The authors would particularly like to thank the following individuals for their technical and intellectual contributions to the development of these methods through the years: Bruce Bagwell (Verity Software House), Nad&egrave;ge Bercovici (IDM), Lizanne Breslin (Zynaxis Cell Science and PTI Research), Brian Gray (PTI Research), Jan Fisher (Dartmouth Medical School), Alice Givan (Dartmouth Medical School), Betsy Ohlsson-Wilhelm (SciGro, Inc.), and Mary Waugh (Dartmouth Medical School). They would also like to thank the Bowdoin class of 2006 from the Annual Courses in Research Methods and Applications of Flow Cytometry, which generated the data shown in Figure 2.
Flow cytometry was performed at Roswell Park Cancer Institute's Flow Cytometry Laboratory, which was established in part by equipment grants from the NIH Shared Instrument Program, and receives support from the Core Grant (5 P30 CA016056-29) from the National Cancer Institute to the Roswell Park Cancer Institute, and at the Abramson Cancer Center Flow Cytometry and Cell Sorting Resource Laboratory of the University of Pennsylvania, which was established in part by equipment grants from the NIH Shared Instrument Program, and receives support from NIH #2P30 CA016520 from the National Cancer Institute. The work shown in Figures 3 and 5 was also supported in part by SBIR grant EB00228 from the National Institutes of Biomedical Imaging and Bioengineering (NIBIB) awarded to PTI Research, Inc.

1. General Membrane Labeling with PKH26 Cell Tracking Dye (Ref. 25; Figure 1)
<ol>
	<li>Use sterile technique for steps 1.1 - 1.9. Prepare ~107 human peripheral blood mononuclear cells or lymphocytes (hPBMC, hPBL) using the laboratory's standard method with addition of a final 300 x g spin to minimize platelet contamination. Resuspend cells at 107/ml in HBSS+1% BSA and place on ice, reserving a 500 &mu;l aliquot (5x106 cells) for use in Step 2.</li>
	<li>Place 5x106 cells (500 &mu;l) in a 12 x 75 mm conical polypropylene tube. Wash once with 3.5 ml HBSS. Carefully aspirate the supernatant, leaving no more than 15-25 &mu;l of residual fluid, but taking care not to remove cells. Use this tube to prepare a 2x cell suspension in Step 1.4.</li>
	<li>During the cell washing in Step 1.2, add 0.5 ml of Diluent C labeling vehicle (from the PKH26GL kit) to a 12 x 75 mm conical polypropylene tube. Use this tube to prepare a 2x PKH26 solution in Step 1.5.</li>
	<li>Add 0.5 ml of Diluent C labeling vehicle to the washed cell pellet from Step 1.2 and aspirate and dispense 3-4 times to obtain a single cell suspension (2x cells). Avoid bubble formation and excessive mixing, which may reduce cell viability and recovery.</li>
	<li>Immediately after preparing the 2x cell suspension in Step 1.4, prepare a 2x (4 &mu;M) dye solution by adding 2.0 &mu;l of 1.0 mM PKH26 dye stock in ethanol (from the PKH26GL kit) to the Diluent C tube prepared in Step 1.3 and vortex to disperse uniformly.</li>
	<li>Immediately after preparing the 2x dye solution in Step 1.5, rapidly pipette the 2x cell suspension from Step 1.4 into the 2x dye solution and simultaneously aspirate and dispense 3-4 times to completely disperse cells in dye. Do not: add 1.0 mM dye directly to cells; pour 2x cells into 2x dye; or add 2x cells to 2x dye while vortexing. Because staining is nearly instantaneous, such methods yield less uniform intensities than the recommended method (Figure 2).</li>
	<li>After 1 min, add 1.0 ml of heat inactivated serum or HBSS+5% BSA to stop dye uptake into cell membranes. Failure to use enough protein risks the formation of dye aggregates, which can pellet with cells during the washing steps and cause unintended labeling of other cells present in an experiment. If medium with 10% heat inactivated serum (CM) or HBSS+1% BSA is to be used as the stop reagent, carry out staining in a 15 ml conical polypropylene tube and add at least 5.0 ml of stop reagent to insure adsorption of all unincorporated dye.</li>
	<li>Centrifuge the labeled cells for 5 min @ ~400 x g. Carefully aspirate the supernatant without removing cells. Wash the pellet twice with 4 ml of CM or HBSS+1% BSA, dispersing the pellet well before recentrifugation. To minimize carryover of dye adsorbed on tube walls and maximize washing efficiency, transfer the cells to a fresh polypropylene tube after the first resuspension. Note: adequately stained cells will exhibit a distinct pink tinge in the pellet.</li>
	<li>Resuspend the washed cell pellet in 1.0 ml HBSS+1% BSA. Count the cells, determine cell recovery, and adjust volume to give a final concentration of 107/ml. With careful aspiration, cell recovery should be &ge;85%. If cell recovery is &lt; 70%, determine the cause before proceeding further. Withdraw a 150 &mu;l aliquot (1.5x106 cells) and place on ice for use in Step 2.</li>
</ol>
2. Preparation of Instrument and Assay Setup Controls (Table 1)
<ol>
	<li>Aliquot 50 &mu;l (5x105) of unstained cells from the cell suspension saved in Step 1.1 into each of five 1.8 ml Eppendorf tubes: 1, 3, 4, 5, and 7. Aliquot 50 &mu;l (5x105) of PKH26pos cells from Step 1.9 into each of three tubes: 2, 6 and 8.</li>
	<li>Add 10 &mu;l IgG block (100 &mu;g/tube of IgG; see table of Reagents) to Tubes 1-8 and incubate for 10 min at ambient temperature (20-25 &deg;C).</li>
	<li>Add a saturating amount of the antibody(ies) indicated in Table 1 to Tubes 4, 5, 7, and 8 and incubate all samples (Tubes 1-8) for 30 min at ambient temperature and protected from light.</li>
	<li>Add 1.5 ml of HBSS+1% BSA to all samples, pellet the cells by centrifugation (5 min @ 400 x g) and wash once with 1.5 ml of HBSS+1% BSA, with careful aspiration to avoid cell loss.</li>
	<li>Resuspend each sample in 500 &mu;l of HBSS+1% BSA. If required for samples to be analyzed on your cytometer, transfer to a 12 x 75 mm round bottom tube. Add 10 &mu;l of 100 &mu;g/ml 7-AAD daily working stock (see table of Reagents) to Tubes 3, 6, 7 and 8 as indicated in Table 1. Incubate on ice for 30 min prior to use in flow cytometer setup and staining verification (Step 3).</li>
</ol>
3. Flow Cytometer Setup and Staining Verification
<ol>
	<li>Verify that the flow cytometer is operating properly, using the laboratory's established procedures for daily quality control. Verify that signals can readily be detected in each spectral window to be used, and that detector responses are linearly proportional to signal intensity in the window to be used for proliferation monitoring14.</li>
	<li>Acquire FSC vs. SSC data for Tube 1 using linear display scales. Adjust each detector's amplification such that the lymphocyte population falls in the lower left quadrant of the dot plot, is not off-scale in either parameter, and is not interrupted due to thresholding. Collect ungated FSC vs. SSC data for all samples in Steps 3.3 - 3.7.</li>
	<li>Acquire data for Tube 1, using no color compensation and logarithmic display scales for all 4 fluorescence detectors. Adjust each detector's high voltage (HV) to place the autofluorescence of unstained lymphocytes on scale with few/no cells accumulating in the first channel. Set an analysis boundary for each histogram corresponding to the brightest 2% of unstained cells.</li>
	<li>Using no color compensation and the HV settings established in Steps 3.2 and 3.3, acquire data for Tube 2, collecting FSC, SSC and signals in all 4 fluorescence detectors. For the detector used to monitor PKH26 fluorescence, verify that all PKH26pos cells appear on scale as a single symmetrical peak in the 3rd-4th decade, with few/no cells in the last channel. If there are multiple peaks or peak shape is skewed, repeat Step 1 with careful attention to salt minimization and mixing technique (Figure 2). If necessary, adjust dye concentration.</li>
	<li>Using settings established in Step 3.3, acquire data for Tube 3, collecting FSC, SSC and signals in all 4 fluorescence detectors. For the detector used to monitor 7-AAD fluorescence, verify that 7-AADpos cells fall above the 2% boundary established in Step 3.3 (i.e., that non-viable cells are well resolved from viable 7-AADneg cells).</li>
	<li>Using settings established in Step 3.3, acquire data for Tube 4, collecting FSC, SSC and signals in all 4 fluorescence detectors. Verify that CD8pos cells are well resolved from unstained cells (i.e., fall above the 2% boundary established in Step 3.3 for the FITC detector). Repeat with Tube 5 and verify that CD8pos cells are well resolved from unstained cells (i.e. fall above the 2% boundary established in Step 3.3 for the APC detector).</li>
	<li>Using settings established in Step 3.3, acquire data for Tubes 6, 7 and 8, collecting FSC, SSC and signals in all 4 fluorescence detectors.</li>
	<li>Use the list mode files collected for Tubes 1-5 and your color compensation software to establish a color overlap matrix for each fluorochrome in the detectors being used to monitor the three other fluorochromes. Apply this matrix to the list mode file for Sample 6 and verify that the presence of PKH26 labeling does not alter the ability to detect 7-AADpos cells.</li>
	<li>Apply the color overlap matrix from Step 3.8 to the list mode file for Sample 7 and verify that: a) 3 well resolved subpopulations (CD3negCD4neg, CD3posCD4neg and CD3posCD4pos) can be identified on an FITC vs. APC dot plot; and b) the presence of anti-CD3-FITC and anti-CD4-APC does not alter the ability to detect 7-AADpos cells. If the presence of anti-CD3-FITC alters the upper 2% boundary for data collected on the PKH26 detector, readjust the boundary as required.</li>
	<li>Apply the color overlap matrix from Step 3.8 to the list mode file for Sample 8. If the presence of PKH26 labeling alters the 2% boundary for the FITC, 7-AAD or APC detectors from those set using the autofluorescence control in Step 3.3, readjust the boundary(ies) as needed using Tube 7 of Table 1 and verify that it is still possible to distinguish CD3posCD4pos, CD3posCD4neg, and CD3negCD4neg cells using the adjusted boundary(ies).</li>
</ol>
4. Proliferation Model Selection for Cell Division Monitoring by Dye Dilution
<ol>
	<li>Determine the spacing between generations, which is dependent on the number of channels and logarithmic decades on your cytometer. For digital instruments, this value, typically 4 or 5 decades, is determined by the number of bins in the digital signal processor. On analog instruments, where the number of decades is rarely a whole number, accurate modeling requires the precise number of decades to be experimentally determined. To do this, data from a mixture of calibrated fluorescent beads with manufacturer-assigned relative intensities is acquired at the same detector high voltage settings used in the experiment. The position of the bead peaks allows for the calibration of the logarithmic scale in terms of intensity range per log decade. Specifically, this is done by plotting the channel number for each bead type against the log of the manufacturer-assigned value. The slope of a best-fit straight line for the bead data values gives the number of relative intensity units per channel. Multiplied by the number of channels, this value will then be the number of log decades for the full scale, from which the number of channels corresponding to a two-fold decrease in intensity (i.e., daughter generation spacing) can be calculated14.</li>
	<li>Decide whether to use a fixed spacing between generations or to allow the spacing to float. A Standard (fixed) setting will use the generational spacing value determined in Step 4.5 to assign the location of each generation, and is typically used when the histogram lacks distinct peaks. A Float setting allows each generational peak position to be determined by the histogram shape and is typically used when distinguishable generational peaks are evident.</li>
	<li>Decide whether to use a fixed peak width for all generations or a floating width. A fixed width uses the SD calculated for the unstimulated control sample to model all generations and is typically chosen when the sample lacks distinguishable generational peaks. A floating width allows the program to independently vary the SD for each generation and is best used with distinguishable generational peaks.</li>
	<li>Run a program containing a proliferation analysis module (here ModFit LT Version 3.3). Load the stimulated PKH26pos file from the data set to be analyzed (e.g., an stimulated 96 hr culture of PKH26pos cells counterstained as for Tube 8 in Table 1).</li>
	<li>Select the parameters for analysis, in this case PKH26 (585/42) gated on viable (7-AADneg) CD3pos lymphocytes and FSC vs. SSC to exclude small debris and large aggregates (Figure 3). In defining these regions be careful to include the high forward scatter area where blasts are typically found and note that CD3 expression may be down-modulated in stimulated cultures.</li>
	<li>Create a new proliferation model using the Proliferation Wizard. Using the Open Data File (Start tab), load the unstimulated PKH26pos control file and define the location of the peak channel in the parental distribution corresponding to undivided cells.</li>
	<li>Analyze the file for the unstimulated PKH26pos control, noting the values for parental peak position and width (standard deviation). If a fixed peak width (SD) is desired, check Lock SD.</li>
	<li>Load the PKH26neg control (e.g., a 96 hr culture of PKH26neg cells counterstained as for Tube 7 in Table 1). Adjust the number of generations by setting the peak channel for the dimmest generation above the PKH26neg control. This determines the number of daughter generations the model can accurately fit and is typically 6-9 generations.</li>
	<li>Open the data file for the stimulated sample (e.g., a stimulated 96 hr culture of PKH26pos cells counterstained as for Tube 8 in Table 1) and confirm that the regions for the parental peak position and the SD as defined in Step 4.7 remain unchanged. If fixed generational spacing is desired, select Standard model option; otherwise select the Floating option.</li>
	<li>Analyze each experimental file in the data set, using the same model defined in Step 4.9. Minor adjustments to the parental peak position may be necessary for the very best fit as defined both visually and by the reduced chi square (RCS) value.</li>
	<li>Record the desired proliferation metrics resulting from the best-fit for each experimental file in the data set. For a comprehensive description of possible metrics see Ref. 22.</li>
</ol>

<ol>
	<li>Poon, R. Y., Ohlsson-Wilhelm, B. M., Bagwell, C. B., Muirhead, K. A., Diamond, R. A., DeMaggio, S. <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+PKH+Membrane+Intercalating+Dyes+to+Monitor+Cell+Trafficking+and+Function.">Use of PKH Membrane Intercalating Dyes to Monitor Cell Trafficking and Function.</a> Living Color: Flow Cytometry and Cell Sorting Protocols. , 302-352 (2000).</li><li>Wallace, P. K., Muirhead, K. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell+Tracking+2007:+A+Proliferation+of+Probes+and+Applications.">Cell Tracking 2007: A Proliferation of Probes and Applications.</a> Immunol. Invest. 36, 527-562 (2007).</li><li>Hawkins, E. D., Hommel, M., Turner, M. L., Battye, F. L., Markham, J. F., Hodgkin, P. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+lymphocyte+proliferation,+survival+and+differentiation+using+CFSE+time-series+data.">Measuring lymphocyte proliferation, survival and differentiation using CFSE time-series data.</a> Nat. Protoc. 2, 2057-2067 (2007).</li><li>Quah, B. J., Warren, H. S., Parish, C. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Monitoring+lymphocyte+proliferation+in+vitro+and+in+vivo+with+the+intracellular+fluorescent+dye+carboxyfluorescein+diacetate+succinimidyl+ester.">Monitoring lymphocyte proliferation in vitro and in vivo with the intracellular fluorescent dye carboxyfluorescein diacetate succinimidyl ester.</a> Nat. Protoc. 2, 2049-2056 (2007).</li><li>Bolton, D. L., Minang, J. T., Trivett, M. T., Song, K., Tuscher, J. J., Li, Y., Piatak, M., O'Connor, D., Lifson, J. D., Roederer, M., Ohlen, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Trafficking,+Persistence,+and+Activation+State+of+Adoptively+Transferred+Allogeneic+and+Autologous+Simian+Immunodeficiency+Virus-Specific+CD8++T+Cell+Clones+during+Acute+and+Chronic+Infection+of+Rhesus+Macaques.">Trafficking, Persistence, and Activation State of Adoptively Transferred Allogeneic and Autologous Simian Immunodeficiency Virus-Specific CD8+ T Cell Clones during Acute and Chronic Infection of Rhesus Macaques.</a> J. Immunol. 184, 303-314 (2010).</li><li>Juopperi, T. A., Sharkis, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Isolation+of+Quiescent+Murine+Hematopoietic+Stem+Cells+by+Homing+Properties.">Isolation of Quiescent Murine Hematopoietic Stem Cells by Homing Properties.</a> Meth. Mol. Biol. 430, 21-30 (2008).</li><li>Kusumbe, A. P., Bapat, S. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cancer+stem+cells+and+aneuploid+populations+within+developing+tumors+are+the+major+determinants+of+tumor+dormancy.">Cancer stem cells and aneuploid populations within developing tumors are the major determinants of tumor dormancy.</a> Cancer Res. 69, 9245-9253 (2009).</li><li>Pece, S., Tosonim, D., Confalonieri, S., Mazzarol, G., Vecchi, M., Ronzoni, S., Bernard, L., Viale, G., Pelicci, P. G., Fiore, P. P. D. i. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biological+and+Molecular+Heterogeneity+of+Breast+Cancers+Correlates+with+Their+Cancer+Stem+Cell+Content.">Biological and Molecular Heterogeneity of Breast Cancers Correlates with Their Cancer Stem Cell Content.</a> Cell. 140, 62-73 (2010).</li><li>Gertner-Dardenne, J., Poupot, M., Gray, B. D., Fourni&#233;, J. -. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lipophilic+fluorochrome+trackers+of+membrane+transfers+between+immune+cells.">Lipophilic fluorochrome trackers of membrane transfers between immune cells.</a> Immunol. Invest. 36, 665-685 (2007).</li><li>Bercovici, N., Givan, A. L., Waugh, M. G., Fisher, J. L., Vernel-Pauillac, F., Ernstoff, M. S., Abastado, J. P., Wallace, P. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiparameter+precursor+analysis+of+T-cell+responses+to+antigen.">Multiparameter precursor analysis of T-cell responses to antigen.</a> J. Immunol. Methods. 276, 5-17 (2003).</li><li>Givan, A. L., Fisher, J. L., Waugh, M. G., Bercovici, N., Wallace, P. K. <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+cell-tracking+dyes+to+determine+proliferation+precursor+frequencies+of+antigen-specific+T+cells.">Use of cell-tracking dyes to determine proliferation precursor frequencies of antigen-specific T cells.</a> Methods Mol. Biol. 263, 109-124 (2004).</li><li>Schwaab, T., Tretter, C. P., Gibson, J. J., Cole, B. F., Schned, A. R., Harris, R., Fisher, J. L., Crosby, N., Stempkowski, L. M., Heaney, J. A., Ernstoff, M. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tumor-related+immunity+in+prostate+cancer+patients+treated+with+human+recombinant+granulocyte+monocyte-colony+stimulating+factor+(GM-CSF).">Tumor-related immunity in prostate cancer patients treated with human recombinant granulocyte monocyte-colony stimulating factor (GM-CSF).</a> Prostate. 66 (6), 667-674 (2006).</li><li>Bantly, A. D., Gray, B. D., Breslin, E., Weinstein, E. G., Muirhead, K. A., Ohlsson-Wilhelm, B. M., Moore, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CellVue+Claret,+a+New+Far-Red+Dye,+Facilitates+Polychromatic+Assessment+of+Immune+Cell+Proliferation.">CellVue Claret, a New Far-Red Dye, Facilitates Polychromatic Assessment of Immune Cell Proliferation.</a> Immunol. Invest. 36, 581-605 (2007).</li><li>Givan, A. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+flow+cytometric+assay+for+quantitation+of+rare+antigen-specific+T-cells:+using+cell-tracking+dyes+to+calculate+precursor+frequencies+for+proliferation.">A flow cytometric assay for quantitation of rare antigen-specific T-cells: using cell-tracking dyes to calculate precursor frequencies for proliferation.</a> Immunol. Invest. 36, 563-580 (2007).</li><li>Tario, J. D., Gray, B. D., Wallace, S. S., Muirhead, K. A., Ohlsson-Wilhelm, B. M., Wallace, P. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Novel+lipophilic+tracking+dyes+for+monitoring+cell+proliferation.">Novel lipophilic tracking dyes for monitoring cell proliferation.</a> Immunol Invest. 36, 861-885 (2007).</li><li>Wallace, P. K., Tario, J. D., Fisher, J. L., Wallace, S. S., Ernstoff, M. S., , ., Muirhead, K. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tracking+Antigen-Driven+Responses+by+Flow+Cytometry:+Monitoring+Proliferation+by+Dye+Dilution.">Tracking Antigen-Driven Responses by Flow Cytometry: Monitoring Proliferation by Dye Dilution.</a> Cytometry. 73, 1019-1034 (2008).</li><li>Barth, R. J., Fisher, D. A., Wallace, P. K., Channon, J. Y., Noelle, R. L., Gui, J., Ernstoff, M. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Randomized+Trial+of+Ex+vivo+CD40L+Activation+of+a+Dendritic+Cell+Vaccine+in+Colorectal+Cancer+Patients:+Tumor-Specific+Immune+Responses+Are+Associated+with+Improved+Survival.">A Randomized Trial of Ex vivo CD40L Activation of a Dendritic Cell Vaccine in Colorectal Cancer Patients: Tumor-Specific Immune Responses Are Associated with Improved Survival.</a> Clin. Cancer Res. 16, 5548-5556 (2010).</li><li>Tario, J. D., Muirhead, K. A., Pan, D., Munson, M., Wallace, P. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tracking+Immune+Cell+Proliferation+and+Cytotoxic+Potential+Using+Flow+Cytometry.">Tracking Immune Cell Proliferation and Cytotoxic Potential Using Flow Cytometry.</a> Meth. Mol. Biol. 699, 119-164 (2011).</li><li>Fuse, S., Underwood, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Simultaneous+Analysis+of+In+Vivo+CD8++T+Cell+Cytotoxicity+Against+Multiple+Epitopes+using+Multicolor+Flow+Cytometry.">Simultaneous Analysis of In Vivo CD8+ T Cell Cytotoxicity Against Multiple Epitopes using Multicolor Flow Cytometry.</a> Immunol. Invest. 36, 829-845 (2007).</li><li>Sch&#252;tz, C., Fleck, M., Mackensen, A., Zoso, A., Halbritter, D., Schneck, J. P., Oelke, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Killer+artificial+antigen-presenting+cells:+a+novel+strategy+to+delete+specific+T+cells.">Killer artificial antigen-presenting cells: a novel strategy to delete specific T cells.</a> Blood. 111, 3546-3552 (2008).</li><li>Zaritskaya, L., Shurin, M. R., Sayers, T. J., Malyguine, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=New+flow+cytometric+assays+for+monitoring+cell-mediated+cytotoxicity.">New flow cytometric assays for monitoring cell-mediated cytotoxicity.</a> Expert Rev. Vaccines. 9, 601-616 (2010).</li><li>Roederer, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interpretation+of+cellular+proliferation+data:+Avoid+the+panglossian.">Interpretation of cellular proliferation data: Avoid the panglossian.</a> Cytometry. 79A, 95-101 (2011).</li><li>Quah, B. J. C., Parish, C. 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+Carboxyfluorescein+Diacetate+Succinimidyl+Ester+(CFSE)+to+Monitor+Lymphocyte+Proliferation.">The Use of Carboxyfluorescein Diacetate Succinimidyl Ester (CFSE) to Monitor Lymphocyte Proliferation.</a> J. Vis. Exp. (44), e2259 (2010).</li><li>Houlihan, D. D., Newsome, P. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Critical+Review+of+Clinical+Trials+of+Bone+Marrow+Stem+Cells+in+Liver+Disease.">Critical Review of Clinical Trials of Bone Marrow Stem Cells in Liver Disease.</a> Gastroenterology. 135, 438-450 (2008).</li><li>Brusko, T. M., Hulme, M. A., Myhr, C. B., Haller, M. J., Atkinson, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessing+the+In+Vitro+Suppressive+Capacity+of+Regulatory.">Assessing the In Vitro Suppressive Capacity of Regulatory.</a> T Cells. Immunol. Invest. 36, 607-628 (2007).</li></ol>

K. Humphrey, J.D. Tario, Jr. and P.K. Wallace have received pre-commercial cell tracking reagents for evaluation from Life Technologies, Inc. and BD Biosciences. A.D. Bantly and J.S. Moore have received pre-commercial cell tracking reagents for evaluation from Life Technologies, Inc. and funding from PTI Research, Inc. for pre-commercial characterization of various CellVue cell tracking dyes. K. Muirhead is employed by SciGro, Inc., which provides consulting services to Phanos Technologies, Inc. (owner of the PKH and CellVue dyes) and provides backup technical support for Sigma-Aldrich and Molecular Targeting Technologies, Inc. (distributors of these dyes). Production and Free Access to this article is sponsored by Sigma-Aldrich.

The methods described here are those found in our combined laboratories to most reliably give optimal results for hPBMC labeling using membrane dyes13,16,18 and for lymphocyte subset phenotyping and proliferation tracking using either membrane or protein dyes2,11,13,16,18. As illustrated in Figures 1 and 2, bright uniform labeling is most readily achieved by limiting the presence of physiologic salts and using a mixing technique that results in rapid, homogeneous exposure of all cells to the same concentration of dye. Because staining with membrane dyes occurs by partitioning into the lipid bilayer, other variables that alter free dye concentration can also impact labeling efficiency. For example, labeling in round bottom polystyrene tubes results in less efficient washout of salts prior to resuspension in Diluent C and also in reduced free dye concentration due to dye adsorption on tube walls, particularly at lower dye concentrations. Both factors tend to give broader staining distributions than when labeling is done using conical bottom polypropylene tubes (Figures 3, 4 and unpublished results). Sample age and type can also affect peak width even when optimized staining procedures are used. For example, CVs for PKH26pos lymphocytes isolated from freshly drawn blood range from 14-20% (Figure 3, Ref. 13 and unpublished results) whereas CVs for lymphocytes isolated from 24 hr old blood samples or TRIMA pheresis filters range from 25-30% (Figure 4 and Ref. 18).
Staining uniformity and the extent to which non-viable cells can be excluded from analysis both affect whether distinguishable daughter peaks are evident in the dye dilution profile, which in turn affects choice of a proliferation model to fit the observed data (Figures 5 and 6). Although ModFit (Verity Software House, Topsham, ME) is used here as an example of software used to generate metrics such as Proliferation Index and Precursor Frequency (Figures 3, 5 and 6; Table 3), other software packages also contain modules to analyze proliferation data. These include FCSExpress (De Novo Software, Los Angeles, CA) and FlowJo (Tree Star, Inc., Ashland, OR). All of these programs use a non-linear least squares analysis to iteratively find the best fit to the raw data by changing the position, height and SD (or width) of Gaussian peaks representing sequential daughter generations. Proliferative Index (PI) and Precursor Frequency (PF) are the most commonly used measures of extent of proliferation. PI, as defined by ModFit, is a measure of the increase in cell number over the course of the assay, analogous to the 'stimulation index' of a thymidine uptake assay. PF returns the fraction of cells in the initial population that responded to the stimulus by proliferating. Caution is advised, however, when reading the literature since terminology varies somewhat among software packages (e.g., FlowJo and ModFit use different definitions and calculations for what is meant by "Proliferation Index")22.
The critical issues discussed here for labeling and proliferation analysis with membrane dyes are also encountered when using protein dyes. For example, careful attention to mixing technique must also be observed, along with exclusion of dead/dying cells, in order to obtain uniform distributions and distinguishable daughter peaks when using CFSE (Figure 6)2-4,13,18,24. Appropriate choice of fluorochromes for phenotyping and viability assessment is also important to avoid excessive spectral overlap and inability to recognize antibody positive cells, particularly with visible emitting protein dyes such as CFSE2-4,11,13,16,18. Reducing the concentration of tracking dye decreases compensation problems in adjacent spectral channels but also limits the number of cell divisions that can be monitored before daughter cell intensities begin to overlap with autofluorescence. Alternatively, use of newer cell tracking dyes such as the far red emitting CellVue Claret (Sigma-Aldrich, St. Louis, MO) or violet emitting CellTrace Violet (Life Technologies, Grand Island, NY) may reduce compensation problems (Figure 6). Finally, although membrane dyes generally tend to exhibit less toxicity11,26, it is possible to over-label cells with either class of dye. It is therefore always necessary to verify that the concentration of tracking dye used has not altered the functionality of the cells to be tracked (Figure 6)3,13,16,18.

<table border="1">
	<tbody>
		<tr>
			<td>Reagent or equipment</td>
			<td>Company</td>
			<td>Catalogue number</td>
			<td>Comments</td>
		</tr>
		<tr>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
			<td>Commercially Purchased</td>
		</tr>
		<tr>
			<td>7-Aminoactinomycin D</td>
			<td>Sigma-Aldrich</td>
			<td>A9400</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Bovine serum albumin (BSA)</td>
			<td>Sigma-Aldrich</td>
			<td>A4503</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Fetal bovine serum (FBS)</td>
			<td>Atlanta Biologicals</td>
			<td>S11150</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Hanks balanced salt solution (HBSS)</td>
			<td>Life Technologies</td>
			<td>14175-079</td>
			<td>Calcium and magnesium free, without phenol red</td>
		</tr>
		<tr>
			<td>Human IgG Cohn fraction II and III globulins</td>
			<td>Sigma-Aldrich</td>
			<td>G-4386</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Mouse anti-human CD3-FITC</td>
			<td>BD Biosciences</td>
			<td>349201</td>
			<td>Saturating concentration as determined by laboratory titration</td>
		</tr>
		<tr>
			<td>Mouse anti-human CD4-APC</td>
			<td>BD Biosciences</td>
			<td>340672</td>
			<td>Saturating concentration as determined by laboratory titration</td>
		</tr>
		<tr>
			<td>Mouse anti-human CD4-PECy7</td>
			<td>BD Biosciences</td>
			<td>348799</td>
			<td>Saturating concentration as determined by laboratory titration</td>
		</tr>
		<tr>
			<td>Mouse anti-human CD8-FITC</td>
			<td>BD Biosciences</td>
			<td>347313</td>
			<td>Saturating concentration as determined by laboratory titration</td>
		</tr>
		<tr>
			<td>Mouse anti-human CD8-APC</td>
			<td>Caltag (Life Technologies)</td>
			<td>MHCD0805</td>
			<td>Saturating concentration as determined by laboratory titration</td>
		</tr>
		<tr>
			<td>Mouse anti-human CD19-APC</td>
			<td>Caltag (Life Technologies)</td>
			<td>MHCD1905</td>
			<td>Saturating concentration as determined by laboratory titration</td>
		</tr>
		<tr>
			<td>Mouse anti-human CD25-APC</td>
			<td>BD Biosciences</td>
			<td>340938</td>
			<td>Saturating concentration as determined by laboratory titration</td>
		</tr>
		<tr>
			<td>Mouse anti-human CD127-PE</td>
			<td>BD Biosciences</td>
			<td>557938</td>
			<td>Saturating concentration as determined by laboratory titration</td>
		</tr>
		<tr>
			<td>Mouse anti-human CD3</td>
			<td>eBiosciences</td>
			<td>16-0037-85</td>
			<td>1.0 mg/ml; azide free</td>
		</tr>
		<tr>
			<td>Mouse anti-human CD28</td>
			<td>eBiosciences</td>
			<td>16-0289-85</td>
			<td>1.0 mg/ml; azide free</td>
		</tr>
		<tr>
			<td>PBS</td>
			<td>Gibco</td>
			<td>21300-058</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>PKH26 red fluorescent cell linker kit containing 10-3M PKH26 in EtOH and Diluent C</td>
			<td>Sigma-Aldrich</td>
			<td>PKH26GL-1KT 
			or 
			MINI26-1KT</td>
			<td>Procedures in Step 1 also apply to kits containing PKH67 or other CellVue dyes</td>
		</tr>
		<tr>
			<td>CellVue Claret far red fluorescent cell linker kit containing 10-3M CellVue Claret in EtOH and Diluent C</td>
			<td>Sigma-Aldrich</td>
			<td>MINCLARET-1KT or MIDCLARET-1KT</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>5-(and-6)-carboxyfluorescein diacetate, succinimidyl ester (CFDA-SE)</td>
			<td>Invitrogen (Life Technologies)</td>
			<td>C34554</td>
			<td>Non-fluorescent; cleaved by membrane esterases to form fluorescent amino-reactive carboxyfluorescein succinimidyl ester (CFSE)</td>
		</tr>
		<tr>
			<td>LIVE/DEAD Fixable Violet</td>
			<td>Invitrogen (Life Technologies)</td>
			<td>L34955</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Sterile 12 x 75 mm conical 
			polypropylene tubes &amp; caps</td>
			<td>VWR</td>
			<td>60818-102</td>
			<td>Gives better membrane dye staining efficiency (reduced dye adsorption; less cell loss during supernatant aspiration)</td>
		</tr>
		<tr>
			<td>12 x 75 mm round bottom 
			polystyrene tubes</td>
			<td>Becton Dickinson</td>
			<td>21008-936</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Flow cytometer</td>
			<td>BD Bioscience</td>
			<td>FACSCalibur 
			LSRFortessa</td>
			<td>Any cytometer able to detect FITC, PKH26, and 7-AAD (ex. 488 nm; em. 520 nm, 567 nm, and 655 nm, respectively) and APC ex. 633-640 nm; em. 660 nm)</td>
		</tr>
		<tr>
			<td>Flow cytometer</td>
			<td>Beckman Coulter</td>
			<td>LSRII CyAn</td>
			<td>Any cytometer able to detect FITC, PKH26, and 7-AAD (ex. 488 nm; em. 520 nm, 567 nm, and 655 nm, respectively) and APC ex. 633-640 nm; em. 660 nm)</td>
		</tr>
		<tr>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
			<td>Laboratory Prepared</td>
		</tr>
		<tr>
			<td>7-Aminoactinomycin D, 
			concentrated stock</td>
			<td>NA</td>
			<td>NA</td>
			<td>1 mg/ml in PBS. Freeze in aliquots and store at -20 &deg;C.</td>
		</tr>
		<tr>
			<td>7-Aminoactinomycin D, 
			working stock</td>
			<td>NA</td>
			<td>NA</td>
			<td>100 &mu;g/ml in PBS; prepare daily from 1 mg/ml frozen stock.</td>
		</tr>
		<tr>
			<td>IgG block</td>
			<td>NA</td>
			<td>NA</td>
			<td>HBSS + 10 mg/ml Human IgG Cohn fraction II and III globulins + 10 mg/ml BSA.</td>
		</tr>
	</tbody>
</table>

optimized staining and proliferation modeling methods for cell division monitoring using cell tracking dyes

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

Watch this Scientific Journal Video about Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes at JoVE.com

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

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

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

optimized-staining-proliferation-modeling-methods-for-cell-division

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Biology

35.4K vues.  Roswell Park Cancer Institute. L'utilisation réussie des colorants cellulaires de suivi pour surveiller la fonction des cellules immunitaires et la prolifération implique plusieurs étapes cruciales. Nous décrivons des méthodes pour: 1) l'obtention brillante et uniforme, reproductible étiquette-ment avec des colorants membrane; 2) la sélection des fluorochromes et les conditions d'acquisition de données, et 3) le choix d'un modèle pour quantifier la prolifération cellulaire bas...

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

Selective Labelling of Cell-surface Proteins using CyDye DIGE Fluor Minimal Dyes

Surface  proteins are central to the cell's ability to react to its environment and to interact with neighboring cells. They are known to be inducers of almost all intracellular signaling. Moreover, they play an important role in environmental adaptation and drug treatment, and are often involved in disease pathogenesis and pathology (1). Protein-protein interactions are intrinsic to signaling pathways, and to gain more insight in these complex biological processes, sensitive and reliable methods are needed for studying cell surface proteins. Two-dimensional (2-D) electrophoresis is used extensively for detection of biomarkers and other targets in complex protein samples to study differential changes. Cell surface proteins, partly due to their low abundance (1 2% of cellular proteins), are difficult to detect in a 2-D gel without fractionation or some other type of enrichment. They are also often poorly represented in 2-D gels due to their hydrophobic nature and high molecular weight (2). In this study, we present a new protocol for intact cells using CyDye  DIGE Fluor minimal dyes for specific labeling and detection of this important group of proteins. The results showed specific labeling of a large number of cell surface proteins with minimal labeling of intracellular proteins. This protocol is rapid, simple to use, and all three CyDye DIGE Fluor minimal dyes (Cy 2, Cy 3 and Cy 5) can be used to label cell-surface proteins. These features allow for multiplexing using the 2-D Fluorescence Difference Gel Electrophoresis (2-D DIGE) with Ettan  DIGE technology and analysis of protein expression changes using DeCyder  2-D Differential Analysis Software. The level of cell-surface proteins was followed during serum starvation of CHO cells for various lengths of time (see Table 1). Small changes in abundance were detected with high accuracy, and results are supported by defined statistical methods.

A simple and specific method was demonstrated for fluorescent labeling and enhanced detection of cell surface proteins without a fractionation step. Differential abundance in cell surface proteins was analyzed using two-dimensional (2-D) electrophoresis and Ettan&#x2122; DIGE technology.

Surface  proteins are central to the cell's ability to react to its environment and to interact with neighboring cells. They are known to be inducers of ...

Live Dissection of Drosophila Embryos: Streamlined Methods for Screening Mutant Collections by Antibody Staining

Drosophila embryos between stages 14 and 17 of embryonic development can be readily dissected to generate "fillet" preparations. In these preparations, the central nervous system runs down the middle, and is flanked by the body walls. Many different phenotypes have been examined using such preparations. In most cases, the fillets were generated by dissection of antibody-stained fixed whole-mount embryos. These "fixed dissections" have some disadvantages, however. They are time-consuming to execute, and it is difficult to sort mutant (GFP-negative) embryos from stocks in which mutations are maintained over GFP balancer chromosomes. Since 2002, our group has been conducting deficiency and ectopic expression screens to identify ligands for orphan receptors. In order to do this, we developed streamlined protocols for live embryo dissection and antibody staining of collections containing hundreds of balanced lines. We have concluded that it is considerably more efficient to examine phenotypes in large collections of stocks by live dissection than by fixed dissection. Using the protocol described here, a single trained individual can screen up to 10 lines per day for phenotypes, examining 4-7 mutant embryos from each line under a compound microscope. This allows the identification of mutations conferring subtle, low-penetrance phenotypes, since up to 70 hemisegments per line are scored at high magnification with a 40X water-immersion lens.

Live Dissection of Drosophila Embryos: Streamlined Methods for Screening Mutant Collections by Antibody Staining

We describe a streamlined protocol for generating "fillet" preparations of Drosophila embryos of specific genotypes. This protocol allows efficient execution of a variety of genetic screens. It also allows excellent visualization of structures in the late embryo.

Drosophila embryos between stages 14 and 17 of embryonic development can be readily dissected to generate "fillet" preparations.  In these preparations, ...

Monitoring Dynamic Changes In Mitochondrial Calcium Levels During Apoptosis Using A Genetically Encoded Calcium Sensor

Dynamic changes in intracellular calcium concentration in response to various stimuli regulates many cellular processes such as proliferation, differentiation, and apoptosis1. During apoptosis, calcium accumulation in mitochondria promotes the release of pro-apoptotic factors from the mitochondria into the cytosol2. It is therefore of interest to directly measure mitochondrial calcium in living cells in situ during apoptosis. High-resolution fluorescent imaging of cells loaded with dual-excitation ratiometric and non-ratiometric synthetic calcium indicator dyes has been proven to be a reliable and versatile tool to study various aspects of intracellular calcium signaling. Measuring cytosolic calcium fluxes using these techniques is relatively straightforward. However, measuring intramitochondrial calcium levels in intact cells using synthetic calcium indicators such as rhod-2 and rhod-FF is more challenging. Synthetic indicators targeted to mitochondria have blunted responses to repetitive increases in mitochondrial calcium, and disrupt mitochondrial morphology3. Additionally, synthetic indicators tend to leak out of mitochondria over several hours which makes them unsuitable for long-term experiments. Thus, genetically encoded calcium indicators based upon green fluorescent protein (GFP)4 or aequorin5 targeted to mitochondria have greatly facilitated measurement of mitochondrial calcium dynamics. Here, we describe a simple method for real-time measurement of mitochondrial calcium fluxes in response to different stimuli. The method is based on fluorescence microscopy of 'ratiometric-pericam' which is selectively targeted to mitochondria. Ratiometric pericam is a calcium indicator based on a fusion of circularly permuted yellow fluorescent protein and calmodulin4. Binding of calcium to ratiometric pericam causes a shift of its excitation peak from 415 nm to 494 nm, while the emission spectrum, which peaks around 515 nm, remains unchanged. Ratiometric pericam binds a single calcium ion with a dissociation constant in vitro of ~1.7 &mu;M4. These properties of ratiometric pericam allow the quantification of rapid and long-term changes in mitochondrial calcium concentration. Furthermore, we describe adaptation of this methodology to a standard wide-field calcium imaging microscope with commonly available filter sets. Using two distinct agonists, the purinergic agonist ATP and apoptosis-inducing drug staurosporine, we demonstrate that this method is appropriate for monitoring changes in mitochondrial calcium concentration with a temporal resolution of seconds to hours. Furthermore, we also demonstrate that ratiometric pericam is also useful for measuring mitochondrial fission/fragmentation during apoptosis. Thus, ratiometric pericam is particularly well suited for continuous long-term measurement of mitochondrial calcium dynamics during apoptosis.

This protocol describes a method for real-time measurement of mitochondrial calcium fluxes by fluorescent imaging. The method takes advantage of a circularly permutated YFP-based dual-excitation ratiometric calcium sensor (ratiometric pericam-mt) selectively expressed in mitochondria.

Dynamic changes in intracellular calcium concentration in response to various stimuli regulates many cellular processes such as proliferation, ...

Cell Tracking Using Photoconvertible Proteins During Zebrafish Development

Embryogenesis is a dynamic process that is best studied by using techniques that allow the documentation of developmental changes in vivo. The use of genetically-encoded fluorescent proteins has proven a valuable strategy for elucidating dynamic morphogenetic processes as they occur in the intact organism. During the past decade, the development of photoactivatable and photoconvertible fluorescent proteins has opened the possibility to investigate the fate of discrete subpopulations of tagged proteins1. Unlike photoactivatable proteins, photoconvertible fluorescent proteins (PCFPs) are readily tracked and imaged in their native emission state prior to photoconversion, making it easier to identify and select regions by optical inspection. PCFPs, such as Kaede2, KikGR3, Dendra4 and EosFP5, can be shifted from green to red upon exposure to UV or blue light due to a His-Tyr-Gly tripeptide sequence which forms a green chromophore that can be photoconverted to a red one by a light-catalyzed &beta;-elimination and subsequent extension of a &pi;-conjugated system3. PCFPs and their monomeric variants are useful tools for tracking cells6-10 and studying protein dynamics11-14, respectively. During recent years, PCFPs have been expressed in different animal model, such as zebrafish6, chicken7,8 and mouse9,10 for cell fate tracking. Here we report a protocol for cell-specific photoconversion of PCFPs in the living zebrafish embryo and further tracking of photoconverted proteins at later developmental stages. This methodology allows studying, in a tissue-specific manner, cell biological events underlying morphogenesis in the zebrafish animal model.

Here, we present a method for the photoactivated switch of photoconvertible fluorescent proteins (PCFPs) in the living zebrafish embryo and further tracking of photoconverted protein at specific time points during development. This methodology allows monitoring of cell biological events underlying different developmental processes in a live vertebrate organism.

Embryogenesis is a dynamic process that is best studied by using techniques that allow the documentation of developmental changes in vivo. The use of ...

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Here, we present methods for the development of assays to query potentially clinically significant nonsynonymous changes using in vivo complementation in zebrafish. Zebrafish (Danio rerio) are a useful animal system due to their experimental tractability; embryos are transparent to enable facile viewing, undergo rapid development ex vivo, and can be genetically manipulated.1 These aspects have allowed for significant advances in the analysis of embryogenesis, molecular processes, and morphogenetic signaling. Taken together, the advantages of this vertebrate model make zebrafish highly amenable to modeling the developmental defects in pediatric disease, and in some cases, adult-onset disorders. Because the zebrafish genome is highly conserved with that of humans (~70% orthologous), it is possible to recapitulate human disease states in zebrafish. This is accomplished either through the injection of mutant human mRNA to induce dominant negative or gain of function alleles, or utilization of morpholino (MO) antisense oligonucleotides to suppress genes to mimic loss of function variants. Through complementation of MO-induced phenotypes with capped human mRNA, our approach enables the interpretation of the deleterious effect of mutations on human protein sequence based on the ability of mutant mRNA to rescue a measurable, physiologically relevant phenotype. Modeling of the human disease alleles occurs through microinjection of zebrafish embryos with MO and/or human mRNA at the 1-4 cell stage, and phenotyping up to seven days post fertilization (dpf). This general strategy can be extended to a wide range of disease phenotypes, as demonstrated in the following protocol. We present our established models for morphogenetic signaling, craniofacial, cardiac, vascular integrity, renal function, and skeletal muscle disorder phenotypes, as well as others.

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Here, we present a systematic approach for developing physiologically relevant, sensitive and specific in vivo assays for interpreting variation in human pathology. Transient genetic manipulation via microinjection of WT and mutant human mRNA and morpholino (MO) antisense oligonucleotides harness the tractability of the developing zebrafish embryo to rapidly assay pathogenic mutations, especially, but not exclusively, in the context of human developmental disorders.

Here,  we present methods for the development of assays to query potentially clinically  significant nonsynonymous changes using in  vivo complementation ...

Histochemical Staining of Arabidopsis thaliana Secondary Cell Wall Elements

Arabidopsis thaliana is a model organism commonly used to understand and manipulate various cellular processes in plants, and it has been used extensively in the study of secondary cell wall formation. Secondary cell wall deposition occurs after the primary cell wall is laid down, a process carried out exclusively by specialized cells such as those forming vessel and fiber tissues. Most secondary cell walls are composed of cellulose (40&#8211;50%), hemicellulose (25&#8211;30%), and lignin (20&#8211;30%). Several mutations affecting secondary cell wall biosynthesis have been isolated, and the corresponding mutants may or may not exhibit obvious biochemical composition changes or visual phenotypes since these mutations could be masked by compensatory responses. Staining procedures have historically been used to show differences on a cellular basis. These methods are exclusively visual means of analysis; nevertheless their role in rapid and critical analysis is of great importance. Congo red and calcofluor white are stains used to detect polysaccharides, whereas M&#228;ule and phloroglucinol are commonly used to determine differences in lignin, and toluidine blue O is used to differentially stain polysaccharides and lignin. The seemingly simple techniques of sectioning, staining, and imaging can be a challenge for beginners. Starting with sample preparation using the A. thaliana model, this study details the protocols of a variety of staining methodologies that can be easily implemented for observation of cell and tissue organization in secondary cell walls of plants.

Histochemical Staining of Arabidopsis thaliana Secondary Cell Wall Elements

Plant cell wall composition varies between tissue types and can include lignin, cellulose, hemicelluloses, and pectin. Various staining techniques have been developed to visualize differences at the cell-type level. This paper is a compilation of commonly used cell wall staining techniques.

Arabidopsis thaliana is a model organism commonly used to understand and manipulate various cellular processes in plants, and it has been used extensively ...

Methods for Skin Wounding and Assays for Wound Responses in C. elegans

The C. elegans epidermis and cuticle form a simple yet sophisticated skin layer that can repair localized damage resulting from wounding. Studies of wound responses and repair in this model have illuminated our understanding of the cytoskeletal and genomic responses to tissue damage. The two most commonly used methods to wound the C. elegans adult skin are pricks with microinjection needles, and local laser irradiation. Needle wounding locally disrupts the cuticle, epidermis, and associated extracellular matrix, and may also damage internal tissues. Laser irradiation results in more localized damage. Wounding triggers a succession of readily assayed responses including elevated epidermal Ca2+ (seconds-minutes), formation and closure of an actin-containing ring at the wound site (1-2 hr), elevated transcription of antimicrobial peptide genes (2-24 hr), and scar formation. Essentially all wild type adult animals survive wounding, whereas mutants defective in wound repair or other responses show decreased survival. Detailed protocols for needle and laser wounding, and assays for quantitation and visualization of wound responses and repair processes (Ca dynamics, actin dynamics, antimicrobial peptide induction, and survival) are presented.

Methods for Skin Wounding and Assays for Wound Responses in C. elegans

The adult C. elegans skin is a tractable model for studies of epithelial wound responses, including wound closure, scar formation, and innate immunity.

The C. elegans epidermis and cuticle form a simple yet sophisticated skin layer that can repair localized damage resulting from wounding. Studies of wound ...

Temporal Tracking of Cell Cycle Progression Using Flow Cytometry without the Need for Synchronization

This protocol describes a method to permit the tracking of cells through the cell cycle without requiring the cells to be synchronized. Achieving cell synchronization can be difficult for many cell systems. Standard practice is to block cell cycle progression at a specific stage and then release the accumulated cells producing a wave of cells progressing through the cycle in unison. However, some cell types find this block toxic resulting in abnormal cell cycling, or even mass death. Bromodeoxyuridine (BrdU) uptake can be used to track the cell cycle stage of individual cells. Cells incorporate this synthetic thymidine analog, while synthesizing new DNA during S phase. By providing BrdU for a brief period it is possible to mark a pool of cells that were in S phase while the BrdU was present. These cells can then be tracked through the remainder of the cell cycle and into the next round of replication, permitting the duration of the cell cycle phases to be determined without the need to induce a potentially toxic cell cycle block. It is also possible to determine and correlate the expression of both internal and external proteins during subsequent stages of the cell cycle. These can be used to further refine the assignment of cell cycle stage or assess effects on other cellular functions such as checkpoint activation or cell death.

This protocol describes the use of bromodeoxyuridine (BrdU) uptake to permit the temporal tracking of cells that were in S phase at a specific point in time. Addition of DNA dyes and antibody labeling facilitates detailed analysis of the fate of the S phase cells at later times.

This protocol describes a method to permit the tracking of cells through the cell cycle without requiring the cells to be synchronized. Achieving cell ...

Comparison of Three Different Methods for Determining Cell Proliferation in Breast Cancer Cell Lines

Measuring cell proliferation can be performed by a number of different methods, each with varying levels of sensitivity, reproducibility and compatibility with high-throughput formatting. This protocol describes the use of three different methods for measuring cell proliferation in vitro including conventional hemocytometer counting chamber, a luminescence-based assay that utilizes the change in the metabolic activity of viable cells as a measure of the relative number of cells, and a multi-mode cell imager that measures cell number using a counting algorithm. Each method presents its own advantages and disadvantages for the measurement of cell proliferation, including time, cost and high-throughput compatibility. This protocol demonstrates that each method could accurately measure cell proliferation over time, and was sensitive to detect growth at differing cellular densities. Additionally, measurement of cell proliferation using a cell imager was able to provide further information such as morphology, confluence and allowed for a continual monitoring of cell proliferation over time. In conclusion, each method is capable of measuring cell proliferation, but the chosen method is user-dependent.

This protocol describes the use of three different methods for analyzing cell proliferation in breast cancer cell lines. This includes the use of conventional cell counting, luminescence-based cell viability, and cell counting through the use of a cell imager. Each offers advantages for the reproducible measurement of cell proliferation.

Measuring cell proliferation can be performed by a number of different methods, each with varying levels of sensitivity, reproducibility and compatibility ...

Xenopus Oocytes: Optimized Methods for Microinjection, Removal of Follicular Cell Layers, and Fast Solution Changes in Electrophysiological Experiments

The Xenopus oocyte as a heterologous expression system for proteins, was first described by Gurdon et al.1 and has been widely used since its discovery (References 2 - 3, and references therein). A characteristic that makes the oocyte attractive for foreign channel expression is the poor abundance of endogenous ion channels4. This expression system has proven useful for the characterization of many proteins, among them ligand-gated ion channels.
The expression of GABAA receptors in Xenopus oocytes and their functional characterization is described here, including the isolation of oocytes, microinjections with cRNA, the removal of follicular cell layers, and fast solution changes in electrophysiological experiments. The procedures were optimized in this laboratory5,6 and deviate from the ones routinely used7-9. Traditionally, denuded oocytes are prepared with a prolonged collagenase treatment of ovary lobes at RT, and these denuded oocytes are microinjected with mRNA. Using the optimized methods, diverse membrane proteins have been expressed and studied with this system, such as recombinant GABAA receptors10-12, human recombinant chloride channels13, Trypanosome potassium channels14, and a myo-inositol transporter15, 16.
The methods detailed here may be applied to the expression of any protein of choice in Xenopus oocytes, and the rapid solution change can be used to study other ligand-gated ion channels.

Xenopus Oocytes: Optimized Methods for Microinjection, Removal of Follicular Cell Layers, and Fast Solution Changes in Electrophysiological Experiments

Optimized procedures for the isolation of single follicles, cytoplasmic RNA microinjections, the removal of surrounding cell layers, and protein expression in Xenopus oocytes are described. In addition, a simple method for fast solution changes in electrophysiological experiments with ligand-gated ion channels is presented.

The Xenopus oocyte as a heterologous expression system for proteins, was first described by Gurdon et al.1 and has been widely used since its discovery ...