1. Compensation: It’s not just for pretty pictures

2. Fluorescence Spillover Compensation Simple in concept… Correct spillover into different parameters Straightforward in execution… Proper settings based on controls …But a lifetime to understand? Profound impact on visualization Nonintuitive aspects Subtle interactions that can be hard to diagnose

3. FITC Single Stain Control 450500550600 Argon Laser FL1 FL2 FITCPE

4. FL2 PE FL1 FITC Total signal detected in FL1 Unwanted signal detected in FL2 = roughly 15% True PE = Total FL2 – 15% FL1 FITC Single Stain Control

5. FITC Compensation Control PE - no stain Unwanted signal detected in FL2 roughly 15% Total signal detected in FL1 FITC CD3

6. FL2-15%FL1 UncompensatedCompensated FITC Compensation Control FITC CD3 PE - no stain

7. Compensation in 2 colors: Mostly aesthetic Accurate identification and enumeration of subsets is still easy in two color experiments

8. Compensation: Mostly aesthetic Accurate discrimination of subsets is possible with uncompensated data However, this is true only when the expression of all antigens is uniform on each subset (e.g., CD45 / CD3 / CD4 / CD8) Otherwise, it may not be possible to gate on subsets (with current tools)

9. Compensation for more colors: It’s not just pretty pictures Spillover from unviewed measurement channel can alter event positions– without obvious visual evidence (no diagnostic diagonals!) Thus, gate positions may depend on unviewed measurement channels and be different for various tubes in a panel Separation of populations may require multi-dimensional surfaces (rather than lines of a polygon).

10. Is this data properly compensated? 10 0 1 2 3 0 1 2 3 Yes No Can’t Tell

11. Impact of Compensation on Visualization and Analysis of Data “Visualization artifacts” lead to: –Manual overcompensation –Incorrect gate settings Specific staining controls become essential What causes this apparent visualization artifact (data spreading)?

12. Spreading due to Measurement Error Why do these populations look funny?

13. 10 1 2 3 4 5 1 2 3 4 5 PE-A: CD8 Cy7PE-A: CD20 Lymphocytes UncompensatedCompensated 10 1 2 3 4 5 1 2 3 4 5 : CD8 : CD20 Lymphocytes Multicolor Compensation

14. 10 1 2 3 4 5 1 2 3 4 5 : CD8 : CD20 Lymphocytes The Actual Spread

15. Imperfect Measurement Leads to Apparent Spread in Compensation Why is there a 400-unit spread? Photon counting statistics.

16. Log Transformation of Data Display Leads to Manual Overcompensation

17. Overcompensation Cannot Correct Error-induced Spread

18. Compensation Does NOT Introduce or Increase Error: Compensation Only Reveals It! All measurements have errors; often the error is proportional to the square-root of the measurement. The measurement error is present before compensation. Compensation does not increase this error, it does not change it, it does not introduce any more error. Compensation simply makes the error more apparent by shifting it to the low end of the log-scale.

19. Spread of Compensated Data Properly compensated data may not appear rectilinear (“rectangular”), because of measurement errors. This effect on compensated data is unavoidable, and it cannot be “corrected”. It is important to distinguish between incorrect compensation and the effects of measurement errors.

20. How can we identify undercompensated data? Diagonals in the data indicate poorly compensated data: but ONLY AT 45˚! Other slopes indicate nonlinear correlations that have nothing to do with compensation. Visual estimation is very difficult. Proportional to intensity (spillover) Proportional to square root of intensity

21. Controls Experimental controls fall into three categories: Instrument setup and validation (compensation, brightness) Staining/gating controls (Viability, FMO / ABO) Biological Experimental controls are necessary to properly analyze and interpret data.

22. Instrument Setup Controls Typically, fluorescent beads… with a range of fluorescences from “negative” to very bright. Use these to validate: Laser stability & focusing Filter performance PMT sensitivity (voltage) Fluidics performance Daily variability Consider setting target fluorescence values for beads during alignment: this allows for greatest consistency in analysis (gating) between experiments.

23. Compensation Controls Single-stained samples…must be at least as bright as the reagent you are using in the experiment! Can use any “carrier”, as long as the positive & negative populations for each control have the same fluorescence when unstained: Cells (mix stained & unstained) Subpopulations (CD8 within total T) Beads (antibody-capture) One compensation for every color… and one for each unique lot of a tandem (Cy5PE, Cy7PE, Cy7APC, TRPE)

24. Compensation Controls Note: you can mix bead controls on some colors and cell controls on other colors. However – the negative control for each type must match the positive control: do not use a “universal negative” unstained beads (or cells) for all controls! Collect sufficient events to precisely estimate fluorescence medians: Beads: At least 5-10,000 Cells: At least 20-50,000

25. Using Beads to Compensate Exact reagent used in experiment 100% positive (not ever rare) Small CV, very bright (precise fluorescence measurement) Sonicate weekly to reduce aggregates Some reagents may not work (Ig, non mouse, too dim, EMA/PI) – for these, use cell-based compensations

26. Why Bright Comp Controls? Autofluorescence FITC spillover into Cy7PE (1%) Unstained cells Bright cells Dimmer cells Estimating a low spillover fluorescence accurately is impossible (autofluorescence). Therefore, compensation is generally only valid for samples that are duller than the compensation control.

27. Insufficiently-Bright Comp Control Is …. Bad! Note that either under- or over-compensation can result from using comp controls that are too dim!

28. Compensation of Multicolor FACS Data It is impossible to set proper compensation using visual guides (dot plots, histograms). –Use statistics (medians of gated cells) –Use automated compensation tools Antibody-capture beads are an excellent way to set compensation! Compensation controls must be matched to your experiment and at least as bright as any of your reagents.

29. Some Examples of Problems The following four examples illustrate some types of problems that can be occur related to compensation. In each case, compensation itself is not the problem: there is an underlying reagent, instrumentation, or analysis problem. However, the manifestation of this problem is an apparent incorrect compensation!

30. Good Instrument Alignment Is Critical! Day 1Day 2 Uncompensated PE TR-PE While the amount of compensation did not differ, the measurement error (correlation) decreased leading to much better visualization of the population! Compensated

31. CFSE: A Special Case Cells stained with CFSE-DA, FITC-CD8, or unstained were collected uncompensated. CFSE has a slightly redder spectrum than FITC… must use CFSE as a comp control!

32. Note that this exacerbates the higher “IL4+” gate required for CD8 cells. The undercompensation would not have been detected except by looking at the APC vs. Cy7APC graphic… Fix/Perm Changes Cy7APC Compensation Requirement The longer Cy7APC is in fixative, the more it “falls apart”, leading to more APC signal

33. Different lots of tandems can require different compensation! TR-PE reagent 1 Median = 21,100 TR-PE reagent 2 Median = 8,720 PE Median = 484 PE Median = 698 Compensation Required (∆PE / ∆TRPE) 2.3% 8.0%

34. Wrong TR-PE comp control Compensating with the wrong TRPE Right TR-PE comp control

35. Staining Controls Staining controls are necessary to identify cells which do or do not express a given antigen. The threshold for positivity may depend on the amount of fluorescence in other channels!

36. Staining Controls Unstained cells or complete isotype control stains are improper controls for determining positive vs. negative expression in multi- color experiments. The best control is to stain cells with all reagents except the one of interest. FMO Control “Fluorescence Minus One” Also known as “ABO” (all-but-one)

37. Identifying CD4 cells with 4 colors PBMC were stained as shown in a 4-color experiment. Compensation was properly set for all spillovers

38. Complex Interactions in Compensation The same data is shown with correct or wrong Cy5PE->Cy7PE comp setting. Note that neither of these channels is shown here!

39. FMO controls aid even when compensation is improper Incorrect Cy5PE into Cy7PE compensation

40. FMO Controls FMO controls are a much better way to identify positive vs. negative cells FMO controls can also help identify problems in compensation that are not immediately visible FMO controls should be used whenever accurate discrimination is essential or when antigen expression is relatively low

41. Quad-Gates of the Future Present

42. Compensation & Data Visualization These “new” distributions are much more frequently seen nowadays, with the use of red dyes (Cy7PE, Cy7APC) and with more precise instruments. Some users have questioned the correctness of these distributions, leading some manufacturers to try to provide “corrections”. However, this cannot be “corrected”–what is needed is education! 10 0 1 2 3 0 1 2 3 Spread of positives Events piling up on axes

43. Compensation Offset??? At least two FACS data analysis software programs offer a “feature” to turn on a “compensation offset”–to try to make data look more like what we have expected. The term “Compensation Offset” simply means that random noise is added to the data to hide the “spread” in compensated data. This “feature” reduces sensitivity!

44. Compensation Offset??? Artificially increase error does make distributions rectangular again--but at a significant expense! –Low level antigen expression will be lost –In >4 color experiments, problems multiply!

45. Compensation Offset??? An even more insidious problem is that overcompensation becomes impossible to detect: Turn OFF this “feature” in your software Correct 5% over- compensated StandardTransformedOffset

46. Is There A Solution? The spread in compensated data is unavoidable (basic physics) Can we visualize data so that the distributions are more intuitive? Nearly all immunophenotyping data is shown on a logarithmic scale… why? –Dynamic range of expression (4 logs) –Often, distributions are in fact log-normal

47. Alternatives to a Log Scale Compensation reveals a linear-domain spreading in the distribution. This is most obvious at the low end of fluorescence, because the measurement error is small compared to bright cells. Can we re-scale the low end of the fluorescence scale to effect a different compression in this domain? What about negative values? –Remember, this is just a fluorescence from which we subtract an estimated value with measurement error

48. “Bi-exponential” Scaling Positive Log Negative Log Linear Wayne Moore Dave Parks

49. “Bi-exponential” Transformation Makes Compensated Data More Intuitive Negative Log Scale: Events with measured fluorescence < 0 Linear Scale: Compression of the amount of visual space devoted to this range Gated for CD3+ Lymphocytes; Stained for CD3, CD4, CD8, CCR5, CD103, and 7 other reagents Question: What is the expression of CCR5 and CD103 on CD4 Lymphocytes? How many events are on the axis? LOTS! Transformed Distributions Result: No events hidden on the axes Populations are visually identifiable

50. “Bi-exponential” Transformation Makes Compensated Data More Intuitive Only changes the visualization of data Does not affect gating or statistics Cannot change the overlap (or lack thereof) of two populations. Supports the basic goal of graphing data: showing it in an intuitive, aesthetic manner Note: the transformation is complex: it is different for each measurement channel and compensation matrix, and depends on the autofluorescence distribution. However, these parameters can be automatically selected by the software.

51. Median Transformed Transformation Confirms Compensation

52. Summary Compensation is straightforward…. But: Interpretation of compensated data is complicated by “spillover spreading” arising from measurement errors Proper compensation requires careful attention to compensation controls: bright, matched reagents & staining conditions Proper analysis of compensated data requires careful attention to staining controls Biexponential transformation of displays is critical for analysis and interpretation of compensated data

53. Bagwell & Adams: Ann NYAS 677:167 (1993) Software compensation Stewart & Stewart: Cytometry 38:162 (1999) 4-color compensation, pitfalls Roederer: Current Protocols in Cytometry Protocol, explanation, discussion Roederer: Cytometry 45:194 (2001) Visualization artifacts, FMO controls http://www.drmr.com/compensation/ Compensation Resources