> Alice Given wrote: > > One additional warning: we have found that CFSE at slightly high > concentrations (5 > uM) inhibits antigen specific T-cell proliferation. The PKH dyes do not > appear to > have this effect -- perhaps because they are labeling lipids rather than > proteins. > ConA-induced proliferation did not appear to be as sensitive to CFSE as did > tetanus-induced proliferation. We have extensive experience using CFSE to visualize T cell division in vitro and in vivo in response to mitogens (J. Clin. Invest. 100:3173, 1997), nominal antigens (J. Immunol. 162:5212, 1999) and alloantigens (J. Immunol. 166:973, 2001). By comparing the function of labeled and unlabeled T cells in functional assays that do not depend on the dye for a readout (e.g., dye exclusion, activation marker expression, DNA content, BrdU or H3-TdR incorporation, Ca2+ flux and kinase activation - see J. Immunol. 165:2432, 2000 and J. Clin. Invest 108:895, 2001), we have become quite confident over the years that CFSE at concentrations as high as 5 uM (we most commonly use 2 uM during the labeling) does <not> negatively impact on the function of <naïve> T cells. We have, however, noted that at least under some circumstances, labeling <recently activated> T cells with CFSE can inhibit their cell cycle progression. This, as Alice points out, is most likely because CFSE covalently attaches to proteins. When stimulated, naïve T cells must synthesize de novo much of the proteins required to progress through the cell cycle and gain the effector function of activated T cells - the initial CFSE labeling would therefore not affect the activity of this de novo machinery. However, exposing activated T cells to a covalent protein modifier most likely poisons the new machinery that these cells have synthesized on now depend on for their function. This, therefore, is a very important consideration in planning an experiment using a cell labeling approach such as CFSE. As for the relative utility of pkh vs. CFSE dyes, I think there are some important distinctions between the two that should be made clear to those who do not use these dyes regularly. It is my opinion that the pkh dyes simply do not approach the functionality of CFSE for measuring division in either in vivo or in vitro assays (as long as one is starting with a naïve or resting cell population). While I have no particular personal interest in which dye investigators choose to use (i.e., I do not hold stock in Molecular Probes, and I do not hold a patent on any technique based on the dyes), I do have experience with both dyes, and my opinion is based on three main aspects: 1) the intensity of fluorescence, 2) the stability of the dye over time, and 3) the homogeneity of labeling. CFSE labels more intensely than pkh, and the half-life of CFSE independent of division is longer - the half-life of pkh26 for me in mouse T cells in culture is less than a day, although it may be greater in human T cells. For in vivo tracking and proliferation analysis, CFSE performs much better than the pkh dyes, especially in any experiment that lasts more than ~5 days. But by far the most notable difference between the two is in the third aspect mentioned above, homogeneity of labeling. Because the starting cell population can be labeled with CFSE such that the intensity of the vast majority of cells varies less than ~50%, each divided population, representing 2^n-fold diminutions in the original intensity, can be visualized as distinct populations and a logical gating strategy can be used to mark each population. The number of events under each peak can be directly counted, and used to model the response. Indeed, we first reported in 1997 on the use of this simple modeling technique to determine precursor frequencies and proliferative capacities from complex cell mixtures without the need for limiting dilution (J. Clin. Invest. 100:3173, 1997). However, pkh-labeled cells do not generate distinct division populations, and the data must be analyzed using deconvolution software (e.g., ModFit). This is not necessary when using CFSE, because the data are not convoluted in the first place. I have spent some time using CFSE data to test some of the proliferation algorithms currently available, including the best one, which has been developed by Verity. In my opinion, none are particularly good yet at modeling proliferation dye data. For instance, in may hands, roughly 2/3 of the CFSE distributions of labeled T cells stimulated under various conditions fail to generate a good fit when analyzed using ModFit's current proliferation module (and FloJo's module is significantly less successful at generating good fits). Remember, CFSE shows you the 'real' distribution of the division populations, so if the model doesn't generate a fit, then it's the model that is wrong. The reason for the discrepancy between the actual and predicted distributions is what the pro's in this field constantly remind us of in this user group - that the detectors are not perfectly logarithmic. All the modeling algorithms are currently stuck with the assumption that the log amp is perfect, and while this is not a problem for DNA content data, which spans only 2- to 4-fold along the amp, it is the reason for the failure to fit a significant proportion of proliferation data. According to Verity, this is <the> major hurdle in designing algorithms to model proliferation data that span several decades along the logamp. So, when using a dye that does not show you the actual distribution of cell divisions within your population of interest, one must realize that somewhere around two thirds of the time your modeling software is giving you inaccurate numbers. I realize that this assertion may be somewhat inflammatory for those investigators that rely on deconvolution software for the analysis of their proliferation data, but I think it is true, and it represents a serious limitation to the interpretation of pkh-determined precursor frequencies, especially low frequencies. So I think there are important uses as well as caveats for both dyes, and further discussion within this forum can be of use to those who are trying to decide which of the many techniques out their for measuring cell cycle progression are the most appropriate for their particular needs. -Andrew Andrew D. Wells, Ph.D. Department of Medicine University of Pennsylvania 728 Clinical Research Building 415 Curie Boulevard Philadelphia, PA 19104 (215) 573-1840 [office] (215) 898-1951 [laboratory] (215) 573-2880 [FAX] adwells@mail.med.upenn.edu
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