Interpreting flow cytometry data: a guide for the perplexed

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2 26 Nature Publishing Group subset that is visible in the logicle displays (centered near zero on both axes) seems to be missing from the logarithmic displays. However, as indicated above, this subset is present but is represented mainly by data points and contours that are piled up on the plot axes. The location of the median fluorescence value in each dimension (Fig. 1, dark red crosses) further demonstrates the problem with logarithmic data visualization. By definition, half of the data values in each rectangular region are greater than the median and half are less than that value. However, whereas the locations of the median values in the logicle displays (Fig. 1, right) correspond to the visual centers of the subsets, the locations of the median values in the logarithmic displays (Fig. 1, left) are substantially offset from the apparent peak of the subset. In fact, each of the subsets in the logarithmic display is broken up into a false peak above the median value, a sparsely populated region between this peak and the baseline, and a pileup of a large fraction of the cells on the baseline. This artificial subdivision is also visible when data are plotted as a one-dimensional histogram on a logarithmic scale (Fig. 1, bottom left). The sharp rise at the lowest value on the scale reflects the pileup of the minimally fluorescent cells at the lowest value on the scale. This peak tends to be overlooked because it is nearly coincident with the axis. However, it represents roughly 4% of the minimally fluorescent population and 2% of the cells in the overall population. The valley and the false peak are also in the histogram, creating a total of three peaks rather than the two that actually exist. This problem does not occur in histograms plotted on logicle axes (Fig. 1, bottom right), in which the minimally fluorescent cells form a peak centered at or near zero and extending symmetrically above and below the peak center, thereby representing the cells whose measured fluorescence values are below zero. In two-dimensional logarithmic displays, cells whose fluorescence measurements are at or below zero in one or both dimensions are piled up on the axes and are represented by contours along both axes in contour displays (Fig. 1, middle left) and by colored dots, visible with keen eyes, on the axes in color density displays (Fig. 1, top left). The contours (and dots) range along a large proportion of both the x and y axes, indicating that cells with fluorescence values at or below zero in one dimension may have equivalently low fluorescence in the other dimension or may have very high fluorescence values in this second dimension. Surprisingly, given how modest the contours along the axes seem, they represent about 4% lgd CD5 3% 5% CD5 Logarithmic of the cells in the sample. Notably, these missing cells are readily visible in logicle displays, which enable visualization of fluorescent measurements for essentially all cells in the sample, including those whose fluorescence values are at or below zero in either dimension (Fig. 1, right) % 5% Logicle Figure 1 Logicle displays provide improved representation of cells with minimal fluorescence. Cells with minimal fluorescence can be visualized with logicle displays (right) but are piled up on the axis with logarithmic displays (left). The true center of each gated population (median fluorescence value in each dimension; dark red crosses) matches the visual peak for that population in logicle displays (right, top and middle) but does not match the visual peak in the logarithmic displays (left, top and middle). Because logarithmic scales cannot display cells with zero or negative values, these cells are piled up on the axis in the logarithmic displays. However, they are properly visualized in the logicle display (bottom right, red shaded region). Data provided by E. Ghosn (Stanford University, Stanford, California). The importance of seeing less than nothing As FACS instruments measure cell-associated fluorescence that can be essentially zero but not negative, how can a FACS data set contain values below zero and why are some cell populations found to be distributed symmetrically around zero? The answer lies in the way the FACS instrument collects data and routinely corrects (compensates for) the data for spectral overlap before visualization in histograms, dot plots and contour maps. In essence, background light and electronic noise make a small but important contribution to the overall signals the FACS instrument obtains for each cell. The FACS apparatus measures this background when no cells are present and subtracts it from the signals the detectors record for each cell. This instrument background correction introduces statistical variation, which may cause the measurements that the detectors record for cells that have no cell-associated fluorescence to be a little above or a little below the measurement set as the instrument zero. As a result, the corrected measurements distribute symmetrically around zero. This distribution is often broadened sub- 682 VOLUME 7 NUMBER 7 JULY 26 NATURE IMMUNOLOGY

3 26 Nature Publishing Group Yellow PE Green FITC Logarithmic Logicle 1 Uncompensated 5 Compensated stantially by the effects of spectral overlap between dyes when two or more dyes are used to stain a cell suspension. During FACS data collection, separate detectors are dedicated to each fluorochrome and measure most light from the target fluorochrome. Some of the light from this fluorochrome, however, may be emitted and detected in the wavelength range assigned to a different fluorochrome. Fluorescence compensation is the process by which the amounts of spectral overlap are estimated and subtracted from the total detected signals to yield an estimate of the actual amount of each dye. Single-laser, two-color FACS experiments in the mid-197s showed that fluorescence compensation is important 6. It is now recognized as an obligate step in most FACS analyses. For measurement of the spectral overlaps, the fluorescence detected on all measurement channels is evaluated for compensation control samples, which have each been labeled with only one of the fluorochromes used in the composite stain set. These measurements are used to make the fluorescence compensation corrections. That is, they are used to estimate and subtract the contribution of the spectral overlap to the overall signal that each detector measures for cells stained with the composite stain set. When a population has little or no detectable cell-associated fluorescence for a particular dye, the subtraction of overlapping fluorescence by the fluorescence compensation process results in a population distributed symmetrically around zero (or cell autofluorescence) in the corresponding data channel. Statistical uncertainties inherent in primary and overlapping fluorescence measurements will determine the width of this distribution. As these uncertainties become more pronounced as the size of the correction increases, larger corrections will result in broader distributions that nonetheless will still be centered on the average autofluorescence of the cells (Fig. 2, bottom right, vertical dimension). Applying the compensation to the data for the compensation control samples and using logicle displays to visualize the results for these samples provide a straightforward way to determine whether the applied compensation is correct. This is demonstrated by the position of populations displaced from the origin along the x or y axis. A population centered above the unstained cell background indicates undercompensation; a population centered below the unstained cell background indicates overcompensation; and a population centered at the unstained background indicates that the compensation correction is, like Goldilocks porridge, just right. Collect, then compensate Given the difficulty or impossibility of setting correct compensation visually using logarithmic displays, the appropriate way to set correct compensation is to rely on values computed by software from appropriately gated compensation control samples. As noted above, logicle displays of compensation control data can be used to confirm that nothing has gone wrong in the process and the resulting compensation is correct. Setting compensation manually from logarithmic displays is in effect flying blind and routinely leads to substantial under- or overcompensation. This is a shocking realization, as the instrument-based method (compensate by eye first; collect the data after) was the only method Figure 2 Logicle displays are superior to logarithmic displays in determining whether compensation has been properly applied to the sample. Uncompensated data for a sample stained with fluorescein isothiocyanate conjugated antibody are presented in logarithmic (top left) and logicle (top right) displays. Fluorescent colors (yellow and green) are used to designate the axes because they represent uncompensated color measurements. Many events are piled up on the axis in the logarithmic display, whereas all events are visible in the logicle display. Bottom, compensated data for samples above. In a properly compensated sample, the location of the median phycoerythrin fluorescence in the FITC population by definition matches the location of the median phycoerythrin fluorescence in the FITC population. This match is apparent in the logicle display but not the logarithmic display (bottom right versus bottom left). In the logicle display (bottom right), the distribution of the phycoerythrin fluorescence in the FITC population is broader than the distribution of the low fluorescence measured for the FITC population. This difference is due to statistical variance of the fluorescence measurements, which increases with the amount of overlap fluorescence that must be subtracted. Red dashed lines above the populations (bottom right) indicate the thresholds (gates) needed to identify the PE events in the FITC and FITC populations in this simple analysis. With more complex stain sets, FMO controls are useful for setting these thresholds. available for many years and is still widely used today. Thus, much of the older FACS data, and a fair part of today s FACS data, have been compromised to some extent, particularly with respect to distinguishing dull (weakly positive) from negative populations or evaluating small differences in the expression of a marker on two subsets. The solution to this problem is simple and straightforward: data should be collected and stored uncompensated (that is, written to data files before compensation is applied); data for compensation samples should also be collected and written to data files; and the compensation should be computed and applied when the data are analyzed. This recommendation, which is made in the strongest terms, goes against common practice. It is time for that practice to change. The errors introduced by compensating the data before collection are no longer tolerable in an era where fluorescence compensation can readily be done after the (uncompensated) data have been collected and stored. Recognizing the necessity for this shift to post-collection compensation, FACS software designers have already introduced software packages to provide this (and related) functionality. Among those that are already familiar (Supplementary Note online), the FacsXpert protocol design software ( provides labels for plot axes and table columns and specifies the necessary compensation and other controls; the FlowJo FACS data analysis software ( computes and applies compensation corrections, enables visualization of all data on logicle axes and provides quantile ( probability ) contour displays; and the DIVA software on newer Becton-Dickinson NATURE IMMUNOLOGY VOLUME 7 NUMBER 7 JULY

5 26 Nature Publishing Group BOX 1 SUGGESTED GUIDELINES FOR FACS DATA PRESENTATION 4 Instrument: Identify the FACS instrument and the software used to collect, compensateand analyze the data. Include model and version number where more than one exists. Graphic displays: Choose smoothing, graph and display options according to the dictates of the study. Be consistent across all displays in an analysis. Indicate the number of cells for which data are displayed and, where applicable, the contour or color density intervals used in the figure. Scaling: Show all parts of the plot axis necessary to indicate the scaling that was used (such as log, linear or logicle ). Numerical values for axis ticks can be eliminated except when necessary to clarify the scaling. For univariate (onedimensional) histograms, the scale for the abscissa (y axis) should be linear and should begin at zero unless otherwise indicated. Numerical axis values should not be included with the zero-based linear axes but should be shown for other axes. Gating: Display the gates used at each step in the gating sequence when gates are set manually (subjective gating). Show data for control samples when these are used to set gates. If necessary, present this information in supplementary figures. When an algorithm is used to set gates, define it explicitly and state that it has been used. Gating is assumed to be subjective unless otherwise stated. Frequency measurements: Show the frequencies (or percentages) of cells in gates of importance in the study. Compute these values relative to the total number of cells presented in the display on which the values appear. If a different frequency computation is used, define the method that was used and where it was applied. The graph itself cannot convey this requisite information. Intensity measurements: Explicitly define the statistic applied (mean, median or a particular percentile). All statistics should be applied to the scaled intensity measurement rather than to channel numbers. Once a cell triggers the placement of a dot on the screen, hundreds or thousands of cells measured at the same fluorescence will effectively be invisible because they will all be represented on the screen by the initial dot. In addition, if enough cells are analyzed, sparse regions will become populated with enough dots at a density of one cell per dot to form a contiguous (black) region indistinguishable from and often merged with highly populated black regions saturated with thousands of dots and potentially thousands of signals per dot (Fig. 3). Curiously, although this problem with live dots plots is fairly well recognized, many investigators have adopted computer-generated dot plots as their preferred method for viewing and publishing data. Some of these investigators believe (erroneously) that dot plots provide the most accurate representation of the subsets that are present. However, quantile contour plots (sometimes called probability plots) and color density plots actually provide a much more accurate representation of the data. By representing the frequencies of cells present at each point in the plot, these plots avoid the dynamic range problems encountered with monochrome dot plots. Thus, they become more rather than less accurate as the number of cells for which data are collected increases (Fig. 3). Furthermore, because they facilitate the discrimination of densely and sparsely populated regions, these contour and color density plots enable precise localization of the peaks and valleys that distinguish subsets of cells. The FACS Tower of Babel From a reader s or a reviewer s point of view, the lesson here is that comparison of FACS data can be difficult even in the same laboratory when data are collected on the same FACS instrument. Dully stained cell populations can seem to be negative, unstained populations can seem to be positive, and irritating controversies can arise, all because one investigator used online data visualization and compensation methods based on logarithmically displayed data, whereas the other collected uncompensated data and used offline compensation and logicle visualizations for analysis. This gap can be even greater when the comparison is between uncompensated data collected on the older FACS machines with logarithmic amplifiers and peak detection electronics and data collected on the newer machines with linear digital signal processing. Even when the data are correctly compensated in both cases and when the data are visualized with the same analysis program, cell populations at both ends of the scale tend to be more accurately represented when data are collected with the newer machines, because the older electronic systems tend to render signals less accurately in the lowest and often also the highest regions. Thus, readers and reviewers accustomed to the older FACS instruments and the older displays may encounter surprises in data acquired with the newer technologies, whereas readers and reviewers steeped in data acquired with the newer technologies may miss data features they are accustomed to seeing. Overall, this means that as the field transitions from the older to the newer technologies (which may take some time), it will be necessary for investigators at all levels to recognize the differences between the technologies and for authors submitting papers to ensure that the information that accompanies FACS data is sufficient for reviewers to understand which technologies were used for data collection, compensation and display. Although this kind of information has sometimes been considered superfluous, it is now essential for making FACS data interpretable. Therefore, the development of a standard to specify the minimal acceptable information necessary to accompany FACS data is obviously in order, the sooner the better (Box 1). 1. Parks, D.R., Roederer, M. & Moore, W.A. Cytometry A (in the press). 2. Tung, J.W., Parks, D.R., Moore, W.A., Herzenberg, L.A. & Herzenberg, L.A. Clin. Immunol. 11, (24). 3. Tung, J.W., Parks, D.R., Moore, W.A., Herzenberg, L.A. & Herzenberg, L.A. Methods Mol. Biol. 271, (24). 4. Roederer, M., Darzynkiewicz, Z. & Parks, D.R. Methods Cell Biol. 75, (24). 5. Herzenberg, L.A. et al. Clin. Chem. 48, (22). 6. Loken, M.R., Parks, D.R. & Herzenberg, L.A. J. Histochem. Cytochem. 25, (1977). 7. Roederer, M. Cytometry 45, (21). NATURE IMMUNOLOGY VOLUME 7 NUMBER 7 JULY

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