This work was funded by the Natural Science Foundation of China 81572599, 81773124, and 81972787; Natural Science Foundation of Tianjin City (China) 19JCYBJC27300; Tianjin People&#8217;s Hospital &#38;&#160;Nankai University Collaborative Research Grant 2016rmnk005; Fundamental Research Funds for the Central Universities, Nankai University 63191153.

1. Cell preparation
<ol>
	<li>Cell digestion and neutralization
	<ol>
		<li>Seed tumor cells (such as MDA-MB-231 cells) in a 6 well plate, and culture them in a 37 &#176;C incubator supplied with 5% CO2.</li>
		<li>Harvest cells when their density reaches about 90%. Aspirate the culture medium thoroughly and wash the cells 2x with 3 mL of phosphate buffered saline (PBS). 
		NOTE: To examine the effects of signaling pathway inhibitors (e.g., FRA1 inhibitor), or activators (e.g., STAT3 activator) on stemness features of tumor cells, tumor cells are seeded in a 6 well plate and pretreated with inhibitors or activators for a certain amount of time before harvest.</li>
		<li>Add 500 &#181;L of 0.25% trypsin-EDTA to each well of the 6 well plate, place the plate in an incubator at 37 &#176;C for 1&#8211;3 minutes (min), and gently tap the plate to detach the cells. 
		NOTE: Prolonged digestion will affect the SP profile due to changes in cell viability.</li>
		<li>Add 3 mL of PBS supplemented with 2% fetal bovine serum (FBS) to each well of the 6 well plate&#160;to terminate the digestion and gently pipette the cells up and down 3&#8211;5x to disperse the cell clumps.</li>
		<li>Add 0.5 mL of cell suspension to one well of a new 6 well plate and examine it under a microscope. If cell clumps are observed, pass the cell suspension through a 70 &#181;M cell strainer. 
		NOTE: This is an optional step.</li>
		<li>Transfer the cells to a 15 mL&#160;centrifuge tube. Centrifuge cells at 200 x g for 5 min to pellet cells. Remove the supernatant and resuspend them in 3 mL of PBS supplemented with 2% FBS. Pipette the cells up and down 3&#8211;5x to mix thoroughly.</li>
	</ol>
	</li>
	<li>Cell counts
	<ol>
		<li>Add 50 &#956;L of the cell suspension into a 1.5 mL microcentrifuge tube and mix it with 50 &#956;L trypan blue solution. Pipette 10 &#956;L of the mixture to count the number of living cells using a standard method, such as a hemocytometer.</li>
		<li>Dilute the cells in PBS supplemented with 2% FBS to a final concentration of 1 x 106 cells/mL. Add 1 mL of the cell suspension to a 5 mL polystyrene round bottom test tube and prepare two sample tubes.</li>
	</ol>
	</li>
</ol>
2. Cell staining with Hoechst 33342
<ol>
	<li>Add Hoechst 33342 to one tube to reach an appropriate final concentration. 
	NOTE: For example, the appropriate concentration of Hoechst 33342 is 3 &#181;g/mL for MDA-MB-231 cells.</li>
	<li>Add a blocker&#160;(e.g., Fumitremorgin C, Verapamil, or Reserpine) to another tube to an appropriate final concentration and incubate the tube at 37 &#176;C for 30 min before adding the same concentration of Hoechst 33342 as described in step 2.1. 
	NOTE: For MDA-MB-231 cells, the appropriate blocker is Reserpine (40 &#181;M). Reserpine is used as a blocking control to verify the absence of cells in the gated SP area.</li>
	<li>Prepare several tubes containing cells and add Hoechst 33342 to&#160;different concentration gradients. After a flow cytometry assay, choose the proper concentration according to the profile and proportion of SP. 
	NOTE: This is an optional step used to define the proper concentration of Hoechst 33342.</li>
	<li>Prepare several tubes containing cells, add the blocker to different concentration gradients, incubate the tubes at 37 &#176;C for 30 min, then add Hoechst 33342 to an appropriate concentration. After the flow cytometry assay, choose the proper concentration according to the absence of cells in the gated SP area. 
	NOTE: This is an optional step used to define the proper concentration of blocker.</li>
	<li>Place the tubes in a 37 &#176;C incubator and incubate them for 60 min, shaking the tubes every 10 min. 
	NOTE: Shaking tubes thoroughly is important for staining, because it ensures complete contact between the cells and the dye for better staining results.</li>
	<li>After the incubation, centrifuge the cells at 200 x g, 4 &#176;C for 5 min, and aspirate the supernatant carefully, because the cells form&#160;very loose and unstable pellets.</li>
	<li>Resuspend cells of each tube in 1 mL of ice-cold PBS supplemented with 2% FBS and pipette the cells up and down 3&#8211;5x to mix thoroughly. Add 1 &#956;L of 1 mg/mL propidium iodide (PI) to the suspension of each tube to identify dead cells. 
	NOTE: All procedures in this step should be performed at 4 &#176;C to inhibit the efflux of Hoechst 33342 from the tumor cells. The tubes should be protected from direct exposure to light.</li>
</ol>
3. Analysis by flow cytometry
NOTE: Instructions for use of the flow cytometer software (see Table of Materials) are described in this section and Supplementary Figures 1&#8211;10.
<ol>
	<li>In the flow cytometer software, click the FOLDER button. Click the Experiment button, then click New Experiment (Supplementary Figure 1A,B).</li>
	<li>Click the OK button. &#8220;Experiment_001&#8221; will show up under the FOLDER (Supplementary Figure 2A,B).</li>
	<li>Click the Experiment_001 button to change the folder name to a specific name (e.g., &#34;20191118-SP&#34;). Click Enter. The new name (&#34;20191118-SP&#34;) will show up under FOLDER (Supplementary Figure 3A,B).</li>
	<li>Click the New Specimen button to add a specimen to the new experiment folder (&#34;20191118-SP&#34;). Click the New Tube button to add a tube to the specimen. Click the Arrowhead button (Supplementary Figure 4A-C).</li>
	<li>Click the Parameters button and set up the parameters of the flow cytometer (Supplementary Figure 5).
	<ol>
		<li>Use a 610 nm dichroic mirror short-pass (DMSP) to separate the emission wavelengths. Use a 450/20 nm BP filter to collect the blue fluorescence and a 675 nm EFLP to collect the red fluorescence. 
		NOTE: Hoechst 33342 is excited with a UV laser at 355 nm and PI is excited at 488 nm.</li>
	</ol>
	</li>
	<li>Run the cell samples on the flow cytometer. 
	NOTE: SP cells can be sorted by fluorescence-activated cell sorting (FACS) under sterile conditions. A total of 100,000&#8211;500,000 cells should be collected for the follow-up experiments.
	<ol>
		<li>Run cells stained with Hoechst 33342 on the flow cytometer.
		<ol>
			<li>Place tubes containing cells stained with Hoechst 33342 on the cytometer.</li>
			<li>Click the Dot Plot button to display the dot plot, then click the X-axis and set it to &#8220;FSC-A&#8221;; click the Y-axis and set it to &#8220;PI-A&#8221;. Display the dot plot of forward scatter pulse area (FSC-A, X-axis set to linear mode) versus the PI fluorescence (Y-axis set to logarithmic scale). Adjust the voltages to show the living cells in the right side and the non-living cells, which are brightly&#160;stained with PI, in the upper left&#160;corner. Then, establish a polygon gate to exclude dead cells and cell debris (Figure 1A) by clicking the Polygon Gate button to gate the P1 subset, also known as FSC-A, PI-A subset (Supplementary Figure 6A,B).</li>
			<li>Click the Dot Plot button to display the dot plot, click the X-axis and set it to &#8220;FSC-A&#8221;; click the Y-axis and set it to &#8220;FSC-W&#8221;. Display the dot plot of FSC-A (X-axis) versus forward scatter pulse width (FSC-W, Y-axis). Right-click the dot plot, click the P1 button under the Show Populations button. Click the Rectangular Gate button to create a rectangular gate to gate the P2 subset (also known as FSC-A, FSC-W subset). This will exclude cells with large volumes (Supplementary Figure 7A-C and Figure 1A).</li>
			<li>Click the Dot Plot button to display the dot plot, click the X-axis and set it to &#8220;SSC-A&#8221;; click the Y-axis and set it to &#8220;SSC-W&#8221;. Display the dot plot of side scatter pulse area (SSC-A, X-axis) versus side scatter pulse width (SSC-W, Y-axis). Right-click the dot plot, and click the P2 button under Show Populations button. Then click the Rectangular Gate button to create a rectangular gate to gate the P3 subset (also known as SSC-A, SSC-W subset). This will obtain a cell population with uniform granularity (Supplementary Figure 8A-C and Figure 1A).</li>
			<li>Click the Dot Plot button to display the dot plot, click the X-axis and set it to &#8220;Hoechst Red-A&#8221;; click the Y-axis and set it to &#8220;Hoechst Blue-A&#8221;. Display the dot plot of Hoechst Red-A (X-axis) versus Hoechst Blue-A (Y-axis). Right-click the dot plot, click the P3 button under the Show Populations button. Click the Polygon Gate button to create a polygon gate to gate the P4 subset (also known as Hoechst Red-A, Hoechst Blue-A subset). Right-click the dot plot, click the Show Population Hierarchy button to show the population hierarchy. 
			NOTE: The dot plot will show three different populations: 1) a G0-G1 phase population near the center of the graph; 2) a S-G2/M phase population near the upper right corner; 3) the SP. The SP is then gated for further analysis (Supplementary Figure 9A-D and Figure 1A). If the dot plot of Hoechst Red-A versus Hoechst Blue-A does not show a&#160;SP profile similar to that shown in Figure 1A, the voltages should be adjusted until a similar profile is seen. Meanwhile, adjust all of above gates accordingly.</li>
			<li>After determining the gate region of the SP cells, click Acquire Data to collect 20,000&#8211;100,000 events from each sample to analyze the percentage of SP cells (Supplementary Figure 10).</li>
		</ol>
		</li>
		<li>Run the Hoechst 33342-stained cells treated with blocker on the flow cytometer.
		<ol>
			<li>Place tubes containing blocker on the cytometer and run cells using the same voltages and gates to further check whether the voltages and gates are selected appropriately. 
			NOTE: Compared with Hoechst 33342 staining alone, only&#160;a very small proportion of cells, if any, should appear in the gated area of the SP (Figure 1B).</li>
			<li>Collect 20,000&#8211;100,000 events from each sample to analyze the percentage of SP cells.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
4. Data analysis
NOTE: Instructions for the use of the flow cytometry analysis software (see Table of Materials) are described in this section and Supplementary Figures 11&#8211;16.
<ol>
	<li>Copy the files in fcs format to a computer, open the flow cytometry analysis software, and drag one sample file to the software (Supplementary Figure 11A,B).</li>
	<li>Gate cells and obtain the percentage of SP cells.
	<ol>
		<li>Double click this sample file, click the X-axis and set it to &#8220;FSC-A&#8221;; click the Y-axis and set it to &#8220;PI-A&#8221;. Then, create a polygon gate and click the OK button to obtain the FSC-A, PI-A subset (Supplementary Figure 12A-E).</li>
		<li>Double click the FSC-A, PI-A subset file, click the X-axis and set it to &#8220;FSC-A&#8221;; click the Y-axis and set it to &#8220;FSC-W&#8221;. Then, create a rectangular gate and click the OK button to obtain the FSC-A, FSC-W subset (Supplementary Figure 13A-E).</li>
		<li>Double click the FSC-A, FSC-W subset file, click the X-axis and set it to &#8220;SSC-A&#8221;; click the Y-axis and set it to &#8220;SSC-W&#8221;. Then, create a rectangular gate and click the OK button to obtain the SSC-A, SSC-W subset (Supplementary Figure 14A-E).</li>
		<li>Double click the SSC-A, SSC-W subset file, click the X-axis and set it to &#8220;Hoechst Red-A&#8221;; click the Y-axis and set it to &#8220;Hoechst Blue-A&#8221;. Then, create a polygon gate and click the OK button to obtain the Hoechst Red-A, Hoechst Blue-A subset (Supplementary Figure 15A-E).</li>
		<li>Open the Layout Editor by clicking the Open Layout Editor button. Drag the SSC-A, SSC-W subset file to Layout Editor, then click the Click to save layout window to file button to save the image results (Supplementary Figure 16A-C).</li>
	</ol>
	</li>
	<li>Save the workspace to keep the gating information when closing the software.</li>
	<li>Perform t-test analyses with statistical analysis software to compare the difference between two groups. A value of P &#60; 0.05 was defined as statistically significant.</li>
</ol>

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<li>Koh, S. Y., Moon, J. Y., Unno, T., Cho, S. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Baicalein+Suppresses+Stem+Cell-Like+Characteristics+in+Radio-+and+Chemoresistant+MDA-MB-231+Human+Breast+Cancer+Cells+through+Up-Regulation+of+IFIT2%5BTitle%5D)+AND+%22Nutrients%22%5BJournal%5D)">Baicalein Suppresses Stem Cell-Like Characteristics in Radio- and Chemoresistant MDA-MB-231 Human Breast Cancer Cells through Up-Regulation of IFIT2.</a> Nutrients. 11 (3), 624(2019).</li>
<li>Lee, H., Park, S., Kim, J. B., Kim, J., Kim, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Entrapped+doxorubicin+nanoparticles+for+the+treatment+of+metastatic+anoikis-resistant+cancer+cells%5BTitle%5D)+AND+%22Cancer+Letters%22%5BJournal%5D)">Entrapped doxorubicin nanoparticles for the treatment of metastatic anoikis-resistant cancer cells.</a> Cancer Letters. 332 (1), 110-119 (2013).</li>
<li>Ota, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((ADAM23+is+downregulated+in+side+population+and+suppresses+lung+metastasis+of+lung+carcinoma+cells%5BTitle%5D)+AND+%22Cancer+Science%22%5BJournal%5D)">ADAM23 is downregulated in side population and suppresses lung metastasis of lung carcinoma cells.</a> Cancer Science. 107 (4), 433-443 (2016).</li>
<li>Wei, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+mechanisms+for+lung+cancer+risk+of+PM2.5+%3A+Induction+of+epithelial-mesenchymal+transition+and+cancer+stem+cell+properties+in+human+non-small+cell+lung+cancer+cells%5BTitle%5D)+AND+%22Environmental+Toxicology%22%5BJournal%5D)">The mechanisms for lung cancer risk of PM2.5 : Induction of epithelial-mesenchymal transition and cancer stem cell properties in human non-small cell lung cancer cells.</a> Environmental Toxicology. 32 (11), 2341-2351 (2017).</li>
<li>Prasanphanich, A. F., White, D. E., Gran, M. A., Kemp, M. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Kinetic+Modeling+of+ABCG2+Transporter+Heterogeneity%3A+A+Quantitative%2C+Single-Cell+Analysis+of+the+Side+Population+Assay%5BTitle%5D)+AND+%22PLoS+Computational+Biology%22%5BJournal%5D)">Kinetic Modeling of ABCG2 Transporter Heterogeneity: A Quantitative, Single-Cell Analysis of the Side Population Assay.</a> PLoS Computational Biology. 12 (11), 1005188(2016).</li>
<li>Han, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((An+endogenous+inhibitor+of+angiogenesis+inversely+correlates+with+side+population+phenotype+and+function+in+human+lung+cancer+cells%5BTitle%5D)+AND+%22Oncogene%22%5BJournal%5D)">An endogenous inhibitor of angiogenesis inversely correlates with side population phenotype and function in human lung cancer cells.</a> Oncogene. 33 (9), 1198-1206 (2014).</li>
<li>Loebinger, M. R., Sage, E. K., Davies, D., Janes, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((TRAIL-expressing+mesenchymal+stem+cells+kill+the+putative+cancer+stem+cell+population%5BTitle%5D)+AND+%22British+Journal+of+Cancer%22%5BJournal%5D)">TRAIL-expressing mesenchymal stem cells kill the putative cancer stem cell population.</a> British Journal of Cancer. 103 (11), 1692-1697 (2010).</li>
<li>Chiba, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Side+population+purified+from+hepatocellular+carcinoma+cells+harbors+cancer+stem+cell-like+properties%5BTitle%5D)+AND+%22Hepatology%22%5BJournal%5D)">Side population purified from hepatocellular carcinoma cells harbors cancer stem cell-like properties.</a> Hepatology. 44 (1), 240-251 (2006).</li>
<li>Hirschmann-Jax, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+distinct+%22side+population%22+of+cells+with+high+drug+efflux+capacity+in+human+tumor+cells%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Science+of+the+United+States+of+America%22%5BJournal%5D)">A distinct "side population" of cells with high drug efflux capacity in human tumor cells.</a> Proceedings of the National Academy of Science of the United States of America. 101 (39), 14228-14233 (2004).</li>
<li>Chen, A. Y., Yu, C., Gatto, B., Liu, L. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((DNA+minor+groove-binding+ligands%3A+a+different+class+of+mammalian+DNA+topoisomerase+I+inhibitors%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Science+of+the+United+States+of+America%22%5BJournal%5D)">DNA minor groove-binding ligands: a different class of mammalian DNA topoisomerase I inhibitors.</a> Proceedings of the National Academy of Science of the United States of America. 90 (17), 8131-8135 (1993).</li>
</ol>

The authors have nothing to disclose.

There are several key points to keep in mind for the SP assay. The first is the selection of a proper blocker, such as Verapamil or Reserpine, for each cell line, because the &#34;gate&#34; location of the SP cells is determined according to the position at which a large number of SP cells disappear after the addition of the blocker. For the MDA-MB-231 cell line, Reserpine works well. However, for other cell lines, different blockers might work better.
The second is the concentration of Hoechs...

Cancer stem cells (CSCs) are subsets of cells with self-renewal ability and multiple differentiation potential, which play a vital role in tumor growth, metastasis, and recurrence1,2. Currently, CSCs have been identified to exist in a variety of malignant tumors, including lung, brain, pancreas, prostate, breast, and liver cancers3,4,5,6,7,8,9. Identification of CSCs in these tumors is mainly based on the presence of surface marker proteins, such as high and/or low expression of CD44, CD24, CD133, and Sca-19,10, but a unique marker that can distinguish CSCs from non-CSCs has not been reported so far. Currently, several techniques are used to identify and purify CSCs in tumor tissue or tumor cell lines. These techniques are designed based on the specific properties of CSCs. Among them, assays and sorting of side population (SP) cells are two of the commonly used methods.
SP cells were originally discovered by Goodell et al.11, when they characterized hematopoietic stem cells in mouse bone marrow cells. When the mouse bone marrow cells were labeled with the fluorescent dye Hoechst 33342, a small group of Hoechst 33342 dimly-stained cells appeared in the two-dimensional dot plot of a flow cytometry assay. Hoechst 33342 is a DNA-binding dye and has at least two binding modes that lead to different spectral characteristics. When viewing fluorescence emission at two wavelengths at the same time, multiple populations can be revealed12. In their assay, the Hoechst 33342 was excited at 350 nm and the fluorescence was measured by using the 450/20 nm band-pass (BP) filter and 675 nm edge filter long-pass (EFLP)11. Compared with whole population of bone marrow cells, this group of cells was enriched with hematopoietic stem cells called SP cells11. SP cells are capable of rapidly expelling Hoechst 33342. The efflux of this dye is related to ATP-binding cassette (ABC) transporters13, which can be inhibited by some agents such as Fumitremorgin C14, Verapamil and Reserpine15,16. After that, different proportions of SP cells were detected in a variety of tissues, organs, tumor tissues, and tumor cell lines17,18,19. These SP cells have many characteristics of stem cells17,19.
This manuscript describes Hoechst 33342 labeling and staining of cultured tumor cells and the analysis of SP cells by flow cytometry. Moreover, optimization of the Hoechst 33342 concentration and the proper blocker selection for a specific tumor cell line using this approach are shown. Finally, the effects of stemness promotion or inhibition signals on the proportion of SP in tumor cells are demonstrated. The experimental examples demonstrate that analysis of SP can be used to explore the effects of various signals, such as gene expression, small inhibitors, activators, cytokines, and chemokines, on tumor stemness. Compared to other methods for isolation and purification of CSCs, such as sorting of CD44+/CD24&#8211; population, aldehyde dehydrogenase (ALDH) analysis, and tumor sphere formation assays, this method is easier for manipulation and is cost-effective.

<table><tbody><tr><td>6 well cell culture plate</td><td>CORNING</td><td>3516</td><td>9.5 cm&lt;sup&gt;2&lt;/sup&gt; (approx.)</td></tr><tr><td>Colivelin</td><td>MCE</td><td>HY-P1061A</td><td>Ser-Ala-Leu-Leu-Arg-Ser-Ile-Pro-Ala-Pro-Ala-Gly-Ala-Ser-Arg-Leu-Leu-Leu-Leu-Thr-Gly-Glu-Ile-Asp-Leu-Pro</td></tr><tr><td>Fetal bovine serum (FBS)</td><td>Biological Industries (BIOIND)</td><td>04-001-1ACS</td><td></td></tr><tr><td>Flow cytometer</td><td>BD Biosciences</td><td></td><td>BD LSRFortessa</td></tr><tr><td>Flow cytometer software</td><td>BD Biosciences</td><td></td><td>FACSDiva</td></tr><tr><td>Flow cytometry analysis software</td><td>BD Biosciences</td><td></td><td>FlowJo</td></tr><tr><td>Hoechst 33342</td><td>Sigma-Aldrich</td><td>B2261</td><td>bisBenzimide H 33342 trihydrochloride</td></tr><tr><td>Polystyrene round bottom test tube</td><td>CORNING</td><td>352054</td><td>12 x 75 mm, 5 mL</td></tr><tr><td>Propidium iodide (PI)</td><td>Sigma-Aldrich</td><td>P4170</td><td>3,8-Diamino-5-[3-(diethylmethylammonio)propyl]-6-phenylphenanthridinium diiodide</td></tr><tr><td>Reserpine</td><td>Sigma-Aldrich</td><td>83580</td><td>(3&amp;beta;, 16&amp;beta;, 17&amp;alpha;, 18&amp;beta;, 20&amp;alpha;)-11,17-Dimethoxy-18-[(3,4,5-trimethoxybenzoyl)oxy]yohimban-16-carboxylic acid methyl ester</td></tr><tr><td>SKLB816</td><td>Provided by Dr. Shengyong Yang, Sichuan University</td><td></td><td></td></tr><tr><td>Trypsin-EDTA (0.25%), phenol red</td><td>Gibco</td><td>25200072</td><td></td></tr><tr><td>Verapamil hydrochloride</td><td>Sigma-Aldrich</td><td>V4629</td><td>5-[&lt;em&gt;N&lt;/em&gt;-(3,4-Dimethoxyphenylethyl)methylamino]
-2-(3,4-dimethoxyphenyl)-2-isopropylvaleronitrile hydrochloride</td></tr></tbody></table>

analysis of side population in solid tumor cell lines

Cancer stem cells (CSCs) are an important cause of tumor growth, metastasis, and recurrence. Isolation and identification of CSCs are of great significance for tumor research. Currently, several techniques are used for the identification and purification of CSCs from tumor tissues and tumor cell lines. Separation and analysis of side population (SP) cells are two of the commonly used methods. The methods rely on the ability of CSCs to rapidly expel fluorescent dyes, such as Hoechst 33342. The efflux of the dye is associated with the ATP-binding cassette (ABC) transporters and can be inhibited by ABC transporter inhibitors. Methods for staining cultured tumor cells with Hoechst 33342 and analyzing the proportion of their SP cells by flow cytometry are described. This assay is convenient, fast, and cost-effective. Data generated in this assay can contribute to a better understanding of the effect of genes or other extracellular and intracellular signals on the stemness properties of tumor cells.

Four experimental SP analyses were performed according to this method. In the first one, we detected the proportion of SP cells in MDA-MB-231, which is a triple negative human breast cancer cell line, under normal conditions. After cell counting, Hoechst 33342 was added into one tube containing 1 x 106 cells to a final concentration of 3 &#181;g/mL. Reserpine and Hoechst 33342 were added to another tube to&#160;final concentrations of 40 &#181;M and 3 &#181;g/mL, respectively. PI was added to both tubes. The d...

Watch this Scientific Journal Video about Analysis of Side Population in Solid Tumor Cell Lines at JoVE.com

Analysis of Side Population in Solid Tumor Cell Lines

A convenient, fast, and cost-effective method to measure the proportion of side population cells in solid tumor cell lines is presented.

Cancer stem cells (CSCs) are an important cause of tumor growth, metastasis, and recurrence. Isolation and identification of CSCs are of great ...

analysis-of-side-population-in-solid-tumor-cell-lines

Research

JoVE Journal

Cancer Research

4.3K Views.  Nankai University. A convenient, fast, and cost-effective method to measure the proportion of side population cells in solid tumor cell lines is presented.

Video: Analysis of Side Population in Solid Tumor Cell Lines

Harnessing the DNA Dye-triggered Side Population Phenotype to Detect and Purify Cancer Stem Cells from Biological Samples

Cancer is a stem cell-driven disease and eradication of these cells has become a major therapeutic goal. Deciphering vulnerabilities of Cancer Stem Cells (CSCs) and identifying suitable molecular targets relies on methods that allow their specific discrimination in heterogeneous samples such as cell lines and ex vivo tumor tissue. Flow cytometry/FACS is a powerful technology to multi-parametrically dissect biological samples at the single cell level and is to date the method of choice to recover live cells for downstream analyses. Surface markers such as CD44 and CD133 as well as detection of aldehyde dehydrogenase enzymatic activity have often been used to define and sort out CSCs from tumor samples by FACS. A complementary approach, depicted here in methodological detail, makes use of functional dye extrusion by ABC drug transporters, which identifies a distinct population of fluorescence-dim cells commonly referred to as side population (SP). SP&#160;cancer cells exhibit canonical stem cell characteristics and can be abrogated and functionally confirmed using agents that inhibit the dye-extruding drug transporter (most frequently ABCB1/P-glycoprotein/MDR1/CD243 and ABCG2/Bcrp1/CD338). Moreover, the SP&#160;assay is compatible with other flow cytometric evaluations such as staining of surface antigens, aldehyde dehydrogenase detection and dead cell discrimination (e.g., with 7-AAD or propidium iodide (PI)). Thus, we describe a valuable and broadly applicable method for CSC identification, isolation and sub-characterization mechanistically based on a functional, rather than a phenotypic parameter. Although originally performed with Hoechst 33342 as triggering dye, we here focus on the more recent Violet dye-based SP phenotype that is resolvable on any flow cytometer equipped with a violet laser source.

Methods allowing the characterization and isolation of stem cell populations from biological samples are critical for the advance of stem cell-targeted treatments in cancer and beyond. Here, we provide a detailed protocol for cancer stem cell isolation using the dye-triggered side population phenotype.

Cancer is a stem cell-driven disease and eradication of these cells has become a major therapeutic goal. Deciphering vulnerabilities of Cancer Stem Cells ...

Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors

The tumor immune microenvironment (TIME) has recently been recognized as a critical mediator of treatment response in solid tumors, especially for immunotherapies. Recent clinical advances in immunotherapy highlight the need for reproducible methods to accurately and thoroughly characterize the tumor and its associated immune infiltrate. Tumor enzymatic digestion and flow cytometric analysis allow broad characterization of numerous immune cell subsets and phenotypes; however, depth of analysis is often limited by fluorophore restrictions on panel design and the need to acquire large tumor samples to observe rare immune populations of interest. Thus, we have developed an effective and high throughput method for separating and enriching the tumor immune infiltrate from the non-immune tumor components. The described tumor digestion and centrifugal density-based separation technique allows separate characterization of tumor and tumor immune infiltrate fractions and preserves cellular viability, and thus, provides a broad characterization of the tumor immunologic state. This method was used to characterize the extensive spatial immune heterogeneity in solid tumors, which further demonstrates the need for consistent whole tumor immunologic profiling techniques. Overall, this method provides an effective and adaptable technique for the immunologic characterization of subcutaneous solid murine tumors; as such, this tool can be used to better characterize the tumoral immunologic features and in the preclinical evaluation of novel immunotherapeutic strategies.

Here we describe a method to separate and enrich components of the tumor immune and non-immune microenvironment in established subcutaneous tumors. This technique allows for the separate analysis of tumor immune infiltrate and non-immune tumor fractions which can permit comprehensive characterization of the tumor immune microenvironment.

The tumor immune microenvironment (TIME) has recently been recognized as a critical mediator of treatment response in solid tumors, especially for ...

Semi-automatic PD-L1 Characterization and Enumeration of Circulating Tumor Cells from Non-small Cell Lung Cancer Patients by Immunofluorescence

Circulating tumor cells (CTCs) derived from the primary tumor are shed into the bloodstream or lymphatic system. These rare cells (1&#8722;10 cells per mL of blood) warrant a poor prognosis and are correlated with shorter overall survival in several cancers (e.g., breast, prostate and colorectal). Currently, the anti-EpCAM-coated magnetic bead-based CTC capturing system is the gold standard test approved by the U.S. Food and Drug Administration (FDA) for enumerating CTCs in the bloodstream. This test is based on the use of magnetic beads coated with anti-EpCAM markers, which specifically target epithelial cancer cells. Many studies have illustrated that EpCAM is not the optimal marker for CTC detection. Indeed, CTCs are a heterogeneous subpopulation of cancer cells and are able to undergo an epithelial-to-mesenchymal transition (EMT) associated with metastatic proliferation and invasion. These CTCs are able to reduce the expression of cell surface epithelial marker EpCAM, while increasing mesenchymal markers such as vimentin. To address this technical hurdle, other isolation methods based on physical properties of CTCs have been developed. Microfluidic technologies enable a label-free approach to CTC enrichment from whole blood samples. The spiral microfluidic technology uses the inertial and Dean drag forces with continuous flow in curved channels generated within a spiral microfluidic chip. The cells are separated based on the differences in size and plasticity between normal blood cells and tumoral cells. This protocol details the different steps to characterize the programmed death-ligand 1 (PD-L1) expression of CTCs, combining a spiral microfluidic device with customizable immunofluorescence (IF) marker set.

The characterization of circulating tumor cells (CTCs) is a popular topic in translational research. This protocol describes a semi-automatic immunofluorescence (IF) assay for PD-L1 characterization and enumeration of CTCs in non-small cell lung cancer (NSCLC) patient samples.

Circulating tumor cells (CTCs) derived from the primary tumor are shed into the bloodstream or lymphatic system. These rare cells (1&#8722;10 cells per mL ...

Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors

In-depth analyses of cancer cell proteomes are needed to elucidate oncogenic pathomechanisms, as well as to identify potential drug targets and diagnostic biomarkers. However, methods for quantitative proteomic characterization of patient-derived tumors and in particular their cellular subpopulations are largely lacking. Here we describe an experimental set-up that allows quantitative analysis of proteomes of cancer cell subpopulations derived from either liquid or solid tumors. This is achieved by combining cellular enrichment strategies with quantitative Super-SILAC-based mass spectrometry followed by bioinformatic data analysis. To enrich specific cellular subsets, liquid tumors are first immunophenotyped by flow cytometry followed by FACS-sorting; for solid tumors, laser-capture microdissection is used to purify specific cellular subpopulations. In a second step, proteins are extracted from the purified cells and subsequently combined with a tumor-specific, SILAC-labeled spike-in standard that enables protein quantification. The resulting protein mixture is subjected to either gel electrophoresis or Filter Aided Sample Preparation (FASP) followed by tryptic digestion. Finally, tryptic peptides are analyzed using a hybrid quadrupole-orbitrap mass spectrometer, and the data obtained are processed with bioinformatic software suites including MaxQuant. By means of the workflow presented here, up to 8,000 proteins can be identified and quantified in patient-derived samples, and the resulting protein expression profiles can be compared among patients to identify diagnostic proteomic signatures or potential drug targets.

In-depth analyses of cancer cell proteomes facilitate identification of novel drug targets and diagnostic biomarkers. We describe an experimental workflow for quantitative analysis of (phospho-)proteomes in cancer cell subpopulations derived from liquid and solid tumors. This is achieved by combining cellular enrichment strategies with quantitative Super-SILAC-based mass spectrometry.

In-depth analyses of cancer cell proteomes are needed to elucidate oncogenic pathomechanisms, as well as to identify potential drug targets and diagnostic ...

Medicine

Alternative Laser Excitation Method for Easy Side Population Cell Detection

Effective Detection of Hoechst Side Population Cells by Flow Cytometry

The side population (SP) cells are identified through Hoechst 33342 staining and analyzed using flow cytometry (FCM). The Hoechst SP method is utilized for the isolation of stem cells based on the dye efflux properties of ATP-binding cassette (ABC) transporters. The method was initially employed for the identification and isolation of hematopoietic stem cells (HSCs), but it has now evolved to primarily focus on the identification and isolation of cancer stem cells (CSCs). The traditional detection method of FCM uses a 355 nm laser to excite the dye to detect SP cells. Through this study, we have successfully identified alternative approaches for dye excitation that can effectively replace the detection of SP cells using a 355 nm laser. This is achieved through the utilization of high-power 375 nm or 405 nm lasers. This allows us to exercise enhanced selectivity in the detection of SP cells rather than being solely limited to the 355 nm laser flow cytometry.

Author Spotlight: Innovative Laser Techniques for Hoechst Staining to Analyze Side Population Cells

Here, we show that high-power 375 nm and 405 nm lasers can effectively excite Hoechst 33342 and serve as a viable alternative to the 355 nm laser for side population (SP) cell detection, thereby expanding the range of available lasers in flow cytometry applications.

The side population (SP) cells are identified through Hoechst 33342 staining and analyzed using flow cytometry (FCM). The Hoechst SP method is utilized ...

Analysis of Human T Cell Activity in an Allogeneic Co-Culture Setting of Pre-Treated Tumor Cells

Cytotoxic T cells play a key role in the elimination of tumor cells and are, therefore, intensively studied in cancer immunology. The frequency and activity of cytotoxic T cells within tumors and their tumor microenvironment (TME) are now well-established prognostic and predictive biomarkers for numerous tumor types. However, it is well-known that various tumor treatment modalities, including radiotherapy, chemotherapy, immunotherapy, and targeted therapy, modulate not only the immunogenicity of the tumor but also the immune system itself. Consequently, the interaction between tumor cells and T cells requires more intensive study in different therapeutic contexts to fully understand the complex role of T cells during tumor therapy. To address this need, a protocol was developed to analyze the activity and proliferative capacity of human cytotoxic (CD8+) T cells in co-culture with pre-treated tumor cells. Specifically, CD8+ T cells from healthy donors are stained with the non-toxic proliferation marker carboxyfluorescein diacetate succinimidyl ester (CFSE) and stimulated using CD3/CD28-coated plates. Subsequently, T cells are co-cultured with pre-treated tumor cells. As a readout, T cell proliferation is quantified by measuring CFSE signal distribution and assessing the expression of surface activation markers via flow cytometry. This can be further complemented by quantifying cytokine release using enzyme-linked immunosorbent assay (ELISA). This method facilitates the evaluation of treatment-induced changes in the interaction between tumor cells and T cells, providing a foundation for more detailed analyses of tumor treatment modalities and their immunogenicity in a human ex vivo setting. Additionally, it contributes to the reduction of pre-clinical in vivo analyses.

The present protocol describes an experimental workflow that allows for the ex vivo analysis of human T cell stimulation in an allogenic co-culture system with pre-treated tumor cells.

Cytotoxic T cells play a key role in the elimination of tumor cells and are, therefore, intensively studied in cancer immunology. The frequency and ...

Isolation of the Side Population in Myc-induced T-cell Acute Lymphoblastic Leukemia in Zebrafish

Heterogeneous cell populations, from either healthy or malignant tissues, may contain a population of cells characterized by a differential ability to efflux the DNA-binding dye Hoechst 33342. This "side population" of cells can be identified using flow cytometric methods after the Hoechst 33342 dye is excited by an ultraviolet (UV) laser. The side population of many cell types contains stem- or progenitor-like cells. However, not all cell types have an identifiable side population. Danio rerio, zebrafish, have a robust in vivo model of T-cell acute lymphoblastic leukemia (T-ALL), but whether these zebrafish T-ALLs have a side population is unknown. The method described here outlines how to isolate the side population cells in zebrafish T-ALL. To begin, the T-ALL in zebrafish is generated via the microinjection of tol2 plasmids into one-cell stage embryos. Once the tumors have grown to a stage at which they expand into more than half of the animal's body, the T-ALL cells can be harvested. The cells are then stained with Hoechst 33342 and examined by flow cytometry for side population cells. This method has broad applications in zebrafish T-ALL research. While there are no known cell surface markers in zebrafish that confirm whether these side population cells are cancer stem cell-like, in vivo functional transplantation assays are possible. Furthermore, single-cell transcriptomics could be applied to identify the genetic features of these side population cells.

Here, we describe a technique to isolate the side population cells from a zebrafish model of myc-induced T-cell acute lymphoblastic leukemia (T-ALL). This side population assay is highly sensitive and is described for zebrafish T-ALL, but it may be applicable to other malignant and non-malignant zebrafish cell types.

Heterogeneous cell populations, from either healthy or malignant tissues, may contain a population of cells characterized by a differential ability to ...

Immunology and Infection

Isolation and Characterization of a Head and Neck Squamous Cell Carcinoma Subpopulation Having Stem Cell Characteristics

Despite advances in the understanding of head and neck squamous cell carcinomas (HNSCC) progression, the five-year survival rate remains low due to local recurrence and distant metastasis. One hypothesis to explain this recurrence is the presence of cancer stem-like cells (CSCs) that present inherent chemo- and radio-resistance. In order to develop new therapeutic strategies, it is necessary to have experimental models that validate the effectiveness of targeted treatments and therefore to have reliable methods for the identification and isolation of CSCs. To this end, we present a protocol for the isolation of CSCs from human HNSCC cell lines that relies on the combination of two successive cell sortings performed by fluorescence activated cell sorting (FACS). The first one is based on the property of CSCs to overexpress ATP-Binding Cassette (ABC) transporter proteins and thus exclude, among others, vital DNA dyes such as Hoechst 33342. The cells sorted with this method are identified as a &#34;side population&#34; (SP). As the SP cells represent a low percentage (&#60;5%) of parental cells, a growing phase is necessary in order to increase their number before the second cell sorting. The next step allows for the selection of cells that possess two other HNSCC stem cell characteristics i.e. high expression level of the cell surface marker CD44 (CD44high) and the over-expression of aldehyde dehydrogenase (ALDHhigh). Since the use of a single marker has numerous limitations and pitfalls for the isolation of CSCs, the combination of SP, CD44 and ALDH markers will provide a useful tool to isolate CSCs for further analytical and functional assays requiring viable cells. The stem-like characteristics of CSCs was finally validated in vitro by the formation of tumorispheres and the expression of &#946;-catenin.

Understanding the role of cancer stem-like cells in tumor recurrence and resistance to therapies has become a topic of great interest in the last decade. This article describes the isolation and characterization of the sub-population of cancer stem-like cells from head and neck squamous carcinoma cell lines (HNSCC).

Despite advances in the understanding of head and neck squamous cell carcinomas (HNSCC) progression, the five-year survival rate remains low due to local ...

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Cellular processes are complex and result from the interplay between multiple cell types and their environment. Existing cell biology techniques often do not allow for accurate interpretation of this interplay. Using a quantitative imaging-based approach, we present a high-content protocol for characterizing the dynamic phenotypic responses (i.e. morphology changes, proliferation, apoptosis) of heterogeneous cell populations to changes in environmental stimuli. We highlight our ability to distinguish between cell types based upon either fluorescence intensity or inherent morphology features depending on the application. This platform allows for a more comprehensive characterization of subpopulation response to perturbation while utilizing shorter time, smaller amounts of reagents, and lower likelihood of error than traditional cell biology assays. However, in some cases, cell populations may be difficult to identify and quantitate based on complex cellular features and will require additional troubleshooting; we highlight some of these circumstances in the protocol. We demonstrate this application using response to drug in a cancer model; however, it can easily be applied more broadly to other physiological processes. This protocol allows one to identify subpopulations within a co-culture system and characterize the particular response of each to external stimuli.

We have developed a novel protocol for studying heterocellular population dynamics in response to perturbations. This manuscript describes an imaging-based platform that produces quantitative datasets for simultaneous characterization of multiple cellular phenotypes of heterocellular populations in a robust manner.

Cellular processes are complex and result from the interplay between multiple cell types and their environment. Existing cell biology techniques often do ...

Biology

Side Population Assay: A Method to Isolate Side Population of Tumor Cells from Zebrafish

Source: Pruitt, M. M., et al. <a href="https://www.jove.com/t/55711">Isolation of the Side Population in Myc-induced T-cell Acute Lymphoblastic Leukemia in Zebrafish.</a> J. Vis. Exp. (2017).
The following protocol describes a method for isolating the side population of T-cell Acute Lymphoblastic Leukemia (T-ALL) tumor cells, consisting of stem cells, from an adult Zebrafish. The side population lacks prominent cell markers and hence we use Hoechst 33342 dye efflux assay to isolate them.

Source: Pruitt, M. M., et al. Isolation of the Side Population in Myc-induced T-cell Acute Lymphoblastic Leukemia in Zebrafish. J. Vis. Exp. (2017).
The ...