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 border="0" cellpadding="0" cellspacing="0" width="1012">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">15 mL centrifuge tube</td>
			<td style="width:244px;">Falcon</td>
			<td style="width:161px;">352096</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">50 mL centrifuge tube</td>
			<td>Falcon</td>
			<td>352070</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="121">
			<td height="121" style="height:121px;">AF647 labeled Donkey anti-Mouse - IgG&#160;</td>
			<td>Invitrogen</td>
			<td>A-31571</td>
			<td style="width:299px;">Donkey anti-Mouse IgG (H+L) Secondary Antibody, Alexa Fluor 647. Concntration 2mg/mL. https://www.thermofisher.com/antibody/product/Donkey-anti-Mouse-IgG-H-L-Secondary-Antibody-Polyclonal/A-31571</td>
		</tr>
		<tr height="62">
			<td height="62" style="height:62px;width:308px;">anti-human CD107a-PE (LAMP1)</td>
			<td>BioLegend</td>
			<td>328607</td>
			<td style="width:299px;">Clone H4A3. Concentration 400 &#181;g/mL. http://www.biolegend.com/pe-anti-human-cd107a-lamp-1-antibody-4967.html</td>
		</tr>
		<tr height="81">
			<td height="81" style="height:81px;">anti-human CD45- AF488</td>
			<td>BioLegend</td>
			<td>304017</td>
			<td style="width:299px;">Clone HI30. http://www.biolegend.com/alexa-fluor-488-anti-human-cd45-antibody-2738.html</td>
		</tr>
		<tr height="61">
			<td height="61" style="height:61px;">anti-human CD45- PE&#160;</td>
			<td>BioLegend</td>
			<td>304008</td>
			<td style="width:299px;">Clone HI30.&#160; http://www.biolegend.com/pe-anti-human-cd45-antibody-708.html</td>
		</tr>
		<tr height="81">
			<td height="81" style="height:81px;">anti-human CD45-AF647&#160;</td>
			<td>BioLegend</td>
			<td>304018</td>
			<td style="width:299px;">Clone HI30.&#160; http://www.biolegend.com/alexa-fluor-647-anti-human-cd45-antibody-2739.html</td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:308px;">Anti-LC3 (Human) mAb clone 4E12</td>
			<td>MBL International Corporation</td>
			<td>M152-3</td>
			<td style="width:299px;">Clone 4E+12.&#160; Concentration 2 mg/mL.&#160; https://www.mblintl.com/products/m152-3</td>
		</tr>
		<tr height="81">
			<td height="81" style="height:81px;width:308px;">Anti-SQSTM1 / p62 antibody [EPR4844] - Autophagosome Marker (Alexa Fluor 488)</td>
			<td>abcam</td>
			<td>ab185015</td>
			<td style="width:299px;">Clone EPR4844.&#160; Concentration 0.5 mg/mL.&#160; http://www.abcam.com/sqstm1-p62-antibody-epr4844-autophagosome-marker-alexa-fluor-488-ab185015.html</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">Chloroquine diphosphate Salt</td>
			<td>Sigma-Aldrich</td>
			<td>C6628-25G</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="101">
			<td height="101" style="height:101px;width:308px;">Cleanser - Coulter Clenz&#160;</td>
			<td>Beckman Coulter</td>
			<td>8546931</td>
			<td style="width:299px;">Fill container with 200 mL of Cleanser.&#160; https://www.beckmancoulter.com/wsrportal/page/itemDetails?itemNumber=8546931#2/10//0/25/1/0/asc/2/8546931///0/1//0/</td>
		</tr>
		<tr height="61">
			<td height="61" style="height:61px;width:308px;">DAPI Stain 5mg/mL</td>
			<td>MilliporeSigma</td>
			<td>508741</td>
			<td style="width:299px;">http://www.emdmillipore.com/US/en/product/DAPI-Stain---CAS-28718-90-3---Calbiochem,EMD_BIO-508741</td>
		</tr>
		<tr height="121">
			<td height="121" style="height:121px;width:308px;">Debubbler - 70% Isopropanol</td>
			<td>MilliporeSigma</td>
			<td>1.3704</td>
			<td style="width:299px;">Fill container with 200 mL of Debubbler.&#160; 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 height="41">
			<td height="41" style="height:41px;width:308px;">Dulbecco&#39;s Phosphate Buffered Saline 10X</td>
			<td>MilliporeSigma</td>
			<td>BSS-2010-B</td>
			<td style="width:299px;">&#160;Ca++Mg++ Free&#160;</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">Dulbecco&#39;s Phosphate Buffered Saline 1X</td>
			<td>MilliporeSigma</td>
			<td>BSS-1006-B</td>
			<td style="width:299px;">PBS Ca++Mg++ Free&#160;</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">Earle&#39;s Balanced Salt Solution 1X</td>
			<td>Gibco</td>
			<td>14155</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">Fetal Bovine Serum</td>
			<td>HyClone</td>
			<td>SH30071.03</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="241">
			<td height="241" style="height:241px;width:308px;">Formaldehyde, 10%, methanol free, Ultra Pure</td>
			<td>Polysciences, Inc.</td>
			<td>04018</td>
			<td style="width:299px;">This is what is used for the 4% and 1% Formalin. CAUTION: Formalin/Formaldehyde toxic by inhalation and if swallowed.&#160; Irritating to the eyes, respiratory systems and skin.&#160; May cause sensitization by inhalation or skin contact. Risk of serious damage to eyes.&#160; Potential cancer hazard.&#160; http://www.polysciences.com/default/catalog-products/life-sciences/histology-microscopy/fixatives/formaldehydes/formaldehyde-10-methanol-free-pure/</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">Hanks&#39; Balanced Salt Solution 1X</td>
			<td>Gibco</td>
			<td>14175</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:308px;">Jurkat, Clone E6-1 (ATCC TIB-152)</td>
			<td>ATCC</td>
			<td>TIB-152</td>
			<td style="width:299px;">https://www.atcc.org/products/all/TIB-152.aspx</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">MEM Non-Essential Amino Acids 100X</td>
			<td>HyClone</td>
			<td>SH30238.01</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="201">
			<td height="201" style="height:201px;width:308px;">MIFC - ImageStreamX Mark II</td>
			<td>MilliporeSigma</td>
			<td>100220</td>
			<td style="width:299px;">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 height="61">
			<td height="61" style="height:61px;width:308px;">MIFC analysis software - IDEAS 6.2</td>
			<td>MilliporeSigma</td>
			<td>100220</td>
			<td style="width:299px;">The companion software to the MIFC (ImageStreamX MKII).&#160; IDEAS version 6.2</td>
		</tr>
		<tr height="61">
			<td height="61" style="height:61px;width:308px;">MIFC software - INSPIRE</td>
			<td>MilliporeSigma</td>
			<td>100220</td>
			<td style="width:299px;">This is the software that runs the MIFC (ImageStreamX MKII). INSPIRE version 200.1.388.0</td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:308px;">PE anti-human CD107a (LAMP-1) Antibody clone H4A3</td>
			<td>BioLegend</td>
			<td>328608</td>
			<td style="width:299px;">http://www.biolegend.com/pe-anti-human-cd107a-lamp-1-antibody-4967.html</td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:308px;">Penicllin/Streptomycin/Glutamine solution 100X</td>
			<td>HyClone</td>
			<td>SV30082.1</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="61">
			<td height="61" style="height:61px;width:308px;">Rinse - Ultrapure water or deionized water</td>
			<td>NA</td>
			<td>NA</td>
			<td style="width:299px;">You can use any ultrapure water or deionized water.&#160; Fill container with 900 mL of Rinse.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">RPMI-1640 Medium 1X</td>
			<td>HyClone</td>
			<td>SH30027.01</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="81">
			<td height="81" style="height:81px;width:308px;">Sheath - PBS</td>
			<td>MilliporeSigma</td>
			<td>BSS-1006-B</td>
			<td style="width:299px;">This is the same as Dulbecco&#39;s Phosphate Buffered Saline 1X&#160; Ca++MG++ free.&#160; Fill container with 900mL of Sheath.</td>
		</tr>
		<tr height="101">
			<td height="101" style="height:101px;width:308px;">Siliconized polypropylene microcentrifuge tubes</td>
			<td>Fisherbrand</td>
			<td>02-681-320</td>
			<td style="width:299px;">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 height="21">
			<td height="21" style="height:21px;width:308px;">Sodium Pyruvate solution (100mM)</td>
			<td>HyClone</td>
			<td>SH30239.01</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="81">
			<td height="81" style="height:81px;width:308px;">Sterilizer - 0.4-0.7% Hypochlorite</td>
			<td>VWR</td>
			<td>JT9416-1</td>
			<td style="width:299px;">This is assentually 10% Clorox bleach that can be made by deluting Clorox bleach with water.&#160; Fill container with 200 mL of Sterilzer.</td>
		</tr>
		<tr height="121">
			<td height="121" style="height:121px;width:308px;">System Calibration Reagent - SpeedBead</td>
			<td>MilliporeSigma</td>
			<td>400041</td>
			<td style="width:299px;">Each tube holds ~10 mL.&#160; https://www.emdmillipore.com/US/en/life-science-research/cell-analysis/amnis-imaging-flow-cytometers/support-training/XDqb.qB.wQMAAAFLBDUp.zHu,nav&#160;</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">T75 flask</td>
			<td>Falcon</td>
			<td>353136</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="101">
			<td height="101" style="height:101px;width:308px;">TRITON X-100, PROTEIN GRADE Detergent, 10% Solution, Sterile-Filtered</td>
			<td>MilliporeSigma</td>
			<td>648463-50ML</td>
			<td style="width:299px;">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 height="21">
			<td height="21" style="height:21px;width:308px;">Water, Cell Culture Grade&#160;</td>
			<td>HyClone</td>
			<td>SH30529.03</td>
			<td style="width:299px;"></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="http://cloudflare.jove.com/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="http://cloudflare.jove.com/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="http://cloudflare.jove.com/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="http://cloudflare.jove.com/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="http://cloudflare.jove.com/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="http://cloudflare.jove.com/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="http://cloudflare.jove.com/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

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31.0K 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

IP-FCM:  Immunoprecipitation Detected by Flow Cytometry

Immunoprecipitation detected by flow cytometry (IP-FCM) is an efficient method for detecting and quantifying protein-protein interactions. The basic principle extends that of sandwich ELISA, wherein the captured primary analyte can be detected together with other molecules physically associated within multiprotein complexes. The procedure involves covalent coupling of polystyrene latex microbeads with immunoprecipitating monoclonal antibodies (mAb) specific for a protein of interest, incubating these beads with cell lysates, probing captured protein complexes with fluorochrome-conjugated probes, and analyzing bead-associated fluorescence by flow cytometry. IP-FCM is extremely sensitive, allows analysis of proteins in their native (non-denatured) state, and is amenable to either semi-quantitative or quantitative analysis. As additional advantages, IP-FCM requires no genetic engineering or specialized equipment, other than a flow cytometer, and it can be readily adapted for high-throughput applications.

The IP-FCM method is presented, which allows a sensitive, robust, biochemical assessment of native protein-protein interactions, without requiring genetic engineering or large sample sizes.

Immunoprecipitation detected by flow cytometry (IP-FCM) is an efficient method for detecting and quantifying protein-protein interactions. The basic ...

Flow Cytometry Purification of Mouse Meiotic Cells

The heterogeneous nature of cell types in the testis and the absence of meiotic cell culture models have been significant hurdles to the study of the unique differentiation programs that are manifest during meiosis. Two principal methods have been developed to purify, to varying degrees, various meiotic fractions from both adult and immature animals: elutriation or Staput (sedimentation) using BSA and/or percoll gradients. Both of these methods rely on cell size and density to separate meiotic cells1-5. Overall, except for few cell populations6, these protocols fail to yield sufficient purity of the numerous meiotic cell populations that are necessary for detailed molecular analyses. Moreover, with such methods usually one type of meiotic cells can be purified at a given time, which adds an extra level of complexity regarding the reproducibility and homogeneity when comparing meiotic cell samples.
Here, we describe a refined method that allows one to easily visualize, identify, and purify meiotic cells, from germ cells to round spermatids, using FACS combined with Hoechst 33342 staining7,8. This method provides an overall snapshot of the entire meiotic process and allows one to highly purify viable cells from most stage of meiosis. These purified cells can then be analyzed in detail for molecular changes that accompany progression through meiosis, for example changes in gene expression9,10and the dynamics of nucleosome occupancy at hotspots of meiotic recombination11.

An efficient method to obtain highly purified viable meiotic fractions from mouse testis is described, which combines a refined cell dissociation protocol with fluorescent activated cell sorting (FACS). This method takes advantage of differences in the DNA content and nuclear density of discrete meiotic fractions.

The heterogeneous nature of cell types in the testis and the absence of meiotic cell culture models have been significant hurdles to the study of the ...

Measuring Cell Cycle Progression Kinetics with Metabolic Labeling and Flow Cytometry

Precise control of the initiation and subsequent progression through the various phases of the cell cycle are of paramount importance in proliferating cells. Cell cycle division is an integral part of growth and reproduction and deregulation of key cell cycle components have been implicated in the precipitating events of carcinogenesis 1,2. Molecular agents in anti-cancer therapies frequently target biological pathways responsible for the regulation and coordination of cell cycle division 3. Although cell cycle kinetics tend to vary according to cell type, the distribution of cells amongst the four stages of the cell cycle is rather consistent within a particular cell line due to the consistent pattern of mitogen and growth factor expression. Genotoxic events and other cellular stressors can result in a temporary block of cell cycle progression, resulting in arrest or a temporary pause in a particular cell cycle phase to allow for instigation of the appropriate response mechanism. 
The ability to experimentally observe the behavior of a cell population with reference to their cell cycle progression stage is an important advance in cell biology. Common procedures such as mitotic shake off, differential centrifugation or flow cytometry-based sorting are used to isolate cells at specific stages of the cell cycle 4-6. These fractionated, cell cycle phase-enriched populations are then subjected to experimental treatments. Yield, purity and viability of the separated fractions can often be compromised using these physical separation methods. As well, the time lapse between separation of the cell populations and the start of experimental treatment, whereby the fractionated cells can progress from the selected cell cycle stage, can pose significant challenges in the successful implementation and interpretation of these experiments.
Other approaches to study cell cycle stages include the use of chemicals to synchronize cells. Treatment of cells with chemical inhibitors of key metabolic processes for each cell cycle stage are useful in blocking the progression of the cell cycle to the next stage. For example, the ribonucleotide reductase inhibitor hydroxyurea halts cells at the G1/S juncture by limiting the supply of deoxynucleotides, the building blocks of DNA. Other notable chemicals include treatment with aphidicolin, a polymerase alpha inhibitor for G1 arrest, treatment with colchicine and nocodazole, both of which interfere with mitotic spindle formation to halt cells in M phase and finally, treatment with the DNA chain terminator 5-fluorodeoxyridine to initiate S phase arrest 7-9. Treatment with these chemicals is an effective means of synchronizing an entire population of cells at a particular phase. With removal of the chemical, cells rejoin the cell cycle in unison. Treatment of the test agent following release from the cell cycle blocking chemical ensures that the drug response elicited is from a uniform, cell cycle stage-specific population. However, since many of the chemical synchronizers are known genotoxic compounds, teasing apart the participation of various response pathways (to the synchronizers vs. the test agents) is challenging. 
Here we describe a metabolic labeling method for following a subpopulation of actively cycling cells through their progression from the DNA replication phase, through to the division and separation of their daughter cells. Coupled with flow cytometry quantification, this protocol enables for measurement of kinetic progression of the cell cycle in the absence of either mechanically- or chemically- induced cellular stresses commonly associated with other cell cycle synchronization methodologies 10. In the following sections we will discuss the methodology, as well as some of its applications in biomedical research.

Tracking subtle changes in the progression and kinetics of cell cycle stages can be accomplished by use of a combination of metabolic labeling of nucleic acids with BrdU and total genomic DNA staining via Propidium Iodide. This method avoids the need of chemical synchronization of cycling cells, thereby preventing the introduction of non-specific DNA damage, which in turn affects cell cycle progression.

Precise control of the initiation and subsequent progression through the various phases of the cell cycle are of paramount importance in proliferating ...

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Fluorescent cell tracking dyes, in combination with flow and image cytometry, are powerful tools with which to study the interactions and fates of different cell types in vitro and in vivo.1-5 Although there are literally thousands of publications using such dyes, some of the most commonly encountered cell tracking applications include monitoring of:
<ol>
	<li>stem and progenitor cell quiescence, proliferation and/or differentiation6-8</li>
	<li>antigen-driven membrane transfer9 and/or precursor cell proliferation3,4,10-18 and</li>
	<li>immune regulatory and effector cell function1,18-21.</li>
</ol>
Commercially available cell tracking dyes vary widely in their chemistries and fluorescence properties but the great majority fall into one of two classes based on their mechanism of cell labeling. "Membrane dyes", typified by PKH26, are highly lipophilic dyes that partition stably but non-covalently into cell membranes1,2,11. "Protein dyes", typified by CFSE, are amino-reactive dyes that form stable covalent bonds with cell proteins4,16,18. Each class has its own advantages and limitations. The key to their successful use, particularly in multicolor studies where multiple dyes are used to track different cell types, is therefore to understand the critical issues enabling optimal use of each class2-4,16,18,24.
The protocols included here highlight three common causes of poor or variable results when using cell-tracking dyes. These are:
<ol>
	<li>Failure to achieve bright, uniform, reproducible labeling. This is a necessary starting point for any cell tracking study but requires attention to different variables when using membrane dyes than when using protein dyes or equilibrium binding reagents such as antibodies.</li>
	<li>Suboptimal fluorochrome combinations and/or failure to include critical compensation controls. Tracking dye fluorescence is typically 102 - 103 times brighter than antibody fluorescence. It is therefore essential to verify that the presence of tracking dye does not compromise the ability to detect other probes being used.</li>
	<li>Failure to obtain a good fit with peak modeling software. Such software allows quantitative comparison of proliferative responses across different populations or stimuli based on precursor frequency or other metrics. Obtaining a good fit, however, requires exclusion of dead/dying cells that can distort dye dilution profiles and matching of the assumptions underlying the model with characteristics of the observed dye dilution profile.</li>
</ol>
Examples given here illustrate how these variables can affect results when using membrane and/or protein dyes to monitor cell proliferation.

Successful use of cell tracking dyes to monitor immune cell function and proliferation involves several critical steps. We describe methods for: 1) obtaining bright, uniform, reproducible label-ing with membrane dyes; 2) selecting fluorochromes and data acquisition conditions; and 3) choosing a model to quantify cell proliferation based on dye dilution.

Fluorescent cell tracking dyes, in combination with flow and image cytometry, are powerful tools with which to study the interactions and fates of ...

Measuring Intracellular Ca2+ Changes in Human Sperm using Four Techniques: Conventional Fluorometry, Stopped Flow Fluorometry, Flow Cytometry and Single Cell Imaging

Spermatozoa are male reproductive cells especially designed to reach, recognize and fuse with the egg. To perform these tasks, sperm cells must be prepared to face a constantly changing environment and to overcome several physical barriers. Being in essence transcriptionally and translationally silent, these motile cells rely profoundly on diverse signaling mechanisms to orient themselves and swim in a directed fashion, and to contend with challenging environmental conditions during their journey to find the egg. In particular, Ca2+-mediated signaling is pivotal for several sperm functions: activation of motility, capacitation (a complex process that prepares sperm for the acrosome reaction) and the acrosome reaction (an exocytotic event that allows sperm-egg fusion). The use of fluorescent dyes to track intracellular fluctuations of this ion is of remarkable importance due to their ease of application, sensitivity, and versatility of detection. Using one single dye-loading protocol we utilize four different fluorometric techniques to monitor sperm Ca2+ dynamics. Each technique provides distinct information that enables spatial and/or temporal resolution, generating data both at single cell and cell population levels.

Measuring Intracellular Ca2+ Changes in Human Sperm using Four Techniques: Conventional Fluorometry, Stopped Flow Fluorometry, Flow Cytometry and Single Cell Imaging

Intracellular Ca2+ dynamics are very important in sperm physiology and Ca2+-sensitive fluorescent dyes constitute a versatile tool to study them. Population experiments (fluorometry and stopped flow fluorometry) and single cell experiments (flow cytometry and single cell imaging) are used to track spatio-temporal [Ca2+] changes in human sperm cells.

Spermatozoa  are male reproductive cells especially designed to reach, recognize and fuse  with the egg. To perform these tasks, sperm cells must be ...

Identification of a Murine Erythroblast Subpopulation  Enriched in Enucleating Events by Multi-spectral Imaging Flow Cytometry

Erythropoiesis in mammals concludes with the dramatic process of enucleation that results in reticulocyte formation. The mechanism of enucleation has not yet been fully elucidated. A common problem encountered when studying the localization of key proteins and structures within enucleating erythroblasts by microscopy is the difficulty to observe a sufficient number of cells undergoing enucleation.&#160;We have developed a novel analysis protocol using multiparameter high-speed cell imaging in flow (Multi-Spectral Imaging Flow Cytometry), a method that combines immunofluorescent microscopy with flow cytometry, in order to identify efficiently a significant number of enucleating events, that allows to obtain measurements and perform statistical analysis.
We first describe here two in vitro erythropoiesis culture methods used in order to synchronize murine erythroblasts and increase the probability of capturing enucleation at the time of evaluation.&#160;Then, we describe in detail the staining of erythroblasts after fixation and permeabilization in order to study the localization of intracellular proteins or lipid rafts during enucleation by multi-spectral imaging flow cytometry.&#160;Along with size and DNA/Ter119 staining which are used to identify the orthochromatic erythroblasts, we utilize the parameters &#8220;aspect ratio&#8221; of a cell in the bright-field channel that aids in the recognition of elongated cells and &#8220;delta centroid XY Ter119/Draq5&#8221; that allows the identification of cellular events in which the center of Ter119 staining (nascent reticulocyte) is far apart from the center of Draq5 staining (nucleus undergoing extrusion), thus indicating a cell about to enucleate.&#160;The subset of the orthochromatic erythroblast population with high delta centroid and low aspect ratio is highly enriched in enucleating cells.

The present protocol describes a novel method of identifying a population of enucleating orthochromatic erythroblasts by multi-spectral imaging flow cytometry, providing a visualization of the erythroblast enucleation process.

Erythropoiesis in mammals concludes with the dramatic process of enucleation that results in reticulocyte formation. The mechanism of enucleation has not ...

In Vivo 4-Dimensional Tracking of Hematopoietic Stem and Progenitor Cells in Adult Mouse Calvarial Bone Marrow

Through a delicate balance between quiescence and proliferation, self renewal and production of differentiated progeny, hematopoietic stem cells (HSCs) maintain the turnover of all mature blood cell lineages. The coordination of the complex signals leading to specific HSC fates relies upon the interaction between HSCs and the intricate bone marrow microenvironment, which is still poorly understood[1-2].
We describe how by combining a newly developed specimen holder for stable animal positioning with multi-step confocal and two-photon in vivo imaging techniques, it is possible to obtain high-resolution 3D stacks containing HSPCs and their surrounding niches and to monitor them over time through multi-point time-lapse imaging. High definition imaging allows detecting ex vivo labeled hematopoietic stem and progenitor cells (HSPCs) residing within the bone marrow. Moreover, multi-point time-lapse 3D imaging, obtained with faster acquisition settings, provides accurate information about HSPC movement and the reciprocal interactions between HSPCs and stroma cells.
Tracking of HSPCs in relation to GFP positive osteoblastic cells is shown as an exemplary application of this method. This technique can be utilized to track any appropriately labeled hematopoietic or stromal cell of interest within the mouse calvarium bone marrow space.

In Vivo 4-Dimensional Tracking of Hematopoietic Stem and Progenitor Cells in Adult Mouse Calvarial Bone Marrow

The nature of the interactions between hematopoietic stem and progenitor cells (HSPCs) and bone marrow niches is poorly understood. Custom hardware modifications and a multi-step acquisition protocol allow the use of two-photon and confocal microscopy to image ex vivo labeled HSPCs homed within bone marrow areas, tracking interactions and movement.

Through a delicate balance between quiescence and proliferation, self renewal and production of differentiated progeny, hematopoietic stem cells (HSCs) ...

Cytosolic Calcium Measurements in Renal Epithelial Cells by Flow Cytometry

A variety of cellular processes, both physiological and pathophysiological, require or are governed by calcium, including exocytosis, mitochondrial function, cell death, cell metabolism and cell migration to name but a few. Cytosolic calcium is normally maintained at low nanomolar concentrations; rather it is found in high micromolar to millimolar concentrations in the endoplasmic reticulum, mitochondrial matrix and the extracellular compartment. Upon stimulation, a transient increase in cytosolic calcium serves to signal downstream events. Detecting changes in cytosolic calcium is normally performed using a live cell imaging set up with calcium binding dyes that exhibit either an increase in fluorescence intensity or a shift in the emission wavelength upon calcium binding. However, a live cell imaging set up is not freely accessible to all researchers. Alternative detection methods have been optimized for immunological cells with flow cytometry and for non-immunological adherent cells with a fluorescence microplate reader. Here, we describe an optimized, simple method for detecting changes in epithelial cells with flow cytometry using a single wavelength calcium binding dye. Adherent renal proximal tubule epithelial cells, which are normally difficult to load with dyes, were loaded with a fluorescent cell permeable calcium binding dye in the presence of probenecid, brought into suspension and calcium signals were monitored before and after addition of thapsigargin, tunicamycin and ionomycin.

Calcium is involved in numerous physiological and pathophysiological signaling pathways. Live cell imaging requires specialized equipment and can be time consuming. A quick, simple method using a flow cytometer to determine relative changes in cytosolic calcium in adherent epithelial cells brought into suspension was optimized.

A variety of cellular processes, both physiological and pathophysiological, require or are governed by calcium, including exocytosis, mitochondrial ...

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Immunohistochemistry is a commonly used clinical and research lab detection technique for investigating protein expression and localization within tissues. Many semi-quantitative systems have been developed for scoring expression using immunohistochemistry, but inherent subjectivity limits reproducibility and accuracy of results. Furthermore, the investigation of spatially overlapping biomarkers such as nuclear transcription factors is difficult with current immunohistochemistry techniques. We have developed and optimized a system for simultaneous investigation of multiple proteins using high throughput methods of multiplexed immunohistochemistry and multispectral imaging. Multiplexed immunohistochemistry is performed by sequential application of primary antibodies with secondary antibodies conjugated to horseradish peroxidase or alkaline phosphatase. Different chromogens are used to detect each protein of interest. Stained slides are loaded into an automated slide scanner and a protocol is created for automated image acquisition. A spectral library is created by staining a set of slides with a single chromogen on each. A subset of representative stained images are imported into multispectral imaging software and an algorithm for distinguishing tissue type is created by defining tissue compartments on images. Subcellular compartments are segmented by using hematoxylin counterstain and adjusting the intrinsic algorithm. Thresholding is applied to determine positivity and protein co-localization. The final algorithm is then applied to the entire set of tissues. Resulting data allows the user to evaluate protein expression based on tissue type (ex. epithelia vs. stroma) and subcellular compartment (nucleus vs. cytoplasm vs. plasma membrane). Co-localization analysis allows for investigation of double-positive, double-negative, and single-positive cell types. Combining multispectral imaging with multiplexed immunohistochemistry and automated image acquisition is an objective, high-throughput method for investigation of biomarkers within tissues.

Immunohistochemistry is a powerful lab technique for evaluating protein localization and expression within tissues. Current semi-automated methods for quantitation introduce subjectivity and often create irreproducible results. Herein, we describe methods for multiplexed immunohistochemistry and objective quantitation of protein expression and co-localization using multispectral imaging.

Immunohistochemistry is a commonly used clinical and research lab detection technique for investigating protein expression and localization within ...

Measuring Endoreduplication by Flow Cytometry of Isolated Tuber Protoplasts

Endoreduplication, the replication of a cell's nuclear genome without subsequent cytokinesis, yields cells with increased DNA content and is associated with specialization, development and increase in cellular size. In plants, endoreduplication seems to facilitate the growth and expansion of certain tissues and organs. Among them is the tuber of potato (Solanum tuberosum), which undergoes considerable cellular expansion in fulfilling its function of carbohydrate storage. Thus, endoreduplication may play an important role in how tubers are able to accommodate this abundance of carbon. However, the cellular debris resulting from crude nuclear isolation methods of tubers, methods that can be used effectively with leaves, precludes the estimation of the tuber endoreduplication index (EI). This article presents a technique for assessing tuber endoreduplication through the isolation of protoplasts while demonstrating representative results obtained from different genotypes and compartmentalized tuber tissues. The major limitations of the protocol are the time and reagent costs required for sample preparation as well as relatively short lifespan of samples after lysis of protoplasts. While the protocol is sensitive to technical variation, it represents an improvement over traditional methods of nuclear isolation from these large specialized cells. Possibilities for improvements to the protocol such as recycling enzyme, the use of fixatives, and other alterations are proposed.

The protocol described herein is a method for measuring endoreduplication within tubers of potato (Solanum tuberosum). It includes plasmolysis and protoplast extraction steps to decrease the noise and debris in downstream flow cytometric analysis.

Endoreduplication, the replication of a cell's nuclear genome without subsequent cytokinesis, yields cells with increased DNA content and is associated ...