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TASBE Synthetic Biology Tools: Calibrated Flow Cytometry Jacob Beal, Aaron Adler, Fusun Yaman Revised June, 2014.

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Presentation on theme: "TASBE Synthetic Biology Tools: Calibrated Flow Cytometry Jacob Beal, Aaron Adler, Fusun Yaman Revised June, 2014."— Presentation transcript:

1 TASBE Synthetic Biology Tools: Calibrated Flow Cytometry Jacob Beal, Aaron Adler, Fusun Yaman Revised June, 2014

2 Curriculum Why Single Cell Measurements? Why Calibrated Flow Cytometry? https://synbiotools.bbn.com Color Models One-Variable Experiments

3 Distributions Matter Are these groups of cells the same? But they've got the same mean fluorescence…?

4 Engineering must consider variation Biological systems exhibit large variation in behavior due to many classes of cause, including: Inherent process stochasticity e.g., transcription, translation, replication, … Cell-to-cell differences e.g., size, cycle state, health, mutations, location, … Protocol stochasticity e.g., transfection variation, insertion site, … Protocol execution issues e.g., reagent variation, contamination, instrument drift,... System engineering must handle each class differently! Unpredictable, must be detected and appropriately compensated Largely uncorrelated effects on individual system elements Highly correlated effects on individual system elements Predictable distributions, affected by choice of protocol parameters

5 Example: Metabolic Process Composition A A B B C C uncorrelated per-cell variation  decreased overall variation correlated per-cell varation  increased variation, selection pressure protocol variation  batch-to-batch "fickleness" Enzyme 1Enzyme 2 Product IntermediateInput Different classes of variation have different effects: What is the effect of variation in enzyme performance?

6 Example: Detecting Protocol Failure By modeling transfection distribution, can detect failure of batches or individual samples Good Samples Bad Samples Distribution Model Bad Transfection [Beal et al., 2012; Kiani et al., 2014; Davidsohn et al, submitted]

7 Per-Cell Measurements Reveal Variation Spectral Bleed Autofluorescence Dose/Resource Variation Transfection Efficiency Quantifying variation components requires per-cell measurements of large populations of cells Cell Variation Dose Variation Quantization Dose Efficiency Example: RNA Replicon CotransfectionExample: Constitutive mKate in HEK293 [Beal et al., 2012] [Beal et al., submitted]

8 Curriculum Why Single Cell Measurements? Why Calibrated Flow Cytometry? https://synbiotools.bbn.com Color Models One-Variable Experiments

9 First, some metrology… Unit mismatch! Output [R2] [R1] [Output] [R2] R2R1 Arbitrary Red Arbitrary Blue Arbitrary Red Arbitrary Blue

10 Good Engineering Needs Absolute Units Metrology 101: precision measurement enables: –Comparison of results across experiments and labs –Deeper insight into the behavior of devices –Effective dissemination of materials and methods –Testing and validation of materials and systems –Establishment of commercial & industrial services –Safety assurance and traceability of responsibility Many biological measurements are only relative!

11 How Flow Cytometry Works Challenges: Autofluorescence Variation in measurements Spectral overlap Time Contamination Lots of data points! Different protein fluorescence Individual cells behave (very) differently

12 Metrology vs. Flow Cytometry Flow cytometry great for per-cell measurements, but… Yellow (Arbitrary Units) Blue (Arbitrary Units) Arbitrary unit output depends on… Instrument brand, configuration Interference from other colors Choice of instrument settings Run-to-run calibration drift Fortunately, this can be corrected… These are not physical units!

13 Calibrated Flow Cytometry Standardized Units (MEF)Autofluorescence Subtraction Non-Distorted CompensationColorEquivalence Mapping 13 Result: replicable measurements in absolute units (MEFL), rather than non-replicable relative units as usual practice. [Roederer, 2002; Wang et al., 2008; NIST/ISAC, 2012; Beal et al., 2012; Kiani et al., 2014; Davidsohn et al, submitted]

14 Example: Predicting Repressor Cascades Precision dose-response measurement allows high- precision prediction with quantitative models pCAG Dox T2A rtTA3VP16Gal4 pTRE EBFP2 pTRE R1 pUAS-Rep1pUAS-Rep2 EYFP R2 pCAG mkate pCAG 14 TAL14  TAL21 Prediction of Repressor CascadeRange vs. Error for 6 Cascades Each line is a dose/response curve for a different relative number of circuit copies. Subpopulation identified by color on inset mKate histogram +: experimental o: predicted [Davidsohn et al., submitted]

15 How much does calibration matter? [Davidsohn et al., submitted] 8x tighter range just by calibration! (2.8x better high errors, 2.8x better low errors)

16 Example Prediction of 3-RNA Replicon Mix: Range vs. Error for 6 Mixtures Example: Engineering Replicon Expression Per-cell measurement of dose-response gives model allowing high-precision control of expression Example: Prediction of fluorescence vs. time for novel mixtures of 3 Sindbis RNA replicons mVenus nsP1-4 SGP mKate nsP1-4 SGP EBFP2 nsP1-4 SGP 16 Mix 1: 0.1Y, 0.1R, 0.1B Mix 2: 0.3Y, 0.3R, 0.3B Mix 3: 0.1Y, 0.5R, 0.4B Mix 4: 0.2Y, 0.2R, 0.6B Mix 5: 0.01Y, 0.1R, 0.5B Mix 6: 0.4Y, 0.02R, 0.02B Mix Number [Beal et al., 2014]

17 Absolute comparisons, segmentation vs. constitutive fluorescence allow better design and debugging of devices Example: Device Engineering Original intronic design: Example: CRISPR Device Design 17 Modified design to fix intron issues: Distribution analysis identifies introns as a problem area Normal TRE induction no response & no leaky expression  intron failure Max ~5x repression Expected high- copy leak Max ~20x repression Device ig-b Device ig-c [Kiani et al., 2014]

18 Curriculum Why Single Cell Measurements? Why Calibrated Flow Cytometry? https://synbiotools.bbn.com Color Models One-Variable Experiments

19 TASBE Public Web Interface Register: individual accounts or group account? Anonymous access also available (but not private) On first use, you will have to terms of service Your data is secure, and can’t be shared on site. FireFox recommended; Chrome has an image- display bug. https://synbiotools.bbn.com/

20 Online Help TutorialFAQ / Debug Support

21 TASBE Flow Cytometry Workflow

22 Setting up your Flow Cytometer Exactly as it appears in machine Only needs to be done once Add every channel you might ever want to use For your records - not used by tools

23 Curriculum Why Single Cell Measurements? Why Calibrated Flow Cytometry? https://synbiotools.bbn.com Color Models One-Variable Experiments

24 Fluorescent Beads  Absolute Units Run beads every time: flow cytometers drift up to 20 percent! Also can detect instrument problems, mistakes in settings SpheroTech RCP-30-5A Tool currently only supports SpheroTech RCP-30-5A

25 Compensating for Autofluorescence Use your negative control for this

26 Compensating for Spectral Overlap Use a strong positive control for each color Note: only linear when autofluorescence subtracted [cf. Roederer 2002]

27 Translating Fluorescence to MEFL Only FITC channel (e.g. GFP) goes directly Others obtained from triple/dual constitutive controls Must have exact same constitutive promoter! Must have a FITC control protein! This is a new control that you need to add

28 Creating a Color Model (1/5) Pick all channels used When you pick the machine, wait a moment and available channels will fill in

29 Creating a Color Model (2/5) Similar entries for single positive controls Negative control Calibration beads

30 Creating a Color Model (3/5) If you only have two colors, repeat one here Must have at least FITC-X for each other fluorescence X

31 Creating a Color Model (4/5) Make sure you pick this right! Leave these alone unless there are model problems Free choice of labels The same control set can be run with different parameters

32 Creating a Color Model (5/5) Results: –Lots of graphs for you to check result validity –Online model for one- variable experiments –Matlab.mat file that you can use for calibration by hand

33 Bead problems? Usually means either: 1.you didn’t run enough beads, or 2.your low peaks are being affected by sensor noise, or 3.you've got a serious flow cytometer hardware problem First step: adjust peak & minimum thresholds

34 Curriculum Why Single Cell Measurements? Why Calibrated Flow Cytometry? https://synbiotools.bbn.com Color Models One-Variable Experiments

35 R1 = TAL14R1 = TAL21 35 Transfer curve for TAL 14Transfer curve for TAL 21 Output Dox R1 pCAG Dox T2A rtTA3VP16Gal4 pTRE EBFP2 pTRE R1 pUAS-Rep1 EYFP pCAG mkate pCAG Subpopulation identified by color on inset mKate histogram Each line is a dose/response curve for a different relative number of circuit copies. Rapid, high-precision device characterization with reproducible absolute units One-Variable Characterization

36 Experiment Assumptions Each functional unit on its own plasmid (but you can work with combined systems too) 3 colors: input, output and constitutive (but you can kludge your way down to two colors) Significant variation in circuit copy number (but you can get population statistics too)

37 TASBE Characterization Method Transient cotransfection of 5 plasmids Calibrated flow cytometry Analysis by copy-count subpopulations Output Dox R1 pCAG Dox T2A rtTA3VP16Gal4 pTRE EBFP2 pTRE R1 pUAS-Rep1 EYFP pCAG mkate pCAG

38 Multi-plasmid cotransfection!?! Avoids all problems with adjacency, plasmid size, sequence validations Variation appears to be independent

39 One-Variable Analysis (1/5) Make sure you'll be able to understand these several months from now

40 One-Variable Analysis (2/5) If you only have 2 FPs, make Input = Output. If you only have 1 FP, make all the same

41 One-Variable Analysis (3/5) Each row is an independent variable value Units ignored - you can do time, voltage, whatever Adjust rows, columns Each column is a replicate

42 One-Variable Analysis (4/5) Expected range of fluorescence (log10) Generally leave these alone

43 One-Variable Analysis (5/5) Population graphs too, but they are delicate… better to compute from.mat Input Induction Input/Output Constitutive Histogram Results: Matlab.mat file, CSV file, lots of graphs Three key graphs, others are preliminary:

44 Summary Calibrated flow cytometry enables order-of- magnitude increase in accuracy TASBE "color model" tool  calibrated units TASBE "characterization" tool does single- variable experiments –Best with 3-color mammalian cotransfections –Also works with other organisms, less colors, etc. Online tools produce graphs and Matlab files


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