We thank Ms. Danae Sharp and Ms. Sheena Daignault for technical assistance. We thank Dr. Atsushi Miyawaki, RIKEN, Wako-city, Japan, for providing the FUCCI constructs, Dr. Meenhard Herlyn and Ms. Patricia Brafford, The Wistar Institute, Philadelphia, for providing cell lines, the Imaging and Flow Cytometry Facility at the Centenary Institute for outstanding technical support. We thank Mr. Chris Johnson and Dr. Andrew Barlow for Volocity software technical support. N.K.H. is a Cameron fellow of the Melanoma and Skin Cancer Research Institute, Australia. K.A.B. is a fellow of the Cancer Institute New South Wales (13/ECF/1-39). W.W. is a fellow of the Cancer Institute New South Wales (11/CDF/3-39). This work was supported by project grants RG 09-08 and RG 13-06 (Cancer Council New South Wales), 570778 and 1051996 (Priority-driven collaborative cancer research scheme/Cancer Australia/Cure Cancer Australia Foundation), 08/RFG/1-27 (Cancer Institute New South Wales), and APP1003637 and APP1084893 (National Health and Medical Research Council).

1. FUCCI Transduction and Cell Culture
<ol>
	<li>FUCCI transduction
	<ol>
		<li>Create cell lines stably expressing the FUCCI constructs mKO2-hCdt1 (30-120) and mAG-hGem (1-100) 15 using lentivirus co-transduction as previously described 7. 
		Note: The FUCCI system is now commercially available.</li>
		<li>Generate sub-clones with bright fluorescence by single-cell sorting. Sort single cells positive for both AG and KO (yellow) by fluorescence activated cell sorting into a 96-well plate as previously described 7,16.</li>
	</ol>
	</li>
	<li>Melanoma cell culture
	<ol>
		<li>Culture C8161 human melanoma cells as previously described 7,17.</li>
	</ol>
	</li>
</ol>
2. 3D Spheroid Formation (as previously described 3,9)
<ol>
	<li>Agarose plate preparation
	<ol>
		<li>Dissolve 1.5% agarose (or noble agar) in 100 ml of Hank&#8217;s balanced salt solution (HBSS) or phosphate buffered saline (PBS) by boiling in the microwave for 3-5 min with swirling. Make sure the agarose is fully dissolved.</li>
		<li>Immediately dispense 100 &#956;l of the agarose solution per well to a flat-bottomed 96-well tissue culture plate using a multi-channel pipette. 
		Note: Agarose may solidify in the tips after a short period of time, to overcome this problem tips must be changed between plates if making more than one agarose plate.</li>
		<li>Leave 96-well plate on a flat surface to harden agarose for at least 1 hr.</li>
	</ol>
	</li>
	<li>Cell preparation and overlay onto agarose
	<ol>
		<li>Grow cells to approximately 80% confluency in a T75 flask. Wash with 10 ml of HBSS.</li>
		<li>Detach cells using 1.5 ml of 0.05% trypsin/ethylenediaminetetraacetic acid (EDTA) and resuspend in 10 ml of normal medium.</li>
		<li>Count cells using a hemocytometer 18 or other automated counting method.</li>
		<li>Resuspend cells to a final concentration of 25,000 cells/ml in normal medium.</li>
		<li>Overlay agarose wells with 200 &#956;l of the cell suspension for a final number of 5,000 cells per well.</li>
		<li>Return plates to the cell culture incubator (5% CO2 and 37 &#730;C) to form spheroids over approximately 3 days. 
		Note: The time taken to form a spheroid varies between different cell lines. Some cell lines do not form spheroids using this method at all.</li>
		<li>Image spheroid morphology with an inverted microscope using a 4X&#160;objective and a phase contrast filter. A cell line that successfully forms spheroids will produce compact, roughly spherical cell aggregates, and there should be only one spheroid per well. 
		Note: Optional: Once spheroids have formed, they can be transplanted into collagen or other matrix for further analysis of growth and invasion 3,7,17. To remove spheroids from collagen, treat with 500 &#956;l of 2 mg/ml collagenase at 37 &#176;C until the collagen is dissolved enough for the spheroids to come free into the media (approximately 30 min). Gently remove spheroids using a 1 ml pipette.</li>
		<li>For sectioning/imaging fix spheroids (either isolated from collagen or grown for 3-5 days on agarose) in 10% neutral buffered formalin (CAUTION: Formalin should be used in a fume cupboard) for at least 2 hr at room temperature (RT), or overnight (O/N) at 4 &#176;C. Spheroids may be stored in HBSS at 4 &#176;C for several days.</li>
	</ol>
	</li>
</ol>
3. Spheroid Vibratome Sectioning
<ol>
	<li>Dissolve 5 g of low melting point agarose in 100 ml of HBSS in a 500 ml glass bottle in a microwave. Use a medium heat setting with swirling. CAUTION: Keep the lid loose when boiling the solution so that the pressure can be released. Use protective heatproof gloves to protect hands from burns when handling the hot glass bottle.</li>
	<li>Wait until the agarose cools slightly &#8211; so the bottle can be touched without burning hands .</li>
	<li>Pour approximately 2 ml of agarose into the bottom of a Coulter counter cup or similar plastic mold. Immediately aspirate a fixed spheroid (see section 2.2.8) using a 1 ml pipette in the smallest volume of liquid possible. Add spheroid to the agarose. A few spheroids (up to 10) may be added per cup.</li>
	<li>Cover the spheroids with 2 ml more of agarose. Do this quickly to avoid the agarose hardening before the second layer is added.</li>
	<li>Allow the agarose to harden at RT in the dark for at least 3 hr. At this point cups may be stored for a short time at 4 &#176;C with 2-3 ml of HBSS overlaid to prevent dehydration.</li>
	<li>Remove agarose from the cup. Trim the agarose containing the spheroids so that it fits onto the vibratome metal block, and spheroids are close to the top of the agarose. Spheroids can be seen as small white objects inside the agarose. Super glue the agarose to the block. &#160;</li>
	<li>Fill the vibratome with distilled water and mount the block in the vibratome. Set the vibratome to cut 100 &#181;m sections using low speed (setting 2) and high vibration (setting 8). Set the cutting blade angle to 16&#176;. Turn vibratome on, turn on the vibratome light and cut sections from the top of the agarose until 100 &#181;m spheroid sections are cut. Place cut spheroid sections into HBSS in a 24-well plate.</li>
	<li>Choose middle sections for image analysis. Mount sections on slides by transferring the section flat onto the slide using tweezers. Fill a 10 ml syringe barrel with vacuum grease; add a thin line of vacuum grease around the edges of the section in order to prevent the mounting media from running off the edges of the slide and help seal the section under the coverslip. Cover the section with mounting medium and a coverslip. Seal edges of coverslip with nail polish. Store slides at 4 &#176;C before imaging.</li>
</ol>
4. Confocal Image Acquisition
<ol>
	<li>Take a z-slice image of a middle FUCCI spheroid section using a 10X&#160;or 20X&#160;objective as previously described 7. Make sure no overlap or crosstalk occurs between the Kusabira Orange2 and Azami Green. If comparing image analysis between multiple spheroid sections, keep laser power and other settings the same between samples.</li>
</ol>
5. Hoechst Dye Diffusion Assay for Flow Sorting
<ol>
	<li>Transfer live spheroids (3-5 days after agarose seeding) to a 15 ml tube. Let spheroids gravity settle to the bottom of the tube. Several spheroids may be stained per tube. Approximately 20 spheroids are needed to gain sufficient cell numbers for flow cytometry.</li>
	<li>Remove excess medium and add 1 ml of 10 &#956;M Hoechst diluted in normal medium. Incubate spheroids at 37 &#176;C for approximately 1 hr. Use gentle flicking to resuspend the spheroids once or twice during incubation. 
	Note: The incubation time may be varied to alter how far the Hoechst dye penetrates into the spheroids. This will vary based on the spheroid size, density and cell line. For some large/dense spheroids there may be a maximum dye penetration that is less than 100%.</li>
	<li>Wash the spheroids gently with 5 ml of HBSS using gravity settling.
	<ol>
		<li>Optional: For imaging of dye penetration: Fix spheroids with 10% neutral buffered formalin for at least 2 hr at RT or O/N at 4 &#176;C. Prepare vibratome sections, mount on slides and image on the confocal as described in steps 3 and 4 above.</li>
	</ol>
	</li>
	<li>To prepare cells for flow cytometry, trypsinize spheroids with 1 ml of 0.05% trypsin/EDTA at 37 &#176;C for approximately 30 min with flicking every 10 min to break the spheroids apart. 
	Note: Spheroids are difficult to get into single cell suspension, conditions may need to be optimized. Viability of C8161 4 day old spheroids after this process is approximately 95%.</li>
	<li>Stain cells with a near-infrared live/dead stain according to the manufacturers protocol. Wash with HBSS then fix with 1 ml of 10% neutral buffered formalin for approximately 30 min. Cells may be stored in HBSS at 4 &#176;C for several days before flow analysis.</li>
</ol>
6. Flow Cytometry Analysis of Hoechst Stained FUCCI Spheroids
<ol>
	<li>Ensure cells are not clumped together by passing them through a cell strainer. Resuspend the Hoechst stained FUCCI spheroids in ice cold FACS wash (PBS with 5% Fetal Calf Serum) at a concentration of 1-5 x 106 cells/ml. Transfer 200 &#181;l of sample to 5 ml round bottom tubes.</li>
	<li>Prepare the following single colour control samples as described in 6.1: un-stained melanoma cells; adherent melanoma cells stained with 10 &#181;M Hoechst for 1 hr; Azami Green only expressing melanoma cells; and Kusabira Orange only expressing melanoma cells. 
	Note: Single colour controls may be fixed in formalin and stored in FACS wash with 0.1% Sodium Azide at 4 &#176;C for weeks or even months.</li>
	<li>Run single cell suspensions on a flow cytometry analyzer with appropriate lasers and single colour/unstained controls as previously described 7,16.</li>
	<li>Use commercial cytometry software such as FlowJo to analyze the inner vs. outer spheroid cells as follows:
	<ol>
		<li>Remove debris by gating on the main cell population in Forward Scattered Light (FSC) vs. Side Scattered Light (SSC).</li>
		<li>Remove doublets by gating on single cells using FSC (Area) vs. FSC (Height)</li>
		<li>Gate for live cells by gating on the live/dead low population.</li>
		<li>Define Hoechst high cells, gated based on the positive signal from the adherent cells stained with Hoechst, as &#8220;outer&#8221; cells.</li>
		<li>Define Hoechst low or negative cells, gated based on the signal from the un-stained control, as &#8220;inner&#8221; cells.</li>
		<li>After gating for the inner and outer populations, cells may be further gated for FUCCI red (G1), yellow (early S), green (S/G2/M) or negative (early G1). 
		Note: Compensation using the single colour controls may be required to properly define the FUCCI red, yellow, green and negative populations.</li>
	</ol>
	</li>
	<li>Once the assay has been optimized and Hoechst penetration validated via confocal microscopy, run cells without fixing on a flow cell sorter as previously described 7,16 for further analysis of live cell sub-populations.</li>
</ol>
7. Image Analysis of FUCCI Spheroid Sections
<ol>
	<li>Open software e.g. Volocity, choose the Quantitation ONLY configuration.</li>
	<li>Create a new library. Import the FUCCI spheroid confocal image file into the software by dragging the raw data file from the confocal into the library. Alternatively, use the File &#62;Import&#160;command.</li>
	<li>Go to the measurements tab to build an image analysis protocol. The Protocol is built by dragging and dropping the appropriate commands in the correct order as follows in the protocol window.</li>
	<li>Find objects in the green channel: Drag the find objects command (found in &#8216;Finding&#8217;) into the protocol window, select the correct channel in the find objects protocol.Add an open command (found in &#8216;Processing&#8217;), then a separate touching objects command (found in &#8216;Processing&#8217; - this will separate cells). Exclude objects by size &#60;50 &#956;m (found in &#8216;Filtering&#8217; - this will exclude small non-cellular objects). 
	Note: Variables such as the find objects threshold, number of open iterations, the size of objects to be separated and the size of objects to exclude must be manually optimized until the object masks match the image.</li>
	<li>Find objects in the red channel: Repeat find objects as above for the red channel. Green and red protocols may need to be adjusted separately. 
	Note: Due to nuclei being in G2, green nuclei are often slightly larger.</li>
	<li>Confirm objects found are accurate: Confirm red and green objects match the red and green nuclei in the image by turning off and on the green and red channels (using the hide or show channel command) while displaying the red and green masks found by the protocol. Feedback options (Measurements &#62;Feedback options) may be used to modify the appearance of the object masks.</li>
	<li>Find yellow objects (early S phase cells): To find yellow cells, add an intersect red with green cells command (found in &#8216;Combining&#8217;). Exclude objects by size (&#60;50 &#956;m, found in &#8216;Filtering&#8217;) to exclude any small non-cellular objects.</li>
	<li>Find exclusively red objects (G1 phase cells): To find exclusively red nuclei, subtract the yellow cells from the red cells (found in &#8216;Combining&#8217;). Exclude objects by size &#60;50 &#956;m, (found in &#8216;Filtering&#8217;) to remove any small non-cellular objects created.</li>
	<li>Find exclusively green objects (S/G2/M phase cells): Repeat for the green channel to find exclusively green. 
	Note: If there are still non-cellular objects remaining, a filter population command may be added to the Exclusive green or Exclusive red protocols (found in &#8216;Filtering&#8217;) to retain only objects with a shape factor greater than 0.25. This will remove non-circular objects.</li>
	<li>Find the spheroid outline: Find the spheroid outline using a find objects command (found in &#8216;Finding&#8217;) with the green or red channel (whichever has more cells around the edge of the spheroid). A much lower threshold (found in the find objects variables) will need to be used to find the spheroid outline compared to individual cells.
	<ol>
		<li>Use a close command to join objects (found in &#8216;Processing&#8217;), then fill holes in objects (found in &#8216;Processing&#8217;), then use a fine filter (found in &#8216;Filtering&#8217;) to remove noise from objects. Finally, exclude objects by size to remove smaller objects &#60;30,000 &#956;m (depending on the spheroid size). 
		Note: This protocol will need to be optimized manually till the spheroid outline is accurate. A brightfield image may also be used &#8211; however this is usually less accurate with a spheroid section, and an invert command (found in &#8216;Combining&#8217;) will need to be used.</li>
	</ol>
	</li>
	<li>Measure distances of nuclei to spheroid outline: Measure distances (found in &#8216;Relating&#8217;) from the yellow, exclusively green and exclusively red populations from the cell centroid to the edge of the spheroid outline.To visualize the minimum distances, which should be from the cell to the nearest spheroid edge, turn on show distances in the relationships tab in feedback options (Measurements &#62;Feedback options &#62;Relationships).</li>
	<li>Save the protocol. This protocol may be re-applied to other images using the Measurements &#62;Restore protocol command.</li>
	<li>Create a measurements item (Measurements &#62;Make Measurement Item). Data may be analyzed using the analysis functions.
	<ol>
		<li>Go to the analysis tab in the measurements item. Go to the Analysis menu (Analysis &#62;Analyze) and analyze the minimum distance from the cell centroid (red, green or yellow) to the spheroid edge, summarized by count and organized by population.</li>
		<li>To count the number of cells found at a certain distance from the edge create a filter (Analysis &#62;Filter). The filter for minimum distance in Figure 2 is based on Hoechst penetration (e.g. minimum distance is less than 80 gives the &#8220;outer&#8221; population). Alternatively, export raw data to a spreadsheet for further analysis.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>LaBarbera, D. V., Reid, B. G., Yoo, B. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+multicellular+tumor+spheroid+model+for+high-throughput+cancer+drug+discovery.">The multicellular tumor spheroid model for high-throughput cancer drug discovery.</a> Expert opinion on drug discovery. 7, 819-830 (2012).</li><li>Beaumont, K. A., Mohana-Kumaran, N., Haass, N. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+Melanoma+In+Vitro+and+In+Vivo.">Modeling Melanoma In Vitro and In Vivo.</a> Healthcare. 2, 27-46 (2014).</li><li>Smalley, K. S., Lioni, M., Noma, K., Haass, N. K., Herlyn, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+vitro+three-dimensional+tumor+microenvironment+models+for+anticancer+drug+discovery.">In vitro three-dimensional tumor microenvironment models for anticancer drug discovery.</a> Expert opinion on drug discovery. 3, 1-10 (2008).</li><li>Reid, B. 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=Live+multicellular+tumor+spheroid+models+for+high-content+imaging+and+screening+in+cancer+drug+discovery.">Live multicellular tumor spheroid models for high-content imaging and screening in cancer drug discovery.</a> Current chemical genomics and translational medicine. 8, 27-35 (2014).</li><li>Kunz-Schughart, L. A., Freyer, J. P., Hofstaedter, F., Ebner, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+use+of+3-D+cultures+for+high-throughput+screening:+the+multicellular+spheroid+model.">The use of 3-D cultures for high-throughput screening: the multicellular spheroid model.</a> Journal of biomolecular screening. 9, 273-285 (2004).</li><li>Hirschhaeuser, F., 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=Multicellular+tumor+spheroids:+an+underestimated+tool+is+catching+up+again.">Multicellular tumor spheroids: an underestimated tool is catching up again.</a> Journal of biotechnology. 148, 3-15 (2010).</li><li>Haass, N. 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=Real-time+cell+cycle+imaging+during+melanoma+growth,+invasion,+and+drug+response.">Real-time cell cycle imaging during melanoma growth, invasion, and drug response.</a> Pigment cell &amp; melanoma research. 27, 764-776 (2014).</li><li>Lucas, K. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modulation+of+NOXA+and+MCL-1+as+a+strategy+for+sensitizing+melanoma+cells+to+the+BH3-mimetic+ABT-737.">Modulation of NOXA and MCL-1 as a strategy for sensitizing melanoma cells to the BH3-mimetic ABT-737.</a> Clinical cancer research : an official journal of the American Association for Cancer Research. 18, 783-795 (2012).</li><li>Smalley, K. 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=Multiple+signaling+pathways+must+be+targeted+to+overcome+drug+resistance+in+cell+lines+derived+from+melanoma+metastases.">Multiple signaling pathways must be targeted to overcome drug resistance in cell lines derived from melanoma metastases.</a> Molecular cancer therapeutics. 5, 1136-1144 (2006).</li><li>Thoma, C. R., Zimmermann, M., Agarkova, I., Kelm, J. M., Krek, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3D+cell+culture+systems+modeling+tumor+growth+determinants+in+cancer+target+discovery.">3D cell culture systems modeling tumor growth determinants in cancer target discovery.</a> Advanced drug delivery reviews. 69-70, 29-41 (2014).</li><li>Santini, M. T., Rainaldi, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Three-dimensional+spheroid+model+in+tumor+biology.">Three-dimensional spheroid model in tumor biology.</a> Pathobiology : journal of immunopathology, molecular and cellular biology. 67, 148-157 (1999).</li><li>Sutherland, R. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell+and+environment+interactions+in+tumor+microregions:+the+multicell+spheroid+model.">Cell and environment interactions in tumor microregions: the multicell spheroid model.</a> Science. 240, 177-184 (1988).</li><li>Haass, N. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+tumour+heterogeneity+in+melanoma+therapy:+how+do+we+address+this+in+a+novel+model+system?.">Dynamic tumour heterogeneity in melanoma therapy: how do we address this in a novel model system?.</a> Melanoma Manag. 2, 93-95 (2015).</li><li>Desoize, B., Jardillier, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multicellular+resistance:+a+paradigm+for+clinical+resistance.">Multicellular resistance: a paradigm for clinical resistance.</a> Critical reviews in oncology/hematology. 36, 193-207 (2000).</li><li>Sakaue-Sawano, A., 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=Visualizing+spatiotemporal+dynamics+of+multicellular+cell-cycle+progression.">Visualizing spatiotemporal dynamics of multicellular cell-cycle progression.</a> Cell. 132, 487-498 (2008).</li><li>Basu, S., Campbell, H. M., Dittel, B. N., Ray, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Purification+of+specific+cell+population+by+fluorescence+activated+cell+sorting+(FACS).">Purification of specific cell population by fluorescence activated cell sorting (FACS).</a> Journal of visualized experiments : JoVE. , (2010).</li><li>Smalley, K. 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=Up-regulated+expression+of+zonula+occludens+protein-1+in+human+melanoma+associates+with+N-cadherin+and+contributes+to+invasion+and+adhesion.">Up-regulated expression of zonula occludens protein-1 in human melanoma associates with N-cadherin and contributes to invasion and adhesion.</a> The American journal of pathology. 166, 1541-1554 (2005).</li><li>Wang, Q., 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=Targeting+glutamine+transport+to+suppress+melanoma+cell+growth.">Targeting glutamine transport to suppress melanoma cell growth.</a> International journal of cancer. Journal international du cancer. 135, 1060-1071 (2014).</li><li>Lorenzo, C., 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=Live+cell+division+dynamics+monitoring+in+3D+large+spheroid+tumor+models+using+light+sheet+microscopy.">Live cell division dynamics monitoring in 3D large spheroid tumor models using light sheet microscopy.</a> Cell division. 6, 22 (2011).</li><li>Pampaloni, F., Ansari, N., Stelzer, E. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-resolution+deep+imaging+of+live+cellular+spheroids+with+light-sheet-based+fluorescence+microscopy.">High-resolution deep imaging of live cellular spheroids with light-sheet-based fluorescence microscopy.</a> Cell and tissue research. 352, 161-177 (2013).</li><li>Durand, R. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Use+of+Hoechst+33342+for+cell+selection+from+multicell+systems.">Use of Hoechst 33342 for cell selection from multicell systems.</a> The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society. 30, 117-122 (1982).</li><li>Laurent, 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=Multicellular+tumor+spheroid+models+to+explore+cell+cycle+checkpoints+in+3D.">Multicellular tumor spheroid models to explore cell cycle checkpoints in 3D.</a> BMC cancer. 13, 73 (2013).</li><li>LaRue, K. E., Khalil, M., Freyer, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microenvironmental+regulation+of+proliferation+in+multicellular+spheroids+is+mediated+through+differential+expression+of+cyclin-dependent+kinase+inhibitors.">Microenvironmental regulation of proliferation in multicellular spheroids is mediated through differential expression of cyclin-dependent kinase inhibitors.</a> Cancer research. 64, 1621-1631 (2004).</li><li>Kyle, A. H., Huxham, L. A., Baker, J. H., Burston, H. E., Minchinton, A. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tumor+distribution+of+bromodeoxyuridine-labeled+cells+is+strongly+dose+dependent.">Tumor distribution of bromodeoxyuridine-labeled cells is strongly dose dependent.</a> Cancer research. 63, 5707-5711 (2003).</li><li>Minchinton, A. I., Tannock, I. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Drug+penetration+in+solid+tumours.">Drug penetration in solid tumours.</a> Nature reviews. Cancer. 6, 583-592 (2006).</li><li>Giesbrecht, J. L., Wilson, W. R., Hill, R. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Radiobiological+studies+of+cells+in+multicellular+spheroids+using+a+sequential+trypsinization+technique.">Radiobiological studies of cells in multicellular spheroids using a sequential trypsinization technique.</a> Radiation research. 86, 368-386 (1981).</li></ol>

PerkinElmer contributed to the costs of publication.

Semi-automated image analysis identified the spheroid inner G1 arrested region, and proliferating outer layers. This method may be used on live spheroids using an optical section, or in fixed spheroid sections, to identify changes in not only the cell cycle but marker expression (via immunofluorescence), cell death, or cell morphology in these different regions. Cell motility within different spheroid regions may also be quantified &#8211; if live confocal time lapse imaging along with a cell tracking image analysis step...

Multicellular 3D spheroids have been known as a tumor model for decades, however it is only recently that they have come into more common usage as an in vitro model for many solid cancers. They are increasingly being used in high-throughput drug discovery screens as an intermediate between complex, expensive and time-consuming in vivo models and the simple, low cost 2D monolayer model 1-6. Studies in 2D culture are often unable to be replicated in vivo. Spheroid models of many types of cancer are able to mimic the growth characteristics, drug sensitivity, drug penetration, cell-cell interactions, restricted availability of oxygen and nutrients and development of necrosis that is seen in vivo in solid tumors 6-11. Spheroids develop a necrotic core, a quiescent or G1 arrested region surrounding the core, and proliferating cells at the periphery of the spheroid 7. The development of these regions may vary depending on the cell density, proliferation rate and the size of the spheroid 12. It has been hypothesized that the cellular heterogeneity seen in these different sub-regions may contribute to cancer therapy resistance 13,14. Therefore the ability to analyze cells in these regions separately is crucial to understanding tumor drug responses.
The fluorescence ubiquitination cell cycle indicator (FUCCI) system is based on the red (Kusabira Orange &#8211; KO) and green (Azami Green &#8211; AG) fluorescent tagging of Cdt1 and geminin, which are degraded in different phases of the cell cycle 15. Thus cell nuclei appear red in G1, yellow in early S and green in S/G2/M phase. We describe here two complementary methods both using FUCCI to identify the cell cycle, along with the use of imaging software or a dye diffusion flow cytometry assay to determine whether cells reside in the G1 arrested center or the outer proliferating ring, and the distance of individual cells from the edge of the spheroid. These methods were developed in our previous publication, where we demonstrated that melanoma cells in hypoxic regions in the center of the spheroid or/and in the presence of targeted therapies are able to remain in G1 arrest for extended periods of time, and can re-enter the cell cycle when more favorable conditions arise 7.

<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Hoechst 33342</td>
			<td>Life Technologies</td>
			<td>H3570</td>
			<td></td>
		</tr>
		<tr>
			<td>agarose low melting point</td>
			<td>Life Technologies</td>
			<td>16520-050</td>
			<td>For sectioning</td>
		</tr>
		<tr>
			<td>noble agar&#160;</td>
			<td>Sigma</td>
			<td>A5431</td>
			<td>For making spheroids</td>
		</tr>
		<tr>
			<td>agarose for spheroids</td>
			<td>Fisher Scientific</td>
			<td>BP1356-100</td>
			<td>For making spheroids</td>
		</tr>
		<tr>
			<td>0.05% trypsin/EDTA</td>
			<td>Life Technologies</td>
			<td>25300-054</td>
			<td></td>
		</tr>
		<tr>
			<td>HBSS</td>
			<td>Life Technologies</td>
			<td>14175-103</td>
			<td></td>
		</tr>
		<tr>
			<td>10% formalin</td>
			<td>Sigma</td>
			<td>HT5014-1CS</td>
			<td>CAUTION: Harmful, corrosive. Use Personal Protective Equipment, do &#160;not breath fumes (open in a fume cupboard).</td>
		</tr>
		<tr>
			<td>live/dead near IR</td>
			<td>Life Technologies</td>
			<td>L10119</td>
			<td></td>
		</tr>
		<tr>
			<td>vibratome</td>
			<td>Technical Products International, Inc</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>coulture cup</td>
			<td>Thermo-Fisher Scientific</td>
			<td>SIE936</td>
			<td>Mold for sectioning spheroids</td>
		</tr>
		<tr>
			<td>hemocytometer</td>
			<td>Sigma</td>
			<td>Z359629</td>
			<td></td>
		</tr>
		<tr>
			<td>96-well tissue culture plate</td>
			<td>Invitro</td>
			<td>FAL353072</td>
			<td></td>
		</tr>
		<tr>
			<td>collagenase</td>
			<td>Sigma</td>
			<td>C5138&#160;</td>
			<td></td>
		</tr>
		<tr>
			<td>confocal microscope</td>
			<td>Leica</td>
			<td>TCS SP5</td>
			<td></td>
		</tr>
		<tr>
			<td>Flow cytometer analyser</td>
			<td>Becton Dickinson</td>
			<td>LSRFortessa</td>
			<td></td>
		</tr>
		<tr>
			<td>volocity</td>
			<td>PerkinElmer</td>
			<td></td>
			<td>Imaging software</td>
		</tr>
		<tr>
			<td>flowjo</td>
			<td>Tree Star</td>
			<td></td>
			<td>Flow cytometry software</td>
		</tr>
		<tr>
			<td>Vaccuum grease</td>
			<td>Sigma</td>
			<td>Z273554</td>
			<td></td>
		</tr>
		<tr>
			<td>Mounting media</td>
			<td>Vector Laboratories</td>
			<td>H1000</td>
			<td></td>
		</tr>
		<tr>
			<td>FUCCI (commercial constructs)</td>
			<td>Life Technologies</td>
			<td>P36238</td>
			<td>Transient transfection only</td>
		</tr>
		<tr>
			<td>Cell strainer 70 um</td>
			<td>In Vitro</td>
			<td>FAL352350</td>
			<td></td>
		</tr>
		<tr>
			<td>Round bottom 5 mL tubes (sterile)</td>
			<td>In Vitro</td>
			<td>FAL352003</td>
			<td></td>
		</tr>
		<tr>
			<td>Round bottom 5 mL tubes (non-sterile)</td>
			<td>In Vitro</td>
			<td>FAL352008</td>
			<td></td>
		</tr>
	</tbody>
</table>

imaging- and flow cytometry-based analysis of cell position and the cell cycle in 3d melanoma spheroids

Three-dimensional (3D) tumor spheroids are utilized in cancer research as a more accurate model of the in vivo tumor microenvironment, compared to traditional two-dimensional (2D) cell culture. The spheroid model is able to mimic the effects of cell-cell interaction, hypoxia and nutrient deprivation, and drug penetration. One characteristic of this model is the development of a necrotic core, surrounded by a ring of G1 arrested cells, with proliferating cells on the outer layers of the spheroid. Of interest in the cancer field is how different regions of the spheroid respond to drug therapies as well as genetic or environmental manipulation. We describe here the use of the fluorescence ubiquitination cell cycle indicator (FUCCI) system along with cytometry and image analysis using commercial software to characterize the cell cycle status of cells with respect to their position inside melanoma spheroids. These methods may be used to track changes in cell cycle status, gene/protein expression or cell viability in different sub-regions of tumor spheroids over time and under different conditions.

There are several methods of producing tumor spheroids, this protocol uses the non-adherent growth method, where cells are cultured on agar or agarose 3,7,9. Figure 1 shows an example of a C8161 melanoma spheroid after 3 days on agar. C8161 spheroids form regular sized spheroids with a diameter of 500 - 600 &#956;m (mean = 565, SD = 19, n = 3) after 3 days. Other melanoma cell lines that will form spheroids include: WM793, WM983C, WM983B, WM164, 1205lu (spheroids formed with this cell line are...

Watch this Scientific Journal Video about Imaging- and Flow Cytometry-based Analysis of Cell Position and the Cell Cycle in 3D Melanoma Spheroids at JoVE.com

Imaging- and Flow Cytometry-based Analysis of Cell Position and the Cell Cycle in 3D Melanoma Spheroids

We describe two complementary methods using the fluorescence ubiquitination cell cycle indicator (FUCCI) and image analysis or flow cytometry to identify and isolate cells in the inner G1 arrested and outer proliferating regions of 3D spheroids.

Three-dimensional (3D) tumor spheroids are utilized in cancer research as a more accurate model of the in vivo tumor microenvironment, compared to ...

imaging-flow-cytometry-based-analysis-cell-position-cell-cycle-3d

Research

JoVE Journal

Medicine

12.9K Views.  The Centenary Institute. We describe two complementary methods using the fluorescence ubiquitination cell cycle indicator (FUCCI) and image analysis or flow cytometry to identify and isolate cells in the inner G1 arrested and outer proliferating regions of 3D spheroids. 

Video: Imaging- and Flow Cytometry-based Analysis of Cell Position and the Cell Cycle in 3D Melanoma Spheroids

Multi-photon Imaging of Tumor Cell Invasion in an Orthotopic Mouse Model of Oral Squamous Cell Carcinoma

Loco-regional invasion of head and neck cancer is linked to metastatic risk and presents a difficult challenge in designing and implementing patient management strategies. Orthotopic mouse models of oral cancer have been developed to facilitate the study of factors that impact invasion and serve as model system for evaluating anti-tumor therapeutics. In these systems, visualization of disseminated tumor cells within oral cavity tissues has typically been conducted by either conventional histology or with in vivo bioluminescent methods. A primary drawback of these techniques is the inherent inability to accurately visualize and quantify early tumor cell invasion arising from the primary site in three dimensions. Here we describe a protocol that combines an established model for squamous cell carcinoma of the tongue (SCOT) with two-photon imaging to allow multi-vectorial visualization of lingual tumor spread. The OSC-19 head and neck tumor cell line was stably engineered to express the F-actin binding peptide LifeAct fused to the mCherry fluorescent protein (LifeAct-mCherry). Fox1nu/nu mice injected with these cells reliably form tumors that allow the tongue to be visualized by ex-vivo application of two-photon microscopy. This technique allows for the orthotopic visualization of the tumor mass and locally invading cells in excised tongues without disruption of the regional tumor microenvironment. In addition, this system allows for the quantification of tumor cell invasion by calculating distances that invaded cells move from the primary tumor site. Overall this procedure provides an enhanced model system for analyzing factors that contribute to SCOT invasion and therapeutic treatments tailored to prevent local invasion and distant metastatic spread. This method also has the potential to be ultimately combined with other imaging modalities in an in vivo setting.

A comprehensive overview of the techniques involved in generating a mouse model of oral cancer and quantitative monitoring of tumor invasion within the tongue through multi-photon microscopy of labeled cells is presented. This system can serve as a useful platform for the molecular assessment and drug efficacy of anti-invasive compounds.

Loco-regional invasion of head and neck cancer is linked to metastatic risk and presents a difficult challenge in designing and implementing patient ...

Modeling and Imaging 3-Dimensional Collective Cell Invasion

A defining characteristic of cancer malignancy is invasion and metastasis 1. In some cancers (e.g. glioma 2), local invasion into surrounding healthy tissue is the root cause of disease and death. For other cancers (e.g. breast, lung, etc.), it is the process of metastasis, in which tumor cells move from a primary tumor mass, colonize distal sites and ultimately contribute to organ failure, that eventually leads to morbidity and mortality 3. It has been estimated that invasion and metastasis are responsible for 90&#37; of cancer deaths 4. As a result, there has been intense interest in identifying the molecular processes and critical protein mediators of invasion and metastasis for the purposes of improving diagnosis and treatment 5.
A challenge for cancer scientists is to develop invasion assays that sufficiently resemble the in vivo situation to enable accurate disease modeling 6. Two-dimensional cell motility assays are only informative about one aspect of invasion and do not take into account extracellular matrix (ECM) protein remodeling which is also a critical element. Recently, research has refined our understanding of tumor cell invasion and revealed that individual cells may move by elongated or rounded modes 7. In addition, there has been greater appreciation of the contribution of collective invasion, in which cells invade in strands, sheets and clusters, particularly in highly differentiated tumors that maintain epithelial characteristics, to the spread of cancer 8.
We present a refined method 9 for examining the contributions of candidate proteins to collective invasion 10. In particular, by engineering separate pools of cells to express different fluorescent proteins, it is possible to molecularly dissect the activities and proteins required in leading cells versus those required in following cells. The use of RNAi provides the molecular tool to experimentally disassemble the processes involved in individual cell invasion as well as in different positions of collective invasion. In this procedure, mixtures of fluorescently-labeled cells are plated on the bottom of a Transwell insert previously filled with Matrigel ECM protein, then allowed to invade "upwards" through the filter and into the Matrigel. Reconstruction of z-series image stacks, obtained by confocal imaging, into three-dimensional representations allows for visualization of collectively invading strands and analysis of the representation of fluorescently-labeled cells in leading versus following positions.

Models of tumor cell invasion into three-dimensional extracellular matrix better reflect the in vivo situation than two-dimensional motility assays. Using matrix invasion assays combined with confocal imaging of fluorescently-labeled cells, detailed information on invasion modes and the distinct contributions of leading versus following cells can be obtained.

A defining characteristic of cancer malignancy is invasion and metastasis 1. In some cancers (e.g. glioma 2), local invasion into surrounding healthy ...

Processing of Primary Brain Tumor Tissue for Stem Cell Assays and Flow Sorting

Brain tumors are typically comprised of morphologically diverse cells that express a variety of neural lineage markers. Only a relatively small fraction of cells in the tumor with stem cell properties, termed brain tumor initiating cells (BTICs), possess an ability to differentiate along multiple lineages, self-renew, and initiate tumors in vivo. We applied culture conditions originally used for normal neural stem cells (NSCs) to a variety of human brain tumors and found that this culture method specifically selects for stem-like populations. Serum-free medium (NSC) allows for the maintenance of an undifferentiated stem cell state, and the addition of bFGF and EGF allows for the proliferation of multi-potent, self-renewing, and expandable tumorspheres.
To further characterize each tumor's BTIC population, we evaluate cell surface markers by flow cytometry. We may also sort populations of interest for more specific characterization. Self-renewal assays are performed on single BTICs sorted into 96 well plates; the formation of tumorspheres following incubation at 37 &deg;C indicates the presence of a stem or progenitor cell. Multiple cell numbers of a particular population can also be sorted in different wells for limiting dilution analysis, to analyze self-renewal capacity. We can also study differential gene expression within a particular cell population by using single cell RT-PCR.
The following protocols describe our procedures for the dissociation and culturing of primary human samples to enrich for BTIC populations, as well as the dissociation of tumorspheres. Also included are protocols for staining for flow cytometry analysis or sorting, self-renewal assays, and single cell RT-PCR.

The identification of brain tumor initiating cells (BTICs), the rare cells within a heterogeneous tumor possessing stem cell properties, provides new insights into human brain tumor pathogenesis. We have refined specific culture conditions to enrich for BTICs, and we routinely use flow cytometry to further enrich these populations. Self-renewal assays and transcript analysis by single cell RT-PCR can subsequently be performed on these isolated cells.

Brain tumors are typically comprised of morphologically diverse cells that express a variety of neural lineage markers. Only a relatively small fraction ...

Patient Derived Cell Culture and Isolation of CD133+ Putative Cancer Stem Cells from Melanoma

Despite improved treatments options for melanoma available today, patients with advanced malignant melanoma still have a poor prognosis for progression-free and overall survival. Therefore, translational research needs to provide further molecular evidence to improve targeted therapies for malignant melanomas. In the past, oncogenic mechanisms related to melanoma were extensively studied in established cell lines. On the way to more personalized treatment regimens based on individual genetic profiles, we propose to use patient-derived cell lines instead of generic cell lines. Together with high quality clinical data, especially on patient follow-up, these cells will be instrumental to better understand the molecular mechanisms behind melanoma progression.
Here, we report the establishment of primary melanoma cultures from dissected fresh tumor tissue. This procedure includes mincing and dissociation of the tissue into single cells, removal of contaminations with erythrocytes and fibroblasts as well as primary culture and reliable verification of the cells' melanoma origin.
Recent reports revealed that melanomas, like the majority of tumors, harbor a small subpopulation of cancer stem cells (CSCs), which seem to exclusively fuel tumor initiation and progression towards the metastatic state. One of the key markers for CSC identification and isolation in melanoma is CD133. To isolate CD133+ CSCs from primary melanoma cultures, we have modified and optimized the Magnetic-Activated Cell Sorting (MACS) procedure from Miltenyi resulting in high sorting purity and viability of CD133+ CSCs and CD133- bulk, which can be cultivated and functionally analyzed thereafter.

Patient Derived Cell Culture and Isolation of CD133+ Putative Cancer Stem Cells from Melanoma

This article describes the preparation of freshly obtained melanoma tissue into primary cell cultures, and how to remove contaminations of erythrocytes and fibroblasts from the tumor cells. Finally, we describe how CD133+ putative melanoma stem cells are sorted from the CD133- bulk using Magnetic Activated Cell Sorting (MACS).

Despite improved treatments options for melanoma available today, patients with advanced malignant melanoma still have a poor prognosis for ...

High-throughput Flow Cytometry Cell-based Assay to Detect Antibodies to N-Methyl-D-aspartate Receptor or Dopamine-2 Receptor in Human Serum

Over the recent years, antibodies against surface and conformational proteins involved in neurotransmission have been detected in autoimmune CNS diseases in children and adults. These antibodies have been used to guide diagnosis and treatment. Cell-based assays have improved the detection of antibodies in patient serum. They are based on the surface expression of brain antigens on eukaryotic cells, which are then incubated with diluted patient sera followed by fluorochrome-conjugated secondary antibodies. After washing, secondary antibody binding is then analyzed by flow cytometry. Our group has developed a high-throughput flow cytometry live cell-based assay to reliably detect antibodies against specific neurotransmitter receptors. This flow cytometry method is straight forward, quantitative, efficient, and the use of a high-throughput sampler system allows for large patient cohorts to be easily assayed in a short space of time. Additionally, this cell-based assay can be easily adapted to detect antibodies to many different antigenic targets, both from the central nervous system and periphery. Discovering additional novel antibody biomarkers will enable prompt and accurate diagnosis and improve treatment of immune-mediated disorders.

Over the recent years, live cell-based assays have been used successfully to detect antibodies against surface and conformational antigens. Here, we describe a method using high-throughput flow cytometry enabling the analysis of large cohorts of patients. Detection of novel antibodies will improve diagnosis and treatment of immune-mediated disorders.

Over the recent years, antibodies against surface and conformational proteins involved in neurotransmission have been detected in autoimmune CNS diseases ...

Adaptation of Semiautomated Circulating Tumor Cell (CTC) Assays for Clinical and Preclinical Research Applications

The majority of cancer-related deaths occur subsequent to the development of metastatic disease. This highly lethal disease stage is associated with the presence of circulating tumor cells (CTCs). These rare cells have been demonstrated to be of clinical significance in metastatic breast, prostate, and colorectal cancers. The current gold standard in clinical CTC detection and enumeration is the FDA-cleared CellSearch system (CSS). This manuscript outlines the standard protocol utilized by this platform as well as two&#160;additional adapted protocols that describe the detailed process of user-defined marker optimization for protein characterization of patient CTCs and a comparable protocol for CTC capture in very low volumes of blood, using standard CSS reagents, for studying in vivo preclinical mouse models of metastasis. In addition, differences in CTC quality between healthy donor blood spiked with cells from tissue culture versus patient blood samples are highlighted. Finally, several commonly discrepant items that can lead to CTC misclassification errors are outlined. Taken together, these protocols will provide a useful resource for users of this platform interested in preclinical and clinical research pertaining to metastasis and CTCs.

Adaptation of Semiautomated Circulating Tumor Cell CTC Assays for Clinical and Preclinical Research Applications

Circulating tumor cells (CTCs) are prognostic in several metastatic cancers. This manuscript describes the gold standard CellSearch system (CSS) CTC enumeration platform and highlights common misclassification errors. In addition, two&#160;adapted protocols are described for user-defined marker characterization of CTCs and CTC enumeration in preclinical mouse models of metastasis using this technology.

The majority of cancer-related deaths occur subsequent to the development of metastatic disease. This highly lethal disease stage is associated with the ...

Rose Bengal Photothrombosis by Confocal Optical Imaging In Vivo: A Model of Single Vessel Stroke

In vivo imaging techniques have increased in utilization due to recent advances in imaging dyes and optical technologies, allowing for the ability to image cellular events in an intact animal. Additionally, the ability to induce physiological disease states such as stroke in vivo increases its utility. The technique described herein allows for physiological assessment of cellular responses within the CNS following a stroke and can be adapted for other pathological conditions being studied. The technique presented uses laser excitation of the photosensitive dye Rose Bengal in vivo to induce a focal ischemic event in a single blood vessel. 
The video protocol demonstrates the preparation of a thin-skulled cranial window over the somatosensory cortex in a mouse for the induction of a Rose Bengal photothrombotic event keeping injury to the underlying dura matter and brain at a minimum. Surgical preparation is initially performed under a dissecting microscope with a custom-made surgical/imaging platform, which is then transferred to a confocal microscope equipped with an inverted objective adaptor. Representative images acquired utilizing this protocol are presented as well as time-lapse sequences of stroke induction. This technique is powerful in that the same area can be imaged repeatedly on subsequent days facilitating longitudinal in vivo studies of pathological processes following stroke.

Rose Bengal Photothrombosis by Confocal Optical Imaging In Vivo:  A Model of Single Vessel Stroke

Here, we describe a semi-invasive optical microscopy approach for the induction of a Rose Bengal photothrombotic clot in the somatosensory cortex of a mouse in vivo. The technical aspects of the imaging procedure are described from induction of a photothrombotic event to application and data collection.

In vivo imaging techniques have increased in utilization due to recent advances in imaging dyes and optical technologies, allowing for the ability to ...

A Flow Cytometry-based Assay for Measuring Mitochondrial Membrane Potential in Cardiac Myocytes After Hypoxia/Reoxygenation

Timely and efficient reperfusion of the occluded coronary artery is the best strategy for decreasing myocardial infarct size in patients with a ST-segment elevated myocardial infarction. However, reperfusion per se can result in further cardiomyocyte death, a phenomenon known as reperfusion injury. The opening of the mitochondrial permeability transition pore (mPTP), with the decrease of the mitochondrial membrane potential (MMP), or mitochondrial depolarization, is universally recognized as the final step of reperfusion injury and is responsible for mitochondrial and cardiomyocyte death. JC-1 is a lipophilic cationic dye that accumulates in mitochondria depending on the value of MMP. The higher the MMP is, the more JC-1 accumulates in the mitochondria. The increasing amounts of JC-1 in mitochondria can be reflected by a fluorescence emission shift from green (~530 nm) to red (~590 nm). Therefore, the reduction of the red/green fluorescence intensity ratio can indicate the depolarization of mitochondria. Here, we take advantage of JC-1 to measure MMP, or the opening of mPTP in human cardiac myocytes after hypoxia/reoxygenation, detected by flow cytometry.

Here, we present a protocol that uses JC-1 dye to assess the mitochondrial membrane potential of cells after being exposed to hypoxia/reoxygenation with or without a protective agent.

Timely and efficient reperfusion of the occluded coronary artery is the best strategy for decreasing myocardial infarct size in patients with a ST-segment ...

Flow Cytometry Analysis of Immune Cell Subsets within the Murine Spleen, Bone Marrow, Lymph Nodes and Synovial Tissue in an Osteoarthritis Model

Osteoarthritis (OA) is one of the most prevalent musculoskeletal diseases, affecting patients suffering from pain and physical limitations. Recent evidence indicates a potential inflammatory component of the disease, with both T-cells and monocytes/macrophages potentially associated with the pathogenesis of OA. Further studies postulated an important role for subsets of both inflammatory cell lineages, such as Th1, Th2, Th17, and T-regulatory lymphocytes, and M1, M2, and synovium-tissue-resident macrophages. However, the interaction between the local synovial and systemic inflammatory cellular response and the structural changes in the joint is unknown. To fully understand how T-cells and monocytes/macrophages contribute towards OA, it is important to be able to quantitively identify these cells and their subsets simultaneously in synovial tissue, secondary lymphatic organs and systemically (the spleen and bone marrow). Nowadays, the different inflammatory cell subsets can be identified by a combination of cell-surface markers making multi-color flow cytometry a powerful technique in investigating these cellular processes. In this protocol, we describe detailed steps regarding the harvest of synovial tissue and secondary lymphatic organs as well as generation of single cell suspensions. Furthermore, we present both an extracellular staining assay to identify monocytes/macrophages and their subsets as well as an extra- and intra-cellular staining assay to identify T-cells and their subsets within the murine spleen, bone marrow, lymph nodes and synovial tissue. Each step of this protocol was optimized and tested, resulting in a highly reproducible assay that can be utilized for other surgical and non-surgical OA mouse models.

Here, we describe a detailed and reproducible flow cytometry protocol to identify monocyte/macrophage and T-cell subsets using both extra- and intracellular staining assays within the murine spleen, bone marrow, lymph nodes and synovial tissue, utilizing an established surgical model of murine osteoarthritis.

Osteoarthritis (OA) is one of the most prevalent musculoskeletal diseases, affecting patients suffering from pain and physical limitations. Recent ...

Imaging and Analysis of Oil Red O-Stained Whole Aorta Lesions in an Aneurysm Hyperlipidemia Mouse Model

Apolipoprotein E (Apoe)- or low density lipoprotein receptor (Ldlr)-deficient hyperlipidemic mice are the two most commonly used models for atherosclerosis research. They are used to study the impact of a various genetic factors and different cell types on atherosclerotic lesion formation and as well as test the development of new therapies. Isolation, excision of the whole aorta, and quantification of Oil Red O-stained atherosclerotic lesions are basic morphometric methods used to evaluate atherosclerotic burden. The goal of this protocol is to describe an optimized, step-by-step surgical method to dissect, perfuse-fix, isolate, stain, image and analyze atherosclerotic lesions in mouse aortas with Oil Red O. Because atherosclerotic lesions can form anywhere in the entire aortic tree, this whole aorta Oil Red O staining method has the advantage of evaluating lipid-laden plaques in the entire aorta and all branches in a single mouse. In addition to Oil Red O staining, fresh isolated whole aortas can be used for variety of in vitro and in vivo experiments and cell isolations.

This protocol provides a step-by-step procedure to analyze atherosclerotic burden in mice. Investigators can use this protocol to compare the abundance, location, and size of atherosclerotic lesions in different animals.