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Cyto 2015 Forensic Flow Cytometry Tutorial

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Troubleshooting flow cytometry data. (Please excuse any formatting issues that arose in the slideshare translation of this powerpoint presentation.)

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Cyto 2015 Forensic Flow Cytometry Tutorial

  1. 1. Forensic Flow Cytometry: Troubleshooting Flow Cytometry Data Pratip K. Chattopadhyay, Ph.D. ISAC Scholar ImmunoTechnology Section Vaccine Research Center, NIH Jennifer Wilshire, Ph.D. Assistant Manager Flow Cytometry Core Facility Memorial Sloan-Kettering Cancer Center
  2. 2. These slides were presented at the CYTO2015 Conference. They are distributed here for the benefit of the flow cytometry community. Please do not: 1.  Take any material from these presentations (copied or adapted) without obtaining the express consent of Pratip Chattopadhyay, pchattop@mail.nih.gov. 2.  Modify this material in any way or distribute it outside of this source. Please do: 1.  Send your flow cytometry troubleshooting examples. I’d love to create a publicly available database of examples like the ones presented here. 2.  Send me your questions and comments! 3.  Learn and Enjoy!  
  3. 3. Disclaimer : “Forensic” Flow Cytometry   If you are here to learn about how flow cytometry is used to solve real crimes (e.g., murder, robbery, etc.) … Sorry, we’ll be talking about other heinous acts: Poor experimental Planning, Execution, and Analysis.
  4. 4. Today   Recognize Problematic Staining Patterns (See how easy it is to fall prey to criminal cytometry) Troubleshoot Experiments and Analysis (Become a crime-stopper) Guidelines to Improve Cytometry Experiments. (Learn how to live a virtuous life)
  5. 5. Guidelines to Improve Cytometry Experiments   Experiment Planning: “Don’t run the red light. Spend time planning your experiments properly.” Start with a robust system for instrument setup & monitoring.
  6. 6. Guidelines to Improve Cytometry Experiments   Experiment Planning: “Don’t run the red light. Spend time planning your experiments properly.” Start with a robust system for instrument setup & monitoring. Why bother? Reliability! Set Panel Instruments Panel Panel Panel PanelSingle  panel  performs  reliably   in  mul2-­‐instrument  (center)  study.   Single  instrument   works  for  most  panels  
  7. 7. Instrument Setup and Monitoring: Voltages   How to set voltages? Option 1: Just set all unstained signals to 102. What’s the crime here? (Audience?)
  8. 8. Instrument Setup and Monitoring: Voltages   How to set voltages? Option 1: Just set all unstained signals to 102. Criminal neglect. This method ignores: - dynamic range of PMT, - linearity of PMT, - the tenet that signal from dye should be highest in the detector designed to read that dye.
  9. 9. “Neglecting” Dynamic Range and Linearity of PMTs   Hard to know relative performance of different PMTs in your instrument. CVs of fluorescent signals may not be optimized, leading to issues with resolution and gating.
  10. 10. “Neglecting” Dynamic Range and Linearity of PMTs   Hard to know relative performance of different PMTs in your instrument. CVs may not be optimized, leading to issues with resolution and gating. Quality Control Panel Design Data Analysis This complicates…
  11. 11. “Neglecting” Dynamic Range and Linearity of PMTs   Hard to know relative performance of different PMTs in your instrument. CVs may not be optimized, leading to issues with resolution and gating. Quality Control Panel Design Data Analysis This complicates… “I will never know if my PMTs are working properly.” “I never resolve dim markers on the 1st detector off green laser, so I don’t use Cy7PE.” “My percent CD4+ never matches any other site in multicenter study.” Leading to criminal confessions:
  12. 12. “Neglecting” Dynamic Range and Linearity of PMTs   Is this always a problem? No… sometimes this will be a victimless crime. But troubleshooting experiments depends on your confidence in instrument setup! Poor instrument setup makes forensic flow harder! Guideline: Consider linearity and dynamic range when choosing PMT voltage.
  13. 13. Another Rule for Choosing Voltages   Signal from a dye should be strongest in channel set up to detect that dye. So, signal from Alexa 680 (or R700APC or Cy55APC) should be brightest when measured off of the red laser detector with 710nm filters in it.  
  14. 14. Another Rule for Setting Voltages   Signal from a dye should be strongest on channel set up to detect that dye. So, signal from Alexa 680 (or R700APC or Cy55APC) should be brightest when measured off of the red laser detector with 710nm filters in it, even in systems also measuring Cy55PE. Ensure this by optimizing detector voltages.  
  15. 15. If voltages across two detectors aren’t optimized…   You may detect more secondary (spillover) signal from Cy55PE in your RedLaser/710nm channel, than primary (Alexa 680). What happens as a consequence? (Audience?)
  16. 16. If voltages across two detectors aren’t optimized…   You may be detect more secondary (spillover) signal from Cy5PE in your RedLaser/710nm channel, than primary (Alexa 680). What happens as a consequence? (Audience?) 0 10 2 10 3 10 4 10 5 Cy5PE (Secondary) 0 102 103 104 10 5 Alexa680(Primary) Hint
  17. 17. If voltages across two detectors aren’t optimized…   You may be detect more secondary (spillover) signal from Cy5PE in your RedLaser/710nm channel, than primary (Alexa 680). What happens as a consequence? (Audience?) 0 10 2 10 3 10 4 10 5 Cy5PE (Secondary) 0 102 103 104 10 5 Alexa680(Primary) Hint Compensation values > 100%. Will this sink your panels? Not always… a multicolor panel can work just fine with compensations > 100% in some color combinations. But… troubleshooting becomes harder. And panel development is more complex.
  18. 18. Instrument Setup and Monitoring: Voltages   How to do a better job setting voltages? Option 1: Just set all unstained signals to 102. Option 2: Bead-based protocols. Good practice. Perfetto (Nature Protocols), CS&T (BD) Provide quantitative measure of instrument performance, in the form of metrics like Q (sensitivity) and B (resolution). Beads have limitations, though: - high intrinsic CV - loaded with broad-spectrum dyes, not our fluors - beads and dyes vary by lot. So, the metrics from these methods are a bit inaccurate.
  19. 19. Instrument Setup and Monitoring: Voltages   How to do an even better job setting voltages? Option 1: Just set all unstained signals to 102. Option 2: Bead-based protocols. Option 3: LED Pulser New device (Jim Wood, Wake) that emits consistent signals to PMT for accurate measurement of sensitivity and resolution (Q & B). Reliable method to compare detectors (track them for QC), or compare instruments. Can use Q and B values to 1) set voltages properly, 2) predict how a panel will stain on different instruments, and 3) predict whether a putative staining panel will work. More info: CYTO U webinar, Steve Perfetto’s Oral Presentation
  20. 20. Review: Guidelines to Improve Cytometry Experiments   Experiment Planning: Spend time planning your experiments properly. 1)  Set up instrument with bead-based protocols or LED Pulser.
  21. 21. Guidelines to Improve Cytometry Experiments   Experiment Planning: “Spend time planning your experiments properly.” 1)  Set up instrument with bead-based protocols or LED Pulser. 2)  Put some effort into panel design!
  22. 22. Panel Design: Step 1… Titrate Every Reagent   Titration is fundamental to successful flow and good data. Don’t rely on the manufacturer’s titer… … they can’t test all experimental conditions, and suggested titers may be significantly higher than what will work just fine in your system.
  23. 23. Titration Guidelines   Choose a wide range of concentrations that captures: Saturation (where signal does not increase with more antibody) Loss of positive staining (or at least diminished staining). O/n rt 0 5 10 15 20 Dilution 0 102 10 3 104 105 B515-A:CD8FITCV1002 O/n 37C 0 5 10 15 20 Dilution O/n 4C 0 5 10 15 20 Dilution Example of a good titration experiment (courtesy Margaret Beddall)
  24. 24. Titration Guidelines   1) Choose a wide range of concentrations. 2) Perform titrations under the same conditions as experiment. If you titrate antibody at room temperature, but need to stain cells at 37C for one experiment, Redo the titration at 37C first! Example of this in case studies later today.
  25. 25. Titration Guidelines   1) Choose a wide range of concentrations. 2) Perform titrations under the same conditions as experiment. 3) Analyze titrations on same cell type as in your study. Some markers differ in expression level by tissue, or are expressed across different cell types at different levels. Need to account for this by using right sample type, or including other markers in titration.
  26. 26. How Do Analyze Titration Data? 10 -3 10 -2 10 -1 10 0 10 1 Dilution 0 10 2 10 3 10 4 10 5 B515-A:CD3Ax488
  27. 27. 10 -3 10 -2 10 -1 10 0 10 1 Dilution 0 10 2 10 3 10 4 10 5 B515-A:CD3Ax488 Saturating Titer: The concentration at which positive signal plateaus, more antibody does not increase positive signal. Use this concentration when quantifying receptor density, working across changing experimental conditions. How Do Analyze Titration Data?
  28. 28. 10 -3 10 -2 10 -1 10 0 10 1 Dilution 0 10 2 10 3 10 4 10 5 B515-A:CD3Ax488 Saturating Titer: Going over saturation increases non- specific binding. How Do Analyze Titration Data?
  29. 29. 10 -3 10 -2 10 -1 10 0 10 1 Dilution 0 10 2 10 3 10 4 10 5 B515-A:CD3Ax488 Separating Titer: Subjective, lower concentration where staining still resolves well. Use this concentration in most experiments, where staining time/temp are constant. Saves antibody. Helps reduce spreading error in other channels when working with complex panels. How Do Analyze Titration Data?
  30. 30. 10 -3 10 -2 10 -1 10 0 10 1 Dilution 0 10 2 10 3 10 4 10 5 B515-A:CD3Ax488 Blasphemy! How can you work below saturation? How Do Analyze Titration Data?
  31. 31. 10 -3 10 -2 10 -1 10 0 10 1 Dilution 0 10 2 10 3 10 4 10 5 B515-A:CD3Ax488 Blasphemy! How can you work below saturation? You can… because we are still at antibody excess. This is, in fact, why we needn’t be terribly precise in our antibody volumes. There is often at least a 4x range of concentrations that are just fine. 100x   50x   25x   How Do Analyze Titration Data?
  32. 32. 10 -3 10 -2 10 -1 10 0 10 1 Dilution 0 10 2 10 3 10 4 10 5 B515-A:CD3Ax488 But then you will underestimate %+! No… Rarely happens. 50   60   70   80   90   100   0   2   4   6   8   10   100x   50x   25x   %  CD3+   How Do Analyze Titration Data?
  33. 33. Guidelines to Improve Cytometry Experiments   Experiment Planning: “Spend time planning your experiments properly.” 1)  Set up instrument with bead-based protocols or LED Pulser. 2)  Put some effort into panel design! + Titration – range of concentrations that works.
  34. 34. Guidelines to Improve Cytometry Experiments   Experiment Planning: “Spend time planning your experiments properly.” 1)  Set up instrument with bead-based protocols or LED Pulser. 2)  Put some effort into panel design! + Titration – range of concentrations that works. + Panel Design – Choosing which reagents will work together, and at what concentrations. We do this by considering spectral overlap and spreading error.
  35. 35. Review of Panel Design* www.isac-net.org Don’t randomly combine antibodies and fluorochromes. Quick & Easy (Spectral Overlap) Pick fluorochromes that don’t overlap. Qdots and Brilliant Violet fluorochromes are particularly useful for this. Pair highly overlapping fluorochromes with markers that aren’t co-expressed. Choose bright dyes for dim markers. The Better Way (Spreading Error) *See ISAC’s Polychromatic Flow Cytometry Course (2011) and my Tutorials (2010, 2013).
  36. 36. Review of Panel Design* www.isac-net.org Don’t randomly combine antibodies and fluorochromes. Quick & Easy (Spectral Overlap) Pick fluorochromes that don’t overlap. Qdots and Brilliant Violet fluorochromes are particularly useful for this. Pair highly overlapping fluorochromes with markers that aren’t co-expressed. Choose bright dyes for dim markers. Limitations *See ISAC’s Polychromatic Flow Cytometry Course (2011) and my Tutorials (2010, 2013). Multicolor flow will always use dyes that overlap (and that overlap is no big deal!). Cells are so heterogeneous that there is usually some co-expression of markers in any (interesting) phenotyping experiment. Reagent brightness doesn’t depend only on the dye used for conjugation. Antibody clones differ in signal strength, too. So, charts and guides ranking dye brightness may be of limited utility.
  37. 37. Review of Panel Design* www.isac-net.org Don’t randomly combine antibodies and fluorochromes. Quick & Easy (Spectral Overlap) Pick fluorochromes that don’t overlap. Qdots and Brilliant Violet fluorochromes are particularly useful for this. Pair highly overlapping fluorochromes with markers that aren’t co-expressed. Choose bright dyes for dim markers. The Better Way (Spreading Error) Characterize spreading error for the fluorochromes and instruments you use. Place dim markers on channels with low spreading error. Iteratively add markers to panel to characterize effect of various combinations on other channels. *See ISAC’s Polychromatic Flow Cytometry Course (2011) and my Tutorials (2010, 2013).
  38. 38. Why Worry About Spreading Error? When fluorescence is low, error associated with photon detection is easily observed. Particularly high photon counting errors occur for fluorochromes that give off very few photons (e.g., Cy7APC). The result: the negative population spreads out and masks dim populations in neighboring channels.
  39. 39. How do you calculate spreading error www.isac-net.org (See PC Previous Tutorials for Manual Example.)
  40. 40. How do you calculate spreading error www.isac-net.org (See PC Previous Tutorials for Manual Example.)
  41. 41. How do you calculate spreading error www.isac-net.org (See PC Previous Tutorials for Manual Example.)
  42. 42. How do use spreading error in panel design? Save the resulting table. You can even copy table to spreadsheet and make a heat map. Dye for Plot vs. Detector for Plot DyeforPlot A-BUV395 B-BUV490 C-BUV550 D-BUV680 E-BUV737 F-BUV815 G-BV421 H-BV480 I-BV570 J-BV605 K-BV650 L-BV711 M-BV745 N-BV786 O-BB515 P-BB630 Q-BB667 R-BB700 S-BB796 T-PE U-CF594PE V-CY5PE W-CY55PE X-CY7PE Y-APC Z-R700APC ZZ-H7APC A-BUV395 B-BUV490 C-BUV550 D-BUV680 E-BUV737 F-BUV815 G-BV421 H-BV480 I-BV570 J-BV605 K-BV650 L-BV711 M-BV745 N-BV786 O-BB515 P-BB630 Q-BB667 R-BB700 S-BB796 T-PE U-CF594PE V-CY5PE W-CY55PE X-CY7PE Y-APC Z-R700APC ZZ-H7APC Detector for Plot Spread 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Dye Detector
  43. 43. Dye for Plot vs. Detector for Plot DyeforPlot A-BUV395 B-BUV490 C-BUV550 D-BUV680 E-BUV737 F-BUV815 G-BV421 H-BV480 I-BV570 J-BV605 K-BV650 L-BV711 M-BV745 N-BV786 O-BB515 P-BB630 Q-BB667 R-BB700 S-BB796 T-PE U-CF594PE V-CY5PE W-CY55PE X-CY7PE Y-APC Z-R700APC ZZ-H7APC A-BUV395 B-BUV490 C-BUV550 D-BUV680 E-BUV737 F-BUV815 G-BV421 H-BV480 I-BV570 J-BV605 K-BV650 L-BV711 M-BV745 N-BV786 O-BB515 P-BB630 Q-BB667 R-BB700 S-BB796 T-PE U-CF594PE V-CY5PE W-CY55PE X-CY7PE Y-APC Z-R700APC ZZ-H7APC Detector for Plot Spread 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Dye Detector Now you can see (FOR ONE PARTICULAR INSTRUMENT) how including Cy5PE in the panel, induces spreading error into a number of other detectors off of the green laser. Don’t put dim markers into those detectors.   How do use spreading error in panel design?
  44. 44. Dye for Plot vs. Detector for Plot DyeforPlot A-BUV395 B-BUV490 C-BUV550 D-BUV680 E-BUV737 F-BUV815 G-BV421 H-BV480 I-BV570 J-BV605 K-BV650 L-BV711 M-BV745 N-BV786 O-BB515 P-BB630 Q-BB667 R-BB700 S-BB796 T-PE U-CF594PE V-CY5PE W-CY55PE X-CY7PE Y-APC Z-R700APC ZZ-H7APC A-BUV395 B-BUV490 C-BUV550 D-BUV680 E-BUV737 F-BUV815 G-BV421 H-BV480 I-BV570 J-BV605 K-BV650 L-BV711 M-BV745 N-BV786 O-BB515 P-BB630 Q-BB667 R-BB700 S-BB796 T-PE U-CF594PE V-CY5PE W-CY55PE X-CY7PE Y-APC Z-R700APC ZZ-H7APC Detector for Plot Spread 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Dye Detector You can also see how low the spreading error is off violet laser detectors. Consider putting dim markers on those channels.   How do use spreading error in panel design?
  45. 45. Dye for Plot vs. Detector for Plot DyeforPlot A-BUV395 B-BUV490 C-BUV550 D-BUV680 E-BUV737 F-BUV815 G-BV421 H-BV480 I-BV570 J-BV605 K-BV650 L-BV711 M-BV745 N-BV786 O-BB515 P-BB630 Q-BB667 R-BB700 S-BB796 T-PE U-CF594PE V-CY5PE W-CY55PE X-CY7PE Y-APC Z-R700APC ZZ-H7APC A-BUV395 B-BUV490 C-BUV550 D-BUV680 E-BUV737 F-BUV815 G-BV421 H-BV480 I-BV570 J-BV605 K-BV650 L-BV711 M-BV745 N-BV786 O-BB515 P-BB630 Q-BB667 R-BB700 S-BB796 T-PE U-CF594PE V-CY5PE W-CY55PE X-CY7PE Y-APC Z-R700APC ZZ-H7APC Detector for Plot Spread 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Dye Detector This analysis is now EASY to do (using FlowJo), and can be done with any of the panels you run on your instrument!   How do use spreading error in panel design?
  46. 46. Guidelines to Improve Cytometry Experiments   Experiment Planning: “Spend time planning your experiments properly.” 1)  Set up instrument with bead-based protocols or LED Pulser. 2)  Put some effort into panel design! + Titration – range of concentrations that works. + Panel Design – Quick and Dirty Spectral Overlap Method Better Spreading Error Method
  47. 47. Guidelines to Improve Cytometry Experiments   Experiment Execution: Proceed cautiously & follow protocols when staining. Two examples of vendor protocols I didn’t follow. (Things you may not be aware of.)
  48. 48. Pratip’s Criminal Past (1st Offense) Ignored manufacturer’s recommendation to stain on ice when using fix/perm kits for intra-cellular cytokine staining with surface Qdot antibodies.
  49. 49. Pratip’s Criminal Past (1st Offense) Ignored manufacturer’s recommendation to stain on ice when using fix/perm kits for intra-cellular cytokine staining.
  50. 50. Pratip’s Criminal Past (2nd Offense) Stained a multicolor panel with many BD and BioLegend “Brilliant” dyes. BD recommends use of Brilliant Stain Buffer, to minimize non-specific interactions between reagents. Non-specific interactions are worst in whole blood lysed protocols, but can appear when staining ficolled PBMC. I would see these interactions essentially as slightly undercompensated staining patterns.
  51. 51. Pratip’s Criminal Past (2nd Offense) Example from BD: CD8 CD19 Withour Brilliant Stain Buffer.
  52. 52. Pratip’s Criminal Past (2nd Offense) Example from BD: CD8 CD19 Without Brilliant Stain Buffer.
  53. 53. Pratip’s Criminal Past (2nd Offense) Example from BD: CD8 CD19 Without Brilliant Stain Buffer. With Brilliant Stain Buffer.
  54. 54. Guidelines to Improve Cytometry Experiments   Experiment Execution: Proceed cautiously & follow protocols when staining. Follow new vendor protocols exactly. There’s a reason for their recommended conditions.
  55. 55. Guidelines to Improve Cytometry Experiments   Analysis of Data: Use tools that help check data quality. 1) NXN Plot.
  56. 56. N X N PlotsFITC   PE   TRPE   Cy5PE   Cy5.5PE   PE   TRPE   Cy5PE   Cy5.5PE   TRPE   Cy5PE   Cy5.5PE   Cy5PE   Cy5.5PE   And so on… For every marker combination in panel.
  57. 57. N X N Plot for Troubleshooting A rapid means to identify problems. Over Compensation Under Compensation Over Compensation Transformation/Compensation
  58. 58. N X N Plot for Troubleshooting Let’s look more closely at Transformation/Compensation example…
  59. 59. Negative and Super-Negative www.isac-­‐net.org   ALer  bi-­‐exponen2al  transforma2on,  nega2ve  popula2ons  should  have  a   uniform  distribu2on.     “Super-­‐nega2ve”  =  “really  pregnant”  or  “extremely  dead.”     Why  does  this  happen?   0 103 104 105 <R710-A>: CD69 0 10 3 104 10 5 <G780-A>:CD4 CD4-­‐Nega2ve     CD4  “Super”  Nega2ve  
  60. 60. Negative and Super-Negative www.isac-net.org After bi-exponential transformation, negative populations should have a uniform distribution. “Super-negative” = “really pregnant” or “extremely dead.” Why does this happen? Bad compensation in another dimension. 0 103 104 105 <R710-A>: CD69 0 10 3 104 10 5 <G780-A>:CD4 0 102 103 104 105 <V800-A>: CD57 0 10 3 104 105 <G780-A>:CD4
  61. 61. Negative and Super-Negative www.isac-net.org As soon as we fix comps, problem is solved. 0 10 3 10 4 10 5 <R710-A>: CD69 0 10 3 10 4 10 5 <G780-A>:CD4 0 10 2 10 3 10 4 10 5 <V800-A>: CD57 0 10 3 10 4 10 5 <G780-A>:CD4 Sort of. There are still a few super-negative events. Why?
  62. 62. Fluorochrome Aggregates www.isac-net.org What are they? High-order complexes of fluorochromes that non-specifically bind cells. How do you recognize them? Random, punctate staining. Brighter than the larger population of cells that is specifically staining for the marker. Why worry about aggregates? Affect accuracy of subset identification, don’t phenotype junk! Effect on transformation and data visualization. 0 10 3 10 4 10 5 <G610-A>: `HLA-DR 0 10 2 10 3 10 4 10 5 <V800-A>:CD57
  63. 63. Fluorochrome Aggregates : Solutions www.isac-net.org Staining Time Make staining cocktail and spin at full speed in microfuge for a few minutes. Mark cap to indicate where pellet would be, and transfer cocktail from other side to new tube. Analysis Time Make gates that exclude the aggregates. Perform transformation after excluding aggregates. 0 10 3 10 4 10 5 <G610-A>: `HLA-DR 0 10 2 10 3 10 4 10 5 <V800-A>:CD57
  64. 64. Guidelines to Improve Cytometry Experiments   Analysis of Data: Use tools that help check data quality. 1)  NXN Plot. 2)  FlowClean: gates out problems in HTS data.
  65. 65. FlowClean   As number of parameters increases, we need to study more samples to make statistically robust conclusions. High-throughput systems (HTS) for cytometers will help with this. Quality control of HTS data is challenging. Cytometer pressure is often disrupted by bubbles, debris, or clogs. This can alter fluorescent signal, resulting in abnormal populations of cells. We developed FlowClean because existing methods for finding HTS problems were subjective, slow, or confusing to use. Cha$opadhyay  and  Fletez-­‐Brant  
  66. 66. FlowClean  Concept   Most  flow  experiments  aim  to  count  cell  popula2ons  based   on  marker  expression.       Since  the  sample  tube  contains  cells  mixed  together,  the   frequency  of  each  popula2on  should  be  stable  over  the   collec2on  period.     When  it’s  not  stable  (i.e.,  new  popula2ons  appear  and  exis2ng   ones  are  lost)  collec2on/pressure  problems  are  happening.       FlowClean  is  an  automated  tool  to  detect  these.  
  67. 67. FlowClean  Workflow   FCS File Partition Distributions Generate Cellular Address #Cells 50%50% #Cells 50%50% <V705> <G560> 0 0 1 1 Partition Cell <V705> <G560> <B515> 1 0 1 0 2 0 1 0 3 1 1 0 4 0 0 1 Define "Populations" by matching addresses:! Population 1! Population 2! Population 3 Track Populations Over Time Transform Population! Frequencies into CLR Flag Anamolous Events Amend FCS File
  68. 68. FlowClean  on  Real  Experimental  Data   0 102 10 3 10 4 10 5 0 10 2 10 3 10 4 105 10 4 10 5 <R710-A>:CD38 LSG RV152 212341-0200_B cel.fcsEvent Count: 164602 Time 03006009001200 Time 101 10 2 103 10 4 Good_vs_Bad LSG RV152 212341-0200_B cel.fcs Event Count: 164602 2 3 4 5 0 102 10 3 10 4 10 5 <R710-A>:CD38 LSG RV152 212341-0200_B cel.fcsEvent Count: 164602 03006009001200 Time 0 10 2 10 3 10 4 10 5 <R710-A>:CD38 03006009001200 Time 10 1 10 2 10 3 10 4 Good_vs_Bad LSG RV152 212341-0200_B cel.fcs Event Count: 164602 bad 010 2 103 104 105 SSC-A 0 102 10 3 104 105 <R710-A>:CD38 good RV152 212341-0200_B cel.fcs 010 2 10 3 10 4 10 5 SSC-A 0 102 10 3 10 4 10 5 <R710-A>:CD38 A B CBad Good Time <R710>CD38 Time ChangePointMeans Good_Bad Time <R710>CD38 Time SSC-A <R710>CD38 •  679  files  from  large  study   (RV152)   •  12-­‐14  colors,  HTS   •  Each  file  =  seconds,  each   plate  <  5min    
  69. 69. FlowClean  on  Real  Experimental  Data   12% 88% Files   Flagged   No   Problems   1 2 3 4 5 6 7 8 9 10 11 12 A 1 111 1 1 111 11 1 111 11 111 1 B 11 11 11 111 11 1 1 1 1 11 1 1 C 1 1 1 11 11 1 1 D E F 1 1 1 G 11 1 1 1 1 H 1 1 A Are  there  any  posi2ons  in  plate  where  problems  are  most  common?   Perhaps  at  early  wells,  sugges2ng  need  for  beder  cleaning  of  HTS  equipment.  
  70. 70. FlowClean  Summary   •  Developed  a  computa2onal  tool  that  makes  flow  data  more   powerful,  by  elimina2ng  ar2facts  and  noise.   •  We  proved  it  worked  in  a  large  dataset,  not  on  a  “toy”  case,   as  is  oLen  done  for  new  algorithms.   •  We  showed  that  it’s  unique  features  are  necessary,  classical/ simple  methods  don’t  work.     •  We  compared  it  to  exis2ng  algorithms,  and  objec2vely   showed  that  it  was  beder.   Available  on  BioConductor  and  GenePadern  (J.  Spidlin).    
  71. 71. Guidelines to Improve Cytometry Experiments   Analysis of Data: Use tools that help check data quality. 1)  NXN Plot. 2)  FlowClean: gates out problems in HTS data. 3) Concatenation of all study files.
  72. 72. Concatenation for QC When performing a large study, with many samples, covering many weeks of experiments, you need good ways to detect experimental or instrument issues and flag them. One method: Add a parameter to each FCS file that indicates sample number. (Numbers should be consecutive: 1, 2, 3…) Export a randomly selected subset events from each fcs file into a single “concatenated” file.
  73. 73. 0 50 100 150 200 Sequence 0 10 2 103 104 10 5 <B515-A>:CD45RA-FITC 0 50 100 150 200 Sequence 0 10 2 10 3 104 10 5 <G560-A>:KI67-PE 0 50 100 150 200 Sequence 0 102 103 104 10 5 <G610-A>:CD4-TRPE 0 50 100 150 200 Sequence 0 102 103 104 10 5 <V800-A>:CD8-BV780 0 50 100 150 200 Sequence 0 10 2 10 3 104 10 5 <G780-A>:CCR5-CY7PE 0 50 100 150 200 Sequence 0 10 2 10 3 104 10 5 <R660-A>:CD95-APC 0 50 100 150 200 Sequence 0 10 2 10 3 104 10 5 <R710-A>:CCR7-A680 0 50 100 150 200 Sequence 0 10 2 10 3 104 10 5 <V450-A>:HLA-DR-CB 0 50 100 150 200 Sequence 0 10 2 10 3 104 10 5 <G660-A>:CD27-CY5PE 0 50 100 150 200 Sequence 0 10 2 10 3 104 10 5 <V655-A>:CD38-QD655 0 50 100 150 200 Sequence 0 10 2 10 3 104 10 5 <V705-A>:CD57-QD705 Concatenation for QC From  this,  you  can  pick  out  the   experiment  days  and  markers  that  are   problema2c.     It  can  also  help  iden2fy  universal  gates   for  analysis  of  the  complete  data  set.  
  74. 74. Guidelines to Improve Cytometry Experiments   Planning:  Stop  to  Do  Setup  and  Design       ExecuEon:  CauEon  with  Reagent  Protocols       Analysis:  Use  Tools  that  Drive  Data  Quality  
  75. 75. Case studies
  76. 76. Crimes Against Flow Cytometry Flow Cytometry Unit Punk E. CytometriskiFLOW CYTOMETRY UNIT (FCU) Suspect  DescripEon    Talented  graduate  student,  with  some  flow  cytometry  experience.     Indicted  on  five  counts,  described  herein.    
  77. 77. Count 1: Filing False Police Report Suspect  visited  FCU  claiming  that  CD25  staining  “totally   different”  across  two  panels.     Even  though  he  was  “100%  sure”  that  an2body  was   added  to  both  tubes  at  same  concentra2on,  and  that   same  reagent  was  used.      
  78. 78. Count 1: Filing False Police Report Panel  1   Panel  2  
  79. 79. Count 1: Filing False Police Report www.isac-­‐net.org   Panel  1   Panel  2   Bright   Dim   Nega2ve   “The  padern   is  totally   different,”   said  suspect.   Upon  interroga2on,  suspect  claimed  there  were  no  differences  in   staining  or  analysis.    “I  don’t  know  what  the  #$@#%  is  happening!”  he   exclaimed.  
  80. 80. How Do These Plots Differ? Panel  1   Panel  2  
  81. 81. How Do These Plots Differ? Panel  1   Panel  2   The  axes  differ…   Compensa2on  and/or  transforma2on  must  be  different.  
  82. 82. How Do These Plots Differ? Panel  1   Panel  2   Lower  resolu2on  plot  (bigger  dot/per  event)  shows  lots  of  “junk”   in  Panel  2.    Fluorochrome  aggregates.  
  83. 83. Panel  1   Panel  2   Count 1: Filing False Police Report When  fluorochrome  aggregates  are  gated  out,  and  then  the  data  is   transformed…  paderns  are  very  similar.   Bright   Dim   Nega2ve  
  84. 84. Count 2: Unauthorized Entry Searching  the  suspect’s  FCS  files,  FCU   inves2gators  discover  unusual   popula2ons.     We  oLen  see  three  popula2ons   (bright,  dim,  and  nega2ve)  in  flow   cytometry  staining,  but  four  is   unusual.     Also,  highly  correlated  expression   (diagonals)  are  unusual.  
  85. 85. Count 2: Unauthorized Entry 0 10 2 10 3 10 4 10 5 SSC-A 0 50K 100K 150K 200K 250K FSC-A Original  lymphocyte  gate   Diagonal  popula2on  overlaid   Inves2ga2ve  process  called  “backga2ng.”   The  diagonal  popula2on  is  not  distributed  throughout  lymphocyte  gate.    Cells   entering  lymphocyte  gate  from  border,  where  SSC  is  larger.      
  86. 86. Count 2: Unauthorized Entry 0 10 2 10 3 10 4 10 5 SSC-A 0 50K 100K 150K 200K 250K FSC-A Solu2on:    A  2ghter  lymphocyte  gate  that  excludes  border  dwellers.       New  lymph  gate  overlaid  on  old  gate.     Diagonal  popula2on  from  old  gate.   New,  2ghter  lymph  gate.  
  87. 87. Count 2: Unauthorized Entry Suspect’s  gate   FCU  Inves2ga2ve  Task  Force  
  88. 88. Count 3: T.W.I. T  .  W  .  I.      “Titra2on  Without  Intracellular  (staining).”     1:20  dilu2on  of  an2body   1:20  dilu2on  of  an2body   Suspect  asks,  “why  don’t  they  look  the  same?”  
  89. 89. Count 3: T.W.I. T  .  W  .  I.      “Titra2on  Without  Intracellular  (staining).”     1:20  dilu2on  of  an2body   Intracellular   1:20  dilu2on  of  an2body   Surface   Suspect  admits  one  added  before  fix/perm,  another  aLer.   Titrate  under  same  condi2ons  as  experiment.  
  90. 90. Count 4: Felony Failure to Wash FCU  inves2gators  found  staining  that   didn’t  make  biological  sense.     Chadopadhyay,  et  al.     Perforin  always  expressed  with  Granzyme   B,  but  Granzyme  B  can  be  expressed   without  Perforin.     What  had  the  suspect  done  to  the   Granzyme  B+  Perforin-­‐  cells?  
  91. 91. Count 4: Felony Failure to Wash During  a  vigorous  interroga2on,  suspect  broke   down  and  admided  that  he  had  forgoden  to   add  GrzB  an2body  during  staining.     Tried  to  cover  up  crime  by  adding  an2body  at   the  cytometer,  but  forgot  to  wash  it  out.     Moral:  Inves2gate  paderns  that  aren’t   consistent  with  biology.  Technical  mistakes   can  lead  to  non-­‐specific  binding.  
  92. 92. Count 5: Misrepresentation without Titration At  trial,  defense  adorney  submided  evidence  claiming   that  “Pra2p’s  an2body  conjugates  never  work  reliably,”   hoping  to  undermine  the  case  against  his  client.          
  93. 93. Count 5: Misrepresentation without Titration Witnesses  report,  however,  that  defendant  “is  always   rushing,”  and  “seems  to  finish  staining  way  before   anyone  else  in  the  lab.”     Surveillance  footage  revealed  that  suspect  stained  at   4C  for  5  minutes.     FCU  2tra2ons  showed  that  reagent  was  fine.     Titra2on  under  same  condiEons  as  experiment  shows   that  staining  will  work  even  at  4C,  but  you  need  much   more  an2body.           0 5 10 15 20 Dilution 0 102 103 104 105 B515-A:CD8FITC 0 102 103 104 105 B515-A:CD8FITC 2' 37C 2' 4C 0 5 5' 4C 5' 37C 15 20 tion 15' rt 0 5 10 15 20 Dilution5' rt 0 5 10 15 20 Dilution 2hrs rt 0 5 10 15 20 Dilution 6hrs rt 0 5 5  minutes   4C   15  minutes   RT   Dilu6on  
  94. 94. Summary Resolving Problems in Flow Cytometry Data •  is easier when instrument, reagents, and staining procedures are optimized. •  requires examination of ALL parameters (hidden problems can exist with certain marker combinations that aren’t present in gating tree). •  is based on pattern recognition, with some patterns indicating a very easy to diagnose problem. www.isac-net.org
  95. 95. Welcome to FCU, Now Here Are Your Cases “Report of missing CCR7+ central memory cells.” CD45RA  FITC   CCR7  A700   Mul2color  Panel   Percentage of CD45RA- CCR7+ cells is too low in multicolor panel. CD45RA  FITC   CCR7  PE   2-­‐color  Test   What happened?
  96. 96. Welcome to FCU, Now Here Are Your Cases “Report of missing CCR7+ central memory cells.” CD45RA  FITC   CCR7  A700   Mul2color  Panel   Percentage of CD45RA- CCR7+ cells is too low in multicolor panel. CD45RA  FITC   CCR7  PE   2-­‐color  Test   What happened? 1)  Titration of A700 reagent? 2)  Spreading error from another marker in panel? 3)  Poor reagent choice for CCR7?
  97. 97. Welcome to FCU, Now Here Are Your Cases “Domestic dispute, disagreement about whether new antibody works.” 10 0 10 1 10 2 10 3 10 0 10 1 10 2 10 3 Compensated New  AnEbody   CD4  Cy5PE   Who’s right? *  Compensated  data  
  98. 98. Welcome to FCU, Now Here Are Your Cases “Domestic dispute, disagreement about whether new antibody works.” 10 0 10 1 10 2 10 3 10 0 10 1 10 2 10 3 Compensated New  AnEbody   CD4  Cy5PE   Who’s right? *  Compensated  data   Events piling up on axes Spreading of negative distribution “Holes” in distribution: appear to separate uniform populations
  99. 99. Welcome to FCU, Now Here Are Your Cases “Domestic dispute, disagreement about whether new antibody works.” 10 0 10 1 10 2 10 3 10 0 10 1 10 2 10 3 Compensated New  AnEbody   CD4  Cy5PE   Who’s right? *  Compensated  data   Events piling up on axes Spreading of negative distribution “Holes” in distribution: appear to separate uniform populations
  100. 100. Welcome to FCU, Now Here Are Your Cases “Fraud: Vendor sold me a kit for making cell lines from single sorted cells. Most of the cells didn’t grown into lines. Vendor claims user error.” What could have gone wrong? Full Panel
  101. 101. Welcome to FCU, Now Here Are Your Cases “Fraud: Vendor sold me a kit for making cell lines from single sorted cells. Most of the cells didn’t grown into lines. Vendor claims user error.” What could have gone wrong? 1)  Not really CMV+, just fluorochrome aggregates. 2)  No viability marker, all/most cells are dead. Full Panel
  102. 102. Welcome to FCU, Now Here Are Your Cases “Fraud: Vendor sold me a kit for making cell lines from single sorted cells. Most of the cells didn’t grown into lines. Vendor claims user error.” What could have gone wrong? 1)  Not really CMV+, just fluorochrome aggregates. 2)  No viability marker, all/most cells are dead. Full Panel Viability   Gate  
  103. 103. Welcome to FCU, Now Here Are Your Cases “Serial criminal on the loose. Find the crimes…” Experimental   Stain   CD4 A488 CD3 PE CD8 Cy7PE DAPI Lymphs   (Stained  today)   Beads   No stain 1.  Set  voltages   Beads   CD3 FITC Lymphs   CD3 PE DAPI Beads   CD4 Cy7PE Beads   DAPI Single     Colors   Universal     Nega2ve   Beads   No stain (Stained  last  week  and  fixed)  2.  Compensa2on   Lymphs   No stain 3.  Ga2ng  
  104. 104. Welcome to FCU, Now Here Are Your Cases “Serial criminal on the loose. Find the crimes…” Experimental   Stain   CD4 A488 CD3 PE CD8 Cy7PE DAPI Lymphs   (Stained  today)   Beads   No stain 1.  Set  voltages   Beads   CD3 FITC Lymphs   CD3 PE DAPI Beads   CD4 Cy7PE Beads   DAPI Single     Colors   Universal     Nega2ve   Beads   No stain (Stained  last  week  and  fixed)  2.  Compensa2on   Lymphs   No stain 3.  Ga2ng  
  105. 105. Review   Recognize Problematic Staining Patterns Troubleshoot Experiments and Analysis Characteristic patterns, tools to identify trouble, strategies to troubleshoot Guidelines to Improve Cytometry Experiments. Stop and Setup, Cautiously Experiment, Drive New Tools for QC
  106. 106.   Thank  you!     Help  us  compile  more  troubleshoo2ng  examples!           If  you  have  good  troubleshoo2ng  examples,  please  email:   pchadop@mail.nih.gov       Perhaps  we  can  have  a  public,  anonymous  repository  for   such  data  through  FlowRepository  or  CytoU.  

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