASS) involved in the synthesis of the amino acid arginine1. This condition causes the tumor cells to become dependent on exogenous arginine, and they undergo metabolic stress when free arginine is depleted by arginine deiminase (ADI)1,10. Indeed, we have shown that human prostate cancer cells CWR22Rv1 are effectively killed by ADI with caspase-independent apoptosis and aggressive autophagy (or macroautophagy)1,2,3. Autophagy is an evolutionarily-conserved process that allows cells to metabolize unwanted proteins by lysosomal breakdown during nutritional starvation4,5. Although the essential components of this pathway are well-characterized6,7,8,9, many aspects of the molecular mechanism are still unclear - in particular, what is the role of autophagy in the death-response of prostate cancer cells after ADI treatment? In order to address this question, we required an experimental method to measure the level and extent of autophagic response in cells - and since there are no known molecular markers that can accurately track this process, we chose to develop an imaging-based approach, using quantitative 3D fluorescence microscopy11,12.
Using CWR22Rv1 cells specifically-labeled with fluorescent probes for autophagosomes and lysosomes, we show that 3D image stacks acquired with either widefield deconvolution microscopy (and later, with super-resolution, structured-illumination microscopy) can clearly capture the early stages of autophagy induction. With commercially available digital image analysis applications, we can readily obtain statistical information about autophagosome and lysosome number, size, distribution, and degree of colocalization from any imaged cell. This information allows us to precisely track the progress of autophagy in living cells and enables our continued investigation into the role of autophagy in cancer chemotherapy.

Autophagy is a ubiquitous process that enables cells to degrade and recycle proteins and organelles. We apply advanced fluorescence microscopy to visualize and quantify the small, but essential, physical changes associated with the induction of autophagy, including the formation and distribution of autophagosomes and lysosomes, and their fusion into autolysosomes.

Prostate cancer is the leading form of malignancies among men in the U.S. While surgery carries a significant risk of impotence and incontinence, ...

Live Cell Imaging of Early Autophagy Events: Omegasomes and Beyond

Autophagy is a cellular response triggered by the lack of nutrients, especially the absence of amino acids. Autophagy is defined by the formation of double membrane structures, called autophagosomes, that sequester cytoplasm, long-lived proteins and protein aggregates, defective organelles, and even viruses or bacteria. Autophagosomes eventually fuse with lysosomes leading to bulk degradation of their content, with the produced nutrients being recycled back to the cytoplasm. Therefore, autophagy is crucial for cell homeostasis, and dysregulation of autophagy can lead to disease, most notably neurodegeneration, ageing and cancer.	
	Autophagosome formation is a very elaborate process, for which cells have allocated a specific group of proteins, called the core autophagy machinery. The core autophagy machinery is functionally complemented by additional proteins involved in diverse cellular processes, e.g. in membrane trafficking, in mitochondrial and lysosomal biology. Coordination of these proteins for the formation and degradation of autophagosomes constitutes the highly dynamic and sophisticated response of autophagy. Live cell imaging allows one to follow the molecular contribution of each autophagy-related protein down to the level of a single autophagosome formation event and in real time, therefore this technique offers a high temporal and spatial resolution.	
	Here we use a cell line stably expressing GFP-DFCP1, to establish a spatial and temporal context for our analysis. DFCP1 marks omegasomes, which are precursor structures leading to autophagosomes formation. A protein of interest (POI) can be marked with either a red or cyan fluorescent tag. Different organelles, like the ER, mitochondria and lysosomes, are all involved in different steps of autophagosome formation, and can be marked using a specific tracker dye. Time-lapse microscopy of autophagy in this experimental set up, allows information to be extracted about the fourth dimension, i.e. time. Hence we can follow the contribution of the POI to autophagy in space and time.

Time-lapse microscopy of fluorescently labeled autophagy markers allows monitoring of the dynamic autophagy response with high temporal resolution. Using specific autophagy and organelle markers in a combination of 3 different colors, we can follow the contribution of a protein to autophagosome formation in a robust spatial and temporal context.

Autophagy is a  cellular response triggered by the lack of nutrients, especially the absence of  amino acids. Autophagy is defined by the formation of ...

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 ...

Sensitive Measurement of Mitophagy by Flow Cytometry Using the pH-dependent Fluorescent Reporter mt-Keima

Mitophagy is a process of selective removal of damaged or unnecessary mitochondria using autophagy machinery. Close links have been found between defective mitophagy and various human diseases, including neurodegenerative diseases, cancer, and metabolic diseases. In addition, recent studies have shown that mitophagy is involved in normal cellular processes, such as differentiation and development. However, the precise role of and molecular mechanisms underlying mitophagy require further study. Therefore, it is critical to develop a robust and convenient method for measuring changes in mitophagy activity. Here, we describe a detailed protocol for quantitatively assessing mitophagy activity through flow cytometry using the mitochondria-targeted fluorescent protein Keima (mt-Keima). This flow cytometry assay can analyze mitophagy activity more rapidly and sensitively than conventional microscopy- or immunoblotting-based methods. This protocol can be applied to analyze mitophagy activity in various cell types.

Mitophagy, the selective degradation of mitochondria, has been implicated in mitochondrial homeostasis and is deregulated in various human diseases. However, convenient experimental methods for measuring mitophagy activity are very limited. Here, we provide a sensitive assay for measuring mitophagy activity using flow cytometry.

Mitophagy is a process of selective removal of damaged or unnecessary mitochondria using autophagy machinery. Close links have been found between ...

Measuring Diurnal Rhythms in Autophagic and Proteasomal Flux

Cells employ several methods for recycling unwanted proteins and other material, including lysosomal and non-lysosomal pathways. The main lysosome-dependent pathway is called autophagy, while the primary non-lysosomal method for protein catabolism is the ubiquitin-proteasome system. Recent studies in model organisms suggest that the activity of both autophagy and the ubiquitin-proteasome system is not constant across the day but instead varies according to a daily (circadian) rhythm. The ability to measure biological rhythms in protein turnover is important for understanding how cellular quality control is achieved and for understanding the dynamics of specific proteins of interest. Here we present a standardized protocol for quantifying autophagic and proteasomal flux in vivo that captures the circadian component of protein turnover. Our protocol includes details for mouse handling, tissue processing, fractionation, and autophagic flux quantification using mouse liver as the starting material.

We describe our protocol for measuring biological rhythms in protein catabolism via autophagy and the proteasome in mouse liver.

Cells employ several methods for recycling unwanted proteins and other material, including lysosomal and non-lysosomal pathways. The main ...

Flux-Based Assay for the Identification of Autophagy Modulators for Osteoarthritis

Autophagy is a central mechanism to regulate homeostasis. Alterations of autophagy contribute to aging-related diseases. Phenotypic methods to identify regulators of autophagy could be used for the identification of novel therapeutics. This article describes a cell-based imaging screening workflow developed to monitor autophagic flux using LC3 as a reporter of autophagic flux (mCherry-EGFP-LC3B) in human chondrocytes. Data acquisition is performed using an automated High Content Imaging Screening System microscope. An algorithm-based automated image analysis protocol was developed and validated to identify molecules activating autophagic flux. Critical steps, explanatory notes, and improvements over current autophagy monitoring protocols are reported. Physiologically relevant phenotypic screening approaches to target hallmarks of aging can facilitate more effective drug discovery strategies for age-related musculoskeletal diseases.

This paper details monitoring autophagy flux to identify new molecules by cell-based imaging screening.

Autophagy is a central mechanism to regulate homeostasis. Alterations of autophagy contribute to aging-related diseases. Phenotypic methods to identify ...

Visualizing Mitophagy with Fluorescent Dyes for Mitochondria and Lysosome

Mitochondria, being the powerhouses of the cell, play important roles in bioenergetics, free radical generation, calcium homeostasis, and apoptosis. Mitophagy is the primary mechanism of mitochondrial quality control and is generally studied using microscopic observation, however in vivo mitophagy assays are difficult to perform. Evaluating mitophagy by imaging live organelles is an alternative and necessary method for mitochondrial research. This protocol describes the procedures for using the cell-permeant green-fluorescent mitochondria dye MitoTracker Green and the red-fluorescent lysosome dye LysoTracker Red in live cells, including the loading of the dyes, visualization of the mitochondria and the lysosome, and expected outcomes. Detailed steps for the evaluation of mitophagy in live cells, as well as technical notes about microscope software settings, are also provided. This method can help researchers observe mitophagy using live-cell fluorescent microscopy. In addition, it can be used to quantify mitochondria and lysosomes and assess mitochondrial morphology.

Mitophagy is the primary mechanism of mitochondrial quality control. However, the evaluation of mitophagy in vivo is hindered by the lack of reliable quantitative assays. Presented here is a protocol for the observation of mitophagy in living cells using a cell-permeant green-fluorescent mitochondria dye and a red-fluorescent lysosome dye.

Mitochondria, being the powerhouses of the cell, play important roles in bioenergetics, free radical generation, calcium homeostasis, and apoptosis. ...

Ultrastructural Localization of Endogenous LC3 by On-Section Correlative Light-Electron Microscopy

The visualization of autophagic organelles at the ultrastructural level by electron microscopy (EM) is essential to establish their identity and reveal details that are important for understanding the autophagic process. However, EM methods often lack molecular information, obstructing the correlation of ultrastructural information obtained by EM to fluorescence microscopy-based localization of specific autophagy proteins. Furthermore, the rarity of autophagosomes in unaltered cellular conditions hampers investigation by EM, which requires high magnification, and hence provides a limited field of view.
In answer to both challenges, an on-section correlative light-electron microscopy (CLEM) method based on fluorescent labeling was applied to correlate a common autophagosomal marker, LC3, to EM ultrastructure. The method was used to rapidly screen cells in fluorescence microscopy for LC3 labeling in combination with other relevant markers. Subsequently, the underlying ultrastructural features of selected LC3-labeled spots were identified by CLEM. The method was applied to starved cells without adding inhibitors of lysosomal acidification.
In these conditions, LC3 was found predominantly on autophagosomes and rarely in autolysosomes, in which LC3 is rapidly degraded. These data show both the feasibility and sensitivity of this approach, demonstrating that CLEM can be used to provide ultrastructural insights on LC3-mediated autophagy in native conditions-without drug treatments or genetic alterations. Overall, this method presents a valuable tool for ultrastructural localization studies of autophagy proteins and other scarce antigens by bridging light microscopy to EM data.

Here, we present a protocol for optimized on-section correlative light-electron microscopy based on endogenous, fluorescent labeling as a tool to investigate the localization of rare proteins in relation to cellular ultrastructure. The power of this approach is demonstrated by ultrastructural localization of endogenous LC3 in starved cells without Bafilomycin treatment.

The visualization of autophagic organelles at the ultrastructural level by electron microscopy (EM) is essential to establish their identity and reveal ...

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

Summary

Explore More Videos

Chapters in this video

A Fluorescence Microscopy Assay for Monitoring Mitophagy in the Yeast Saccharomyces cerevisiae

Evaluation of LC3-II Release via Extracellular Vesicles in Relation to the Accumulation of Intracellular LC3-positive Vesicles

Quantitative Analysis of Autophagy using Advanced 3D Fluorescence Microscopy

Live Cell Imaging of Early Autophagy Events: Omegasomes and Beyond

The Lactate Dehydrogenase Sequestration Assay — A Simple and Reliable Method to Determine Bulk Autophagic Sequestration Activity in Mammalian Cells

Sensitive Measurement of Mitophagy by Flow Cytometry Using the pH-dependent Fluorescent Reporter mt-Keima

Measuring Diurnal Rhythms in Autophagic and Proteasomal Flux

Flux-Based Assay for the Identification of Autophagy Modulators for Osteoarthritis

Visualizing Mitophagy with Fluorescent Dyes for Mitochondria and Lysosome

Ultrastructural Localization of Endogenous LC3 by On-Section Correlative Light-Electron Microscopy